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+Data[[:space:]]Analitics/assessment/.ipynb_checkpoints/Assignment-A-checkpoint.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/assessment/Assignment-A.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/assessment/Bank-Research-Paper[[:space:]](1).pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]0/L0-Module-Admin.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]1/L1-Introduction.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]1/Lab1-Introduction.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]10/L10-Clustering-Data.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]10/Lab10-Clustering-Data.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]11/L11-Text-Mining.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]11/Lab11-Text-Mining[[:space:]](1).pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]11/Lab11-Text-Mining.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]3/1230-Article[[:space:]]Text-1227-1-10-20080129.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]3/L2-Data-Science-Life-Cycle.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]3/Lab1-Introduction.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]3/Lab2-Data-Understanding.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]4/L3-Data-Exploration-Preparation.pptx filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]5/1-Assessment-Advice.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]5/Assessment-A-Guidance.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]5/decistion_tree.png filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]5/L5-Classification-Part-1.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]5/Lab5-Classification-Part-1.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]6/1-Assessment-Advice.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]6/Assessment-A-Guidance.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]6/L6-Classification-Part-2.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]6/Lab6-Classification-Part-2.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]7/.ipynb_checkpoints/Assessment-A-Guidance-checkpoint.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]7/Assessment-A-Guidance.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]7/L7-Tuning-AutoML.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]7/Lab7-Tuning-AutoML.pdf filter=lfs diff=lfs merge=lfs -text
+Data[[:space:]]Analitics/Week[[:space:]]9/L9-Association-Rules.pdf filter=lfs diff=lfs merge=lfs -text
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Data Analitics/FAQ Data Analytics.txt

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+FAQ for TU257 Data Analytics
+: My chart This page will contain Questions I've received about the module, the topics, labs, and assignments.
+
+It can be challenging to ask questions, in class or after class etc.  This FAQ attempts to address these many  challenges and allows for the sharing of knowledge.
+
+Students can contact me with their questions. I will attempt to respond promptly with answers. To assist with knowledge sharing with the whole class, I will post the questions I receive on this page, along with the answers. [The names of the student asking the question will not be listed]
+
+IMPORTANT: When I say, I will attempt to respond promptly, it means I will endeavor to respond within a day or two of getting the question. If I don't respond as quickly as you would like, just remember I have other classes, task and roles to perform each day. If I haven't responded with three days, then Yes Please get onto me and Gently remind me :-)
+
+IMPORTANT: There will be a delay to any questions asked during weekends, holidays and vacation periods. Questions will be answered after such periods.
+
+If you see a week with No questions, that means no one asked me a question.
+
+IMPORTANT: It is important that students check this page regularly for new Q&A. There will be no notices posted to the class group when new Q&A are added.
+
+Week 0 - Course Admin
+
+Q: Is there an exam?
+
+A: Nope, no exam.  There are two assessments. The combination of marks from these makes up your final mark for the module. See the Module Introduction & Admin for the breakdown of these marks.
+
+Q: How quickly will we get feedback on the assessment?
+
+A: Typically within 2-3 working weeks. Depending on dates/timing, etc it might be a little longer.  Feedback consists of a short paragraph on your assessment highlighting good things and things that needed some additional work
+
+Week 1 - Introduction
+
+Q: Will we be coding or doing lab work during the Week 1
+
+A: There will be a bit of an overlap with your other module. Similar tasks to complete with getting your environment setup and installing software. These tasks aren't very complex, but some people might have minor issues. It's important to get these resolved asap.
+
+Week 2 - Bank Holiday
+
+Q: Do we have a class or work to do this week?
+
+A: There is NO class and technically NO work you need to complete. If you'd like to do some learning, I've links on a webpage with some tutorials with using Pandas to process data. Check out the link to the webpage. NB. You don't have to do this. 
+
+Week 3 
+
+Q: I cannot get the library to install in Anaconda. Is there another way to do this
+
+A: Yes, run the following command on the command-line, it will do the install, and the library will then appear in your Anaconda environment
+
+conda install -c conda-forge ydata-profiling
+
+Q: I get an error when I change the directory to the location of the data file
+
+A: This means you have typed the full directory path to the file incorrectly. Double check the full path and make sure the path in the notebook matches. If you get an error message it means there is a typo in the path. Keep checking until it works.
+
+Q: In the directory path should I use / or \  in the directory path?
+
+A: It shouldn't matter if you use / in the path.  But if you get an error message (and you are using a Windows machine) just change it to \.  If you are using a Mac machine you can use /
+
+Week 4 
+
+Q: My chart/graph doesn't display. All I get is an empty cell.  What is wrong and how do I fix it?
+
+A: There is nothing wrong. Go to the cell containing the code to display the chart/graph and run the cell for a second time. It should now display it.
+
+Week 5
+
+Week 6
+
+Assignment A
+
+Q: I'm trying to load the data set into a pandas dataframe, but it isn't loading the header correctly. Can you suggest what I need to do to make this work correctly for me?
+A:  The read_csv function assumes the file is in CSV format. By default, the column separator for CSV is a comma, but any character can be used as a separator.  See the documentation for this function  https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
+
+For example, if the data set has a semicolon separator, you can use the following.
+
+# import dataset
+
+df = pd.read_csv('.........', sep=';')
+
+Q: Is there a sample report/solution you can give us, for example, a sample from last year?
+
+A: The problem with providing sample solutions for assignments is, I'll just get 20+ reports which are exactly the same as the sample. Effectively it will be a copy and paste from the sample. There is no learning experience with doing this and for this reason NO sample solutions will be provided. I have given you a template notebook you can use. This contains a structured layout of sections you will typically have to work through. These sections also correspond to the marking scheme.  Each student/team is responsible for completing these sections with code and clear documentation covering the work completed and discussions of your work and findings.  This makes every report/notebook/assignment submission individual to each student/team.
+
+Q: Is there a marking rubric?
+
+A: The marking scheme is provided on the assignment handout. Although it isn't in a rubric format, it does give you the available marks for each section. Marks will be awarded on a range from Zero to the maximum mark for a section, and this range of marks will be based on the depth of detail, analysis, and discussions completed for each section.  For example, if you write a few lines of code and only give a short general comment for these, then you will get the minimum of marks. Similarly, if you just reuse the code from the Demo Notebooks, I've supplied and provided no additional comments/discussions/analysis, then you will be looking at potential marks in the lower ranges, possibly getting zero marks. On the other hand, if you write lots of code, the code is well documented, and you provide in-depth analysis of the results and perform additional analysis, then you might be looking at a mark towards the upper range of marks.
+
+Q: Do we need to write a separate report, in addition to the notebook?
+
+A: There is no need for a separate report document. Everything should be in the notebook and it is this notebook you should submit.
+
+Q: Does each person need to submit an assignment on BrightSpace?
+
+A: Only one submission from a team should be submitted on BrightSpace.  The name, student number, etc must be clearly given at the top of each notebook.  Feedback will consist of a short paragraph and a mark. This will be entered into BrightSpace, and it is the responsibility of the person receiving this, to share it with other students in the team.  The short feedback paragraph will focus on areas in your submission that needed more work (i.e. areas where you lost marks).  You can learn from this, and try to address these for Assignment-2.
+
+Q: I'm a little confused if I need to use Up Sampling or Under Sampling. Can you help guide us on this?
+
+A: Up/Down Sampling allows you to create a modified data set. Most of the classification algorithms like to see a similar number of cases/records for each value in the Target variable. In some instances, they like to see a 50:50 split (for binary classification). In most data sets and work related problems for classification, one of the values in the Target variable is be a small percentage of the overall data set. To over come this small percentage, Up Sampling can be used to increase the number of cases/records for this particular variable/feature. This will typically be used when dealing with small data sets, like what we have for the Assignment. When you have LARGE data sets (Big Data), consisting of several millions or tens of millions of cases/records, you might want to use Down Sampling to bring the data set down to a manageable size to work with and for the algorithms to work, without running out of computer memory (RAM).
+
+Q: Is the assignment based on what is covered in weeks 5+6?
+
+A: The assessment work is based on everything we have covered up to and including week 7.
+
+Q: Should we spend any time understanding the domain knowledge of the dataset?
+
+A: You don't have to spend a lot of time on this. Every probably has some knowledge of the domains for the two problem data sets. Use this knowledge and try to think about if you were an employee and what you might say or tell your manager about the work you did.
+
+Q: If columns are correlated should we remove them?
+
+A: Have a look back over the notes/demo relating to correlation analysis. It's also vital to have a look at the meta-data or the descriptive information that's provided with the data set. This will give you some hints about certain attributes/features in the data set.
+
+Q: I ran Naive Bayes and Decision Tree on the data (Supermarket case study) and got 100% accuracy on both. Is that beginner's luck? totally random?
+
+A: Yes that's a bit of beginner's luck or perhaps bad luck. It is commonly referred to as having an 'overfitted model'. I've mentioned this several times in class. Have a look at the previous question (above) about correlated data and have a look at the notes and examples that cover this. Hopefully, this is a good hint towards what you might have to do. Also, check out the documentation for the data set and any related articles.
+
+Q: Is it ok to pick 2-3 algorithms to build a model?
+
+A: Remember the quote from George Box, "all models are wrong but some are more useful than others". Something to consider is what algorithms you can test and evaluate, and if they are sufficient.
+
+Q: For problem set 2, should we perform any data manipulations based on the date the data was extracted or the current date?
+
+A: It should be based on the date the data was extracted. You need to use this to correctly calculate any additional information you think is necessary. If you used the current date (e.g. today date) then all calculations would be incorrect. Additionally, all calculations would give a different outcome depending on whether you run those calculations today, tomorrow, or the day after, etc. FYI, the date of extraction is 23rd February, 1998.
+
+Assessment -  General Questions & Comments 
+
+Q: What feedback will we get?
+
+A: For a group assessment, one person should submit the assignment on BrightSpace. Feedback will consist of a short paragraph and a mark. This will be entered into BrightSpace for the assignment submission. The student receiving it should share it with the other members of the team. For individual assessments, feedback will be similar with a paragraph and mark.
+
+Q: How much detail should we include in the assignment for each topic?
+
+A: It's important to remember when completing this assignment you explain your work, why you are doing a task, what the outcomes are, what they mean, how it feeds into the next step/cell etc. Document all of this as code comments and MarkDown. This helps the reader to see and understand what you are doing and (most importantly) why you are doing it, and (even more importantly) you can explain the outcomes and what they mean. This will be useful for potential employers who might ask to see examples of your work. The more detail you include beyond the copy & paste of the code examples I've given, the better it will be for you and the more marks I can award. Copy&Paste of what I've given will not gain many marks. 
+
+Q: Can I get regular feedback on my assessment work, during the weeks leading up to the Due Date?
+
+A: All the materials necessary to complete the assessment have been covered. Your task is to apply these to the specified problem/use case.  This is standard for assessments. If you have a specific question about the problem/user case, this will be addressed and shared with the class and on this FAQ page. Questions such as, is this code correct, or is this the correct answer, what is missing, etc will not be answered.
+
+Q: Can I get a higher mark/grade?
+
+A: The final mark/grade for the assessment has been carefully considered and will be in keeping with the marking and standards of previous students on this module and with other similar modules. All marks are classed as provisional and subject to change as per TU Dublin General Assessment Regulations. All assessments and marks/grades are reviewed by an External Examiner to ensure the standard of marking and feedback.
+
+Check the TU Dublin General Assessment Regulations.
+
+Some students expect to receive a very high mark for their work, typically in the 80%-100% range. This is an incorrect assumption. Marks are awarded on a 0%-100% range. Only a small percentage of students will attain a mark of >70%. Most students will achieve a mark between 50%-69%
+
+
+Q: I'm not happy with the mark/grade I've received. What can I do?
+
+A: Check the TU Dublin General Assessment Regulations for details on what you can do regarding this. The procedure for appealing your grade is outlined along with the fees for requesting this.
+
+Q: I attempted a section of the assessment and I didn't get full marks, Why?
+
+A: Marks are awarded on a sliding scale and are based on several factors such as completeness, correctness, depth of detail, explanations, etc. If you didn't get full marks then some details were probably missing.  
+
+Week 7
+
+Q: When I first run TPOT with the default hyperparameters setting, all 5 generations gave the same CV score  (see cells 51 & 52 below). When I changed some of the hyperparameters'  in the TPOT algorithm it appeared to execute without any issue but came back with the following message: "Pipeline encountered that has previously been evaluated during the optimization process. Using the score from the previous evaluation."
+
+A: Check out this post on the TPOP Github repository, which contains details about this message, and what can be done  https://github.com/EpistasisLab/tpot/issues/927
+
+Q: AutoML has inbuilt steps to perform Data Select and Data Transformations. Does this mean I don't have to do any of these steps manually?
+
+A: Data cleaning and preparation still need to be performed before using the dataset for AutoML.  With AutoML they perform some additional Feature Engineering and Feature Selection. For example, with Feature Selection they will run a variety of statistical tests to select which features to include in each iteration. This typically involved selecting a random subset of the features to include in the model.
+
+Week 8
+
+
+Week 9
+
+Q: I cannot install or find mlxtends library in Anaconda. How can I install it?
+
+A: If you go to the Anaconda webpage for this library you will find a command for you to run in a Terminal/Command Line window. You had to do similar when installing tpot library for AutoML 
+
+conda install -c conda-forge mlxtend
+
+You might need to close Anaconda and reopen (or refresh the environment), it should then appear in your list of installed libraries. If you had Jupyter Notebook open, you will need to restart the Kernal (see menu option)
+
+Assignment B (See section above on Assessment - General Questions & Coments)
+
+Q: How many references would be enough for each notebook?
+
+A: The answer to everything in IT is "it depends". It depends on the topic you are covering and how much detail is included. This can be a useful section to illustrate to employers your reading on the topic and your understanding of the topic. I'd suggest having a minimum of three references. One of these can be the reference to the data set you are using. But consider adding 4, 5, 6 references, up to a maximum of 8.
+
+Q: For Assignment B, is it OK to use the dataset for Assignment A that we didn't work on?
+
+A: The assignment handout, see section Important, mentions you "should not include any of the datasets and examples used throughout the module".  This will include the data set not used in Assignment A. 
+
+The only exception to this is for AutoML.
+
+Q: How many different data sets should we use for Assessment B?
+
+A: You need to use two different data sets.  Given the nature of the topics, you'll need to use two different datasets. 
+
+Q: How can we share the datasets we used in the assignment with you?
+
+A: There are a number of ways to do this. If you downloaded the data set from the internet using python code (using pandas or otherwise) then just leave that code in your notebook submission. If you encounter/discover there are some download limits or rate limits or the download
+can be temperamental you can give a Dropbox or Google Drive link. An alternative is to include a ZIP file with your submission in Brightspace
+
+Week 10
+
+Q: I've seen examples using PCA (and other approaches) for Clustering. Should we use this (for our assignment)?
+
+A: PCA or Principal Component Analysis, is a dimensional reduction method. It is complex to use and understand what it produces and because of this PCA and other similar approaches/algorithms are outside the scope of this module. If you decide to continue your studies to higher levels you will encounter PCA etc in other modules such as Machine Learning where the various challenges of using it will be explored. 
+
+For Assessment-B, you don't have to use PCA, and using it may limit your ability to explain your work and discoveries in the data.
+
+Week 11
+
+Week 12
+
+Q: Is there a class/topic for this week?
+
+A: There is a topic for this week but most people will be busy finishing Assessment-B and maybe you are still working on your final assessment in your other module. It will be a busy week.  I've recorded videos of the lecture and you can view these at any time, along with the additional readings. The topic is not assessed.  Instead of covering the topic live during our scheduled class time, we will instead have a Q&A session for Assessment-B.  I think most people found the same session for Assessment-A useful earlier in the semester. We'll do similar this week
+
+Week 13
+
+Q: Is there a class/topic for this week?  
+
+A: There is no Class this week as people will be busy completing and submitting Assessment-B. 
+

Data Analitics/Week 0/L0-Module-Admin.pdf

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Data Analitics/Week 1/Additional Reading and Links Week 1.txt

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+### Abaixo estao os links de "additional Reading" complementares a materia de "Week 1":
+
+https://oralytics.com/2019/04/18/data-sets-for-analytics/
+
+https://practicalanalytics.wordpress.com/predictive-analytics-101/
+
+https://www.datasciencecentral.com/?s=&q=Data+science+use+cases
+
+https://www.gartner.com/en/newsroom
+
+https://www.dropbox.com/scl/fi/yp3s9zs37d6x2xvh8zvov/it-has-26-words-for-data-mining.pd?rlkey=oad38rok3znuqabfv9u4qhscc&e=1
+
+https://www.dropbox.com/scl/fi/nacqj76um27pc0ree2q0c/DM-in-Business.pdf?rlkey=py6t5uffbpy5fx82v7ofsnlxl&e=1
+
+https://www.dropbox.com/scl/fi/u9ibgc1dvrrlav11j5jl8/fayyad96from.pdf?rlkey=kjhcseyvn3t4l4vgsz1qiw7d1&e=1
+
+
+###Abaixo estao os links dos videos no YouTube sobre a materia, feitas, em sua maioria, pelo professor Brendan:
+
+https://youtu.be/nmsrSWad254
+
+https://youtu.be/iLL_HxzpsDA
+
+https://youtu.be/wSgX8gCZwsg
+

Data Analitics/Week 1/L1-Introduction.pdf

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Data Analitics/Week 1/Lab1-Introduction.pdf

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Data Analitics/Week 1/TU257_Lab1-Introduction-Solution.ipynb

@@ -0,0 +1,337 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 16:52:21) \n",
+      "[Clang 6.0 (clang-600.0.57)]\n",
+      "sys.version_info(major=3, minor=7, micro=3, releaselevel='final', serial=0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "#Print Python version details\n",
+    "print(sys.version)\n",
+    "print(sys.version_info)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x86_64\n",
+      "Darwin Kernel Version 19.6.0: Tue Jun 21 21:18:39 PDT 2022; root:xnu-6153.141.66~1/RELEASE_X86_64\n",
+      "Darwin\n",
+      "i386\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Print details about your computer\n",
+    "\n",
+    "print(platform.machine())\n",
+    "print(platform.version())\n",
+    "print(platform.system())\n",
+    "print(platform.processor())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Write code to print your name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Brendan Tierney\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Brendan Tierney')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Crate a variable containing your name, and print it to the screen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My name is  Brendan Tierney\n"
+     ]
+    }
+   ],
+   "source": [
+    "name='Brendan Tierney'\n",
+    "print('My name is ', name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter your name:\n",
+      "brendan\n",
+      "Hello, brendan how are you today?\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Use the input command to allow a user to enter a value\n",
+    "#for eample, use the input command to ask the use to enter a string\n",
+    "#save the inputted value to a variable\n",
+    "#print the variable\n",
+    "print('Enter your name:')\n",
+    "x = input()\n",
+    "print('Hello, ' + x + ' how are you today?') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use an IF condition to decide what mesage to print\n",
+    "#Use the input command to ask the use to enter a number\n",
+    "#Use the IF condition to determine if the number if Postive or Negative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter a number :\n",
+      "1\n",
+      "The number  1  is postive\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Enter a number :')\n",
+    "x = input()\n",
+    "if int(x) >= 0:\n",
+    "    print('The number ', x, ' is postive')\n",
+    "elif int(x) < 0:\n",
+    "    print('The number ', x, ' is negative')\n",
+    "else:\n",
+    "    print('Unkown number')\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Add an 'else' condition to the above IF condition\n",
+    "#You can decide what the statement should be, what message should be printed?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "#the following will print a range of values\n",
+    "for i in range(0,5):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Take a copy of this code and paste it into the next cell\n",
+    "#Change the code to add two variables to contain a number, assign a number to these variables\n",
+    "#Change the code in the 'for' loop to print the numbers between the values in the two variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11\n",
+      "12\n",
+      "13\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 11\n",
+    "y = 14\n",
+    "for i in range(x, y):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Take a copy of the code in the previous cell\n",
+    "#Expand it to ask the user to input two values.\n",
+    "#Change the loop to print the values between these two values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter the Start number: \n",
+      "31\n",
+      "Enter the End number: \n",
+      "44\n",
+      "----------\n",
+      "31\n",
+      "32\n",
+      "33\n",
+      "34\n",
+      "35\n",
+      "36\n",
+      "37\n",
+      "38\n",
+      "39\n",
+      "40\n",
+      "41\n",
+      "42\n",
+      "43\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Enter the Start number: ')\n",
+    "x = input()\n",
+    "print('Enter the End number: ')\n",
+    "y = input()\n",
+    "print(('----------'))\n",
+    "for i in range(int(x), int(y)):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#what else can you do?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Data Analitics/Week 1/TU257_Lab1-Introduction.ipynb

@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 16:52:21) \n",
+      "[Clang 6.0 (clang-600.0.57)]\n",
+      "sys.version_info(major=3, minor=7, micro=3, releaselevel='final', serial=0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "#Print Python version details\n",
+    "print(sys.version)\n",
+    "print(sys.version_info)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x86_64\n",
+      "Darwin Kernel Version 19.6.0: Tue Jun 21 21:18:39 PDT 2022; root:xnu-6153.141.66~1/RELEASE_X86_64\n",
+      "Darwin\n",
+      "i386\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Print details about your computer\n",
+    "\n",
+    "print(platform.machine())\n",
+    "print(platform.version())\n",
+    "print(platform.system())\n",
+    "print(platform.processor())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Write code to print your name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Crate a variable containing your name, and print it to the screen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use the input command to allow a user to enter a value\n",
+    "#for eample, use the input command to ask the use to enter a string\n",
+    "#save the inputted value to a variable\n",
+    "#print the variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use an IF condition to decide what mesage to print\n",
+    "#Use the input command to ask the use to enter a number\n",
+    "#Use the IF condition to determine if the number if Postive or Negative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Add an 'else' condition to the above IF condition\n",
+    "#You can decide what the statement should be, what message should be printed?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "#the following will print a range of values\n",
+    "for i in range(0,5):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Take a copy of this code and paste it into the next cell\n",
+    "#Change the code to add two variables to contain a number, assign a number to these variables\n",
+    "#Change the code in the 'for' loop to print the numbers between the values in the two variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Take a copy of the code in the previous cell\n",
+    "#Expand it to ask the user to input two values.\n",
+    "#Change the loop to print the values between these two values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#what else can you do?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Data Analitics/Week 10/L10-Clustering-Data.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1bcef526c6bc5e2ee4395eeaf3313956ed549cd8ea91b030c37caf97b668a1b3
+size 4691849

Data Analitics/Week 10/Lab10-Clustering-Data.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:caa26cb042538e1f17832c5681a029770fad12e102ecfefc0288496d7608496b
+size 100254

Data Analitics/Week 10/Week 10 Additional Reading and links.txt

@@ -0,0 +1,23 @@
+### Week 10 Additional Reading and Links
+
+### YouTube links for classes Lab:
+
+https://youtu.be/f0wYy9SsuVA
+
+https://youtu.be/RWLmcq1ykVw
+
+https://youtu.be/N4beiC6sNmk
+
+### Additional Reading:
+
+https://oralytics.com/2019/04/18/data-sets-for-analytics/
+
+https://oralytics.com/2021/10/18/dbscan-clustering-in-python/
+
+https://www.kdnuggets.com/2023/05/clustering-scikitlearn-tutorial-unsupervised-learning.html
+
+https://www.kdnuggets.com/2023/05/principal-component-analysis-pca-scikitlearn.html
+
+https://drive.google.com/file/d/13Wyub3clCDMzt5Z8i1zhM7xcTSsE_v2B/view
+
+https://drive.google.com/file/d/1kRDtiM3e_sl_FqJH2tLlg4XvDg1FVJkT/view

