Machine Learning Projects

Welcome to my machine learning projects repository! This portfolio contains a series of hands-on projects designed to demonstrate various machine learning techniques, algorithms, and workflows. Each project explores different aspects of data science, from data cleaning and preprocessing to model training and evaluation.

Recent Project

Classification: Image Classification using Neural Networks (FasionMNIST)

• Objective: Build a neural network to classify images from the FashionMNIST dataset into 10 categories (e.g., T-shirt, Trouser, Dress).

• Techniques:

  • Data exploration and processing
  • Neural network design (Convolutional Neural Networks - CNNs)
  • Model training, validation, and testing
  • Evaluation using metrics like accuracy and confusion matrix

• Tools & Libraries:

  • PyTorch, NumPy, Matplotlib, Seaborn

Future Projects

This repository will continue to grow as I add more machine learning projects. Here are the general areas that will be covered:

• Supervised Learning: Classification and Regression problems
• Unsupervised Learning: Clustering and Dimensionality Reduction
• Deep Learning: Neural Networks and Computer Vision

List of Projects

Sr. No.	Project Name	Category	Short Description
1	Housing Price Prediction	Regression	Develops a regression model to predict house prices based on various features such as amenities, area, etc.
2	FMNIST Image Classification	Classification	Implements a neural network to classify Fashion-MNIST images into categories like shirts, shoes, and bags.

Folder Structure

Here's the organization of the repository:

/Machine-Learning-Projects
│
├── /Regression
│   └── /Housing Price Prediction
│
├── /Classification
│   └── /FMNIST-Image-Classification
│       ├── saved_models
│		│	└── fmnist_model.pth
│		├──src
│	    │   ├── __init__.py
│		│	├── data_preparation.py
│		│	├── model.py
│		│	├── train.py
│		│	├── predict.py
│	    │   ├── evaluate.py
│       │   ├── utils.py
│       ├── main.py
│       ├── NN-Classification-FMNIST.ipynb
│       └── README.md
│
└── README.md

How to Run

To run the code in this repository, perform the following steps:

1. Clone the repository

git clone https://github.com/asitdave/Machine-Learning-Projects.git

2. Create a virtual environment to install required dependencies.

pip install -r requirements.txt

or (for Anaconda)

conda env create -f ML-proj-venv.yml
conda activate ML-proj-venv.yml

Contributing

Feel free to fork this repository, explore the code, and contribute by submitting issues or pull requests. Suggestions are always welcome!