🚀 Machine Learning Project: Optimizing k-Nearest Neighbors Classifier 🤖

In this project, we optimize the k-Nearest Neighbors (k-NN) classifier, a machine learning algorithm that can find patterns in large, high-dimensional datasets. We use a waveform dataset with 5000 instances and 21 features as a case study. The project involves tuning the best k of a kNN classifier by cross-validation, reducing the complexity by running the Data Reduction algorithms, comparing the two methods studied in class for speeding-up the calculation of the 1NN with a brute force 1NN algorithm, and generating artificially imbalancy in the training data and analyzing the impact on the accuracy.

🎯 Objectives

• Tune the best k of a kNN classifier by cross-validation
• Reduce the complexity by running the Data Reduction algorithms
• Compare the two methods studied in class for speeding-up the calculation of the 1NN with a brute force 1NN algorithm
• Generate artificially imbalancy in the training data and analyze the impact on the accuracy

📁 Project Structure

• data: Contains the waveform dataset used in the project.
• notebooks: Jupyter Notebooks for implementing the tasks.
• reports: Contains a detailed project report.

📊 Usage

• Navigate to the notebooks folder.
• Run the Jupyter Notebooks for implementing the tasks.
• Customize the implementation as needed for your exploration.

🌐 Additional Information:

• Optimal Bayes classification rate = 86% accuracy## Tech Stack

🚀 Languages:

• Python

📊 Data Analysis and Visualization:

• Pandas: Data manipulation and analysis
• Matplotlib: Creating static, interactive, and animated visualizations
• Seaborn: Statistical data visualization
• NumPy: Numerical operations

🤖 Machine Learning:

• Scikit-Learn: Machine learning models and tools

📚 Other Tools and Frameworks:

• Jupyter Notebooks: Interactive computing and data exploration

🔧 Version Control:

• Git: Version control system

💻 Development Environment:

• Visual Studio Code

🔧 Deployment

To deploy this project run

  git clone https://github.com/ZahirAhmadChaudhry/Optimizing-k-Nearest-Neighbors-Classifier.git

  cd Optimizing-k-Nearest-Neighbors-Classifier

  pip install -r requirements.txt

🤝 Contributing:

Contributions are always welcome!

• Fork the repository.
• Create a new branch: git checkout -b feature-branch
• Make changes and commit: git commit -m "Description of changes"
• Push changes to your fork: git push origin feature-branch
• Create a pull request.

📰 Project Report:

For a detailed report on the project, analysis, and conclusions, refer to the Project Report.

📄 License

This project is licensed under the MIT License. See LICENSE for details.