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 Will Ship to GitHub
Hiring managers don't want to see another cookie-cutter Titanic or Iris tutorial. You will build a professional repository with modular scripts and executive business memos that demonstrate commercial analytical maturity.
What You Will Master
5 Guided Project Milestones
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.
Exploratory Data Analysis & Feature Preparation
Explore the customer churn dataset, analyze class distribution, and engineer predictive features.
Logistic Regression Baseline & Odds Ratio Interpretability
Core MilestoneTrain a standardized Logistic Regression model, compute odds ratios, and interpret feature coefficients.
Random Forest Classifier & Model Evaluation
Train a Random Forest ensemble, tune key parameters, compare feature importance, and evaluate trade-offs.
Actionable Retention Strategy & Executive Readout
Review Pass RequiredGenerate a prioritized high-risk customer target list, calculate saved revenue impact, and deliver executive recommendations.
ATS Keywords & Resume Ready Bullets
"Authored an end-to-end data analytics project on Customer Churn Prediction with Machine Learning utilizing Python (Pandas, NumPy, Scikit-Learn), PostgreSQL / CSV, Jupyter Notebook — delivered production-grade queries, cohort distributions, and an executive stakeholder summary published as a portfolio GitHub repository."
Ready to build and ship Customer Churn Prediction with Machine Learning?
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Free Customer Churn Prediction with Machine Learning portfolio project — with AI review
Build a real customer churn prediction with machine learning project for your data analyst portfolio in your own public GitHub repo — free brief, starter template, and milestone guides. When your code is ready, the ₹99 pass gets every milestone reviewed against a fixed rubric (customer churn prediction ml walkthrough included) and issues a verifiable certificate on completion.
Is the Customer Churn Prediction with Machine Learning project free?
The full project brief, milestone guides, and starter template are free to audit. The AI-powered review pass and verified certificate unlock at ₹99.
Can I add the Customer Churn Prediction with Machine Learning project to my resume and GitHub?
Yes — that is the point. You build in your own public GitHub repo, every milestone is reviewed against a fixed rubric, and the certificate links to the repo employers can inspect. Only list what is visible in your repo.
How long does the Customer Churn Prediction with Machine Learning project take?
Most students finish the 5 milestones in 2–4 weekends. Milestones unlock in order, and you can re-submit any milestone that needs work.
Build your portfolio & get certified
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