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Developed a Bengaluru house price prediction project using Python libraries like Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn. Implemented data cleaning, feature engineering, and model evaluation with techniques such as median imputation, outlier removal, and Random Forest modeling (R-square: 0.72, MAPE: 0.18). Deployed the model via Flask

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panubhav2001/bengaluru-house-price

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Bengaluru House Price Prediction

This project aims to predict house prices in Bengaluru using various machine learning models. The process involves data preprocessing, outlier removal, feature engineering, model training, and evaluation.

git clone https://github.com/panubhav2001/bengaluru-house-price.git
cd bengaluru-house-price
pip install -r requirements.txt
python main.py

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Developed a Bengaluru house price prediction project using Python libraries like Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn. Implemented data cleaning, feature engineering, and model evaluation with techniques such as median imputation, outlier removal, and Random Forest modeling (R-square: 0.72, MAPE: 0.18). Deployed the model via Flask

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