Welcome to this repository. The objective of this repo is to best understand logistic regression with the help of the example of employee attrition predictions.
I am a mechanical engineer by educaion. Now, I want to deep dive in the world of Machine Learning, hence the name, mechanic of ML :D. I have taken up this project to understand the in-depth mathematics involved in regularly used ML algorithms. Under this project, I will be sharing useful material and links as I explore this domain.The objective is to learn and spread the same. Stay tuned to my github for updates!
The goal of the notebook is to develop a risk model that forecasts the likelihood of employee attrition in a business based on historic data.
- To understand and implement logistic regression
- To visualize and understand the data
- To select features which can best predict costs based on attribute-value pair.
- To derive conclusions from the data and suggest solutions for business.
- Logistic Regression: https://www.youtube.com/watch?v=yIYKR4sgzI8
- https://www.youtube.com/watch?v=nk2CQITm_eo&t=265s
- https://www.youtube.com/watch?v=het9HFqo1TQ
- Likelyhood: https://www.youtube.com/watch?v=pYxNSUDSFH4
- https://www.kaggle.com/gulsahdemiryurek/seaborn-exercise
- https://www.kaggle.com/mnassrib/titanic-logistic-regression-with-python
- https://www.kaggle.com/gianzlupko/predicting-employee-churn-proposing-intervention/data