Comprehensive exploration of recommendation systems using Python, pandas, matplotlib, seaborn. Repository includes Jupyter notebook, CSV data, and PDF report. Dive into building systems for movie and product datasets.

Embark on a journey through three diverse datasets—Churn, Adult Income, and Credit Card—to build Adaboost and Logistic Regression models from scratch. Dive into extensive data preprocessing to maximize model accuracy. Explore binary classification challenges, handle outliers, normalize data, and balance class distribution. Craft a robust, reusable model for future applications. Uncover insights and tackle imbalanced datasets with finesse