Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 2

This video titled "Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 2" is the Part-2 video of Multiple Linear Regression using Spark(PySpark) MLLib series which explains Feature Engineering of Spark MLLib like StringIndexer, OneHotEncoderEstimator, VectorAssembler to transform data containing Categorical Variables into Machine-readable format.

FOLLOW ME ON:

About this Channel:
The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge.

Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you.

*******Other AI, ML and Deep Learning Related Video Series*****

******************************************************************

#StringIndexer #OneHotEncoderEstimator #VectorAssembler

[TheAIUniversity]

Trivia question from the video: What is the role of
the StringIndexer and in which scenario do we use it in feature engineering step?

[ahmedeldib]

Hi Nathan, great work, I have an error on this Line "data =
the error is "Field "cut_catVec" does not exist" where "cut" it the a column name that I have in my dataset and "_catVec" is the added part to the column name