IBM Cognos Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe221fdd-1796-4326-a2f9-b1abec2c6a68",
   "metadata": {},
   "source": [
    "# ETL(Extract Transform Load)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76fe476b-b286-4694-a643-052e2e7e1e53",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49d90fa5-0048-4faa-91f3-96243cf00ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import mysql.connector\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6dda34c-8a9c-4922-89ed-b92f3c9ac2e1",
   "metadata": {},
   "source": [
    "### Establishing connection with mysql server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84a1baae-c07a-462d-901b-c92675bb7cf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<mysql.connector.connection.MySQLConnection object at 0x00000296BA5858D0>\n"
     ]
    }
   ],
   "source": [
    "# Database parameter\n",
    "host = \"127.0.0.1\"\n",
    "user = \"root\"\n",
    "passwd = os.getenv(\"password\")\n",
    "dbname = \"CustomerChurn\"\n",
    "\n",
    "\n",
    "mydb = mysql.connector.connect(host=host, user=user, password=passwd)\n",
    "\n",
    "print(mydb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c45a7a3-ee34-4274-a995-34bef9722bc4",
   "metadata": {},
   "source": [
    "### Create Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f221b35d-34d4-47b0-a343-4de936617532",
   "metadata": {},
   "outputs": [],
   "source": [
    "mycursor = mydb.cursor()\n",
    "\n",
    "mycursor.execute(f\"CREATE DATABASE IF NOT EXISTS  {dbname}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538da070-f746-4ac2-9a72-77ee99c08c7a",
   "metadata": {},
   "source": [
    "### Importing `Telco_customer_churn` Data before creating table in Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "817516a4-9e81-4279-a630-698786209da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Customer ID</th>\n",
       "      <th>Count</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Lat Long</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90022</td>\n",
       "      <td>34.02381, -118.156582</td>\n",
       "      <td>34.023810</td>\n",
       "      <td>-118.156582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7495-OOKFY</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90063</td>\n",
       "      <td>34.044271, -118.185237</td>\n",
       "      <td>34.044271</td>\n",
       "      <td>-118.185237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1658-BYGOY</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90065</td>\n",
       "      <td>34.108833, -118.229715</td>\n",
       "      <td>34.108833</td>\n",
       "      <td>-118.229715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4598-XLKNJ</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Inglewood</td>\n",
       "      <td>90303</td>\n",
       "      <td>33.936291, -118.332639</td>\n",
       "      <td>33.936291</td>\n",
       "      <td>-118.332639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4846-WHAFZ</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Whittier</td>\n",
       "      <td>90602</td>\n",
       "      <td>33.972119, -118.020188</td>\n",
       "      <td>33.972119</td>\n",
       "      <td>-118.020188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID  Count        Country       State         City  Zip Code  \\\n",
       "0  8779-QRDMV      1  United States  California  Los Angeles     90022   \n",
       "1  7495-OOKFY      1  United States  California  Los Angeles     90063   \n",
       "2  1658-BYGOY      1  United States  California  Los Angeles     90065   \n",
       "3  4598-XLKNJ      1  United States  California    Inglewood     90303   \n",
       "4  4846-WHAFZ      1  United States  California     Whittier     90602   \n",
       "\n",
       "                 Lat Long   Latitude   Longitude  \n",
       "0   34.02381, -118.156582  34.023810 -118.156582  \n",
       "1  34.044271, -118.185237  34.044271 -118.185237  \n",
       "2  34.108833, -118.229715  34.108833 -118.229715  \n",
       "3  33.936291, -118.332639  33.936291 -118.332639  \n",
       "4  33.972119, -118.020188  33.972119 -118.020188  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_location = pd.read_excel(\n",
    "    \"Datasets/Telco_customer_churn_location.xlsx\"\n",
    ")\n",
    "Telco_customer_churn_location.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dda508e9-30a9-47ae-b99e-8ef9e6864bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count\n",
       "1    7043\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_location.Count.value_counts()  # Since every value is 1 in the  Count column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8015125a-92f8-47fa-8eb9-5b9822c093a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID    0\n",
       "Count          0\n",
       "Country        0\n",
       "State          0\n",
       "City           0\n",
       "Zip Code       0\n",
       "Lat Long       0\n",
       "Latitude       0\n",
       "Longitude      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_location.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7418b9e-c744-437e-95d5-ddcf42ffc8ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "Telco_customer_churn_location.drop(\"Count\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86df35ea-33bf-4cda-bc99-5f6fd32f6cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Lat Long</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90022</td>\n",
       "      <td>34.02381, -118.156582</td>\n",
       "      <td>34.023810</td>\n",
       "      <td>-118.156582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7495-OOKFY</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90063</td>\n",
       "      <td>34.044271, -118.185237</td>\n",
       "      <td>34.044271</td>\n",
       "      <td>-118.185237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1658-BYGOY</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>90065</td>\n",
       "      <td>34.108833, -118.229715</td>\n",
       "      <td>34.108833</td>\n",
       "      <td>-118.229715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4598-XLKNJ</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Inglewood</td>\n",
       "      <td>90303</td>\n",
       "      <td>33.936291, -118.332639</td>\n",
       "      <td>33.936291</td>\n",
       "      <td>-118.332639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4846-WHAFZ</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>Whittier</td>\n",
       "      <td>90602</td>\n",
       "      <td>33.972119, -118.020188</td>\n",
       "      <td>33.972119</td>\n",
       "      <td>-118.020188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID        Country       State         City  Zip Code  \\\n",
       "0  8779-QRDMV  United States  California  Los Angeles     90022   \n",
       "1  7495-OOKFY  United States  California  Los Angeles     90063   \n",
       "2  1658-BYGOY  United States  California  Los Angeles     90065   \n",
       "3  4598-XLKNJ  United States  California    Inglewood     90303   \n",
       "4  4846-WHAFZ  United States  California     Whittier     90602   \n",
       "\n",
       "                 Lat Long   Latitude   Longitude  \n",
       "0   34.02381, -118.156582  34.023810 -118.156582  \n",
       "1  34.044271, -118.185237  34.044271 -118.185237  \n",
       "2  34.108833, -118.229715  34.108833 -118.229715  \n",
       "3  33.936291, -118.332639  33.936291 -118.332639  \n",
       "4  33.972119, -118.020188  33.972119 -118.020188  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_location.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a36e593c-2e12-40af-a0d0-68e957953f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8bf8da0-b65e-4240-ba0a-98bddb86b7dc",
   "metadata": {},
   "source": [
    "### Creating table in the Database for Telco_customer_churn_location data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd8b6b1b-0d35-4e60-9a5a-21b384bbbea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydb = mysql.connector.connect(\n",
    "    host=host,\n",
    "    user=user,\n",
    "    password=passwd,\n",
    "    database=dbname,\n",
    ")\n",
    "\n",
    "mycursor = mydb.cursor()\n",
    "\n",
    "# Creating table\n",
    "mycursor.execute(\n",
    "    \"CREATE TABLE IF NOT EXISTS Telco_customer_churn_location(CustomerID VARCHAR(20) PRIMARY KEY,Country VARCHAR(50),State VARCHAR(50),City VARCHAR(50),ZipCode INT,LatLong VARCHAR(30),Latitude DECIMAL(10, 8),Longitude DECIMAL(11, 8));\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c086089-4b5c-4c1b-8c16-b7d4514148e0",
   "metadata": {},
   "source": [
    "### Inserting values into the table created above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "664699e3-3d57-421c-a7b0-80e4744e100f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute a SELECT query\n",
    "mycursor.execute(\"SELECT * FROM Telco_customer_churn_location\")\n",
    "\n",
    "if mycursor.fetchone() == None:\n",
    "    for _, row in Telco_customer_churn_location.iterrows():\n",
    "        mycursor.execute(\n",
    "            \"INSERT INTO  Telco_customer_churn_location(CustomerID, Country, State, City, ZipCode, LatLong, Latitude, Longitude) \"\n",
    "            \"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\",\n",
    "            tuple(row),\n",
    "        )\n",
    "\n",
    "    # Commit changes and close connection\n",
    "    mydb.commit()\n",
    "    mydb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32c79e7-e2ec-4cc7-8250-78f547a827d3",
   "metadata": {},
   "source": [
    "### Importing `Telco_customer_churn_demographics` Data before creating table in Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c2c26c5-980c-4632-8085-1a3d370a5dae",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Count</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Under 30</th>\n",
       "      <th>Senior Citizen</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Number of Dependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>78</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7495-OOKFY</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>74</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1658-BYGOY</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>71</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4598-XLKNJ</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>78</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4846-WHAFZ</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>80</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID  Count  Gender  Age Under 30 Senior Citizen Married Dependents  \\\n",
       "0  8779-QRDMV      1    Male   78       No            Yes      No         No   \n",
       "1  7495-OOKFY      1  Female   74       No            Yes     Yes        Yes   \n",
       "2  1658-BYGOY      1    Male   71       No            Yes      No        Yes   \n",
       "3  4598-XLKNJ      1  Female   78       No            Yes     Yes        Yes   \n",
       "4  4846-WHAFZ      1  Female   80       No            Yes     Yes        Yes   \n",
       "\n",
       "   Number of Dependents  \n",
       "0                     0  \n",
       "1                     1  \n",
       "2                     3  \n",
       "3                     1  \n",
       "4                     1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_demographics = pd.read_excel(\n",
    "    \"Datasets/Telco_customer_churn_demographics.xlsx\"\n",
    ")\n",
    "Telco_customer_churn_demographics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ea69e88-bbc6-4e35-abdf-3d968b6e5f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count\n",
       "1    7043\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_demographics.Count.value_counts()  # Since every value is 1 in the  Count column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "afd37d32-cf15-4852-84b9-ba4c621d28db",
   "metadata": {},
   "outputs": [],
   "source": [
    "Telco_customer_churn_demographics.drop(\"Count\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9e0e406-2c22-4e1f-9be3-fa7e127ab05f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID             0\n",
       "Gender                  0\n",
       "Age                     0\n",
       "Under 30                0\n",
       "Senior Citizen          0\n",
       "Married                 0\n",
       "Dependents              0\n",
       "Number of Dependents    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_demographics.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b098ab6-0963-46db-b4f3-77d1c46c809e",
   "metadata": {},
   "source": [
    "### Creating Table  `Telco_customer_churn_demographics`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "33268cb7-85f3-4c08-92df-8cc1f09518ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydb = mysql.connector.connect(\n",
    "    host=host,\n",
    "    user=user,\n",
    "    password=passwd,\n",
    "    database=dbname,\n",
    ")\n",
    "\n",
    "mycursor = mydb.cursor()\n",
    "\n",
    "# Creating table\n",
    "mycursor.execute(\n",
    "    \"CREATE TABLE IF NOT EXISTS Telco_customer_churn_demographics(CustomerID VARCHAR(20) PRIMARY KEY,  Gender VARCHAR(10), Age INT,  Under30 VARCHAR(3), SeniorCitizen VARCHAR(3), Married VARCHAR(3), Dependents VARCHAR(3),    NumberOfDependents INT);\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a1398ab-cfae-40af-b1b9-3894a367e6b3",
   "metadata": {},
   "source": [
    "### Inserting values into the table created above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73658a3a-1f27-448b-be4a-947ae0a844b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Under 30</th>\n",
       "      <th>Senior Citizen</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Number of Dependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>Male</td>\n",
       "      <td>78</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7495-OOKFY</td>\n",
       "      <td>Female</td>\n",
       "      <td>74</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1658-BYGOY</td>\n",
       "      <td>Male</td>\n",
       "      <td>71</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4598-XLKNJ</td>\n",
       "      <td>Female</td>\n",
       "      <td>78</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4846-WHAFZ</td>\n",
       "      <td>Female</td>\n",
       "      <td>80</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID  Gender  Age Under 30 Senior Citizen Married Dependents  \\\n",
       "0  8779-QRDMV    Male   78       No            Yes      No         No   \n",
       "1  7495-OOKFY  Female   74       No            Yes     Yes        Yes   \n",
       "2  1658-BYGOY    Male   71       No            Yes      No        Yes   \n",
       "3  4598-XLKNJ  Female   78       No            Yes     Yes        Yes   \n",
       "4  4846-WHAFZ  Female   80       No            Yes     Yes        Yes   \n",
       "\n",
       "   Number of Dependents  \n",
       "0                     0  \n",
       "1                     1  \n",
       "2                     3  \n",
       "3                     1  \n",
