Forest Cover Type Prediction using Machine Learning Approaches

Overview

This project aims to predict the forest cover type using machine learning algorithms. The application leverages Random Forest and XGBoost algorithms to classify the type of forest cover. The application is built using Streamlit for an interactive web interface.

Features

• Utilizes Random Forest and XGBoost algorithms for prediction.
• Provides an interactive web interface using Streamlit.
• Supports various input features for accurate prediction.

Requirements

• Python 3.6 or above
• pandas
• numpy
• matplotlib
• streamlit
• scikit-learn
• xgboost

Installation

Step 1: Clone the Repository

Clone the repository to your local machine using the following command:

git clone https://github.com/yourusername/forest-cover-type-prediction.git cd forest-cover-type-prediction

Step 2: Install Dependencies

Install the required Python libraries using the following command:

pip install -r requirements.txt

Step 3: Run the Application

Run the Streamlit application using the following command:

streamlit run app.py

This command will generate a URL. Open this URL in your web browser to access the application.

Usage

• Navigate to the URL generated by the Streamlit command.
• Upload or input the required features for the forest cover type prediction.
• Click on the predict button to get the predicted forest cover type.

Acknowledgements

Thanks to the creators of Streamlit for providing an easy-to-use framework for creating web applications for machine learning projects. The scikit-learn and XGBoost development teams for their powerful and user-friendly machine learning libraries.

Contact

For any questions or inquiries, please contact [].