Muhammad-Zeeshan-chandia/Regression-Model-of-Algerian-Forest-Dataset

# Algerian Forest Fires: EDA, Feature Selection & Model Training This project involves exploratory data analysis (EDA), feature selection, and machine learning model training on the **Algerian Forest Fires Dataset**. The objective is to analyze environmental factors contributing to forest fires and build a predictive model to classify fire occurrence. ## 🔍 Notebooks Overview ### 1. `eda_feature_selection_algerian_forest.ipynb` - Performs EDA on the dataset. - Cleans and preprocesses the data. - Visualizes trends and relationships between features. - Implements feature selection techniques to identify important predictors. ### 2. `model_training.ipynb` - Trains classification models to predict forest fires. - Compares multiple algorithms for optimal results. ## 📁 Dataset The dataset used contains meteorological and forest-related features from two regions in Algeria. ## 🛠️ Requirements Install the required Python packages - numpy - pandas - matplotlib - seaborn - scikit-learn