🏠 House Price Prediction - Machine Learning Web App

This project predicts house prices in King County, USA using a trained Random Forest Regressor model and a Flask web app.

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📊 Dataset

Dataset used: kc_house_data.csv

• Contains 21,000+ housing records.
• Includes features like bedrooms, bathrooms, sqft, location, etc.

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##⚙️ Features

• Cleaned & preprocessed dataset (outlier removal, encoding).
• Trained RandomForestRegressor model with 86–87% accuracy.
• Location-based dropdown selection with lat/long and zipcode_encoded mapping.
• Fully interactive Flask-based web UI with dropdowns.
• Predicts house price based on 15 input features.

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🚀 Tech Stack

• Python
• Pandas, NumPy, Scikit-Learn, Seaborn, Matplotlib
• Flask (Backend)
• HTML, CSS (Frontend)

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🛠️ How to Run Locally

1. Clone this repo:

   git clone https://github.com/yourusername/house-price-predictor.git
   cd house-price-predictor
   

⚠️ Note: The trained model file house_price_model.pkl is not included in this repository due to file size limits. To test the app, either train the model yourself using train_model.py or contact the author.