In today’s world, data plays a very important role in decision making. This is especially true in real estate, where property prices can vary a lot depending on different factors.
In this project, we build a House Price Prediction Web Application using Python, Flask, and Machine Learning. This app helps users estimate the price of a house based on simple inputs.
Project Overview
This is a complete web application where users can enter details like:
- City (location)
- Size of the property
- Number of rooms
Based on these inputs, the system predicts the house price using trained machine learning models.
Main Features
🔮 Price Prediction
Users can enter house details and get an estimated price instantly.
📊 Interactive Dashboard
The app shows useful information like:
- Price trends in different cities
- Data charts
- Important features affecting price
🔗 REST API
The project also provides APIs so developers can use it in other applications.
🌆 Multi-City Support
The app supports major Indian cities:
- Mumbai
- Delhi
- Bangalore
- Chennai
- Kolkata
- Hyderabad
🧰 Technologies Used
- Backend: Python, Flask
- Machine Learning: scikit-learn, XGBoost
- Frontend: HTML, CSS, JavaScript
- Charts: Chart.js
⚙️ How It Works
1. Data Collection
The project uses house price data from different Indian cities.
This data includes price, area, location, and other details.
2. Data Preparation
The data is cleaned and prepared before training the model.
python src/preprocess.py3. Model Training
Different models are trained to predict prices:
- Linear Regression
- Random Forest
- XGBoost
python src/train.py4. Run the Application
Start the Flask server:
python src/api.pyOpen in browser:
👉 http://localhost:5000
Download the project source code zip file👇
buymeacoffee.com/synchlabcoq/e/523411