       "4                     1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_demographics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "06cba122-26ee-4265-b178-6c081fdaf1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mycursor.execute(\"SELECT * FROM Telco_customer_churn_demographics\")\n",
    "\n",
    "if mycursor.fetchone() == None:\n",
    "    for _, row in Telco_customer_churn_demographics.iterrows():\n",
    "        mycursor.execute(\n",
    "            \"INSERT INTO  Telco_customer_churn_demographics(CustomerID, Gender, Age, Under30, SeniorCitizen, Married, Dependents, NumberOfDependents)\"\n",
    "            \"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\",\n",
    "            tuple(row),\n",
    "        )\n",
    "\n",
    "# Commit changes and close connection\n",
    "mydb.commit()\n",
    "mydb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ea7fac-14e9-4b7b-8b3a-ec5a996f39e2",
   "metadata": {},
   "source": [
    "### Importing `Telco_customer_churn_services` Data before creating table in Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da77e2b5-b1bc-47ce-b4b8-11f513cf0299",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Count</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Referred a Friend</th>\n",
       "      <th>Number of Referrals</th>\n",
       "      <th>Tenure in Months</th>\n",
       "      <th>Offer</th>\n",
       "      <th>Phone Service</th>\n",
       "      <th>Avg Monthly Long Distance Charges</th>\n",
       "      <th>Multiple Lines</th>\n",
       "      <th>...</th>\n",
       "      <th>Unlimited Data</th>\n",
       "      <th>Contract</th>\n",
       "      <th>Paperless Billing</th>\n",
       "      <th>Payment Method</th>\n",
       "      <th>Monthly Charge</th>\n",
       "      <th>Total Charges</th>\n",
       "      <th>Total Refunds</th>\n",
       "      <th>Total Extra Data Charges</th>\n",
       "      <th>Total Long Distance Charges</th>\n",
       "      <th>Total Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>39.65</td>\n",
       "      <td>39.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7495-OOKFY</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Offer E</td>\n",
       "      <td>Yes</td>\n",
       "      <td>48.85</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>80.65</td>\n",
       "      <td>633.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>390.80</td>\n",
       "      <td>1024.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1658-BYGOY</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>Offer D</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>95.45</td>\n",
       "      <td>1752.55</td>\n",
       "      <td>45.61</td>\n",
       "      <td>0</td>\n",
       "      <td>203.94</td>\n",
       "      <td>1910.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4598-XLKNJ</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>Offer C</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.76</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>98.50</td>\n",
       "      <td>2514.50</td>\n",
       "      <td>13.43</td>\n",
       "      <td>0</td>\n",
       "      <td>494.00</td>\n",
       "      <td>2995.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4846-WHAFZ</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>Offer C</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>76.50</td>\n",
       "      <td>2868.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>234.21</td>\n",
       "      <td>3102.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID  Count Quarter Referred a Friend  Number of Referrals  \\\n",
       "0  8779-QRDMV      1      Q3                No                    0   \n",
       "1  7495-OOKFY      1      Q3               Yes                    1   \n",
       "2  1658-BYGOY      1      Q3                No                    0   \n",
       "3  4598-XLKNJ      1      Q3               Yes                    1   \n",
       "4  4846-WHAFZ      1      Q3               Yes                    1   \n",
       "\n",
       "   Tenure in Months    Offer Phone Service  Avg Monthly Long Distance Charges  \\\n",
       "0                 1      NaN            No                               0.00   \n",
       "1                 8  Offer E           Yes                              48.85   \n",
       "2                18  Offer D           Yes                              11.33   \n",
       "3                25  Offer C           Yes                              19.76   \n",
       "4                37  Offer C           Yes                               6.33   \n",
       "\n",
       "  Multiple Lines  ... Unlimited Data        Contract  Paperless Billing  \\\n",
       "0             No  ...             No  Month-to-Month                Yes   \n",
       "1            Yes  ...            Yes  Month-to-Month                Yes   \n",
       "2            Yes  ...            Yes  Month-to-Month                Yes   \n",
       "3             No  ...            Yes  Month-to-Month                Yes   \n",
       "4            Yes  ...            Yes  Month-to-Month                Yes   \n",
       "\n",
       "    Payment Method Monthly Charge Total Charges Total Refunds  \\\n",
       "0  Bank Withdrawal          39.65         39.65          0.00   \n",
       "1      Credit Card          80.65        633.30          0.00   \n",
       "2  Bank Withdrawal          95.45       1752.55         45.61   \n",
       "3  Bank Withdrawal          98.50       2514.50         13.43   \n",
       "4  Bank Withdrawal          76.50       2868.15          0.00   \n",
       "\n",
       "  Total Extra Data Charges Total Long Distance Charges Total Revenue  \n",
       "0                       20                        0.00         59.65  \n",
       "1                        0                      390.80       1024.10  \n",
       "2                        0                      203.94       1910.88  \n",
       "3                        0                      494.00       2995.07  \n",
       "4                        0                      234.21       3102.36  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_services = pd.read_excel(\n",
    "    \"Datasets/Telco_customer_churn_services.xlsx\"\n",
    ")\n",
    "Telco_customer_churn_services.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "20b4737e-7934-43a7-94a1-3d7e795638b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count\n",
       "1    7043\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_services.Count.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c529f3d-5c5e-49a6-90f6-14535ad1bc0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer ID', 'Count', 'Quarter', 'Referred a Friend',\n",
       "       'Number of Referrals', 'Tenure in Months', 'Offer', 'Phone Service',\n",
       "       'Avg Monthly Long Distance Charges', 'Multiple Lines',\n",
       "       'Internet Service', 'Internet Type', 'Avg Monthly GB Download',\n",
       "       'Online Security', 'Online Backup', 'Device Protection Plan',\n",
       "       'Premium Tech Support', 'Streaming TV', 'Streaming Movies',\n",
       "       'Streaming Music', 'Unlimited Data', 'Contract', 'Paperless Billing',\n",
       "       'Payment Method', 'Monthly Charge', 'Total Charges', 'Total Refunds',\n",
       "       'Total Extra Data Charges', 'Total Long Distance Charges',\n",
       "       'Total Revenue'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_services.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f0a5db4-ca26-478d-b004-496f0684b8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Quarter</th>\n",
       "      <th>Referred a Friend</th>\n",
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       "      <th>Tenure in Months</th>\n",
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       "      <th>Multiple Lines</th>\n",
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       "      <th>...</th>\n",
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       "      <th>Contract</th>\n",
       "      <th>Paperless Billing</th>\n",
       "      <th>Payment Method</th>\n",
       "      <th>Monthly Charge</th>\n",
       "      <th>Total Charges</th>\n",
       "      <th>Total Refunds</th>\n",
       "      <th>Total Extra Data Charges</th>\n",
       "      <th>Total Long Distance Charges</th>\n",
       "      <th>Total Revenue</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>Q3</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>39.65</td>\n",
       "      <td>39.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7495-OOKFY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Offer E</td>\n",
       "      <td>Yes</td>\n",
       "      <td>48.85</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>80.65</td>\n",
       "      <td>633.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>390.80</td>\n",
       "      <td>1024.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1658-BYGOY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>Offer D</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>95.45</td>\n",
       "      <td>1752.55</td>\n",
       "      <td>45.61</td>\n",
       "      <td>0</td>\n",
       "      <td>203.94</td>\n",
       "      <td>1910.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4598-XLKNJ</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>Offer C</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.76</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>98.50</td>\n",
       "      <td>2514.50</td>\n",
       "      <td>13.43</td>\n",
       "      <td>0</td>\n",
       "      <td>494.00</td>\n",
       "      <td>2995.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4846-WHAFZ</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>Offer C</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>76.50</td>\n",
       "      <td>2868.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>234.21</td>\n",
       "      <td>3102.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID Quarter Referred a Friend  Number of Referrals  \\\n",
       "0  8779-QRDMV      Q3                No                    0   \n",
       "1  7495-OOKFY      Q3               Yes                    1   \n",
       "2  1658-BYGOY      Q3                No                    0   \n",
       "3  4598-XLKNJ      Q3               Yes                    1   \n",
       "4  4846-WHAFZ      Q3               Yes                    1   \n",
       "\n",
       "   Tenure in Months    Offer Phone Service  Avg Monthly Long Distance Charges  \\\n",
       "0                 1      NaN            No                               0.00   \n",
       "1                 8  Offer E           Yes                              48.85   \n",
       "2                18  Offer D           Yes                              11.33   \n",
       "3                25  Offer C           Yes                              19.76   \n",
       "4                37  Offer C           Yes                               6.33   \n",
       "\n",
       "  Multiple Lines Internet Service  ... Unlimited Data        Contract  \\\n",
       "0             No              Yes  ...             No  Month-to-Month   \n",
       "1            Yes              Yes  ...            Yes  Month-to-Month   \n",
       "2            Yes              Yes  ...            Yes  Month-to-Month   \n",
       "3             No              Yes  ...            Yes  Month-to-Month   \n",
       "4            Yes              Yes  ...            Yes  Month-to-Month   \n",
       "\n",
       "  Paperless Billing   Payment Method Monthly Charge Total Charges  \\\n",
       "0               Yes  Bank Withdrawal          39.65         39.65   \n",
       "1               Yes      Credit Card          80.65        633.30   \n",
       "2               Yes  Bank Withdrawal          95.45       1752.55   \n",
       "3               Yes  Bank Withdrawal          98.50       2514.50   \n",
       "4               Yes  Bank Withdrawal          76.50       2868.15   \n",
       "\n",
       "  Total Refunds Total Extra Data Charges Total Long Distance Charges  \\\n",
       "0          0.00                       20                        0.00   \n",
       "1          0.00                        0                      390.80   \n",
       "2         45.61                        0                      203.94   \n",
       "3         13.43                        0                      494.00   \n",
       "4          0.00                        0                      234.21   \n",
       "\n",
       "  Total Revenue  \n",
       "0         59.65  \n",
       "1       1024.10  \n",
       "2       1910.88  \n",
       "3       2995.07  \n",
       "4       3102.36  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_services.drop(\"Count\", axis=1, inplace=True)\n",
    "Telco_customer_churn_services.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c539a4ba-41e2-4138-8cbf-f89960566cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID                             0\n",
       "Quarter                                 0\n",
       "Referred a Friend                       0\n",
       "Number of Referrals                     0\n",
       "Tenure in Months                        0\n",
       "Offer                                3877\n",
       "Phone Service                           0\n",
       "Avg Monthly Long Distance Charges       0\n",
       "Multiple Lines                          0\n",
       "Internet Service                        0\n",
       "Internet Type                        1526\n",
       "Avg Monthly GB Download                 0\n",
       "Online Security                         0\n",
       "Online Backup                           0\n",
       "Device Protection Plan                  0\n",
       "Premium Tech Support                    0\n",
       "Streaming TV                            0\n",
       "Streaming Movies                        0\n",
       "Streaming Music                         0\n",
       "Unlimited Data                          0\n",
       "Contract                                0\n",
       "Paperless Billing                       0\n",
       "Payment Method                          0\n",
       "Monthly Charge                          0\n",
       "Total Charges                           0\n",
       "Total Refunds                           0\n",
       "Total Extra Data Charges                0\n",
       "Total Long Distance Charges             0\n",
       "Total Revenue                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_services.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df3ae9f-3512-490f-a4f1-e755c45b1b50",
   "metadata": {},
   "source": [
    "### Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "02c7a1a5-a23d-4eaf-a1aa-a5fed864c941",
   "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>Customer ID</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Referred a Friend</th>\n",
       "      <th>Number of Referrals</th>\n",
       "      <th>Tenure in Months</th>\n",
       "      <th>Offer</th>\n",
       "      <th>Phone Service</th>\n",
       "      <th>Avg Monthly Long Distance Charges</th>\n",
       "      <th>Multiple Lines</th>\n",
       "      <th>Internet Service</th>\n",
       "      <th>...</th>\n",
       "      <th>Unlimited Data</th>\n",
       "      <th>Contract</th>\n",
       "      <th>Paperless Billing</th>\n",
