Churn Prediction Project | Interview Manual with Mohit Garg

Churn Prediction Interview Manual

Knowing how to build a Machine Learning model is one thing. Explaining your project confidently in an interview is another.

This practical interview manual is designed around a real-world Telecom Churn Prediction case study and helps you prepare for the questions interviewers are likely to ask across the complete project lifecycle.

What you'll find inside:

✅ Project & Business Questions

Understand how to explain the business problem, objectives, requirements, and expected business impact.

✅ Data & EDA Questions

Prepare for questions around data understanding, data quality, missing values, outliers, and exploratory analysis.

✅ Feature Engineering Questions

Learn how to explain feature selection, transformations, derived features, and the reasoning behind your approach.

✅ Machine Learning Questions

Prepare for questions on model selection, training, hyperparameter tuning, overfitting, and model comparison.

✅ Evaluation & Imbalanced Data

Understand how to discuss precision, recall, F1-score, ROC-AUC, class imbalance, and choosing the right evaluation strategy.

✅ Business & Scenario-Based Questions

Practice "What would you do if..." situations that test your problem-solving and business thinking.

✅ Project Explanation Framework

Learn how to structure your answer when an interviewer says:

"Walk me through your Data Science project."

Who is this for?

• Data Science aspirants preparing for interviews
• Professionals switching into Data Science
• Candidates with ML knowledge but limited real-world project exposure
• Anyone who wants to confidently explain their end-to-end churn prediction project

The goal

Not just to memorize interview answers.

The goal is to understand the project deeply enough to explain your decisions, handle follow-up questions, and demonstrate real-world Data Science thinking.

Use this manual alongside the Churn Prediction Domain eBook to learn the project AND prepare to defend it in interviews.