Machine Learning Algorithms

Welcome to my Machine Learning repository! 🚀

This repository contains implementations of Machine Learning algorithms using Python, NumPy, Pandas, Matplotlib, and Scikit-learn. The goal is to build a complete collection of ML algorithms from beginner to advanced level with well-documented Jupyter Notebooks.

📚 Topics Covered

Supervised Learning

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Logistic Regression
• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)
• Naive Bayes

Unsupervised Learning

• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• Principal Component Analysis (PCA)

Model Evaluation

• Train-Test Split
• Cross Validation
• Confusion Matrix
• Accuracy, Precision, Recall
• F1 Score
• ROC-AUC
• Mean Squared Error (MSE)
• R² Score

Data Preprocessing

• Handling Missing Values
• Feature Scaling
• Label Encoding
• One-Hot Encoding
• Feature Engineering

Future Topics

• Deep Learning
• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Natural Language Processing (NLP)
• Time Series Forecasting
• Recommendation Systems

🛠️ Tech Stack

• Python
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn

📂 Repository Structure

Machine-Learning/
│
├── Datasets/
├── Linear Regression/
├── Logistic Regression/
├── Decision Tree/
├── Random Forest/
├── KNN/
├── SVM/
├── Naive Bayes/
├── Clustering/
├── PCA/
├── Deep Learning/
└── README.md

🎯 Objectives

• Learn Machine Learning from scratch
• Implement algorithms step by step
• Understand mathematical concepts
• Practice with real-world datasets
• Build a strong ML portfolio

⭐ If you find this repository helpful, consider giving it a star!