       "      <th>Payment Method</th>\n",
       "      <th>Monthly Charge</th>\n",
       "      <th>Total Charges</th>\n",
       "      <th>Total Refunds</th>\n",
       "      <th>Total Extra Data Charges</th>\n",
       "      <th>Total Long Distance Charges</th>\n",
       "      <th>Total Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>Q3</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No Offer</td>\n",
       "      <td>No</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>39.65</td>\n",
       "      <td>39.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7495-OOKFY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Offer E</td>\n",
       "      <td>Yes</td>\n",
       "      <td>48.85</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>80.65</td>\n",
       "      <td>633.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>390.80</td>\n",
       "      <td>1024.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1658-BYGOY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>Offer D</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>95.45</td>\n",
       "      <td>1752.55</td>\n",
       "      <td>45.61</td>\n",
       "      <td>0</td>\n",
       "      <td>203.94</td>\n",
       "      <td>1910.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4598-XLKNJ</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>Offer C</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.76</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>98.50</td>\n",
       "      <td>2514.50</td>\n",
       "      <td>13.43</td>\n",
       "      <td>0</td>\n",
       "      <td>494.00</td>\n",
       "      <td>2995.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4846-WHAFZ</td>\n",
       "      <td>Q3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>Offer C</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-Month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank Withdrawal</td>\n",
       "      <td>76.50</td>\n",
       "      <td>2868.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>234.21</td>\n",
       "      <td>3102.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID Quarter Referred a Friend  Number of Referrals  \\\n",
       "0  8779-QRDMV      Q3                No                    0   \n",
       "1  7495-OOKFY      Q3               Yes                    1   \n",
       "2  1658-BYGOY      Q3                No                    0   \n",
       "3  4598-XLKNJ      Q3               Yes                    1   \n",
       "4  4846-WHAFZ      Q3               Yes                    1   \n",
       "\n",
       "   Tenure in Months     Offer Phone Service  \\\n",
       "0                 1  No Offer            No   \n",
       "1                 8   Offer E           Yes   \n",
       "2                18   Offer D           Yes   \n",
       "3                25   Offer C           Yes   \n",
       "4                37   Offer C           Yes   \n",
       "\n",
       "   Avg Monthly Long Distance Charges Multiple Lines Internet Service  ...  \\\n",
       "0                               0.00             No              Yes  ...   \n",
       "1                              48.85            Yes              Yes  ...   \n",
       "2                              11.33            Yes              Yes  ...   \n",
       "3                              19.76             No              Yes  ...   \n",
       "4                               6.33            Yes              Yes  ...   \n",
       "\n",
       "  Unlimited Data        Contract Paperless Billing   Payment Method  \\\n",
       "0             No  Month-to-Month               Yes  Bank Withdrawal   \n",
       "1            Yes  Month-to-Month               Yes      Credit Card   \n",
       "2            Yes  Month-to-Month               Yes  Bank Withdrawal   \n",
       "3            Yes  Month-to-Month               Yes  Bank Withdrawal   \n",
       "4            Yes  Month-to-Month               Yes  Bank Withdrawal   \n",
       "\n",
       "  Monthly Charge Total Charges Total Refunds Total Extra Data Charges  \\\n",
       "0          39.65         39.65          0.00                       20   \n",
       "1          80.65        633.30          0.00                        0   \n",
       "2          95.45       1752.55         45.61                        0   \n",
       "3          98.50       2514.50         13.43                        0   \n",
       "4          76.50       2868.15          0.00                        0   \n",
       "\n",
       "  Total Long Distance Charges Total Revenue  \n",
       "0                        0.00         59.65  \n",
       "1                      390.80       1024.10  \n",
       "2                      203.94       1910.88  \n",
       "3                      494.00       2995.07  \n",
       "4                      234.21       3102.36  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_services[\"Offer\"] = Telco_customer_churn_services[\"Offer\"].apply(\n",
    "    lambda x: \"No Offer\" if pd.isna(x) else x\n",
    ")\n",
    "Telco_customer_churn_services.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f69c3fec-7abd-4500-83d0-8ebfbaeb54c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               DSL\n",
       "1       Fiber Optic\n",
       "2       Fiber Optic\n",
       "3       Fiber Optic\n",
       "4       Fiber Optic\n",
       "           ...     \n",
       "7038        Unknown\n",
       "7039          Cable\n",
       "7040    Fiber Optic\n",
       "7041            DSL\n",
       "7042    Fiber Optic\n",
       "Name: Internet Type, Length: 7043, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_services[\"Internet Type\"] = Telco_customer_churn_services[\n",
    "    \"Internet Type\"\n",
    "].apply(lambda x: \"Unknown\" if pd.isna(x) else x)\n",
    "Telco_customer_churn_services[\"Internet Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "36d1be05-11c0-4db3-a1a2-efb515e1a30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID                          0\n",
       "Quarter                              0\n",
       "Referred a Friend                    0\n",
       "Number of Referrals                  0\n",
       "Tenure in Months                     0\n",
       "Offer                                0\n",
       "Phone Service                        0\n",
       "Avg Monthly Long Distance Charges    0\n",
       "Multiple Lines                       0\n",
       "Internet Service                     0\n",
       "Internet Type                        0\n",
       "Avg Monthly GB Download              0\n",
       "Online Security                      0\n",
       "Online Backup                        0\n",
       "Device Protection Plan               0\n",
       "Premium Tech Support                 0\n",
       "Streaming TV                         0\n",
       "Streaming Movies                     0\n",
       "Streaming Music                      0\n",
       "Unlimited Data                       0\n",
       "Contract                             0\n",
       "Paperless Billing                    0\n",
       "Payment Method                       0\n",
       "Monthly Charge                       0\n",
       "Total Charges                        0\n",
       "Total Refunds                        0\n",
       "Total Extra Data Charges             0\n",
       "Total Long Distance Charges          0\n",
       "Total Revenue                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_services.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27274cc-13f4-4bb2-bea4-1c4147a5edc1",
   "metadata": {},
   "source": [
    "### Creating Table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "38d07b95-cac4-49a0-ae2c-d90a0552987a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydb = mysql.connector.connect(\n",
    "    host=host,\n",
    "    user=user,\n",
    "    password=passwd,\n",
    "    database=dbname,\n",
    ")\n",
    "\n",
    "mycursor = mydb.cursor()\n",
    "\n",
    "# Creating table\n",
    "mycursor.execute(\n",
    "    \"\"\"CREATE TABLE IF NOT EXISTS Telco_customer_churn_services(\n",
    "    CustomerID VARCHAR(20) PRIMARY KEY,\n",
    "    Quarter VARCHAR(2),\n",
    "    ReferredFriend VARCHAR(3),\n",
    "    NumberOfReferrals INT,\n",
    "    TenureInMonths INT,\n",
    "    Offer VARCHAR(50),\n",
    "    PhoneService VARCHAR(3),\n",
    "    AvgMonthlyLongDistanceCharges DECIMAL(10, 2),\n",
    "    MultipleLines VARCHAR(3),\n",
    "    InternetService VARCHAR(3),\n",
    "    InternetType VARCHAR(20),\n",
    "    AvgMonthlyGBDownload INT,\n",
    "    OnlineSecurity VARCHAR(3),\n",
    "    OnlineBackup VARCHAR(3),\n",
    "    DeviceProtectionPlan VARCHAR(3),\n",
    "    PremiumTechSupport VARCHAR(3),\n",
    "    StreamingTV VARCHAR(3),\n",
    "    StreamingMovies VARCHAR(3),\n",
    "    StreamingMusic VARCHAR(3),\n",
    "    UnlimitedData VARCHAR(3),\n",
    "    Contract VARCHAR(20),\n",
    "    PaperlessBilling VARCHAR(3),\n",
    "    PaymentMethod VARCHAR(50),\n",
    "    MonthlyCharge DECIMAL(10, 2),\n",
    "    TotalCharges DECIMAL(10, 2),\n",
    "    TotalRefunds DECIMAL(10, 2),\n",
    "    TotalExtraDataCharges INT,\n",
    "    TotalLongDistanceCharges DECIMAL(10, 2),\n",
    "    TotalRevenue DECIMAL(10, 2)\n",
    ");\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f33c9ba-de7d-43b0-8503-cf4f65d1ed5e",
   "metadata": {},
   "source": [
    "### Inserting values into the table created above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "35e7bd59-20b6-4e20-8846-b7d3bf6114c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "mycursor.execute(\"SELECT * FROM Telco_customer_churn_services\")\n",
    "\n",
    "if mycursor.fetchone() == None:\n",
    "    for _, row in Telco_customer_churn_services.iterrows():\n",
    "        mycursor.execute(\n",
    "            \"\"\"\n",
    "            INSERT INTO Telco_customer_churn_services (\n",
    "                CustomerID, Quarter, ReferredFriend, NumberOfReferrals, TenureInMonths, Offer,\n",
    "                PhoneService, AvgMonthlyLongDistanceCharges, MultipleLines, InternetService,\n",
    "                InternetType, AvgMonthlyGBDownload, OnlineSecurity, OnlineBackup, DeviceProtectionPlan,\n",
    "                PremiumTechSupport, StreamingTV, StreamingMovies, StreamingMusic, UnlimitedData,\n",
    "                Contract, PaperlessBilling, PaymentMethod, MonthlyCharge, TotalCharges, TotalRefunds,\n",
    "                TotalExtraDataCharges, TotalLongDistanceCharges, TotalRevenue\n",
    "            )\n",
    "            VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n",
    "        \"\"\",\n",
    "            tuple(row),\n",
    "        )\n",
    "\n",
    "# Commit changes and close connection\n",
    "mydb.commit()\n",
    "mydb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54a5c52-fe60-427a-9501-b04106dea8bc",
   "metadata": {},
   "source": [
    "### Importing `Telco_customer_churn_status` Data before creating table in Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e87039ee-b70d-425a-9951-47f4e123d516",
   "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>Customer ID</th>\n",
       "      <th>Count</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Customer Status</th>\n",
       "      <th>Churn Label</th>\n",
       "      <th>Churn Value</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>CLTV</th>\n",
       "      <th>Churn Category</th>\n",
       "      <th>Churn Reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>3</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>5433</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor offered more data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7495-OOKFY</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>3</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>5302</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor made better offer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1658-BYGOY</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>2</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>3179</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor made better offer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4598-XLKNJ</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>2</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>5337</td>\n",
       "      <td>Dissatisfaction</td>\n",
       "      <td>Limited range of services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4846-WHAFZ</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>2</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>2793</td>\n",
       "      <td>Price</td>\n",
       "      <td>Extra data charges</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>2569-WGERO</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>5</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>5306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>2140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>4</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>5560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>4</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>2793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>1</td>\n",
       "      <td>Q3</td>\n",
       "      <td>4</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>5097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID  Count Quarter  Satisfaction Score Customer Status  \\\n",
       "0     8779-QRDMV      1      Q3                   3         Churned   \n",
       "1     7495-OOKFY      1      Q3                   3         Churned   \n",
       "2     1658-BYGOY      1      Q3                   2         Churned   \n",
       "3     4598-XLKNJ      1      Q3                   2         Churned   \n",
       "4     4846-WHAFZ      1      Q3                   2         Churned   \n",
       "...          ...    ...     ...                 ...             ...   \n",
       "7038  2569-WGERO      1      Q3                   5          Stayed   \n",
       "7039  6840-RESVB      1      Q3                   3          Stayed   \n",
       "7040  2234-XADUH      1      Q3                   4          Stayed   \n",
       "7041  4801-JZAZL      1      Q3                   4          Stayed   \n",
       "7042  3186-AJIEK      1      Q3                   4          Stayed   \n",
       "\n",
       "     Churn Label  Churn Value  Churn Score  CLTV   Churn Category  \\\n",
       "0            Yes            1           91  5433       Competitor   \n",
       "1            Yes            1           69  5302       Competitor   \n",
       "2            Yes            1           81  3179       Competitor   \n",
       "3            Yes            1           88  5337  Dissatisfaction   \n",
       "4            Yes            1           67  2793            Price   \n",
       "...          ...          ...          ...   ...              ...   \n",
       "7038          No            0           45  5306              NaN   \n",
       "7039          No            0           59  2140              NaN   \n",
       "7040          No            0           71  5560              NaN   \n",
