House Price Prediction Using Machine Learning

Before you download

House Price Prediction Using Machine Learning is an advanced web-based ML application that estimates property prices based on location and property characteristics. Users can enter details such as city, locality, area in square feet, BHK, bathrooms, property age, parking, floor, furnishing status and distance from the city center. The system processes these inputs using an advanced Stacking Ensemble model combining Random Forest, Gradient Boosting and Ridge Regression to generate an estimated property price. The application also maintains prediction history and provides an analytics dashboard with model performance information.

Getting started

1. Extract the downloaded ZIP file.

2. Open the extracted project folder in VS Code.

3. Open Terminal.

4. Create virtual environment:

python -m venv venv

5. Activate virtual environment:

venv\Scripts\activate

6. Install required packages:

pip install -r requirements.txt

7. Train the Machine Learning model:

python train_model.py

8. After model training is completed, start the Flask application:

python app.py

9. Open your browser and visit:

http://127.0.0.1:5000/

Important:
Run python train_model.py at least once before starting predictions.

Requirements

Python 3.10 or above, pip, Flask, Pandas, NumPy, Scikit-learn, Joblib, SQLite and a modern web browser. VS Code is recommended. Minimum 4 GB RAM; 8 GB recommended for smoother model training.

Build your understanding

Use this resource as a learning reference. Explore how the code works, test it with your own inputs and document your changes for your presentation.