Project

customer-churn-prediction

A machine learning project that predicts customer churn by analyzing historical customer data. It uses data preprocessing, feature engineering, and classification models to forecast churn risk and provide actionable insights for business retention strategies.

PythonpandasNumPyscikit-learnMatplotlibseaborn+3

View Folder Live Demo Repository

Categorymachine-learning

Stack9 tools

Updated2026-04-02T15:19:17.972233+00:00

Timeline7 steps

Key Features

machine-learning
churn-prediction
data-science

Project Timeline

Step 1

Identified customer churn as a critical business problem impacting revenue and retention.

step 1

Step 2

Collected and explored historical customer data to understand churn patterns and trends.

step 2

Step 3

step 3

Performed data cleaning, preprocessing, and feature engineering to prepare data for modeling.

Step 4

step 4

Trained multiple supervised machine learning classification models to predict churn.

Step 5

step 5

Evaluated model performance using accuracy, precision, recall, F1-score, and confusion matrix.

Step 6

step 6

Analyzed feature importance to identify key drivers of customer churn and extract business insights.

Step 7

step 7

Documented the full end-to-end pipeline with visualizations and actionable conclusions.

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