Developed a machine learning model to predict real estate prices based on diverse features such as location, size, and property characteristics. The project emphasizes the mathematical foundations of learning algorithms and data preprocessing.
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Data Preprocessing: Handled missing values, performed feature scaling, and executed One-Hot Encoding for categorical variables to prepare the dataset for regression.
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Conducted statistical analysis and visualization to identify correlations between property features and market prices.
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Implemented and compared multiple regression models, including Linear Regression and Decision Trees, to optimize prediction accuracy.
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Utilized metrics Mean Squared Error (MSE)
- Python
- Jupyter Notebook
- Machine learning
Course: Mathematics for Machine Learning | Institution: Ruppin Academic Center