FireWatch: Fire Prediction AI Model

Project Overview

FireWatch AI utilizes an advanced machine learning model to predict future wildfires based on key features identified by the same AI.

Features

Fire Prediction: Predict the occurrence of wildfires based on historical data and weather conditions.

Real-Time Heat Maps: Visualize predicted fire locations using interactive heat maps.

Installation

To run this project, you need to install the following libraries:

• pandas
• numpy
• joblib
• folium

pip install xgboost pandas scikit-learn folium geopandas joblib

Usage

1. Load the Dataset: Update the file_path variable with the path to your dataset.
2. Train the Model: The script will preprocess the data, train the XGBoost classifier, and evaluate its performance.
3. Generate Predictions: The script will generate predictions for the entire dataset and create a heat map of predicted fire locations.
4. Visualize: The heat map will be saved as an HTML file (fire_prediction_heatmap.html).

Code Structure

• Data Preprocessing: Handles missing values and converts feature columns to integers.
• Model Training: Splits the data into training, validation, and test sets. Trains the XGBoost model and evaluates its performance.
• Model Evaluation: Outputs accuracy, classification report, and confusion matrix.
• Model Saving: Saves the trained model using joblib.
• Visualization: Creates a heat map of predicted fire locations using Folium.