Assist with Python Fitting a Ridge Regression Model and Lasso

dataset on google drive: https://drive.google.com/file/d/13PmNaMib43506pAx97812yXGuJSOa7KR/view?usp=sharing

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The HOUSES dataset contains a collection of recent real estate listings in San Luis Obispo county and around it. The dataset is provided in Rea]Estate.csv. You may use \"onehotkeying\" to expand the categorical variables. The dataset contains the following useful elds (You may excluding the Location and MLS in your linear regression model). You can use any package for this question. (b) Price: the most recent listing price of the house (in dollars). Bedrooms: number of bedrooms. Bathrooms: number of bathrooms. Size: size of the house in square feet. Price/Sth: price of the house per square foot. Status: Short Sale, Foreclosure and Regular. (10 points) Fit the Ridge regression model to predict Price from all variable. You can use one-hot keying to expand the categorical variable Status. Use 5-fold cross validation to select the regular- izer optimal parameter, and show the CV curve. Report the tted model (i.e., the parameters), and the sumofsquares residuals. You can use any package. (10 points) Use lasso to select variables. Use 5-fold cross validation to select the regularizer optimal parameter, and show the CV curve. Report the tted model (i.e., the parameters selected and their coefcient). Show the Lasso solution path. You can use any package for this