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Mastering Big Data with PySpark

Quiz: Predicting Diabetes Using PySpark MLlib

Explore building a diabetes prediction model using PySpark MLlib. Learn to preprocess data, convert categorical variables, assemble features, and train a logistic regression model. Understand model evaluation and prediction within a PySpark ML pipeline.

We'll cover the following...

Task 1: Load the diabetes prediction data into a PySpark DataFrame

To commence, create a SparkSession as previously learned. Utilize it to load the data into a PySpark DataFrame and display the initial rows.

Python 3.8
# Import libraries
from pyspark.sql import SparkSession
# Create a SparkSession
spark = 
# Write code to read "diabetes.csv" that is available in your path in a variable called 'diabetes_df'. Make sure you include options 'header=T' and 'inferSchema=True'
diabetes_df = 
# Write code to display the initial rows of the dataframe
# Write code to check the column types of diabetes_df

Task 2: Data preprocessing and EDA

In the data preprocessing task, we’ll apply essential data preparation techniques to ensure the dataset is in a suitable format for the model training. ...