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index.pynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predicting Customer Attrition with Survival Analysis\n",
"------------\n",
"#### <b>1. Objective <b>\n",
"* Customer Attrition or Churn refers to a decision made by the customer about ending the business relationship. \n",
"* Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%.\n",
"* Churn is undesirable and it is the firm's responsibility to understand why customers are churning and prevent that \n",
"--------\n",
"#### <b>2. Approach <b>\n",
"* Usually when it comes to Predicting customer churn, we look at classification techniques such as Logistic regression but the problem with that approach is that it doesn't take time into consideration\n",
"* So we will be using a tool from an unlikely place - Survival analysis. It was first developed by actuaries & medical professionals to predict survival rates\n",
"* Here we will be defining -\n",
" - Birth event: For eg, a customer subsribing to your product or service\n",
" - Death event: For eg, a customer ending the relationship with the company\n",
" \n",
"* Component that makes SA superior to other models is its ability to deal with \"censorship\" in data\n",
"* Censorship basically refers to losing track of a customer or the customer \"not dying\" before the end of the observation period\n",
"* This data is considered \"censored\" since everyone dies eventually, we are just missing the data\n",
"* Similarly, we would expect to lose all customers eventually. Just because we haven't observed them cancelling their subscription doesn't mean they never will.\n",
"\n",
"We have all come across \"Teleco Customer Churn\" dataset which we usually use to Predict customer churn by binary classification method. <br>\n",
"Now, we will use the same dataset and apply our newly learnt Survival analysis skills.\n",
"\n",
"---------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### <b> 3. Data Preparation <b>\n",
"\n",
"<b> 3.1 - Import necessary libs and read data "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customerID</th>\n",
" <th>gender</th>\n",
" <th>SeniorCitizen</th>\n",
" <th>Partner</th>\n",
" <th>Dependents</th>\n",
" <th>tenure</th>\n",
" <th>PhoneService</th>\n",
" <th>MultipleLines</th>\n",
" <th>InternetService</th>\n",
" <th>OnlineSecurity</th>\n",
" <th>...</th>\n",
" <th>DeviceProtection</th>\n",
" <th>TechSupport</th>\n",
" <th>StreamingTV</th>\n",
" <th>StreamingMovies</th>\n",
" <th>Contract</th>\n",
" <th>PaperlessBilling</th>\n",
" <th>PaymentMethod</th>\n",
" <th>MonthlyCharges</th>\n",
" <th>TotalCharges</th>\n",
" <th>Churn</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7590-VHVEG</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>No</td>\n",
" <td>No phone service</td>\n",
" <td>DSL</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Month-to-month</td>\n",
" <td>Yes</td>\n",
" <td>Electronic check</td>\n",
" <td>29.85</td>\n",
" <td>29.85</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5575-GNVDE</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>34</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>DSL</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>One year</td>\n",
" <td>No</td>\n",
" <td>Mailed check</td>\n",
" <td>56.95</td>\n",
" <td>1889.5</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3668-QPYBK</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>DSL</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Month-to-month</td>\n",
" <td>Yes</td>\n",
" <td>Mailed check</td>\n",
" <td>53.85</td>\n",
" <td>108.15</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" customerID gender SeniorCitizen Partner Dependents tenure PhoneService \\\n",
"0 7590-VHVEG Female 0 Yes No 1 No \n",
"1 5575-GNVDE Male 0 No No 34 Yes \n",
"2 3668-QPYBK Male 0 No No 2 Yes \n",
