DhruvGangdev/Algerian_Forest_Fire_Analysis

# Algerian Forest Fires Analysis ## Project Overview This project focuses on analyzing and predicting forest fires in Algeria using machine learning techniques. It includes data cleaning, exploratory data analysis (EDA), feature engineering, and model training. ## Dataset The dataset used in this project consists of forest fire occurrences in Algeria, including meteorological and environmental features. ### Files - **Algerian_forest_fires_dataset.csv**: The raw dataset containing fire occurrences and associated features. - **Algerian_forest_fires_cleaned_dataset.csv**: The cleaned dataset after preprocessing. - **EDA And FE Algerian Forest Fires.ipynb**: Jupyter Notebook containing exploratory data analysis and feature engineering. - **Model Training.ipynb**: Jupyter Notebook for training machine learning models to predict forest fires. ## Steps Involved 1. **Data Preprocessing**: - Handling missing values - Feature selection and transformation - Data normalization 2. **Exploratory Data Analysis (EDA)**: - Visualizing correlations between features - Identifying patterns in fire occurrences - Distribution analysis of meteorological factors 3. **Feature Engineering**: - Creating new meaningful features - Encoding categorical variables - Dimensionality reduction techniques 4. **Model Training & Evaluation**: - Training different machine learning models (e.g., Logistic Regression, Decision Trees, Random Forest, etc.) - Hyperparameter tuning - Model performance evaluation using metrics like accuracy, precision, recall, and F1-score ## How to Use 1. Clone this repository: ```bash git clone ``` 2. Install required dependencies: ```bash pip install -r requirements.txt ``` 3. Run the EDA notebook to understand the dataset. 4. Train models using the Model Training notebook. 5. Evaluate model performance and interpret results. ## Conclusion This project provides insights into factors affecting forest fires in Algeria and builds predictive models to aid in early fire d …