https://github.com/nabilshadman/python-classification-and-generative-models

Applications of classification and generative models with Python
https://github.com/nabilshadman/python-classification-and-generative-models

classification data-science data-visualization generative-model machine-learning matplotlib numpy pandas scikit-learn

Last synced: 2 months ago
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Applications of classification and generative models with Python

• Host: GitHub
• URL: https://github.com/nabilshadman/python-classification-and-generative-models
• Owner: nabilshadman
• Created: 2023-01-13T13:30:58.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2025-09-09T05:52:08.000Z (10 months ago)
• Last Synced: 2025-09-09T08:32:19.423Z (10 months ago)
• Topics: classification, data-science, data-visualization, generative-model, machine-learning, matplotlib, numpy, pandas, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Classification and Generative Modeling with Machine Learning

[![Python](https://img.shields.io/badge/Python-3.7%2B-blue.svg)](https://www.python.org/)

[![Jupyter](https://img.shields.io/badge/Jupyter-Notebook-orange.svg)](https://jupyter.org/)  

## Overview

This repository implements advanced machine learning techniques focusing on classification algorithms and generative models. The project demonstrates practical applications of machine learning through two comprehensive case studies: music genre classification and parameter inference in generative models.

## Project Structure

The project consists of two main components:

1. **Music Genre Classification**

   - Implementation of multiple classification algorithms

   - Analysis of the GTZAN Dataset

   - Comparative evaluation of model performance

   

2. **Generative Models**

   - Multivariate Gaussian distribution analysis

   - Parameter estimation and inference

   - Gaussian Mixture Model (GMM) implementation

   - Classification experiments with limited training data

## Technologies

- Python Data Science Stack:

  - NumPy

  - Pandas

  - Matplotlib

  - Scikit-learn

  - Jupyter Notebook

## Part 1: Music Genre Classification

This section utilizes a modified version of the [GTZAN Dataset](https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification), comprising 1000 music samples across 10 genres. The implementation includes:

- Comprehensive exploratory data analysis

- Data preprocessing and feature engineering

- Implementation of multiple classifiers:

  - Logistic Regression

  - Support Vector Machines

- Detailed model evaluation and performance comparison

- Visualization of results and insights

## Part 2: Generative Models and Parameter Inference

The second component focuses on probabilistic modeling and includes:

- Implementation of multivariate Gaussian distributions

- Sample generation and parameter estimation

- Analysis of classification performance with limited training data

- Gaussian Mixture Model parameter optimization

- Empirical evaluation of model performance

## Installation and Setup

### Prerequisites

- Anaconda Distribution (Recommended)

- Python 3.7 or higher

### Setup Instructions

1. **Install Anaconda**

   - Download [Anaconda Distribution](https://www.anaconda.com/download)

   - Follow the installation wizard for your operating system

2. **Launch Jupyter Notebook**

   - Open Anaconda Navigator

   - Click "Launch" under Jupyter Notebook

3. **Run the Project**

   - Navigate to the project directory

   - Open `classification_and_generative_models.ipynb`

   - Select "Run" → "Run All Cells" from the toolbar

## Notebook Access

The complete implementation is available in our [Jupyter notebook](https://github.com/nabilshadman/python-classification-and-generative-models/blob/main/classification_and_generative_models.ipynb).