🚀 Machine Learning Portfolio

From-Scratch Implementations & Library Benchmarks

📌 Overview

This repository showcases a machine learning portfolio focused on implementing core ML algorithms from scratch and benchmarking them against standard machine learning libraries.
The goal is to develop a strong understanding of model internals, optimization techniques, and evaluation rather than relying solely on high-level APIs.

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🎯 Objectives

• Implement fundamental machine learning algorithms from first principles
• Understand optimization, loss functions, and convergence behavior
• Compare custom implementations with scikit-learn models
• Evaluate models using appropriate metrics
• Build clean, reproducible, and well-documented ML code

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🧠 Algorithms Implemented

✅ From Scratch

• Linear Regression (Gradient Descent, Mean Squared Error)
• Logistic Regression (Sigmoid)
• K-Means Clustering (Distance-based clustering)
• Evaluation Metrics (Accuracy, Precision, Recall, RMSE)

⚙️ Using Libraries (Benchmarking)

• Linear Regression – scikit-learn
• Logistic Regression – scikit-learn
• K-Means – scikit-learn
• Model evaluation and comparison

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🗂 Repository Structure

ML-Algorithms/
│
├── Linear-Regression/
│   ├── linear-regression-from-scratch.ipynb
│   └── linear-regression.ipynb
│
├── classification/
│   ├── logistic_regression_from_scratch.py
│   └── logistic_regression_sklearn.py
│
├── clustering/
│   ├── kmeans_from_scratch.py
│   └── kmeans_sklearn.py
│
├── data/
│   └── sample_datasets.csv
│
├── README.md
├── requirements.txt
└── .gitignore


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🔬 Methodology

Each algorithm follows a consistent ML workflow:

1. Data preprocessing and feature scaling
2. Algorithm implementation from scratch
3. Model training using iterative optimization
4. Performance evaluation using suitable metrics
5. Comparison with library-based implementations
6. Visualization and error analysis

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📊 Results & Insights

• Custom implementations achieve performance comparable to scikit-learn baselines on benchmark datasets
• Loss curves and visualizations help analyze convergence behavior
• Error analysis highlights strengths and limitations of each model

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🛠 Tech Stack

• Language: Python
• Libraries: NumPy, Pandas, Matplotlib, scikit-learn
• Tools: Jupyter Notebook, Git, GitHub

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✅ Note

This project emphasizes fundamental understanding of machine learning algorithms.
Libraries are used intentionally after validating concepts through from-scratch implementations.