Telecom Customer Churn Prediction

Difficulty
Advanced
Estimated Time
30 Hours
Portfolio Value
Very High
Interview Value
Very High
Telecom Customer Churn Prediction

The Business Problem

The telecom company is losing 20% of its customer base every year. They need to know which demographic or service factors cause churn, so the retention team can offer targeted discounts.

Project Overview

Acquiring a new customer is 5x more expensive than retaining an existing one. In this advanced project, you will step into the role of a Data Scientist. You will analyze a telecom company's database to find out exactly *why* customers cancel their subscriptions, and then use Machine Learning to predict which current customers are at high risk of leaving.

Step-by-Step Implementation

Follow this structured path to complete the project successfully.

1

Step 1: EDA

Load the data into a Pandas DataFrame. Use Seaborn to plot the correlation between contract length, monthly charges, and churn rate.

2

Step 2: Feature Engineering

Convert categorical variables (like 'Yes/No' for Tech Support) into numerical dummy variables. Handle the blank spaces in the 'TotalCharges' column.

3

Step 3: Train/Test Split

Split the dataset into 80% training data and 20% testing data.

4

Step 4: Modeling

Import Logistic Regression and Random Forest classifiers. Fit them to the training data and predict on the test data.

5

Step 5: Evaluation

Calculate the Accuracy, Precision, Recall, and plot a Confusion Matrix. Realize that 'Recall' is the most important metric for churn.

Expected Output

A well-structured notebook that tells a story, concluding with a model that correctly identifies 80% of customers who are about to leave.

Common Mistakes to Avoid

  • Focusing only on 'Accuracy' rather than 'Recall' (missing churning customers is worse than accidentally predicting a loyal customer will churn).
  • Data leakage (scaling data before splitting).

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