heba14101998/Algerian-Forest-Fire-Prediction

Predicting forest fire occurrences in Algeria using the "Algerian Forest Fires Dataset" from Kaggle. The project aims to develop a robust model that can effectively identify potential fire risks, thereby supporting proactive measures for prevention, mitigation, and resource allocation. # Algerian Forest Fire Prediction This repository contains a machine learning project focused on predicting forest fire occurrences in Algeria using the "Algerian Forest Fires Dataset" from Kaggle. The project aims to develop a robust model that can effectively identify potential fire risks, thereby supporting proactive measures for prevention, mitigation, and resource allocation. ## 🚩 Table of Contents - Project Overview - Problem Statement - Project Goals - Getting Started - Prerequisites - Installation - Running the Project - Step 1: Setup Kaggle API - Step 2: Configure DVC with Remote Storage - Step 3: Run the Project - Dataset - Methodology - Tools - Project Structure - Contributing - License - Acknowledgments ## Project Overview Forest fires pose a significant threat to the environment and human safety. This project aims to develop a a machine learning model that can accurately predict whether a forest fire will occur based on input features based on environmental and weather data. This is a binary classification problem, where the model needs to learn the patterns that distinguish between instances where a fire occurred ("fire") and instances where no fire occurred ("not fire"). ### Project Goals 1. **Data Acquisition and Preprocessing:** - Download and prepare the "Algerian Forest Fires Dataset" for analysis. - Cleanse the data to handle missing values, inconsistencies, and outliers. 2. **Model Development:** - Train a machine learning model capable of predicting whether a forest fire will occur based on environmental and weather factors. - Explore and compare different machine learning algorithms to identify the most suitable model. - Tune hyperparameters to optimize the model's performance. 3. **Model Evaluation:** - Evaluate the trained model using relevant metrics (e.g., accuracy, precision, recall, F1-score, ROC AUC). - Analyze the model's predictions and identify any potential areas for improvement. 4. **Pipeline Creation:** - Develop a stream …