This project aims to analyze customer transaction data using market basket analysis techniques. The objective is to discover associations between products, identify frequently purchased item combinations, and support retail decision-making through data-driven product placement and promotion strategies.
Collect customer datasets including usage patterns, complaints, and subscription details.
Clean and preprocess data by handling missing values and encoding categorical variables.
Perform exploratory data analysis to identify churn-related trends.
Select important features influencing customer churn.
Split data into training and testing datasets.
Implement machine learning models such as logistic regression or random forest.
Evaluate model performance using accuracy, precision, recall, and ROC curves.
Compare multiple models and select the best-performing one.
Visualize churn patterns using charts and dashboards.
Document findings and recommend retention strategies.
