Conducted thorough exploratory data analysis (EDA) on a comprehensive house prices dataset. Employed Pandas and Numpy for efficient data manipulation, preprocessing, and feature engineering. Visualized key insights and findings using advanced data visualization libraries, Seaborn and Matplotlib. Implemented two powerful machine learning models: Decision Tree Regressor and Linear Regression with Polynomial Features. Evaluated the performance using mean absolute percentage error (MAPE) metric to quantify the accuracy of house price predictions. Conducted and Visualized the comparison between the Decision Tree Regressor and Linear Regression models.
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Exploratory Data Analysis and Regression Analysis, Created our Machine Learning of Decsion Tree and Linear Regression for Predicting House Prices in Future
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