{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TU257 - Assignment 1\n",
    "\n",
    "#### Student 1: Guilherme\n",
    "#### Student 2: Lohana Azevedo Rodrigues - D24126847 - (TU257)\n",
    "#### Student 3: Rafael\n",
    "\n",
    "#### Group Num= 3\n",
    "#### Problem Set= 1 \n",
    "\n",
    "####  **Portuguese Banking Marking Campaign -** The data set is related to a direct marketing campaign for a Portuguese banking institution. The bank conducts marketing campaigns and uses their call center to contact their customers using phone calls.\n",
    "#### ***GOAL***: The purpose of this project is to identify customers who are most likely to subscribe to a term deposit account based on previous marketing campaigns.\n",
    "#### https://archive.ics.uci.edu/ml/datasets/Bank+Marketing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________________________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sweetviz as sv\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Importing the Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For this project we used the bank-additional data set with 4119 examples.\n",
    "mkt = pd.read_csv('Data/bank-additional-full.csv', sep = ';')\n",
    "mkt_raw = mkt  #keeping the original data untouched for comparisons at the end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### I choose to use the sweetviz to explore my data. It is a good way to explore an unknown data base. The disvantage is ith the increase in the size of the data the time to generate the report also increases a lot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 values\n",
      "\n"
     ]
    },
    {
     "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's start seeing the head and the shape of the data \n",
    "print('First 5 values\\n')\n",
    "mkt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41852d460fbe432097273ee42d0271b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "                                             |          | [  0%]   00:00 -> (? left)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report mkt_analyze.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n"
     ]
    }
   ],
   "source": [
    "# Using the Sweetviz module to generate a report that I will use to explore my data\n",
    "analysis=sv.analyze(mkt)\n",
    "analysis.show_html('mkt_analyze.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "(Rows, Columns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print('\\n\\n(Rows, Columns)')\n",
    "#mkt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Data information\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  float64\n",
      " 20  y               41188 non-null  object \n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#print('\\n\\nData information\\n')\n",
    "#mkt.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mkt.hist();\n",
    "#plt.gcf().set_size_inches(20, 10)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 - Data Insights Discovered sofar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The dataset is a sample with a size of **41,188**, where **12** are duplicate records that will be removed.  \n",
    "- It contains **21 variables**, of which **8 are numerical** and **13 are categorical**.  \n",
    "\n",
    "## Variable Analysis  \n",
    "\n",
    "### - ***Numerical Variables:***\n",
    "### Age  \n",
    "- **Type:** Continue  \n",
    "- **Range:** 17 to 98  \n",
    "- **Average:** 40  \n",
    "- **Missing values:** None  \n",
    "- **Observations:** The numbers appear to be real. No need for modifications (data cleaning).  \n",
    "\n",
    "### Duration  \n",
    "- **Type:** Continue  \n",
    "- **Insights:**  \n",
    "  - \\>80% of values are above 135 seconds.\n",
    "  - **Important note:** This attribute highly affects the output target y.  \n",
    "  - If duration=0, then y='no'.  \n",
    "  - **Since Duration is only known after a call, it should only be included for benchmarking and not for a realistic predictive model.**  \n",
    "\n",
    "### Campaign  \n",
    "- **Type:** Continue  \n",
    "- **Definition:** Number of contacts performed during this campaign for this client (includes last contact).  \n",
    "- **Insights:**  \n",
    "  - 95% of clients were contacted up to 7 times.  \n",
    "  - 48% were contacted only once.  \n",
    "\n",
    "### Pdays  \n",
    "- **Type:** Binary \n",
    "- **Definition:** Number of days since the client was last contacted from a previous campaign.  \n",
    "- **Insights:**  \n",
    "  - 96% of clients were last contacted 999 days ago.  \n",
    "  - This variable does not explain much.  \n",
    "\n",
    "### Previous  \n",
    "- **Type:** Continue \n",
    "- **Definition:** Number of contacts before this campaign for this client.  \n",
    "- **Insights:**  \n",
    "  - 86% = 0 contacts.  \n",
    "  - 14% = 1 or more contacts.\n",
    "    \n",
    "### Emp.var.rate  \n",
    "- **Type:** Continue \n",
    "- **Definition:** Employment variation rate (quarterly frequency).  \n",
    "- **Distinct categories:** 8  \n",
    "\n",
    "### Cons.price.idx  \n",
    "- **Type:** Continue  \n",
    "- **Definition:** Monthly average consumer price index.  \n",
    "- **Range:** Min 92.20, Max 94.77, Avg 93.58.  \n",
    "\n",
    "### Cons.conf.idx  \n",
    "- **Type:** Continue  \n",
    "- **Definition:** Monthly average consumer confidence index.  \n",
    "- **Range:** -26.9 to -50.8, Avg -40.5.  \n",
    "\n",
    "### Euribor3m  \n",
    "- **Type:** Continue \n",
    "- **Definition:** Three-month Euribor rate computed by the European Central Bank (ECB).  \n",
    "- **Insights:** Correlation of 0.31 with the target variable.  \n",
    "\n",
    "### Nr.employed  \n",
    "- **Type:** Continue  \n",
    "- **Definition:** Quarterly average of the total number of employed citizens.  \n",
    "- **Insights:** Correlation of 0.65 with the target variable.\n",
    "\n",
    "\n",
    "### - ***Categorical Variables:***\n",
    "### Job  \n",
    "- **Type:** Continue   \n",
