gaurav-bhadane/Fire_Weather_Index_Predictor

Fire Weather Predictor made using Ridge Regression on Algerian Forest Fire Dataset to forecast fire occurrences based on meteorological parameters. Utilizes Python with Flask for web interface. Predictions delivered via an interactive dashboard. # Fire Weather Index Predictor ## Overview This project aims to predict the Fire Weather Index (FWI) using ridge regression on the Algerian forest fire dataset. The FWI is a numerical rating of fire intensity potential, representing the difficulty of controlling fires. The project utilizes Flask for building the web application. ## Dataset The Algerian Forest Fire Dataset contains meteorological data and fire occurrences for two regions in Algeria (Bejaia region and Sidi Bel-Abbes region) over a period from June 2012 to September 2012. The dataset includes the following columns: 1. **Date**: Date of observation 2. **Temperature**: Temperature in Celsius degrees 3. **RH**: Relative Humidity in percentage 4. **Ws**: Wind speed in km/h 5. **Rain**: Total day in mm 6. **FFMC**: Fine Fuel Moisture Code 7. **DMC**: Duff Moisture Code 8. **DC**: Drought Code 9. **ISI**: Initial Spread Index 10. **BUI**: Buildup Index 11. **FWI**: Fire Weather Index 12. **Classes**: Fire occurrence (Yes/No) ## Requirements - Python 3 - Flask - NumPy - Pandas - Scikit-learn - Matplotlib - Seaborn ## Installation 1. Clone the repository: `git clone github.com` 2. 2. Install dependencies: `pip install -r requirements.txt` ## Model Training and Evaluation The model is trained using Ridge Regression, a type of linear regression that includes L2 regularization to prevent overfitting. The steps for model training and evaluation are as follows: 1. **Data Preprocessing**: - Handle missing values - Encode categorical variables - Normalize/standardize numerical features 2. **Feature Selection**: - Select relevant features based on domain knowledge and exploratory data analysis 3. **Model Training**: - Split the dataset into training and testing sets - Train the Ridge Regression model on the training set 4. **Model Evaluation**: - Evaluate the model on the testing set using metrics like Mean Squared Error (MSE) and R-squared. ## Usage 1 …