🏠 House Price Prediction

---

---

---

---

---

A clean, modern, and fully documented machine learning project designed to predict residential property prices using advanced data preprocessing and regression models.

🧠 Overview

This project aims to predict house prices using machine learning techniques based on various features of residential properties. The workflow includes data cleaning, feature engineering, handling missing values, encoding categorical variables, feature scaling, and training multiple regression models. The system also includes a full-stack web interface built by Aniruddha Nagesh Salvankar, allowing prediction and model training directly through a browser.

---

⭐ Features

• Predicts house prices using multiple machine learning models
• Handles missing values, categorical encoding, and feature scaling
• Includes a full Flask backend and modern responsive frontend
• Saves and loads trained ML models automatically
• Supports model comparison and selection
• Visualizes training progress and prediction results (Frontend UI)

---

🛠️ Technologies Used

• Python 3.12.10

1. Pandas
2. NumPy
3. Matplotlib
4. Seaborn
5. Scikit-learn
6. XGBoost

• Flask (for backend API)
• HTML, CSS, JavaScript (for frontend UI)

---

🚀 How to Use?

1. Clone the repository.

2. Install dependencies using:

   pip install -r requirements.txt

3. Run the Flask application:

   python app.py

4. Open your browser and visit: http://127.0.0.1:5000

5. Use the interface to train the model and predict house prices.