Absenteeism Prediction

Project Description

This project will address and predict the absenteeism at a company during work hours.

Absenteeism: Absence from work during normal working hours, resulting in temporary incapacity to execute regular working activity.

Problems

• higher competitiveness → increased pressure
• unachievable business goals → raised stress levels
• elevated risk of becoming unemployed → raised stress levels

can raise the problems of being excessively absent at work hours.

Questions

• Based on what information should we predict whether an employee is expected to be absent or not?
• How would we measure absenteeism?

Purpose

Explore whether a person presenting certain characteristics is expected to be away from work at some point in time or not.

We want know for how many working hours an employee could be away from work based on:

• How far they live from their workplace?
• How many children and pets they have?
• Do they have higher education?

And so on…

Project Requirements

Install the following Python libraries on your local system:

pip install numpy

pip install pandas

pip install scikit-learn

or you can install using requirements.txt file

pip install -r requirements.txt

How to Run the Jupyter Source Files

1. Clone the repository on your local system
2. Run jupyter notebook command in the directory where you have cloned the repository. Make sure Anaconda or Jupyter Labs is already installed on your local system.
3. Run Absenteeism Raw CSV Data Preprocessing.ipynb in the jupyter lab. A Absenteeism_preprocessed.csv will be created which contains the information for building the Logistic Regression Model.
4. Run Absenteeism - Logistic Regression.ipynb to build the model. This will save the model which can be implemented to predict the Excessive Absenteeism.

Integrating the Model

1. Create a new folder say Integration.
2. Make sure this folder contains 5 files for sure - Absenteeism_new_data.csv, absenteeism_module.py, absenteeism_model, absenteeism_scaler, Absenteeism-Integration.ipynb
3. Run Absenteeism-Integration.ipynb and the prediction values will be saved in a file named as Absenteeism_predictions.csv

Alternatively, you can directly go into the Integration folder and run the jupyter file to use the pre-trained model.