📊 Machine Learning Algorithm Code Examples

A collection of basic machine learning implementations for educational purposes. These notebooks contain simplified code to demonstrate core ML concepts.

📚 Notebook Contents

File	Description	Key Concepts
Gini_index.ipynb	Decision tree impurity measurement	Gini impurity, node splitting
K-means.ipynb	Unsupervised clustering algorithm	Centroids, Euclidean distance, elbow method
Linear_regression.ipynb	Basic linear regression implementation	MSE, gradient descent, OLS
Linear_regression_2.ipynb	Advanced linear regression	Regularization, polynomial features
ML_project.ipynb	Sample ML project template	EDA, feature engineering, model evaluation
[Random Forest Classification.ipynb](Random Forest Classification.ipynb)	Ensemble tree method	Bootstrap aggregating, feature importance
confusion_matrix.ipynb	Classification evaluation metrics	Precision, recall, F1-score, ROC curves

🛠️ Setup Instructions

1. Environment Setup:

conda create -n ml-basics python=3.8
conda activate ml-basics
pip install numpy pandas matplotlib scikit-learn jupyter

2. Run Notebooks:

jupyter notebook

🧑‍🎓 Learning Objectives

Understand fundamental ML algorithms Learn proper evaluation techniques See clean implementation patterns Gain intuition through visualizations

⚠️ Note About This Repository

These are simplified implementations designed for:

Educational demonstrations Algorithm intuition building Quick reference examples For production-grade implementations, always use established libraries like scikit-learn.