"\n",
" MultipleLines InternetService OnlineSecurity ... DeviceProtection \\\n",
"0 No phone service DSL No ... No \n",
"1 No DSL Yes ... Yes \n",
"2 No DSL Yes ... No \n",
"\n",
" TechSupport StreamingTV StreamingMovies Contract PaperlessBilling \\\n",
"0 No No No Month-to-month Yes \n",
"1 No No No One year No \n",
"2 No No No Month-to-month Yes \n",
"\n",
" PaymentMethod MonthlyCharges TotalCharges Churn \n",
"0 Electronic check 29.85 29.85 No \n",
"1 Mailed check 56.95 1889.5 No \n",
"2 Mailed check 53.85 108.15 Yes \n",
"\n",
"[3 rows x 21 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import lifelines\n",
"\n",
"churn_data = pd.read_csv('../data/Telco-Customer-Attrition-Data.csv')\n",
"churn_data.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>3.2 - Cleaning the dataset <b>\n",
" \n",
"For each customer, we will need to calculate the following\n",
"* Tenure: How long has the customer been with the firm?\n",
"* Churn: Has the customer left the firm?"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customerID</th>\n",
" <th>gender</th>\n",
" <th>SeniorCitizen</th>\n",
" <th>Partner</th>\n",
" <th>Dependents</th>\n",
" <th>tenure</th>\n",
" <th>PhoneService</th>\n",
" <th>MultipleLines</th>\n",
" <th>InternetService</th>\n",
" <th>OnlineSecurity</th>\n",
" <th>...</th>\n",
" <th>DeviceProtection</th>\n",
" <th>TechSupport</th>\n",
" <th>StreamingTV</th>\n",
" <th>StreamingMovies</th>\n",
" <th>Contract</th>\n",
" <th>PaperlessBilling</th>\n",
" <th>PaymentMethod</th>\n",
" <th>MonthlyCharges</th>\n",
" <th>TotalCharges</th>\n",
" <th>Churn</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7590-VHVEG</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" <td>No phone service</td>\n",
" <td>DSL</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Month-to-month</td>\n",
" <td>Yes</td>\n",
" <td>Electronic check</td>\n",
" <td>29.85</td>\n",
" <td>29.85</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5575-GNVDE</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>34.0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>DSL</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>One year</td>\n",
" <td>No</td>\n",
" <td>Mailed check</td>\n",
" <td>56.95</td>\n",
" <td>1889.5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3668-QPYBK</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2.0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>DSL</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Month-to-month</td>\n",
" <td>Yes</td>\n",
" <td>Mailed check</td>\n",
" <td>53.85</td>\n",
" <td>108.15</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7795-CFOCW</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>45.0</td>\n",
" <td>No</td>\n",
" <td>No phone service</td>\n",
" <td>DSL</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>One year</td>\n",
" <td>No</td>\n",
" <td>Bank transfer (automatic)</td>\n",
" <td>42.30</td>\n",
" <td>1840.75</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9237-HQITU</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2.0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Fiber optic</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Month-to-month</td>\n",
" <td>Yes</td>\n",
" <td>Electronic check</td>\n",
" <td>70.70</td>\n",
" <td>151.65</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" customerID gender SeniorCitizen Partner Dependents tenure PhoneService \\\n",
"0 7590-VHVEG Female 0 Yes No 1.0 No \n",
"1 5575-GNVDE Male 0 No No 34.0 Yes \n",
"2 3668-QPYBK Male 0 No No 2.0 Yes \n",
"3 7795-CFOCW Male 0 No No 45.0 No \n",
"4 9237-HQITU Female 0 No No 2.0 Yes \n",
"\n",
" MultipleLines InternetService OnlineSecurity ... DeviceProtection \\\n",
"0 No phone service DSL No ... No \n",
"1 No DSL Yes ... Yes \n",
"2 No DSL Yes ... No \n",
"3 No phone service DSL Yes ... Yes \n",
