🏠 House Price Prediction using Linear Regression

This project is part of Task 01 of the SkillCraft Technology internship program. It implements a Linear Regression model to predict house prices based on:

• Square footage (GrLivArea)
• Number of bedrooms (BedroomAbvGr)
• Number of full bathrooms (FullBath)

📌 Technologies Used

• Python
• Pandas
• scikit-learn

📦 Installation

To run this project locally, follow the steps below:

1. Clone the repository:

git clone https://github.com/Agent-A345/SCT_ML_01.git

2. Install Dependencies

pip install pandas sklearn

3. Run the program

python task1.py

🧠 How It Works

1. Load the dataset (train.csv)
2. Select 3 key features affecting house prices
3. Train a Linear Regression model
4. Evaluate model using R² and Mean Squared Error
5. Take user input and predict house price
6. Show similar past examples for validation

🙌 Acknowledgements

Thanks to SkillCraft Technology for the opportunity to work on this internship project.

License

This project is licensed under the MIT License.