jenil001/project3_wildfire-ml-algeria

Data analysis and ML models for Algerian Forest Fires dataset — includes preprocessing, regression modeling, and reproducible Jupyter notebooks. # project3_wildfire-ml-algeria Data analysis and ML models for Algerian Forest Fires dataset — includes preprocessing, regression modeling, and reproducible Jupyter notebooks. # Algerian Forest Fires ML Project This repository contains an end-to-end machine learning workflow on the **Algerian Forest Fires dataset**. The main objective is to study wildfire behavior, clean and prepare the data, and build predictive models to understand the factors that influence fire occurrence. --- ## Contents - `Algerian_forest_fires_dataset_updated.csv` → Original dataset - `Algerian_forest_fires_cleaned_dataset.csv` → Preprocessed dataset - `data_cleaning_eda.ipynb` → Data exploration and preprocessing (EDA). - `Model Training.ipynb` → Training baseline ML models .Applying Ridge, Lasso and ElasticNet regression. Along with cross validations. --- ## Dataset The dataset includes meteorological conditions and fire indicators from two Algerian regions during summer 2012. Some key variables are: - Temperature - Humidity - Wind - Rain - Fire Weather Index codes (FFMC, DMC, DC, ISI) - Target: Fire vs No Fire --- ## Steps Followed 1. **Data preprocessing** → Cleaning, handling missing values, encoding, and scaling 2. **Exploratory Data Analysis** → Visualizing patterns and relationships 3. **Model training** → Building baseline machine learning models 4. **Regularized regression** → Using Ridge and Lasso for better generalization 5. **Evaluation** → Comparing model accuracy and performance metrics --- ## Tools & Technologies Used ### Languages & Environment - Python - Jupyter Notebook ### Libraries - **Data Handling** → Pandas, NumPy - **Visualization** → Matplotlib, Seaborn - **Modeling** → Scikit-learn ### Visualizations - Histograms and bar plots - Boxplots and violin plots (for outlier and distribution analysis) - Correlation heatmaps - Line plots (for trends across regions and months) - Pair plots (to study relationships between features) - Scatter plots with regression l …