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🏡 House Price Prediction - Flask App

📌 Project Overview

This is a Flask-based web application that predicts house prices based on user inputs such as:

• Number of Bedrooms 🛏️
• Number of Bathrooms 🚿
• Size in Square Feet 📏
• Location 📍
• Median Income (in Lakhs ₹)

The application uses a Machine Learning Model trained on an open-source housing dataset (California) to provide accurate price predictions.

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🚀 Installation Guide

Follow these steps to set up the project on your local machine.

1️⃣ Clone the Repository

git clone https://github.com/https://github.com/jd20000/House_Prediction/house-price-prediction.git

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2️⃣ Navigate to the Project Directory

cd house-price-prediction

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3️⃣ Create a Virtual Environment (Recommended)

A virtual environment helps isolate dependencies. Run:

python -m venv venv

Activate it:

• Windows:

  venv\Scripts\activate

• Mac/Linux:

  source venv/bin/activate

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4️⃣ Install Dependencies

Install the required libraries from requirements.txt:

pip install -r requirements.txt

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5️⃣ Train the Model (If Not Already Trained)

If the model file house_price_model.pkl is missing, run:

python model_training.py

This will train the model and save it inside the model/ directory.

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6️⃣ Run the Flask Application

python app.py

The app will start on http://127.0.0.1:5000/.

If another project is running on this port, change it:

python app.py --port 5001

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7️⃣ Open in Browser

Go to:
👉 http://127.0.0.1:5000/

Enter house details and get the predicted price in Indian Rupees (₹).

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8️⃣ Deactivate Virtual Environment (When Done)

deactivate

screenshots

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📊 Machine Learning Model Details

🔹 Algorithm Used:

We use Linear Regression, a simple yet effective ML algorithm for price prediction.

🔹 How It Works?

• The model takes in various features like size, bedrooms, location, median income, etc.
• It learns the relationship between these features and house prices.
• When new inputs are given, it predicts the most likely price based on past data.

🔹 Key Features Explained:

Feature	Meaning
Median Income	Average annual income of residents in Lakhs (₹1 Lakh = 1.0)
House Age	How old the house is (in years) 🏠
Average Rooms	Average number of rooms per house in the area 📏
Average Occupancy	Number of people living per house 👨‍👩‍👧‍👦

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📜 License

This project is open-source. Feel free to modify and enhance it! 🚀

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💡 Contributing

If you'd like to contribute, feel free to submit a pull request. 😊

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📞 Contact

For any queries, contact Jay - 📧 [email protected]

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House Price Prediction - Flask App

Overview

This is a Flask-based web application that predicts house prices based on input features using a machine learning model.

Features

• User inputs features like median income, house age, average rooms, and average occupancy.
• The machine learning model predicts house prices in Indian Rupees (₹).
• Simple and easy-to-use web interface.

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/house-price-prediction.git

0a3c4e7 (Initial commit with existing README)