churn_prediction_PIPELINE.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Churn Prediction - Eduardo Chaves\n",
    "\n",
    "## Pipeline (Data Preprocessing and Machine Learning Modeling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import joblib\n",
    "\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No       1   \n",
       "1     5575-GNVDE    Male              0      No         No      34   \n",
       "2     3668-QPYBK    Male              0      No         No       2   \n",
       "3     7795-CFOCW    Male              0      No         No      45   \n",
       "4     9237-HQITU  Female              0      No         No       2   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
       "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
       "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
       "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
       "7042  3186-AJIEK    Male              0      No         No      66   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0              No  No phone service             DSL             No  ...   \n",
       "1             Yes                No             DSL            Yes  ...   \n",
       "2             Yes                No             DSL            Yes  ...   \n",
       "3              No  No phone service             DSL            Yes  ...   \n",
       "4             Yes                No     Fiber optic             No  ...   \n",
       "...           ...               ...             ...            ...  ...   \n",
       "7038          Yes               Yes             DSL            Yes  ...   \n",
       "7039          Yes               Yes     Fiber optic             No  ...   \n",
       "7040           No  No phone service             DSL            Yes  ...   \n",
       "7041          Yes               Yes     Fiber optic             No  ...   \n",
       "7042          Yes                No     Fiber optic            Yes  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0                  No          No          No              No  Month-to-month   \n",
       "1                 Yes          No          No              No        One year   \n",
       "2                  No          No          No              No  Month-to-month   \n",
       "3                 Yes         Yes          No              No        One year   \n",
       "4                  No          No          No              No  Month-to-month   \n",
       "...               ...         ...         ...             ...             ...   \n",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0                 Yes           Electronic check          29.85         29.85   \n",
       "1                  No               Mailed check          56.95        1889.5   \n",
       "2                 Yes               Mailed check          53.85        108.15   \n",
       "3                  No  Bank transfer (automatic)          42.30       1840.75   \n",
       "4                 Yes           Electronic check          70.70        151.65   \n",
       "...               ...                        ...            ...           ...   \n",
       "7038              Yes               Mailed check          84.80        1990.5   \n",
       "7039              Yes    Credit card (automatic)         103.20        7362.9   \n",
       "7040              Yes           Electronic check          29.60        346.45   \n",
       "7041              Yes               Mailed check          74.40         306.6   \n",
       "7042              Yes  Bank transfer (automatic)         105.65        6844.5   \n",
       "\n",
       "     Churn  \n",
       "0       No  \n",
       "1       No  \n",
       "2      Yes  \n",
       "3       No  \n",
       "4      Yes  \n",
       "...    ...  \n",
       "7038    No  \n",
       "7039    No  \n",
       "7040    No  \n",
       "7041   Yes  \n",
       "7042    No  \n",
       "\n",
       "[7043 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./assets/WA_Fn-UseC_-Telco-Customer-Churn.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['customerID'], axis=1)\n",
    "# dropping unnecessary feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()\n",
    "# checking duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7043 non-null   object \n",
      " 1   SeniorCitizen     7043 non-null   int64  \n",
      " 2   Partner           7043 non-null   object \n",
      " 3   Dependents        7043 non-null   object \n",
      " 4   tenure            7043 non-null   int64  \n",
      " 5   PhoneService      7043 non-null   object \n",
      " 6   MultipleLines     7043 non-null   object \n",
      " 7   InternetService   7043 non-null   object \n",
      " 8   OnlineSecurity    7043 non-null   object \n",
      " 9   OnlineBackup      7043 non-null   object \n",
      " 10  DeviceProtection  7043 non-null   object \n",
      " 11  TechSupport       7043 non-null   object \n",
      " 12  StreamingTV       7043 non-null   object \n",
      " 13  StreamingMovies   7043 non-null   object \n",
      " 14  Contract          7043 non-null   object \n",
      " 15  PaperlessBilling  7043 non-null   object \n",
      " 16  PaymentMethod     7043 non-null   object \n",
      " 17  MonthlyCharges    7043 non-null   float64\n",
      " 18  TotalCharges      7043 non-null   object \n",
      " 19  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# overview of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Series name: TotalCharges\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "7043 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 55.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.TotalCharges.info()\n",
    "# previously on EDA process was discovered that this\n",
    "# column is not in a numerical data type"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting TotalCharges to a numeric data type (float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.TotalCharges = pd.to_numeric(df.TotalCharges, downcast='float', errors='coerce')\n",