Data Analitics/Week 11/L11-Text-Mining.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e43275de38fbb61d964280a3deb7ae49f6f63ee7616aeeddcf58337224cfa73
+size 1196030

Data Analitics/Week 11/Lab11-Text-Mining (1).pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b05ea9b2454e0190cee970d577a57e0dfa85626218fd633b75c264a0282a32f
+size 290828

Data Analitics/Week 11/Lab11-Text-Mining.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b05ea9b2454e0190cee970d577a57e0dfa85626218fd633b75c264a0282a32f
+size 290828

Data Analitics/Week 11/Week 11 Additional reading and links.txt

@@ -0,0 +1,22 @@
+### Additional reading and links
+
+### Additional Links
+
+https://oralytics.com/2021/11/01/combining-nlp-and-machine-learning-for-document-classification/
+
+https://oralytics.com/2020/01/21/ge2020-analysing-party-manifestos-using-python/
+
+https://oralytics.com/2020/01/24/ge2020-comparing-party-manifestos-to-2016/
+
+https://oralytics.com/2019/04/18/data-sets-for-analytics/
+
+https://www.kdnuggets.com/2015/01/text-analysis-101-document-classification.html
+
+
+### Links Youtube:
+
+https://youtu.be/4vHwcbFKM2Q
+
+https://youtu.be/xfphUjk9jz0
+
+https://youtu.be/k-QMStX-ZWw

Data Analitics/Week 11/review_polarity.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc0dccc2671af5db3c5d8f81f77a1ebfec953ecdd422334062df61ede36b2179
+size 3127238

Data Analitics/Week 2/Week 2 Material Complementar.txt

@@ -0,0 +1,29 @@
+### A Week 2 foi um feriado, entao nao houve aula. No entanto há materiais importantes e relevantes para a materia.
+
+### Abaixo um curso de 4h de duracao sobre Pandas. Link:
+
+https://www.kaggle.com/learn/pandas
+
+
+### Abaixo o link para uma aula sobre "Exploratory Data with Pandas":
+
+
+https://www.kaggle.com/code/kashnitsky/topic-1-exploratory-data-analysis-with-pandas
+
+### Abaixo uma aula sobre "Visual Data analysis in Python":
+
+https://www.kaggle.com/code/kashnitsky/topic-2-visual-data-analysis-in-python
+
+
+
+### Alguns links de videos no Youtube que complementam a materia:
+
+https://youtu.be/X3paOmcrTjQ
+
+https://youtu.be/yZvFH7B6gKI
+
+https://youtu.be/lYWt-aCnE2U
+
+https://youtu.be/lIFLeHDanmA
+
+https://youtu.be/C_Q_L0wdPNg

Data Analitics/Week 3/### Week 3 Additional Reading.txt

@@ -0,0 +1,8 @@
+### Week 3 Additional Reading:
+
+https://www.dropbox.com/scl/fi/pile8auga8sux1c7eb00y/Berry-Chapter-1.pdf?rlkey=ioba8cbfie5cc2bxj8p6owqgz&e=1
+
+https://www.dropbox.com/scl/fi/nacqj76um27pc0ree2q0c/DM-in-Business.pdf?rlkey=py6t5uffbpy5fx82v7ofsnlxl&e=1
+
+https://b-tierney.com/wp-content/uploads/2018/07/CRISPDM.pdf
+

Data Analitics/Week 3/.ipynb_checkpoints/Lab2-1-checkpoint.ipynb

@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.container { width:85% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, HTML\n",
+    "display(HTML(\"<style>.container { width:85% !important; }</style>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (403486649.py, line 4)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[1], line 4\u001b[1;36m\u001b[0m\n\u001b[1;33m    df = pd.read_csv(\"C:\\Users\\Rafael\\Documents\\DataScience\\Data Analitics\\Week 3\\TU257-Lab2-1-Automated-Data-Profiling.ipynb\")\u001b[0m\n\u001b[1;37m                     ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "#Change this next command to the location of train.csv on your Computer\n",
+    "df = pd.read_csv(\"C:\\Users\\Rafael\\Documents\\DataScience\\Data Analitics\\Week 3\\TU257-Lab2-1-Automated-Data-Profiling.ipynb\")\n",
+    "#df = pd.read_csv(\"C:\\Studies\\TU257\\DataAnalytics\\Week2\\train.csv\")\n",
+    "df.head(8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = df.iloc[:,[1,2,4,5,6,7,8,10,11]]\n",
+    "df2.head(8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Make sure to install 'ydata_profiling' library before running the following\n",
+    "#see Lab Notes\n",
+    "\n",
+    "from ydata_profiling import ProfileReport\n",
+    "\n",
+    "profile = ProfileReport(df2, title=\"Profiling Report\")\n",
+    "profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Can you save the Data Profile Report to a file?\n",
+    "#Check the package Github site for examples (link to this is in the Lab Notes)\n",
+    "#  https://github.com/ydataai/ydata-profiling\n",
+    "# Scroll to the bottom of the main GitHub page for examples of saving the report\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Enter the code here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### See lots more examples of using this library/package for analysing datasets on the Github page. Scroll to bottom of main page to get the links"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Data Analitics/Week 3/.ipynb_checkpoints/TU257-Lab2-2-Data-Exploration-checkpoint.ipynb

