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C

Customer Churn Prediction

Complete learning notes

1. Project Overview

This project predicts which customers are likely to cancel (churn) a subscription service, based on their usage patterns and account details. It's a classic, high-value business classification problem that brings together classification algorithms (Module 4), careful evaluation metric selection (Module 6), and class imbalance handling.

2. Problem Statement

Subscription-based businesses (streaming services, telecom providers, SaaS companies) lose revenue every time a customer cancels. Given historical data on past customers — including whether they eventually churned — can we identify which CURRENT customers are at high risk of churning, so the business can intervene proactively?

3. Project Objective

Build a binary classification model that predicts the probability a customer will churn, prioritizing Recall (Module 6, Topic 1) to minimize missed at-risk customers, while still maintaining reasonable Precision to avoid wasting retention resources on customers who wouldn't have churned anyway.

4. Dataset Requirements

  • A dataset with one row per customer, including account/usage details and a "churned" (yes/no) label based on historical outcomes.
  • The "Telco Customer Churn" dataset is a popular, freely available option.
  • Should include a mix of numeric (e.g., monthly charges, tenure) and categorical (e.g., contract type, payment method) features.

5. Features

  • Tenure (how long the customer has been subscribed)
  • Monthly charges / total charges
  • Contract type (month-to-month, one-year, two-year)
  • Payment method
  • Services used (e.g., internet, streaming add-ons)
  • Customer support interactions (if available)

6. Target Variable

Churned — a binary label (Yes/No), making this a binary classification problem.

7. Data Preprocessing

  • Handle missing values (Module 3, Topic 3) — e.g., missing total charges for very new customers.
  • Encode categorical features (contract type, payment method) using One-Hot Encoding (Module 3, Topic 6).
  • Scale numeric features if using KNN, SVM, or Logistic Regression.
  • Check for class imbalance (churned customers are usually the minority class) and consider stratified splitting.

8. Model/Algorithm Selection

  • Logistic Regression (Module 4, Topic 3) — interpretable baseline, useful for understanding which factors drive churn.
  • Random Forest (Module 4, Topic 6) — often captures complex feature interactions well for this kind of business data.
  • Gradient Boosting (Module 7, Topic 5) — frequently a top performer for churn-prediction-style problems.

9. Training Process

  1. Split data using stratified sampling to preserve the churn/non-churn ratio.
  2. Train baseline Logistic Regression, then compare against Random Forest/Gradient Boosting.
  3. Use Cross-Validation and Hyperparameter Tuning (Module 6-7) to optimize the chosen model.
  4. Consider adjusting the classification threshold (Module 4, Topic 3) away from the default 0.5 to prioritize Recall, given the business context.

10. Model Evaluation

  • Recall — critical, since missing an at-risk customer means a likely lost sale.
  • Precision — still relevant, to avoid excessive false alarms straining retention team resources.
  • ROC-AUC (Module 6, Topic 3) — for overall model comparison across thresholds.
  • Confusion Matrix — to clearly see the specific tradeoff between missed churners and false alarms.

11. Expected Output

For each current customer, the model outputs a churn probability (e.g., "68% likely to churn"), allowing the business to rank customers by risk and prioritize retention outreach (special offers, check-in calls) toward the highest-risk group.

12. Suggested Folder Structure

text
customer_churn_prediction/
├── data/
│   └── telco_churn.csv
├── notebooks/
│   └── churn_analysis.ipynb
├── src/
│   ├── preprocessing.py
│   ├── train_model.py
│   └── evaluate.py
└── README.md

13. Technologies/Libraries

  • Pandas, NumPy — data handling.
  • Scikit-learn — LogisticRegression, RandomForestClassifier, GradientBoostingClassifier, GridSearchCV, metrics.
  • Matplotlib/Seaborn — visualizing churn rates by customer segment.

14. Step-by-Step Implementation Plan

  1. Load the dataset and explore churn rate by different customer segments (contract type, tenure, etc.) using groupby() and visualizations.
  2. Clean and preprocess the data (missing values, encoding, scaling).
  3. Split into training/test sets with stratification.
  4. Train and evaluate a Logistic Regression baseline.
  5. Train and evaluate Random Forest and/or Gradient Boosting models.
  6. Tune the best-performing model's hyperparameters.
  7. Analyze feature importance to understand key churn drivers.
  8. Finalize the model and document the recommended probability threshold for flagging at-risk customers.

15. Possible Improvements

  • Engineer features like "recent usage decline" or "support ticket frequency trend" for richer signal.
  • Combine with Customer Segmentation (Project 6) to tailor retention strategies by customer type.
  • Deploy the model to score customers automatically on a recurring schedule (previewed in Module 10's Model Deployment topic).

16. Real-World Relevance

Churn prediction models are used extensively by telecom companies, streaming services, SaaS businesses, and gyms — anywhere a recurring subscription relationship exists — to proactively identify and retain at-risk customers before they cancel, directly protecting recurring revenue.