Bilalbaddi/AlgerianForestFireDetection

The Algerian Forest Fire Detection project utilizes the Algerian Forest Fire Dataset to predict the likelihood of forest fires using Logistic Regression The dataset includes features like temperature, humidity, wind speed, and rainfall from specific regions in Algeria,To enhance usability, a Flask web application was developed, Here’s the updated README content without hashes (#) or double asterisks (**): --- Algerian Forest Fire FWI Prediction This project demonstrates a machine learning regression model built using the Algerian Forest Fire dataset to predict the Fire Weather Index (FWI). The model is implemented using LassoCV Regressor and is deployed as a web application using Flask. --- Features - Dataset: Algerian Forest Fire dataset - Target Variable: Fire Weather Index (FWI) - Regression Algorithm: LassoCV Regressor - Web Application: Flask-based interface to input feature values and predict FWI - Objective: Provide accurate predictions of FWI based on the dataset for better forest fire management. --- Technologies Used - Python - Machine Learning Libraries: - scikit-learn (LassoCV) - pandas - numpy - Web Framework: Flask - Other Libraries: matplotlib (for visualization), seaborn (for exploratory data analysis) --- Dataset The Algerian Forest Fire dataset contains meteorological data and FWI values. It includes features like temperature, humidity, wind speed, and others that are significant in predicting forest fire intensity. --- Workflow 1. Data Preprocessing: - Cleaned and preprocessed the dataset. - Handled missing values and performed feature scaling. 2. Feature Engineering: - Analyzed the correlation between features and the target variable. - Selected significant features for prediction. 3. Model Training: - Implemented LassoCV Regressor for regression and feature selection. - Performed hyperparameter tuning to achieve optimal results. 4. Deployment: - Built a Flask-based web server to provide predictions based on user inputs. --- Flask Application The Flask web application allows users to: - Input meteorological data manually. - Receive a predicted FWI value instantly. How to Run the Flask App 1. Clone the repository: git clone 2. Navigate to the project directory: cd 3. Install the required dependencies: pip install -r requirements.txt 4. Run the Flask applica …