@@ -0,0 +1,1024 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TU258 - Lab 2-2 - Data Exploration\n",
+    "\n",
+    "This lab gives an example of using Pandas dataframes for analysing data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import pandas\n",
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reading a CSV File into a Panda\n",
+    "videoReview = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/Video_Games_Sales_as_at_22_Dec_2016.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# print first 3 rows\n",
+      "                Name Platform  Year_of_Release     Genre Publisher  NA_Sales  \\\n",
+      "0         Wii Sports      Wii           2006.0    Sports  Nintendo     41.36   \n",
+      "1  Super Mario Bros.      NES           1985.0  Platform  Nintendo     29.08   \n",
+      "2     Mario Kart Wii      Wii           2008.0    Racing  Nintendo     15.68   \n",
+      "\n",
+      "   EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  Critic_Count  \\\n",
+      "0     28.96      3.77         8.45         82.53          76.0          51.0   \n",
+      "1      3.58      6.81         0.77         40.24           NaN           NaN   \n",
+      "2     12.76      3.79         3.29         35.52          82.0          73.0   \n",
+      "\n",
+      "  User_Score  User_Count Developer Rating  \n",
+      "0          8       322.0  Nintendo      E  \n",
+      "1        NaN         NaN       NaN    NaN  \n",
+      "2        8.3       709.0  Nintendo      E  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print('# print first 3 rows')\n",
+    "print(videoReview[:3])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Platform</th>\n",
+       "      <th>Year_of_Release</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Publisher</th>\n",
+       "      <th>NA_Sales</th>\n",
+       "      <th>EU_Sales</th>\n",
+       "      <th>JP_Sales</th>\n",
+       "      <th>Other_Sales</th>\n",
+       "      <th>Global_Sales</th>\n",
+       "      <th>Critic_Score</th>\n",
+       "      <th>Critic_Count</th>\n",
+       "      <th>User_Score</th>\n",
+       "      <th>User_Count</th>\n",
+       "      <th>Developer</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Wii Sports</td>\n",
+       "      <td>Wii</td>\n",
+       "      <td>2006.0</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>41.36</td>\n",
+       "      <td>28.96</td>\n",
+       "      <td>3.77</td>\n",
+       "      <td>8.45</td>\n",
+       "      <td>82.53</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>E</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Super Mario Bros.</td>\n",
+       "      <td>NES</td>\n",
+       "      <td>1985.0</td>\n",
+       "      <td>Platform</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>29.08</td>\n",
+       "      <td>3.58</td>\n",
+       "      <td>6.81</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>40.24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mario Kart Wii</td>\n",
+       "      <td>Wii</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>Racing</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>15.68</td>\n",
+       "      <td>12.76</td>\n",
+       "      <td>3.79</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>35.52</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>709.0</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>E</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Name Platform  Year_of_Release     Genre Publisher  NA_Sales  \\\n",
+       "0         Wii Sports      Wii           2006.0    Sports  Nintendo     41.36   \n",
+       "1  Super Mario Bros.      NES           1985.0  Platform  Nintendo     29.08   \n",
+       "2     Mario Kart Wii      Wii           2008.0    Racing  Nintendo     15.68   \n",
+       "\n",
+       "   EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  Critic_Count  \\\n",
+       "0     28.96      3.77         8.45         82.53          76.0          51.0   \n",
+       "1      3.58      6.81         0.77         40.24           NaN           NaN   \n",
+       "2     12.76      3.79         3.29         35.52          82.0          73.0   \n",
+       "\n",
+       "  User_Score  User_Count Developer Rating  \n",
+       "0          8       322.0  Nintendo      E  \n",
+       "1        NaN         NaN       NaN    NaN  \n",
+       "2        8.3       709.0  Nintendo      E  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "videoReview.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "# print columns\n",
+      "0                           Wii Sports\n",
+      "1                    Super Mario Bros.\n",
+      "2                       Mario Kart Wii\n",
+      "3                    Wii Sports Resort\n",
+      "4             Pokemon Red/Pokemon Blue\n",
+      "                     ...              \n",
+      "16714    Samurai Warriors: Sanada Maru\n",
+      "16715                 LMA Manager 2007\n",
+      "16716          Haitaka no Psychedelica\n",
+      "16717                 Spirits & Spells\n",
+      "16718              Winning Post 8 2016\n",
+      "Name: Name, Length: 16719, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('# print columns')\n",
+    "print(videoReview['Name'])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                           Wii Sports\n",
+       "1                    Super Mario Bros.\n",
+       "2                       Mario Kart Wii\n",
+       "3                    Wii Sports Resort\n",
+       "4             Pokemon Red/Pokemon Blue\n",
+       "                     ...              \n",
+       "16714    Samurai Warriors: Sanada Maru\n",
+       "16715                 LMA Manager 2007\n",
+       "16716          Haitaka no Psychedelica\n",
+       "16717                 Spirits & Spells\n",
+       "16718              Winning Post 8 2016\n",
+       "Name: Name, Length: 16719, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "videoReview['Name']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "# print columns, first 5 rows\n",
+      "0                  Wii Sports\n",
+      "1           Super Mario Bros.\n",
+      "2              Mario Kart Wii\n",
+      "3           Wii Sports Resort\n",
+      "4    Pokemon Red/Pokemon Blue\n",
+      "Name: Name, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('# print columns, first 5 rows')\n",
+    "print(videoReview['Name'][:5])\n",
+    "\n",
+    "#videoReview['Name'].head(5)\n",
+    "#videoReview['Name'].tail(5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "# Platform #\n",
+      "Platform\n",
+      "PS2     2161\n",
+      "DS      2152\n",
+      "PS3     1331\n",
+      "Wii     1320\n",
+      "X360    1262\n",
+      "PSP     1209\n",
+      "PS      1197\n",
+      "PC       974\n",
+      "XB       824\n",
+      "GBA      822\n",
+      "GC       556\n",
+      "3DS      520\n",
+      "PSV      432\n",
+      "PS4      393\n",
+      "N64      319\n",
+      "XOne     247\n",
+      "SNES     239\n",
+      "SAT      173\n",
+      "WiiU     147\n",
+      "2600     133\n",
+      "NES       98\n",
+      "GB        98\n",
+      "DC        52\n",
+      "GEN       29\n",
+      "NG        12\n",
+      "SCD        6\n",
+      "WS         6\n",
+      "3DO        3\n",
+      "TG16       2\n",
+      "GG         1\n",
+      "PCFX       1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('# Platform #')\n",
+    "print(videoReview['Platform'].value_counts())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#shape\n",
+      "Number of rows =  16719\n",
+      "Number of columns =  16\n",
+      "Shape =  (16719, 16)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#shape')\n",
+    "print('Number of rows = ', videoReview.shape[0])\n",
+    "print('Number of columns = ', videoReview.shape[1])\n",
+    "print('Shape = ', videoReview.shape)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "# Name #\n",
+      "Name\n",
+      "Need for Speed: Most Wanted                         12\n",
+      "FIFA 14                                              9\n",
+      "Ratatouille                                          9\n",
+      "LEGO Marvel Super Heroes                             9\n",
+      "Madden NFL 07                                        9\n",
+      "                                                    ..\n",
+      "Jewels of the Tropical Lost Island                   1\n",
+      "Sherlock Holmes and the Mystery of Osborne House     1\n",
+      "The King of Fighters '95 (CD)                        1\n",
+      "Megamind: Mega Team Unite                            1\n",
+      "Haitaka no Psychedelica                              1\n",
+      "Name: count, Length: 11562, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('# Name #')\n",
+    "print(videoReview['Name'].value_counts())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#head\n",
+      "                       Name Platform  Year_of_Release         Genre Publisher  \\\n",
+      "0                Wii Sports      Wii           2006.0        Sports  Nintendo   \n",
+      "1         Super Mario Bros.      NES           1985.0      Platform  Nintendo   \n",
+      "2            Mario Kart Wii      Wii           2008.0        Racing  Nintendo   \n",
+      "3         Wii Sports Resort      Wii           2009.0        Sports  Nintendo   \n",
+      "4  Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing  Nintendo   \n",
+      "\n",
+      "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
+      "0     41.36     28.96      3.77         8.45         82.53          76.0   \n",
+      "1     29.08      3.58      6.81         0.77         40.24           NaN   \n",
+      "2     15.68     12.76      3.79         3.29         35.52          82.0   \n",
+      "3     15.61     10.93      3.28         2.95         32.77          80.0   \n",
+      "4     11.27      8.89     10.22         1.00         31.37           NaN   \n",
+      "\n",
+      "   Critic_Count User_Score  User_Count Developer Rating  \n",
+      "0          51.0          8       322.0  Nintendo      E  \n",
+      "1           NaN        NaN         NaN       NaN    NaN  \n",
+      "2          73.0        8.3       709.0  Nintendo      E  \n",
+      "3          73.0          8       192.0  Nintendo      E  \n",
+      "4           NaN        NaN         NaN       NaN    NaN  \n",
+      "                       Name Platform  Year_of_Release         Genre Publisher  \\\n",
+      "0                Wii Sports      Wii           2006.0        Sports  Nintendo   \n",
+      "1         Super Mario Bros.      NES           1985.0      Platform  Nintendo   \n",
+      "2            Mario Kart Wii      Wii           2008.0        Racing  Nintendo   \n",
+      "3         Wii Sports Resort      Wii           2009.0        Sports  Nintendo   \n",
+      "4  Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing  Nintendo   \n",
+      "5                    Tetris       GB           1989.0        Puzzle  Nintendo   \n",
+      "6     New Super Mario Bros.       DS           2006.0      Platform  Nintendo   \n",
+      "7                  Wii Play      Wii           2006.0          Misc  Nintendo   \n",
+      "\n",
+      "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
+      "0     41.36     28.96      3.77         8.45         82.53          76.0   \n",
+      "1     29.08      3.58      6.81         0.77         40.24           NaN   \n",
+      "2     15.68     12.76      3.79         3.29         35.52          82.0   \n",
+      "3     15.61     10.93      3.28         2.95         32.77          80.0   \n",
+      "4     11.27      8.89     10.22         1.00         31.37           NaN   \n",
+      "5     23.20      2.26      4.22         0.58         30.26           NaN   \n",
+      "6     11.28      9.14      6.50         2.88         29.80          89.0   \n",
+      "7     13.96      9.18      2.93         2.84         28.92          58.0   \n",
+      "\n",
+      "   Critic_Count User_Score  User_Count Developer Rating  \n",
+      "0          51.0          8       322.0  Nintendo      E  \n",
+      "1           NaN        NaN         NaN       NaN    NaN  \n",
+      "2          73.0        8.3       709.0  Nintendo      E  \n",
+      "3          73.0          8       192.0  Nintendo      E  \n",
+      "4           NaN        NaN         NaN       NaN    NaN  \n",
+      "5           NaN        NaN         NaN       NaN    NaN  \n",
+      "6          65.0        8.5       431.0  Nintendo      E  \n",
+      "7          41.0        6.6       129.0  Nintendo      E  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#head')\n",
+    "print(videoReview.head())\n",
+    "print(videoReview.head(8))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#tail\n",
+      "                                Name Platform  Year_of_Release       Genre  \\\n",
+      "16714  Samurai Warriors: Sanada Maru      PS3           2016.0      Action   \n",
+      "16715               LMA Manager 2007     X360           2006.0      Sports   \n",
+      "16716        Haitaka no Psychedelica      PSV           2016.0   Adventure   \n",
+      "16717               Spirits & Spells      GBA           2003.0    Platform   \n",
+      "16718            Winning Post 8 2016      PSV           2016.0  Simulation   \n",
+      "\n",
+      "          Publisher  NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \\\n",
+      "16714    Tecmo Koei      0.00      0.00      0.01          0.0          0.01   \n",
+      "16715   Codemasters      0.00      0.01      0.00          0.0          0.01   \n",
+      "16716  Idea Factory      0.00      0.00      0.01          0.0          0.01   \n",
+      "16717       Wanadoo      0.01      0.00      0.00          0.0          0.01   \n",
+      "16718    Tecmo Koei      0.00      0.00      0.01          0.0          0.01   \n",
+      "\n",
+      "       Critic_Score  Critic_Count User_Score  User_Count Developer Rating  \n",
+      "16714           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "16715           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "16716           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "16717           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "16718           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "                                                   Name Platform  \\\n",
+      "16711                                  Aiyoku no Eustia      PSV   \n",
+      "16712                Woody Woodpecker in Crazy Castle 5      GBA   \n",
+      "16713  SCORE International Baja 1000: The Official Game      PS2   \n",
+      "16714                     Samurai Warriors: Sanada Maru      PS3   \n",
+      "16715                                  LMA Manager 2007     X360   \n",
+      "16716                           Haitaka no Psychedelica      PSV   \n",
+      "16717                                  Spirits & Spells      GBA   \n",
+      "16718                               Winning Post 8 2016      PSV   \n",
+      "\n",
+      "       Year_of_Release       Genre        Publisher  NA_Sales  EU_Sales  \\\n",
+      "16711           2014.0        Misc  dramatic create      0.00      0.00   \n",
+      "16712           2002.0    Platform            Kemco      0.01      0.00   \n",
+      "16713           2008.0      Racing       Activision      0.00      0.00   \n",
+      "16714           2016.0      Action       Tecmo Koei      0.00      0.00   \n",
+      "16715           2006.0      Sports      Codemasters      0.00      0.01   \n",
+      "16716           2016.0   Adventure     Idea Factory      0.00      0.00   \n",
+      "16717           2003.0    Platform          Wanadoo      0.01      0.00   \n",
+      "16718           2016.0  Simulation       Tecmo Koei      0.00      0.00   \n",
+      "\n",
+      "       JP_Sales  Other_Sales  Global_Sales  Critic_Score  Critic_Count  \\\n",
+      "16711      0.01          0.0          0.01           NaN           NaN   \n",
+      "16712      0.00          0.0          0.01           NaN           NaN   \n",
+      "16713      0.00          0.0          0.01           NaN           NaN   \n",
+      "16714      0.01          0.0          0.01           NaN           NaN   \n",
+      "16715      0.00          0.0          0.01           NaN           NaN   \n",
+      "16716      0.01          0.0          0.01           NaN           NaN   \n",
+      "16717      0.00          0.0          0.01           NaN           NaN   \n",
+      "16718      0.01          0.0          0.01           NaN           NaN   \n",
+      "\n",
+      "      User_Score  User_Count Developer Rating  \n",
+      "16711        NaN         NaN       NaN    NaN  \n",
+      "16712        NaN         NaN       NaN    NaN  \n",
+      "16713        NaN         NaN       NaN    NaN  \n",
+      "16714        NaN         NaN       NaN    NaN  \n",
+      "16715        NaN         NaN       NaN    NaN  \n",
+      "16716        NaN         NaN       NaN    NaN  \n",
+      "16717        NaN         NaN       NaN    NaN  \n",
+      "16718        NaN         NaN       NaN    NaN  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#tail')\n",
+    "print(videoReview.tail())\n",
+    "print(videoReview.tail(8))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#Describe\n",
+      "       Year_of_Release      NA_Sales      EU_Sales      JP_Sales  \\\n",
+      "count     16450.000000  16719.000000  16719.000000  16719.000000   \n",
+      "mean       2006.487356      0.263330      0.145025      0.077602   \n",
+      "std           5.878995      0.813514      0.503283      0.308818   \n",
+      "min        1980.000000      0.000000      0.000000      0.000000   \n",
+      "25%        2003.000000      0.000000      0.000000      0.000000   \n",
+      "50%        2007.000000      0.080000      0.020000      0.000000   \n",
+      "75%        2010.000000      0.240000      0.110000      0.040000   \n",
+      "max        2020.000000     41.360000     28.960000     10.220000   \n",
+      "\n",
+      "        Other_Sales  Global_Sales  Critic_Score  Critic_Count    User_Count  \n",
+      "count  16719.000000  16719.000000   8137.000000   8137.000000   7590.000000  \n",
+      "mean       0.047332      0.533543     68.967679     26.360821    162.229908  \n",
+      "std        0.186710      1.547935     13.938165     18.980495    561.282326  \n",
+      "min        0.000000      0.010000     13.000000      3.000000      4.000000  \n",
+      "25%        0.000000      0.060000     60.000000     12.000000     10.000000  \n",
+      "50%        0.010000      0.170000     71.000000     21.000000     24.000000  \n",
+      "75%        0.030000      0.470000     79.000000     36.000000     81.000000  \n",
+      "max       10.570000     82.530000     98.000000    113.000000  10665.000000  \n",
+      "count     16719\n",
+      "unique       31\n",
+      "top         PS2\n",
+      "freq       2161\n",
+      "Name: Platform, dtype: object\n",
+      "count    16450.000000\n",
+      "mean      2006.487356\n",
+      "std          5.878995\n",
+      "min       1980.000000\n",
+      "25%       2003.000000\n",
+      "50%       2007.000000\n",
+      "75%       2010.000000\n",
+      "max       2020.000000\n",
+      "Name: Year_of_Release, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#Describe')\n",
+    "print(videoReview.describe()) # calculates measures of central tendency\n",
+    "print(videoReview['Platform'].describe())\n",
+    "print(videoReview['Year_of_Release'].describe())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#info\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 16719 entries, 0 to 16718\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   Name             16717 non-null  object \n",
+      " 1   Platform         16719 non-null  object \n",
+      " 2   Year_of_Release  16450 non-null  float64\n",
+      " 3   Genre            16717 non-null  object \n",
+      " 4   Publisher        16665 non-null  object \n",
+      " 5   NA_Sales         16719 non-null  float64\n",
+      " 6   EU_Sales         16719 non-null  float64\n",
+      " 7   JP_Sales         16719 non-null  float64\n",
+      " 8   Other_Sales      16719 non-null  float64\n",
+      " 9   Global_Sales     16719 non-null  float64\n",
+      " 10  Critic_Score     8137 non-null   float64\n",
+      " 11  Critic_Count     8137 non-null   float64\n",
+      " 12  User_Score       10015 non-null  object \n",
+      " 13  User_Count       7590 non-null   float64\n",
+      " 14  Developer        10096 non-null  object \n",
+      " 15  Rating           9950 non-null   object \n",
+      "dtypes: float64(9), object(7)\n",
+      "memory usage: 2.0+ MB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#info')\n",
+    "print(videoReview.info()) # memory footprint and datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Transpose - Describe\n",
+      "                   count         mean         std      min      25%      50%  \\\n",
+      "Year_of_Release  16450.0  2006.487356    5.878995  1980.00  2003.00  2007.00   \n",
+      "NA_Sales         16719.0     0.263330    0.813514     0.00     0.00     0.08   \n",
+      "EU_Sales         16719.0     0.145025    0.503283     0.00     0.00     0.02   \n",
+      "JP_Sales         16719.0     0.077602    0.308818     0.00     0.00     0.00   \n",
+      "Other_Sales      16719.0     0.047332    0.186710     0.00     0.00     0.01   \n",
+      "Global_Sales     16719.0     0.533543    1.547935     0.01     0.06     0.17   \n",
+      "Critic_Score      8137.0    68.967679   13.938165    13.00    60.00    71.00   \n",
+      "Critic_Count      8137.0    26.360821   18.980495     3.00    12.00    21.00   \n",
+      "User_Count        7590.0   162.229908  561.282326     4.00    10.00    24.00   \n",
+      "\n",
+      "                     75%       max  \n",
+      "Year_of_Release  2010.00   2020.00  \n",
+      "NA_Sales            0.24     41.36  \n",
+      "EU_Sales            0.11     28.96  \n",
+      "JP_Sales            0.04     10.22  \n",
+      "Other_Sales         0.03     10.57  \n",
+      "Global_Sales        0.47     82.53  \n",
+      "Critic_Score       79.00     98.00  \n",
+      "Critic_Count       36.00    113.00  \n",
+      "User_Count         81.00  10665.00  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('Transpose - Describe')\n",
+    "print(videoReview.describe().transpose())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Iterate some rows from DF\n",
+      "#### Printing row ####\n",
+      "Wii Sports\n",
+      "#### Printing row ####\n",
+      "Super Mario Bros.\n",
+      "#### Printing row ####\n",
+      "Mario Kart Wii\n",
+      "#### Printing row ####\n",
+      "Wii Sports Resort\n",
+      "#### Printing row ####\n",
+      "Pokemon Red/Pokemon Blue\n",
+      "#### Printing row ####\n",
+      "Tetris\n",
+      "#### Printing row ####\n",
+      "New Super Mario Bros.\n",
+      "#### Printing row ####\n",
+      "Wii Play\n",
+      "#### Printing row ####\n",
+      "New Super Mario Bros. Wii\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('Iterate some rows from DF')\n",
+    "for i, row in videoReview[:9].iterrows():\n",
+    " print('#### Printing row ####')\n",
+    " print(row['Name'])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Group by Year, Platform by Count : \n",
+      "                          Name  Genre  Publisher  NA_Sales  EU_Sales  \\\n",
+      "Year_of_Release Platform                                               \n",
+      "1980.0          2600         9      9          9         9         9   \n",
+      "1981.0          2600        46     46         46        46        46   \n",
+      "1982.0          2600        36     36         36        36        36   \n",
+      "1983.0          2600        11     11         11        11        11   \n",
+      "                NES          6      6          6         6         6   \n",
+      "...                        ...    ...        ...       ...       ...   \n",
+      "2016.0          X360        13     13         13        13        13   \n",
+      "                XOne        87     87         87        87        87   \n",
+      "2017.0          PS4          1      1          1         1         1   \n",
+      "                PSV          2      2          2         2         2   \n",
+      "2020.0          DS           1      1          1         1         1   \n",
+      "\n",
+      "                          JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
+      "Year_of_Release Platform                                                      \n",
+      "1980.0          2600             9            9             9             0   \n",
+      "1981.0          2600            46           46            46             0   \n",
+      "1982.0          2600            36           36            36             0   \n",
+      "1983.0          2600            11           11            11             0   \n",
+      "                NES              6            6             6             0   \n",
+      "...                            ...          ...           ...           ...   \n",
+      "2016.0          X360            13           13            13             0   \n",
+      "                XOne            87           87            87            60   \n",
+      "2017.0          PS4              1            1             1             0   \n",
+      "                PSV              2            2             2             0   \n",
+      "2020.0          DS               1            1             1             0   \n",
+      "\n",
+      "                          Critic_Count  User_Score  User_Count  Developer  \\\n",
+      "Year_of_Release Platform                                                    \n",
+      "1980.0          2600                 0           0           0          0   \n",
+      "1981.0          2600                 0           0           0          0   \n",
+      "1982.0          2600                 0           0           0          0   \n",
+      "1983.0          2600                 0           0           0          0   \n",
+      "                NES                  0           0           0          0   \n",
+      "...                                ...         ...         ...        ...   \n",
+      "2016.0          X360                 0          12           7         12   \n",
+      "                XOne                60          74          66         74   \n",
+      "2017.0          PS4                  0           0           0          0   \n",
+      "                PSV                  0           0           0          0   \n",
+      "2020.0          DS                   0           1           0          1   \n",
+      "\n",
+      "                          Rating  \n",
+      "Year_of_Release Platform          \n",
+      "1980.0          2600           0  \n",
+      "1981.0          2600           0  \n",
+      "1982.0          2600           0  \n",
+      "1983.0          2600           0  \n",
+      "                NES            0  \n",
+      "...                          ...  \n",
+      "2016.0          X360          12  \n",
+      "                XOne          71  \n",
+      "2017.0          PS4            0  \n",
+      "                PSV            0  \n",
+      "2020.0          DS             1  \n",
+      "\n",
+      "[241 rows x 14 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Subsetting and ordering Pandas\n",
+    "print('----------')\n",
+    "print('Group by Year, Platform by Count : ')\n",
+    "print(videoReview.groupby(['Year_of_Release','Platform']).count())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Group by : for Year=2016 group by Platform and count Global Sales\n",
+      "Platform\n",
+      "3DS     15.14\n",
+      "PC       5.27\n",
+      "PS3      3.58\n",
+      "PS4     69.29\n",
+      "PSV      4.27\n",
+      "Wii      0.18\n",
+      "WiiU     4.58\n",
+      "X360     1.52\n",
+      "XOne    26.27\n",
+      "Name: Global_Sales, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Group by : for Year=2016 group by Platform and count Global Sales')\n",
+    "print(videoReview[videoReview.Year_of_Release==2016.0].groupby('Platform')['Global_Sales'].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Sorting and Ordering\n",
+      "                         Name Platform  Year_of_Release     Genre Publisher  \\\n",
+      "15               Wii Fit Plus      Wii           2009.0    Sports  Nintendo   \n",
+      "8   New Super Mario Bros. Wii      Wii           2009.0  Platform  Nintendo   \n",
+      "7                    Wii Play      Wii           2006.0      Misc  Nintendo   \n",
+      "3           Wii Sports Resort      Wii           2009.0    Sports  Nintendo   \n",
+      "2              Mario Kart Wii      Wii           2008.0    Racing  Nintendo   \n",
+      "0                  Wii Sports      Wii           2006.0    Sports  Nintendo   \n",
+      "\n",
+      "    NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
+      "15      9.01      8.49      2.53         1.77         21.79          80.0   \n",
+      "8      14.44      6.94      4.70         2.24         28.32          87.0   \n",
+      "7      13.96      9.18      2.93         2.84         28.92          58.0   \n",
+      "3      15.61     10.93      3.28         2.95         32.77          80.0   \n",
+      "2      15.68     12.76      3.79         3.29         35.52          82.0   \n",
+      "0      41.36     28.96      3.77         8.45         82.53          76.0   \n",
+      "\n",
+      "    Critic_Count User_Score  User_Count Developer Rating  \n",
+      "15          33.0        7.4        52.0  Nintendo      E  \n",
+      "8           80.0        8.4       594.0  Nintendo      E  \n",
+      "7           41.0        6.6       129.0  Nintendo      E  \n",
+      "3           73.0          8       192.0  Nintendo      E  \n",
+      "2           73.0        8.3       709.0  Nintendo      E  \n",
+      "0           51.0          8       322.0  Nintendo      E  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#More Panda functions - Sorting & Ordering\n",
+    "print('----------')\n",
+    "print('Sorting and Ordering')\n",
+    "df = videoReview[(videoReview.Platform=='Wii') & (videoReview.NA_Sales>9)]\n",
+    "print(df.sort_values('Global_Sales', ascending=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Writing a Panda to a CSV file\n",
+    "df.to_csv('/Users/brendan.tierney/video_games_wii.csv', sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Go inspect the CSV file created.\n",
+    "#Is it what you expected?\n",
+    "#Could the output be formatted differently?\n",
+    "#If so, look up the Pandas 'to_csv' function to see what you can change\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Plotting - Histogram\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jjDnfJ9XU1Mjn8+mtt95SdPT3H1Srq6vT3XffrWeffVaxsbEhN5SUlKR58+bpvvvuk9frVV5enh5++GFJ3+8GpaSkaM6cORozZowCgYA6deqkV155RSNHjpQkHThwQKmpqXr//fd10003afv27erRo4fWrl2rjIwMSdLatWuVmZmpTz/9VN26dWtQX5WVlXK73QoEAkpMTAx5PgBoLJc98l64W7DCl7OHhrsFXICG/v4OaYcoNjZWb7zxhj799FMtWbJES5cu1a5du/SHP/wh5DBUW1urwsJCHT16VJmZmdq9e7f8fr+ysrKcmri4OPXv31+rV6+WJG3atEknTpwIqvF6vUpPT3dq1qxZI7fb7YQhSerbt6/cbrdTcybV1dWqrKwMugEAgNYppO8hOuXKK6/UlVdeeUENbNmyRZmZmTp+/Lh+8pOfaNmyZerRo4cTVlJSUoLqU1JStGfPHkmS3+9XbGys2rdvX6/G7/c7NcnJyfVeNzk52ak5k1mzZunxxx+/oNkAAEBkCCkQ1dbWavHixfrrX/+q8vJy1dXVBT2+YsWKBp+rW7duKi0t1eHDh/XHP/5Ro0aNUklJifO4y+UKqjfG1Dt2utNrzlR/rvNMmzZNkyZNcu5XVlYqNTX1nPMAAIDIE1IgmjhxohYvXqyhQ4cqPT39nAHlbGJjY3XFFVdIkvr06aMNGzboqaeecq4b8vv9uuSSS5z68vJyZ9fI4/GopqZGFRUVQbtE5eXl6tevn1Nz8ODBeq976NChertPPxQXF6e4uLiQ5wIAAJEjpEBUWFioN998U7fccktj9yNjjKqrq9W1a1d5PB4VFxfrmmuukfT9xdwlJSWaM2eOJKl3796KiYlRcXGxRowYIUkqKyvT1q1bNXfuXElSZmamAoGA1q9fr5///OeSpHXr1ikQCDihCQAA2C2kQPTDXZ0L8dvf/lY333yzUlNTVVVVpcLCQv3tb39TUVGRXC6X8vLyNHPmTKWlpSktLU0zZ85Uu3btlJubK0lyu90aPXq0Jk+erA4dOigpKUlTpkxRz549NWjQIElS9+7dNWTIEN1///1auHChJOmBBx5QdnZ2gz9hBgAAWreQAtHkyZP11FNPqaCg4ILeLjt48KB8Pp/KysrkdrvVq1cvFRUVafDgwZKkqVOn6tixYxo7dqwqKiqUkZGh5cuXKyEhwTnH/PnzFR0drREjRujYsWMaOHCgFi9erKioKKdmyZIlmjBhgvNptJycHBUUFITcNwAAaF1C+h6iYcOGaeXKlUpKStJVV12lmJiYoMeXLl3aaA22FHwPEYCWhu8hah58D1Fka+jv75B2iC6++GINGzYs5OYAAABakpAC0aJFixq7DwAAgLAJ6ZuqJenkyZP64IMPtHDhQudvix04cEBHjhxptOYAAACaQ0g7RHv27NGQIUO0d+9eVVdXa/DgwUpISNDcuXN1/PhxPfvss43dJwAAQJMJaYdo4sSJ6tOnjyoqKtS2bVvn+LBhw/TXv/610ZoDAABoDiHtEK1atUofffRRvT/k2qVLF3311VeN0hgAAEBzCWmHqK6uTrW1tfWO79+/P+g7ggAAACJBSIFo8ODBWrBggXPf5XLpyJEjmj59epP8OQ8AAICmFNJbZvPnz9cNN9ygHj166Pjx48rNzdXOnTvVsWNHvf76643dIwAAQJMKKRB5vV6Vlpbq9ddf18cff6y6ujqNHj1ad911V9BF1gAAAJEgpEAkSW3bttV9992n++67rzH7AQAAaHYhBaKXX375rI/ffffdITUDAAAQDiEFookTJwbdP3HihL777jvFxsaqXbt2BCIAABBRQvqUWUVFRdDtyJEj2rFjh6677jouqgYAABEn5L9ldrq0tDTNnj273u4RAABAS9dogUiSoqKidODAgcY8JQAAQJML6Rqid999N+i+MUZlZWUqKCjQv/zLvzRKYwAAAM0lpEB0++23B913uVzq1KmTbrzxRj355JON0RcAAECzCSkQ1dXVNXYfAAAAYdOo1xABAABEopB2iCZNmtTg2vz8/FBeAgAAoNmEFIg2b96sjz/+WCdPnlS3bt0kSZ999pmioqJ07bXXOnUul6txugQAAGhCIQWiW2+9VQkJCXrppZfUvn17Sd9/WeO9996r66+/XpMnT27UJgEAAJpSSNcQPfnkk5o1a5YThiSpffv2euKJJ/iUGQAAiDghBaLKykodPHiw3vHy8nJVVVVdcFMAAADNKaRANGzYMN177716++23tX//fu3fv19vv/22Ro8ereHDhzd2jwAAAE0qpGuInn32WU2ZMkW//OUvdeLEie9PFB2t0aNHa968eY3aIAAAQFMLKRC1a9dOv//97zVv3jzt2rVLxhhdccUVio+Pb+z+AAAAmtwFfTFjWVmZysrKdOWVVyo+Pl7GmMbqCwAAoNmEFIi++eYbDRw4UFdeeaVuueUWlZWVSZJ+9atf8ZF7AAAQcUIKRL/5zW8UExOjvXv3ql27ds7xkSNHqqioqNGaAwAAaA4hXUO0fPly/eUvf1Hnzp2DjqelpWnPnj2N0hgANKfLHnkv3C0ACKOQdoiOHj0atDN0ytdff624uLgLbgoAAKA5hRSIfvGLX+jll1927rtcLtXV1WnevHm64YYbGq05AACA5hDSW2bz5s3TgAEDtHHjRtXU1Gjq1Knatm2bvv32W3300UeN3SMAAGETiW+nfjl7aLhbiDgh7RD16NFDn3zyiX7+859r8ODBOnr0qIYPH67Nmzfr8ssvb+weAQAAmtR57xCdOHFCWVlZWrhwoR5//PGm6AkAAKBZnfcOUUxMjLZu3SqXy9UU/QAAADS7kN4yu/vuu/Xiiy82di8AAABhEdJF1TU1NXrhhRdUXFysPn361PsbZvn5+Y3SHAAAQHM4r0D0xRdf6LLLLtPWrVt17bXXSpI+++yzoBreSgMAAJHmvAJRWlqaysrKtHLlSknf/6mOp59+WikpKU3SHAAAQHM4r2uITv9r9n/+85919OjRRm0IAACguYV0UfUppwckAACASHRegcjlctW7RohrhgAAQKQ7r2uIjDG65557nD/gevz4cT344IP1PmW2dOnSxusQAACgiZ1XIBo1alTQ/V/+8peN2gwAAEA4nFcgWrRoUVP1AQAAEDYXdFE1AABAa0AgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYL6yBaNasWfrZz36mhIQEJScn6/bbb9eOHTuCaowxmjFjhrxer9q2basBAwZo27ZtQTXV1dUaP368OnbsqPj4eOXk5Gj//v1BNRUVFfL5fHK73XK73fL5fDp8+HBTjwgAACJAWANRSUmJHnroIa1du1bFxcU6efKksrKydPToUadm7ty5ys/PV0FBgTZs2CCPx6PBgwerqqrKqcnLy9OyZctUWFioVatW6ciRI8rOzlZtba1Tk5ubq9LSUhUVFamoqEilpaXy+XzNOi8AAGiZXMYYE+4mTjl06JCSk5NVUlKiX/ziFzLGyOv1Ki8vTw8//LCk73eDUlJSNGfOHI0ZM0aBQECdOnXSK6+8opEjR0qSDhw4oNTUVL3//vu66aabtH37dvXo0UNr165VRkaGJGnt2rXKzMzUp59+qm7dup2zt8rKSrndbgUCASUmJjbdDwFAWFz2yHvhbgFoNF/OHhruFlqMhv7+blHXEAUCAUlSUlKSJGn37t3y+/3KyspyauLi4tS/f3+tXr1akrRp0yadOHEiqMbr9So9Pd2pWbNmjdxutxOGJKlv375yu91Ozemqq6tVWVkZdAMAAK1TiwlExhhNmjRJ1113ndLT0yVJfr9fkpSSkhJUm5KS4jzm9/sVGxur9u3bn7UmOTm53msmJyc7NaebNWuWc72R2+1WamrqhQ0IAABarBYTiMaNG6dPPvlEr7/+er3HXC5X0H1jTL1jpzu95kz1ZzvPtGnTFAgEnNu+ffsaMgYAAIhALSIQjR8/Xu+++65Wrlypzp07O8c9Ho8k1dvFKS8vd3aNPB6PampqVFFRcdaagwcP1nvdQ4cO1dt9OiUuLk6JiYlBNwAA0DqFNRAZYzRu3DgtXbpUK1asUNeuXYMe79q1qzwej4qLi51jNTU1KikpUb9+/SRJvXv3VkxMTFBNWVmZtm7d6tRkZmYqEAho/fr1Ts26desUCAScGgAAYK/ocL74Qw89pNdee01/+tOflJCQ4OwEud1utW3bVi6XS3l5eZo5c6bS0tKUlpammTNnql27dsrNzXVqR48ercmTJ6tDhw5KSkrSlClT1LNnTw0aNEiS1L17dw0ZMkT333+/Fi5cKEl64IEHlJ2d3aBPmAEAgNYtrIHomWeekSQNGDAg6PiiRYt0zz33SJKmTp2qY8eOaezYsaqoqFBGRoaWL1+uhIQEp37+/PmKjo7WiBEjdOzYMQ0cOFCLFy9WVFSUU7NkyRJNmDDB+TRaTk6OCgoKmnZAAAAQEVrU9xC1ZHwPEdC68T1EaE34HqL/E5HfQwQAABAOBCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgvehwNwCg9bnskffC3QIAnBd2iAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYL6yB6MMPP9Stt94qr9crl8uld955J+hxY4xmzJghr9ertm3basCAAdq2bVtQTXV1tcaPH6+OHTsqPj5eOTk52r9/f1BNRUWFfD6f3G633G63fD6fDh8+3MTTAQCASBHWQHT06FFdffXVKigoOOPjc+fOVX5+vgoKCrRhwwZ5PB4NHjxYVVVVTk1eXp6WLVumwsJCrVq1SkeOHFF2drZqa2udmtzcXJWWlqqoqEhFRUUqLS2Vz+dr8vkAAEBkcBljTLibkCSXy6Vly5bp9ttvl/T97pDX61VeXp4efvhhSd/vBqWkpGjOnDkaM2aMAoGAOnXqpFdeeUUjR46UJB04cECpqal6//33ddNNN2n79u3q0aOH1q5dq4yMDEnS2rVrlZmZqU8//VTdunVrUH+VlZVyu90KBAJKTExs/B8A0Ipc9sh74W4BsNqXs4eGu4UWo6G/v1vsNUS7d++W3+9XVlaWcywuLk79+/fX6tWrJUmbNm3SiRMngmq8Xq/S09OdmjVr1sjtdjthSJL69u0rt9vt1JxJdXW1Kisrg24AAKB1arGByO/3S5JSUlKCjqekpDiP+f1+xcbGqn379metSU5Ornf+5ORkp+ZMZs2a5Vxz5Ha7lZqaekHzAACAlqvFBqJTXC5X0H1jTL1jpzu95kz15zrPtGnTFAgEnNu+ffvOs3MAABApWmwg8ng8klRvF6e8vNzZNfJ4PKqpqVFFRcVZaw4ePFjv/IcOHaq3+/RDcXFxSkxMDLoBAIDWqcUGoq5du8rj8ai4uNg5VlNTo5KSEvXr10+S1Lt3b8XExATVlJWVaevWrU5NZmamAoGA1q9f79SsW7dOgUDAqQEAAHaLDueLHzlyRJ9//rlzf/fu3SotLVVSUpIuvfRS5eXlaebMmUpLS1NaWppmzpypdu3aKTc3V5Lkdrs1evRoTZ48WR06dFBSUpKmTJminj17atCgQZKk7t27a8iQIbr//vu1cOFCSdIDDzyg7OzsBn/CDAAAtG5hDUQbN27UDTfc4NyfNGmSJGnUqFFavHixpk6dqmPHjmns2LGqqKhQRkaGli9froSEBOc58+fPV3R0tEaMGKFjx45p4MCBWrx4saKiopyaJUuWaMKECc6n0XJycn70u48AAIB9Wsz3ELV0fA8R0HB8DxEQXnwP0f+J+O8hAgAAaC4EIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYL3ocDcAAAAa12WPvBfuFs7bl7OHhvX12SECAADWIxABAADrEYgAAID1uIYIaOEi8VoAAIg07BABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYLzrcDUC67JH3wt3Cefty9tBwtwAAQKMhEMEqkRg+AQBNj7fMAACA9azaIfr973+vefPmqaysTFdddZUWLFig66+/PtxtRSR2WgAArYk1O0RvvPGG8vLy9Oijj2rz5s26/vrrdfPNN2vv3r3hbg0AAISZNYEoPz9fo0eP1q9+9St1795dCxYsUGpqqp555plwtwYAAMLMirfMampqtGnTJj3yyCNBx7OysrR69eozPqe6ulrV1dXO/UAgIEmqrKxs9P7qqr9r9HMCABBJmuL36w/Pa4w5a50Vgejrr79WbW2tUlJSgo6npKTI7/ef8TmzZs3S448/Xu94ampqk/QIAIDN3Aua9vxVVVVyu90/+rgVgegUl8sVdN8YU+/YKdOmTdOkSZOc+3V1dfr222/VoUOHH31OKCorK5Wamqp9+/YpMTGx0c7bkrT2GZkv8rX2GVv7fFLrn5H5QmeMUVVVlbxe71nrrAhEHTt2VFRUVL3doPLy8nq7RqfExcUpLi4u6NjFF1/cVC0qMTGxVf6f/Ida+4zMF/la+4ytfT6p9c/IfKE5287QKVZcVB0bG6vevXuruLg46HhxcbH69esXpq4AAEBLYcUOkSRNmjRJPp9Pffr0UWZmpp577jnt3btXDz74YLhbAwAAYWZNIBo5cqS++eYb/cd//IfKysqUnp6u999/X126dAlrX3FxcZo+fXq9t+dak9Y+I/NFvtY+Y2ufT2r9MzJf03OZc30ODQAAoJWz4hoiAACAsyEQAQAA6xGIAACA9QhEAADAegSiRvDhhx/q1ltvldfrlcvl0jvvvBP0+MGDB3XPPffI6/WqXbt2GjJkiHbu3BlU4/f75fP55PF4FB8fr2uvvVZvv/12UE1FRYV8Pp/cbrfcbrd8Pp8OHz7cxNM133yXXXaZXC5X0O30vz/XVBpjxl27dmnYsGHq1KmTEhMTNWLECB08eDCoJpLXsCHzhWsNZ82apZ/97GdKSEhQcnKybr/9du3YsSOoxhijGTNmyOv1qm3bthowYIC2bdsWVFNdXa3x48erY8eOio+PV05Ojvbv3x9UE441bM75In0Nn3vuOQ0YMECJiYlyuVxnXJtIXsOGzBeONWyM+b799luNHz9e3bp1U7t27XTppZdqwoQJzt8SPaWp1o9A1AiOHj2qq6++WgUFBfUeM8bo9ttv1xdffKE//elP2rx5s7p06aJBgwbp6NGjTp3P59OOHTv07rvvasuWLRo+fLhGjhypzZs3OzW5ubkqLS1VUVGRioqKVFpaKp/P12rmk+R8LcKp22OPPdbk80kXPuPRo0eVlZUll8ulFStW6KOPPlJNTY1uvfVW1dXVOeeK1DVs6HxSeNawpKREDz30kNauXavi4mKdPHlSWVlZQf8fnDt3rvLz81VQUKANGzbI4/Fo8ODBqqqqcmry8vK0bNkyFRYWatWqVTpy5Iiys7NVW1vr1IRjDZtzPimy1/C7777TkCFD9Nvf/vZHXyuS17Ah80nNv4aNMd+BAwd04MAB/e53v9OWLVu0ePFiFRUVafTo0UGv1WTrZ9CoJJlly5Y593fs2GEkma1btzrHTp48aZKSkszzzz/vHIuPjzcvv/xy0LmSkpLMCy+8YIwx5n//93+NJLN27Vrn8TVr1hhJ5tNPP22iaeprqvmMMaZLly5m/vz5TdZ7Q4Uy41/+8hfTpk0bEwgEnJpvv/3WSDLFxcXGmMhew4bMZ0zLWcPy8nIjyZSUlBhjjKmrqzMej8fMnj3bqTl+/Lhxu93m2WefNcYYc/jwYRMTE2MKCwudmq+++sq0adPGFBUVGWNazho21XzGRPYa/tDKlSuNJFNRURF0PJLX8Id+bD5jWsYaXuh8p7z55psmNjbWnDhxwhjTtOvHDlETq66uliRddNFFzrGoqCjFxsZq1apVzrHrrrtOb7zxhr799lvV1dWpsLBQ1dXVGjBggCRpzZo1crvdysjIcJ7Tt29fud1urV69unmGOYPGmu+UOXPmqEOHDvrnf/5n/dd//ZdqamqaZY6zaciM1dXVcrlcQV8qdtFFF6lNmzZOTSSvYUPmO6UlrOGpLfakpCRJ0u7du+X3+5WVleXUxMXFqX///s7PftOmTTpx4kRQjdfrVXp6ulPTUtawqeY7JVLXsCEieQ3PR7jXsLHmCwQCSkxMVHT0998j3ZTrRyBqYj/96U/VpUsXTZs2TRUVFaqpqdHs2bPl9/tVVlbm1L3xxhs6efKkOnTooLi4OI0ZM0bLli3T5ZdfLun7a3CSk5PrnT85ObneH61tTo01nyRNnDhRhYWFWrlypcaNG6cFCxZo7Nix4RgrSENm7Nu3r+Lj4/Xwww/ru+++09GjR/Xv//7vqqurc2oieQ0bMp/UMtbQGKNJkybpuuuuU3p6uiQ5P9/T/5hzSkqK85jf71dsbKzat29/1ppwr2FTzidF9ho2RCSvYUOFew0ba75vvvlG//mf/6kxY8Y4x5py/az50x3hEhMToz/+8Y8aPXq0kpKSFBUVpUGDBunmm28OqnvsscdUUVGhDz74QB07dtQ777yjf/u3f9Pf//539ezZU5Lkcrnqnd8Yc8bjzaUx5/vNb37j1Pfq1Uvt27fXv/7rvzr/pRMuDZmxU6dOeuutt/TrX/9aTz/9tNq0aaM777xT1157raKiopy6SF3Dhs7XEtZw3Lhx+uSTT+rtXEn1f/4N+dmfXhPuNWzq+VrjGp7rHKGeJ1RNPV+417Ax5qusrNTQoUPVo0cPTZ8+/aznONt5zgeBqBn07t1bpaWlCgQCqqmpUadOnZSRkaE+ffpI+v7TOwUFBdq6dauuuuoqSdLVV1+tv//97/rv//5vPfvss/J4PPU+0SNJhw4dqpe4m1tjzHcmffv2lSR9/vnnYQ1E0rlnlKSsrCzt2rVLX3/9taKjo3XxxRfL4/Goa9eukhTRayide74zae41HD9+vN599119+OGH6ty5s3Pc4/FI+v6/Li+55BLneHl5ufOz93g8qqmpUUVFRdAuSnl5ufr16+fUhHMNm3q+M4mkNWyISF7DUDXnGjbGfFVVVRoyZIh+8pOfaNmyZYqJiQk6T1OtH2+ZNSO3261OnTpp586d2rhxo2677TZJ339qQJLatAlejqioKOcTPJmZmQoEAlq/fr3z+Lp16xQIBM76L7PmdCHzncmpT6D98B+ecPuxGX+oY8eOuvjii7VixQqVl5crJydHUmSv4Q/92Hxn0lxraIzRuHHjtHTpUq1YsaJeSOvatas8Ho+Ki4udYzU1NSopKXF+9r1791ZMTExQTVlZmbZu3erUhGsNm2u+M4mkNWyISF7DUDXHGjbWfJWVlcrKylJsbKzefffdoGsbpSZevwu6JBvGGGOqqqrM5s2bzebNm40kk5+fbzZv3mz27NljjPn+KvmVK1eaXbt2mXfeecd06dLFDB8+3Hl+TU2NueKKK8z1119v1q1bZz7//HPzu9/9zrhcLvPee+85dUOGDDG9evUya9asMWvWrDE9e/Y02dnZrWK+1atXO+f94osvzBtvvGG8Xq/Jyclp8vkaY0ZjjPnDH/5g1qxZYz7//HPzyiuvmKSkJDNp0qSgmkhdw4bMF841/PWvf23cbrf529/+ZsrKypzbd99959TMnj3buN1us3TpUrNlyxZz5513mksuucRUVlY6NQ8++KDp3Lmz+eCDD8zHH39sbrzxRnP11VebkydPOjXhWMPmmq81rGFZWZnZvHmzef75540k8+GHH5rNmzebb775xqmJ5DU813zhWsPGmK+ystJkZGSYnj17ms8//zzoPM3xzyCBqBGc+vjj6bdRo0YZY4x56qmnTOfOnU1MTIy59NJLzWOPPWaqq6uDzvHZZ5+Z4cOHm+TkZNOuXTvTq1eveh9T/+abb8xdd91lEhISTEJCgrnrrrvO+JHLSJxv06ZNJiMjw7jdbnPRRReZbt26menTp5ujR482+XyNNePDDz9sUlJSTExMjElLSzNPPvmkqaurC6qJ5DU813zhXMMzzSbJLFq0yKmpq6sz06dPNx6Px8TFxZlf/OIXZsuWLUHnOXbsmBk3bpxJSkoybdu2NdnZ2Wbv3r1BNeFYw+aarzWs4fTp0895nkhew3PNF641bIz5fuzfU5LM7t27nbqmWj/X/x8EAADAWlxDBAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1/h9D3BrRbVZwuAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Creating Graphs for a Panda\n",
+    "#plotting\n",
+    "print('----------')\n",
+    "print('Plotting - Histogram')\n",
+    "videoReview['Year_of_Release'].plot(kind='hist')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#If the chart does not appear in the above cell, just go back to that cell and rerun. It should appear now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Can you create any other plots?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Data Analitics/Week 3/.ipynb_checkpoints/train-checkpoint.csv

