🏠 House Price Prediction

A Machine Learning project that predicts house prices based on various features using regression algorithms. The project includes data preprocessing, model training, evaluation, and a Streamlit web application for real-time predictions.

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🚀 Project Demo

Application Screenshot

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📌 Features

✅ Data preprocessing and cleaning
✅ Exploratory Data Analysis (EDA)
✅ Machine Learning model training
✅ House price prediction
✅ User-friendly Streamlit interface
✅ Model evaluation

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Streamlit

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📂 Project Structure

House-Price-Prediction/
│
├── app.py                  # Streamlit application
├── model.pkl               # Trained ML model
├── requirements.txt        # Required libraries
├── README.md               # Project documentation
│
├── data/
│   └── house_data.csv
│
├── notebooks/
│   └── model_training.ipynb
│
└── screenshots/
    └── app.png

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⚙️ Installation & Setup

Clone the repository:

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

Go to project directory:

cd House-Price-Prediction

Create virtual environment:

python -m venv venv

Activate environment:

Windows:

venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Run the application:

streamlit run app.py

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🤖 Machine Learning Workflow

1. Data Collection
2. Data Cleaning
3. Exploratory Data Analysis
4. Feature Engineering
5. Model Training
6. Model Evaluation
7. Deployment using Streamlit

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📊 Model Used

• Linear Regression
• Decision Tree Regression
• Random Forest Regression

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🎯 Future Improvements

• Improve model accuracy
• Add more real-world datasets
• Deploy using cloud platforms
• Add advanced ML algorithms

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👨‍💻 Author

Shabarish Kandagatla

GitHub: https://github.com/Shabarishkandagatla