Machine Learning Algorithms

A comprehensive collection of Machine Learning algorithms implemented during my Master's degree program. This repository showcases hands-on implementations of supervised, unsupervised, and advanced ML techniques using Python and scikit-learn.

📚 About This Repository

This repository contains Colab notebooks demonstrating various machine learning algorithms learned and implemented throughout my graduate studies. Each notebook includes theoretical explanations, practical implementations, and real-world applications of the algorithms.

🎓 Academic Background

Program: Master's Degree
Focus Area: Artificial Intelligence & Machine Learning Duration: 2 years of intensive study and implementation

📑 Table of Contents

• Supervised Learning
• Unsupervised Learning
• Model Optimization
• Technologies Used
• Getting Started

🤖 Algorithms Implemented

Supervised Learning

Regression Algorithms

• Linear Regression - Simple linear regression for continuous variable prediction
• Multivariate Linear Regression - Multiple feature regression analysis
• Multivariate Linear Regression (Extended) - Advanced multivariate techniques

Classification Algorithms

• Logistic Regression - Binary and multiclass classification
• Decision Tree - Tree-based classification and regression
• Support Vector Machine (SVM) - Margin-based classification
• SVM with ROC-AUC Analysis - Performance evaluation using ROC curves
• SVM vs Logistic Regression - Comparative analysis

Probabilistic Models

• Naive Bayes (Multinomial) - Text classification and probability-based predictions
• Complement Naive Bayes - Enhanced NB for imbalanced datasets

Unsupervised Learning

Clustering Algorithms

• K-Means Clustering - Centroid-based clustering
• Finding Optimal K Value - Elbow method and silhouette analysis
• DBSCAN Clustering - Density-based spatial clustering
• Hierarchical Clustering - Agglomerative and divisive clustering

Model Optimization

• Regularization Techniques - Ridge, Lasso, and Elastic Net for overfitting prevention

🛠️ Technologies Used

• Python 3.x
• Jupyter Notebook / Google Colab
• Libraries:
  • NumPy - Numerical computing
  • Pandas - Data manipulation
  • Scikit-learn - Machine learning algorithms
  • Matplotlib - Data visualization
  • Seaborn - Statistical plotting
  • SciPy - Scientific computing

📊 Key Concepts Covered

Regression Analysis

• Simple and multiple linear regression
• Polynomial regression
• Residual analysis
• R-squared and adjusted R-squared

Classification Techniques

• Binary and multiclass classification
• Decision boundaries
• Confusion matrix analysis
• Precision, recall, and F1-score
• ROC-AUC curves

Clustering Methods

• Centroid-based clustering (K-Means)
• Density-based clustering (DBSCAN)
• Hierarchical clustering dendrograms
• Cluster evaluation metrics

Model Evaluation

• Train-test split
• Cross-validation
• Hyperparameter tuning
• Overfitting and underfitting
• Regularization (L1, L2)

🚀 Getting Started

Prerequisites

pip install numpy pandas scikit-learn matplotlib seaborn jupyter

Running the Notebooks

1. Clone the repository

git clone https://github.com/Manya123-max/Machine-Learning-Algorithms.git
cd Machine-Learning-Algorithms

2. Launch Jupyter Notebook

jupyter notebook

3. Or open in Google Colab

• Upload the .ipynb files to Google Drive
• Open with Google Colaboratory

📈 Learning Outcomes

Through these implementations, I have gained proficiency in:

• ✅ Understanding mathematical foundations of ML algorithms
• ✅ Implementing algorithms from scratch and using libraries
• ✅ Data preprocessing and feature engineering
• ✅ Model selection and evaluation
• ✅ Handling imbalanced datasets
• ✅ Interpreting model performance metrics
• ✅ Applying appropriate algorithms to real-world problems
• ✅ Optimizing model performance through regularization

📝 Project Structure

Machine-Learning-Algorithms/
│
├── Regression/
│   ├── ML1_LinearRegiression.ipynb
│   ├── MULTIVARIATE.ipynb
│   └── multivariatelinearregression.ipynb
│
├── Classification/
│   ├── LogisticRegression.ipynb
│   ├── DecisionTree.ipynb
│   ├── SuppoortVectorMachineExample.ipynb
│   ├── SupportVectorMachineROC_AUC.ipynb
│   ├── SupportVectorMachine_Logistic.ipynb
│   ├── NaiveBayesMultinomial.ipynb
│   └── COMPLEMENT_NB.ipynb
│
├── Clustering/
│   ├── K_MeansClustering1.ipynb
│   ├── FINDING__k_value_AND_cluster2.ipynb
│   ├── DBSCAN_CLUSTERING3.ipynb
│   └── HierarchicalClustering.ipynb
│
├── Optimization/
│   └── Regularization.ipynb
│
└── README.md

🔍 Notable Implementations

1. Support Vector Machine Analysis

Comprehensive implementation including kernel tricks, margin optimization, and comparative analysis with logistic regression using ROC-AUC metrics.

2. Clustering Ensemble

Complete clustering workflow from finding optimal K values using elbow method to advanced density-based and hierarchical clustering techniques.

3. Regularization Study

In-depth exploration of L1 (Lasso) and L2 (Ridge) regularization for preventing overfitting in linear models.

📧 Contact

Manya

📄 License

This project is open source and available for educational purposes.