Hyperparameter Tuning Tool

This tool allows you to tune hyperparameters for various machine learning algorithms and visualize the decision boundaries. It supports the following algorithms:

• Random Forest
• Support Vector Classifier (SVC)
• Decision Tree
• AdaBoost
• XGBoost
• Gradient Boosting Machine (GBM)

You can select an algorithm, adjust its hyperparameters, train the model, and visualize the decision boundary with a 2D scatter plot.

Streamlit App

You can access the tool via the following link: Hyperparameter Tuning Tool

Features

• Interactive UI: Adjust hyperparameters using sliders and dropdown menus.
• Real-time Visualization: See the decision boundary of the trained model.
• Support for Multiple Algorithms: Easily switch between different machine learning algorithms.

Getting Started

Prerequisites

• Python 3.12
• Streamlit
• scikit-learn
• xgboost
• matplotlib
• numpy
• seaborn

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/Hyperparameter-Tuning-Tool.git
   cd Hyperparameter-Tuning-Tool

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run toolsapp.py

Contact

For any queries or suggestions, feel free to reach out:

• LinkedIn: Lavish Gangwani
• Email: [email protected]

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Thank you for using the Hyperparameter Tuning Tool!