       "7041          No            0           59  2793              NaN   \n",
       "7042          No            0           38  5097              NaN   \n",
       "\n",
       "                      Churn Reason  \n",
       "0     Competitor offered more data  \n",
       "1     Competitor made better offer  \n",
       "2     Competitor made better offer  \n",
       "3        Limited range of services  \n",
       "4               Extra data charges  \n",
       "...                            ...  \n",
       "7038                           NaN  \n",
       "7039                           NaN  \n",
       "7040                           NaN  \n",
       "7041                           NaN  \n",
       "7042                           NaN  \n",
       "\n",
       "[7043 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_status = pd.read_excel(\"Datasets/Telco_customer_churn_status.xlsx\")\n",
    "Telco_customer_churn_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3b6127ca-6999-4fbc-be91-53ae01030958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count\n",
       "1    7043\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_status.Count.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fbe0374b-d034-45ad-b3e4-7d30ddb41cc4",
   "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>Customer ID</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Customer Status</th>\n",
       "      <th>Churn Label</th>\n",
       "      <th>Churn Value</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>CLTV</th>\n",
       "      <th>Churn Category</th>\n",
       "      <th>Churn Reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>Q3</td>\n",
       "      <td>3</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>5433</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor offered more data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7495-OOKFY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>3</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>5302</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor made better offer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1658-BYGOY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>2</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>3179</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor made better offer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4598-XLKNJ</td>\n",
       "      <td>Q3</td>\n",
       "      <td>2</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>5337</td>\n",
       "      <td>Dissatisfaction</td>\n",
       "      <td>Limited range of services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4846-WHAFZ</td>\n",
       "      <td>Q3</td>\n",
       "      <td>2</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>2793</td>\n",
       "      <td>Price</td>\n",
       "      <td>Extra data charges</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID Quarter  Satisfaction Score Customer Status Churn Label  \\\n",
       "0  8779-QRDMV      Q3                   3         Churned         Yes   \n",
       "1  7495-OOKFY      Q3                   3         Churned         Yes   \n",
       "2  1658-BYGOY      Q3                   2         Churned         Yes   \n",
       "3  4598-XLKNJ      Q3                   2         Churned         Yes   \n",
       "4  4846-WHAFZ      Q3                   2         Churned         Yes   \n",
       "\n",
       "   Churn Value  Churn Score  CLTV   Churn Category  \\\n",
       "0            1           91  5433       Competitor   \n",
       "1            1           69  5302       Competitor   \n",
       "2            1           81  3179       Competitor   \n",
       "3            1           88  5337  Dissatisfaction   \n",
       "4            1           67  2793            Price   \n",
       "\n",
       "                   Churn Reason  \n",
       "0  Competitor offered more data  \n",
       "1  Competitor made better offer  \n",
       "2  Competitor made better offer  \n",
       "3     Limited range of services  \n",
       "4            Extra data charges  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_status.drop(\"Count\", axis=1, inplace=True)\n",
    "Telco_customer_churn_status.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "770d8c08-91d4-446f-a60e-18953f0cac48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID              0\n",
       "Quarter                  0\n",
       "Satisfaction Score       0\n",
       "Customer Status          0\n",
       "Churn Label              0\n",
       "Churn Value              0\n",
       "Churn Score              0\n",
       "CLTV                     0\n",
       "Churn Category        5174\n",
       "Churn Reason          5174\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_status.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0ecc4b9b-c022-46a2-8129-65ea9d048ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            Competitor\n",
       "1            Competitor\n",
       "2            Competitor\n",
       "3       Dissatisfaction\n",
       "4                 Price\n",
       "             ...       \n",
       "7038            Unknown\n",
       "7039            Unknown\n",
       "7040            Unknown\n",
       "7041            Unknown\n",
       "7042            Unknown\n",
       "Name: Churn Category, Length: 7043, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_status[\"Churn Category\"] = Telco_customer_churn_status[\n",
    "    \"Churn Category\"\n",
    "].apply(lambda x: \"Unknown\" if pd.isna(x) else x)\n",
    "Telco_customer_churn_status[\"Churn Category\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "84c58868-20ae-49d9-a55b-5e365b50b97d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Competitor offered more data\n",
       "1       Competitor made better offer\n",
       "2       Competitor made better offer\n",
       "3          Limited range of services\n",
       "4                 Extra data charges\n",
       "                    ...             \n",
       "7038                         Unknown\n",
       "7039                         Unknown\n",
       "7040                         Unknown\n",
       "7041                         Unknown\n",
       "7042                         Unknown\n",
       "Name: Churn Reason, Length: 7043, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_status[\"Churn Reason\"] = Telco_customer_churn_status[\n",
    "    \"Churn Reason\"\n",
    "].apply(lambda x: \"Unknown\" if pd.isna(x) else x)\n",
    "Telco_customer_churn_status[\"Churn Reason\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "09b8b6fe-6c13-41e3-8cc8-ad32da8ed237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer ID           0\n",
       "Quarter               0\n",
       "Satisfaction Score    0\n",
       "Customer Status       0\n",
       "Churn Label           0\n",
       "Churn Value           0\n",
       "Churn Score           0\n",
       "CLTV                  0\n",
       "Churn Category        0\n",
       "Churn Reason          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_status.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e78e710-8907-4490-b30d-1aa899358f12",
   "metadata": {},
   "source": [
    "### Creating Table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1aad6e19-1e2e-4280-b42b-6648c4647e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydb = mysql.connector.connect(\n",
    "    host=host,\n",
    "    user=user,\n",
    "    password=passwd,\n",
    "    database=dbname,\n",
    ")\n",
    "\n",
    "mycursor = mydb.cursor()\n",
    "\n",
    "# Creating table\n",
    "mycursor.execute(\n",
    "    \"\"\"CREATE TABLE IF NOT EXISTS Telco_customer_churn_status (\n",
    "    CustomerID VARCHAR(20) PRIMARY KEY,\n",
    "    Quarter VARCHAR(10),\n",
    "    SatisfactionScore INT,\n",
    "    CustomerStatus VARCHAR(20),\n",
    "    ChurnLabel VARCHAR(10),\n",
    "    ChurnValue INT,\n",
    "    ChurnScore INT,\n",
    "    CLTV DECIMAL(10, 2),\n",
    "    ChurnCategory VARCHAR(20),\n",
    "    ChurnReason VARCHAR(100)\n",
    ");;\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f9da14-4774-4961-9c48-60f5afb9ca61",
   "metadata": {},
   "source": [
    "### Inserting values into the table created above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "27864072-5af7-43a6-a94c-676549dca459",
   "metadata": {},
   "outputs": [],
   "source": [
    "mycursor.execute(\"SELECT * FROM Telco_customer_churn_status\")\n",
    "\n",
    "if mycursor.fetchone() == None:\n",
    "    for _, row in Telco_customer_churn_status.iterrows():\n",
    "        mycursor.execute(\n",
    "            \"\"\"\n",
    "            INSERT INTO Telco_customer_churn_status (\n",
    "                CustomerID, Quarter, SatisfactionScore, CustomerStatus, ChurnLabel, ChurnValue, ChurnScore, CLTV, ChurnCategory, ChurnReason)\n",
    "            VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n",
    "        \"\"\",\n",
    "            tuple(row),\n",
    "        )\n",
    "\n",
    "# Commit changes and close connection\n",
    "mydb.commit()\n",
    "mydb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47b8f47-245f-4861-9ad2-a3fe30538b26",
   "metadata": {},
   "source": [
    "### Importing `Telco_customer_churn_population` Data before creating table in Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "485d90e4-3952-4f4e-b57d-c7cef6cecc4a",
   "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>ID</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>90001</td>\n",
       "      <td>54492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>90002</td>\n",
       "      <td>44586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90003</td>\n",
       "      <td>58198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>90004</td>\n",
       "      <td>67852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>90005</td>\n",
       "      <td>43019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>1667</td>\n",
       "      <td>96145</td>\n",
       "      <td>4002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>1668</td>\n",
       "      <td>96146</td>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668</th>\n",
       "      <td>1669</td>\n",
       "      <td>96148</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>1670</td>\n",
       "      <td>96150</td>\n",
       "      <td>33038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>1671</td>\n",
       "      <td>96161</td>\n",
       "      <td>15783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1671 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Zip Code  Population\n",
       "0        1     90001       54492\n",
       "1        2     90002       44586\n",
       "2        3     90003       58198\n",
       "3        4     90004       67852\n",
       "4        5     90005       43019\n",
       "...    ...       ...         ...\n",
       "1666  1667     96145        4002\n",
       "1667  1668     96146         942\n",
       "1668  1669     96148         678\n",
       "1669  1670     96150       33038\n",
       "1670  1671     96161       15783\n",
       "\n",
       "[1671 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_population = pd.read_excel(\n",
    "    \"Datasets/Telco_customer_churn_population.xlsx\"\n",
    ")\n",
    "Telco_customer_churn_population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "213929cc-92c8-4bde-9dc8-f533659618d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID            0\n",
       "Zip Code      0\n",
       "Population    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_population.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffa1f9d-1480-422a-849a-cecae9b0df90",
   "metadata": {},
   "source": [
    "### Creating Table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5596690a-1f3e-42f1-bd6a-36ffbd021df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydb = mysql.connector.connect(host=host, user=user, password=passwd, database=dbname)\n",
    "\n",
    "mycursor = mydb.cursor()\n",
    "\n",
    "# Creating table\n",
    "mycursor.execute(\n",
    "    \"\"\"CREATE TABLE IF NOT EXISTS Telco_customer_churn_population (\n",
    "    id INT PRIMARY KEY AUTO_INCREMENT,\n",
    "    zip VARCHAR(10) UNIQUE,\n",
    "    population INT);\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca9c2470-fb56-4145-b2eb-c37673ea6ac2",
   "metadata": {},
   "source": [
    "### Inserting values into the table created above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "48cfd418-ac9e-4d6b-899d-9f932de3aec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mycursor.execute(\"SELECT * FROM Telco_customer_churn_population\")\n",
    "\n",
    "if mycursor.fetchone() == None:\n",
    "    for _, row in Telco_customer_churn_population.iterrows():\n",
    "        mycursor.execute(\n",
    "            \"\"\"\n",
    "            INSERT INTO Telco_customer_churn_population (\n",
    "                id,zip,population)\n",
    "            VALUES (%s, %s, %s)\n",
    "        \"\"\",\n",
    "            tuple(row),\n",
    "        )\n",
    "\n",
    "# Commit changes and close connection\n",
    "mydb.commit()\n",
    "mydb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ff7102-1e89-44f7-bb99-a5e93727b62a",
   "metadata": {},
   "source": [
    "### Data Modeling Using MySQL Query"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca2720a-b68d-4ac2-a7f1-481d1aaa7712",
   "metadata": {},
   "source": [
    "### Inner Joining tables having CustomerID in common and Population table using locations Zip Code and population tables's zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0a1ec9b5-a26a-493e-8e7c-d74248403137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CustomerID',\n",
       " 'Gender',\n",
       " 'Age',\n",
       " 'Under30',\n",
       " 'SeniorCitizen',\n",
       " 'Married',\n",
       " 'Dependents',\n",
       " 'NumberOfDependents',\n",
       " 'CustomerID',\n",
       " 'Country']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydb = mysql.connector.connect(\n",
    "    host=host,\n",
    "    user=user,\n",
    "    password=passwd,\n",
    "    database=dbname,\n",
    ")\n",
    "\n",
    "mycursor = mydb.cursor()\n",
    "\n",
    "mycursor.execute(\n",
    "    \"select * from telco_customer_churn_demographics demo inner join telco_customer_churn_location loc on demo.CustomerID=loc.CustomerID inner join telco_customer_churn_services serv on loc.CustomerID=serv.CustomerID inner join telco_customer_churn_status stat on stat.CustomerID=serv.CustomerID inner join telco_customer_churn_population popu on popu.zip=loc.ZipCode;\"\n",
    ")\n",
    "\n",
    "rows = mycursor.fetchall()\n",
    "\n",
    "num_fields = len(mycursor.description)\n",
    "field_names = [i[0] for i in mycursor.description]\n",
    "field_names[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3f436fa5-7608-47a6-901b-5a0fd9c1e816",
   "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>Age</th>\n",
       "      <th>Under30</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>...</th>\n",
       "      <th>CustomerStatus</th>\n",
       "      <th>ChurnLabel</th>\n",
       "      <th>ChurnValue</th>\n",
       "      <th>ChurnScore</th>\n",
       "      <th>CLTV</th>\n",
