{
 "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Importing the Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>30</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.313</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>4</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>4.855</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</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>jun</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.959</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</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>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.191</td>\n",
       "      <td>5195.8</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  \\\n",
       "0   30  blue-collar  married           basic.9y      no      yes       no   \n",
       "1   39     services   single        high.school      no       no       no   \n",
       "2   25     services  married        high.school      no      yes       no   \n",
       "3   38     services  married           basic.9y      no  unknown  unknown   \n",
       "4   47       admin.  married  university.degree      no      yes       no   \n",
       "\n",
       "     contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
       "0   cellular   may         fri  ...         2    999         0  nonexistent   \n",
       "1  telephone   may         fri  ...         4    999         0  nonexistent   \n",
       "2  telephone   jun         wed  ...         1    999         0  nonexistent   \n",
       "3  telephone   jun         fri  ...         3    999         0  nonexistent   \n",
       "4   cellular   nov         mon  ...         1    999         0  nonexistent   \n",
       "\n",
       "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0         -1.8          92.893          -46.2      1.313       5099.1  no  \n",
       "1          1.1          93.994          -36.4      4.855       5191.0  no  \n",
       "2          1.4          94.465          -41.8      4.962       5228.1  no  \n",
       "3          1.4          94.465          -41.8      4.959       5228.1  no  \n",
       "4         -0.1          93.200          -42.0      4.191       5195.8  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For this project we used the bank-additional data set with 4119 examples.\n",
    "mkt = pd.read_csv('Data/bank-additional.csv', sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 values\n",
      "\n",
      "   age          job  marital          education default  housing     loan  \\\n",
      "0   30  blue-collar  married           basic.9y      no      yes       no   \n",
      "1   39     services   single        high.school      no       no       no   \n",
      "2   25     services  married        high.school      no      yes       no   \n",
      "3   38     services  married           basic.9y      no  unknown  unknown   \n",
      "4   47       admin.  married  university.degree      no      yes       no   \n",
      "\n",
      "     contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
      "0   cellular   may         fri  ...         2    999         0  nonexistent   \n",
      "1  telephone   may         fri  ...         4    999         0  nonexistent   \n",
      "2  telephone   jun         wed  ...         1    999         0  nonexistent   \n",
      "3  telephone   jun         fri  ...         3    999         0  nonexistent   \n",
      "4   cellular   nov         mon  ...         1    999         0  nonexistent   \n",
      "\n",
      "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
      "0         -1.8          92.893          -46.2      1.313       5099.1  no  \n",
      "1          1.1          93.994          -36.4      4.855       5191.0  no  \n",
      "2          1.4          94.465          -41.8      4.962       5228.1  no  \n",
      "3          1.4          94.465          -41.8      4.959       5228.1  no  \n",
      "4         -0.1          93.200          -42.0      4.191       5195.8  no  \n",
      "\n",
      "[5 rows x 21 columns]\n",
      "\n",
      "\n",
      "(Rows, Columns)\n",
      "(4119, 21)\n",
      "\n",
      "\n",
      "Data information\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4119 entries, 0 to 4118\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             4119 non-null   int64  \n",
      " 1   job             4119 non-null   object \n",
      " 2   marital         4119 non-null   object \n",
      " 3   education       4119 non-null   object \n",
      " 4   default         4119 non-null   object \n",
      " 5   housing         4119 non-null   object \n",
      " 6   loan            4119 non-null   object \n",
      " 7   contact         4119 non-null   object \n",
      " 8   month           4119 non-null   object \n",
      " 9   day_of_week     4119 non-null   object \n",
      " 10  duration        4119 non-null   int64  \n",
      " 11  campaign        4119 non-null   int64  \n",
      " 12  pdays           4119 non-null   int64  \n",
      " 13  previous        4119 non-null   int64  \n",
      " 14  poutcome        4119 non-null   object \n",
      " 15  emp.var.rate    4119 non-null   float64\n",
      " 16  cons.price.idx  4119 non-null   float64\n",
      " 17  cons.conf.idx   4119 non-null   float64\n",
      " 18  euribor3m       4119 non-null   float64\n",
      " 19  nr.employed     4119 non-null   float64\n",
      " 20  y               4119 non-null   object \n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 675.9+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#Lets start seing the head and the shape of the data \n",
    "print('First 5 values\\n')\n",
    "print(mkt.head())\n",
    "print('\\n\\n(Rows, Columns)')\n",
    "print(mkt.shape)\n",
    "print('\\n\\nData information\\n')\n",
    "print(mkt.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": [
    "##### We have a total of 4119 examples and 21 different variables. The data doesn't contain null values, but it contains unknown values. \n",
    "##### Some of the variables are an object. There are also discrete variables and continuous variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 - Data Clearning & Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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 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": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}