This project focused on predicting real estate prices using machine learning algorithms.
I collected and preprocessed a dataset of real estate properties, including features like location, size, amenities, and historical prices. I then trained various regression models, such as linear regression, decision trees, and random forests, to predict property prices.
The best-performing model achieved an R-squared value of [mention R-squared value, e.g., 0.82], indicating a strong predictive ability.
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