Rkdixit01/Forest-Fire-Prediction-Using-Regression-Models

This project analyzes the Algerian Forest Fires Dataset to predict fire occurrences using linear regression models like Elastic Net, Ridge, and Lasso. The workflow includes data cleaning, EDA, feature engineering, and regularization to optimize accuracy, providing insights for better forest fire risk prediction. # Forest-Fire-Prediction-Using-Regression-Models This project analyzes and predicts forest fire occurrences using the Algerian Forest Fires Dataset from Kaggle. The dataset includes various meteorological and environmental factors that influence fires in Algeria. The goal was to develop a predictive model using linear regression techniques. 💠Feature Engineering: Meaningful features were created and selected to improve model performance, enhancing the accuracy of the predictions. 💠Exploratory Data Analysis (EDA): In-depth EDA identified correlations and patterns in the data, guiding feature engineering and model development. 💠Data Cleaning: Missing values, inconsistencies, and outliers were handled to ensure the dataset’s quality and improve model accuracy. 💠Linear Regression Models: Techniques like Elastic Net, Ridge, and Lasso Regression were applied to predict fire occurrences. These models handled multicollinearity and overfitting through regularization. Conclusion: This project shows how advanced regression techniques, combined with proper data cleaning and feature engineering, can significantly improve forest fire prediction. The results can help in fire risk management and prevention.