ML_mini_Project1

Project Overview

This project focuses on predicting forest fires in Algeria using machine learning techniques. It utilizes a linear regression modeles to analyze and predict the occurrence and severity of forest fires based on historical data.

File Structure

ML_mini_Project1/
├── dataset/
│   └── Algerian_forest_fires_dataset_cleaned.csv
├── models/
│   ├── ridge2.pkl
│   └── scaler2.pkl
├── notebook/
│   └── Linear_Regression.ipynb
├── templates/
│   ├── home.html
│   └── index.html
├── README.md
├── app.py
└── requirements.txt

Description of Files and Directories

• dataset/

  • Algerian_forest_fires_dataset_cleaned.csv: This file contains the cleaned dataset used for training and testing the machine learning model.

• models/

  • ridge2.pkl: This file contains the serialized ridge regression model.
  • scaler2.pkl: This file contains the serialized scaler used for feature scaling.

• notebook/

  • Linear_Regression.ipynb: Jupyter notebook containing the exploratory data analysis (EDA), data preprocessing, model training, and evaluation for the linear regression model.

• templates/

  • home.html: HTML template for the home page of the web application.
  • index.html: HTML template for the main page of the web application.

• README.md: Documentation file providing an overview of the project and instructions for setup and usage.

• app.py: Flask web application script that serves the model and renders the HTML templates.

• requirements.txt: A list of Python packages required to run the project. This file can be used to install the dependencies using pip.

Setup Instructions

1. Clone the repository:

   git clone https://github.com/NK278/ML_mini_Project1.git
   cd ML_mini_Project1

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

4. Run the web application:

   python app.py

5. Access the web application: Open a web browser and go to http://127.0.0.1:5000.

Usage

• Web Application: The web application allows users to interact with the trained model through a user-friendly interface. Users can input relevant features and get predictions about forest fires.

• Jupyter Notebook: The Linear_Regression.ipynb notebook provides a comprehensive walkthrough of the data analysis, preprocessing, model training, and evaluation steps.

Dependencies

The project requires the following Python packages:

• Flask
• pandas
• scikit-learn
• Jupyter

All required packages are listed in requirements.txt and can be installed using pip.

Contact

For any questions or issues, please contact [email protected].

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Feel free to contribute to this project by submitting issues or pull requests. We hope this project helps in understanding and predicting forest fires using machine learning techniques.