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telecomchurn's Introduction

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

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