@@ -0,0 +1,892 @@
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+874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
+875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
+877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
+878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q

Data Analitics/Week 3/1230-Article Text-1227-1-10-20080129.pdf

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Data Analitics/Week 3/L2-Data-Science-Life-Cycle.pdf

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Data Analitics/Week 3/Lab2-Data-Understanding.pdf

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Data Analitics/Week 3/TU257-Lab2-2-Data-Exploration.ipynb

@@ -0,0 +1,1083 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TU258 - Lab 2-2 - Data Exploration\n",
+    "\n",
+    "This lab gives an example of using Pandas dataframes for analysing data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import pandas\n",
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reading a CSV File into a Panda\n",
+    "videoReview = pd.read_csv(r\"C:\\\\Users\\\\Rafael\\\\Documents\\\\DataScience\\\\Data Analitics\\\\Week 3\\\\Video_Games_Sales_as_at_22_Dec_2016.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# print first 3 rows\n",
+      "                Name Platform  Year_of_Release     Genre Publisher  NA_Sales  \\\n",
+      "0         Wii Sports      Wii           2006.0    Sports  Nintendo     41.36   \n",
+      "1  Super Mario Bros.      NES           1985.0  Platform  Nintendo     29.08   \n",
+      "2     Mario Kart Wii      Wii           2008.0    Racing  Nintendo     15.68   \n",
+      "\n",
+      "   EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  Critic_Count  \\\n",
+      "0     28.96      3.77         8.45         82.53          76.0          51.0   \n",
+      "1      3.58      6.81         0.77         40.24           NaN           NaN   \n",
+      "2     12.76      3.79         3.29         35.52          82.0          73.0   \n",
+      "\n",
+      "  User_Score  User_Count Developer Rating  \n",
+      "0          8       322.0  Nintendo      E  \n",
+      "1        NaN         NaN       NaN    NaN  \n",
+      "2        8.3       709.0  Nintendo      E  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print('# print first 3 rows')\n",
+    "print(videoReview[:3])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Platform</th>\n",
+       "      <th>Year_of_Release</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Publisher</th>\n",
+       "      <th>NA_Sales</th>\n",
+       "      <th>EU_Sales</th>\n",
+       "      <th>JP_Sales</th>\n",
+       "      <th>Other_Sales</th>\n",
+       "      <th>Global_Sales</th>\n",
+       "      <th>Critic_Score</th>\n",
+       "      <th>Critic_Count</th>\n",
+       "      <th>User_Score</th>\n",
+       "      <th>User_Count</th>\n",
+       "      <th>Developer</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Wii Sports</td>\n",
+       "      <td>Wii</td>\n",
+       "      <td>2006.0</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>41.36</td>\n",
+       "      <td>28.96</td>\n",
+       "      <td>3.77</td>\n",
+       "      <td>8.45</td>\n",
+       "      <td>82.53</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>E</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Super Mario Bros.</td>\n",
+       "      <td>NES</td>\n",
+       "      <td>1985.0</td>\n",
+       "      <td>Platform</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>29.08</td>\n",
+       "      <td>3.58</td>\n",
+       "      <td>6.81</td>\n",
+       "      <td>0.77</td>\n",
+       "      <td>40.24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mario Kart Wii</td>\n",
+       "      <td>Wii</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>Racing</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>15.68</td>\n",
+       "      <td>12.76</td>\n",
+       "      <td>3.79</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>35.52</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>709.0</td>\n",
+       "      <td>Nintendo</td>\n",
+       "      <td>E</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Name Platform  Year_of_Release     Genre Publisher  NA_Sales  \\\n",
+       "0         Wii Sports      Wii           2006.0    Sports  Nintendo     41.36   \n",
+       "1  Super Mario Bros.      NES           1985.0  Platform  Nintendo     29.08   \n",
+       "2     Mario Kart Wii      Wii           2008.0    Racing  Nintendo     15.68   \n",
+       "\n",
+       "   EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  Critic_Count  \\\n",
+       "0     28.96      3.77         8.45         82.53          76.0          51.0   \n",
+       "1      3.58      6.81         0.77         40.24           NaN           NaN   \n",
+       "2     12.76      3.79         3.29         35.52          82.0          73.0   \n",
+       "\n",
+       "  User_Score  User_Count Developer Rating  \n",
+       "0          8       322.0  Nintendo      E  \n",
+       "1        NaN         NaN       NaN    NaN  \n",
+       "2        8.3       709.0  Nintendo      E  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "videoReview.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "# print columns\n",
+      "0                           Wii Sports\n",
+      "1                    Super Mario Bros.\n",
+      "2                       Mario Kart Wii\n",
+      "3                    Wii Sports Resort\n",
+      "4             Pokemon Red/Pokemon Blue\n",
+      "                     ...              \n",
+      "16714    Samurai Warriors: Sanada Maru\n",
+      "16715                 LMA Manager 2007\n",
+      "16716          Haitaka no Psychedelica\n",
+      "16717                 Spirits & Spells\n",
+      "16718              Winning Post 8 2016\n",
+      "Name: Name, Length: 16719, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('# print columns')\n",
+    "print(videoReview['Name'])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                           Wii Sports\n",
+       "1                    Super Mario Bros.\n",
+       "2                       Mario Kart Wii\n",
+       "3                    Wii Sports Resort\n",
+       "4             Pokemon Red/Pokemon Blue\n",
+       "                     ...              \n",
+       "16714    Samurai Warriors: Sanada Maru\n",
+       "16715                 LMA Manager 2007\n",
+       "16716          Haitaka no Psychedelica\n",
+       "16717                 Spirits & Spells\n",
+       "16718              Winning Post 8 2016\n",
+       "Name: Name, Length: 16719, dtype: object"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "videoReview['Name']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "# print columns, first 5 rows\n",
+      "0                  Wii Sports\n",
+      "1           Super Mario Bros.\n",
+      "2              Mario Kart Wii\n",
+      "3           Wii Sports Resort\n",
+      "4    Pokemon Red/Pokemon Blue\n",
+      "Name: Name, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('# print columns, first 5 rows')\n",
+    "print(videoReview['Name'][:5])\n",
+    "\n",
+    "#videoReview['Name'].head(5)\n",
+    "#videoReview['Name'].tail(5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "# Platform #\n",
+      "Platform\n",
+      "PS2     2161\n",
+      "DS      2152\n",
+      "PS3     1331\n",
+      "Wii     1320\n",
+      "X360    1262\n",
+      "PSP     1209\n",
+      "PS      1197\n",
+      "PC       974\n",
+      "XB       824\n",
+      "GBA      822\n",
+      "GC       556\n",
+      "3DS      520\n",
+      "PSV      432\n",
+      "PS4      393\n",
+      "N64      319\n",
+      "XOne     247\n",
+      "SNES     239\n",
+      "SAT      173\n",
+      "WiiU     147\n",
+      "2600     133\n",
+      "NES       98\n",
+      "GB        98\n",
+      "DC        52\n",
+      "GEN       29\n",
+      "NG        12\n",
+      "SCD        6\n",
+      "WS         6\n",
+      "3DO        3\n",
+      "TG16       2\n",
+      "GG         1\n",
+      "PCFX       1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('# Platform #')\n",
+    "print(videoReview['Platform'].value_counts())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#shape\n",
+      "Number of rows =  16719\n",
+      "Number of columns =  16\n",
+      "Shape =  (16719, 16)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#shape')\n",
+    "print('Number of rows = ', videoReview.shape[0])\n",
+    "print('Number of columns = ', videoReview.shape[1])\n",
+    "print('Shape = ', videoReview.shape)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "# Name #\n",
+      "Name\n",
+      "Need for Speed: Most Wanted                         12\n",
+      "FIFA 14                                              9\n",
+      "Ratatouille                                          9\n",
+      "LEGO Marvel Super Heroes                             9\n",
+      "Madden NFL 07                                        9\n",
+      "                                                    ..\n",
+      "Jewels of the Tropical Lost Island                   1\n",
+      "Sherlock Holmes and the Mystery of Osborne House     1\n",
+      "The King of Fighters '95 (CD)                        1\n",
+      "Megamind: Mega Team Unite                            1\n",
+      "Haitaka no Psychedelica                              1\n",
+      "Name: count, Length: 11562, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('# Name #')\n",
+    "print(videoReview['Name'].value_counts())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#head\n",
+      "                       Name Platform  Year_of_Release         Genre Publisher  \\\n",
+      "0                Wii Sports      Wii           2006.0        Sports  Nintendo   \n",
+      "1         Super Mario Bros.      NES           1985.0      Platform  Nintendo   \n",
+      "2            Mario Kart Wii      Wii           2008.0        Racing  Nintendo   \n",
+      "3         Wii Sports Resort      Wii           2009.0        Sports  Nintendo   \n",
+      "4  Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing  Nintendo   \n",
+      "\n",
+      "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
+      "0     41.36     28.96      3.77         8.45         82.53          76.0   \n",
+      "1     29.08      3.58      6.81         0.77         40.24           NaN   \n",
+      "2     15.68     12.76      3.79         3.29         35.52          82.0   \n",
+      "3     15.61     10.93      3.28         2.95         32.77          80.0   \n",
+      "4     11.27      8.89     10.22         1.00         31.37           NaN   \n",
+      "\n",
+      "   Critic_Count User_Score  User_Count Developer Rating  \n",
+      "0          51.0          8       322.0  Nintendo      E  \n",
+      "1           NaN        NaN         NaN       NaN    NaN  \n",
+      "2          73.0        8.3       709.0  Nintendo      E  \n",
+      "3          73.0          8       192.0  Nintendo      E  \n",
+      "4           NaN        NaN         NaN       NaN    NaN  \n",
+      "                       Name Platform  Year_of_Release         Genre Publisher  \\\n",
+      "0                Wii Sports      Wii           2006.0        Sports  Nintendo   \n",
+      "1         Super Mario Bros.      NES           1985.0      Platform  Nintendo   \n",
+      "2            Mario Kart Wii      Wii           2008.0        Racing  Nintendo   \n",
+      "3         Wii Sports Resort      Wii           2009.0        Sports  Nintendo   \n",
+      "4  Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing  Nintendo   \n",
+      "5                    Tetris       GB           1989.0        Puzzle  Nintendo   \n",
+      "6     New Super Mario Bros.       DS           2006.0      Platform  Nintendo   \n",
+      "7                  Wii Play      Wii           2006.0          Misc  Nintendo   \n",
+      "\n",
+      "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
+      "0     41.36     28.96      3.77         8.45         82.53          76.0   \n",
+      "1     29.08      3.58      6.81         0.77         40.24           NaN   \n",
+      "2     15.68     12.76      3.79         3.29         35.52          82.0   \n",
+      "3     15.61     10.93      3.28         2.95         32.77          80.0   \n",
+      "4     11.27      8.89     10.22         1.00         31.37           NaN   \n",
+      "5     23.20      2.26      4.22         0.58         30.26           NaN   \n",
+      "6     11.28      9.14      6.50         2.88         29.80          89.0   \n",
+      "7     13.96      9.18      2.93         2.84         28.92          58.0   \n",
+      "\n",
+      "   Critic_Count User_Score  User_Count Developer Rating  \n",
+      "0          51.0          8       322.0  Nintendo      E  \n",
+      "1           NaN        NaN         NaN       NaN    NaN  \n",
+      "2          73.0        8.3       709.0  Nintendo      E  \n",
+      "3          73.0          8       192.0  Nintendo      E  \n",
+      "4           NaN        NaN         NaN       NaN    NaN  \n",
+      "5           NaN        NaN         NaN       NaN    NaN  \n",
+      "6          65.0        8.5       431.0  Nintendo      E  \n",
+      "7          41.0        6.6       129.0  Nintendo      E  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#head')\n",
+    "print(videoReview.head())\n",
+    "print(videoReview.head(8))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#tail\n",
+      "                                Name Platform  Year_of_Release       Genre  \\\n",
+      "16714  Samurai Warriors: Sanada Maru      PS3           2016.0      Action   \n",
+      "16715               LMA Manager 2007     X360           2006.0      Sports   \n",
+      "16716        Haitaka no Psychedelica      PSV           2016.0   Adventure   \n",
+      "16717               Spirits & Spells      GBA           2003.0    Platform   \n",
+      "16718            Winning Post 8 2016      PSV           2016.0  Simulation   \n",
+      "\n",
+      "          Publisher  NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \\\n",
+      "16714    Tecmo Koei      0.00      0.00      0.01          0.0          0.01   \n",
+      "16715   Codemasters      0.00      0.01      0.00          0.0          0.01   \n",
+      "16716  Idea Factory      0.00      0.00      0.01          0.0          0.01   \n",
+      "16717       Wanadoo      0.01      0.00      0.00          0.0          0.01   \n",
+      "16718    Tecmo Koei      0.00      0.00      0.01          0.0          0.01   \n",
+      "\n",
+      "       Critic_Score  Critic_Count User_Score  User_Count Developer Rating  \n",
+      "16714           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "16715           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "16716           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "16717           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "16718           NaN           NaN        NaN         NaN       NaN    NaN  \n",
+      "                                                   Name Platform  \\\n",
+      "16711                                  Aiyoku no Eustia      PSV   \n",
+      "16712                Woody Woodpecker in Crazy Castle 5      GBA   \n",
+      "16713  SCORE International Baja 1000: The Official Game      PS2   \n",
+      "16714                     Samurai Warriors: Sanada Maru      PS3   \n",
+      "16715                                  LMA Manager 2007     X360   \n",
+      "16716                           Haitaka no Psychedelica      PSV   \n",
+      "16717                                  Spirits & Spells      GBA   \n",
+      "16718                               Winning Post 8 2016      PSV   \n",
+      "\n",
+      "       Year_of_Release       Genre        Publisher  NA_Sales  EU_Sales  \\\n",
+      "16711           2014.0        Misc  dramatic create      0.00      0.00   \n",
+      "16712           2002.0    Platform            Kemco      0.01      0.00   \n",
+      "16713           2008.0      Racing       Activision      0.00      0.00   \n",
+      "16714           2016.0      Action       Tecmo Koei      0.00      0.00   \n",
+      "16715           2006.0      Sports      Codemasters      0.00      0.01   \n",
+      "16716           2016.0   Adventure     Idea Factory      0.00      0.00   \n",
+      "16717           2003.0    Platform          Wanadoo      0.01      0.00   \n",
+      "16718           2016.0  Simulation       Tecmo Koei      0.00      0.00   \n",
+      "\n",
+      "       JP_Sales  Other_Sales  Global_Sales  Critic_Score  Critic_Count  \\\n",
+      "16711      0.01          0.0          0.01           NaN           NaN   \n",
+      "16712      0.00          0.0          0.01           NaN           NaN   \n",
+      "16713      0.00          0.0          0.01           NaN           NaN   \n",
+      "16714      0.01          0.0          0.01           NaN           NaN   \n",
+      "16715      0.00          0.0          0.01           NaN           NaN   \n",
+      "16716      0.01          0.0          0.01           NaN           NaN   \n",
+      "16717      0.00          0.0          0.01           NaN           NaN   \n",
+      "16718      0.01          0.0          0.01           NaN           NaN   \n",
+      "\n",
+      "      User_Score  User_Count Developer Rating  \n",
+      "16711        NaN         NaN       NaN    NaN  \n",
+      "16712        NaN         NaN       NaN    NaN  \n",
+      "16713        NaN         NaN       NaN    NaN  \n",
+      "16714        NaN         NaN       NaN    NaN  \n",
+      "16715        NaN         NaN       NaN    NaN  \n",
+      "16716        NaN         NaN       NaN    NaN  \n",
+      "16717        NaN         NaN       NaN    NaN  \n",
+      "16718        NaN         NaN       NaN    NaN  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#tail')\n",
+    "print(videoReview.tail())\n",
+    "print(videoReview.tail(8))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#Describe\n",
+      "       Year_of_Release      NA_Sales      EU_Sales      JP_Sales  \\\n",
+      "count     16450.000000  16719.000000  16719.000000  16719.000000   \n",
+      "mean       2006.487356      0.263330      0.145025      0.077602   \n",
+      "std           5.878995      0.813514      0.503283      0.308818   \n",
+      "min        1980.000000      0.000000      0.000000      0.000000   \n",
+      "25%        2003.000000      0.000000      0.000000      0.000000   \n",
+      "50%        2007.000000      0.080000      0.020000      0.000000   \n",
+      "75%        2010.000000      0.240000      0.110000      0.040000   \n",
+      "max        2020.000000     41.360000     28.960000     10.220000   \n",
+      "\n",
+      "        Other_Sales  Global_Sales  Critic_Score  Critic_Count    User_Count  \n",
+      "count  16719.000000  16719.000000   8137.000000   8137.000000   7590.000000  \n",
+      "mean       0.047332      0.533543     68.967679     26.360821    162.229908  \n",
+      "std        0.186710      1.547935     13.938165     18.980495    561.282326  \n",
+      "min        0.000000      0.010000     13.000000      3.000000      4.000000  \n",
+      "25%        0.000000      0.060000     60.000000     12.000000     10.000000  \n",
+      "50%        0.010000      0.170000     71.000000     21.000000     24.000000  \n",
+      "75%        0.030000      0.470000     79.000000     36.000000     81.000000  \n",
+      "max       10.570000     82.530000     98.000000    113.000000  10665.000000  \n",
+      "count     16719\n",
+      "unique       31\n",
+      "top         PS2\n",
+      "freq       2161\n",
+      "Name: Platform, dtype: object\n",
+      "count    16450.000000\n",
+      "mean      2006.487356\n",
+      "std          5.878995\n",
+      "min       1980.000000\n",
+      "25%       2003.000000\n",
+      "50%       2007.000000\n",
+      "75%       2010.000000\n",
+      "max       2020.000000\n",
+      "Name: Year_of_Release, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#Describe')\n",
+    "print(videoReview.describe()) # calculates measures of central tendency\n",
+    "print(videoReview['Platform'].describe())\n",
+    "print(videoReview['Year_of_Release'].describe())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "#info\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 16719 entries, 0 to 16718\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   Name             16717 non-null  object \n",
+      " 1   Platform         16719 non-null  object \n",
+      " 2   Year_of_Release  16450 non-null  float64\n",
+      " 3   Genre            16717 non-null  object \n",
+      " 4   Publisher        16665 non-null  object \n",
+      " 5   NA_Sales         16719 non-null  float64\n",
+      " 6   EU_Sales         16719 non-null  float64\n",
+      " 7   JP_Sales         16719 non-null  float64\n",
+      " 8   Other_Sales      16719 non-null  float64\n",
+      " 9   Global_Sales     16719 non-null  float64\n",
+      " 10  Critic_Score     8137 non-null   float64\n",
+      " 11  Critic_Count     8137 non-null   float64\n",
+      " 12  User_Score       10015 non-null  object \n",
+      " 13  User_Count       7590 non-null   float64\n",
+      " 14  Developer        10096 non-null  object \n",
+      " 15  Rating           9950 non-null   object \n",
+      "dtypes: float64(9), object(7)\n",
+      "memory usage: 2.0+ MB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('#info')\n",
+    "print(videoReview.info()) # memory footprint and datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Transpose - Describe\n",
+      "                   count         mean         std      min      25%      50%  \\\n",
+      "Year_of_Release  16450.0  2006.487356    5.878995  1980.00  2003.00  2007.00   \n",
+      "NA_Sales         16719.0     0.263330    0.813514     0.00     0.00     0.08   \n",
+      "EU_Sales         16719.0     0.145025    0.503283     0.00     0.00     0.02   \n",
+      "JP_Sales         16719.0     0.077602    0.308818     0.00     0.00     0.00   \n",
+      "Other_Sales      16719.0     0.047332    0.186710     0.00     0.00     0.01   \n",
+      "Global_Sales     16719.0     0.533543    1.547935     0.01     0.06     0.17   \n",
+      "Critic_Score      8137.0    68.967679   13.938165    13.00    60.00    71.00   \n",
+      "Critic_Count      8137.0    26.360821   18.980495     3.00    12.00    21.00   \n",
