Predictive Customer Churn Model
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Project Overview
Take a raw, messy CSV dataset of customer behaviors, clean the data, perform exploratory data analysis, and train a machine learning model to predict churn probability. You will build an end-to-end pipeline from data ingestion to model evaluation.
Real World Use Case
A telecom company predicting which users are likely to cancel their subscription next month.
Steps to Build
Perform data cleaning and handle missing values using Pandas.
Visualize feature correlations using Matplotlib and Seaborn.
Engineer new features (e.g., tenure length, average spend).
Train a Random Forest or XGBoost classification model.
Evaluate model performance using precision, recall, and a confusion matrix.
Resume Impact
Directly solves a massive business problem (retaining customers). Business-driven data projects are highly valued.
Skills Learned
- Data Cleaning
- Machine Learning
- Feature Engineering
- Data Visualization
Ready to build?
Complete the project and earn your free certificate.