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🏠 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.

Bar Plot

📈 Dataset View

Dataset of house prices along with structural information.

Bar Plot

🚀 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.

Bar Plot

🚩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.