+      "User_Count        7590.0   162.229908  561.282326     4.00    10.00    24.00   \n",
+      "\n",
+      "                     75%       max  \n",
+      "Year_of_Release  2010.00   2020.00  \n",
+      "NA_Sales            0.24     41.36  \n",
+      "EU_Sales            0.11     28.96  \n",
+      "JP_Sales            0.04     10.22  \n",
+      "Other_Sales         0.03     10.57  \n",
+      "Global_Sales        0.47     82.53  \n",
+      "Critic_Score       79.00     98.00  \n",
+      "Critic_Count       36.00    113.00  \n",
+      "User_Count         81.00  10665.00  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('Transpose - Describe')\n",
+    "print(videoReview.describe().transpose())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Iterate some rows from DF\n",
+      "#### Printing row ####\n",
+      "Wii Sports\n",
+      "#### Printing row ####\n",
+      "Super Mario Bros.\n",
+      "#### Printing row ####\n",
+      "Mario Kart Wii\n",
+      "#### Printing row ####\n",
+      "Wii Sports Resort\n",
+      "#### Printing row ####\n",
+      "Pokemon Red/Pokemon Blue\n",
+      "#### Printing row ####\n",
+      "Tetris\n",
+      "#### Printing row ####\n",
+      "New Super Mario Bros.\n",
+      "#### Printing row ####\n",
+      "Wii Play\n",
+      "#### Printing row ####\n",
+      "New Super Mario Bros. Wii\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('----------')\n",
+    "print('Iterate some rows from DF')\n",
+    "for i, row in videoReview[:9].iterrows():\n",
+    " print('#### Printing row ####')\n",
+    " print(row['Name'])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Group by Year, Platform by Count : \n",
+      "                          Name  Genre  Publisher  NA_Sales  EU_Sales  \\\n",
+      "Year_of_Release Platform                                               \n",
+      "1980.0          2600         9      9          9         9         9   \n",
+      "1981.0          2600        46     46         46        46        46   \n",
+      "1982.0          2600        36     36         36        36        36   \n",
+      "1983.0          2600        11     11         11        11        11   \n",
+      "                NES          6      6          6         6         6   \n",
+      "...                        ...    ...        ...       ...       ...   \n",
+      "2016.0          X360        13     13         13        13        13   \n",
+      "                XOne        87     87         87        87        87   \n",
+      "2017.0          PS4          1      1          1         1         1   \n",
+      "                PSV          2      2          2         2         2   \n",
+      "2020.0          DS           1      1          1         1         1   \n",
+      "\n",
+      "                          JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
+      "Year_of_Release Platform                                                      \n",
+      "1980.0          2600             9            9             9             0   \n",
+      "1981.0          2600            46           46            46             0   \n",
+      "1982.0          2600            36           36            36             0   \n",
+      "1983.0          2600            11           11            11             0   \n",
+      "                NES              6            6             6             0   \n",
+      "...                            ...          ...           ...           ...   \n",
+      "2016.0          X360            13           13            13             0   \n",
+      "                XOne            87           87            87            60   \n",
+      "2017.0          PS4              1            1             1             0   \n",
+      "                PSV              2            2             2             0   \n",
+      "2020.0          DS               1            1             1             0   \n",
+      "\n",
+      "                          Critic_Count  User_Score  User_Count  Developer  \\\n",
+      "Year_of_Release Platform                                                    \n",
+      "1980.0          2600                 0           0           0          0   \n",
+      "1981.0          2600                 0           0           0          0   \n",
+      "1982.0          2600                 0           0           0          0   \n",
+      "1983.0          2600                 0           0           0          0   \n",
+      "                NES                  0           0           0          0   \n",
+      "...                                ...         ...         ...        ...   \n",
+      "2016.0          X360                 0          12           7         12   \n",
+      "                XOne                60          74          66         74   \n",
+      "2017.0          PS4                  0           0           0          0   \n",
+      "                PSV                  0           0           0          0   \n",
+      "2020.0          DS                   0           1           0          1   \n",
+      "\n",
+      "                          Rating  \n",
+      "Year_of_Release Platform          \n",
+      "1980.0          2600           0  \n",
+      "1981.0          2600           0  \n",
+      "1982.0          2600           0  \n",
+      "1983.0          2600           0  \n",
+      "                NES            0  \n",
+      "...                          ...  \n",
+      "2016.0          X360          12  \n",
+      "                XOne          71  \n",
+      "2017.0          PS4            0  \n",
+      "                PSV            0  \n",
+      "2020.0          DS             1  \n",
+      "\n",
+      "[241 rows x 14 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Subsetting and ordering Pandas\n",
+    "print('----------')\n",
+    "print('Group by Year, Platform by Count : ')\n",
+    "print(videoReview.groupby(['Year_of_Release','Platform']).count())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Group by : for Year=2016 group by Platform and count Global Sales\n",
+      "Platform\n",
+      "3DS     15.14\n",
+      "PC       5.27\n",
+      "PS3      3.58\n",
+      "PS4     69.29\n",
+      "PSV      4.27\n",
+      "Wii      0.18\n",
+      "WiiU     4.58\n",
+      "X360     1.52\n",
+      "XOne    26.27\n",
+      "Name: Global_Sales, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Group by : for Year=2016 group by Platform and count Global Sales')\n",
+    "print(videoReview[videoReview.Year_of_Release==2016.0].groupby('Platform')['Global_Sales'].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Sorting and Ordering\n",
+      "                         Name Platform  Year_of_Release     Genre Publisher  \\\n",
+      "15               Wii Fit Plus      Wii           2009.0    Sports  Nintendo   \n",
+      "8   New Super Mario Bros. Wii      Wii           2009.0  Platform  Nintendo   \n",
+      "7                    Wii Play      Wii           2006.0      Misc  Nintendo   \n",
+      "3           Wii Sports Resort      Wii           2009.0    Sports  Nintendo   \n",
+      "2              Mario Kart Wii      Wii           2008.0    Racing  Nintendo   \n",
+      "0                  Wii Sports      Wii           2006.0    Sports  Nintendo   \n",
+      "\n",
+      "    NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
+      "15      9.01      8.49      2.53         1.77         21.79          80.0   \n",
+      "8      14.44      6.94      4.70         2.24         28.32          87.0   \n",
+      "7      13.96      9.18      2.93         2.84         28.92          58.0   \n",
+      "3      15.61     10.93      3.28         2.95         32.77          80.0   \n",
+      "2      15.68     12.76      3.79         3.29         35.52          82.0   \n",
+      "0      41.36     28.96      3.77         8.45         82.53          76.0   \n",
+      "\n",
+      "    Critic_Count User_Score  User_Count Developer Rating  \n",
+      "15          33.0        7.4        52.0  Nintendo      E  \n",
+      "8           80.0        8.4       594.0  Nintendo      E  \n",
+      "7           41.0        6.6       129.0  Nintendo      E  \n",
+      "3           73.0          8       192.0  Nintendo      E  \n",
+      "2           73.0        8.3       709.0  Nintendo      E  \n",
+      "0           51.0          8       322.0  Nintendo      E  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#More Panda functions - Sorting & Ordering\n",
+    "print('----------')\n",
+    "print('Sorting and Ordering')\n",
+    "df = videoReview[(videoReview.Platform=='Wii') & (videoReview.NA_Sales>9)]\n",
+    "print(df.sort_values('Global_Sales', ascending=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Writing a Panda to a CSV file\n",
+    "df.to_csv('/Users/Rafael/video_games_wii.csv', sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Unnamed: 0                       Name Platform  Year_of_Release     Genre  \\\n",
+      "0           0                 Wii Sports      Wii           2006.0    Sports   \n",
+      "1           2             Mario Kart Wii      Wii           2008.0    Racing   \n",
+      "2           3          Wii Sports Resort      Wii           2009.0    Sports   \n",
+      "3           7                   Wii Play      Wii           2006.0      Misc   \n",
+      "4           8  New Super Mario Bros. Wii      Wii           2009.0  Platform   \n",
+      "\n",
+      "  Publisher  NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \\\n",
+      "0  Nintendo     41.36     28.96      3.77         8.45         82.53   \n",
+      "1  Nintendo     15.68     12.76      3.79         3.29         35.52   \n",
+      "2  Nintendo     15.61     10.93      3.28         2.95         32.77   \n",
+      "3  Nintendo     13.96      9.18      2.93         2.84         28.92   \n",
+      "4  Nintendo     14.44      6.94      4.70         2.24         28.32   \n",
+      "\n",
+      "   Critic_Score  Critic_Count  User_Score  User_Count Developer Rating  \n",
+      "0          76.0          51.0         8.0       322.0  Nintendo      E  \n",
+      "1          82.0          73.0         8.3       709.0  Nintendo      E  \n",
+      "2          80.0          73.0         8.0       192.0  Nintendo      E  \n",
+      "3          58.0          41.0         6.6       129.0  Nintendo      E  \n",
+      "4          87.0          80.0         8.4       594.0  Nintendo      E  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Go inspect the CSV file created.\n",
+    "#Is it what you expected?\n",
+    "#Could the output be formatted differently?\n",
+    "#If so, look up the Pandas 'to_csv' function to see what you can change\n",
+    "df_loaded = pd.read_csv('/Users/Rafael/video_games_wii.csv', sep=',');\n",
+    "print(df_loaded.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Get' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[41], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Get\u001b[38;5;241m-\u001b[39mContent \u001b[38;5;241m/\u001b[39mUsers\u001b[38;5;241m/\u001b[39mRafael\u001b[38;5;241m/\u001b[39mvideo_games_wii\u001b[38;5;241m.\u001b[39mcsv\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'Get' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "Get-Content /Users/Rafael/video_games_wii.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('/Users/Rafael/video_games_wii.csv', sep=',', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------\n",
+      "Plotting - Histogram\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jjDnfJ9XU1Mjn8+mtt95SdPT3H1Srq6vT3XffrWeffVaxsbEhN5SUlKR58+bpvvvuk9frVV5enh5++GFJ3+8GpaSkaM6cORozZowCgYA6deqkV155RSNHjpQkHThwQKmpqXr//fd10003afv27erRo4fWrl2rjIwMSdLatWuVmZmpTz/9VN26dWtQX5WVlXK73QoEAkpMTAx5PgBoLJc98l64W7DCl7OHhrsFXICG/v4OaYcoNjZWb7zxhj799FMtWbJES5cu1a5du/SHP/wh5DBUW1urwsJCHT16VJmZmdq9e7f8fr+ysrKcmri4OPXv31+rV6+WJG3atEknTpwIqvF6vUpPT3dq1qxZI7fb7YQhSerbt6/cbrdTcybV1dWqrKwMugEAgNYppO8hOuXKK6/UlVdeeUENbNmyRZmZmTp+/Lh+8pOfaNmyZerRo4cTVlJSUoLqU1JStGfPHkmS3+9XbGys2rdvX6/G7/c7NcnJyfVeNzk52ak5k1mzZunxxx+/oNkAAEBkCCkQ1dbWavHixfrrX/+q8vJy1dXVBT2+YsWKBp+rW7duKi0t1eHDh/XHP/5Ro0aNUklJifO4y+UKqjfG1Dt2utNrzlR/rvNMmzZNkyZNcu5XVlYqNTX1nPMAAIDIE1IgmjhxohYvXqyhQ4cqPT39nAHlbGJjY3XFFVdIkvr06aMNGzboqaeecq4b8vv9uuSSS5z68vJyZ9fI4/GopqZGFRUVQbtE5eXl6tevn1Nz8ODBeq976NChertPPxQXF6e4uLiQ5wIAAJEjpEBUWFioN998U7fccktj9yNjjKqrq9W1a1d5PB4VFxfrmmuukfT9xdwlJSWaM2eOJKl3796KiYlRcXGxRowYIUkqKyvT1q1bNXfuXElSZmamAoGA1q9fr5///OeSpHXr1ikQCDihCQAA2C2kQPTDXZ0L8dvf/lY333yzUlNTVVVVpcLCQv3tb39TUVGRXC6X8vLyNHPmTKWlpSktLU0zZ85Uu3btlJubK0lyu90aPXq0Jk+erA4dOigpKUlTpkxRz549NWjQIElS9+7dNWTIEN1///1auHChJOmBBx5QdnZ2gz9hBgAAWreQAtHkyZP11FNPqaCg4ILeLjt48KB8Pp/KysrkdrvVq1cvFRUVafDgwZKkqVOn6tixYxo7dqwqKiqUkZGh5cuXKyEhwTnH/PnzFR0drREjRujYsWMaOHCgFi9erKioKKdmyZIlmjBhgvNptJycHBUUFITcNwAAaF1C+h6iYcOGaeXKlUpKStJVV12lmJiYoMeXLl3aaA22FHwPEYCWhu8hah58D1Fka+jv75B2iC6++GINGzYs5OYAAABakpAC0aJFixq7DwAAgLAJ6ZuqJenkyZP64IMPtHDhQudvix04cEBHjhxptOYAAACaQ0g7RHv27NGQIUO0d+9eVVdXa/DgwUpISNDcuXN1/PhxPfvss43dJwAAQJMJaYdo4sSJ6tOnjyoqKtS2bVvn+LBhw/TXv/610ZoDAABoDiHtEK1atUofffRRvT/k2qVLF3311VeN0hgAAEBzCWmHqK6uTrW1tfWO79+/P+g7ggAAACJBSIFo8ODBWrBggXPf5XLpyJEjmj59epP8OQ8AAICmFNJbZvPnz9cNN9ygHj166Pjx48rNzdXOnTvVsWNHvf76643dIwAAQJMKKRB5vV6Vlpbq9ddf18cff6y6ujqNHj1ad911V9BF1gAAAJEgpEAkSW3bttV9992n++67rzH7AQAAaHYhBaKXX375rI/ffffdITUDAAAQDiEFookTJwbdP3HihL777jvFxsaqXbt2BCIAABBRQvqUWUVFRdDtyJEj2rFjh6677jouqgYAABEn5L9ldrq0tDTNnj273u4RAABAS9dogUiSoqKidODAgcY8JQAAQJML6Rqid999N+i+MUZlZWUqKCjQv/zLvzRKYwAAAM0lpEB0++23B913uVzq1KmTbrzxRj355JON0RcAAECzCSkQ1dXVNXYfAAAAYdOo1xABAABEopB2iCZNmtTg2vz8/FBeAgAAoNmEFIg2b96sjz/+WCdPnlS3bt0kSZ999pmioqJ07bXXOnUul6txugQAAGhCIQWiW2+9VQkJCXrppZfUvn17Sd9/WeO9996r66+/XpMnT27UJgEAAJpSSNcQPfnkk5o1a5YThiSpffv2euKJJ/iUGQAAiDghBaLKykodPHiw3vHy8nJVVVVdcFMAAADNKaRANGzYMN177716++23tX//fu3fv19vv/22Ro8ereHDhzd2jwAAAE0qpGuInn32WU2ZMkW//OUvdeLEie9PFB2t0aNHa968eY3aIAAAQFMLKRC1a9dOv//97zVv3jzt2rVLxhhdccUVio+Pb+z+AAAAmtwFfTFjWVmZysrKdOWVVyo+Pl7GmMbqCwAAoNmEFIi++eYbDRw4UFdeeaVuueUWlZWVSZJ+9atf8ZF7AAAQcUIKRL/5zW8UExOjvXv3ql27ds7xkSNHqqioqNGaAwAAaA4hXUO0fPly/eUvf1Hnzp2DjqelpWnPnj2N0hgANKfLHnkv3C0ACKOQdoiOHj0atDN0ytdff624uLgLbgoAAKA5hRSIfvGLX+jll1927rtcLtXV1WnevHm64YYbGq05AACA5hDSW2bz5s3TgAEDtHHjRtXU1Gjq1Knatm2bvv32W3300UeN3SMAAGETiW+nfjl7aLhbiDgh7RD16NFDn3zyiX7+859r8ODBOnr0qIYPH67Nmzfr8ssvb+weAQAAmtR57xCdOHFCWVlZWrhwoR5//PGm6AkAAKBZnfcOUUxMjLZu3SqXy9UU/QAAADS7kN4yu/vuu/Xiiy82di8AAABhEdJF1TU1NXrhhRdUXFysPn361PsbZvn5+Y3SHAAAQHM4r0D0xRdf6LLLLtPWrVt17bXXSpI+++yzoBreSgMAAJHmvAJRWlqaysrKtHLlSknf/6mOp59+WikpKU3SHAAAQHM4r2uITv9r9n/+85919OjRRm0IAACguYV0UfUppwckAACASHRegcjlctW7RohrhgAAQKQ7r2uIjDG65557nD/gevz4cT344IP1PmW2dOnSxusQAACgiZ1XIBo1alTQ/V/+8peN2gwAAEA4nFcgWrRoUVP1AQAAEDYXdFE1AABAa0AgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYL6yBaNasWfrZz36mhIQEJScn6/bbb9eOHTuCaowxmjFjhrxer9q2basBAwZo27ZtQTXV1dUaP368OnbsqPj4eOXk5Gj//v1BNRUVFfL5fHK73XK73fL5fDp8+HBTjwgAACJAWANRSUmJHnroIa1du1bFxcU6efKksrKydPToUadm7ty5ys/PV0FBgTZs2CCPx6PBgwerqqrKqcnLy9OyZctUWFioVatW6ciRI8rOzlZtba1Tk5ubq9LSUhUVFamoqEilpaXy+XzNOi8AAGiZXMYYE+4mTjl06JCSk5NVUlKiX/ziFzLGyOv1Ki8vTw8//LCk73eDUlJSNGfOHI0ZM0aBQECdOnXSK6+8opEjR0qSDhw4oNTUVL3//vu66aabtH37dvXo0UNr165VRkaGJGnt2rXKzMzUp59+qm7dup2zt8rKSrndbgUCASUmJjbdDwFAWFz2yHvhbgFoNF/OHhruFlqMhv7+blHXEAUCAUlSUlKSJGn37t3y+/3KyspyauLi4tS/f3+tXr1akrRp0yadOHEiqMbr9So9Pd2pWbNmjdxutxOGJKlv375yu91Ozemqq6tVWVkZdAMAAK1TiwlExhhNmjRJ1113ndLT0yVJfr9fkpSSkhJUm5KS4jzm9/sVGxur9u3bn7UmOTm53msmJyc7NaebNWuWc72R2+1WamrqhQ0IAABarBYTiMaNG6dPPvlEr7/+er3HXC5X0H1jTL1jpzu95kz1ZzvPtGnTFAgEnNu+ffsaMgYAAIhALSIQjR8/Xu+++65Wrlypzp07O8c9Ho8k1dvFKS8vd3aNPB6PampqVFFRcdaagwcP1nvdQ4cO1dt9OiUuLk6JiYlBNwAA0DqFNRAZYzRu3DgtXbpUK1asUNeuXYMe79q1qzwej4qLi51jNTU1KikpUb9+/SRJvXv3VkxMTFBNWVmZtm7d6tRkZmYqEAho/fr1Ts26desUCAScGgAAYK/ocL74Qw89pNdee01/+tOflJCQ4OwEud1utW3bVi6XS3l5eZo5c6bS0tKUlpammTNnql27dsrNzXVqR48ercmTJ6tDhw5KSkrSlClT1LNnTw0aNEiS1L17dw0ZMkT333+/Fi5cKEl64IEHlJ2d3aBPmAEAgNYtrIHomWeekSQNGDAg6PiiRYt0zz33SJKmTp2qY8eOaezYsaqoqFBGRoaWL1+uhIQEp37+/PmKjo7WiBEjdOzYMQ0cOFCLFy9WVFSUU7NkyRJNmDDB+TRaTk6OCgoKmnZAAAAQEVrU9xC1ZHwPEdC68T1EaE34HqL/E5HfQwQAABAOBCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgvehwNwCg9bnskffC3QIAnBd2iAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYL6yB6MMPP9Stt94qr9crl8uld955J+hxY4xmzJghr9ertm3basCAAdq2bVtQTXV1tcaPH6+OHTsqPj5eOTk52r9/f1BNRUWFfD6f3G633G63fD6fDh8+3MTTAQCASBHWQHT06FFdffXVKigoOOPjc+fOVX5+vgoKCrRhwwZ5PB4NHjxYVVVVTk1eXp6WLVumwsJCrVq1SkeOHFF2drZqa2udmtzcXJWWlqqoqEhFRUUqLS2Vz+dr8vkAAEBkcBljTLibkCSXy6Vly5bp9ttvl/T97pDX61VeXp4efvhhSd/vBqWkpGjOnDkaM2aMAoGAOnXqpFdeeUUjR46UJB04cECpqal6//33ddNNN2n79u3q0aOH1q5dq4yMDEnS2rVrlZmZqU8//VTdunVrUH+VlZVyu90KBAJKTExs/B8A0Ipc9sh74W4BsNqXs4eGu4UWo6G/v1vsNUS7d++W3+9XVlaWcywuLk79+/fX6tWrJUmbNm3SiRMngmq8Xq/S09OdmjVr1sjtdjthSJL69u0rt9vt1JxJdXW1Kisrg24AAKB1arGByO/3S5JSUlKCjqekpDiP+f1+xcbGqn379metSU5Ornf+5ORkp+ZMZs2a5Vxz5Ha7lZqaekHzAACAlqvFBqJTXC5X0H1jTL1jpzu95kz15zrPtGnTFAgEnNu+ffvOs3MAABApWmwg8ng8klRvF6e8vNzZNfJ4PKqpqVFFRcVZaw4ePFjv/IcOHaq3+/RDcXFxSkxMDLoBAIDWqcUGoq5du8rj8ai4uNg5VlNTo5KSEvXr10+S1Lt3b8XExATVlJWVaevWrU5NZmamAoGA1q9f79SsW7dOgUDAqQEAAHaLDueLHzlyRJ9//rlzf/fu3SotLVVSUpIuvfRS5eXlaebMmUpLS1NaWppmzpypdu3aKTc3V5Lkdrs1evRoTZ48WR06dFBSUpKmTJminj17atCgQZKk7t27a8iQIbr//vu1cOFCSdIDDzyg7OzsBn/CDAAAtG5hDUQbN27UDTfc4NyfNGmSJGnUqFFavHixpk6dqmPHjmns2LGqqKhQRkaGli9froSEBOc58+fPV3R0tEaMGKFjx45p4MCBWrx4saKiopyaJUuWaMKECc6n0XJycn70u48AAIB9Wsz3ELV0fA8R0HB8DxEQXnwP0f+J+O8hAgAAaC4EIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYL3ocDcAAAAa12WPvBfuFs7bl7OHhvX12SECAADWIxABAADrEYgAAID1uIYIaOEi8VoAAIg07BABAADrEYgAAID1CEQAAMB6BCIAAGA9AhEAALAegQgAAFiPQAQAAKxHIAIAANYjEAEAAOsRiAAAgPUIRAAAwHoEIgAAYD0CEQAAsB6BCAAAWI9ABAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYLzrcDUC67JH3wt3Cefty9tBwtwAAQKMhEMEqkRg+AQBNj7fMAACA9azaIfr973+vefPmqaysTFdddZUWLFig66+/PtxtRSR2WgAArYk1O0RvvPGG8vLy9Oijj2rz5s26/vrrdfPNN2vv3r3hbg0AAISZNYEoPz9fo0eP1q9+9St1795dCxYsUGpqqp555plwtwYAAMLMirfMampqtGnTJj3yyCNBx7OysrR69eozPqe6ulrV1dXO/UAgIEmqrKxs9P7qqr9r9HMCABBJmuL36w/Pa4w5a50Vgejrr79WbW2tUlJSgo6npKTI7/ef8TmzZs3S448/Xu94ampqk/QIAIDN3Aua9vxVVVVyu90/+rgVgegUl8sVdN8YU+/YKdOmTdOkSZOc+3V1dfr222/VoUOHH31OKCorK5Wamqp9+/YpMTGx0c7bkrT2GZkv8rX2GVv7fFLrn5H5QmeMUVVVlbxe71nrrAhEHTt2VFRUVL3doPLy8nq7RqfExcUpLi4u6NjFF1/cVC0qMTGxVf6f/Ida+4zMF/la+4ytfT6p9c/IfKE5287QKVZcVB0bG6vevXuruLg46HhxcbH69esXpq4AAEBLYcUOkSRNmjRJPp9Pffr0UWZmpp577jnt3btXDz74YLhbAwAAYWZNIBo5cqS++eYb/cd//IfKysqUnp6u999/X126dAlrX3FxcZo+fXq9t+dak9Y+I/NFvtY+Y2ufT2r9MzJf03OZc30ODQAAoJWz4hoiAACAsyEQAQAA6xGIAACA9QhEAADAegSiRvDhhx/q1ltvldfrlcvl0jvvvBP0+MGDB3XPPffI6/WqXbt2GjJkiHbu3BlU4/f75fP55PF4FB8fr2uvvVZvv/12UE1FRYV8Pp/cbrfcbrd8Pp8OHz7cxNM133yXXXaZXC5X0O30vz/XVBpjxl27dmnYsGHq1KmTEhMTNWLECB08eDCoJpLXsCHzhWsNZ82apZ/97GdKSEhQcnKybr/9du3YsSOoxhijGTNmyOv1qm3bthowYIC2bdsWVFNdXa3x48erY8eOio+PV05Ojvbv3x9UE441bM75In0Nn3vuOQ0YMECJiYlyuVxnXJtIXsOGzBeONWyM+b799luNHz9e3bp1U7t27XTppZdqwoQJzt8SPaWp1o9A1AiOHj2qq6++WgUFBfUeM8bo9ttv1xdffKE//elP2rx5s7p06aJBgwbp6NGjTp3P59OOHTv07rvvasuWLRo+fLhGjhypzZs3OzW5ubkqLS1VUVGRioqKVFpaKp/P12rmk+R8LcKp22OPPdbk80kXPuPRo0eVlZUll8ulFStW6KOPPlJNTY1uvfVW1dXVOeeK1DVs6HxSeNawpKREDz30kNauXavi4mKdPHlSWVlZQf8fnDt3rvLz81VQUKANGzbI4/Fo8ODBqqqqcmry8vK0bNkyFRYWatWqVTpy5Iiys7NVW1vr1IRjDZtzPimy1/C7777TkCFD9Nvf/vZHXyuS17Ah80nNv4aNMd+BAwd04MAB/e53v9OWLVu0ePFiFRUVafTo0UGv1WTrZ9CoJJlly5Y593fs2GEkma1btzrHTp48aZKSkszzzz/vHIuPjzcvv/xy0LmSkpLMCy+8YIwx5n//93+NJLN27Vrn8TVr1hhJ5tNPP22iaeprqvmMMaZLly5m/vz5TdZ7Q4Uy41/+8hfTpk0bEwgEnJpvv/3WSDLFxcXGmMhew4bMZ0zLWcPy8nIjyZSUlBhjjKmrqzMej8fMnj3bqTl+/Lhxu93m2WefNcYYc/jwYRMTE2MKCwudmq+++sq0adPGFBUVGWNazho21XzGRPYa/tDKlSuNJFNRURF0PJLX8Id+bD5jWsYaXuh8p7z55psmNjbWnDhxwhjTtOvHDlETq66uliRddNFFzrGoqCjFxsZq1apVzrHrrrtOb7zxhr799lvV1dWpsLBQ1dXVGjBggCRpzZo1crvdysjIcJ7Tt29fud1urV69unmGOYPGmu+UOXPmqEOHDvrnf/5n/dd//ZdqamqaZY6zaciM1dXVcrlcQV8qdtFFF6lNmzZOTSSvYUPmO6UlrOGpLfakpCRJ0u7du+X3+5WVleXUxMXFqX///s7PftOmTTpx4kRQjdfrVXp6ulPTUtawqeY7JVLXsCEieQ3PR7jXsLHmCwQCSkxMVHT0998j3ZTrRyBqYj/96U/VpUsXTZs2TRUVFaqpqdHs2bPl9/tVVlbm1L3xxhs6efKkOnTooLi4OI0ZM0bLli3T5ZdfLun7a3CSk5PrnT85ObneH61tTo01nyRNnDhRhYWFWrlypcaNG6cFCxZo7Nix4RgrSENm7Nu3r+Lj4/Xwww/ru+++09GjR/Xv//7vqqurc2oieQ0bMp/UMtbQGKNJkybpuuuuU3p6uiQ5P9/T/5hzSkqK85jf71dsbKzat29/1ppwr2FTzidF9ho2RCSvYUOFew0ba75vvvlG//mf/6kxY8Y4x5py/az50x3hEhMToz/+8Y8aPXq0kpKSFBUVpUGDBunmm28OqnvsscdUUVGhDz74QB07dtQ777yjf/u3f9Pf//539ezZU5Lkcrnqnd8Yc8bjzaUx5/vNb37j1Pfq1Uvt27fXv/7rvzr/pRMuDZmxU6dOeuutt/TrX/9aTz/9tNq0aaM777xT1157raKiopy6SF3Dhs7XEtZw3Lhx+uSTT+rtXEn1f/4N+dmfXhPuNWzq+VrjGp7rHKGeJ1RNPV+417Ax5qusrNTQoUPVo0cPTZ8+/aznONt5zgeBqBn07t1bpaWlCgQCqqmpUadOnZSRkaE+ffpI+v7TOwUFBdq6dauuuuoqSdLVV1+tv//97/rv//5vPfvss/J4PPU+0SNJhw4dqpe4m1tjzHcmffv2lSR9/vnnYQ1E0rlnlKSsrCzt2rVLX3/9taKjo3XxxRfL4/Goa9eukhTRayide74zae41HD9+vN599119+OGH6ty5s3Pc4/FI+v6/Li+55BLneHl5ufOz93g8qqmpUUVFRdAuSnl5ufr16+fUhHMNm3q+M4mkNWyISF7DUDXnGjbGfFVVVRoyZIh+8pOfaNmyZYqJiQk6T1OtH2+ZNSO3261OnTpp586d2rhxo2677TZJ339qQJLatAlejqioKOcTPJmZmQoEAlq/fr3z+Lp16xQIBM76L7PmdCHzncmpT6D98B+ecPuxGX+oY8eOuvjii7VixQqVl5crJydHUmSv4Q/92Hxn0lxraIzRuHHjtHTpUq1YsaJeSOvatas8Ho+Ki4udYzU1NSopKXF+9r1791ZMTExQTVlZmbZu3erUhGsNm2u+M4mkNWyISF7DUDXHGjbWfJWVlcrKylJsbKzefffdoGsbpSZevwu6JBvGGGOqqqrM5s2bzebNm40kk5+fbzZv3mz27NljjPn+KvmVK1eaXbt2mXfeecd06dLFDB8+3Hl+TU2NueKKK8z1119v1q1bZz7//HPzu9/9zrhcLvPee+85dUOGDDG9evUya9asMWvWrDE9e/Y02dnZrWK+1atXO+f94osvzBtvvGG8Xq/Jyclp8vkaY0ZjjPnDH/5g1qxZYz7//HPzyiuvmKSkJDNp0qSgmkhdw4bMF841/PWvf23cbrf529/+ZsrKypzbd99959TMnj3buN1us3TpUrNlyxZz5513mksuucRUVlY6NQ8++KDp3Lmz+eCDD8zHH39sbrzxRnP11VebkydPOjXhWMPmmq81rGFZWZnZvHmzef75540k8+GHH5rNmzebb775xqmJ5DU813zhWsPGmK+ystJkZGSYnj17ms8//zzoPM3xzyCBqBGc+vjj6bdRo0YZY4x56qmnTOfOnU1MTIy59NJLzWOPPWaqq6uDzvHZZ5+Z4cOHm+TkZNOuXTvTq1eveh9T/+abb8xdd91lEhISTEJCgrnrrrvO+JHLSJxv06ZNJiMjw7jdbnPRRReZbt26menTp5ujR482+XyNNePDDz9sUlJSTExMjElLSzNPPvmkqaurC6qJ5DU813zhXMMzzSbJLFq0yKmpq6sz06dPNx6Px8TFxZlf/OIXZsuWLUHnOXbsmBk3bpxJSkoybdu2NdnZ2Wbv3r1BNeFYw+aarzWs4fTp0895nkhew3PNF641bIz5fuzfU5LM7t27nbqmWj/X/x8EAADAWlxDBAAArEcgAgAA1iMQAQAA6xGIAACA9QhEAADAegQiAABgPQIRAACwHoEIAABYj0AEAACsRyACAADWIxABAADrEYgAAID1/h9D3BrRbVZwuAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Creating Graphs for a Panda\n",
+    "#plotting\n",
+    "print('----------')\n",
+    "print('Plotting - Histogram')\n",
+    "videoReview['Year_of_Release'].plot(kind='hist')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#If the chart does not appear in the above cell, just go back to that cell and rerun. It should appear now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Can you create any other plots?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Data Analitics/Week 3/TU257_Lab1-Introduction.ipynb