       "      <th>ChurnCategory</th>\n",
       "      <th>ChurnReason</th>\n",
       "      <th>id</th>\n",
       "      <th>zip</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0136-IFMYD</td>\n",
       "      <td>Male</td>\n",
       "      <td>71</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0136-IFMYD</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>4297.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>90001</td>\n",
       "      <td>54492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0060-FUALY</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0060-FUALY</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>5238.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>90003</td>\n",
       "      <td>58198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0253-ZTEOB</td>\n",
       "      <td>Female</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0253-ZTEOB</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>3246.00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>4</td>\n",
       "      <td>90004</td>\n",
       "      <td>67852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0280-XJGEX</td>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0280-XJGEX</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>5340.00</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor had better devices</td>\n",
       "      <td>14</td>\n",
       "      <td>90015</td>\n",
       "      <td>15140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0697-ZMSWS</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0697-ZMSWS</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>2330.00</td>\n",
       "      <td>Dissatisfaction</td>\n",
       "      <td>Limited range of services</td>\n",
       "      <td>22</td>\n",
       "      <td>90023</td>\n",
       "      <td>47487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age Under30 SeniorCitizen Married Dependents  \\\n",
       "0  0136-IFMYD    Male   71      No           Yes     Yes         No   \n",
       "1  0060-FUALY  Female   60      No            No     Yes         No   \n",
       "2  0253-ZTEOB  Female   44      No            No     Yes        Yes   \n",
       "3  0280-XJGEX    Male   38      No            No      No        Yes   \n",
       "4  0697-ZMSWS    Male   19     Yes            No      No         No   \n",
       "\n",
       "   NumberOfDependents  CustomerID        Country  ... CustomerStatus  \\\n",
       "0                   0  0136-IFMYD  United States  ...         Stayed   \n",
       "1                   0  0060-FUALY  United States  ...         Stayed   \n",
       "2                   1  0253-ZTEOB  United States  ...         Stayed   \n",
       "3                   1  0280-XJGEX  United States  ...        Churned   \n",
       "4                   0  0697-ZMSWS  United States  ...        Churned   \n",
       "\n",
       "  ChurnLabel  ChurnValue ChurnScore     CLTV    ChurnCategory  \\\n",
       "0         No           0         63  4297.00          Unknown   \n",
       "1         No           0         26  5238.00          Unknown   \n",
       "2         No           0         50  3246.00          Unknown   \n",
       "3        Yes           1         89  5340.00       Competitor   \n",
       "4        Yes           1         87  2330.00  Dissatisfaction   \n",
       "\n",
       "                     ChurnReason  id    zip  population  \n",
       "0                        Unknown   1  90001       54492  \n",
       "1                        Unknown   3  90003       58198  \n",
       "2                        Unknown   4  90004       67852  \n",
       "3  Competitor had better devices  14  90015       15140  \n",
       "4      Limited range of services  22  90023       47487  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Database = pd.DataFrame(rows, columns=field_names)\n",
    "\n",
    "mycursor.close()\n",
    "mydb.close()\n",
    "\n",
    "Database.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0045e726-c4cf-49cb-a2db-52f046aec113",
   "metadata": {},
   "source": [
    "### Data Modeling Using  Pandas "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4944c9-b023-48b0-b0f3-0e9fcba2674b",
   "metadata": {},
   "source": [
    "### Merging tables having CustomerID in common and Population table using locations Zip Code and population tables's zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6ecf1293-9265-4593-a97f-075706ed1910",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer ID', 'Country', 'State', 'City', 'Zip Code', 'Lat Long',\n",
       "       'Latitude', 'Longitude'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_location.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6a1aef08-b96f-4323-b11f-e689c51528c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Zip Code', 'Population'], dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Telco_customer_churn_population.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a62c8543-a879-4f26-b02b-afdf9e6a9ce0",
   "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>Customer ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Under 30</th>\n",
       "      <th>Senior Citizen</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Number of Dependents</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>...</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Customer Status</th>\n",
       "      <th>Churn Label</th>\n",
       "      <th>Churn Value</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>CLTV</th>\n",
       "      <th>Churn Category</th>\n",
       "      <th>Churn Reason</th>\n",
       "      <th>ID</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>Male</td>\n",
       "      <td>78</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>5433</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor offered more data</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4737-AQCPU</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>4658</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5043-TRZWM</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>Joined</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>3503</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8165-CBKXO</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>5748</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9979-RGMZT</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>5545</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID  Gender  Age Under 30 Senior Citizen Married Dependents  \\\n",
       "0  8779-QRDMV    Male   78       No            Yes      No         No   \n",
       "1  4737-AQCPU    Male   39       No             No     Yes         No   \n",
       "2  5043-TRZWM  Female   32       No             No      No         No   \n",
       "3  8165-CBKXO    Male   35       No             No     Yes        Yes   \n",
       "4  9979-RGMZT  Female   20      Yes             No      No         No   \n",
       "\n",
       "   Number of Dependents        Country       State  ... Satisfaction Score  \\\n",
       "0                     0  United States  California  ...                  3   \n",
       "1                     0  United States  California  ...                  3   \n",
       "2                     0  United States  California  ...                  5   \n",
       "3                     3  United States  California  ...                  3   \n",
       "4                     0  United States  California  ...                  3   \n",
       "\n",
       "   Customer Status Churn Label  Churn Value  Churn Score  CLTV Churn Category  \\\n",
       "0          Churned         Yes            1           91  5433     Competitor   \n",
       "1           Stayed          No            0           42  4658        Unknown   \n",
       "2           Joined          No            0           34  3503        Unknown   \n",
       "3           Stayed          No            0           46  5748        Unknown   \n",
       "4           Stayed          No            0           38  5545        Unknown   \n",
       "\n",
       "                   Churn Reason  ID Population  \n",
       "0  Competitor offered more data  21      68701  \n",
       "1                       Unknown  21      68701  \n",
       "2                       Unknown  21      68701  \n",
       "3                       Unknown  21      68701  \n",
       "4                       Unknown  21      68701  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PandasDf = pd.merge(\n",
    "    pd.merge(\n",
    "        pd.merge(\n",
    "            pd.merge(\n",
    "                Telco_customer_churn_demographics,\n",
    "                Telco_customer_churn_location,\n",
    "                on=\"Customer ID\",\n",
    "            ),\n",
    "            Telco_customer_churn_services,\n",
    "            on=\"Customer ID\",\n",
    "        ),\n",
    "        Telco_customer_churn_status,\n",
    "        on=\"Customer ID\",\n",
    "    ),\n",
    "    Telco_customer_churn_population,\n",
    "    on=\"Zip Code\",\n",
    ")\n",
    "\n",
    "PandasDf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7bf1fe-c5a1-4f25-b74b-32feda511ec6",
   "metadata": {},
   "source": [
    "### Preserving for future usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "100de61e-29a0-4278-9374-43f8744837ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "PandasDf.to_excel(\"Datasets/DataModel.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a1eed7-ab50-4bd4-b1b4-fc28c3adb693",
   "metadata": {},
   "source": [
    "# Data Analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "56f186b3-1d92-487e-a1bb-bbd52bb53813",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a96b227b-5754-41a1-bf9e-e24a90c7f1e1",
   "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>Customer ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Under 30</th>\n",
       "      <th>Senior Citizen</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Number of Dependents</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>...</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Customer Status</th>\n",
       "      <th>Churn Label</th>\n",
       "      <th>Churn Value</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>CLTV</th>\n",
       "      <th>Churn Category</th>\n",
       "      <th>Churn Reason</th>\n",
       "      <th>ID</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>Male</td>\n",
       "      <td>78</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>5433</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor offered more data</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4737-AQCPU</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>4658</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5043-TRZWM</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>Joined</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>3503</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8165-CBKXO</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>5748</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9979-RGMZT</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>5545</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID  Gender  Age Under 30 Senior Citizen Married Dependents  \\\n",
       "0  8779-QRDMV    Male   78       No            Yes      No         No   \n",
       "1  4737-AQCPU    Male   39       No             No     Yes         No   \n",
       "2  5043-TRZWM  Female   32       No             No      No         No   \n",
       "3  8165-CBKXO    Male   35       No             No     Yes        Yes   \n",
       "4  9979-RGMZT  Female   20      Yes             No      No         No   \n",
       "\n",
       "   Number of Dependents        Country       State  ... Satisfaction Score  \\\n",
       "0                     0  United States  California  ...                  3   \n",
       "1                     0  United States  California  ...                  3   \n",
       "2                     0  United States  California  ...                  5   \n",
       "3                     3  United States  California  ...                  3   \n",
       "4                     0  United States  California  ...                  3   \n",
       "\n",
       "   Customer Status Churn Label  Churn Value  Churn Score  CLTV Churn Category  \\\n",
       "0          Churned         Yes            1           91  5433     Competitor   \n",
       "1           Stayed          No            0           42  4658        Unknown   \n",
       "2           Joined          No            0           34  3503        Unknown   \n",
       "3           Stayed          No            0           46  5748        Unknown   \n",
       "4           Stayed          No            0           38  5545        Unknown   \n",
       "\n",
       "                   Churn Reason  ID Population  \n",
       "0  Competitor offered more data  21      68701  \n",
       "1                       Unknown  21      68701  \n",
       "2                       Unknown  21      68701  \n",
       "3                       Unknown  21      68701  \n",
       "4                       Unknown  21      68701  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"Datasets/DataModel.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6f11400a-eb28-4507-933c-108d7e2761bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 54 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   Customer ID                        7043 non-null   object \n",
      " 1   Gender                             7043 non-null   object \n",
      " 2   Age                                7043 non-null   int64  \n",
      " 3   Under 30                           7043 non-null   object \n",
      " 4   Senior Citizen                     7043 non-null   object \n",
      " 5   Married                            7043 non-null   object \n",
      " 6   Dependents                         7043 non-null   object \n",
      " 7   Number of Dependents               7043 non-null   int64  \n",
      " 8   Country                            7043 non-null   object \n",
      " 9   State                              7043 non-null   object \n",
      " 10  City                               7043 non-null   object \n",
      " 11  Zip Code                           7043 non-null   int64  \n",
      " 12  Lat Long                           7043 non-null   object \n",
      " 13  Latitude                           7043 non-null   float64\n",
      " 14  Longitude                          7043 non-null   float64\n",
      " 15  Quarter_x                          7043 non-null   object \n",
      " 16  Referred a Friend                  7043 non-null   object \n",
      " 17  Number of Referrals                7043 non-null   int64  \n",
      " 18  Tenure in Months                   7043 non-null   int64  \n",
      " 19  Offer                              7043 non-null   object \n",
      " 20  Phone Service                      7043 non-null   object \n",
      " 21  Avg Monthly Long Distance Charges  7043 non-null   float64\n",
      " 22  Multiple Lines                     7043 non-null   object \n",
      " 23  Internet Service                   7043 non-null   object \n",
      " 24  Internet Type                      7043 non-null   object \n",
