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!🌟