House Price AI: Machine Learning Predictor

House Price AI is an interactive web application that estimates residential property prices based on key features like square footage, build quality, and garage capacity.

Unlike static Jupyter Notebooks, this project deploys a Linear Regression model into a live, user-friendly interface, allowing users to tweak input parameters and see instant price predictions along with the model's accuracy metrics.

Live Demo: [https://house-price-predictor-d235puue4xxatph2r25hdj.streamlit.app/]

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Key Features

• Real-Time Inference: Instantly calculates the estimated price as you adjust sliders and inputs.
• Transparent Metrics: Displays the model's R² Score (Accuracy) dynamically to show how well the model fits the data.
• Interactive Inputs: Users can experiment with:
  • Overall Material Quality (1-10 Scale)
  • Living Area (sq ft)
  • Garage Capacity (Car count)
  • Basement Size & Year Built
• Automated Training: The app retrains the model on the fly using the latest dataset (train.csv) whenever the app loads, ensuring transparency in the modeling process.

Tech Stack

• Frontend: Streamlit (Python Web Framework)
• Machine Learning: Scikit-Learn (Linear Regression, Train-Test Splitting)
• Data Processing: Pandas (Data Cleaning & Feature Engineering)
• Dataset: Kaggle House Prices - Advanced Regression Techniques

How to Run Locally

1. Clone the repository:

   git clone [https://github.com/yourusername/House-Price-Predictor.git](https://github.com/yourusername/House-Price-Predictor.git)

2. Install dependencies:

   pip install -r requirements.txt

3. Run the app:

   streamlit run app.py

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Note: This model is trained on a subset of the Ames Housing Dataset and is intended for educational and demonstration purposes.