{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "976978fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "file='telecom_customer_churn.csv'\n",
    "\n",
    "df=pd.read_csv(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ce5a7bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(columns=['customerID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "618d1fb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7043 non-null   str    \n",
      " 1   SeniorCitizen     7043 non-null   int64  \n",
      " 2   Partner           7043 non-null   str    \n",
      " 3   Dependents        7043 non-null   str    \n",
      " 4   tenure            7043 non-null   int64  \n",
      " 5   PhoneService      7043 non-null   str    \n",
      " 6   MultipleLines     7043 non-null   str    \n",
      " 7   InternetService   7043 non-null   str    \n",
      " 8   OnlineSecurity    7043 non-null   str    \n",
      " 9   OnlineBackup      7043 non-null   str    \n",
      " 10  DeviceProtection  7043 non-null   str    \n",
      " 11  TechSupport       7043 non-null   str    \n",
      " 12  StreamingTV       7043 non-null   str    \n",
      " 13  StreamingMovies   7043 non-null   str    \n",
      " 14  Contract          7043 non-null   str    \n",
      " 15  PaperlessBilling  7043 non-null   str    \n",
      " 16  PaymentMethod     7043 non-null   str    \n",
      " 17  MonthlyCharges    7043 non-null   float64\n",
      " 18  TotalCharges      7043 non-null   str    \n",
      " 19  Churn             7043 non-null   str    \n",
      "dtypes: float64(1), int64(2), str(17)\n",
      "memory usage: 1.7 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ecce6f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Analysis of Features of IBM Churn Dataset')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(nrows=2, ncols=3, figsize=(15,10))\n",
    "\n",
    "axes[0,0].pie(df['gender'].value_counts(), autopct='%1.1f',labels=['Male','Female'])\n",
    "axes[0,0].set_title('Gender Percentages')\n",
    "axes[0,1].pie(df['SeniorCitizen'].value_counts(), autopct='%1.1f', labels=['Non Senior Citizen','Senior Citizen'])\n",
    "axes[0,1].set_title('Senior or Non senior citizens')\n",
    "axes[0,2].pie(df['Partner'].value_counts(), autopct='%1.1f', labels=['No Partner', 'Has Partner'])\n",
    "axes[0,2].set_title('Partner Analysis')\n",
    "\n",
    "sns.histplot(df['MonthlyCharges'],bins=range(0,151,10),ax=axes[1,0])\n",
    "axes[1,0].set_xlabel('Monthly Plan Price')\n",
    "axes[1,0].set_ylabel('No. of users')\n",
    "axes[1,0].set_xticks(range(0,151,30))\n",
    "axes[1,0].set_title('Monthly Payment Plan Analysis')\n",
    "\n",
    "axes[1,1].pie(df['MultipleLines'].value_counts(), autopct='%1.1f', labels=['No','Yes','No Phone'])\n",
    "axes[1,1].set_title('Multiple Phone Lines')\n",
    "\n",
    "axes[1,2].pie(df['Churn'].value_counts(),autopct='%1.1f', labels=df['Churn'].value_counts().index)\n",
    "axes[1,2].set_title('Churn Percentages')\n",
    "\n",
    "fig.suptitle('Analysis of Features of IBM Churn Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ee2be83",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender']=df['gender'].map({'Female':0,'Male':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40fbc2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['has_partner']=df['Partner'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e37c5911",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Partner'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "818c7f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['has_dependents']=df['Dependents'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a84959c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Dependents'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f86cf466",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['has_phoneservice']=df['PhoneService'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0dcf33a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['PhoneService'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5c58243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultipleLines\n",
       "No                  3390\n",
       "Yes                 2971\n",
       "No phone service     682\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MultipleLines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "76f45726",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MultipleLines'] = df['MultipleLines'].replace('No phone service', 'No')\n",
    "df['has_multiplelines'] = df['MultipleLines'].map({'No': 0, 'Yes': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5419be8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['MultipleLines'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d9fa9b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InternetService\n",
       "Fiber optic    3096\n",
       "DSL            2421\n",
       "No             1526\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['InternetService'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ce104b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['internet_service_encoded'] = df['InternetService'].map({\n",
    "    'No': 0, \n",
    "    'DSL': 1, \n",
    "    'Fiber optic': 2\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dc60a052",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['InternetService'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ca4cd388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PaymentMethod\n",
       "Electronic check             2365\n",
       "Mailed check                 1612\n",
       "Bank transfer (automatic)    1544\n",
       "Credit card (automatic)      1522\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PaymentMethod'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "31dfa205",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_automatic']=df['PaymentMethod'].str.contains('automatic').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b926cd24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        No\n",
       "1       Yes\n",
       "2       Yes\n",
       "3       Yes\n",
       "4        No\n",
       "       ... \n",
       "7038    Yes\n",
       "7039     No\n",
       "7040    Yes\n",
       "7041     No\n",
       "7042    Yes\n",
       "Name: OnlineSecurity, Length: 7043, dtype: str"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OnlineSecurity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "57d85cfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                 No        Yes\n",
      "Product_Count                      \n",
      "0              78.593961  21.406039\n",
      "1              54.244306  45.755694\n",
      "2              64.181994  35.818006\n",
      "3              72.629696  27.370304\n",
      "4              77.699531  22.300469\n",
