This project is a simple machine learning model deployment that predicts the price of houses based on the area (in square feet) and the number of rooms. The model is trained on a dummy dataset and deployed on Render.
You can access the live demo of the deployed model here.
- Predict House Prices: Get the predicted price of a house based on input features.
- Input Features:
- Area of the house (in square feet)
- Number of rooms
- Visit the live demo.
- Enter the area of the house in square feet.
- Enter the number of rooms.
- Click on the "Predict" button to get the predicted price of the house.
The model is trained on a dummy dataset with the following features:
- Area of the house (in square feet)
- Number of rooms
The target variable is the price of the house.
The model is deployed on Render using Flask. Flask is used to create a simple web interface where users can input the features and get the predicted house price.