Welcome to the House Price Predictor App! This application utilizes machine learning to predict house prices based on various features. Here's how it works:
This app is designed to provide insights into house prices using a Linear Regression model trained on the California housing dataset.
- Discover the magic of predicting house prices.
- Evaluate model performance using Mean Squared Error, Root Mean Squared Error, and R^2 score.
- Visualize actual vs predicted house prices with an interactive scatter plot.
- Predict new house prices by adjusting sliders for different features.
- Model Performance: Explore the evaluation metrics to assess the accuracy of the model's predictions.
- Actual vs Predicted: Visualize the performance of the model through an interactive scatter plot.
- Predict New House Price: Adjust the sliders to enter details of the house you want to predict the price for. The predicted house price will be displayed instantly.
To use this application:
- Ensure you have the necessary dependencies installed. You can install them using
pip install -r requirements.txt. - Run the script locally by executing
streamlit run script_name.pyin your terminal.
The model is trained on the California housing dataset, which consists of various features such as longitude, latitude, housing median age, total rooms, total bedrooms, population, households, and median income.
(https://github.com/khairuldzulqarnain)
We welcome feedback and contributions! If you encounter any issues or have suggestions for improvement, please open an issue.
Happy predicting! 🏡