Load the data stored in the tab$-$delimited file nyc$.$txt into a DataFrame named nyc$.$ Use head$()$ to display the first $10$

rows of this DataFrame.

The columns contained in this DataFrame are described below.

$$ Price $$ The average price in US dollars for a meal for two.

$$ Food $$ The Zagat customer rating of the quality of the food. On a scale from $130.$

$$ Decor $$ The Zagat customer rating of the quality of the decor. On a scale from $130.$

$$ Service $$ The Zagat customer rating of the quality of the service. On a scale from $130.$

$$ Wait $$ The average wait time, in minutes, to be seated during dinner rush on a Friday evening.

$$ East $$ A binary variable indicating if the restaurant is East $(1)$ or West $(0)$ of $5$th Avenue.

Our goal in this problem will be to create a linear regression model to predict the value of Price using the other five

columns as features.

$2$

Perform the following steps in a single code cell:

$$ Create a $2$D feature array named X$1$ containing the relevant features, as well as a $1$D label array named y$1$

containing the labels. $($Note: These should be arrays, and not DataFrames or Series. See note below.$)$

$$ Use train$\_$test$\_$split$()$ to split the data into training and testing sets using an $80/20$ split. Name the

resulting arrays X$\_$train$\_1,$ X$\_$test$\_1,$ y$\_$train$\_1,$ and y$\_$test$\_1.$ Set random state$=1.$

$$ Print the shapes of X$\_$train$\_1$ and X$\_$test$\_1.$ Include text labeling the two results as shown below. Add

spacing to ensure that the shape tuples are left$-$aligned.

Training Features Shape: xxxx

Test Features Shape: xxxx

Note: You can extract a NumPy array from a pandas DataFrame or series object by adding $.$values to the end of it$.$ For

example, if df is a DataFrame object, if you run the statement X $=$ df$.$iloc$[$:$,$ some$\_$columns$].$values, then X will

be a $2$D array containing the information for the selected columns.

We will now create a linear regression model that can be used to estimate the price at a similar restaurant.

Create a linear regression model named nyc$\_$mod and then fit it to the training data. Display the intercepts and

coefficients for the final model with text labels as shown below. Add spacing to ensure that the values replacing the

xxxx characters are left$-$aligned. The intercept should appear as a single number, The coefficients should be in the form

of an array and should be displayed on a single line.

Intercept: xxxx

Coefficients: xxxx

We will now calculate the r$-$squared score for the model on both the training set and the test set.

Calculate and print the training and testing r$-$squared values for your model, rounded to four decimal places. Include the

text labels explaining which value is which, as shown below. Add spacing to ensure that the scores are left$-$aligned.

Training r$-$Squared: xxxx

Testing r$-$Squared: xxxx

We will now use the model to generate predictions for the restaurants in the test set.

Use your model to generate price estimates based on the feature values in the test set. Store the results in a variable

named test$\_$pred$\_1.$ Print the first $10$ observed y$-$values for the test set, and then the first $10$ predictions, rounded to

$2$ decimal places. Include text labels with your output as shown below. Each price array should be displayed on a single

line, and the two arrays should be left$-$aligned.

Observed Prices: xxxx

Estimated Prices: xxxx

Suppose that you wish to use the model to estimate the price for three new restaurants that were not included in the

original dataset. Assume that the feature values for these restaurants are as follows:

Food Decor Service Wait East

$221220150$

$181922341$

$252218360$

Create a DataFrame named nyc$\_$new that contains the feature values for these $3$ restaurants. Pass this DataFrame to

the predict$()$ method of your model, storing the results in a variable named new$\_$pred$\_1.$ Print the price predictions

stored in this variable, rounded to $2$ decimal places, with a message as shown below.

Estimated Prices: xxxx