This project focuses on predicting customer churn — the likelihood that a customer will stop using a company’s product or service. By identifying at-risk customers in advance, businesses can take proactive measures to improve retention and reduce revenue loss. The project includes; Data Collection: Import dataset (CSV / SQL ).

Exploratory Data Analysis (EDA):

Visualize distributions, churn rates, and feature importance.

Data Preprocessing:

Handle missing values, encode categorical features, scale numerical values.

Modeling:

Train multiple ML models (Logistic Regression, Random Forest).

Evaluate using accuracy, precision, recall, F1-score.