This repository contains a comprehensive case study on predicting 365 Data Science customer subscriptions using real-world student engagement data.

Predict whether a customer will make a purchase on an e-commerce website using Machine Learning models, with 89% accuracy achieved via Random Forest.

A machine learning project that predicts online shopping purchase intent using a k-nearest neighbor classifier. The model analyzes visitor behavior features like page visits, browsing duration, bounce rates, and user characteristics to predict whether a visitor will make a purchase. Built with scikit-learn.

Understanding and forecasting online shoppers’ purchase behavior is crucial for e-commerce platforms aiming to improve personalization, marketing effectiveness, and customer retention. This study proposes a combined analytical framework integrating clustering and logistic regression

Data mining project for predicting customer purchase behavior using machine learning

Модель прогнозирования покупок клиентов интернет-магазина в течение 90 дней с использованием LightGBM. Достигнута высокая точность при сильном дисбалансе классов. Использованы Python, Scikit-learn, LightGBM.

"Logistic Regression model built on the Social Network Ads dataset to predict whether a user will purchase a product based on their Age and Estimated Salary. This project includes data visualization, model training, evaluation, and visualization of decision boundaries."

Predicting iPhone purchase behavior using a Decision Tree Classifier with EDA, feature preprocessing, and ROC–AUC evaluation.

Predicting potential buyers using ML classification on purchase trends & event analysis — Gradient Boosting, Random Forest & ED

Leveraging the Kaggle Online Retail Dataset (2009-2011), this system optimizes decision-making with: RFM Modeling for high-value customer identification, Ensemble Learning for purchase behavior prediction, Game Theory-Based Pricing for dynamic strategy optimization.

Explorations in the use of GNNs to make purchase predictions using Amazon sales data.