telco_customer_churn_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Telco Customer Churn Analysis using IV and WOE\n",
    "\n",
    "One of the most important metrics for a business to evaluate is customer churn. It measures number of customers who stopped using the company's product or service during a certain time frame. It's crucial to keep track of this metric since it costs more to acquire new customers than it does to retain existing customers. Being aware what factors influence customers' retention and how to distinguish clients who are more likely to churn is a key to success and enables businesses to take appropriate actions.\n",
    "\n",
    "Telco dataset contains data about customer churn in a telephone service company. It includes information about customers who left within the last month, services that each customer has signed up for, customer account information and demographic information.\n",
    "\n",
    "The goal of this analysis is to explore factors that influence customer churn and to define churner profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.api.types import is_numeric_dtype\n",
    "\n",
    "from Features import *\n",
    "from AttributeRelevance import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "pd.set_option('display.max_columns', None)\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Overview\n",
    "\n",
    "The dataset contains 7043 records representing company's customers. Churn column indicates who opt out of services within the last month. There are 20 explanatory variables - both categorical and continuous features.\n",
    "\n",
    "In the analysis variables of numeric type (int, float) were recognized as continuous features, while string variables were treated as categorical features.\n",
    "\n",
    "Services as: online security, tech support, online backup, streaming TV, streaming movies and device protection are dedicated only to customers with internet service. Because of that the analysis is divided into 2 parts:\n",
    "* the analysis of features referring to all customers (excluding the above features)\n",
    "* the analysis of customers who use the internet service (only the above features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/telco_churn.csv', na_values=[' '])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [c[0].lower() + c[1:] for c in df.columns]\n",
    "df['label'] = df['churn'].map({'Yes': 0, 'No': 1})\n",
    "df['seniorCitizen'] = df['seniorCitizen'].map({1: 'Yes', 0: 'No'})\n",
    "df.drop(['customerID', 'churn'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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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>gender</th>\n",
       "      <th>seniorCitizen</th>\n",
       "      <th>partner</th>\n",
       "      <th>dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>phoneService</th>\n",
       "      <th>multipleLines</th>\n",
       "      <th>internetService</th>\n",
       "      <th>onlineSecurity</th>\n",
       "      <th>onlineBackup</th>\n",
       "      <th>deviceProtection</th>\n",
       "      <th>techSupport</th>\n",
       "      <th>streamingTV</th>\n",
       "      <th>streamingMovies</th>\n",
       "      <th>contract</th>\n",
       "      <th>paperlessBilling</th>\n",
       "      <th>paymentMethod</th>\n",
       "      <th>monthlyCharges</th>\n",
       "      <th>totalCharges</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>71</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>76.05</td>\n",
       "      <td>5,436.45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3832</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>51</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>78.65</td>\n",
       "      <td>3,950.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4051</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>97.70</td>\n",
       "      <td>6,869.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6883</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.35</td>\n",
       "      <td>3,688.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>70.85</td>\n",
       "      <td>4,738.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender seniorCitizen partner dependents  tenure phoneService  \\\n",
       "296   Female            No     Yes        Yes      71          Yes   \n",
       "3832  Female            No     Yes         No      51          Yes   \n",
       "4051  Female            No     Yes         No      72          Yes   \n",
       "6883  Female            No      No        Yes      34          Yes   \n",
       "1490  Female           Yes     Yes         No      66          Yes   \n",
       "\n",
       "     multipleLines internetService onlineSecurity onlineBackup  \\\n",
       "296             No     Fiber optic             No           No   \n",
       "3832           Yes             DSL             No          Yes   \n",
       "4051           Yes     Fiber optic             No          Yes   \n",
       "6883            No     Fiber optic            Yes          Yes   \n",
       "1490           Yes             DSL             No          Yes   \n",
       "\n",
       "     deviceProtection techSupport streamingTV streamingMovies        contract  \\\n",
       "296               Yes          No          No              No        Two year   \n",
       "3832               No         Yes         Yes             Yes        One year   \n",
       "4051               No          No         Yes             Yes        Two year   \n",