"4 No Fiber optic No ... No \n",
"\n",
" TechSupport StreamingTV StreamingMovies Contract PaperlessBilling \\\n",
"0 No No No Month-to-month Yes \n",
"1 No No No One year No \n",
"2 No No No Month-to-month Yes \n",
"3 Yes No No One year No \n",
"4 No No No Month-to-month Yes \n",
"\n",
" PaymentMethod MonthlyCharges TotalCharges Churn \n",
"0 Electronic check 29.85 29.85 False \n",
"1 Mailed check 56.95 1889.5 False \n",
"2 Mailed check 53.85 108.15 True \n",
"3 Bank transfer (automatic) 42.30 1840.75 False \n",
"4 Electronic check 70.70 151.65 True \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Transform tenure and churn features\n",
"churn_data['tenure'] = churn_data['tenure'].astype(float)\n",
"churn_data['Churn'] = churn_data['Churn'] == 'Yes'\n",
"churn_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: Many customers in our data have not left yet. Here we deal with censorship as discussed earlier."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---------\n",
"\n",
"#### <b> 4. Exploratory Data Analysis\n",
"\n",
"Let's look at survival rate for the average customer using a Kaplan-Meier survival curve. We will fit a KM survival curve to our dataset & plot our survival curve with confidence interval.\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# fitting kmf to churn data\n",
"t = churn_data['tenure'].values\n",
"churn = churn_data['Churn'].values\n",
"kmf = lifelines.KaplanMeierFitter()\n",
"kmf.fit(t, event_observed=churn, label='Estimate for Average Customer')\n",
"\n",
"# plotting kmf curve\n",
"fig, ax = plt.subplots(figsize=(10,7))\n",
"kmf.plot(ax=ax)\n",
"ax.set_title('Kaplan-Meier Survival Curve - All Customers')\n",
"ax.set_xlabel('Customer Tenure (Months)')\n",
"ax.set_ylabel('Customer Survival Chance (%)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: Survival curve is cumulative. After 20 months, the chance of a customer not canceling service is just above 80%\n",
"\n",
"Insights:\n",
"* We can see that even after 70 months, the company is able to retain 60% of its customers\n",
"* Churn is relatively low when it comes to Telcos\n",
"\n",
"Now we will examine the affect of different features on the survival rate. <br>\n",
"For this we will use \"Cox Proportional Hazards Model\", we can also think of this as a Survival Regression Model\n",
"\n",
"* Hazards can be something that would increase or decrease the chances of survival. In our case, a hazard can be type of contract. \n",
"* Customer with multi-year contracts would probably cancel less frequently than those with monthly contracts\n",
"* Restriction - Here the model assumes a constant ratio of hazards over time across groups.\n",
"* Lifelines offers a check_assumptions method for CoxPHFitter Object\n",
"\n",
"<b> 4.1 - Data cleaning, encoding categorical variables (k-1 dummies)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"features_to_drop = ['customerID', 'gender', 'PhoneService', 'InternetService']\n",
"\n",
"# engineering numeric columns for Cox Proportional Hazard estimation\n",
"churn_hazard = churn_data.drop(features_to_drop, axis=1).copy()\n",
"\n",
"# convert some stuff to integers\n",
"churn_hazard['TotalCharges'] = pd.to_numeric(churn_hazard['TotalCharges'], errors='coerce')\n",
"churn_hazard['TotalCharges'].fillna(0, inplace=True)\n",
"\n",
"# a lot of variables are encoded as 'Yes' or 'No', lets get these all done at once\n",
"binary_features = ['Partner', 'Dependents', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', \n",
" 'StreamingTV','StreamingMovies', 'PaperlessBilling']\n",
"for feat in binary_features:\n",
" churn_hazard[feat] = churn_hazard[feat] == 'Yes'\n",
" \n",
"# let's one hot encode the remaining categorical features\n",
"ohe_features = ['MultipleLines', 'Contract', 'PaymentMethod']\n",
"churn_hazard = pd.get_dummies(churn_hazard, \n",