      "5              87.565674  12.434326\n",
      "6              94.718310   5.281690\n"
     ]
    }
   ],
   "source": [
    "# List of the 6 core internet add-on services\n",
    "addons = ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\n",
    "\n",
    "# Count how many times a user has 'Yes' across these columns\n",
    "df['Product_Count'] = (df[addons] == 'Yes').sum(axis=1)\n",
    "\n",
    "# Calculate the churn rate for users with 0, 1, 2... up to 6 services\n",
    "density_churn = df.groupby('Product_Count')['Churn'].value_counts(normalize=True).unstack() * 100\n",
    "print(density_churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e3a23ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Capture the high-risk zone (1 to 3 products)\n",
    "df['Is_High_Risk_Integration'] = df['Product_Count'].isin([1, 2, 3]).astype(int)\n",
    "\n",
    "# 2. Capture the ultimate ecosystem lock-in (5 or 6 products)\n",
    "df['Is_Fully_Integrated'] = (df['Product_Count'] >= 5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0166e7ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Is_High_Risk_Integration\n",
       "0    3926\n",
       "1    3117\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Is_High_Risk_Integration'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e3cc9bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Contract        Churn\n",
       "Month-to-month  No       0.572903\n",
       "                Yes      0.427097\n",
       "One year        No       0.887305\n",
       "                Yes      0.112695\n",
       "Two year        No       0.971681\n",
       "                Yes      0.028319\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Contract')['Churn'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b5d6fa60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n60% of the people prefer paperless bill, while 40% prefer the digital bill\\n'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PaperlessBilling'].value_counts(normalize=True)\n",
    "\n",
    "'''\n",
    "60% of the people prefer paperless bill, while 40% prefer the digital bill\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8cf1efad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nChurn is higher in those who prefer paperless billing, as they might search for replacements digitally\\nwhere as those who prefer the billing delivered, they might not be searching actively for the better subscription\\nleading to lower churns overall \\n'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('PaperlessBilling')['Churn'].value_counts(normalize=True)\n",
    "\n",
    "'''\n",
    "Churn is higher in those who prefer paperless billing, as they might search for replacements digitally\n",
    "where as those who prefer the billing delivered, they might not be searching actively for the better subscription\n",
    "leading to lower churns overall \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "66700edb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        29.85\n",
       "1        56.95\n",
       "2        53.85\n",
       "3        42.30\n",
       "4        70.70\n",
       "         ...  \n",
       "7038     84.80\n",
       "7039    103.20\n",
       "7040     29.60\n",
       "7041     74.40\n",
       "7042    105.65\n",
       "Name: MonthlyCharges, Length: 7043, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MonthlyCharges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ba969ec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn                       No        Yes\n",
      "OnlineSecurity                           \n",
      "No                   58.233276  41.766724\n",
      "No internet service  92.595020   7.404980\n",
      "Yes                  85.388806  14.611194\n",
      "Churn                No        Yes\n",
      "Mod_Security                      \n",
      "0             68.670382  31.329618\n",
      "1             85.388806  14.611194\n"
     ]
    }
   ],
   "source": [
    "# Check the raw, unmerged churn distribution for OnlineSecurity\n",
    "verification_table = df.groupby('OnlineSecurity')['Churn'].value_counts(normalize=True).unstack() * 100\n",
    "print(verification_table)\n",
    "\n",
    "df['Mod_Security']=df['OnlineSecurity'].map({'No':0,'Yes':1,'No internet service':0})\n",
    "df['Mod_Security'].value_counts()\n",
    "\n",
    "verification_table = df.groupby('Mod_Security')['Churn'].value_counts(normalize=True).unstack() * 100\n",
    "print(verification_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3e1753b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='MonthlyCharges'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['MonthlyCharges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "51721bb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='MonthlyCharges'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df['MonthlyCharges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "50806902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "488     52.55\n",
       "753     20.25\n",
       "936     80.85\n",
       "1082    25.75\n",
       "1340    56.05\n",
       "3331    19.85\n",
       "3826    25.35\n",
       "4380    20.00\n",
       "5218    19.70\n",
       "6670    73.35\n",
       "6754    61.90\n",
       "Name: MonthlyCharges, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['TotalCharges']==' ']['MonthlyCharges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "36daca50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "488     52.55\n",
       "753     20.25\n",
       "936     80.85\n",
       "1082    25.75\n",
       "1340    56.05\n",
       "3331    19.85\n",
       "3826    25.35\n",
       "4380    20.00\n",
       "5218    19.70\n",
       "6670    73.35\n",
       "6754    61.90\n",
       "Name: MonthlyCharges, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['TotalCharges']==' ']['MonthlyCharges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e2363933",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['TotalCharges'] == ' ', 'TotalCharges'] = df[df['TotalCharges'] == ' ']['MonthlyCharges'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8be222a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TotalCharges']=df['TotalCharges'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2ad77a47",
   "metadata": {},
   "outputs": [],
   "source": [
    "values=['OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies']\n",
    "for value in values:\n",
    "\n",
    "    df[value]=df[value].map({\"No\":-1,'Yes':1,\"No internet service\":0})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b47ede34",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc3cdbc0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml (3.13.9)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}