@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 16:52:21) \n",
+      "[Clang 6.0 (clang-600.0.57)]\n",
+      "sys.version_info(major=3, minor=7, micro=3, releaselevel='final', serial=0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import platform\n",
+    "\n",
+    "#Print Python version details\n",
+    "print(sys.version)\n",
+    "print(sys.version_info)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x86_64\n",
+      "Darwin Kernel Version 19.6.0: Tue Jun 21 21:18:39 PDT 2022; root:xnu-6153.141.66~1/RELEASE_X86_64\n",
+      "Darwin\n",
+      "i386\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Print details about your computer\n",
+    "\n",
+    "print(platform.machine())\n",
+    "print(platform.version())\n",
+    "print(platform.system())\n",
+    "print(platform.processor())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Write code to print your name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Crate a variable containing your name, and print it to the screen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use the input command to allow a user to enter a value\n",
+    "#for eample, use the input command to ask the use to enter a string\n",
+    "#save the inputted value to a variable\n",
+    "#print the variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Use an IF condition to decide what mesage to print\n",
+    "#Use the input command to ask the use to enter a number\n",
+    "#Use the IF condition to determine if the number if Postive or Negative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Add an 'else' condition to the above IF condition\n",
+    "#You can decide what the statement should be, what message should be printed?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "#the following will print a range of values\n",
+    "for i in range(0,5):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Take a copy of this code and paste it into the next cell\n",
+    "#Change the code to add two variables to contain a number, assign a number to these variables\n",
+    "#Change the code in the 'for' loop to print the numbers between the values in the two variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Take a copy of the code in the previous cell\n",
+    "#Expand it to ask the user to input two values.\n",
+    "#Change the loop to print the values between these two values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#what else can you do?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Data Analitics/Week 3/train.csv

@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
+100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
+101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
+102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
+103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
+104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
+105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
+106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
+107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
+108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
+109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
+110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
+111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
+112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
+113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
+114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
+115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
+116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
+117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
+118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
+119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
+120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
+121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
+122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
+123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
+124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
+125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
+126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
+127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
+128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
+129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
+130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
+131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
+132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
+133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
+134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
+135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
+136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
+137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
+138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
+139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
+140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
+141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
+142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
+143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
+144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
+145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
+146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
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+812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
+813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
+814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
+815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
+816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
+817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
+818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
+819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
+820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
+821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
+822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
+823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
+824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
+825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
+826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
+827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
+828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
+829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
+830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
+831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
+832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
+833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
+834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
+835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
+836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
+837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
+838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
+839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
+840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
+841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
+842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
+843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
+844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
+845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
+846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
+847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
+848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
+849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
+850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
+851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
+852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
+853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
+854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
+855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
+856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
+857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
+858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
+859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
+860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
+861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
+862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
+863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
+864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
+865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
+866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
+867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
+868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
+869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
+870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
+871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
+872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
+873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
+874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
+875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
+877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
+878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q

Data Analitics/Week 4/.ipynb_checkpoints/TU257-Lab3-3-Scaling-Data-checkpoint.ipynb