" drop_first=True,\n",
" columns=ohe_features)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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"<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",
" text-align: right;\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>SeniorCitizen</th>\n",
" <th>Partner</th>\n",
" <th>Dependents</th>\n",
" <th>tenure</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>...</th>\n",
" <th>MonthlyCharges</th>\n",
" <th>TotalCharges</th>\n",
" <th>Churn</th>\n",
" <th>MultipleLines_No phone service</th>\n",
" <th>MultipleLines_Yes</th>\n",
" <th>Contract_One year</th>\n",
" <th>Contract_Two year</th>\n",
" <th>PaymentMethod_Credit card (automatic)</th>\n",
" <th>PaymentMethod_Electronic check</th>\n",
" <th>PaymentMethod_Mailed check</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>29.85</td>\n",
" <td>29.85</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>34.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>56.95</td>\n",
" <td>1889.50</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>2.0</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>53.85</td>\n",
" <td>108.15</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>45.0</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>42.30</td>\n",
" <td>1840.75</td>\n",
" <td>False</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>2.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>70.70</td>\n",
" <td>151.65</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" SeniorCitizen Partner Dependents tenure OnlineSecurity OnlineBackup \\\n",
"0 0 True False 1.0 False True \n",
"1 0 False False 34.0 True False \n",
"2 0 False False 2.0 True True \n",
"3 0 False False 45.0 True False \n",
"4 0 False False 2.0 False False \n",
"\n",
" DeviceProtection TechSupport StreamingTV StreamingMovies \\\n",
"0 False False False False \n",
"1 True False False False \n",
"2 False False False False \n",
"3 True True False False \n",
"4 False False False False \n",
"\n",
" ... MonthlyCharges TotalCharges Churn \\\n",
"0 ... 29.85 29.85 False \n",
"1 ... 56.95 1889.50 False \n",
"2 ... 53.85 108.15 True \n",
"3 ... 42.30 1840.75 False \n",
"4 ... 70.70 151.65 True \n",
"\n",
" MultipleLines_No phone service MultipleLines_Yes Contract_One year \\\n",
"0 1 0 0 \n",
"1 0 0 1 \n",
"2 0 0 0 \n",
"3 1 0 1 \n",
"4 0 0 0 \n",
"\n",
" Contract_Two year PaymentMethod_Credit card (automatic) \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" PaymentMethod_Electronic check PaymentMethod_Mailed check \n",
"0 1 0 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 0 \n",
"4 1 0 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"churn_hazard.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---------\n",
"\n",
"#### <b> 5. Modeling"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
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" }\n",
"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>model</th>\n",
" <td>lifelines.CoxPHFitter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>duration col</th>\n",
" <td>'tenure'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>event col</th>\n",
" <td>'Churn'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>baseline estimation</th>\n",
" <td>breslow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of observations</th>\n",
" <td>7043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of events observed</th>\n",
" <td>1869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial log-likelihood</th>\n",
" <td>-12688.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time fit was run</th>\n",
" <td>2021-06-15 09:14:33 UTC</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th style=\"min-width: 12;\"></th>\n",
" <th style=\"min-width: 12;\">coef</th>\n",
" <th style=\"min-width: 12;\">exp(coef)</th>\n",
