🤖 Machine Learning Fundamentals

A collection of machine learning programs and practice exercises implemented in Python.

📚 Topics Covered

• Data Preprocessing
• Label Encoding
• One Hot Encoding
• Linear Regression
• Polynomial Regression
• Logistic Regression
• Ridge Regression
• Lasso Regression
• K-Nearest Neighbors
• Decision Trees
• Random Forest
• Clustering
• Evaluation Metrics
• Confusion Matrix
• F1 Score

🛠 Libraries Used

• Python
• NumPy
• Pandas
• Matplotlib
• Scikit-learn

🎯 Goal

This repository documents my journey in learning machine learning fundamentals through hands-on coding and practical implementations.

📂 Repository Structure

Machine-Learning-Fundamentals
│
├── Problems
├── 01_data_preprocessing.py
├── 02_encoding.py
├── ...
├── 19_kmeans_clustering.py
└── 20_student_performance_prediction.py

Author

-Harsh Mishra