    "- **Distinct categories:** 12 (<1% is labeled as 'unknown')  \n",
    "- **Correlation:** High correlation with Age, but we cannot determine someone’s job based on their age.  \n",
    "- **Insights:** There is a 20% chance that Education explains this variable (the inverse makes more sense).  \n",
    "\n",
    "### Marital  \n",
    "- **Type:** Continue   \n",
    "- **Distinct categories:** 4 (<1% is 'unknown')  \n",
    "- **Correlation:** High correlation with Age.  \n",
    "\n",
    "### Education  \n",
    "- **Type:** Continue  \n",
    "- **Distinct categories:** 8 (4% are 'unknown')  \n",
    "- **Insights:**  \n",
    "  - Categories below high school could be grouped, totaling 31%.  \n",
    "  - Moderate correlation with Age.  \n",
    "  - 23% chance that Job explains this variable (which makes sense).  \n",
    "\n",
    "### Default  \n",
    "- **Type:** Continue  \n",
    "- **Categories:** 3 (<1% is \"yes\", 28% is 'unknown')  \n",
    "- **Insights:** May not be useful for analysis since it does not provide much information.  \n",
    "\n",
    "### Housing  \n",
    "- **Type:** Continue   \n",
    "- **Categories:** 3 (2% unknown)  \n",
    "\n",
    "### Loan  \n",
    "- **Type:** Continue   \n",
    "- **Categories:** 3 (2% unknown)  \n",
    "- **Insights:** Housing may explain this variable.  \n",
    "\n",
    "### Contact  \n",
    "- **Type:** Binary  \n",
    "- **Categories:** 2  \n",
    "- **Insights:** Likely not a good explanatory variable.  \n",
    "\n",
    "### Month  \n",
    "- **Type:** Continue   \n",
    "- **Categories:** Jan to Dec  \n",
    "- **Insights:** May + Jun + Aug = 65%  \n",
    "\n",
    "### Day_of_week  \n",
    "- **Type:** Continue   \n",
    "- **Categories:** Mon to Fri  \n",
    "- **Insights:** About 20% for each day.  \n",
    "  \n",
    "\n",
    "### Poutcome  \n",
    "- **Type:** Continue  \n",
    "- **Definition:** Outcome of the previous marketing campaign (`failure`, `nonexistent`, `success`).  \n",
    "- **Insights:**  \n",
    "  - Among the 14% who were previously contacted, 75.6% had a successful outcome.  \n",
    "\n",
    "### Target Variable - Y  \n",
    "- **Type:** Binary \n",
    "- **Definition:** Has the client subscribed to a term deposit?  \n",
    "- **Distribution:** 89% = No  \n",
    "\n",
    "#### *Correlation with Target Variable (Y)*  \n",
    "\n",
    "| **Variable**         | **Correlation** |\n",
    "|-----------------|------------|\n",
    "| Duration       | **0.41** |\n",
    "| Nr.employed    | **0.35** |\n",
    "| Pdays          | **0.32** |\n",
    "| Euribor3m      | **0.31** |\n",
    "| Cons.price.idx | **0.14** |\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 - Data Clearning & Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This database has 12 duplicate records. Let's remove them!\n",
    "mkt.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41176, 21)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I had before 41,188 rows. What about now? - 41176\n",
    "mkt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#O que fazer com os 'unknown'? Excluo?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming object variables to numeric\n",
    "##### Ps.: alguns algoritimos nao precisam dessa transformacao."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13 variables are categorical. Some models don't accept categorical variables. Let's transform them into numeric!\n",
    "enconder = LabelEncoder()\n",
    "\n",
    "for variable in mkt.columns:\n",
    "    if mkt[variable].dtype == 'object':\n",
    "        mkt[variable] = enconder.fit_transform(mkt[variable])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  default  housing  loan  contact  month  \\\n",
       "0   56    3        1          0        0        0     0        1      6   \n",
       "1   57    7        1          3        1        0     0        1      6   \n",
       "2   37    7        1          3        0        2     0        1      6   \n",
       "3   40    0        1          1        0        0     0        1      6   \n",
       "4   56    7        1          3        0        0     2        1      6   \n",
       "\n",
       "   day_of_week  ...  campaign  pdays  previous  poutcome  emp.var.rate  \\\n",
       "0            1  ...         1    999         0         1           1.1   \n",
       "1            1  ...         1    999         0         1           1.1   \n",
       "2            1  ...         1    999         0         1           1.1   \n",
       "3            1  ...         1    999         0         1           1.1   \n",
       "4            1  ...         1    999         0         1           1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "0          93.994          -36.4      4.857       5191.0  0  \n",
       "1          93.994          -36.4      4.857       5191.0  0  \n",
       "2          93.994          -36.4      4.857       5191.0  0  \n",
       "3          93.994          -36.4      4.857       5191.0  0  \n",
       "4          93.994          -36.4      4.857       5191.0  0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now I can plot the histogram of all variables\n",
    "mkt.hist();\n",
    "plt.gcf().set_size_inches(20, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ps.: A relacao do meu alvo com a minha variavel tem que ser preferencialment linear. Tenho que prestar atencao nas variaveil continuas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# agrupar a variavel age em intervalos?\n",
    "#Agrupar a variavel month com os meses que nao teve muita interacao?\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5 - Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6 - Creating the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7 - Evaluating the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8 - Recommendation & Findings"
   ]
  },
  {
   "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 [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
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 },
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}