🏠 Lahore House Price Predictor

This is a machine learning web application built with Flask that predicts house prices in Lahore. It uses an XGBoost model trained on custom data and a clean frontend with HTML, CSS, and JavaScript.

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📌 Features

• Trained XGBoost model for regression
• OneHotEncoding on categorical columns
• Flask backend for prediction API
• Responsive frontend with dark mode
• Custom logo and design
• Deployed on Render

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🧠 Model Info

• Model: XGBoost Regressor
• Preprocessing: OneHotEncoder
• Trained on custom housing data
• Saved as: xgb.pkl

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📂 Dataset

• Created manually and cleaned
• Columns used:
  • Area Name (categorical)
  • House Type (categorical)
  • Furnishing (categorical)
  • Area (numeric)
  • Bedrooms (numeric)
  • Bathrooms (numeric)
  • Year Built (numeric)

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🚀 How to Run Locally

1. Clone the repo

git clone https://github.com/your-username/lahore-house-price-predictor.git
cd lahore-house-price-predictor
Install packages

bash
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pip install -r requirements.txt
Run the app

bash
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python app.py
Open in browser:

arduino
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http://localhost:5000/
🖼 UI
Dropdowns: Area Name, House Type, Furnishing

Inputs: Area (Marla), Bedrooms, Bathrooms, Year Built

Submit button to get predicted price

Logo on top

Dark mode toggle

Mobile responsive layout

🌐 Deployment
Hosted on Render.com

Requirements:

pip install -r requirements.txt

Start command: python app.py

👨‍💻 Developer
Ali Ahmad

Software Engineering Student

The Islamia University of Bahawalpur

Passionate about ML & Web Development

📁 Project Structure
cpp
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📦 lahore-house-price-predictor
├── app.py
├── xgb.pkl
├── Cleaned_data.csv
├── requirements.txt
├── templates/
│   └── index.html
├── static/
│   ├── style.css
│   └── logo.png
└── README.md