" <th style=\"min-width: 12;\">se(coef)</th>\n",
" <th style=\"min-width: 12;\">coef lower 95%</th>\n",
" <th style=\"min-width: 12;\">coef upper 95%</th>\n",
" <th style=\"min-width: 12;\">exp(coef) lower 95%</th>\n",
" <th style=\"min-width: 12;\">exp(coef) upper 95%</th>\n",
" <th style=\"min-width: 12;\">z</th>\n",
" <th style=\"min-width: 12;\">p</th>\n",
" <th style=\"min-width: 12;\">-log2(p)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">SeniorCitizen</th>\n",
" <td>0.03</td>\n",
" <td>1.03</td>\n",
" <td>0.06</td>\n",
" <td>-0.08</td>\n",
" <td>0.14</td>\n",
" <td>0.93</td>\n",
" <td>1.16</td>\n",
" <td>0.60</td>\n",
" <td>0.55</td>\n",
" <td>0.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">Partner</th>\n",
" <td>-0.19</td>\n",
" <td>0.82</td>\n",
" <td>0.06</td>\n",
" <td>-0.30</td>\n",
" <td>-0.09</td>\n",
" <td>0.74</td>\n",
" <td>0.92</td>\n",
" <td>-3.52</td>\n",
" <td>&lt;0.005</td>\n",
" <td>11.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">Dependents</th>\n",
" <td>-0.10</td>\n",
" <td>0.91</td>\n",
" <td>0.07</td>\n",
" <td>-0.23</td>\n",
" <td>0.04</td>\n",
" <td>0.79</td>\n",
" <td>1.04</td>\n",
" <td>-1.39</td>\n",
" <td>0.17</td>\n",
" <td>2.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">OnlineSecurity</th>\n",
" <td>-0.38</td>\n",
" <td>0.68</td>\n",
" <td>0.07</td>\n",
" <td>-0.51</td>\n",
" <td>-0.25</td>\n",
" <td>0.60</td>\n",
" <td>0.78</td>\n",
" <td>-5.65</td>\n",
" <td>&lt;0.005</td>\n",
" <td>25.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">OnlineBackup</th>\n",
" <td>-0.29</td>\n",
" <td>0.75</td>\n",
" <td>0.06</td>\n",
" <td>-0.40</td>\n",
" <td>-0.18</td>\n",
" <td>0.67</td>\n",
" <td>0.83</td>\n",
" <td>-5.22</td>\n",
" <td>&lt;0.005</td>\n",
" <td>22.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">DeviceProtection</th>\n",
" <td>-0.16</td>\n",
" <td>0.85</td>\n",
" <td>0.06</td>\n",
" <td>-0.27</td>\n",
" <td>-0.05</td>\n",
" <td>0.76</td>\n",
" <td>0.95</td>\n",
" <td>-2.85</td>\n",
" <td>&lt;0.005</td>\n",
" <td>7.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">TechSupport</th>\n",
" <td>-0.28</td>\n",
" <td>0.76</td>\n",
" <td>0.07</td>\n",
" <td>-0.41</td>\n",
" <td>-0.15</td>\n",
" <td>0.67</td>\n",
" <td>0.86</td>\n",
" <td>-4.19</td>\n",
" <td>&lt;0.005</td>\n",
" <td>15.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">StreamingTV</th>\n",
" <td>-0.27</td>\n",
" <td>0.77</td>\n",
" <td>0.06</td>\n",
" <td>-0.38</td>\n",
" <td>-0.15</td>\n",
" <td>0.68</td>\n",
" <td>0.86</td>\n",
" <td>-4.46</td>\n",
" <td>&lt;0.005</td>\n",
" <td>16.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">StreamingMovies</th>\n",
" <td>-0.26</td>\n",
" <td>0.77</td>\n",
" <td>0.06</td>\n",
" <td>-0.38</td>\n",
" <td>-0.14</td>\n",
" <td>0.69</td>\n",
" <td>0.87</td>\n",
" <td>-4.36</td>\n",
" <td>&lt;0.005</td>\n",
" <td>16.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">PaperlessBilling</th>\n",
" <td>0.16</td>\n",
" <td>1.17</td>\n",
" <td>0.06</td>\n",
" <td>0.05</td>\n",
" <td>0.27</td>\n",
" <td>1.05</td>\n",
" <td>1.31</td>\n",
" <td>2.79</td>\n",
" <td>0.01</td>\n",
" <td>7.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">MonthlyCharges</th>\n",
" <td>0.07</td>\n",
" <td>1.07</td>\n",
" <td>0.00</td>\n",
" <td>0.06</td>\n",
" <td>0.07</td>\n",
" <td>1.06</td>\n",
" <td>1.07</td>\n",
" <td>26.59</td>\n",
" <td>&lt;0.005</td>\n",
" <td>515.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">TotalCharges</th>\n",
" <td>-0.00</td>\n",
" <td>1.00</td>\n",
" <td>0.00</td>\n",
" <td>-0.00</td>\n",
" <td>-0.00</td>\n",
" <td>1.00</td>\n",
" <td>1.00</td>\n",
" <td>-40.10</td>\n",
" <td>&lt;0.005</td>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">MultipleLines_No phone service</th>\n",
" <td>0.64</td>\n",
" <td>1.89</td>\n",
" <td>0.12</td>\n",
" <td>0.41</td>\n",
