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Employee at Risk! 📌

Here is my notebook on the classification problem of predicting the employees at risk, working with the dataset

https://www.kaggle.com/manasdalakoti/univai-hack-data

Importing the libraries

Hello everyone, ​ This is my take on the binary classification of determining employees who are at a risk of termination or not.

​ It is a Binary Classification Problem. The tools used are:

Pandas for data manipulation and ingestion

Numpy for multidimensional array computing

Matplotlib and seaborn for data visualization

Word Cloud for geeting the most populare string

Imblearn for oversampling of the model

Scikit Learn for Data Preprocessing

For modelling:

Random Forest Classifier: ​

Accuracy Reached: 95.74%

XG Boost Classifier: ​

Accuracy Reached: 93.17%

Light Gradient Boosting: ​

Accuracy Reached: 91.10%

Cat Boost classifier: ​

Accuracy Reached: 95.74%

-> Feel free to leave any suggestions in the comments for the betterment of the notebook.

-> Thank you for your time,CHEERS!🌟