AI & ML Learning Sandbox Overview This repository contains a collection of unorganized scripts and Jupyter notebooks from my self-learning journey and academic coursework. These files represent raw experiments, practice problems, and assignments covering various Artificial Intelligence and Machine Learning concepts.
Note This is not a production-ready library. The code structures vary and are primarily for educational documentation.
Contents
Regression: Linear regression implementations (from scratch and using libraries), income prediction, and gradient descent.
Classification: Customer churn prediction, Decision Trees (Breast Cancer, Social Network Ads), and KNN for housing data.
Clustering: Unsupervised learning for customer segmentation.
Computer Vision: Baseline testing vs. data augmentation.
Simulation & Logic: Roulette wheel simulation and Fuzzy Logic fan control.
Miscellaneous: Kaggle road accident analysis and general algorithm tests.