@@ -0,0 +1,1996 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### In this demo and lab exercise we will look at scaling numerical data\n",
+    "### The examples given below illustrate using a min-max scaler\n",
+    "\n",
+    "\n",
+    "#### Let's beging with importing the data set\n",
+    "#### Download this from webpage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np \n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.preprocessing import MinMaxScaler\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read in the data set\n",
+    "#NB. you will need to edit this command to change it to the directory you are using\n",
+    "data = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/small_purchases.csv', )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10 entries, 0 to 9\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Country    10 non-null     object \n",
+      " 1   Age        9 non-null      float64\n",
+      " 2   Salary     9 non-null      float64\n",
+      " 3   Purchased  10 non-null     object \n",
+      "dtypes: float64(2), object(2)\n",
+      "memory usage: 448.0+ bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Display basic information about the Pandas dataframe\n",
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 4)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#How many rows and columns does the dataframe have?\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Salary</th>\n",
+       "      <th>Purchased</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>France</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>27000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>48000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>54000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>61000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>France</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>58000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>52000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>France</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>79000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>83000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>France</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>67000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Country   Age   Salary Purchased\n",
+       "0   France  44.0  27000.0        No\n",
+       "1    Spain  27.0  48000.0       Yes\n",
+       "2  Germany  30.0  54000.0        No\n",
+       "3    Spain  38.0  61000.0        No\n",
+       "4  Germany  40.0      NaN       Yes\n",
+       "5   France  35.0  58000.0       Yes\n",
+       "6    Spain   NaN  52000.0        No\n",
+       "7   France  48.0  79000.0       Yes\n",
+       "8  Germany  50.0  83000.0        No\n",
+       "9   France  37.0  67000.0       Yes"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Display the first 10 rows\n",
+    "#Question: How many rows does the dataframe contain\n",
+    "#Question: Modify the code to display all the data\n",
+    "\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>38.777778</td>\n",
+       "      <td>58777.777778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>7.693793</td>\n",
+       "      <td>16820.952543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>27000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>52000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>58000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>67000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>83000.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age        Salary\n",
+       "count   9.000000      9.000000\n",
+       "mean   38.777778  58777.777778\n",
+       "std     7.693793  16820.952543\n",
+       "min    27.000000  27000.000000\n",
+       "25%    35.000000  52000.000000\n",
+       "50%    38.000000  58000.000000\n",
+       "75%    44.000000  67000.000000\n",
+       "max    50.000000  83000.000000"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Crate summary statisics about the data in the dataframe\n",
+    "#This only provides summary statistics for Numerical data\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>38.777778</td>\n",
+       "      <td>7.693793</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Salary</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>58777.777778</td>\n",
+       "      <td>16820.952543</td>\n",
+       "      <td>27000.0</td>\n",
+       "      <td>52000.0</td>\n",
+       "      <td>58000.0</td>\n",
+       "      <td>67000.0</td>\n",
+       "      <td>83000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        count          mean           std      min      25%      50%      75%  \\\n",
+       "Age       9.0     38.777778      7.693793     27.0     35.0     38.0     44.0   \n",
+       "Salary    9.0  58777.777778  16820.952543  27000.0  52000.0  58000.0  67000.0   \n",
+       "\n",
+       "            max  \n",
+       "Age        50.0  \n",
+       "Salary  83000.0  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#An alternative was of viewing the summary statistics\n",
+    "data.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Salary</th>\n",
+       "      <th>Purchased</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>France</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>27000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>48000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>54000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>61000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>France</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>58000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>52000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>France</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>79000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>83000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>France</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>67000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Country   Age   Salary Purchased\n",
+       "0   France  44.0  27000.0        No\n",
+       "1    Spain  27.0  48000.0       Yes\n",
+       "2  Germany  30.0  54000.0        No\n",
+       "3    Spain  38.0  61000.0        No\n",
+       "4  Germany  40.0      NaN       Yes\n",
+       "5   France  35.0  58000.0       Yes\n",
+       "6    Spain   NaN  52000.0        No\n",
+       "7   France  48.0  79000.0       Yes\n",
+       "8  Germany  50.0  83000.0        No\n",
+       "9   France  37.0  67000.0       Yes"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Display the data from the dataframe\n",
+    "#NB: notices we have some values to NO values -> See question later in the notebook\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Salary</th>\n",
+       "      <th>Purchased</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>France</td>\n",
+       "      <td>0.739130</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.482143</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>0.478261</td>\n",
+       "      <td>0.607143</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>0.565217</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>France</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.553571</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.446429</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>France</td>\n",
+       "      <td>0.913043</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>France</td>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Country       Age    Salary Purchased\n",
+       "0   France  0.739130  0.000000        No\n",
+       "1    Spain  0.000000  0.375000       Yes\n",
+       "2  Germany  0.130435  0.482143        No\n",
+       "3    Spain  0.478261  0.607143        No\n",
+       "4  Germany  0.565217       NaN       Yes\n",
+       "5   France  0.347826  0.553571       Yes\n",
+       "6    Spain       NaN  0.446429        No\n",
+       "7   France  0.913043  0.928571       Yes\n",
+       "8  Germany  1.000000  1.000000        No\n",
+       "9   France  0.434783  0.714286       Yes"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Setup the MinMaxScaler\n",
+    "#This will only work on Numerical attributes/features\n",
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "#Apply the scaler to the numerical data\n",
+    "#  and save the data back to the dataframe\n",
+    "#  overwriting the original values\n",
+    "data[['Age', 'Salary']] = scaler.fit_transform(data[['Age', 'Salary']])\n",
+    "\n",
+    "#Display the dataframe\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Copy and modify the above code to repace the NaN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Modify the data set to replace the empty data (NaN) with an appropriate values\n",
+    "#### Rerun the scaler with this updated/modified dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Another Example and Exercise - Complete all the steps\n",
+    "\n",
+    "#### Data set = Pima Indian diabetes dataset \n",
+    "#### https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   preg  plas  pres  skin  test  mass   pedi  age  class\n",
+       "0     6   148    72    35     0  33.6  0.627   50      1\n",
+       "1     1    85    66    29     0  26.6  0.351   31      0\n",
+       "2     8   183    64     0     0  23.3  0.672   32      1\n",
+       "3     1    89    66    23    94  28.1  0.167   21      0\n",
+       "4     0   137    40    35   168  43.1  2.288   33      1"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Import the data set\n",
+    "\n",
+    "import pandas as pd\n",
+    "columns = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
+    "data = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/pima-indians-diabetes.csv', names=columns)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.845052</td>\n",
+       "      <td>120.894531</td>\n",
+       "      <td>69.105469</td>\n",
+       "      <td>20.536458</td>\n",
+       "      <td>79.799479</td>\n",
+       "      <td>31.992578</td>\n",
+       "      <td>0.471876</td>\n",
+       "      <td>33.240885</td>\n",
+       "      <td>0.348958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.369578</td>\n",
+       "      <td>31.972618</td>\n",
+       "      <td>19.355807</td>\n",
+       "      <td>15.952218</td>\n",
+       "      <td>115.244002</td>\n",
+       "      <td>7.884160</td>\n",
+       "      <td>0.331329</td>\n",
+       "      <td>11.760232</td>\n",
+       "      <td>0.476951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.078000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>27.300000</td>\n",
+       "      <td>0.243750</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>117.000000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>30.500000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>0.372500</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>140.250000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>127.250000</td>\n",
+       "      <td>36.600000</td>\n",
+       "      <td>0.626250</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>199.000000</td>\n",
+       "      <td>122.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>846.000000</td>\n",
+       "      <td>67.100000</td>\n",
+       "      <td>2.420000</td>\n",
+       "      <td>81.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             preg        plas        pres        skin        test        mass  \\\n",
+       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
+       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
+       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
+       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
+       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
+       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
+       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
+       "\n",
+       "             pedi         age       class  \n",
+       "count  768.000000  768.000000  768.000000  \n",
+       "mean     0.471876   33.240885    0.348958  \n",
+       "std      0.331329   11.760232    0.476951  \n",
+       "min      0.078000   21.000000    0.000000  \n",
+       "25%      0.243750   24.000000    0.000000  \n",
+       "50%      0.372500   29.000000    0.000000  \n",
+       "75%      0.626250   41.000000    1.000000  \n",
+       "max      2.420000   81.000000    1.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Create the summary statistics\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>preg</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>3.845052</td>\n",
+       "      <td>3.369578</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>3.0000</td>\n",
+       "      <td>6.00000</td>\n",
+       "      <td>17.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plas</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>120.894531</td>\n",
+       "      <td>31.972618</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>99.00000</td>\n",
+       "      <td>117.0000</td>\n",
+       "      <td>140.25000</td>\n",
+       "      <td>199.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pres</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>69.105469</td>\n",
+       "      <td>19.355807</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>62.00000</td>\n",
+       "      <td>72.0000</td>\n",
+       "      <td>80.00000</td>\n",
+       "      <td>122.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>skin</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>20.536458</td>\n",
+       "      <td>15.952218</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>23.0000</td>\n",
+       "      <td>32.00000</td>\n",
+       "      <td>99.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>79.799479</td>\n",
+       "      <td>115.244002</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>30.5000</td>\n",
+       "      <td>127.25000</td>\n",
+       "      <td>846.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mass</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>31.992578</td>\n",
+       "      <td>7.884160</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>27.30000</td>\n",
+       "      <td>32.0000</td>\n",
+       "      <td>36.60000</td>\n",
+       "      <td>67.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pedi</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>0.471876</td>\n",
+       "      <td>0.331329</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>0.24375</td>\n",
+       "      <td>0.3725</td>\n",
+       "      <td>0.62625</td>\n",
+       "      <td>2.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>33.240885</td>\n",
+       "      <td>11.760232</td>\n",
+       "      <td>21.000</td>\n",
+       "      <td>24.00000</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>41.00000</td>\n",
+       "      <td>81.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>0.348958</td>\n",
+       "      <td>0.476951</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       count        mean         std     min       25%       50%        75%  \\\n",
+       "preg   768.0    3.845052    3.369578   0.000   1.00000    3.0000    6.00000   \n",
+       "plas   768.0  120.894531   31.972618   0.000  99.00000  117.0000  140.25000   \n",
+       "pres   768.0   69.105469   19.355807   0.000  62.00000   72.0000   80.00000   \n",
+       "skin   768.0   20.536458   15.952218   0.000   0.00000   23.0000   32.00000   \n",
+       "test   768.0   79.799479  115.244002   0.000   0.00000   30.5000  127.25000   \n",
+       "mass   768.0   31.992578    7.884160   0.000  27.30000   32.0000   36.60000   \n",
+       "pedi   768.0    0.471876    0.331329   0.078   0.24375    0.3725    0.62625   \n",
+       "age    768.0   33.240885   11.760232  21.000  24.00000   29.0000   41.00000   \n",
+       "class  768.0    0.348958    0.476951   0.000   0.00000    0.0000    1.00000   \n",
+       "\n",
+       "          max  \n",
+       "preg    17.00  \n",
+       "plas   199.00  \n",
+       "pres   122.00  \n",
+       "skin    99.00  \n",
+       "test   846.00  \n",
+       "mass    67.10  \n",
+       "pedi     2.42  \n",
+       "age     81.00  \n",
+       "class    1.00  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x2160 with 28 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data[columns].hist(stacked=False, bins=100, figsize=(12,30), layout=(14,2));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   preg  plas  pres  skin  test  mass   pedi  age\n",
+       "0     6   148    72    35     0  33.6  0.627   50\n",
+       "1     1    85    66    29     0  26.6  0.351   31\n",
+       "2     8   183    64     0     0  23.3  0.672   32\n",
+       "3     1    89    66    23    94  28.1  0.167   21\n",
+       "4     0   137    40    35   168  43.1  2.288   33"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#The data set contains a Class attribute. \n",
+    "#This is an indicator variable that is non-descriptive and only indicates if the \n",
+    "# descriptive data indicates a particular event\n",
+    "\n",
+    "#Let's separate the data to into 2 dataframes.\n",
+    "#  - The first will contain the descriptive attributes\n",
+    "#  - The second will contain the indication attribute\n",
+    "\n",
+    "#Create a new dataframe (X) to contain the descriptive attributes, droping the indicitor attribute\n",
+    "X = data.drop('class', axis=1)\n",
+    "\n",
+    "#Create a new dataframe (Y) to only contain the indicator attribute\n",
+    "Y = data['class']\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.352941</td>\n",
+       "      <td>0.743719</td>\n",
+       "      <td>0.590164</td>\n",
+       "      <td>0.353535</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500745</td>\n",
+       "      <td>0.234415</td>\n",
+       "      <td>0.483333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.427136</td>\n",
+       "      <td>0.540984</td>\n",
+       "      <td>0.292929</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.396423</td>\n",
+       "      <td>0.116567</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.919598</td>\n",
+       "      <td>0.524590</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.347243</td>\n",
+       "      <td>0.253629</td>\n",
+       "      <td>0.183333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.447236</td>\n",
+       "      <td>0.540984</td>\n",
+       "      <td>0.232323</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.418778</td>\n",
+       "      <td>0.038002</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.688442</td>\n",
+       "      <td>0.327869</td>\n",
+       "      <td>0.353535</td>\n",
+       "      <td>0.198582</td>\n",
+       "      <td>0.642325</td>\n",
+       "      <td>0.943638</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       preg      plas      pres      skin      test      mass      pedi  \\\n",
+       "0  0.352941  0.743719  0.590164  0.353535  0.000000  0.500745  0.234415   \n",
+       "1  0.058824  0.427136  0.540984  0.292929  0.000000  0.396423  0.116567   \n",
+       "2  0.470588  0.919598  0.524590  0.000000  0.000000  0.347243  0.253629   \n",
+       "3  0.058824  0.447236  0.540984  0.232323  0.111111  0.418778  0.038002   \n",
+       "4  0.000000  0.688442  0.327869  0.353535  0.198582  0.642325  0.943638   \n",
+       "\n",
+       "        age  \n",
+       "0  0.483333  \n",
+       "1  0.166667  \n",
+       "2  0.183333  \n",
+       "3  0.000000  \n",
+       "4  0.200000  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "X_copy = X.copy() #We create a copy so we can still refer to the original dataframe later\n",
+    "scaler = MinMaxScaler()\n",
+    "#Create list of Columns to transform/scale\n",
+    "X_columns = X.columns\n",
+    "#Create a new dataframe\n",
+    "X_scaled = pd.DataFrame(scaler.fit_transform(X_copy), columns=X_columns)\n",
+    "X_scaled.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.845052</td>\n",
+       "      <td>120.894531</td>\n",
+       "      <td>69.105469</td>\n",
+       "      <td>20.536458</td>\n",
+       "      <td>79.799479</td>\n",
+       "      <td>31.992578</td>\n",
+       "      <td>0.471876</td>\n",
+       "      <td>33.240885</td>\n",
+       "      <td>0.348958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.369578</td>\n",
+       "      <td>31.972618</td>\n",
+       "      <td>19.355807</td>\n",
+       "      <td>15.952218</td>\n",
+       "      <td>115.244002</td>\n",
+       "      <td>7.884160</td>\n",
+       "      <td>0.331329</td>\n",
+       "      <td>11.760232</td>\n",
+       "      <td>0.476951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.078000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>27.300000</td>\n",
+       "      <td>0.243750</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>117.000000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>30.500000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>0.372500</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>140.250000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>127.250000</td>\n",
+       "      <td>36.600000</td>\n",
+       "      <td>0.626250</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>199.000000</td>\n",
+       "      <td>122.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>846.000000</td>\n",
+       "      <td>67.100000</td>\n",
+       "      <td>2.420000</td>\n",
+       "      <td>81.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             preg        plas        pres        skin        test        mass  \\\n",
+       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
+       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
+       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
+       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
+       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
+       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
+       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
+       "\n",
+       "             pedi         age       class  \n",
+       "count  768.000000  768.000000  768.000000  \n",
+       "mean     0.471876   33.240885    0.348958  \n",
+       "std      0.331329   11.760232    0.476951  \n",
+       "min      0.078000   21.000000    0.000000  \n",
+       "25%      0.243750   24.000000    0.000000  \n",
+       "50%      0.372500   29.000000    0.000000  \n",
+       "75%      0.626250   41.000000    1.000000  \n",
+       "max      2.420000   81.000000    1.000000  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.226180</td>\n",
+       "      <td>0.607510</td>\n",
+       "      <td>0.566438</td>\n",
+       "      <td>0.207439</td>\n",
+       "      <td>0.094326</td>\n",
+       "      <td>0.476790</td>\n",
+       "      <td>0.168179</td>\n",
+       "      <td>0.204015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.198210</td>\n",
+       "      <td>0.160666</td>\n",
+       "      <td>0.158654</td>\n",
+       "      <td>0.161134</td>\n",
+       "      <td>0.136222</td>\n",
+       "      <td>0.117499</td>\n",
+       "      <td>0.141473</td>\n",
+       "      <td>0.196004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.497487</td>\n",
+       "      <td>0.508197</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.406855</td>\n",
+       "      <td>0.070773</td>\n",
+       "      <td>0.050000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.176471</td>\n",
+       "      <td>0.587940</td>\n",
+       "      <td>0.590164</td>\n",
+       "      <td>0.232323</td>\n",
+       "      <td>0.036052</td>\n",
+       "      <td>0.476900</td>\n",
+       "      <td>0.125747</td>\n",
+       "      <td>0.133333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.352941</td>\n",
+       "      <td>0.704774</td>\n",
+       "      <td>0.655738</td>\n",
+       "      <td>0.323232</td>\n",
+       "      <td>0.150414</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.234095</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             preg        plas        pres        skin        test        mass  \\\n",
+       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
+       "mean     0.226180    0.607510    0.566438    0.207439    0.094326    0.476790   \n",
+       "std      0.198210    0.160666    0.158654    0.161134    0.136222    0.117499   \n",
+       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      0.058824    0.497487    0.508197    0.000000    0.000000    0.406855   \n",
+       "50%      0.176471    0.587940    0.590164    0.232323    0.036052    0.476900   \n",
+       "75%      0.352941    0.704774    0.655738    0.323232    0.150414    0.545455   \n",
+       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
+       "\n",
+       "             pedi         age  \n",
+       "count  768.000000  768.000000  \n",
+       "mean     0.168179    0.204015  \n",
+       "std      0.141473    0.196004  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.070773    0.050000  \n",
+       "50%      0.125747    0.133333  \n",
+       "75%      0.234095    0.333333  \n",
+       "max      1.000000    1.000000  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_scaled.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Question: Add code (below) to create a new dataframe, where only the 'preg' and 'plas' attributes are transformed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.352941</td>\n",
+       "      <td>0.743719</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.427136</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.919598</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.447236</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.688442</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       preg      plas  pres  skin  test  mass   pedi  age\n",
+       "0  0.352941  0.743719    72    35     0  33.6  0.627   50\n",
+       "1  0.058824  0.427136    66    29     0  26.6  0.351   31\n",
+       "2  0.470588  0.919598    64     0     0  23.3  0.672   32\n",
+       "3  0.058824  0.447236    66    23    94  28.1  0.167   21\n",
+       "4  0.000000  0.688442    40    35   168  43.1  2.288   33"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "X_copy = X.copy()\n",
+    "scaler = MinMaxScaler()\n",
+    "X_copy[['preg', 'plas']] = scaler.fit_transform(X_copy[['preg', 'plas']])\n",
+    "X_copy.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Data Analitics/Week 4/.ipynb_checkpoints/TU257-Lab3-5-Sampling-and-Unbalanced-checkpoint.ipynb

@@ -0,0 +1,538 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   preg  plas  pres  skin  test  mass   pedi  age  class\n",
+       "0     6   148    72    35     0  33.6  0.627   50      1\n",
+       "1     1    85    66    29     0  26.6  0.351   31      0\n",
+       "2     8   183    64     0     0  23.3  0.672   32      1\n",
+       "3     1    89    66    23    94  28.1  0.167   21      0\n",
+       "4     0   137    40    35   168  43.1  2.288   33      1"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Import the data set\n",
+    "\n",
+    "import pandas as pd\n",
+    "columns = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
+    "data = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/pima-indians-diabetes.csv', names=columns)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(768, 9)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    500\n",
+       "1    268\n",
+       "Name: class, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['class'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#print bar chart\n",
+    "data['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Down Sampling - Majority Class - Using Random Sampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class = 0  500\n",
+      "Class = 1  268\n"
+     ]
+    }
+   ],
+   "source": [
+    "count_class_0, count_class_1 = data['class'].value_counts()\n",
+    "\n",
+    "# Divide by class\n",
+    "df_class_0 = data[data['class'] == 0] #majority class\n",
+    "df_class_1 = data[data['class'] == 1] #minority class\n",
+    "\n",
+    "print('Class = 0 ', df_class_0.shape[0])\n",
+    "print('Class = 1 ', df_class_1.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(268, 9)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sample Majority class (y=0, to have same number of records as minority calls (y=1)\n",
+    "df_class_0_under = df_class_0.sample(count_class_1)\n",
+    "\n",
+    "df_class_0_under.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random under-sampling:\n",
+      "0    268\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "Num records =  536\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# join the dataframes containing y=1 and y=0\n",
+    "df_test_under = pd.concat([df_class_0_under, df_class_1])\n",
+    "\n",
+    "print('Random under-sampling:')\n",
+    "print(df_test_under['class'].value_counts())\n",
+    "print(\"Num records = \", df_test_under.shape[0])\n",
+    "\n",
+    "df_test_under['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Down Sampling - Majority Class - Using imblearn "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "imblearn over-sampling:\n",
+      "0    268\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "Num records =  536\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "\n",
+    "#separate the data in descriptive and target attributes\n",
+    "X = data.drop('class', axis=1)\n",
+    "Y = data['class']\n",
+    "\n",
+    "rus = RandomUnderSampler(random_state=42, replacement=True)\n",
+    "X_rus, Y_rus = rus.fit_resample(X, Y)\n",
+    "\n",
+    "df_rus = pd.concat([pd.DataFrame(X_rus), pd.DataFrame(Y_rus, columns=['class'])], axis=1)\n",
+    "\n",
+    "print('imblearn over-sampling:')\n",
+    "print(df_rus['class'].value_counts())\n",
+    "print(\"Num records = \", df_rus.shape[0])\n",
+    "\n",
+    "df_rus['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we should have the same/similar results as previous. Although the selection of records could be different"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " #### Down/Under sampling the majority class y=1 using Sci-Kit Learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Data distribution\n",
+      "0    500\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "Sci-Kit Learn : resample : Down Sampled data set\n",
+      "0    268\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "Num records =  536\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.utils import resample\n",
+    "\n",
+    "print(\"Original Data distribution\")\n",
+    "print(data['class'].value_counts())\n",
+    "\n",
+    "# Down Sample Majority class\n",
+    "down_sample = resample(data[data['class']==0],\n",
+    "  replace = True, # sample with replacement\n",
+    "  n_samples = data[data['class']==1].shape[0], # to match minority class\n",
+    "  random_state=42) # reproducible results\n",
+    "\n",
+    "# Combine majority class with upsampled minority class\n",
+    "train_downsample = pd.concat([data[data['class']==1], down_sample])\n",
+    "\n",
+    "# Display new class counts\n",
+    "print('Sci-Kit Learn : resample : Down Sampled data set')\n",
+    "print(train_downsample['class'].value_counts())\n",
+    "print(\"Num records = \", train_downsample.shape[0])\n",
+    "train_downsample['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Over sampling the minority call y=0 (using random sampling)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random over-sampling:\n",
+      "0    500\n",
+      "1    500\n",
+      "Name: class, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_class_1_over = df_class_1.sample(count_class_0, replace=True)\n",
+    "\n",
+    "df_test_over = pd.concat([df_class_0, df_class_1_over], axis=0)\n",
+    "\n",
+    "print('Random over-sampling:')\n",
+    "print(df_test_over['class'].value_counts())\n",
+    "\n",
+    "df_test_over['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Over sampling the minority call y=0 using SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    500\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "SMOTE over-sampling:\n",
+      "0    500\n",
+      "1    500\n",
+      "Name: class, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import SMOTE\n",
+    "\n",
+    "print(data['class'].value_counts())\n",
+    "X = data.drop('class', axis=1)\n",
+    "Y = data['class']\n",
+    "\n",
+    "sm = SMOTE(random_state=42)\n",
+    "X_res, Y_res = sm.fit_resample(X, Y)\n",
+    "\n",
+    "df_smote_over = pd.concat([pd.DataFrame(X_res), pd.DataFrame(Y_res, columns=['class'])], axis=1)\n",
+    "\n",
+    "print('SMOTE over-sampling:')\n",
+    "print(df_smote_over['class'].value_counts())\n",
+    "\n",
+    "df_smote_over['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Data Analitics/Week 4/L3-Data-Exploration-Preparation.pptx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ec208d6c7d7f4026f5f36286b088708e382557babc48fd23a4af2e952f56ffd
+size 6927381

