MaissaLkl/Natural-Disaster-Prediction

ML-powered analysis of historical disaster data in Algeria using classification, clustering, and visualization techniques to identify patterns and predict occurrences across different terrain types. # 🌍 Natural Disaster Analysis and Prediction Project ## 📌 Overview This project leverages machine learning models to analyze and predict natural disasters in Algeria based on historical data. It integrates classification, clustering, and visualization techniques to identify disaster patterns, their relationships with terrain types, and their impact metrics. ## 🚀 Features ✅ Data preprocessing and cleaning for disaster datasets ✅ Decision tree-based disaster type prediction ✅ Multiple clustering approaches (KMeans, DBSCAN, GMM, Agglomerative) ✅ Impact analysis and visualization ✅ Terrain-based pattern recognition ✅ Comprehensive visualization suite ## 📦 Requirements ```bash pandas numpy scikit-learn matplotlib seaborn ``` ## 📂 Project Structure ``` ├── Data/ │ └── 1900_2021_DISASTERS.xlsx ├── Scripts/ │ └── script.py └── README.md ``` ## 🔧 Installation 1. **Clone the repository**: ```bash git clone github.com ``` 2. **Install required packages**: ```bash pip install -r requirements.txt ``` ## 📊 Usage ### 🧹 Data Preprocessing Run the cell to clean and preprocess disaster data: ✔ Loads raw disaster data ✔ Handles missing values ✔ Converts data types ✔ Adds terrain information ✔ Exports cleaned dataset ### 🔍 Classification Model Execute the classification cell: Features: - Decision Tree Classifier - Terrain-based disaster prediction - Performance evaluation metrics - Feature importance analysis ### 🔗 Clustering Analysis Run the clustering cell: Includes: - Multiple clustering algorithms - Optimal cluster determination - Impact distribution analysis - Terrain pattern visualization ## 🧠 Model Details ### 🎯 Classification Model - **Algorithm**: Decision Tree - **Hyperparameters**: - `max_depth`: 10 - `min_samples_leaf`: 5 - `min_samples_split`: 10 - `class_weight`: 'balanced' ### 🔄 Clustering Models - **Algorithms**: - KMeans - DBSCAN - Gaussian Mixture Model (GMM) - Agglomerative Clustering - **Features**: - …