Simplify Data Preprocessing with Python's Column Transformer: A Step-by-Step Guide

In this comprehensive KZbin video, we dive into the powerful world of feature transformation using the Column Transformer in Python's machine learning ecosystem. Whether you're a beginner or an experienced practitioner, understanding how to preprocess and transform features is crucial for building robust ML models.
Whether you're working on classification, regression, or any other machine learning task, mastering the art of feature transformation through the Column Transformer can significantly elevate your model's efficiency
Interested in discussing a Data or AI project? Feel free to reach out via email or simply complete the contact form on my website.
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As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector.
This KZbin channel serves as both a platform for sharing knowledge and a personal journey of continuous learning. With a commitment to growth, I aim to expand my skill set by publishing 2 to 3 new videos each week, delving into various aspects of data analytics/science and Artificial Intelligence. Join me on this exciting journey as we explore the endless possibilities of data together.
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[@user-iu5nz2gy6l]

Great video. Feel like this is an important concept and you explain it in 15 mins. Thanks. I do have a few question. 1) if i want to use the odc like u explain in another video, do i clarify another transformer (enc) for example: sizes =['Small', 'Medium', 'Large'] enc =OrdinalEncoder(categories=[sizes]) ct = make_column_transformer ( (ohe,['city']), (enc,['size']), remainder = 'passthrough' )

[@user-ks9fx7cg3r]

Loving all your videos thanks

[@RyanNolanData]

Thank you for checking them out!

[@andyhong3003]

Like your others, there are lots of good stuffs in a concise video. Wonder if you could make the Jupiter notebook available for this video or future's.

[@RyanNolanData]

I do plan on rolling out the code to my website in the future. Just don’t have the resources yet. Once the channel scales a little more I’ll hire someone to help me upload them

[@mariaclara8871]

Thank you!!!

[@RyanNolanData]

Thanks for checking it out

[@onurbltc]

Great video!

[@RyanNolanData]

Thanks!

[@user-iu5nz2gy6l]

Also, you give us a simple data set, which is great. However, I had the trouble when i have a large data set and have a lot of columns, and i don't know how to retrieve the column name after column transform. Will you explain how to do it in later projects/videos? Again, thanks for all the great work. Having a blast following through the playlist.

[@RyanNolanData]

I may have this in my titanic or House pricing projects? Not 100% sure though its been a while since I looked at them

[@cvrbcheppali8214]

Nice Explination

[@RyanNolanData]

Thank you

[@ayushparwal2210]

nice

[@RyanNolanData]

Thanks