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Data Science Portfolio Project: Regression #2 | Data Science with Marco

2020-07-20 Science & Technology
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Description

Part 1: https://www.youtube.com/watch?v=YnGf8G2JJyg Dataset: http://archive.ics.uci.edu/ml/dataset... Full project notebook: https://github.com/marcopeix/datascie... In this video, we walk through the first part of a project to start off or to add to your data science portfolio. The objective is to build a machine learning algorithm to predict the age of abalone from physical measurements only. In this part, we try out different algorithms to improve upon the baseline model. We will use ridge regression, lasso, random forest, bagging, and we will learn how to use the LightGBM library, open-sourced by Microsoft. Finally, I will give you some pointers as to how to make this project your own!

Top Comments (6)

@anuragbhatt6178 2020-10-10

You should add some end-to-end projects

1
@princedube5008 2021-05-23

What if you want the accuracy in percentage and not rmse who would you do that

0 2 replies
@anshulzade6355 2024-08-19

This is great stuff. Really builds up the perspective. Can we continue on this kind of competitive problems so as to build a solid foundation for solving Kaggle projects. !!

0
@williamwambua7710 2021-01-30

Thanks. I am downloading this for future reference purposes. Next lets do one on classification.

0
@naazimjalal7051 2020-10-16

hello sir can you make a video on pattern recognition between numbers with ML

0
@MissWhite21 2021-08-02

Great video but I see that you have done only RMSE test here. I would like to under the R2 score for it as well since when I am building my Regression models the R2 scores are horrifyingly low and I cannot afford to omit the multi collinear columns because that way I won't even have any columns left to use as my feature for label prediction. I will keep looking for ways to improve it or might just switch to classification instead of making this a regression problem hahaha. Anyway please continue the awesome work! Appreciate such content on YT.

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