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Predict Employee Turnover with scikit-learn に戻る

Coursera Project Network による Predict Employee Turnover with scikit-learn の受講者のレビューおよびフィードバック

4.4
249件の評価
41件のレビュー

コースについて

Welcome to this project-based course on Predicting Employee Turnover with Decision Trees and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to grow decision trees and random forests, and apply them to an important business problem. Additionally, you will learn to interpret decision trees and random forest models using feature importance plots. Leverage Jupyter widgets to build interactive controls, you can change the parameters of the models on the fly with graphical controls, and see the results in real time! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed....

人気のレビュー

RS

2020年5月31日

I am glad to have taken this course. I came across some unknown features of Pandas (profile), sklearn library. New python libraries like yellowbrick.

LY

2020年5月4日

I was looking for Elaborated explanation of the project and implement it to clear the concept.\n\nThis course did explain it all.

フィルター:

Predict Employee Turnover with scikit-learn: 26 - 41 / 41 レビュー

by Karan G

2020年6月8日

Most of the things that were used were not discussed on how to install them. This consumes a lot of time searching them over the internet.

Also, some of the python libraries that were used are deprecated and are not running on our notebooks. This is also not discussed in great detail

by Murtuza B

2020年6月28日

Get to learn something new. Like I have not used the interactive dashboard when creating the model. Also get to know about some very useful libraries that I was not using before and I used them more often. Thank you so much for your time and efforts creating this.

by Samridha K

2020年5月7日

Very good content and specific. Very satisfied. However my only complaint is that I hope the creator had explained the interactive decision tree and rf codes such as setting gini, entropy and other min, max values more with reasoning.

by Bhoom S

2020年9月8日

Good practical overview of decision tree and random forest model with Python. The interface for code typing is a bit difficult to navigate with some lag time; hence -1 star in the review.

by Aathira S

2020年6月12日

Doing hands on project on Rhyme was very helpful as we could listen to the instructions and learn and type it ourselves.

by Frank J C I

2020年6月14日

Interesting project! It could be better if the course explores more theorical fundamentals of the algorithms

by Mukthar A O

2020年10月30日

It really worth the time and I was exposed to new approach to codes and algorithm.

by Erwin D

2020年5月13日

Excellent approach to predicting employee turnovers!

by Harsh N

2020年5月4日

Good for Foundation!

by LAKSHAY S

2020年5月24日

nice project

by Oscar J L G

2020年5月7日

very good

by Ahsan R

2020年5月27日

More details about the project and libraries used would be helpful to get a good understanding.

by Amlan C

2020年6月12日

The codeing environment is very bad very much lagging

by mohit g

2020年5月23日

no answers for querries

by Prateek G

2021年9月6日

difficult understanding with it

by Manoj K

2020年5月29日

This is not a project at all. This should include proper EDA, Feature Engineering, Model interpretation (not just showing the visuals, you have to interpret it). Many more basic check points are missing from a practical Project perspective.