Problem $1$: Load Stroke Data

IProblem $1$: Load Stroke Data

In this assignment you will be working with data obtained from

Kaggle.com relating to stroke risk. The data file

for this data set can be found at the following path: $/$FileStore$/$tables$/$stroke$\_$data.csv$.$ Descriptions

of the columns in this dataset, as well as their data types, are provided below. The columns are listed in the

same order in which they appear within the dataset.

Our goal in this assignment will be to create a regularized logistic regression model to predict values of the

stroke column. This column will represent the label in our model. All other columns will represent features.

We will begin by loading the dataset into a DataFrame.

Create a schema for the stroke data using the data types provided in the table on the previous page. Then

read the file stroke$\_$data.csv into a DataFrame named stroke$\_$df$.$ Note that the file is comma$-$

separated and has a header. Call the printSchema$()$ method of the DataFrame.

We will now look at the first few rows of the DataFrame.

Use the show$()$ method to display the first $10$ rows of stroke$\_$df$.$

We will determine the number of observations in the dataset.

Use the count$()$ method to determine the number of records in the stroke$\_$df DataFrame. Store this

value in a variable named $N$ and then print its value.

Next, we will determine the proportion of records in each of the two label categories.

Group the stroke$\_$df DataFrame by the stroke column and calculate the proportion of individuals in each

of the two groups. You can accomplish this by counting the number of observations in each group and then

dividing by the total number of observations. Round the proportions to four decimal places. Display the

results. The displayed DataFrame should have two columns named stroke and prop.