Customer Churn Prediction
with Machine Learning
A SaaS subscription company wants to identify customers at risk of churning before they cancel. You'll explore customer usage and billing records, engineer behavioral features, train and interpret an explainable Logistic Regression baseline (odds ratios & coefficients), build a Random Forest classifier to capture non-linear interactions, evaluate classification trade-offs (Recall vs Precision, ROC-AUC, Confusion Matrix), and deliver a prioritized high-risk customer retention playbook for the Customer Success team.
What you'll learn
- —Frame customer retention and cancellation as a supervised binary classification problem
- —Engineer features from customer usage, billing frequency, and support tickets without leakage
- —Train and interpret Logistic Regression coefficients and odds ratios for stakeholder transparency
- —Train a Random Forest Classifier to handle non-linear feature interactions and evaluate feature importance
- —Evaluate models using Confusion Matrices, Precision, Recall, F1-score, and ROC-AUC curves
- —Translate classification probabilities into a prioritized high-risk target list with estimated saved MRR
The 5 milestones
Each milestone is reviewed before you advance.
- 01
Set up your project repo
Before any analytical work: create a public GitHub repository, push the standard skeleton, and paste the URL into your workspace.
- 02
Exploratory Data Analysis & Feature Preparation
Explore the customer churn dataset, analyze class distribution, and engineer predictive features.
- 03
Logistic Regression Baseline & Odds Ratio Interpretability
Train a standardized Logistic Regression model, compute odds ratios, and interpret feature coefficients.
- 04
Random Forest Classifier & Model Evaluation
Train a Random Forest ensemble, tune key parameters, compare feature importance, and evaluate trade-offs.
- 05
Actionable Retention Strategy & Executive Readout
Generate a prioritized high-risk customer target list, calculate saved revenue impact, and deliver executive recommendations.
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