      " 25  Avg Monthly GB Download            7043 non-null   int64  \n",
      " 26  Online Security                    7043 non-null   object \n",
      " 27  Online Backup                      7043 non-null   object \n",
      " 28  Device Protection Plan             7043 non-null   object \n",
      " 29  Premium Tech Support               7043 non-null   object \n",
      " 30  Streaming TV                       7043 non-null   object \n",
      " 31  Streaming Movies                   7043 non-null   object \n",
      " 32  Streaming Music                    7043 non-null   object \n",
      " 33  Unlimited Data                     7043 non-null   object \n",
      " 34  Contract                           7043 non-null   object \n",
      " 35  Paperless Billing                  7043 non-null   object \n",
      " 36  Payment Method                     7043 non-null   object \n",
      " 37  Monthly Charge                     7043 non-null   float64\n",
      " 38  Total Charges                      7043 non-null   float64\n",
      " 39  Total Refunds                      7043 non-null   float64\n",
      " 40  Total Extra Data Charges           7043 non-null   int64  \n",
      " 41  Total Long Distance Charges        7043 non-null   float64\n",
      " 42  Total Revenue                      7043 non-null   float64\n",
      " 43  Quarter_y                          7043 non-null   object \n",
      " 44  Satisfaction Score                 7043 non-null   int64  \n",
      " 45  Customer Status                    7043 non-null   object \n",
      " 46  Churn Label                        7043 non-null   object \n",
      " 47  Churn Value                        7043 non-null   int64  \n",
      " 48  Churn Score                        7043 non-null   int64  \n",
      " 49  CLTV                               7043 non-null   int64  \n",
      " 50  Churn Category                     7043 non-null   object \n",
      " 51  Churn Reason                       7043 non-null   object \n",
      " 52  ID                                 7043 non-null   int64  \n",
      " 53  Population                         7043 non-null   int64  \n",
      "dtypes: float64(8), int64(13), object(33)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecdf644-172d-4fb6-a5fa-89ffecd524b1",
   "metadata": {},
   "source": [
    "### StatisticalSummary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "43534753-fc06-41ec-810f-53dc13163dd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Number of Dependents</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Number of Referrals</th>\n",
       "      <th>Tenure in Months</th>\n",
       "      <th>Avg Monthly Long Distance Charges</th>\n",
       "      <th>Avg Monthly GB Download</th>\n",
       "      <th>Monthly Charge</th>\n",
       "      <th>...</th>\n",
       "      <th>Total Refunds</th>\n",
       "      <th>Total Extra Data Charges</th>\n",
       "      <th>Total Long Distance Charges</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Churn Value</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>CLTV</th>\n",
       "      <th>ID</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.509726</td>\n",
       "      <td>0.468692</td>\n",
       "      <td>93486.070567</td>\n",
       "      <td>36.197455</td>\n",
       "      <td>-119.756684</td>\n",
       "      <td>1.951867</td>\n",
       "      <td>32.386767</td>\n",
       "      <td>22.958954</td>\n",
       "      <td>20.515405</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>...</td>\n",
       "      <td>1.962182</td>\n",
       "      <td>6.860713</td>\n",
       "      <td>749.099262</td>\n",
       "      <td>3034.379056</td>\n",
       "      <td>3.244924</td>\n",
       "      <td>0.265370</td>\n",
       "      <td>58.505040</td>\n",
       "      <td>4400.295755</td>\n",
       "      <td>784.800653</td>\n",
       "      <td>22139.603294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.750352</td>\n",
       "      <td>0.962802</td>\n",
       "      <td>1856.767505</td>\n",
       "      <td>2.468929</td>\n",
       "      <td>2.154425</td>\n",
       "      <td>3.001199</td>\n",
       "      <td>24.542061</td>\n",
       "      <td>15.448113</td>\n",
       "      <td>20.418940</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>...</td>\n",
       "      <td>7.902614</td>\n",
       "      <td>25.104978</td>\n",
       "      <td>846.660055</td>\n",
       "      <td>2865.204542</td>\n",
       "      <td>1.201657</td>\n",
       "      <td>0.441561</td>\n",
       "      <td>21.170031</td>\n",
       "      <td>1183.057152</td>\n",
       "      <td>489.044050</td>\n",
       "      <td>21152.392837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90001.000000</td>\n",
       "      <td>32.555828</td>\n",
       "      <td>-124.301372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.360000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92101.000000</td>\n",
       "      <td>33.990646</td>\n",
       "      <td>-121.788090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.210000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.545000</td>\n",
       "      <td>605.610000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3469.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>2344.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>93518.000000</td>\n",
       "      <td>36.205465</td>\n",
       "      <td>-119.595293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>22.890000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>401.440000</td>\n",
       "      <td>2108.640000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>4527.000000</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>17554.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>95329.000000</td>\n",
       "      <td>38.161321</td>\n",
       "      <td>-117.969795</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>36.395000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1191.100000</td>\n",
       "      <td>4801.145000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>5380.500000</td>\n",
       "      <td>1208.000000</td>\n",
       "      <td>36125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>96150.000000</td>\n",
       "      <td>41.962127</td>\n",
       "      <td>-114.192901</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>49.990000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>49.790000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>3564.720000</td>\n",
       "      <td>11979.340000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>1670.000000</td>\n",
       "      <td>105285.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  Number of Dependents      Zip Code     Latitude  \\\n",
       "count  7043.000000           7043.000000   7043.000000  7043.000000   \n",
       "mean     46.509726              0.468692  93486.070567    36.197455   \n",
       "std      16.750352              0.962802   1856.767505     2.468929   \n",
       "min      19.000000              0.000000  90001.000000    32.555828   \n",
       "25%      32.000000              0.000000  92101.000000    33.990646   \n",
       "50%      46.000000              0.000000  93518.000000    36.205465   \n",
       "75%      60.000000              0.000000  95329.000000    38.161321   \n",
       "max      80.000000              9.000000  96150.000000    41.962127   \n",
       "\n",
       "         Longitude  Number of Referrals  Tenure in Months  \\\n",
       "count  7043.000000          7043.000000       7043.000000   \n",
       "mean   -119.756684             1.951867         32.386767   \n",
       "std       2.154425             3.001199         24.542061   \n",
       "min    -124.301372             0.000000          1.000000   \n",
       "25%    -121.788090             0.000000          9.000000   \n",
       "50%    -119.595293             0.000000         29.000000   \n",
       "75%    -117.969795             3.000000         55.000000   \n",
       "max    -114.192901            11.000000         72.000000   \n",
       "\n",
       "       Avg Monthly Long Distance Charges  Avg Monthly GB Download  \\\n",
       "count                        7043.000000              7043.000000   \n",
       "mean                           22.958954                20.515405   \n",
       "std                            15.448113                20.418940   \n",
       "min                             0.000000                 0.000000   \n",
       "25%                             9.210000                 3.000000   \n",
       "50%                            22.890000                17.000000   \n",
       "75%                            36.395000                27.000000   \n",
       "max                            49.990000                85.000000   \n",
       "\n",
       "       Monthly Charge  ...  Total Refunds  Total Extra Data Charges  \\\n",
       "count     7043.000000  ...    7043.000000               7043.000000   \n",
       "mean        64.761692  ...       1.962182                  6.860713   \n",
       "std         30.090047  ...       7.902614                 25.104978   \n",
       "min         18.250000  ...       0.000000                  0.000000   \n",
       "25%         35.500000  ...       0.000000                  0.000000   \n",
       "50%         70.350000  ...       0.000000                  0.000000   \n",
       "75%         89.850000  ...       0.000000                  0.000000   \n",
       "max        118.750000  ...      49.790000                150.000000   \n",
       "\n",
       "       Total Long Distance Charges  Total Revenue  Satisfaction Score  \\\n",
       "count                  7043.000000    7043.000000         7043.000000   \n",
       "mean                    749.099262    3034.379056            3.244924   \n",
       "std                     846.660055    2865.204542            1.201657   \n",
       "min                       0.000000      21.360000            1.000000   \n",
       "25%                      70.545000     605.610000            3.000000   \n",
       "50%                     401.440000    2108.640000            3.000000   \n",
       "75%                    1191.100000    4801.145000            4.000000   \n",
       "max                    3564.720000   11979.340000            5.000000   \n",
       "\n",
       "       Churn Value  Churn Score         CLTV           ID     Population  \n",
       "count  7043.000000  7043.000000  7043.000000  7043.000000    7043.000000  \n",
       "mean      0.265370    58.505040  4400.295755   784.800653   22139.603294  \n",
       "std       0.441561    21.170031  1183.057152   489.044050   21152.392837  \n",
       "min       0.000000     5.000000  2003.000000     1.000000      11.000000  \n",
       "25%       0.000000    40.000000  3469.000000   353.000000    2344.000000  \n",
       "50%       0.000000    61.000000  4527.000000   759.000000   17554.000000  \n",
       "75%       1.000000    75.500000  5380.500000  1208.000000   36125.000000  \n",
       "max       1.000000    96.000000  6500.000000  1670.000000  105285.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a89cc8d7-edc7-4b74-8b09-3ef36f92ab3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(\"Number of Dependents\", \"Churn Score\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caed071a-97b0-4b88-9914-bb9c5a0013f5",
   "metadata": {},
   "source": [
    "# Observation\n",
    "- Lower the number of dependents higher is the churn score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "af005286-b1a2-4073-b94b-9a7ec7158038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(\"Satisfaction Score\", \"Churn Value\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79e8da79-ee2f-4cd3-b848-221b4389a2e1",
   "metadata": {},
   "source": [
    "# Observation\n",
    "- Satisfaction Score < 4 then there is a chances of customer to churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3d88aaa9-c0f1-46bc-8cd3-b3ac8f8f4e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    df[\"Churn Score\"],\n",
    "    df[\"Churn Value\"],\n",
    "    marker=\"+\",\n",
    ")\n",
    "plt.xlabel(\"Churn Score\")\n",
    "plt.ylabel(\"Churn Value\")\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3059d133-c58c-4eb7-8f9d-e73b143fd19b",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- Few churn scores > 60 have higher chances of customer churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "80ff1099-53b2-4b1c-aff6-4a639b6be8d3",
   "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>Customer Status</th>\n",
       "      <th>Churned</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Stayed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>922</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>518</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>429</td>\n",
       "      <td>157</td>\n",
       "      <td>2079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>1637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>1004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Customer Status     Churned  Joined  Stayed\n",
       "Satisfaction Score                         \n",
       "1                       922       0       0\n",
       "2                       518       0       0\n",
       "3                       429     157    2079\n",
       "4                         0     152    1637\n",
       "5                         0     145    1004"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"Satisfaction Score\"], df[\"Customer Status\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14717539-ac25-4926-8b13-d317f2433670",
   "metadata": {},
   "source": [
    "# Observation\n",
    "- Customers  who have given Satisfaction Score `1` and `2` have usually churned.\n",
    "- Customers  who have given Satisfaction Score as `3` have  churned and maximum have stayed\n",
    "- Customer with Satisfaction Score 3, have Customer Status as `Stayed` and few as `Churned`\n",
    "- 4720 Customers stayed while 1869 customers churned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5f5b8821-710f-41a5-8488-c491a096687c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(\n",
    "    df[\"Customer Status\"].value_counts().values,\n",
    "    labels=set(df[\"Customer Status\"].values),\n",
    "    autopct=\"%.2f%%\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18bbf7c5-465f-462d-ba85-ccaa9b87c79a",
   "metadata": {},
   "source": [
    "# Observation\n",
    "- Customer Status results in 26% customers churned and 67%  stayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "661a35c6-228e-4631-ac57-055a0fb89b67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "United States    7043\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f7e077e1-fb6e-478c-9d23-2d2ad38d9ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State     \n",
       "California    7043\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"State\"]].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "af4fe1a3-1b55-412b-8f24-84d8f6e251d5",
   "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>Population</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>105285</td>\n",
