Python,Please answer the above question except for Q1.1

Q1.1 Fit a linear regression model on data: USA_housing.csv to predict the Price of the house. "'"'Data description" #Income: Avg. area income #Age: Avg. age of the houses #Bedrooms: Avg. No. of bedrooms #Rooms: Avg. No. of rooms #Population: Population of the area #Price: Avg. price in the area #Address: Think of them as different ZIP codes Q1.2 Also try to see if the model performance can be improved with feature selection. Q2. What is the difference between correlation and regression? Q3. Give three situations when correlation implies causation. Q4. Give three situation when correlation does not imply causation Q5. Explain the importance of bias and variance related to overfitting and underfitting and How to address it using regularization