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Machine Learning Projects Collection

Overview

This repository contains three comprehensive machine learning projects, each demonstrating different aspects of machine learning implementation, including deep learning architectures. These projects showcase both from-scratch implementations and usage of popular libraries like scikit-learn and PyTorch.

Details

Click on each folder for detailed readme files.

ML_Projects

Each folder contains:

  • code files for two different code files corresponding to two different projects
  • Report for a detailed overview of each code file
  • Readme for a brief overview of each code file

Repository Structure

ML_Projects/
├── first/                   # Machine Learning Fundamentals
│   ├── ass1q2.ipynb        # Logistic Regression from scratch
│   ├── ass1q3.ipynb        # Regression analysis using libraries
│   ├── htd1.csv            # Heart Disease dataset
│   ├── htd2.csv            # Electricity Bill dataset
│   ├── readme.md           # Project-specific documentation
│   └── report.pdf          # Detailed analysis and results
│
├── second/                  # Neural Network Implementation
│   ├── ass3q2.ipynb        # Neural Network from scratch
│   ├── ass3q3.ipynb        # sklearn Neural Network implementation
│   ├── NN_models.zip       # Trained neural network models
│   ├── readme.md           # Project-specific documentation
│   └── report.pdf          # Detailed analysis and results
│
├── third/                   # Advanced ML and Deep Learning
│   ├── ass4q2.ipynb        # KMeans clustering implementation
│   ├── ass4q3.ipynb        # CNN & MLP with PyTorch
│   ├── cnn_model.pth       # Trained CNN model weights
│   ├── mlp_model.pth       # Trained MLP model weights
│   ├── readme.md           # Project-specific documentation
│   └── report.pdf          # Detailed analysis and results
│
└── README.md               # This file

Projects Summary

1. Machine Learning Fundamentals (first/)

  • Logistic Regression implementation from scratch
  • Advanced regression techniques using libraries
  • Focus on feature engineering and model evaluation
  • Datasets: Heart Disease and Electricity Bill

2. Neural Network Implementation (second/)

  • Custom Neural Network implementation from scratch
  • sklearn-based neural network models
  • MNIST and Fashion-MNIST dataset analysis
  • Multiple trained models with different configurations

3. Advanced ML and Deep Learning (third/)

  • KMeans clustering implementation from scratch
  • CNN and MLP implementation using PyTorch
  • CIFAR-10 dataset analysis
  • Trained model weights and performance comparisons

Getting Started

Prerequisites

  • Python 3.8+
  • Jupyter Notebook
  • Required libraries:
    • NumPy
    • Pandas
    • Matplotlib
    • scikit-learn
    • PyTorch
    • torchvision

Installation

  1. Clone the repository
git clone <repository-url>
cd ML_Projects
  1. Install dependencies
pip install numpy pandas matplotlib scikit-learn torch torchvision

Usage

Each project directory contains its own:

  • Detailed README with specific instructions
  • Jupyter notebooks with implementation
  • Dataset files or loading instructions
  • Comprehensive report with analysis

Project Status

  • All projects are complete and documented
  • Models are trained and saved
  • Results are analyzed and reported

Additional Information

For detailed information about each project:

  • First Project: See first/readme.md
  • Second Project: See second/readme.md
  • Third Project: See third/readme.md

For technical details, implementation specifics, and comprehensive analysis, refer to the respective report.pdf files in each project directory.