       "6883              Yes          No         Yes             Yes  Month-to-month   \n",
       "1490              Yes          No         Yes              No        One year   \n",
       "\n",
       "     paperlessBilling              paymentMethod  monthlyCharges  \\\n",
       "296                No           Electronic check           76.05   \n",
       "3832              Yes    Credit card (automatic)           78.65   \n",
       "4051              Yes    Credit card (automatic)           97.70   \n",
       "6883              Yes  Bank transfer (automatic)          105.35   \n",
       "1490              Yes  Bank transfer (automatic)           70.85   \n",
       "\n",
       "      totalCharges  label  \n",
       "296       5,436.45      1  \n",
       "3832      3,950.85      1  \n",
       "4051      6,869.70      1  \n",
       "6883      3,688.60      1  \n",
       "1490      4,738.85      1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feats_dict = {}\n",
    "black_list = ['onlineSecurity', 'techSupport', 'onlineBackup', 'streamingTV', 'streamingMovies', 'deviceProtection']\n",
    "\n",
    "for col in [c for c in df.columns if c not in black_list and c != 'label']:\n",
    "    if is_numeric_dtype(df[col]):\n",
    "        feats_dict[col] = ContinuousFeature(df, col)\n",
    "    else:\n",
    "        feats_dict[col] = CategoricalFeature(df, col)\n",
    "\n",
    "feats = list(feats_dict.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Information Value and Weight of Evidence\n",
    "\n",
    "Attribute relevance analysis evaluates attribute values' impact on target variable. It helps in recognizing the most important variables which have the greatest impact on target variable and understanding relations and logic between the most important predictors and the target variable.\n",
    "\n",
    "Information value (IV) and weight of evidence (WOE) are simple and powerful techniques of conducting attribute relevance analysis. They provide a great framework for exploratory analysis and have been used extensively in the credit risk world for several decades.\n",
    "\n",
    "**Information value**\n",
    ">Information Value provides a measure of how well a variable X is able to distinguish between a binary response (e.g. \"good\" versus \"bad\") in some target variable Y. The idea is if a variable X has a low Information Value, it may not do a sufficient job of classifying the target variable, and hence is removed as an explanatory variable.\n",
    "\n",
    "**Weight of evidence**\n",
    ">The weight of evidence tells the predictive power of an independent variable in relation to the dependent variable. Since it evolved from credit scoring world, it is generally described as a measure of the separation of good and bad customers. \"Bad Customers\" refers to the customers who defaulted on a loan. and \"Good Customers\" refers to the customers who paid back loan.\n",
    "\n",
    "**IV values interpretation**\n",
    "\n",
    "| Information value        | Predictive power           |\n",
    "| ------------- |:-------------:|\n",
    "| < 0.02      | useless for prediction |\n",
    "| 0.02 - 0.1      | weak predictor      |\n",
    "| 0.1 - 0.3 | medium predictor      |\n",
    "| 0.3 - 0.5 | strong predictor      |\n",
    "| > 0.5 | suspicious or too good      |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv = IV()\n",
    "s = StatsSignificance()\n",
    "\n",
    "ar = AttributeRelevance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Information value analysis\n",
    "\n",
    "Customers who churned are labeled with value 0 ('bad' customers) and those who retained with value 1 ('good' customers). It implies that positive WOE means higher probability of retention and negative WOE - higher probability of churn.\n",
    "\n",
    "As mentioned before, the analysis was divided into 2 parts: analysis of all customers and analysis of only customers with internet service. It reveals some interesting relations:\n",
    "\n",
    "**Suspicious predictors**\n",
    "\n",
    "This group contains: contract, tenure and internet service. It shows that relation between values of those features and churn is very strong - so strong that it should be examined carefully. It may suggest that there is some error in the data or that explanatory and explained variables are not independent.\n",
    "\n",
    "Analysis of 'contract' feature shows that WOE for month-to-month contracts is negative while it's very high for two year contracts. It can be explained by the fact that customers with shorter contract have many more 'churn moments' (every month they need to renew their service) than customers with long-term contracts, thus their churn rate is much higher.\n",
    "\n",
    "**Strong predictors**\n",
    "\n",
    "Fortunately, this group of features is quite big. It contains: payment method, total charges, online security and tech support. They show strong relation between value of the feature and churn. They can be successfully used to predict churn of the customer.\n",
    "\n",
    "Analysis of total charges shows that customers who have already spent more than \\\\$678 are less likely to churn. To fully understand this relation it's good to notice that total charges is the resultant of monthly charge and the tenure. Monthly charges variable tells us that users who pay \\\\$18 - \\\\$50 monthly are less likely to churn and the risk of churn increases with the increase of monthly fee. In this case, although total charges feature has higher IV, it seems more actionable to use monthly charges and tenure variables separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iv</th>\n",
