ML Project(ForestFirePrediction)

Algerian Forest Fire Analysis This project involves data cleaning, visualization, and predictive modeling of the Algerian Forest Fire dataset. It focuses on building and evaluating multiple regression models to predict fire-related metrics.

Here is my deployment link (hosted on Render): 🔗 https://forestfireprediction-5o4e.onrender.com/

📂 Project Structure Data Cleaning: Handling missing values, converting data types, and cleaning column names. Exploratory Data Analysis (EDA): Visualizing data distributions and relationships using matplotlib and seaborn.

Model Training: Building and evaluating multiple regression models: Linear Regression Lasso Regression Ridge Regression Elastic Net Regression

🛠️ Technologies Used Python 3 pandas numpy matplotlib seaborn scikit-learn

🚀 How to Run the Project

Clone this repository: git clone https://github.com/your-username/your-repo.git cd your-repo

Install required dependencies: pip install -r requirements.txt

Open the Jupyter Notebook: jupyter notebook Cleaning&Visualizing.ipynb

Run all cells to clean the data, visualize, and train models.

📊 Dataset The project uses the Algerian Forest Fire dataset. It includes various features such as: Date (day, month, year) Temperature Relative Humidity (RH) Wind Speed (Ws) FFMC, DMC, DC, ISI indices Region

🔍 Model Evaluation Each regression model is evaluated using cross-validation to determine its performance and select the best regularization method.

📜 License This project is licensed under the MIT License - see the LICENSE file for details.