ML-Project-Prediction-of-employee-Burnout

Summary:

This project consists of using the different Machine Learning techniques with the aim of applying them in a practical way to the problem posed. This problem consists of dealing with the issue of employee attrition by creating a predictive model that is capable of predicting the probability of an employee leaving the company we are dealing with.

Content:

• attrition_available_12.pkl: data set in pickle format.
• Practica2.ipynb: EDA, hyperparameter setting, model selection... The notebook has explanations of the processes, analysis of the results, justifications of the decisions using tables and graphs and loads the final model and use it to make predictions.
• modelo_final.pkl: is the file that contains the final model.

Technologies:

• Python: data collection, data exploration and preprocessing, data visualization anda data partitioning.
• Scikit-learn: model selection, model training, model evaluation, model tuning and model deployment.

Team:

• Arianna Potente Vázquez: https://github.com/Ari-Potente

• Ernesto Gracia Cancho: https://github.com/Ernesto-Gracia-Cancho

Report

In the "Practica2.ipynb" file you can find in detail the process of what is being done and the conclusions reached.