       "      <th>iv_interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>contract</th>\n",
       "      <td>1.24</td>\n",
       "      <td>suspicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>0.78</td>\n",
       "      <td>suspicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internetService</th>\n",
       "      <td>0.62</td>\n",
       "      <td>suspicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paymentMethod</th>\n",
       "      <td>0.46</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalCharges</th>\n",
       "      <td>0.32</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paperlessBilling</th>\n",
       "      <td>0.20</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthlyCharges</th>\n",
       "      <td>0.17</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dependents</th>\n",
       "      <td>0.16</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partner</th>\n",
       "      <td>0.12</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seniorCitizen</th>\n",
       "      <td>0.11</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multipleLines</th>\n",
       "      <td>0.01</td>\n",
       "      <td>useless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phoneService</th>\n",
       "      <td>0.00</td>\n",
       "      <td>useless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.00</td>\n",
       "      <td>useless</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   iv iv_interpretation\n",
       "contract         1.24        suspicious\n",
       "tenure           0.78        suspicious\n",
       "internetService  0.62        suspicious\n",
       "paymentMethod    0.46            strong\n",
       "totalCharges     0.32            strong\n",
       "paperlessBilling 0.20            medium\n",
       "monthlyCharges   0.17            medium\n",
       "dependents       0.16            medium\n",
       "partner          0.12            medium\n",
       "seniorCitizen    0.11            medium\n",
       "multipleLines    0.01           useless\n",
       "phoneService     0.00           useless\n",
       "gender           0.00           useless"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ar.analyze(feats, iv, interpretation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ar.draw_iv(feats, iv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ar.draw_woe_extremes(feats, iv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "feats_dict_black = {}\n",
    "df_black = df[df['internetService']!='No']\n",
    "for b in black_list:\n",
    "    feats_dict_black[b] = CategoricalFeature(df_black, b)\n",
    "\n",
    "feats_black = list(feats_dict_black.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iv</th>\n",
       "      <th>iv_interpretation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>onlineSecurity</th>\n",
       "      <td>0.42</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>techSupport</th>\n",
       "      <td>0.39</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onlineBackup</th>\n",
       "      <td>0.18</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deviceProtection</th>\n",
       "      <td>0.15</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>streamingMovies</th>\n",
       "      <td>0.01</td>\n",
       "      <td>useless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>streamingTV</th>\n",
       "      <td>0.01</td>\n",
       "      <td>useless</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   iv iv_interpretation\n",
       "onlineSecurity   0.42            strong\n",
       "techSupport      0.39            strong\n",
       "onlineBackup     0.18            medium\n",
       "deviceProtection 0.15            medium\n",
       "streamingMovies  0.01           useless\n",
       "streamingTV      0.01           useless"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ar.analyze(feats_black, iv, interpretation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ar.draw_iv(feats_black, iv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ar.draw_woe_extremes(feats_black, iv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight of evidence analysis\n",
    "\n",
    "Detailed examination of WOE provides interesting insights and shows some possible issues in the services offered by the company.\n",
    "\n",
    "**Tenure vs total charges**\n",
    "\n",
    "As mentioned before, those two features show very similar behaviour - and it is totally logical since total charges = tenure * monthly charges. It's good to have it in mind and focus more on monthly charges variable instead of total charges.\n",
    "\n",
    "**Internet service**\n",
    "\n",
    "Among the customers there are those who use DSL network, Fiber optic network or don't use the internet service. It's interesting to see that customers using Fiber optic are much more likely to churn - it may suggest some problems with the service.\n",
    "\n",
    "**Payment method**\n",
    "\n",
    "The company offers four different payment methods: bank transfer, credit card, electronic check and mailed check. The analysis shows that users who pay with electronic check are more likely to churn. In order to understand this relation it's good to check which payment methods are recurring and what are the possible issues that customers have while using electronic check.\n",
    "\n",
    "**Additional Internet services**\n",
