TelecomChurn

Problem Statment

You have a telecom firm which has collected data of all its customers. The main types of attributes are:

• Demographics (age, gender etc.)
• Services availed (internet packs purchased, special offers taken etc.)
• Expenses (amount of recharge done per month etc.)

Based on all this past information, you want to build a model which will predict whether a particular customer will churn or not, i.e. whether they will switch to a different service provider or not. So the variable of interest, i.e. the target variable here is ‘Churn’ which will tell us whether or not a particular customer has churned. It is a binary variable - 1 means that the customer has churned and 0 means the customer has not churned.

Methodology Followed

Data cleaning and preparation

• Combining three dataframes
• Handling categorical variables
  • Mapping categorical variables to integers
  • Dummy variable creation
• Handling missing values

Test-train split and scaling

Model Building Using Logisitc Regression

• Feature elimination based on correlations
• Feature selection using RFE (Coarse Tuning)
• Manual feature elimination (using p-values and VIFs)

Model Evaluation

• Accuracy
• Sensitivity and Specificity
• Optimal cut-off using ROC curve
• Precision and Recall

Predictions on the test set

Achieved a test accuracy of 74.4% with 72.16% sensitivity

This was done as part of the coursework of PostGraduate programme by IIIT-B