    "# converting it to numeric and leaving Null values for the ones that returns an error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float32')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.TotalCharges.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.TotalCharges.isnull().sum()\n",
    "# there were 11 errors so they were passed as nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()\n",
    "# removing nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.500000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.150002</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.750000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.649994</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  Female              0     Yes         No       1           No   \n",
       "1    Male              0      No         No      34          Yes   \n",
       "2    Male              0      No         No       2          Yes   \n",
       "3    Male              0      No         No      45           No   \n",
       "4  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85     29.850000   \n",
       "1               No               Mailed check           56.95   1889.500000   \n",
       "2              Yes               Mailed check           53.85    108.150002   \n",
       "3               No  Bank transfer (automatic)           42.30   1840.750000   \n",
       "4              Yes           Electronic check           70.70    151.649994   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No phone service' 'No' 'Yes']\n",
      "['No' 'Yes' 'No internet service']\n"
     ]
    }
   ],
   "source": [
    "print(df.MultipleLines.unique())\n",
    "print(df.OnlineSecurity.unique())\n",
    "# was detected redundant categories in these features (No & No phone...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No' 'Yes']\n",
      "['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "df = df.replace(('No phone service', 'No internet service'), 'No')\n",
    "\n",
    "print(df.MultipleLines.unique())\n",
    "print(df.OnlineSecurity.unique())\n",
    "# removing this redundancy and was detected a slight\n",
    "# improvement on the machine learning model predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7032 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7032 non-null   object \n",
      " 1   SeniorCitizen     7032 non-null   int64  \n",
      " 2   Partner           7032 non-null   object \n",
      " 3   Dependents        7032 non-null   object \n",
      " 4   tenure            7032 non-null   int64  \n",
      " 5   PhoneService      7032 non-null   object \n",
      " 6   MultipleLines     7032 non-null   object \n",
      " 7   InternetService   7032 non-null   object \n",
      " 8   OnlineSecurity    7032 non-null   object \n",
      " 9   OnlineBackup      7032 non-null   object \n",
      " 10  DeviceProtection  7032 non-null   object \n",
      " 11  TechSupport       7032 non-null   object \n",
      " 12  StreamingTV       7032 non-null   object \n",
      " 13  StreamingMovies   7032 non-null   object \n",
      " 14  Contract          7032 non-null   object \n",
      " 15  PaperlessBilling  7032 non-null   object \n",
      " 16  PaymentMethod     7032 non-null   object \n",
      " 17  MonthlyCharges    7032 non-null   float64\n",
      " 18  TotalCharges      7032 non-null   float32\n",
      " 19  Churn             7032 non-null   object \n",
      "dtypes: float32(1), float64(1), int64(2), object(16)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# looking for the feature that are categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SeniorCitizen'] = df['SeniorCitizen'].replace((0, 1), ('No', 'Yes'))\n",
    "df['SeniorCitizen'].unique()\n",
    "# changing the values to string to later on detect as a categorical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7032 entries, 0 to 7042\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   gender            7032 non-null   object\n",
      " 1   SeniorCitizen     7032 non-null   object\n",
      " 2   Partner           7032 non-null   object\n",
      " 3   Dependents        7032 non-null   object\n",
      " 4   PhoneService      7032 non-null   object\n",
      " 5   MultipleLines     7032 non-null   object\n",
      " 6   InternetService   7032 non-null   object\n",
      " 7   OnlineSecurity    7032 non-null   object\n",
      " 8   OnlineBackup      7032 non-null   object\n",
      " 9   DeviceProtection  7032 non-null   object\n",
      " 10  TechSupport       7032 non-null   object\n",
      " 11  StreamingTV       7032 non-null   object\n",
      " 12  StreamingMovies   7032 non-null   object\n",
      " 13  Contract          7032 non-null   object\n",
      " 14  PaperlessBilling  7032 non-null   object\n",
      " 15  PaymentMethod     7032 non-null   object\n",
      " 16  Churn             7032 non-null   object\n",
      "dtypes: object(17)\n",
      "memory usage: 988.9+ KB\n"
     ]
    }
   ],
   "source": [
    "numeric_features = df.select_dtypes(np.number)\n",
    "categorical_features = df.drop(numeric_features, axis=1)\n",
    "categorical_features.info()\n",
    "# defining two variables for numerical and categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.500000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.150002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.750000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.649994</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              0                0               0             1   \n",
       "1              0                0               1             0   \n",
       "2              0                0               1             1   \n",
       "3              0                0               1             0   \n",
