kompany04/Algerian-forest-fire-prediction

# Algerian-Forest-Fire-Prediction This project aims to predict the occurrence and spread of forest fires in Algeria using machine learning techniques. **Introduction** Forest fires can cause significant environmental and economic damage. Predicting forest fires can help in taking preemptive measures to mitigate their impact. This project uses machine learning algorithms such as Polynomial Regression, Multilinear Regression, and Decision Trees to predict forest fires in Algeria. **Dataset:** The dataset used in this project is sourced from Kaggle and contains various features related to forest fires in Algeria, including meteorological data and fire occurrences. **Features:**\ Temperature\ Relative Humidity\ Wind Speed\ Rain\ FFMC (Fine Fuel Moisture Code)\ DMC (Duff Moisture Code)\ DC (Drought Code)\ ISI (Initial Spread Index)\ BUI (Buildup Index)\ FWI (Fire Weather Index)\ Classes (fire/no fire)\ **This notebook contains:**\ Data loading and preprocessing steps\ Exploratory Data Analysis (EDA)\ Implementation of Polynomial Regression, Multilinear Regression, and Decision Tree models\ Model evaluation and comparison\ **Models Used:**\ Polynomial Regression\ Polynomial Regression is used to model non-linear relationships between the features and the target variable. It extends linear regression by considering polynomial terms of the features. Multilinear Regression\ Multilinear Regression, or multiple linear regression, is a linear approach to model the relationship between a dependent variable and multiple independent variables. Decision Tree\ Decision Tree is a non-linear model that splits the data into subsets based on feature values, creating a tree-like structure. It captures complex interactions between features. **Contributing:**\ Contributions are welcome! Please follow these steps to contribute:\ Fork the repository.\ Create a new branch (git checkout -b feature-branch).\ Commit your changes (git commit -m 'Add new feature').\ Push to the branch (g …