ML model in Real Estate Market Predict

This project leverages the Ames Housing Dataset to predict real estate prices using advanced machine learning techniques. By combining methods like DBScan for outlier detection, K-Means clustering for segmentation, and models including Linear Regression, Neural Networks, and Random Forests, it highlights the importance of data preprocessing and robust algorithms in achieving high prediction accuracy. The approach provides valuable insights into the factors driving housing prices, aiding stakeholders in making informed decisions.

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