" <td>0.87</td>\n",
" <td>1.51</td>\n",
" <td>2.38</td>\n",
" <td>5.50</td>\n",
" <td>&lt;0.005</td>\n",
" <td>24.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">MultipleLines_Yes</th>\n",
" <td>-0.20</td>\n",
" <td>0.82</td>\n",
" <td>0.05</td>\n",
" <td>-0.30</td>\n",
" <td>-0.09</td>\n",
" <td>0.74</td>\n",
" <td>0.91</td>\n",
" <td>-3.68</td>\n",
" <td>&lt;0.005</td>\n",
" <td>12.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">Contract_One year</th>\n",
" <td>-1.40</td>\n",
" <td>0.25</td>\n",
" <td>0.10</td>\n",
" <td>-1.60</td>\n",
" <td>-1.20</td>\n",
" <td>0.20</td>\n",
" <td>0.30</td>\n",
" <td>-13.78</td>\n",
" <td>&lt;0.005</td>\n",
" <td>141.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">Contract_Two year</th>\n",
" <td>-4.05</td>\n",
" <td>0.02</td>\n",
" <td>0.20</td>\n",
" <td>-4.43</td>\n",
" <td>-3.66</td>\n",
" <td>0.01</td>\n",
" <td>0.03</td>\n",
" <td>-20.74</td>\n",
" <td>&lt;0.005</td>\n",
" <td>314.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">PaymentMethod_Credit card (automatic)</th>\n",
" <td>-0.01</td>\n",
" <td>0.99</td>\n",
" <td>0.09</td>\n",
" <td>-0.18</td>\n",
" <td>0.17</td>\n",
" <td>0.83</td>\n",
" <td>1.19</td>\n",
" <td>-0.06</td>\n",
" <td>0.95</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">PaymentMethod_Electronic check</th>\n",
" <td>0.38</td>\n",
" <td>1.46</td>\n",
" <td>0.07</td>\n",
" <td>0.24</td>\n",
" <td>0.52</td>\n",
" <td>1.27</td>\n",
" <td>1.69</td>\n",
" <td>5.20</td>\n",
" <td>&lt;0.005</td>\n",
" <td>22.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th style=\"min-width: 12;\">PaymentMethod_Mailed check</th>\n",
" <td>0.52</td>\n",
" <td>1.68</td>\n",
" <td>0.09</td>\n",
" <td>0.35</td>\n",
" <td>0.69</td>\n",
" <td>1.42</td>\n",
" <td>1.99</td>\n",
" <td>5.96</td>\n",
" <td>&lt;0.005</td>\n",
" <td>28.57</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><br><div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>Concordance</th>\n",
" <td>0.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Partial AIC</th>\n",
" <td>25415.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>log-likelihood ratio test</th>\n",
" <td>5928.67 on 19 df</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-log2(p) of ll-ratio test</th>\n",
" <td>inf</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/latex": [
"\\begin{tabular}{lrrrrrrrrrr}\n",
"\\toprule\n",
"{} & coef & exp(coef) & se(coef) & coef lower 95\\% & coef upper 95\\% & exp(coef) lower 95\\% & exp(coef) upper 95\\% & z & p & -log2(p) \\\\\n",
"covariate & & & & & & & & & & \\\\\n",
"\\midrule\n",
"SeniorCitizen & 0.03 & 1.03 & 0.06 & -0.08 & 0.14 & 0.93 & 1.16 & 0.60 & 5.487665e-01 & 0.87 \\\\\n",
"Partner & -0.19 & 0.82 & 0.06 & -0.30 & -0.09 & 0.74 & 0.92 & -3.52 & 4.241057e-04 & 11.20 \\\\\n",
"Dependents & -0.10 & 0.91 & 0.07 & -0.23 & 0.04 & 0.79 & 1.04 & -1.39 & 1.652144e-01 & 2.60 \\\\\n",
"OnlineSecurity & -0.38 & 0.68 & 0.07 & -0.51 & -0.25 & 0.60 & 0.78 & -5.65 & 1.609159e-08 & 25.89 \\\\\n",
"OnlineBackup & -0.29 & 0.75 & 0.06 & -0.40 & -0.18 & 0.67 & 0.83 & -5.22 & 1.789263e-07 & 22.41 \\\\\n",
"DeviceProtection & -0.16 & 0.85 & 0.06 & -0.27 & -0.05 & 0.76 & 0.95 & -2.85 & 4.346366e-03 & 7.85 \\\\\n",
"TechSupport & -0.28 & 0.76 & 0.07 & -0.41 & -0.15 & 0.67 & 0.86 & -4.19 & 2.758595e-05 & 15.15 \\\\\n",
"StreamingTV & -0.27 & 0.77 & 0.06 & -0.38 & -0.15 & 0.68 & 0.86 & -4.46 & 8.380722e-06 & 16.86 \\\\\n",
"StreamingMovies & -0.26 & 0.77 & 0.06 & -0.38 & -0.14 & 0.69 & 0.87 & -4.36 & 1.272423e-05 & 16.26 \\\\\n",
"PaperlessBilling & 0.16 & 1.17 & 0.06 & 0.05 & 0.27 & 1.05 & 1.31 & 2.79 & 5.245333e-03 & 7.57 \\\\\n",
"MonthlyCharges & 0.07 & 1.07 & 0.00 & 0.06 & 0.07 & 1.06 & 1.07 & 26.59 & 9.259870e-156 & 515.01 \\\\\n",
"TotalCharges & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & -40.10 & 0.000000e+00 & inf \\\\\n",