Data Analitics/Week 4/TU257-Lab3-3-Scaling-Data.ipynb

@@ -0,0 +1,1996 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### In this demo and lab exercise we will look at scaling numerical data\n",
+    "### The examples given below illustrate using a min-max scaler\n",
+    "\n",
+    "\n",
+    "#### Let's beging with importing the data set\n",
+    "#### Download this from webpage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np \n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.preprocessing import MinMaxScaler\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read in the data set\n",
+    "#NB. you will need to edit this command to change it to the directory you are using\n",
+    "data = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/small_purchases.csv', )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10 entries, 0 to 9\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Country    10 non-null     object \n",
+      " 1   Age        9 non-null      float64\n",
+      " 2   Salary     9 non-null      float64\n",
+      " 3   Purchased  10 non-null     object \n",
+      "dtypes: float64(2), object(2)\n",
+      "memory usage: 448.0+ bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Display basic information about the Pandas dataframe\n",
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 4)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#How many rows and columns does the dataframe have?\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Salary</th>\n",
+       "      <th>Purchased</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>France</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>27000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>48000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>54000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>61000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>France</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>58000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>52000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>France</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>79000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>83000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>France</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>67000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Country   Age   Salary Purchased\n",
+       "0   France  44.0  27000.0        No\n",
+       "1    Spain  27.0  48000.0       Yes\n",
+       "2  Germany  30.0  54000.0        No\n",
+       "3    Spain  38.0  61000.0        No\n",
+       "4  Germany  40.0      NaN       Yes\n",
+       "5   France  35.0  58000.0       Yes\n",
+       "6    Spain   NaN  52000.0        No\n",
+       "7   France  48.0  79000.0       Yes\n",
+       "8  Germany  50.0  83000.0        No\n",
+       "9   France  37.0  67000.0       Yes"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Display the first 10 rows\n",
+    "#Question: How many rows does the dataframe contain\n",
+    "#Question: Modify the code to display all the data\n",
+    "\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>38.777778</td>\n",
+       "      <td>58777.777778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>7.693793</td>\n",
+       "      <td>16820.952543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>27000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>52000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>58000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>67000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>83000.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age        Salary\n",
+       "count   9.000000      9.000000\n",
+       "mean   38.777778  58777.777778\n",
+       "std     7.693793  16820.952543\n",
+       "min    27.000000  27000.000000\n",
+       "25%    35.000000  52000.000000\n",
+       "50%    38.000000  58000.000000\n",
+       "75%    44.000000  67000.000000\n",
+       "max    50.000000  83000.000000"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Crate summary statisics about the data in the dataframe\n",
+    "#This only provides summary statistics for Numerical data\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>38.777778</td>\n",
+       "      <td>7.693793</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Salary</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>58777.777778</td>\n",
+       "      <td>16820.952543</td>\n",
+       "      <td>27000.0</td>\n",
+       "      <td>52000.0</td>\n",
+       "      <td>58000.0</td>\n",
+       "      <td>67000.0</td>\n",
+       "      <td>83000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        count          mean           std      min      25%      50%      75%  \\\n",
+       "Age       9.0     38.777778      7.693793     27.0     35.0     38.0     44.0   \n",
+       "Salary    9.0  58777.777778  16820.952543  27000.0  52000.0  58000.0  67000.0   \n",
+       "\n",
+       "            max  \n",
+       "Age        50.0  \n",
+       "Salary  83000.0  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#An alternative was of viewing the summary statistics\n",
+    "data.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Salary</th>\n",
+       "      <th>Purchased</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>France</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>27000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>48000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>54000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>61000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>France</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>58000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>52000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>France</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>79000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>83000.0</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>France</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>67000.0</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Country   Age   Salary Purchased\n",
+       "0   France  44.0  27000.0        No\n",
+       "1    Spain  27.0  48000.0       Yes\n",
+       "2  Germany  30.0  54000.0        No\n",
+       "3    Spain  38.0  61000.0        No\n",
+       "4  Germany  40.0      NaN       Yes\n",
+       "5   France  35.0  58000.0       Yes\n",
+       "6    Spain   NaN  52000.0        No\n",
+       "7   France  48.0  79000.0       Yes\n",
+       "8  Germany  50.0  83000.0        No\n",
+       "9   France  37.0  67000.0       Yes"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Display the data from the dataframe\n",
+    "#NB: notices we have some values to NO values -> See question later in the notebook\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Salary</th>\n",
+       "      <th>Purchased</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>France</td>\n",
+       "      <td>0.739130</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.482143</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>0.478261</td>\n",
+       "      <td>0.607143</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>0.565217</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>France</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.553571</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Spain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.446429</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>France</td>\n",
+       "      <td>0.913043</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Germany</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>France</td>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Country       Age    Salary Purchased\n",
+       "0   France  0.739130  0.000000        No\n",
+       "1    Spain  0.000000  0.375000       Yes\n",
+       "2  Germany  0.130435  0.482143        No\n",
+       "3    Spain  0.478261  0.607143        No\n",
+       "4  Germany  0.565217       NaN       Yes\n",
+       "5   France  0.347826  0.553571       Yes\n",
+       "6    Spain       NaN  0.446429        No\n",
+       "7   France  0.913043  0.928571       Yes\n",
+       "8  Germany  1.000000  1.000000        No\n",
+       "9   France  0.434783  0.714286       Yes"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Setup the MinMaxScaler\n",
+    "#This will only work on Numerical attributes/features\n",
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "#Apply the scaler to the numerical data\n",
+    "#  and save the data back to the dataframe\n",
+    "#  overwriting the original values\n",
+    "data[['Age', 'Salary']] = scaler.fit_transform(data[['Age', 'Salary']])\n",
+    "\n",
+    "#Display the dataframe\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Copy and modify the above code to repace the NaN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Modify the data set to replace the empty data (NaN) with an appropriate values\n",
+    "#### Rerun the scaler with this updated/modified dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Another Example and Exercise - Complete all the steps\n",
+    "\n",
+    "#### Data set = Pima Indian diabetes dataset \n",
+    "#### https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   preg  plas  pres  skin  test  mass   pedi  age  class\n",
+       "0     6   148    72    35     0  33.6  0.627   50      1\n",
+       "1     1    85    66    29     0  26.6  0.351   31      0\n",
+       "2     8   183    64     0     0  23.3  0.672   32      1\n",
+       "3     1    89    66    23    94  28.1  0.167   21      0\n",
+       "4     0   137    40    35   168  43.1  2.288   33      1"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Import the data set\n",
+    "\n",
+    "import pandas as pd\n",
+    "columns = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
+    "data = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/pima-indians-diabetes.csv', names=columns)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.845052</td>\n",
+       "      <td>120.894531</td>\n",
+       "      <td>69.105469</td>\n",
+       "      <td>20.536458</td>\n",
+       "      <td>79.799479</td>\n",
+       "      <td>31.992578</td>\n",
+       "      <td>0.471876</td>\n",
+       "      <td>33.240885</td>\n",
+       "      <td>0.348958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.369578</td>\n",
+       "      <td>31.972618</td>\n",
+       "      <td>19.355807</td>\n",
+       "      <td>15.952218</td>\n",
+       "      <td>115.244002</td>\n",
+       "      <td>7.884160</td>\n",
+       "      <td>0.331329</td>\n",
+       "      <td>11.760232</td>\n",
+       "      <td>0.476951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.078000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>27.300000</td>\n",
+       "      <td>0.243750</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>117.000000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>30.500000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>0.372500</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>140.250000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>127.250000</td>\n",
+       "      <td>36.600000</td>\n",
+       "      <td>0.626250</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>199.000000</td>\n",
+       "      <td>122.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>846.000000</td>\n",
+       "      <td>67.100000</td>\n",
+       "      <td>2.420000</td>\n",
+       "      <td>81.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             preg        plas        pres        skin        test        mass  \\\n",
+       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
+       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
+       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
+       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
+       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
+       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
+       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
+       "\n",
+       "             pedi         age       class  \n",
+       "count  768.000000  768.000000  768.000000  \n",
+       "mean     0.471876   33.240885    0.348958  \n",
+       "std      0.331329   11.760232    0.476951  \n",
+       "min      0.078000   21.000000    0.000000  \n",
+       "25%      0.243750   24.000000    0.000000  \n",
+       "50%      0.372500   29.000000    0.000000  \n",
+       "75%      0.626250   41.000000    1.000000  \n",
+       "max      2.420000   81.000000    1.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Create the summary statistics\n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>preg</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>3.845052</td>\n",
+       "      <td>3.369578</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>3.0000</td>\n",
+       "      <td>6.00000</td>\n",
+       "      <td>17.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>plas</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>120.894531</td>\n",
+       "      <td>31.972618</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>99.00000</td>\n",
+       "      <td>117.0000</td>\n",
+       "      <td>140.25000</td>\n",
+       "      <td>199.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pres</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>69.105469</td>\n",
+       "      <td>19.355807</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>62.00000</td>\n",
+       "      <td>72.0000</td>\n",
+       "      <td>80.00000</td>\n",
+       "      <td>122.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>skin</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>20.536458</td>\n",
+       "      <td>15.952218</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>23.0000</td>\n",
+       "      <td>32.00000</td>\n",
+       "      <td>99.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>79.799479</td>\n",
+       "      <td>115.244002</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>30.5000</td>\n",
+       "      <td>127.25000</td>\n",
+       "      <td>846.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mass</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>31.992578</td>\n",
+       "      <td>7.884160</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>27.30000</td>\n",
+       "      <td>32.0000</td>\n",
+       "      <td>36.60000</td>\n",
+       "      <td>67.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pedi</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>0.471876</td>\n",
+       "      <td>0.331329</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>0.24375</td>\n",
+       "      <td>0.3725</td>\n",
+       "      <td>0.62625</td>\n",
+       "      <td>2.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>33.240885</td>\n",
+       "      <td>11.760232</td>\n",
+       "      <td>21.000</td>\n",
+       "      <td>24.00000</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>41.00000</td>\n",
+       "      <td>81.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class</th>\n",
+       "      <td>768.0</td>\n",
+       "      <td>0.348958</td>\n",
+       "      <td>0.476951</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       count        mean         std     min       25%       50%        75%  \\\n",
+       "preg   768.0    3.845052    3.369578   0.000   1.00000    3.0000    6.00000   \n",
+       "plas   768.0  120.894531   31.972618   0.000  99.00000  117.0000  140.25000   \n",
+       "pres   768.0   69.105469   19.355807   0.000  62.00000   72.0000   80.00000   \n",
+       "skin   768.0   20.536458   15.952218   0.000   0.00000   23.0000   32.00000   \n",
+       "test   768.0   79.799479  115.244002   0.000   0.00000   30.5000  127.25000   \n",
+       "mass   768.0   31.992578    7.884160   0.000  27.30000   32.0000   36.60000   \n",
+       "pedi   768.0    0.471876    0.331329   0.078   0.24375    0.3725    0.62625   \n",
+       "age    768.0   33.240885   11.760232  21.000  24.00000   29.0000   41.00000   \n",
+       "class  768.0    0.348958    0.476951   0.000   0.00000    0.0000    1.00000   \n",
+       "\n",
+       "          max  \n",
+       "preg    17.00  \n",
+       "plas   199.00  \n",
+       "pres   122.00  \n",
+       "skin    99.00  \n",
+       "test   846.00  \n",
+       "mass    67.10  \n",
+       "pedi     2.42  \n",
+       "age     81.00  \n",
+       "class    1.00  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x2160 with 28 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data[columns].hist(stacked=False, bins=100, figsize=(12,30), layout=(14,2));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   preg  plas  pres  skin  test  mass   pedi  age\n",
+       "0     6   148    72    35     0  33.6  0.627   50\n",
+       "1     1    85    66    29     0  26.6  0.351   31\n",
+       "2     8   183    64     0     0  23.3  0.672   32\n",
+       "3     1    89    66    23    94  28.1  0.167   21\n",
+       "4     0   137    40    35   168  43.1  2.288   33"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#The data set contains a Class attribute. \n",
+    "#This is an indicator variable that is non-descriptive and only indicates if the \n",
+    "# descriptive data indicates a particular event\n",
+    "\n",
+    "#Let's separate the data to into 2 dataframes.\n",
+    "#  - The first will contain the descriptive attributes\n",
+    "#  - The second will contain the indication attribute\n",
+    "\n",
+    "#Create a new dataframe (X) to contain the descriptive attributes, droping the indicitor attribute\n",
+    "X = data.drop('class', axis=1)\n",
+    "\n",
+    "#Create a new dataframe (Y) to only contain the indicator attribute\n",
+    "Y = data['class']\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.352941</td>\n",
+       "      <td>0.743719</td>\n",
+       "      <td>0.590164</td>\n",
+       "      <td>0.353535</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500745</td>\n",
+       "      <td>0.234415</td>\n",
+       "      <td>0.483333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.427136</td>\n",
+       "      <td>0.540984</td>\n",
+       "      <td>0.292929</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.396423</td>\n",
+       "      <td>0.116567</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.919598</td>\n",
+       "      <td>0.524590</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.347243</td>\n",
+       "      <td>0.253629</td>\n",
+       "      <td>0.183333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.447236</td>\n",
+       "      <td>0.540984</td>\n",
+       "      <td>0.232323</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.418778</td>\n",
+       "      <td>0.038002</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.688442</td>\n",
+       "      <td>0.327869</td>\n",
+       "      <td>0.353535</td>\n",
+       "      <td>0.198582</td>\n",
+       "      <td>0.642325</td>\n",
+       "      <td>0.943638</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       preg      plas      pres      skin      test      mass      pedi  \\\n",
+       "0  0.352941  0.743719  0.590164  0.353535  0.000000  0.500745  0.234415   \n",
+       "1  0.058824  0.427136  0.540984  0.292929  0.000000  0.396423  0.116567   \n",
+       "2  0.470588  0.919598  0.524590  0.000000  0.000000  0.347243  0.253629   \n",
+       "3  0.058824  0.447236  0.540984  0.232323  0.111111  0.418778  0.038002   \n",
+       "4  0.000000  0.688442  0.327869  0.353535  0.198582  0.642325  0.943638   \n",
+       "\n",
+       "        age  \n",
+       "0  0.483333  \n",
+       "1  0.166667  \n",
+       "2  0.183333  \n",
+       "3  0.000000  \n",
+       "4  0.200000  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "X_copy = X.copy() #We create a copy so we can still refer to the original dataframe later\n",
+    "scaler = MinMaxScaler()\n",
+    "#Create list of Columns to transform/scale\n",
+    "X_columns = X.columns\n",
+    "#Create a new dataframe\n",
+    "X_scaled = pd.DataFrame(scaler.fit_transform(X_copy), columns=X_columns)\n",
+    "X_scaled.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.845052</td>\n",
+       "      <td>120.894531</td>\n",
+       "      <td>69.105469</td>\n",
+       "      <td>20.536458</td>\n",
+       "      <td>79.799479</td>\n",
+       "      <td>31.992578</td>\n",
+       "      <td>0.471876</td>\n",
+       "      <td>33.240885</td>\n",
+       "      <td>0.348958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.369578</td>\n",
+       "      <td>31.972618</td>\n",
+       "      <td>19.355807</td>\n",
+       "      <td>15.952218</td>\n",
+       "      <td>115.244002</td>\n",
+       "      <td>7.884160</td>\n",
+       "      <td>0.331329</td>\n",
+       "      <td>11.760232</td>\n",
+       "      <td>0.476951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.078000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>27.300000</td>\n",
+       "      <td>0.243750</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>117.000000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>30.500000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>0.372500</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>140.250000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>127.250000</td>\n",
+       "      <td>36.600000</td>\n",
+       "      <td>0.626250</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>199.000000</td>\n",
+       "      <td>122.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>846.000000</td>\n",
+       "      <td>67.100000</td>\n",
+       "      <td>2.420000</td>\n",
+       "      <td>81.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             preg        plas        pres        skin        test        mass  \\\n",
+       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
+       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
+       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
+       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
+       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
+       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
+       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
+       "\n",
+       "             pedi         age       class  \n",
+       "count  768.000000  768.000000  768.000000  \n",
+       "mean     0.471876   33.240885    0.348958  \n",
+       "std      0.331329   11.760232    0.476951  \n",
+       "min      0.078000   21.000000    0.000000  \n",
+       "25%      0.243750   24.000000    0.000000  \n",
+       "50%      0.372500   29.000000    0.000000  \n",
+       "75%      0.626250   41.000000    1.000000  \n",
+       "max      2.420000   81.000000    1.000000  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "      <td>768.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.226180</td>\n",
+       "      <td>0.607510</td>\n",
+       "      <td>0.566438</td>\n",
+       "      <td>0.207439</td>\n",
+       "      <td>0.094326</td>\n",
+       "      <td>0.476790</td>\n",
+       "      <td>0.168179</td>\n",
+       "      <td>0.204015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.198210</td>\n",
+       "      <td>0.160666</td>\n",
+       "      <td>0.158654</td>\n",
+       "      <td>0.161134</td>\n",
+       "      <td>0.136222</td>\n",
+       "      <td>0.117499</td>\n",
+       "      <td>0.141473</td>\n",
+       "      <td>0.196004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.497487</td>\n",
+       "      <td>0.508197</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.406855</td>\n",
+       "      <td>0.070773</td>\n",
+       "      <td>0.050000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.176471</td>\n",
+       "      <td>0.587940</td>\n",
+       "      <td>0.590164</td>\n",
+       "      <td>0.232323</td>\n",
+       "      <td>0.036052</td>\n",
+       "      <td>0.476900</td>\n",
+       "      <td>0.125747</td>\n",
+       "      <td>0.133333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.352941</td>\n",
+       "      <td>0.704774</td>\n",
+       "      <td>0.655738</td>\n",
+       "      <td>0.323232</td>\n",
+       "      <td>0.150414</td>\n",
+       "      <td>0.545455</td>\n",
+       "      <td>0.234095</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             preg        plas        pres        skin        test        mass  \\\n",
+       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
+       "mean     0.226180    0.607510    0.566438    0.207439    0.094326    0.476790   \n",
+       "std      0.198210    0.160666    0.158654    0.161134    0.136222    0.117499   \n",
+       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
+       "25%      0.058824    0.497487    0.508197    0.000000    0.000000    0.406855   \n",
+       "50%      0.176471    0.587940    0.590164    0.232323    0.036052    0.476900   \n",
+       "75%      0.352941    0.704774    0.655738    0.323232    0.150414    0.545455   \n",
+       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
+       "\n",
+       "             pedi         age  \n",
+       "count  768.000000  768.000000  \n",
+       "mean     0.168179    0.204015  \n",
+       "std      0.141473    0.196004  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.070773    0.050000  \n",
+       "50%      0.125747    0.133333  \n",
+       "75%      0.234095    0.333333  \n",
+       "max      1.000000    1.000000  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_scaled.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Question: Add code (below) to create a new dataframe, where only the 'preg' and 'plas' attributes are transformed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.352941</td>\n",
+       "      <td>0.743719</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.427136</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.919598</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.447236</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.688442</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       preg      plas  pres  skin  test  mass   pedi  age\n",
+       "0  0.352941  0.743719    72    35     0  33.6  0.627   50\n",
+       "1  0.058824  0.427136    66    29     0  26.6  0.351   31\n",
+       "2  0.470588  0.919598    64     0     0  23.3  0.672   32\n",
+       "3  0.058824  0.447236    66    23    94  28.1  0.167   21\n",
+       "4  0.000000  0.688442    40    35   168  43.1  2.288   33"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "X_copy = X.copy()\n",
+    "scaler = MinMaxScaler()\n",
+    "X_copy[['preg', 'plas']] = scaler.fit_transform(X_copy[['preg', 'plas']])\n",
+    "X_copy.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Data Analitics/Week 4/TU257-Lab3-5-Sampling-and-Unbalanced.ipynb

@@ -0,0 +1,538 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>preg</th>\n",
+       "      <th>plas</th>\n",
+       "      <th>pres</th>\n",
+       "      <th>skin</th>\n",
+       "      <th>test</th>\n",
+       "      <th>mass</th>\n",
+       "      <th>pedi</th>\n",
+       "      <th>age</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   preg  plas  pres  skin  test  mass   pedi  age  class\n",
+       "0     6   148    72    35     0  33.6  0.627   50      1\n",
+       "1     1    85    66    29     0  26.6  0.351   31      0\n",
+       "2     8   183    64     0     0  23.3  0.672   32      1\n",
+       "3     1    89    66    23    94  28.1  0.167   21      0\n",
+       "4     0   137    40    35   168  43.1  2.288   33      1"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Import the data set\n",
+    "\n",
+    "import pandas as pd\n",
+    "columns = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
+    "data = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/pima-indians-diabetes.csv', names=columns)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(768, 9)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    500\n",
+       "1    268\n",
+       "Name: class, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['class'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#print bar chart\n",
+    "data['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Down Sampling - Majority Class - Using Random Sampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class = 0  500\n",
+      "Class = 1  268\n"
+     ]
+    }
+   ],
+   "source": [
+    "count_class_0, count_class_1 = data['class'].value_counts()\n",
+    "\n",
+    "# Divide by class\n",
+    "df_class_0 = data[data['class'] == 0] #majority class\n",
+    "df_class_1 = data[data['class'] == 1] #minority class\n",
+    "\n",
+    "print('Class = 0 ', df_class_0.shape[0])\n",
+    "print('Class = 1 ', df_class_1.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(268, 9)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sample Majority class (y=0, to have same number of records as minority calls (y=1)\n",
+    "df_class_0_under = df_class_0.sample(count_class_1)\n",
+    "\n",
+    "df_class_0_under.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random under-sampling:\n",
+      "0    268\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "Num records =  536\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# join the dataframes containing y=1 and y=0\n",
+    "df_test_under = pd.concat([df_class_0_under, df_class_1])\n",
+    "\n",
+    "print('Random under-sampling:')\n",
+    "print(df_test_under['class'].value_counts())\n",
+    "print(\"Num records = \", df_test_under.shape[0])\n",
+    "\n",
+    "df_test_under['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Down Sampling - Majority Class - Using imblearn "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "imblearn over-sampling:\n",
+      "0    268\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "Num records =  536\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "\n",
+    "#separate the data in descriptive and target attributes\n",
+    "X = data.drop('class', axis=1)\n",
+    "Y = data['class']\n",
+    "\n",
+    "rus = RandomUnderSampler(random_state=42, replacement=True)\n",
+    "X_rus, Y_rus = rus.fit_resample(X, Y)\n",
+    "\n",
+    "df_rus = pd.concat([pd.DataFrame(X_rus), pd.DataFrame(Y_rus, columns=['class'])], axis=1)\n",
+    "\n",
+    "print('imblearn over-sampling:')\n",
+    "print(df_rus['class'].value_counts())\n",
+    "print(\"Num records = \", df_rus.shape[0])\n",
+    "\n",
+    "df_rus['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we should have the same/similar results as previous. Although the selection of records could be different"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " #### Down/Under sampling the majority class y=1 using Sci-Kit Learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Data distribution\n",
+      "0    500\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "Sci-Kit Learn : resample : Down Sampled data set\n",
+      "0    268\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "Num records =  536\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.utils import resample\n",
+    "\n",
+    "print(\"Original Data distribution\")\n",
+    "print(data['class'].value_counts())\n",
+    "\n",
+    "# Down Sample Majority class\n",
+    "down_sample = resample(data[data['class']==0],\n",
+    "  replace = True, # sample with replacement\n",
+    "  n_samples = data[data['class']==1].shape[0], # to match minority class\n",
+    "  random_state=42) # reproducible results\n",
+    "\n",
+    "# Combine majority class with upsampled minority class\n",
+    "train_downsample = pd.concat([data[data['class']==1], down_sample])\n",
+    "\n",
+    "# Display new class counts\n",
+    "print('Sci-Kit Learn : resample : Down Sampled data set')\n",
+    "print(train_downsample['class'].value_counts())\n",
+    "print(\"Num records = \", train_downsample.shape[0])\n",
+    "train_downsample['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Over sampling the minority call y=0 (using random sampling)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random over-sampling:\n",
+      "0    500\n",
+      "1    500\n",
+      "Name: class, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_class_1_over = df_class_1.sample(count_class_0, replace=True)\n",
+    "\n",
+    "df_test_over = pd.concat([df_class_0, df_class_1_over], axis=0)\n",
+    "\n",
+    "print('Random over-sampling:')\n",
+    "print(df_test_over['class'].value_counts())\n",
+    "\n",
+    "df_test_over['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Over sampling the minority call y=0 using SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    500\n",
+      "1    268\n",
+      "Name: class, dtype: int64\n",
+      "SMOTE over-sampling:\n",
+      "0    500\n",
+      "1    500\n",
+      "Name: class, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from imblearn.over_sampling import SMOTE\n",
+    "\n",
+    "print(data['class'].value_counts())\n",
+    "X = data.drop('class', axis=1)\n",
+    "Y = data['class']\n",
+    "\n",
+    "sm = SMOTE(random_state=42)\n",
+    "X_res, Y_res = sm.fit_resample(X, Y)\n",
+    "\n",
+    "df_smote_over = pd.concat([pd.DataFrame(X_res), pd.DataFrame(Y_res, columns=['class'])], axis=1)\n",
+    "\n",
+    "print('SMOTE over-sampling:')\n",
+    "print(df_smote_over['class'].value_counts())\n",
+    "\n",
+    "df_smote_over['class'].value_counts().plot(kind='bar', title='Count (target)');"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Data Analitics/Week 4/Week 4 Complementary Material.txt

@@ -0,0 +1,23 @@
+### Week 4 Complementary Material:
+
+https://oralytics.com/2019/04/18/data-sets-for-analytics/
+
+https://machinelearningmastery.com/decoding-data-descriptive-statistics/
+
+https://machinelearningmastery.com/decoding-data-descriptive-statistics/
+
+https://medium.com/@pca_plus/3-epic-data-quality-blunders-1a1c024c20af
+
+https://www.datasciencecentral.com/one-page-r-a-survival-guide-to-data-science-with-r/
+
+https://medium.com/towards-data-science/reducing-dimensionality-from-dimensionality-reduction-techniques-f658aec24dfe
+
+https://tylervigen.com/
+
+https://learn.g2.com/data-visualization
+
+https://www.kdnuggets.com/2015/05/7-methods-data-dimensionality-reduction.html
+
+https://towardsdatascience.com/3-key-encoding-techniques-for-machine-learning-a-beginner-friendly-guide-aff8a01a7b6a/
+
+https://www.scientificamerican.com/article/how-the-guinness-brewery-invented-the-most-important-statistical-method-in/?utm_source=pocket-newtab-en-gb