       "4              0                1               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 1            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 1            1            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
       "0                 1              2           29.85     29.850000      0  \n",
       "1                 0              3           56.95   1889.500000      0  \n",
       "2                 1              3           53.85    108.150002      1  \n",
       "3                 0              0           42.30   1840.750000      0  \n",
       "4                 1              2           70.70    151.649994      1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "labelEncoder_fit = [(feature, LabelEncoder().fit(df[feature])) for feature in categorical_features]\n",
    "\n",
    "joblib.dump(labelEncoder_fit, './assets/labelEncoder_fit.jbl')\n",
    "# exporting the fitted features from the data tu use later on\n",
    "# the deployed model\n",
    "\n",
    "for feature, fit in labelEncoder_fit:\n",
    "  df[feature] = fit.transform(df[feature])\n",
    "  # transforming the categorical features from string to 1,2,3...\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())\n",
    "# looking for extreme correlations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multicollinearity Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "def calculate_vif(data):\n",
    "  vif_df = pd.DataFrame(columns = ['Var', 'Vif'])\n",
    "  x_var_names = data.columns\n",
    "\n",
    "  for i in range(0, x_var_names.shape[0]):\n",
    "    y = data[x_var_names[i]]\n",
    "    x = data[x_var_names.drop([x_var_names[i]])]\n",
    "    r_squared = sm.OLS(y,x).fit().rsquared\n",
    "    vif = round(1/(1-r_squared), 2)\n",
    "    vif_df.loc[i] = [x_var_names[i], vif]\n",
    "\n",
    "  return vif_df.sort_values(by = 'Vif', axis = 0, ascending = False, inplace = False)\n",
    "\n",
    "# algorithm to look for extreme multicollinearity (VIF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Var</th>\n",
       "      <th>Vif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MonthlyCharges</td>\n",
       "      <td>31.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>16.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TotalCharges</td>\n",
       "      <td>15.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tenure</td>\n",
       "      <td>14.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Var    Vif\n",
       "17   MonthlyCharges  31.49\n",
       "5      PhoneService  16.03\n",
       "18     TotalCharges  15.31\n",
       "4            tenure  14.71\n",
       "7   InternetService   4.43"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_vif(df).head()\n",
    "# vif before removing some features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = ['TotalCharges']\n",
    "df = df.drop(drop_cols, axis=1)\n",
    "# after a lot of testing, I detected the best model\n",
    "# results without this(se) feature(s) above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Var</th>\n",
       "      <th>Vif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MonthlyCharges</td>\n",
       "      <td>28.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>15.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tenure</td>\n",
       "      <td>7.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Contract</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Var    Vif\n",
       "17   MonthlyCharges  28.36\n",
       "5      PhoneService  15.40\n",
       "4            tenure   7.59\n",
       "7   InternetService   4.43\n",
       "14         Contract   4.16"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_vif(df).head()\n",
    "# vif after removing some features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())\n",
    "# correlation after removing some features"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change to Categorical Data Type\n",
    "This will help the model to understand that these features are categorical and not ordinal, so 1 is not lesser than 2, but actually another category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_drop_cols(df, drop_cols):\n",
    "  cols_to_drop = []\n",
    "\n",
    "  for col in drop_cols:\n",
    "    cols_to_drop.append(col) if col in df.columns else None\n",
    "\n",
    "  return df.drop(cols_to_drop, axis=1)\n",
    "\n",
    "# this function will help to remove the columns that were previously\n",
    "# dropped some cell ago, without having to manually do it so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7032 entries, 0 to 7042\n",
      "Data columns (total 19 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   gender            7032 non-null   category\n",
      " 1   SeniorCitizen     7032 non-null   category\n",
      " 2   Partner           7032 non-null   category\n",
      " 3   Dependents        7032 non-null   category\n",
      " 4   tenure            7032 non-null   int64   \n",
      " 5   PhoneService      7032 non-null   category\n",
      " 6   MultipleLines     7032 non-null   category\n",
      " 7   InternetService   7032 non-null   category\n",
      " 8   OnlineSecurity    7032 non-null   category\n",
      " 9   OnlineBackup      7032 non-null   category\n",
      " 10  DeviceProtection  7032 non-null   category\n",
      " 11  TechSupport       7032 non-null   category\n",
      " 12  StreamingTV       7032 non-null   category\n",
      " 13  StreamingMovies   7032 non-null   category\n",
      " 14  Contract          7032 non-null   category\n",
      " 15  PaperlessBilling  7032 non-null   category\n",
      " 16  PaymentMethod     7032 non-null   category\n",
      " 17  MonthlyCharges    7032 non-null   float64 \n",
      " 18  Churn             7032 non-null   category\n",
      "dtypes: category(17), float64(1), int64(1)\n",
      "memory usage: 283.7 KB\n"
     ]
    }
   ],
   "source": [
    "categorical_features = update_drop_cols(categorical_features, drop_cols)\n",
    "\n",
    "for feature in categorical_features.columns:\n",
