Learning Goal: I’m working on a python exercise and need an explanation and answer to help me learn.Q1: Use the same dataset used in the source code (House _dataset.csv). a. Delete all the outlier data for the GarageArea field (for the same data set in the use case: House Prices).
*For this task you need to plot GaurageArea field and SalePrice in scatter plot, then check which numbers
b. Using simple regression select one feature that is positively correlated with ‘SalePrice’ create a regression
model and plot the regression line between the two features. c. Find top 5 most correlated features to the target label(SalePrice) and then build a model using those 5 features. d. Apply PCA on the same dataset then apply regression model on the PCA result and report your observation
after you evaluate the model. e. Evaluate all the models using MAE, MSE, RMSE and R2 score.f. video recording of your solution so I can have a better understanding of your thought process. (The solution needs to be on Jupyter notebook format)
Requirements: as long as it needs
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