"MultipleLines\\_No phone service & 0.64 & 1.89 & 0.12 & 0.41 & 0.87 & 1.51 & 2.38 & 5.50 & 3.877513e-08 & 24.62 \\\\\n",
"MultipleLines\\_Yes & -0.20 & 0.82 & 0.05 & -0.30 & -0.09 & 0.74 & 0.91 & -3.68 & 2.293482e-04 & 12.09 \\\\\n",
"Contract\\_One year & -1.40 & 0.25 & 0.10 & -1.60 & -1.20 & 0.20 & 0.30 & -13.78 & 3.267030e-43 & 141.13 \\\\\n",
"Contract\\_Two year & -4.05 & 0.02 & 0.20 & -4.43 & -3.66 & 0.01 & 0.03 & -20.74 & 1.630391e-95 & 314.88 \\\\\n",
"PaymentMethod\\_Credit card (automatic) & -0.01 & 0.99 & 0.09 & -0.18 & 0.17 & 0.83 & 1.19 & -0.06 & 9.541490e-01 & 0.07 \\\\\n",
"PaymentMethod\\_Electronic check & 0.38 & 1.46 & 0.07 & 0.24 & 0.52 & 1.27 & 1.69 & 5.20 & 1.951765e-07 & 22.29 \\\\\n",
"PaymentMethod\\_Mailed check & 0.52 & 1.68 & 0.09 & 0.35 & 0.69 & 1.42 & 1.99 & 5.96 & 2.502489e-09 & 28.57 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n"
],
"text/plain": [
"<lifelines.CoxPHFitter: fitted with 7043 total observations, 5174 right-censored observations>\n",
" duration col = 'tenure'\n",
" event col = 'Churn'\n",
" baseline estimation = breslow\n",
" number of observations = 7043\n",
"number of events observed = 1869\n",
" partial log-likelihood = -12688.70\n",
" time fit was run = 2021-06-15 09:14:33 UTC\n",
"\n",
"---\n",
" coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
"covariate \n",
"SeniorCitizen 0.03 1.03 0.06 -0.08 0.14 0.93 1.16\n",
"Partner -0.19 0.82 0.06 -0.30 -0.09 0.74 0.92\n",
"Dependents -0.10 0.91 0.07 -0.23 0.04 0.79 1.04\n",
"OnlineSecurity -0.38 0.68 0.07 -0.51 -0.25 0.60 0.78\n",
"OnlineBackup -0.29 0.75 0.06 -0.40 -0.18 0.67 0.83\n",
"DeviceProtection -0.16 0.85 0.06 -0.27 -0.05 0.76 0.95\n",
"TechSupport -0.28 0.76 0.07 -0.41 -0.15 0.67 0.86\n",
"StreamingTV -0.27 0.77 0.06 -0.38 -0.15 0.68 0.86\n",
"StreamingMovies -0.26 0.77 0.06 -0.38 -0.14 0.69 0.87\n",
"PaperlessBilling 0.16 1.17 0.06 0.05 0.27 1.05 1.31\n",
"MonthlyCharges 0.07 1.07 0.00 0.06 0.07 1.06 1.07\n",
"TotalCharges -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n",
"MultipleLines_No phone service 0.64 1.89 0.12 0.41 0.87 1.51 2.38\n",
"MultipleLines_Yes -0.20 0.82 0.05 -0.30 -0.09 0.74 0.91\n",
"Contract_One year -1.40 0.25 0.10 -1.60 -1.20 0.20 0.30\n",
"Contract_Two year -4.05 0.02 0.20 -4.43 -3.66 0.01 0.03\n",
"PaymentMethod_Credit card (automatic) -0.01 0.99 0.09 -0.18 0.17 0.83 1.19\n",
"PaymentMethod_Electronic check 0.38 1.46 0.07 0.24 0.52 1.27 1.69\n",
"PaymentMethod_Mailed check 0.52 1.68 0.09 0.35 0.69 1.42 1.99\n",
"\n",
" z p -log2(p)\n",
"covariate \n",
"SeniorCitizen 0.60 0.55 0.87\n",
"Partner -3.52 <0.005 11.20\n",
"Dependents -1.39 0.17 2.60\n",
"OnlineSecurity -5.65 <0.005 25.89\n",
"OnlineBackup -5.22 <0.005 22.41\n",
"DeviceProtection -2.85 <0.005 7.85\n",
"TechSupport -4.19 <0.005 15.15\n",
"StreamingTV -4.46 <0.005 16.86\n",
"StreamingMovies -4.36 <0.005 16.26\n",
"PaperlessBilling 2.79 0.01 7.57\n",
"MonthlyCharges 26.59 <0.005 515.01\n",
"TotalCharges -40.10 <0.005 inf\n",
"MultipleLines_No phone service 5.50 <0.005 24.62\n",
"MultipleLines_Yes -3.68 <0.005 12.09\n",
"Contract_One year -13.78 <0.005 141.13\n",
"Contract_Two year -20.74 <0.005 314.88\n",
"PaymentMethod_Credit card (automatic) -0.06 0.95 0.07\n",
"PaymentMethod_Electronic check 5.20 <0.005 22.29\n",
"PaymentMethod_Mailed check 5.96 <0.005 28.57\n",
"---\n",
"Concordance = 0.93\n",
"Partial AIC = 25415.41\n",
"log-likelihood ratio test = 5928.67 on 19 df\n",
"-log2(p) of ll-ratio test = inf"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fitting survival regression model \n",
"cph = lifelines.CoxPHFitter()\n",
"cph.fit(churn_hazard, duration_col='tenure', event_col='Churn', show_progress=False)\n",
"cph.print_summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* In the above regression, the key output is exp(coef). This is interpreted as the scaling of hazard risk for each additional unit of the variable, 1.00 being neutral.\n",