    "  df[feature] = df[feature].astype('category')\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization of the Numerical Features\n",
    "\n",
    "By doing the normalization all numeric values will have 1 and 0 as max e min values. This will help the machine model in its learning process. In this case which is a classification problem it probably won't change its precision. This process has a better outcome when dealing with regression training, but I'll do it to record my learning on this particular process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.115423</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.464789</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.385075</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.354229</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.619718</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.239303</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.521891</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender SeniorCitizen Partner Dependents    tenure PhoneService  \\\n",
       "0      0             0       1          0  0.000000            0   \n",
       "1      1             0       0          0  0.464789            1   \n",
       "2      1             0       0          0  0.014085            1   \n",
       "3      1             0       0          0  0.619718            0   \n",
       "4      0             0       0          0  0.014085            1   \n",
       "\n",
       "  MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection  \\\n",
       "0             0               0              0            1                0   \n",
       "1             0               0              1            0                1   \n",
       "2             0               0              1            1                0   \n",
       "3             0               0              1            0                1   \n",
       "4             0               1              0            0                0   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies Contract PaperlessBilling  \\\n",
       "0           0           0               0        0                1   \n",
       "1           0           0               0        1                0   \n",
       "2           0           0               0        0                1   \n",
       "3           1           0               0        1                0   \n",
       "4           0           0               0        0                1   \n",
       "\n",
       "  PaymentMethod  MonthlyCharges Churn  \n",
       "0             2        0.115423     0  \n",
       "1             3        0.385075     0  \n",
       "2             3        0.354229     1  \n",
       "3             0        0.239303     0  \n",
       "4             2        0.521891     1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "numeric_features = update_drop_cols(numeric_features, drop_cols)\n",
    "\n",
    "minMaxScaler_fit = [(feature, MinMaxScaler().fit(df[feature].values.reshape(-1,1))) for feature in numeric_features]\n",
    "\n",
    "joblib.dump(minMaxScaler_fit, './assets/minMaxScaler_fit.jbl')\n",
    "# exporting the fitted features from the data tu use later on\n",
    "# the deployed model\n",
    "\n",
    "for feature, scaler in minMaxScaler_fit:\n",
    "  df[feature] = scaler.transform(df[feature].values.reshape(-1,1))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7032.000000</td>\n",
       "      <td>7032.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.442560</td>\n",
       "      <td>0.463166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.345708</td>\n",
       "      <td>0.299363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.112676</td>\n",
       "      <td>0.172512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.394366</td>\n",
       "      <td>0.518408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.760563</td>\n",
       "      <td>0.712562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tenure  MonthlyCharges\n",
       "count  7032.000000     7032.000000\n",
       "mean      0.442560        0.463166\n",
       "std       0.345708        0.299363\n",
       "min       0.000000        0.000000\n",
       "25%       0.112676        0.172512\n",
       "50%       0.394366        0.518408\n",
       "75%       0.760563        0.712562\n",
       "max       1.000000        1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "# now they're all normalized as you can see in the min and max indexes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['Churn'], axis=1)\n",
    "y = df.Churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "x_train: (4711, 18) | y_train: (4711,)\n",
      "x_test: (2321, 18) | y_test: (2321,)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "x_train: {x_train.shape} | y_train: {y_train.shape}\n",
    "x_test: {x_test.shape} | y_test: {y_test.shape}\n",
    "\"\"\")\n",
    "# Seeing the train and test sizes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balancing the Training Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3452\n",
       "1    1259\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()\n",
    "# Out data set has a big unbalance on the target feature, which makes more\n",
    "# difficult to our model identify future cases of the lesser outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE()\n",
    "# as we don't have a big data set, I dicided to use the SMOTE over sampling method\n",
    "# so we'll equilibate the target feature to the greater amount, by appending new data\n",
    "# based on the data we already have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3452\n",
       "1    3452\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, y_train = smote.fit_resample(x_train, y_train)\n",
    "y_train.value_counts()\n",
    "# perfect balance!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "x_train: (6904, 18) | y_train: (6904,)\n",
