Predict Employee Attrition Using Machine Learning & Python 

Amazing very informative, can't wait to see your upcoming tutorials.

I enjoyed this and found it instructive to follow along. Appreciate your quick pacing, yet somehow unhurried, teaching style.

Simple and easy code. Nice explanation. Thank you so much

You are a pro boss !! Good to see your video. Query - How do you validate the prediction results ? What are the ways and types to validate the same? Your thoughts on classification reports for the same ?

Hii this video is at it's best. But I need a conclusion like on which the attrition is more dependent and how are we going to find out the main factor that's affecting the attrition the most?

can you show how we will predict which staff will leave from the database

Hallo, I have follow your step in medium but in the last step when I want to show importance feature there is eror " Valuue eror = Array must all be same length". I don"t understand with this problem, could you help me?

Thank you for the walkthrough - really helpful. Question: early on in the analysis, you plotted bar graph for Age with Attrition as the hue. But we dont know if Age is correlated with other attribute or attributes so what would be the point of the graph? Age alone does not explain the attrition rate. Why look at that at all?

Can we also find the specific reason of leaving, the variable with the highest value?

Thank you for the appropriate explanation

Question - How do i predict in a current employee with attrit? how do I now test which employees are now on their way out the door and what factors do i look for that show this

It's very nice video,it's really helpful me

At df.corr() , why do I get..? (FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. df.corr() ). and an error on every code after that

How to make conclusions out of the entire coding process for presentation? Like what are the factors under the columns affect the most Attriction

I can't run seaborn, keep getting modulenotfounderror: no module named 'resource' . I'm using windows 10.

Solid tutorial. How do I use this though?

in last forest.fit(x_train,y_train) in shows string cannot convert into float why so???

Fantastic approach. Can you also make a video explaining how can we use this model? like what after creating this prediction?

Thank you for the amazing explanation but i have a query, why did you use label encoder and not one hot encoder to convert non - numeric data into numeric data?

how to calculate employee performance-department wise?

can we fix range of x axis ?? cuz I do have many data points on x axis

Getting error at randomforestclassifier using sklearn ? How to solve this

where is the link to the data set?

Instead of dropping the Age column can’t we change the index to one for attrition? Just asking, cause I am new to Data Science and curious.

how do we show the decision tree?

This is a good straight forward model training for beginners. But the model is weak. Especially in the case of the problem, if you want to make employee attrition you want to know who will quit the job and maybe contact him and the opposite way. Maybe it will be better to choose another metric like Recall or F1 score.

Can you please share GitHub link of this project

Lovely video but please do projects which involves data cleaning , especially handling text data such .what you do is lovely and very much appreciate sir but it's bit too plain and majority of work in data science is DATA CLEANING .so please in upcoming videos focus on this aspect .thank you sir

Friend, can you put the dataset, to apply

Can you please share the code, if you don't mind

Informative. Thanks. But would've been better had you standardized the data and then given an analysis of the data.

why you won't use oversampling in this unbalanced data (yes-237,no-1233)?

When we are fit the x_train and Y_train getting value error : Input contains Nan

where can I find the dataset??

can someone please explain how we get 80% accuracy just by guessing "No" all the time need to understand the math (1233-237)/1233

With this model, how can I check which employee is more likely to attrite?

I keep getting a value error: ValueError Traceback (most recent call last) in () 1 from sklearn.model_selection import train_test_split ----> 2 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = None, random_state = 0) /usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_consistent_length(*arrays) 210 if len(uniques) > 1: 211 raise ValueError("Found input variables with inconsistent numbers of" --> 212 " samples: %r" % [int(l) for l in lengths]) 213 214 ValueError: Found input variables with inconsistent numbers of samples: [1, 1470] Can someone help?

where can I get this data?

where is the dataset used?

please give me hrm dataset

this data sets is imbalanced, so we should use different approach to do project...

Your model by random guessing gives you 80% accuracy. But by machine learning it gives 86% accuracy. This generally makes a case against ML since -+6% accurate results are not that far off from random guess. So it is better to go with guessing than ML. Now that's my opinion. Others are welcome to share theirs as well.

Thank you, and i got an error "ValueError: Input contains NaN, infinity or a value too large for dtype('float32')," when running the scripts of # use the random forest classifier from sklearn.ensemble import RandomForestClassifier forest = RandomForestClassifier(n_estimators=10, criterion='entropy',random_state=0) forest.fit(x_train, y_train) could you shed me some lights how to fix it?

i am getting error after #use random forest classifier from sklearn.ensemble import RandomForestClassifier forest=RandomForestClassifier(n_estimators = 10 , criterion = 'entropy', random_state = 0) forest.fit(X_train, Y_train) and the error is could not convert string to float: 'Non-Travel'