This project uses a simple linear regression model to predict house prices based on house size (in square feet). It uses a dataset stored in a CSV file and visualizes both the raw data and the model's predictions.

The goal is to:

• Load and explore a dataset of house sizes and prices
• Train a linear regression model using scikit-learn
• Make predictions on unseen data
• Visualize actual vs. predicted house prices using matplotlib

• Python 3
• Pandas
• NumPy
• Matplotlib
• scikit-learn

• home_dataset.csv: CSV file containing two columns:
  • HouseSize (in sq.ft)
  • HousePrice (in millions of dollars)
• predict_home_prices.py or your script file: contains the full code for loading data, training the model, and plotting results.

1. Load the dataset using pandas
2. Plot raw data as a scatter plot
3. Split data into training and test sets (80/20 split)
4. Train a linear regression model on the training data
5. Predict house prices for the test set
6. Visualize:
   • Blue dots = actual house prices
   • Red line = predicted prices based on the model

1. Clone the repository:

   git clone https://github.com/alishbamateen/predict_home_prices.git
   cd house-price-prediction

2. Make sure you have the required libraries:

   pip install pandas numpy matplotlib scikit-learn

3. Run the script:

   python predict_home_prices
   

A second plot showing:

Blue dots: actual house prices (test data) Red line: predicted house prices from the linear regression model

• The red line in the final graph may not appear properly unless x_test is sorted before plotting.
• This project is for educational purposes and demonstrates basic regression modeling and visualization.