    "\n",
    "The analysis of customers using Internet service shows that those who use additional services like: online security, tech support and online backup are more likely to retain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_draw = ['contract', 'tenure', 'internetService', 'paymentMethod', 'totalCharges', 'monthlyCharges', 'charges']\n",
    "feats_draw = [v for k, v in feats_dict.items() if k in f_draw]\n",
    "ar.draw_woe_multiplot(feats_draw, iv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_black_draw = ['onlineSecurity', 'techSupport', 'onlineBackup']\n",
    "feats_black_draw = [v for k, v in feats_dict_black.items() if k in f_black_draw]\n",
    "ar.draw_woe_multiplot(feats_black_draw, iv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Churner profile\n",
    "\n",
    "Let's answer the question: which of the company's customers are thinking about resigning from the services?\n",
    "\n",
    "According to the analysis this is the typical churners profile:\n",
    "* **Tenure:** has been using the services for less than 3 months\n",
    "* **Contract:** has month-to-month contract\n",
    "* **Monthly charges:** pays more than \\\\$50 monthly\n",
    "* **Internet service:** Fiber optic\n",
    "* **Payment method:** electronic check\n",
    "* **Additional Internet services:** doesn't use online security and tech support"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Action points\n",
    "\n",
    "Sooo... the analysis revealed interesting relations and now, what's next?\n",
    "\n",
    "The first step is to make Customer Support Team, Product Owners, Marketing Managers familiar with the results of the analysis. There are some actions that should be taken by them in order to increase the customers' retention and speed up the growth of the company.\n",
    "\n",
    "There is a list of possible action points:\n",
    "* maintain the personal contact with customers with more expensive plans to make sure that they are satisfied with the service\n",
    "* allow only recurring payment methods for month-to-month plan to make sure that customers don't forget about the payment\n",
    "* send email notifications about upcoming payments\n",
    "* in the first 3 months of service offer special deal for customers - if they pay in advance, they will get a discount\n",
    "* evaluate Fiber Optic network and electronic check payment method - investigate what is the reason of higher churn and resolve the issue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read more\n",
    "\n",
    "- What about statistical significance? Check [**Information Value vs Statistical Significance Analysis**](https://github.com/klaudia-nazarko/iv-and-woe-python/blob/master/statistical_significance_of_information_value.ipynb).\n",
    "- In order to see how Information Value module is used, check [**IV and WOE in Python**](https://github.com/klaudia-nazarko/iv-and-woe-python/blob/master/iv_and_woe_in_python.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset info\n",
    "\n",
    "Source: [Kaggle](https://www.kaggle.com/blastchar/telco-customer-churn)\n",
    "\n",
    "* customerID - Customer ID\n",
    "* gender - Whether the customer is a male or a female\n",
    "* seniorCitizen - Whether the customer is a senior citizen or not (1, 0)\n",
    "* partner - Whether the customer has a partner or not (Yes, No)\n",
    "* dependents - Whether the customer has dependents or not (Yes, No)\n",
    "* tenure - Number of months the customer has stayed with the company\n",
    "* phoneService - Whether the customer has a phone service or not (Yes, No)\n",
    "* multipleLines - Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
    "* internetService - Customer’s internet service provider (DSL, Fiber optic, No)\n",
    "* onlineSecurity - Whether the customer has online security or not (Yes, No, No internet service)\n",
    "* onlineBackup - Whether the customer has online backup or not (Yes, No, No internet service)\n",
    "* deviceProtection - Whether the customer has device protection or not (Yes, No, No internet service)\n",
    "* techSupport - Whether the customer has tech support or not (Yes, No, No internet service)\n",
    "* streamingTV - Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
    "* streamingMovies - Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
    "* contract - The contract term of the customer (Month-to-month, One year, Two year)\n",
    "* paperlessBilling - Whether the customer has paperless billing or not (Yes, No)\n",
    "* paymentMethod - The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
    "* monthlyCharges - The amount charged to the customer monthly\n",
    "* totalCharges - The total amount charged to the customer\n",
    "* churn - Whether the customer churned or not (Yes or No)"
   ]
  },
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    "### Reference\n",
    "\n",
    "1. https://stats.stackexchange.com/questions/93170/why-do-we-calculate-information-value\n",
    "2. https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html#What-is-Information-Value-IV-\n",
    "3. https://towardsdatascience.com/attribute-relevance-analysis-in-python-iv-and-woe-b5651443fc04\n",
    "4. https://www.kaggle.com/pavansanagapati/weight-of-evidence-woe-information-value-iv\n",
    "5. https://medium.com/@sundarstyles89/weight-of-evidence-and-information-value-using-python-6f05072e83eb\n",
    "6. https://multithreaded.stitchfix.com/blog/2015/08/13/weight-of-evidence/"
   ]
  }
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