"Accurately predict house rental prices using Multiple Linear Regression. This project analyzes various factors like location, size, and amenities to estimate rental costs with precision."
User Interface (UI) Setup:
The UI is built using PyQt5, with labels and input fields for features such as city, area, rooms, parking spaces, etc. A button is provided to trigger the prediction. Model Prediction:
When the button is clicked, the model is expected to take the input values, process them through a trained LinearRegression model, and predict the rent amount. Output:
The predicted rent amount is displayed in a result field on the UI. Warning:
There's a warning indicating that the input features might not be correctly named or aligned with how the LinearRegression model was trained. This is something you might need to address to ensure proper predictions. Accuracy:
The code prints out the accuracy of the model, which seems to be extremely high, though this might require validation as it could indicate overfitting or some other issue.
the gui interaction page
the result page using multiple linear regression