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The project demonstrates practical applications of machine learning through two comprehensive case studies: music genre classification and parameter inference in generative models.\n\n## Project Structure\nThe project consists of two main components:\n\n1. **Music Genre Classification**\n   - Implementation of multiple classification algorithms\n   - Analysis of the GTZAN Dataset\n   - Comparative evaluation of model performance\n   \n2. **Generative Models**\n   - Multivariate Gaussian distribution analysis\n   - Parameter estimation and inference\n   - Gaussian Mixture Model (GMM) implementation\n   - Classification experiments with limited training data\n\n## Technologies\n- Python Data Science Stack:\n  - NumPy\n  - Pandas\n  - Matplotlib\n  - Scikit-learn\n  - Jupyter Notebook\n\n## Part 1: Music Genre Classification\nThis 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:\n\n- Comprehensive exploratory data analysis\n- Data preprocessing and feature engineering\n- Implementation of multiple classifiers:\n  - Logistic Regression\n  - Support Vector Machines\n- Detailed model evaluation and performance comparison\n- Visualization of results and insights\n\n## Part 2: Generative Models and Parameter Inference\nThe second component focuses on probabilistic modeling and includes:\n\n- Implementation of multivariate Gaussian distributions\n- Sample generation and parameter estimation\n- Analysis of classification performance with limited training data\n- Gaussian Mixture Model parameter optimization\n- Empirical evaluation of model performance\n\n## Installation and Setup\n\n### Prerequisites\n- Anaconda Distribution (Recommended)\n- Python 3.7 or higher\n\n### Setup Instructions\n\n1. **Install Anaconda**\n   - Download [Anaconda Distribution](https://www.anaconda.com/download)\n   - Follow the installation wizard for your operating system\n\n2. **Launch Jupyter Notebook**\n   - Open Anaconda Navigator\n   - Click \"Launch\" under Jupyter Notebook\n\n3. **Run the Project**\n   - Navigate to the project directory\n   - Open `classification_and_generative_models.ipynb`\n   - Select \"Run\" → \"Run All Cells\" from the toolbar\n\n## Notebook Access\nThe complete implementation is available in our [Jupyter notebook](https://github.com/nabilshadman/python-classification-and-generative-models/blob/main/classification_and_generative_models.ipynb).  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnabilshadman%2Fpython-classification-and-generative-models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnabilshadman%2Fpython-classification-and-generative-models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnabilshadman%2Fpython-classification-and-generative-models/lists"}