"* For example, the last exp(coefficient), corresponding to PaymentMethod_Mailed check, means a customer that pays by mailing a check is 1.68 times as likely to cancel their service.\n",
"* For the company, exp(coef) below 1.0 is good, meaning a customer less likely to cancel.\n",
"\n",
"To better visualize the above, we can plot the coefficient outputs and their confidence intervals."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plotting coefficients\n",
"fig_coef, ax_coef = plt.subplots(figsize=(12,7))\n",
"ax_coef.set_title('Survival Regression: Coefficients and Confident Intervals')\n",
"cph.plot(ax=ax_coef);"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# function for creating Kaplan-Meier curve plots segmented\n",
"# by categorical variables\n",
"def plot_categorical_survival(feature, t='tenure', event='Churn', df=churn_data, ax=None):\n",
" for cat in df[feature].unique():\n",
" idx = df[feature] == cat\n",
" kmf = lifelines.KaplanMeierFitter()\n",
" kmf.fit(df[idx][t], event_observed=df[idx][event], label=cat)\n",
" kmf.plot(ax=ax, label=cat)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig_pmt, ax_pmt = plt.subplots(figsize=(12,7))\n",
"plot_categorical_survival(feature='PaymentMethod', ax=ax_pmt)\n",
"ax_pmt.set_title('Customer Churn by Payment Method')\n",
"ax_pmt.set_xlabel('Customer Tenure (Months)')\n",
"ax_pmt.set_ylabel('Customer Survival Chance (%)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig_contract, ax_contract = plt.subplots(figsize=(12,7))\n",
"plot_categorical_survival(feature='Contract', ax=ax_contract)\n",
"ax_contract.set_title('Customer Churn by Contract Type')\n",
"ax_contract.set_xlabel('Customer Tenure (Months)')\n",
"ax_contract.set_ylabel('Customer Survival Chance (%)')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"fig_dep, ax_dep = plt.subplots(figsize=(12,7))\n",
"plot_categorical_survival(feature='Dependents', ax=ax_dep)\n",
"ax_dep.set_title('Customer Churn Dependents vs. No Dependents')\n",
"ax_dep.set_xlabel('Customer Tenure (Months)')\n",
"ax_dep.set_ylabel('Customer Survival Chance (%)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---------\n",
"\n",
"#### <b> 6. Recommendations\n",
" \n",
"So coming back to our objective, how can we reduce attrition?\n",
"\n",
"Customer Specification: \n",
"* The most important feature, by far, is the presence of a 1 or 2 year contract. Customers are .25 and .02, respectively, times as likely to cancel their service if they are under contract. Cancellation fees are a possible underlying cause. As long as these fees do not prohibit new sales, we would recommend continuing to put them into as many contracts as possible.\n",
"\n",
"Customer Selection:\n",
"* Customers with a partner or dependents are .82 and .91 times as likely to cancel as normal customers. Families and other large households seem to be less likely to change providers. This could be due to higher incomes, less time to consider options, or another combination of factors.\n",
" \n",
"Payment Systems:\n",
"* There is a reason companies now default to opting employees into 401k plans. It takes effort for people to make a change, even if it is beneficial.\n",
"* Make sure your customer's default is an automatic payment made monthly. This requires little effort from the customer to remain subscribed.\n",
"* Conversely, sending a check, in the mail or electronically, is a pain. It requires effort to remain subscribed.\n",
" \n",
"--------------------\n",
"\n",
"<b> Links: <b>\n",
"\n",
"* Portfolio : https://gofornaman.github.io\n",
"* LinkedIn : https://www.linkedin.com/in/naman-doshi/\n",
"\n",
"------------"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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