🚀 Supervised Machine Learning Algorithms

A comprehensive collection of Supervised Machine Learning algorithms implemented using Python and Scikit-learn. This repository demonstrates both classification and regression techniques, along with modern tree-based ensemble learning algorithms.

📌 Repository Overview

This repository covers the implementation of fundamental and advanced supervised machine learning algorithms with clean, well-structured Jupyter notebooks. It is designed for learning, practice, interview preparation, and reference.

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📚 Algorithms Covered

Classification Algorithms

• K-Nearest Neighbors (KNN)
• Naive Bayes
• Decision Tree Classifier
• Support Vector Machine (SVM)
• Random Forest Classifier
• AdaBoost Classifier
• Gradient Boosting Classifier
• XGBoost Classifier
• CatBoost Classifier
• LightGBM Classifier

Regression Algorithms

• Decision Tree Regressor
• KNN Regressor
• SVM Regressor
• Random Forest Regressor
• AdaBoost Regressor
• Gradient Boosting Regressor
• XGBoost Regressor
• CatBoost Regressor
• LightGBM Regressor

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📂 Repository Structure

Supervised-Machine-Learning-Algorithms
│
├── CatBoost
├── Dataset
├── Decision-Tree
├── KNN
├── LightGBM
├── Naive-Bayes
├── SVM
├── Tree-Based-Ensemble-Learning
├── binary-classification.ipynb
├── requirements.txt
└── README.md

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🛠️ Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• XGBoost
• CatBoost
• LightGBM
• Jupyter Notebook

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📊 Datasets Used

This repository includes practice datasets such as:

• Cardekho Dataset
• Travel Dataset
• Built-in Scikit-learn Datasets
• OpenML Datasets

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🎯 Learning Objectives

• Understand supervised machine learning concepts.
• Learn both classification and regression techniques.
• Compare different machine learning algorithms.
• Understand tree-based ensemble learning methods.
• Practice model training, prediction, and evaluation.
• Build a strong foundation for interviews and real-world projects.

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📈 Evaluation Metrics

Classification

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix
• ROC-AUC

Regression

• MAE
• MSE
• RMSE
• R² Score

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🌟 Future Improvements

🚀 Future Improvements

• Add more real-world datasets for algorithm comparison.
• Perform comprehensive performance benchmarking across algorithms.
• Include model interpretability techniques (SHAP and LIME).
• Add end-to-end machine learning case studies.
• Expand the repository with advanced ensemble learning techniques.
• Improve documentation with detailed algorithm explanations and visualizations.

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📬 Connect With Me

Anjali Sharma

• GitHub: https://github.com/anjali1527
• LinkedIn: https://www.linkedin.com/in/anjali-sharma1527/

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