Projects/Customer Churn Prediction with Machine Learning
intermediate5 milestones · ~12 hours

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.

Full project workspace, live PostgreSQL DB, AI Project Mentor & verified credential

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.

  1. 01

    Set up your project repo

    Milestone 1

    Before any analytical work: create a public GitHub repository, push the standard skeleton, and paste the URL into your workspace.

  2. 02

    Exploratory Data Analysis & Feature Preparation

    Milestone 2

    Explore the customer churn dataset, analyze class distribution, and engineer predictive features.

  3. 03

    Logistic Regression Baseline & Odds Ratio Interpretability

    Milestone 3

    Train a standardized Logistic Regression model, compute odds ratios, and interpret feature coefficients.

  4. 04

    Random Forest Classifier & Model Evaluation

    Milestone 4

    Train a Random Forest ensemble, tune key parameters, compare feature importance, and evaluate trade-offs.

  5. 05

    Actionable Retention Strategy & Executive Readout

    Milestone 5

    Generate a prioritized high-risk customer target list, calculate saved revenue impact, and deliver executive recommendations.

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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.

Rubric-reviewed · Plagiarism-checked · Verifiable certificate