How rizwanashafi/house_price works
This is a small Flask house-price prediction app. A user submits form values; the Flask route converts them to a NumPy array, calls the loaded linear-regression model, and renders the predicted price. Separately, model.py trains that model from house-price data and serializes it for the app to load. The tree omits the referenced data file, serialized model, and template contents, so their exact paths and internal details cannot be verified.
Main entry points: app.py Flask routes · model.py Regression model · templates Prediction page · data House price data
Components 6
Web prediction
- Flask routes
app.py - Prediction page
templates
Model training
- Regression model
model.py - House price data
data - Serialized model
Other components
- User
Connections 7
- User → Flask routes: submits values
- Flask routes → Prediction page: renders page
- Prediction page → User: displays price
- Flask routes → Serialized model: loads model
- Flask routes → Regression model: predicts price
- Regression model → House price data: reads training data
- Regression model → Serialized model: serializes model