      "x_test: (2321, 18) | y_test: (2321,)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "x_train: {x_train.shape} | y_train: {y_train.shape}\n",
    "x_test: {x_test.shape} | y_test: {y_test.shape}\n",
    "\"\"\")\n",
    "# our test set is the same as size and values, but our training set\n",
    "# now have more values to improve our model training"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 3. Creating the Machine Learning Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgbm\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = lgbm.LGBMClassifier(seed=42, objective='binary')\n",
    "# using the LGBM classifier model (light gradient-boosting machine) with\n",
    "# a binary objective (churn or not churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8483581525674525"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(model, x_train, y_train, cv=5).mean()\n",
    "# I used this cross validation to help me see a general precision\n",
    "# when I was testing different preprocessing methods for the pipeline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid Search\n",
    "Searching for the best hyperparameters for the LGBM model\n",
    "\n",
    "> I decided to not use this technique after my testing show none improvements by comparing with the default hyperparameters defined by the LGBM model, and because this step was consuming much time and resources that for me wasn't worth it, especially because of none improvements it got after all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid = GridSearchCV(lgbm.LGBMClassifier(), {\n",
    "#     'boosting_type': ['dart', 'gbdt'],\n",
    "#     'num_iterations': [400, 600],\n",
    "#     'learning_rate': [.1, .05],\n",
    "#     'num_leaves': [50, 70],\n",
    "#     'max_depth': [7, 8],\n",
    "#     'seed': [42],\n",
    "#     'early_stopping_round': [5],\n",
    "# }, cv=5)\n",
    "\n",
    "# # defining the different parameters to combine and test, with a cross validation of 5 folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid.fit(x_train, y_train)\n",
    "# # putting the grid search to work/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid.best_score_\n",
    "# # getting the best cross validation score of all test made from grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_parameters = grid.best_params_\n",
    "# best_parameters\n",
    "# # seeing the best parameters found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = lgbm.LGBMClassifier(\n",
    "#   num_leaves = best_parameters['num_leaves'],\n",
    "#   max_depth = best_parameters['max_depth'],\n",
    "#   boosting_type = best_parameters['boosting_type'],\n",
    "#   learning_rate = best_parameters['learning_rate'],\n",
    "#   num_iterations = best_parameters['num_iterations'],\n",
    "#   seed = 42,\n",
    "#   objective = 'binary',\n",
    "# )\n",
    "# # definig the definitive model with the best parameters found by grid search"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation / Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(objective=&#x27;binary&#x27;, seed=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(objective=&#x27;binary&#x27;, seed=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier(objective='binary', seed=42)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)\n",
    "# putting our definitive model to train is the train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = model.predict(x_test)\n",
    "y_predict_proba = model.predict_proba(x_test)\n",
    "# making the predictions with the test set to evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.83      0.85      1711\n",
      "           1       0.57      0.63      0.60       610\n",
      "\n",
      "    accuracy                           0.78      2321\n",
      "   macro avg       0.72      0.73      0.72      2321\n",
      "weighted avg       0.79      0.78      0.78      2321\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_predict))\n",
    "# seeing the prediction results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "false_positive_roc, true_positive_roc, _ = metrics.roc_curve(y_test, y_predict_proba[:,1])\n",
    "roc_auc_score = metrics.roc_auc_score(y_test, y_predict)\n",
    "\n",
    "plt.plot(false_positive_roc,true_positive_roc,label=\"AUC=\"+str(roc_auc_score))\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.legend(loc=4)\n",
    "plt.show()\n",
    "# seeing the results with the AUC ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAGdCAYAAAB+VCt0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA2B0lEQVR4nO3dfXzN9f/H8edhztmQq9mWmYguEB1j0ZXvl1IRRa5yEUJZGBWFub4IWXQhiykSulguuqLS1TcJkWmbiyZzOWy2kS3ZdszO7499O9/O54Ozcfy26nG/3T63287n/Tmf8z4n2tPr9f58jsXpdDoFAABwEWVKegIAAKD0IzAAAACPCAwAAMAjAgMAAPCIwAAAADwiMAAAAI8IDAAAwCMCAwAA8IjAAAAAPPIp6Qn84Wzm/pKeAlDqhNS7v6SnAJRKx7OSruj5vfk7qVz1ul47V0miwgAAgFHBOe9tl8DhcKhDhw7asmWLaey3335Ty5YttXr1arf9a9asUZs2bWS32zV06FCdPHnSNeZ0OjV79mzdeuutat68uaKiolRQUFCsOREYAAAoRfLy8jRixAjt3bv3vOMvvPCC0tPT3fYlJiZq3LhxioiIUGxsrLKzsxUZGekaf/PNN7VmzRrNmzdPc+fO1SeffKI333yzWPMiMAAAYOQs8N5WDMnJyerevbsOHz583vFt27bphx9+UEBAgNv+5cuXq127durUqZPq16+vqKgorV+/XikpKZKkpUuXavjw4QoLC9Ott96qZ555Rm+//Xax5kZgAADAqKDAe1sxbN26VS1atFBsbKxpzOFwaMKECZo4caKsVqvbWEJCgsLCwlyPa9SooeDgYCUkJOj48eNKTU3VLbfc4hpv1qyZjh49aqpUXEypWfQIAEBp4SxmZeBiHA6HHA6H2z6r1Wr6pS9JvXr1uuB5FixYoIYNG+rOO+80jaWnpyswMNBtn7+/v9LS0pSRkSFJbuPVq1eXJKWlpZmedyEEBgAArqCYmBjNmzfPbV9ERISGDRtW5HMkJyfrvffe08cff3ze8dzcXFMAsVqtcjgcys3NdT3+85gkU5C5GAIDAABGxWwlXEx4eLj69+/vtu981YULcTqdGj9+vIYPH+6qDBjZbDbTL3+HwyE/Pz+3cGCz2Vw/S5Kfn1+R50FgAADAyIstiQu1H4rq2LFj+umnn7Rnzx7NmjVLkpSTk6NJkybp008/1RtvvKGgoCBlZma6PS8zM1MBAQEKCgqSJGVkZCgkJMT1syTT4smLITAAAFCKBQUF6YsvvnDb16dPH/Xp00cPPvigJMlutysuLk6dO3eWJKWmpio1NVV2u11BQUEKDg5WXFycKzDExcUpODi4yOsXJAIDAABml3jDpSvBx8dHtWvXNu3z9/d3VQ969uypPn36qEmTJmrcuLGmT5+uVq1aqVatWq7x2bNn6+qrr5YkzZkzRwMGDCjePLzwXgAA+HvxYkvi/0NoaKimTp2quXPnKisrS3fccYemTZvmGh84cKBOnDihiIgIlS1bVl27dtWjjz5arNewOJ1Op5fnfUn4LgnAjO+SAM7vSn+XhOPgNq+dy1onzPNBfwFUGAAAMPLiVRJ/FwQGAAAMvHnjpr8Lbg0NAAA8osIAAIARLQkTAgMAAEa0JEwIDAAAGJWi+zCUFqxhAAAAHlFhAADAiJaECYEBAAAjFj2a0JIAAAAeUWEAAMCIloQJgQEAACNaEia0JAAAgEdUGAAAMHA6uQ+DEYEBAAAj1jCY0JIAAAAeUWEAAMCIRY8mBAYAAIxoSZgQGAAAMOLLp0xYwwAAADyiwgAAgBEtCRMCAwAARix6NKElAQAAPKLCAACAES0JEwIDAABGtCRMaEkAAACPqDAAAGBEhcGEwAAAgAHfVmlGSwIAAHhEhQEAACNaEiYEBgAAjLis0oTAAACAERUGE9YwAAAAj6gwAABgREvChMAAAIARLQkTWhIAAMAjKgwAABjRkjAhMAAAYERLwoSWBAAA8IgKAwAARlQYTAgMAAAYsYbBhJYEAADwiAoDAABGtCRMCAwAABjRkjAhMAAAYESFwYQ1DAAAwCMqDAAAGNGSMCEwAABgREvChJYEAADwiAoDAABGVBhMqDAAAGDkdHpvuwQOh0MdOnTQli1bXPvi4+PVo0cPhYaG6r777tOKFSvcnrNp0yZ16NBBdrtdffv2VUpKitv4kiVL1LJlS4WGhmrs2LHKyckp1pwIDAAAlCJ5eXkaMWKE9u7d69qXkZGhxx9/XM2bN9cHH3yg4cOHa9q0afr2228lSceOHdPQoUPVuXNnrVy5UtWqVdOQIUPk/G9gWbdunebNm6epU6fqrbfeUkJCgl544YVizYvAAACAUUGB97ZiSE5OVvfu3XX48GG3/V999ZWqV6+uESNGqE6dOmrfvr06deqkTz75RJK0YsUKNWrUSAMGDND111+vmTNn6ujRo9q6daskaenSperXr59at26tm2++WVOmTNGqVauKVWUgMAAAYFRCgWHr1q1q0aKFYmNj3fa3bNlSM2fONB1/+vRpSVJCQoLCwsJc+/38/HTTTTcpPj5e586d044dO9zGmzRporNnzyopKanIc2PRIwAAV5DD4ZDD4XDbZ7VaZbVaTcf26tXrvOcICQlRSEiI6/GJEye0du1aDRs2TFJhyyIwMNDtOf7+/kpLS1N2drby8vLcxn18fFSlShWlpaUV+X1QYQAAwMhZ4LUtJiZGzZo1c9tiYmIueWq5ubkaNmyYqlevrocffliSlJOTYwogVqtVDodDubm5rsfnGy8qKgwAABh58bLK8PBw9e/f323f+aoLRfH7779ryJAhOnjwoN555x35+flJkmw2m+mXv8PhUKVKlWSz2VyPjeN/PL8oCAwAABhd4uWQ53Oh9kNxnT59Wo899pgOHz6st956S3Xq1HGNBQUFKTMz0+34zMxMNWjQQFWqVJHNZlNmZqbq1asnScrPz9epU6cUEBBQ5NenJQEAQClXUFCgiIgIHTlyRMuWLdP111/vNm632xUXF+d6nJOTo927d8tut6tMmTJq3Lix23h8fLx8fHxUv379Is+BCgMAAEal7E6PK1eu1JYtWzR//nxVqlRJGRkZkqRy5cqpSpUq6tKlixYtWqSFCxeqdevWio6OVkhIiFq0aCGpcDHlxIkTdcMNNygwMFCTJ09W9+7daUkAAHBZSllgWLdunQoKChQeHu62v3nz5lq2bJlCQkL06quvasaMGYqOjlZoaKiio6NlsVgkSe3bt9fRo0c1ceJEORwO3XvvvXr22WeLNQeL0+nFRs1lOJu5v6SnAJQ6IfXuL+kpAKXS8ayi3z/gUuQsesZr5/IbONtr5ypJVBgAADBylq4KQ2lAYAAAwMBZUCqK76UKV0kAAACPqDAAAGBUyhY9lgYEBgAAjFjDYEJLAgAAeESFAQAAIxY9mhAYAAAwYg2DCYEBAAAjAoMJaxgAAIBHBIa/MIfDoU6PPKGt2xM9Hns09bhuafNQkY4tqrw8hybMfEm33ddVrR7spSXvrnIb37glTp37DVGz1h3Vud8Qbdj8o9deGzC6ukag3lj6ipIO/qD4n9dryvQxstnO/5XC/77rDn3z/YfafzROKz5arHrXXevVuYyfPEK79m1S0sEfNGHqM677+UvSdddfq9gPFik5ZZt+TPxKT44MdxtHKeF0em/7myAw/EXl5Tn07KRZSj5wqEjHT5s9Tzk5uV6dw5zoN7Qraa8WzX1e40cO1fzFb+uL/2yQJB0+ckxPRk5Tp3b36MPlC9SxXRsNj5yqo6nHvToH4A+Lls6Vn5+vOrZ9ROEDRujedq00evyTpuNurH+d3n5/gT7/9Gvd8++uSkzYrVWfLFH5CuW9Mo8nIvrroa4d1L93hAb2eVJduj2gJyL6S5L8/Hz1zsqFSj12XPe17qYxI6dq0OC+enRgT6+8NryooMB7298EgeEvaN+BQ+o16GmlHEst0vFr1n2j38+cueTXi160XOOem+O270xOrlZ9sk5jnnxCDW+8Tm3+fYcG9O6md1Z9IklKS89U1wfbqm+Ph1SrZg3169FZfr6+2rF7zyXPA7iQ666/VmHNm+ipIWO1JylZWzbHKWr6q+rctYPp2H4De+rHLfGKmvGq9iUf0LSJs5Wd/Zu6dH+gWK/5cK+HtHrNUtP+x5/oo6gZr2rrD9u1ccMWTZs0WwMe7y1JuvX2MFWpUlmjnp6kfckH9PWX3ynmtbfUuZt5nkBpQ2D4C/oxfoeaN71Zb8e86PHYU1nZevG1xZr07HDTmMPh0MyXF+jO+x/Wnfc/rNFTopSV/VuR5rAneb/yz+UrtHED177Qm2/Sjl17VFBQoOZNb9aYp56QJJ3Nz9eqT9bp7NmzatzwxiK+S6Do0tMz9XDnx5SRccJtf6VKFU3H1q4Tou1xCW77knb/orBbmrgeP/3sYCUkfadfDm3Vsvfmq2ZIjSLNI+jqQIXUCtYPm/7Xftv6w3ZdU7umAoMCtHNHkh7tHSGH46zHeaKEFTi9t/1NXPJVEr/++qscDof8/PxUqVIlb84JHvR4qOj/Gomau1APtmuj6+rWNo29EvOWdv78i+bPniqbzapXYpZoxPjpWjT3eY/nzcw8qSqVK6tcuXKuff7VqijP4dCprGxVq1pFUmFr4oFej+vcuQI9Pbi/atYIKvLcgaLKzvpN3379veuxxWLRgEG9tWH9D6ZjM9JPqIbhz2FwzRo69WuWJGngoEfUpfsDGvzYM0o/nqEhwwco9oNFanXbg8rPz7/oPIKuDpAkpaWm/+n1Mv/7GkGK377T9ViSfH1teqRfN33x2X+K+Y5xxXGnR5NiBYYvvvhCy5cvV2JiovLy8lz7fX191ahRI/Xr109t2rTx+iRxaTb/+JO2J+7Sh8sXmMZycnP1zqqPFbtorm6oV7jga+aEZ3Xn/Q/rl30H9Ntvv+uJZyZIks6ezZecTn3xbeH/kBfMnqacvDxZ/xQWJLkeO87+719PVatU1ntvvKKEnUmKenWhrqkZrHta33lF3i/wh4nTnlVje0O1bd3NNPbR6k+19L3X9MHKtfrmqw3q0v0BNWnaSBs3bJUkDX1yoMaMnKpN3xc+fubJSdrxywbd1aaldu1M0oYtayRJZX18VK6cj/YfjZMkPfvUJB05UtgmzMtzuF7vj5+tVvcFmBaLRXPnz1SFihX0yosLvfwJAN5X5MDw5ptvat68eXrssccUEREhf39/Wa1WORwOZWZmatu2bRozZoyefPJJ9enT50rOGUWQm5enKVFzNeGZCPnabKbxI8fSdPZsvnqHj3DbX1BQoEOHj6rl7bdo1ZJoSdLyFR8pPeOERgwZIEkKDPBX5slf3YKB9L+g4Ofr69p3VcUKanDDdWpww3Xad+Cw3l71MYEBV9T4KSM1aHBfDeo/Qkk/7zWN/+fr7zVnVrQWLZsrH5+y2rhhi1a895GuqnSVylcor5ohNRTz5oty/mmxmq+fr+peV0dff/md7mr5kCSp/QP3qMOD92rw489KKqxcXH9DYfi22ayuoPDHlRp/XnRctmxZvbrged1zXyt16zTQreqAUuJv1ErwliIHhsWLF2vWrFnnrSDUq1dPLVq00I033qhp06YRGEqBHbv36MixND017jm3/YNHTtCD7dro4U73S5KWvTZb5cv7uR3jX7WKfG02XRMSLEmqXOkq/f77GddjqTA0nMrKUn7+Ofn4lJUknTjxq3xtNl1VsYKS9x9SVvZvatakkes59a69Rj/+5L3LOgGjGVHj1W9gDw0dNEprP/7igse9PDtGr81drEqVrlJm5kktXPKSUg4fdf1ZfrzfU0pOPuD2nFO/ZuncuXM6uP+wJCkz46RycvJcjyUp9VhhKyIwKEAph4/+9+fqkqTjaRmSJB8fHy1c8qJatb5DvbqFa9vWn7z07uFNzr/R1Q3eUuRFj7m5uQoJCbnoMUFBQfrtt6ItmsOV1bjhjfo0dpFWLYl2bZI0ZcyTini8j2rVrKGyZcvoVHa2rgkJ1jUhwapQobxmzY3RiV9PeTx//evryqesjxJ3/ezatz1xlxo1uF5lypTRtxu3aPKsV+T80zXIu/bsVd06tbz+XgFJGjl6qPoOeFjhA0bqw1WfXvC4h7q017SZkXI4zioz86R8fW26o2ULbfxui7KzflNGeqYCg6rr4P7DOrj/sI6mpGri1Gd0XRHu1XA8LV0ph4+q+a1NXfua39pMKYePKv14YWCY/cpU/bvVHerR5XFt3si9SfDXUeTAcM8992jMmDHatm2baeFPQUGBtm/frrFjx+q+++7z+iRRdJknTio3L89VIfjzJkmBAdXlX7WKKlQory4PtNW02dHauj1R+w4c0thps3X4SKppYeLQgY9o+viRbvv8fH31YLs2mvrCPO34eY++/m6Tlry7Sr27dZIkdbivtTJOnNRL8xfrUMpRvbvqE61Z940e6/Pw/8vngH+W62+oqxGjBuvVl17Xls1xCgis7tokKSCwunx9C1tz+/YdVN8BPXT/A/fo2rq1Nf+N2Tp2NFVff/mdJGlB9BJFTnhK97ZtrWvr1taLr05T8xZNtXfvfrfXjH3nA3Xu0Nc0l7cWv6cJU57R7Xc21+13Ntf4ySP1+oJlkqR/tb5dPR/prMnjZ+nA/sOuOfr7V72SHw8uBVdJmFiczqLdhsrhcGjWrFlauXKlzp07pypVqrjWMJw6dUo+Pj7q2LGjIiMj5funHnZRnc3c7/kgmDS6o50WvzpLzZve7Hr83NgR6tT+Ho/H5uTmava8N/T5198pPz9fzZo01tinBysk+OoivXZObq6mvTBPX67fqKsqVFD/Xl3U5+GHXOMJO3/WrFdi9Mu+gwquEainnxig1i1v9cK7/ucIqXd/SU/hL2HY049r/OSR5x0Lqlxfx7OSNHxwpGLf+UCS1KN3Z40cPURVq1bRhvWbNXrkVFcFoEyZMho1dph69emiq66qqPifdmn8mOnatSOpSHMpU6aMJk0bpR6PPKT8/HN6d9lKPTe58BLoqJcmq9+AHqbnHD50VLfcfPelvPV/rONZRfvvcal+f+4Rr52rwvjlXjtXSSpyYPhDTk6OkpKSlJGRoZycHNlsNgUFBalBgwaXFBT+QGAAzAgMwPld8cAwtbfXzlVh4tteO1dJKvZ9GPz8/BQaGnol5gIAAEopvt4aAAAjrpIwITAAAGD0N1qs6C18lwQAAPCICgMAAEZ8l4QJgQEAACNaEia0JAAAgEdUGAAAMOC7JMwIDAAAGNGSMKElAQAAPKLCAACAERUGEwIDAABGXFZpQmAAAMCICoMJaxgAAIBHVBgAADBwUmEwITAAAGBEYDChJQEAADyiwgAAgBF3ejQhMAAAYERLwoSWBAAA8IgKAwAARlQYTAgMAAAYOJ0EBiNaEgAAwCMqDAAAGNGSMCEwAABgRGAwITAAAGDAraHNWMMAAAA8IjAAAGBU4PTedgkcDoc6dOigLVu2uPalpKTo0UcfVZMmTXT//ffr+++/d3vOpk2b1KFDB9ntdvXt21cpKSlu40uWLFHLli0VGhqqsWPHKicnp1hzIjAAAGBU4MWtmPLy8jRixAjt3bvXtc/pdGro0KGqXr26Vq1apY4dOyoiIkLHjh2TJB07dkxDhw5V586dtXLlSlWrVk1DhgxxXR66bt06zZs3T1OnTtVbb72lhIQEvfDCC8WaF4EBAIBSIjk5Wd27d9fhw4fd9v/www9KSUnR1KlTVa9ePYWHh6tJkyZatWqVJGnFihVq1KiRBgwYoOuvv14zZ87U0aNHtXXrVknS0qVL1a9fP7Vu3Vo333yzpkyZolWrVhWrykBgAADAwFng9NpWHFu3blWLFi0UGxvrtj8hIUENGzZU+fLlXfuaNWum+Ph413hYWJhrzM/PTzfddJPi4+N17tw57dixw228SZMmOnv2rJKSkoo8N66SAADAqISukujVq9d592dkZCgwMNBtn7+/v9LS0jyOZ2dnKy8vz23cx8dHVapUcT2/KAgMAABcQQ6HQw6Hw22f1WqV1Wot8jlycnJMx1utVtd5Lzaem5vrenyh5xcFLQkAAIy8uOgxJiZGzZo1c9tiYmKKNR2bzWb65e5wOOTr63vRcT8/P9lsNtfj840XFRUGAAAMvHnjpvDwcPXv399tX3GqC5IUFBSk5ORkt32ZmZmuNkNQUJAyMzNN4w0aNFCVKlVks9mUmZmpevXqSZLy8/N16tQpBQQEFHkOVBgAALiCrFarKlas6LYVNzDY7Xbt2rXL1V6QpLi4ONntdtd4XFycaywnJ0e7d++W3W5XmTJl1LhxY7fx+Ph4+fj4qH79+kWeA4EBAACjErwPw/k0b95cNWrUUGRkpPbu3auFCxcqMTFRXbt2lSR16dJF27dv18KFC7V3715FRkYqJCRELVq0kFS4mHLRokX66quvlJiYqMmTJ6t79+60JAAAuByl7bskypYtq9dee03jxo1T586dVbt2bUVHRys4OFiSFBISoldffVUzZsxQdHS0QkNDFR0dLYvFIklq3769jh49qokTJ8rhcOjee+/Vs88+W6w5WJx/3AaqhJ3N3F/SUwBKnZB695f0FIBS6XhW0e8fcClOdvy3185V7aP1XjtXSaIlAQAAPKIlAQCAgdNLaw/+TggMAAAYERhMaEkAAACPqDAAAGBAS8KMwAAAgBGBwYSWBAAA8IgKAwAABrQkzAgMAAAYEBjMCAwAABgQGMxYwwAAADyiwgAAgJHTUtIzKHUIDAAAGNCSMKMlAQAAPKLCAACAgbOAloQRgQEAAANaEma0JAAAgEdUGAAAMHBylYQJgQEAAANaEma0JAAAgEdUGAAAMOAqCTMCAwAABk5nSc+g9CEwAABgQIXBjDUMAADAIyoMAAAYUGEwIzAAAGDAGgYzWhIAAMAjKgwAABjQkjAjMAAAYMCtoc1oSQAAAI+oMAAAYMB3SZgRGAAAMCigJWFCSwIAAHhEhQEAAAMWPZoRGAAAMOCySjMCAwAABtzp0Yw1DAAAwCMqDAAAGNCSMCMwAABgwGWVZrQkAACAR1QYAAAw4LJKMwIDAAAGXCVhRksCAAB4RIUBAAADFj2aERgAADBgDYMZLQkAAOARFQYAAAxY9GhGYAAAwIA1DGalJjD4Bbcs6SkApU4T/7olPQXgH4k1DGasYQAAAB4RGAAAMChwWry2FUdqaqrCw8PVtGlT3XXXXVqyZIlrbPfu3erWrZvsdru6dOminTt3uj13zZo1atOmjex2u4YOHaqTJ09646NwITAAAGDg9OJWHE899ZTKly+v1atXa+zYsXr55Zf15Zdf6syZMxo0aJDCwsK0evVqhYaGKjw8XGfOnJEkJSYmaty4cYqIiFBsbKyys7MVGRl5uR+DGwIDAAClQFZWluLj4zV48GDVqVNHbdq0UcuWLbV582Z9+umnstlsGjVqlOrVq6dx48apQoUK+vzzzyVJy5cvV7t27dSpUyfVr19fUVFRWr9+vVJSUrw2PwIDAAAGJdGS8PX1lZ+fn1avXq2zZ89q//792r59uxo0aKCEhAQ1a9ZMFkvh+SwWi5o2bar4+HhJUkJCgsLCwlznqlGjhoKDg5WQkOC1z4TAAACAgdNp8drmcDh0+vRpt83hcJhe02azaeLEiYqNjZXdble7du30r3/9S926dVNGRoYCAwPdjvf391daWpokKT09/aLj3lBqLqsEAODvKCYmRvPmzXPbFxERoWHDhpmO3bdvn1q3bq3+/ftr7969mjZtmm677Tbl5OTIarW6HWu1Wl3BIzc396Lj3kBgAADAoMCL5woPD1f//v3d9hl/uUvS5s2btXLlSq1fv16+vr5q3Lixjh8/rvnz56tWrVqmX/4Oh0O+vr6SCqsT5xv38/Pz2vugJQEAgIFTFq9tVqtVFStWdNvOFxh27typ2rVru0KAJDVs2FDHjh1TUFCQMjMz3Y7PzMx0tSEuNB4QEOC1z4TAAABAKRAYGKhDhw65VQr279+vkJAQ2e12/fTTT3L+90sunE6ntm/fLrvdLkmy2+2Ki4tzPS81NVWpqamucW8gMAAAYFDg9N5WVHfddZfKlSun8ePH68CBA/rmm2+0YMEC9enTR23btlV2dramT5+u5ORkTZ8+XTk5OWrXrp0kqWfPnvroo4+0YsUKJSUladSoUWrVqpVq1arltc/E4nSWju/k8rHWLOkpAKUO3yUBnN+21A1X9PzfBHX32rnuOv5+kY/9IwwkJiaqWrVq6t27t/r16yeLxaLExERNmjRJ+/bt04033qgpU6aoYcOGrueuXr1ac+fOVVZWlu644w5NmzZNVatW9dr7IDAApRiBATi/Kx0Yvg562Gvnuvt4rNfOVZJoSQAAAI+4rBIAAANvXlb5d0FgAADAwKnifcvkPwEtCQAA4BEVBgAADGhJmBEYAAAwIDCY0ZIAAAAeUWEAAMCARY9mBAYAAAwKyAsmtCQAAIBHVBgAADAooCVhQmAAAMCgVHzJUilDYAAAwIDLKs1YwwAAADyiwgAAgEGBhTUMRgQGAAAMWMNgRksCAAB4RIUBAAADFj2aERgAADDgTo9mtCQAAIBHVBgAADDgTo9mBAYAAAy4SsKMlgQAAPCICgMAAAYsejQjMAAAYMBllWYEBgAADFjDYMYaBgAA4BEVBgAADFjDYEZgAADAgDUMZrQkAACAR1QYAAAwoMJgRmAAAMDAyRoGE1oSAADAIyoMAAAY0JIwIzAAAGBAYDCjJQEAADyiwgAAgAG3hjYjMAAAYMCdHs0IDAAAGLCGwYw1DAAAwCMqDAAAGFBhMCMwAABgwKJHM1oSAADAIyoMAAAYcJWEGYEBAAAD1jCY0ZIAAAAeUWEAAMCARY9mBAYAAAwKiAwmtCQAAIBHVBgAADBg0aMZFQYAAAycXtyKw+FwaMqUKbrlllt0++2368UXX5TTWXiW3bt3q1u3brLb7erSpYt27tzp9tw1a9aoTZs2stvtGjp0qE6ePHlJ7/1CCAwAABgUeHErjueee06bNm3SokWLNGfOHL3//vuKjY3VmTNnNGjQIIWFhWn16tUKDQ1VeHi4zpw5I0lKTEzUuHHjFBERodjYWGVnZysyMvJyPwY3tCQAACgFTp06pVWrVunNN9/UzTffLEkaMGCAEhIS5OPjI5vNplGjRslisWjcuHH67rvv9Pnnn6tz585avny52rVrp06dOkmSoqKi1Lp1a6WkpKhWrVpemR8VBgAADAos3tuKKi4uThUrVlTz5s1d+wYNGqSZM2cqISFBzZo1k8VSeEKLxaKmTZsqPj5ekpSQkKCwsDDX82rUqKHg4GAlJCR45fOQCAwAAJgUyOm1zeFw6PTp026bw+EwvWZKSopq1qypDz/8UG3bttXdd9+t6OhoFRQUKCMjQ4GBgW7H+/v7Ky0tTZKUnp5+0XFvoCUBAMAVFBMTo3nz5rnti4iI0LBhw9z2nTlzRocOHdJ7772nmTNnKiMjQxMnTpSfn59ycnJktVrdjrdara7gkZube9FxbyAwAABg4M3bNoWHh6t///5u+4y/3CXJx8dHp0+f1pw5c1SzZk1J0rFjx/Tuu++qdu3apl/+DodDvr6+kiSbzXbecT8/P6+9DwIDAAAG3rwPg9VqPW9AMAoICJDNZnOFBUm69tprlZqaqubNmyszM9Pt+MzMTFcbIigo6LzjAQEBXngHhVjDAABAKWC325WXl6cDBw649u3fv181a9aU3W7XTz/95Long9Pp1Pbt22W3213PjYuLcz0vNTVVqamprnFvIDAAAGDgzUWPRVW3bl21atVKkZGRSkpK0oYNG7Rw4UL17NlTbdu2VXZ2tqZPn67k5GRNnz5dOTk5ateunSSpZ8+e+uijj7RixQolJSVp1KhRatWqldcuqZQIDAAAmJTUnR5nz56ta665Rj179tTo0aPVu3dv9enTRxUrVlRMTIzi4uLUuXNnJSQkaOHChSpfvrwkKTQ0VFOnTlV0dLR69uypypUra+bMmZf7MbixOP+ob5QwH2tNzwcB/zBN/OuW9BSAUmlb6oYrev5RdXp67VxRB9/12rlKEoseAQAw4MunzAgMAAAYFGftwT8FgQEAAAPighmLHgEAgEdUGAAAMGANgxmBAQAAAydNCRNaEgAAwCMqDAAAGNCSMCMwAABgwGWVZrQkAACARwSGv6Dg4KsV+95Cpaft1KED2zQ7apJsNtt5j72/3d3a9uMXOnXyF22P+1IdOtzj1bnMmB6p1KOJSk/bqednjpPFYnGN3XhjPX229h2dzEzS3j2bNWb0MLdxwNtC6tTUq+/O0XfJ67Rm20r1GXzh2/u2atdSK75bpu+S1+mNj6J1Y+MbvDYPq82qCXNG6z9Jn+rz+A/VO/xht/FGTRtq0cev6bvkdVq14W117NXBa68N7yip75IozWhJ/AW9/95C/frrKbW6q7OqVa2i1xe+qHPnzml05HNuxzVu3EAr3n9do8c8p88+/0b33vNvvf/eQt16e3slJu6+7Hk8/VS4ejz8kLp2G6hy5crprSVzlZ6eqRdfipGfn68++WiZvtvwg269vb3q1a2tRW+8pKysbM1f8NZlvzZgZLFY9MqyKO1KSFLvewbqmmtDNH3+JKWnZWjdB1+5HVv3hjp6LnqSZo5+QfFbd6j3oO56ZVmUOt72sPJy8i57Lk9OGKIG9vp6ottTqhESpMmvjFPakeP6eu238g+oprlvz9bKpR9q8pMz1ODmGzXxpUhlHj+hjV9vvuzXhnfQkjCjwvAXc+ON9XTrrc008PER2r37F32/casmT31BPXp0Mh3bs0cn/ec/GzUverH27Tuo+Qve0rffblK3rg8U6zX79umur79cYdo/LGKgJk+ZrY2bftS36zcpctwMDRncX5L0r5a3qlq1KhoydIx++WWfPvv8G70y93X17PHQJb1vwJNqAdW0Z9dePT96tlIOHNHGb37Q1u/j1KT5zaZjb23VXPt/OaC1K9bp6KFjmjcjRtWD/FX3hjrFes1BI/tr0stj3fb5+vmqY68OmjPhFe3Z8Yu+/WyDlr32jroP6CypsLJxIuOEXpu5UCkHjuiLj77Wpys/V9vObS75vQP/HwgMfzFpaRm6v30vpadnuu2vXLmS6dily1Zo7Hjz15tWqnSV6+dxY5/S4YNxykzfrQ8/WKJatYKLNI8aNYJ0zTU1teH7H1z7Nm7cqjp1aunqqwMVn7BLXboOlMPhcH/tylcZTwV4xYn0Exr7xGSd+T1HkmS/pbGa3mpX3KafTMdm/ZqlujdcK/stjWWxWPRAj/t1Ovu0jhw8KkkqZy2nkdOG66tdn+irXZ9o6rwJqlSlaH92b7jpOvmUK6uEbTtd++K3JOqm0IayWCza9M0WTXnK/Pey4lUVL+Vt4wop8OL2d0FL4i8mKytbX3y53vXYYrFo6OD++uY/35uOTUpKdnvcsOENuuuuOxXz+jJJ0tAh/dWz50Pq0zdCacfTNeLpJ/TZp++qSejdys/Pv+g8alwdKEk6duy4a9/x44UhJqRmDW2LS9Dx4xmuMV9fXw0c0Etr1n5ZzHcMFN8nP65QjZCr9d0XG/XN2vWm8S8++kb/uvdOLfr4NeXn58tZ4NRTfUbpt6zTkqShkYPU0F5fT/YepdzcPA2NHKTnF07TkO5PeXzt6kH+OnUyS/ln//d36ETmr/L1s6lytcpKPZKm1CNprrGq/lV0b8e7tXDOm5f/xuE13LjJjMDwFzdr5niFhjbSrbe3v+hx/v5V9X7s69q06Ud9/PE6SdIzI4do2PCxWv9dYd908JDROnL4J7W9r7USEndpR8K3kiQfn7IqV66cTp38pfC4oaOVcrjwX2J5ef/r9/7xs81mdXtti8WixYte0lVXVdSsqHmX/6YBD0Y9Nl7+Af4a8/wIjZgyTLMnvOI2XrlqJfkHVtOsyBe1Y/sude37kCa+FKlH7h2oM2dy1L1/Z/Vp+7j2Je2XJE0c9py+3r1G9erX1VWVK2ru2y9IksqVKyeLxaK7O/xbkjS897Py9fPVWcdZt9c7m1dYabNay7ntt/laFbXoOZ1IP6lVyz66Ip8FLs3fqTLgLQSGv7CZM8Zq+PDH1LP3YO3ateeCxwUGVtfnn72rMmXKqHuPQXI6napQobxq1QrWO2/PV0HB//5q+Pn56vrr6+qzz79Rs1vulSQ91KmdOj90v/r0GyZJOn48Q/VvvE6SZLPZ/hQUCq/UOHMmx3W+smXL6s3FL6v9/W3Utl1Pt6oDcKX8nFD49+GlSVZNi56gl6dGu/2Lf/j4wUr+eb9WLPlAkjT92Sit/G65Huxxv77/arOsNqveXDPf7Zxly5ZV7Xq1tPHrzerVZoAkqcfArgqsUV1zn1sgScpIy5B/QDWVMwSDcv8N0bk5ua59fuX9NGfJTF1Tt5Ye6zjUK4stgSupWIHhxx9/LPKxt9xyS7Eng6J7+aVpeiK8r/o+OkwffPDpBY8LDr5aX37xviTp7jZdlZl5UpLk41P4n75Hz3Dt+WWf23NOnjylc+fOad++g5Kk9PQTysnJdT2WpKPHCkuqV18doEOHjrh+lqTUtHTXa7z7znzd0+bfeuDBPtr8w7bLfNfAhVWrXlWNwxpp/ecbXPv27z0oq82qCldVUNbJLNf++jffqNhFK12PnU6nftmdrKtDrlZZn7KSpMc6DnWth/jDyYyTyst1uNY6ZJ/KVoWrKrgeS1JGWqaqVKussmXL6ty5c5Ik/4Bqys3JdbU8KlQsr7lvz1bItTU1uOuTSjlwxMufBi4XLQmzYgWGqVOnKjm5sC/udF74w7RYLPr5558vb2a4oAnjn1b4oD7q9cgQrV699oLHlS/vp7VrlqugoEBt7unm9q/7rKxsHT+eoaCgAH362deSCsur77z9mubMWaAftsRddA6pqcd16NAR3XFHc1dg+OPntP8GhgXzo9Tm7n+pfYfe2rip6GETuBTB19TQC4ueU/tmXZSRVriepsHNN+pk5q9uYUEq/KV+7fV13PbVrneNdsd/oSMHjyo/P1+Vq1bWL7sK/39X1b+KJrw4Ri9OelVnfj+qi9mza6/yz55To2YNlbB1hySpSYubtSs+SU6nUxaLRVGLpqtm7WAN6jxMh5IPe+kTgDfRkjArVmBYtWqVRowYoSNHjig2NvaCNwvClVO//nUaN/YpzYqap40btyooKMA19kcAyMr6Tbm5uYocM1z16tbR3W26SpLr2JycXGVn/6aXX1moaVNHKyPjhJL2JGvc2Kd0+2236PE9z7i95tJl72vpsvdNc4lZuFQzp4/V0SOpkqQZz0XqpZcXSpLa3N1Sj/Z7WE8MHqXkfQddr33u3DlXlQPwpt3xSfo5cY8mvhSpFye9quCQqzV8wmAtfmWppMJ/5Z/+7bTych368O1PNOnlsdqdkKTEbTvVqVcH1QgJ0pr3P9OZ33P04dtrNOb5kZrxbJROnjilpydHqEZIkI4dTnV7zfMtVMzLydPaFZ9p7KxnNOWpmQqoEaA+T/TQlKcLr4zo2KuDwu4I1Yh+kTqddVr+AdUkSWfPnlX2qd+u8KcEXDqL82KlgvNwOBzq3r27brvtNo0ePdprE/Gx1vTauf7ORj07VDOmjz3vmI+1pvIdRzVg4NNauux97dyx3rXW4M/eWvq+Bj72tMqUKaPJk55R/0d7qFKlqxQXl6CnR05SQsKuIs2lTJkyinp+gvr16678/HN6c8m7Gjuu8H+K0fOeV/igPqbnHDyYoutuuLUY7/ifrYl/3ZKewl9K9SB/jZrxtJrf2Uw5Z3L1/pur9ebcwquCtqVu0OQnZ2jN+59Jkjr2bK9HBvdQYI1A/bJrr2ZPmKs9OwoX9tr8bHpq4lDd8+Bd8inno59+SNAL417WsZTUC772n9n8bIp8fqTuav9vnc7+Xcvmv6t3Xy+8l8ncd2br9tYtTM+J2/STwrsM98bH8I+wLXWD54MuQ5/anb12rmWHVnvtXCWp2IFBkvbt26etW7eqZ88L33a1uAgMgBmBATi/Kx0YHvFiYFj+NwkMl3SVRL169VSvXj1vzwUAAJRSXFYJAIAB3yVhRmAAAMCAyyrN+C4JAADgERUGAAAMuA+DGYEBAAAD1jCYERgAADBgDYMZaxgAAIBHVBgAADBgDYMZgQEAAINLuAny3x4tCQAA4BEVBgAADLhKwozAAACAAWsYzGhJAAAAj6gwAABgwH0YzAgMAAAYsIbBjJYEAADwiAoDAAAG3IfBjMAAAIABV0mYERgAADBg0aMZaxgAAIBHVBgAADDgKgkzAgMAAAYsejSjJQEAADyiwgAAgAEtCTMCAwAABlwlYUZLAgAAeESFAQAAgwIWPZpQYQAAwMDpxe1SDRo0SGPGjHE93r17t7p16ya73a4uXbpo586dbsevWbNGbdq0kd1u19ChQ3Xy5MnLeHUzAgMAAKXM2rVrtX79etfjM2fOaNCgQQoLC9Pq1asVGhqq8PBwnTlzRpKUmJiocePGKSIiQrGxscrOzlZkZKRX50RgAADAoEBOr23FderUKUVFRalx48aufZ9++qlsNptGjRqlevXqady4capQoYI+//xzSdLy5cvVrl07derUSfXr11dUVJTWr1+vlJQUr30mBAYAAAxKMjDMmjVLHTt21HXXXefal5CQoGbNmslisUiSLBaLmjZtqvj4eNd4WFiY6/gaNWooODhYCQkJl/dB/AmBAQAAA6fT6bXN4XDo9OnTbpvD4Tjv627evFnbtm3TkCFD3PZnZGQoMDDQbZ+/v7/S0tIkSenp6Rcd9wYCAwAAV1BMTIyaNWvmtsXExJiOy8vL06RJkzRx4kT5+vq6jeXk5Mhqtbrts1qtruCRm5t70XFv4LJKAAAMvHmnx/DwcPXv399tn/GXuyTNmzdPjRo1UsuWLU1jNpvN9Mvf4XC4gsWFxv38/C53+i4EBgAADLx5p0er1XregGC0du1aZWZmKjQ0VJJcAWDdunXq0KGDMjMz3Y7PzMx0tSGCgoLOOx4QEOCNtyCJwAAAQKmwbNky5efnux7Pnj1bkvTMM8/oxx9/1Ouvvy6n0ymLxSKn06nt27friSeekCTZ7XbFxcWpc+fOkqTU1FSlpqbKbrd7bX4EBgAADEri661r1qzp9rhChQqSpNq1a8vf319z5szR9OnT1aNHD7333nvKyclRu3btJEk9e/ZUnz591KRJEzVu3FjTp09Xq1atVKtWLa/Nj0WPAAAYlORlledTsWJFxcTEuKoICQkJWrhwocqXLy9JCg0N1dSpUxUdHa2ePXuqcuXKmjlzplde+w8WZ0nEqPPwsdb0fBDwD9PEv25JTwEolbalbrii529a406vnWt76vdeO1dJoiUBAIBBKfm3dKlCYAAAwMCbl1X+XbCGAQAAeESFAQAAA2/eh+HvgsAAAIBBAWsYTAgMAAAYUGEwYw0DAADwiAoDAAAGtCTMCAwAABjQkjCjJQEAADyiwgAAgAEtCTMCAwAABrQkzGhJAAAAj6gwAABgQEvCjMAAAIABLQkzWhIAAMAjKgwAABg4nQUlPYVSh8AAAIBBAS0JEwIDAAAGThY9mrCGAQAAeESFAQAAA1oSZgQGAAAMaEmY0ZIAAAAeUWEAAMCAOz2aERgAADDgTo9mtCQAAIBHVBgAADBg0aMZgQEAAAMuqzSjJQEAADyiwgAAgAEtCTMCAwAABlxWaUZgAADAgAqDGWsYAACAR1QYAAAw4CoJMwIDAAAGtCTMaEkAAACPqDAAAGDAVRJmBAYAAAz48ikzWhIAAMAjKgwAABjQkjAjMAAAYMBVEma0JAAAgEdUGAAAMGDRoxmBAQAAA1oSZgQGAAAMCAxmrGEAAAAeUWEAAMCA+oKZxUndBQAAeEBLAgAAeERgAAAAHhEYAACARwQGAADgEYEBAAB4RGAAAAAeERgAAIBHBAYAAOARgQEAAHhEYIBLXl6exo4dq7CwMN15551avHhxSU8JKDUcDoc6dOigLVu2lPRUgBLBd0nAJSoqSjt37tRbb72lY8eOafTo0QoODlbbtm1LempAicrLy9PIkSO1d+/ekp4KUGIIDJAknTlzRitWrNDrr7+um266STfddJP27t2rt99+m8CAf7Tk5GSNHDmSrzvGPx4tCUiSkpKSlJ+fr9DQUNe+Zs2aKSEhQQUFBSU4M6Bkbd26VS1atFBsbGxJTwUoUVQYIEnKyMhQ1apVZbVaXfuqV6+uvLw8nTp1StWqVSvB2QElp1evXiU9BaBUoMIASVJOTo5bWJDkeuxwOEpiSgCAUoTAAEmSzWYzBYM/Hvv6+pbElAAApQiBAZKkoKAg/frrr8rPz3fty8jIkK+vrypVqlSCMwMAlAYEBkiSGjRoIB8fH8XHx7v2xcXFqXHjxipThj8mAPBPx28CSJL8/PzUqVMnTZ48WYmJifrqq6+0ePFi9e3bt6SnBgAoBbhKAi6RkZGaPHmy+vXrp4oVK2rYsGG69957S3paAIBSwOLkbiQAAMADWhIAAMAjAgMAAPCIwAAAADwiMAAAAI8IDAAAwCMCAwAA8IjAAAAAPCIwAAAAjwgMAADAIwIDAADwiMAAAAA8IjAAAACP/g/W68RQv/4N6wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(metrics.confusion_matrix(y_test, y_predict), annot=True)\n",
    "plt.show()\n",
    "# seeing the results with the confusion matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting our Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = lgbm.plot_importance(model, height=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig = fig.figure\n",
    "# fig.savefig('./assets/graphs/feature-importance.png', dpi=300, bbox_inches='tight')\n",
    "# # saving this plot to use in the streamlit front-end application"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Salvando o modelo final em um arquivo a parte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./assets/churn-prediction-model.jbl']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# joblib.dump(model, './assets/churn-prediction-model.jbl')\n",
    "# # saving our final model to use in out deploy application"
   ]
  }
 ],
 "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": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}