Demystify Employee Leaving with Machine Learning. Creation and Evaluation of Handful of Machine Learning Models for Leave Prediction. I will share recent work in the human resource domain to bring some predictive power to any firm struggling to retain their employees.
In previous posts, I tried to predict if a bank customer is likely to leave, OR if an app user is likely to churn or subscribe. Here I will share recent work in the human resource domain to bring some predictive power to any firm struggling to retain their employees.
In this second post, I aim to evaluate and contrast the performances of a handful of different models. As always, it is split into:
1. Data Engineering
2. Data Processing
3. Model Creation & Evaluation
1. Data Engineering
_Having completed a brief data exploration in the first _post, let’s proceed with feature engineering and data encoding. Feature engineering involves creating new features and relationships from current features.
To start off, let’s segregate the categorical variables from numerical ones. We can use the datatype method *to find categorical variables, as their *dtype *would be ‘object’. You may notice data types are already shown when using *employee_df.info().
I recently learned about logistic regression and feed forward neural networks and how either of them can be used for classification.
Linear Regression VS Logistic Regression (MACHINE LEARNING). Linear Regression and Logistic Regression are two algorithms of machine learning and these are mostly used in the data science field.
Artificial Neural Networks — Recurrent Neural Networks. Remembering the history and predicting the future with neural networks. A intuition behind Recurrent neural networks.
Fundamentals of Neural Network in Machine Learning. What is a Neuron? What is the Activation Function? How do Neural Network Works? How do Neural Networks Learn?
Use of Decision Trees and Random Forest in Machine Learning. An Insight into Supervised Learning for Classification Problems