🏠 House Price Prediction

📌 Project Description

This project is a house price prediction application in India using machine learning. The model is trained using a public dataset from Kaggle containing various house features such as the number of bedrooms, bathrooms, building area, condition, and number of nearby schools.

📂 Dataset

Dataset : House Price India.csv (source: https://www.kaggle.com/datasets/sukhmandeepsinghbrar/house-prices-india)

Key features used :

• bedrooms – number of bedrooms
• bathrooms – number of bathrooms
• livingarea – building area (sq ft)
• condition – condition of the house (scale 1–5)
• numberofschools – number of schools near the house

Prediction target : House price (in dataset currency)

⚙️ Methodology

1. Data Preprocessing

• Selecting relevant features from the dataset
• Normalization/Standardization if necessary (depending on the model)

2. Modeling

• Machine Learning models are saved in the model.pkl file (e.g., using Random Forest Regressor or other regression algorithms)

3. Model Evaluation

• Using regression evaluation metrics such as R² Score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).

4. Deployment

• The model is integrated with the Streamlit application for real-time house price prediction.

📦 Library

• pandas
• numpy
• scikit-learn
• joblib
• streamlit
• matplotlib

🎯 Application Usage

1. House Price Prediction

• Provides a house price estimate based on property features: number of bedrooms, bathrooms, building area, house condition, and number of nearby schools.
• Helps prospective buyers, sellers, or property agents make informed pricing decisions.

2. Interactive Web Application

• Built with Streamlit, it allows users to make price predictions directly in their browser without the need for coding or machine learning.
• Predictions are made in real time after entering property data.

3. Property Data Analysis

• Can be used to view price trends based on specific factors (e.g., house condition or number of bedrooms).
• Visualization (bar plots) makes it easier to interpret the relationship between features and house prices.

4. Real-World Application of Machine Learning

• Example of the end-to-end machine learning flow: from dataset collection, preprocessing, model training, evaluation, to deployment.
• Easily learning for those wanting to understand regression and price prediction.

5. Data Source for Research Purposes

• Can be used by students, researchers, or data practitioners to experiment with property price prediction models.
• The Kaggle dataset makes it open and replicable.

6. Flexible for Further Development

• The model can be upgraded with other algorithms or added new features (location, land area, year of construction, etc.).
• Streamlit applications can be deployed to Streamlit Cloud, Heroku, or Docker for widespread use.

📊 Plot Result

Bar plot of average home prices based on home condition from the dataset.

📈 Dataset View

Dataset of house prices along with structural information.

🚀 Run The Application

1. Clone Repository

git clone https://github.com/404-mind72/House-Price-Prediction.git

2. Install Requirements

pip install -r requirements.txt

3. Launch Streamlit App

streamlit run app.py

4. Browser Address

http://localhost:8501

🖥️ Application Usage

• Enter the number of bedrooms, bathrooms, building area, house condition, and number of nearby schools.
• Click Predict Price to see the estimated home price.

🚩Contributions

I would appreciate contributions to improve the model, add or enhance features, and optimize the deployment process. For any queries, reach out to me at joni150703@gmail.com

📄 License

This project is licensed under the MIT License. Free to use, modify, and distribute.