       "      <td>Bell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>103214</td>\n",
       "      <td>Norwalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>101215</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>98239</td>\n",
       "      <td>Oceanside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>97318</td>\n",
       "      <td>Pacoima</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Population         City\n",
       "1625      105285         Bell\n",
       "1624      103214      Norwalk\n",
       "1623      101215  Los Angeles\n",
       "1622       98239    Oceanside\n",
       "1621       97318      Pacoima"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "populations = (\n",
    "    df[[\"Population\", \"City\"]]\n",
    "    .groupby([\"Population\", \"City\"])\n",
    "    .size()\n",
    "    .reset_index()[[\"Population\", \"City\"]]\n",
    ")\n",
    "populations.iloc[::-1][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6c94717d-2c8a-4f39-9bdf-9b42ba0ad865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(height=populations.Population.iloc[::-1][:5], x=populations.City.iloc[::-1][:5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a21518-1789-4a88-b47d-9bfa345abb30",
   "metadata": {},
   "source": [
    "# Obseravtions\n",
    "- Top 5 cities of United States, California with highest customers are Bell, Norwalk, Los Angeles, Oceanside, Pacoima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "87bcd404-ad3f-4f53-b127-0b65d07c42f2",
   "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>Married</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>1800</td>\n",
       "      <td>1688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1841</td>\n",
       "      <td>1714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Married    No   Yes\n",
       "Gender             \n",
       "Female   1800  1688\n",
       "Male     1841  1714"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.Gender, df.Married)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ea6760-3670-4bf8-bcd3-64bdc49a30d7",
   "metadata": {},
   "source": [
    "# Observation\n",
    "- 1800 Female Customer's marital status is unmarried\n",
    "- 1841 Male Customer's marital status is unmarried\n",
    "- 1688 Female Customer's marital status is married\n",
    "- 1714 Male Customer's marital status is married"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "673e35a0-fd03-4810-b050-aa859a571172",
   "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>Dependents</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2690</td>\n",
       "      <td>798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2726</td>\n",
       "      <td>829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Dependents    No  Yes\n",
       "Gender               \n",
       "Female      2690  798\n",
       "Male        2726  829"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.Gender, df.Dependents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca8ace72-b83c-469e-ad29-3b55e3734fb2",
   "metadata": {},
   "source": [
    "# Observation\n",
    "- 2690 Female customers and 2726 Male customers are not dependent \n",
    "- 789 Female customers and 829 Male customers are dependent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b109e47f-3488-444b-accf-b398416a3c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7043.000000\n",
       "mean       64.761692\n",
       "std        30.090047\n",
       "min        18.250000\n",
       "25%        35.500000\n",
       "50%        70.350000\n",
       "75%        89.850000\n",
       "max       118.750000\n",
       "Name: Monthly Charge, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Monthly Charge\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f90019e7-94ef-44ea-8ea7-eb15b681e289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(y=df[\"Total Extra Data Charges\"], x=df[\"Satisfaction Score\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcd7e84-cf42-4765-be22-0ae14bd8071e",
   "metadata": {},
   "source": [
    "# Observation\n",
    "- Extra Data charges has an impact on Satisfaction Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "98599a37-b187-42d8-9153-82b8acd9f02f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(height=df[\"Total Long Distance Charges\"], x=df[\"Satisfaction Score\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5988021e-b82e-4eab-9cd6-95ffecd3c53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(height=df[\"Monthly Charge\"], x=df[\"Satisfaction Score\"])\n",
    "plt.xlabel(\"Satisfaction Score\")\n",
    "plt.ylabel(\"Monthly Charge\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52e3764-282d-4697-a722-a360a96fc4c0",
   "metadata": {},
   "source": [
    "# Observation\n",
    "-  monthly cost doesn't have much impact on Satisfaction Score\n",
    "-  Total Long Distance Charges also doesn't have much impact on Satisfaction Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "908e4bd2-40db-488d-a95f-daabd123a3fd",
   "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>Total Refunds</th>\n",
       "      <th>City</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>1.27</td>\n",
       "      <td>Acampo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3142</th>\n",
       "      <td>1.48</td>\n",
       "      <td>Dutch Flat</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3156</th>\n",
       "      <td>2.93</td>\n",
       "      <td>Rancho Cordova</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3169</th>\n",
       "      <td>4.48</td>\n",
       "      <td>Avalon</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3173</th>\n",
       "      <td>4.73</td>\n",
       "      <td>Novato</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>48.26</td>\n",
       "      <td>Walnut Creek</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3643</th>\n",
       "      <td>48.54</td>\n",
       "      <td>Cupertino</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3648</th>\n",
       "      <td>48.95</td>\n",
       "      <td>Rohnert Park</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3658</th>\n",
       "      <td>49.57</td>\n",
       "      <td>Bieber</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3659</th>\n",
       "      <td>49.57</td>\n",
       "      <td>Long Beach</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total Refunds            City  Satisfaction Score\n",
       "3139           1.27          Acampo                   1\n",
       "3142           1.48      Dutch Flat                   2\n",
       "3156           2.93  Rancho Cordova                   1\n",
       "3169           4.48          Avalon                   1\n",
       "3173           4.73          Novato                   2\n",
       "...             ...             ...                 ...\n",
       "3642          48.26    Walnut Creek                   1\n",
       "3643          48.54       Cupertino                   1\n",
       "3648          48.95    Rohnert Park                   1\n",
       "3658          49.57          Bieber                   2\n",
       "3659          49.57      Long Beach                   1\n",
       "\n",
       "[82 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Refund_amt = (\n",
    "    df[[\"City\", \"Total Refunds\", \"Satisfaction Score\"]]\n",
    "    .groupby([\"Total Refunds\", \"City\", \"Satisfaction Score\"])\n",
    "    .size()\n",
    "    .reset_index()\n",
    ")\n",
    "cities_with_refunds = Refund_amt[\n",
    "    (Refund_amt[\"Total Refunds\"] > 0) & (Refund_amt[\"Satisfaction Score\"] < 3)\n",
    "]\n",
    "cities_with_refunds = cities_with_refunds.iloc[:, :-1]\n",
    "cities_with_refunds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6c15527f-7e2f-4ca3-98b6-c221afc38f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "plt.bar(x=cities_with_refunds.City, height=cities_with_refunds[\"Total Refunds\"])\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d0ed4a-10c0-4328-b64b-02fa9da8a78a",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- Cities with Total Refunds are displayed where customers are not satisfied with the service and claimed for refund and have also given Satisfaction Score < 3\n",
    "- These cities have customers who expect better service and might churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "590aeca5-21fc-4482-ac83-e7fe939f90ff",
   "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>Total Revenue</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>11979.34</td>\n",
       "      <td>Calistoga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>11868.34</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7037</th>\n",
       "      <td>11795.78</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7036</th>\n",
       "      <td>11688.90</td>\n",
       "      <td>Shasta Lake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7035</th>\n",
       "      <td>11634.53</td>\n",
       "      <td>Middletown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.12</td>\n",
       "      <td>Corona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.08</td>\n",
       "      <td>Mira Loma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.61</td>\n",
       "      <td>Smith River</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.40</td>\n",
       "      <td>Nevada City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.36</td>\n",
       "      <td>El Nido</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7040 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total Revenue           City\n",
       "7039       11979.34      Calistoga\n",
       "7038       11868.34  San Francisco\n",
       "7037       11795.78  San Francisco\n",
       "7036       11688.90    Shasta Lake\n",
       "7035       11634.53     Middletown\n",
       "...             ...            ...\n",
       "4             22.12         Corona\n",
       "3             22.08      Mira Loma\n",
       "2             21.61    Smith River\n",
       "1             21.40    Nevada City\n",
       "0             21.36        El Nido\n",
       "\n",
       "[7040 rows x 2 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total_revenue = (\n",
    "    df[[\"Total Revenue\", \"City\"]]\n",
    "    .groupby([\"Total Revenue\", \"City\"])\n",
    "    .size()\n",
    "    .reset_index()\n",
    ")\n",
    "Total_revenue = Total_revenue.iloc[:, :-1][::-1]\n",
    "Total_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a7373009-ae7f-48d0-ad78-51c65a3fc362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "ax[0].pie(\n",
    "    Total_revenue[\"Total Revenue\"][:5],\n",
    "    labels=Total_revenue[\"City\"][:5],\n",
    "    autopct=\"%1.1f%%\",\n",
    "    startangle=140,\n",
    ")\n",
    "ax[0].set_title(\"5 Highest Revenue generated City\")\n",
    "\n",
    "ax[1].pie(\n",
    "    Total_revenue[\"Total Revenue\"][::-1][:5],\n",
    "    labels=Total_revenue[\"City\"][::-1][:5],\n",
    "    autopct=\"%1.1f%%\",\n",
    "    startangle=140,\n",
    ")\n",
    "ax[1].set_title(\"5 Least Revenue generated City\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "eb697f7b-aef9-42f4-b0c3-6f4df38ba039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(\n",
    "    df[\"Churn Category\"].value_counts().values,\n",
    "    labels=df[\"Churn Category\"].unique(),\n",
    "    autopct=\"%1.1f%%\",\n",
    "    startangle=140,\n",
    "    colors=[\"#ff9999\", \"#66b3ff\", \"#99ff99\", \"#ffcc99\", \"#ffca26\", \"#c2c2f0\"],\n",
    ")\n",
    "plt.title(\"Churn Category Distribution\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3453c9e-3da9-4a45-ab59-2bea4293e32e",
   "metadata": {},
   "source": [
    "# Observation\n",
    " -  The Competitors are the highest reason behind customers churn.\n",
    "- 11.9% are Unknown which might be personal reasons of the customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6f63070b-7630-417a-97fd-154142a8409c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(\n",
    "    df[\"Churn Reason\"].value_counts().values[:10],\n",
    "    labels=df[\"Churn Reason\"].unique()[:10],\n",
    "    autopct=\"%1.1f%%\",\n",
    "    colors=[\"#ff9999\", \"#66b3ff\", \"#99ff99\", \"#ffcc99\", \"#c2c2f0\"],\n",
    ")\n",
    "plt.title(\"Churn Reason Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83af31e4-a96f-4756-ba01-c18bee266c32",
   "metadata": {},
   "source": [
    "# Obseravtion\n",
    "- Competitors offer more data at same price point which is main cause of customer's churn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "647fc824-d54f-44ce-81a3-ad26267b1ebd",
   "metadata": {},
   "source": [
    "# Building ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "16698b82-95dc-49bc-bcb5-e7382b8f7e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from imblearn import over_sampling\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d332e7cc-d607-4865-8c67-8bb1a965dbf4",
   "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>Customer ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Under 30</th>\n",
       "      <th>Senior Citizen</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Number of Dependents</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>...</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Customer Status</th>\n",
       "      <th>Churn Label</th>\n",
       "      <th>Churn Value</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>CLTV</th>\n",
       "      <th>Churn Category</th>\n",
       "      <th>Churn Reason</th>\n",
       "      <th>ID</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8779-QRDMV</td>\n",
       "      <td>Male</td>\n",
       "      <td>78</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Churned</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>5433</td>\n",
       "      <td>Competitor</td>\n",
       "      <td>Competitor offered more data</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4737-AQCPU</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>4658</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5043-TRZWM</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>Joined</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>3503</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8165-CBKXO</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>5748</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9979-RGMZT</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Stayed</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>5545</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>68701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID  Gender  Age Under 30 Senior Citizen Married Dependents  \\\n",
       "0  8779-QRDMV    Male   78       No            Yes      No         No   \n",
       "1  4737-AQCPU    Male   39       No             No     Yes         No   \n",
       "2  5043-TRZWM  Female   32       No             No      No         No   \n",
       "3  8165-CBKXO    Male   35       No             No     Yes        Yes   \n",
       "4  9979-RGMZT  Female   20      Yes             No      No         No   \n",
       "\n",
       "   Number of Dependents        Country       State  ... Satisfaction Score  \\\n",
       "0                     0  United States  California  ...                  3   \n",
