PRODIGY_ML_01

The goal of this task is to implement a linear regression model to predict the prices of houses based on their square footage, the number of bedrooms and bathrooms and house location. The model is trained on the Kaggle "House Prices - Advanced Regression Techniques" dataset and deployed using a Flask web interface for easy interaction.

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Features

• House Price Prediction using Machine Learning
• Random Forest Regressor Model
• Data Preprocessing and Feature Engineering
• Interactive Flask Web Application
• User-Friendly Interface with HTML and CSS

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Technologies Used

• Python
• Pandas
• Scikit-learn
• Flask
• HTML
• CSS
• Jupyter Notebook

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Project Structure

task-01-hpp-final
│
├── app.py
├── house_price.ipynb
├── house_price_model.pkl
├── templates/
│   └── index.html
├── static/
│   └── style.css
└── data/

Dataset

Dataset used: House Prices - Advanced Regression Techniques

Source: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques

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How to Run

1. Clone the repository

git clone https://github.com/rah-shresh/PRODIGY_ML_01.git

2. Open project directory

cd PRODIGY_ML_01

3. Install dependencies

pip install flask pandas numpy scikit-learn joblib

4. Run the Flask application

python app.py

5. Open browser and visit

http://127.0.0.1:5000

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Output

Users can enter:

• Square Feet
• Bedrooms
• Bathrooms

The application predicts the estimated house price and displays it on the webpage.

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Internship

Task 01 - House Price Prediction

Machine Learning Internship at Prodigy InfoTech

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Author

Shresh Rahangdale

GitHub: https://github.com/rah-shresh