       "1                     0  United States  California  ...                  3   \n",
       "2                     0  United States  California  ...                  5   \n",
       "3                     3  United States  California  ...                  3   \n",
       "4                     0  United States  California  ...                  3   \n",
       "\n",
       "   Customer Status Churn Label  Churn Value  Churn Score  CLTV Churn Category  \\\n",
       "0          Churned         Yes            1           91  5433     Competitor   \n",
       "1           Stayed          No            0           42  4658        Unknown   \n",
       "2           Joined          No            0           34  3503        Unknown   \n",
       "3           Stayed          No            0           46  5748        Unknown   \n",
       "4           Stayed          No            0           38  5545        Unknown   \n",
       "\n",
       "                   Churn Reason  ID Population  \n",
       "0  Competitor offered more data  21      68701  \n",
       "1                       Unknown  21      68701  \n",
       "2                       Unknown  21      68701  \n",
       "3                       Unknown  21      68701  \n",
       "4                       Unknown  21      68701  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"Datasets/DataModel.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "64495102-f968-4101-8b2f-75f4d35fd3f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "United States    7043\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "784b8015-877f-4290-bd77-a722d2bc5170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "California    7043\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.State.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77bfdb09-b86d-45e3-8f8c-55ec5e2574e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "328    43\n",
       "327    38\n",
       "373    36\n",
       "368    34\n",
       "376    32\n",
       "       ..\n",
       "764     2\n",
       "303     2\n",
       "357     2\n",
       "404     2\n",
       "851     1\n",
       "Name: count, Length: 1626, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ID.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d4e8aec-d297-49e6-82ad-ddbf6071c9c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer ID', 'Gender', 'Age', 'Under 30', 'Senior Citizen', 'Married',\n",
       "       'Dependents', 'Number of Dependents', 'Country', 'State', 'City',\n",
       "       'Zip Code', 'Lat Long', 'Latitude', 'Longitude', 'Quarter_x',\n",
       "       'Referred a Friend', 'Number of Referrals', 'Tenure in Months', 'Offer',\n",
       "       'Phone Service', 'Avg Monthly Long Distance Charges', 'Multiple Lines',\n",
       "       'Internet Service', 'Internet Type', 'Avg Monthly GB Download',\n",
       "       'Online Security', 'Online Backup', 'Device Protection Plan',\n",
       "       'Premium Tech Support', 'Streaming TV', 'Streaming Movies',\n",
       "       'Streaming Music', 'Unlimited Data', 'Contract', 'Paperless Billing',\n",
       "       'Payment Method', 'Monthly Charge', 'Total Charges', 'Total Refunds',\n",
       "       'Total Extra Data Charges', 'Total Long Distance Charges',\n",
       "       'Total Revenue', 'Quarter_y', 'Satisfaction Score', 'Customer Status',\n",
       "       'Churn Label', 'Churn Value', 'Churn Score', 'CLTV', 'Churn Category',\n",
       "       'Churn Reason', 'ID', 'Population'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c85ac8ef-b94b-4f4c-957f-fe9d48649d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Number of Dependents</th>\n",
       "      <th>Married</th>\n",
       "      <th>Monthly Charge</th>\n",
       "      <th>City</th>\n",
       "      <th>Total Extra Data Charges</th>\n",
       "      <th>Unlimited Data</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Churn Score</th>\n",
       "      <th>Churn Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>39.65</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>20</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72.10</td>\n",
       "      <td>Los Angeles</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>75.55</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>56.75</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>94.05</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Number of Dependents Married  Monthly Charge         City  \\\n",
       "0    Male   78                     0      No           39.65  Los Angeles   \n",
       "1    Male   39                     0     Yes           72.10  Los Angeles   \n",
       "2  Female   32                     0      No           75.55  Los Angeles   \n",
       "3    Male   35                     3     Yes           56.75  Los Angeles   \n",
       "4  Female   20                     0      No           94.05  Los Angeles   \n",
       "\n",
       "   Total Extra Data Charges Unlimited Data  Satisfaction Score  Churn Score  \\\n",
       "0                        20             No                   3           91   \n",
       "1                         0            Yes                   3           42   \n",
       "2                         0            Yes                   5           34   \n",
       "3                         0            Yes                   3           46   \n",
       "4                         0            Yes                   3           38   \n",
       "\n",
       "   Churn Value  \n",
       "0            1  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[\n",
    "    [\n",
    "        \"Gender\",\n",
    "        \"Age\",\n",
    "        \"Number of Dependents\",\n",
    "        \"Married\",\n",
    "        \"Monthly Charge\",\n",
    "        \"City\",\n",
    "        \"Total Extra Data Charges\",\n",
    "        \"Unlimited Data\",\n",
    "        \"Satisfaction Score\",\n",
    "        \"Churn Score\",\n",
    "        \"Churn Value\",\n",
    "    ]\n",
    "]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fcc27635-e116-4491-a2a6-9a90a5885cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Number of Dependents</th>\n",
       "      <th>Married</th>\n",
       "      <th>Monthly Charge</th>\n",
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       "      <th>Unlimited Data</th>\n",
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       "      <th>Churn Value</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
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       "      <td>554</td>\n",
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       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>72.10</td>\n",
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       "      <td>42</td>\n",
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       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.55</td>\n",
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       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>35</td>\n",
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       "      <td>1</td>\n",
       "      <td>56.75</td>\n",
       "      <td>554</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.05</td>\n",
       "      <td>554</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  Age  Number of Dependents Married  Monthly Charge City  \\\n",
       "0      1   78                     0       0           39.65  554   \n",
       "1      1   39                     0       1           72.10  554   \n",
       "2      0   32                     0       0           75.55  554   \n",
       "3      1   35                     3       1           56.75  554   \n",
       "4      0   20                     0       0           94.05  554   \n",
       "\n",
       "   Total Extra Data Charges Unlimited Data  Satisfaction Score  Churn Score  \\\n",
       "0                        20              0                   3           91   \n",
       "1                         0              1                   3           42   \n",
       "2                         0              1                   5           34   \n",
       "3                         0              1                   3           46   \n",
       "4                         0              1                   3           38   \n",
       "\n",
       "   Churn Value  \n",
       "0            1  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[:, \"Gender\"] = encoder.fit_transform(df.Gender)\n",
    "df2.loc[:, \"Unlimited Data\"] = encoder.fit_transform(df[\"Unlimited Data\"])\n",
    "df2.loc[:, \"Married\"] = encoder.fit_transform(df.Married)\n",
    "df2.loc[:, \"City\"] = encoder.fit_transform(df2[\"City\"])\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e20c498-8b7f-4342-a8c8-8ca796a89941",
   "metadata": {},
   "source": [
    "### Checking the correlation  using pearson's correlation technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5edbf689-0c97-4fd3-8803-b9f8c40b6dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df2.corr(method=\"pearson\"));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d59d66-853a-4c0c-8f64-aecfe7ff4d02",
   "metadata": {},
   "source": [
    "### Positive correlation\n",
    "Number of Dependent,\n",
    "Monthly Charges,\n",
    "Total Extra Data Charges\n",
    "\n",
    "### Negative correlation\n",
    "Satisfaction Score,\n",
    "Churn Score,\n",
    "Age\n",
    "\n",
    "### Observations\n",
    "\n",
    "* Number of Dependents: Customers with more dependents are more likely to churn because they may have more options for choosing a different service provider. For example, if a customer has multiple family members on their account, they may be more likely to switch to a different service provider if they can get a better deal on a family lan.\n",
    "\n",
    "* Monthly Charge: Customers with higher monthly charges are more likely to churn because they may be more price-sensitive. These customers may be more likely to switch to a different service provider if they can find a better deal on a comparable sevice.\n",
    "\n",
    "* Total Extra Data Charges: Customers with higher total extra data charges are more likely to churn because they may be more frustrated with their current service provider. These customers may feel like they are being overcharged for data usage, and they may be more likely to switch to a different service provider that offers a better data.\n",
    " \n",
    "* Satisfaction Score: Customers with higher satisfaction scores are less likely to churn because they are more happy with their current service provider. These customers are less likely to switch to a different service provider unless they have a very compelling reason.\n",
    "  \n",
    "* Churn Score: Customers with lower churn scores are less likely to churn because they are less likely to be at risk of churning. Churn scores are typically calculated based on a variety of factors, such as customer usage patterns, payment history, and customer support inteactions.\n",
    "\n",
    "* Age: Younger customers are less likely to churn because they may be more willing to try new things. These customers may also be less likely to be tied down to a specific service provider, as they may not have been using their current service for as long as older customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0193a589-c407-4710-ab70-f33c4ca1365d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df2.drop(\"Churn Value\", axis=1)\n",
    "y = df2[\"Churn Value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f166cfe-7145-4374-8864-029105b0ec89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn Value\n",
       "0    5174\n",
       "1    1869\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745a69fa-68f3-4162-9bf3-228aa634dba1",
   "metadata": {},
   "source": [
    "### Dealing with Class imbalance using SMOTE (By Oversampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee0862be-215e-442d-b9c1-872a916aaa70",
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample = over_sampling.SMOTE()\n",
    "X, y = oversample.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7772e436-67c8-4ddd-9550-3ca0ef017988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn Value\n",
       "1    5174\n",
       "0    5174\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c24e2bf3-b4e1-4a13-8119-a566b21ab459",
   "metadata": {},
   "source": [
    "### Standardizing the values to normal scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69697e25-80de-4026-8b2c-e588d029e004",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X1 = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "afc50dc3-5e7c-4aac-8e46-779a591f4620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7761, 10), (2587, 10))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X1, y, shuffle=True, stratify=y, random_state=21\n",
    ")\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "31fe2417-09c8-47c8-acb9-26d3a2e75379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98      1293\n",
      "           1       0.99      0.98      0.98      1294\n",
      "\n",
      "    accuracy                           0.98      2587\n",
      "   macro avg       0.98      0.98      0.98      2587\n",
      "weighted avg       0.98      0.98      0.98      2587\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    \"n_estimators\": [50, 100, 200],\n",
    "    \"max_depth\": [None, 5, 10],\n",
    "    \"min_samples_split\": [2, 5, 10],\n",
    "}\n",
    "\n",
    "model = RandomForestClassifier(\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "model = GridSearchCV(model, param_grid, cv=5, scoring=\"accuracy\")\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7734ec1d-f247-4b22-a70b-e11e4dace4e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': None, 'min_samples_split': 5, 'n_estimators': 100}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "358da977-e93f-42af-9bef-f4824ec265ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1277,   16],\n",
       "       [  25, 1269]], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7a123263-81cf-41de-a291-32eedf1b53c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543525ad-3d01-48af-b4fa-81613d23e65d",
   "metadata": {},
   "source": [
    "* True Positives (TP) = 1277: This means there were 1277 cases where the model correctly predicted that the customer would churn.\n",
    "* True Negatives (TN) = 1269: This means there were 1269 cases where the model correctly predicted that the customer would not churn.\r",
    "* \n",
    "False Positives (FP) = 16: This means there were 16 cases where the model predicted churn, but the customer did not actually churn (Type I error..* \r\n",
    "False Negatives (FN) = 25: This means there were 25 cases where the model predicted non-churn, but the customer actually churned (Type II errro.\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5c1d98-cce5-46ed-aff2-f65bfb1548f6",
   "metadata": {},
   "source": [
    "# Model Persistance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ad17c9d-856e-439e-be93-83ab73741690",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model, open(\"Model.pkl\", \"wb\"))"
   ]
  }
 ],
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