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Analyzing and building a model using Logistic regression analysis to predict absenteeism of people from a workplace, in order to reorganize the workspace and increase productivity. Visualization is performed using Tableau.

Analyzing the causes of absenteeism at work. Predicting whether the employee will be absent using logistic regression

Predicting absenteeism at work

Predicting Absenteeism at work

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Predicting absenteeism at work with Logistic Regression algorithm and then integrating the results with MySQL and Tableau for Visualization.

Model predicting excessive absenteeism at work

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Predicting Absenteeism At Work using Machine Learning Models

Predictive Modeling of Absenteeism at Work Dataset

Model to predict the absenteeism at work

Predicting the probability of a person being absent from the work for more than 3 hours a day given his relevant details. Executed SQL statements from python and sent the data from Jupyter to Workbench by connecting them. Analysed the predicted outputs stored in Workbench via Tableau by connecting them.

A ML model to predict absenteeism at work

Using Logistics Regression to predict Absenteeism at work

Predicting absenteeism of employees at work using tree based learners

About Predicting the probability of a person being absent from the work for more than 3 hours a day given his relevant details. Executed SQL statements from python and sent the data from Jupyter to Workbench by connecting them. Analysed the predicted outputs stored in Workbench via Tableau by connecting them.

Developed a model to Predict the absenteeism at work

Developed a model to Predict the absenteeism at work

Classification based predictive model for absenteeism of employees at work

Trained a model to predict absenteeism at work using Dataset of Employees

Software Integration between Python, SQL and Tableau to predict absenteeism at work

Edwisor project challenge on Employee Absenteeism at work - Data Science Predictive Analytics problem

Assignment in pairs for my Master. Goal is to predict absenteeism at work.

Comparison of two machine learning models to predict the number of hours of absenteeism at work

Working on a data set to predict and analyse the high number of absenteeism at work

The objective of this project is to predict the Absenteeism at work and propose any changes to make to reduce the Absenteeism by using given employee data

Predicting hours of absenteeism at work given 22 features, using various machine learning methods such as SVM, Random Forest, Naive Bayes Classifier, Neural Network.

The experimental data we are going to use is the Absenteeism at work Data Set (https://archive.ics.uci.edu/ml/datasets/Absenteeism+at+work). The first goal with this dataset is to decide what do you want to predict. You must determine what categorical variable to construct over the existing predictors, to classify/predict (binomial or multinomial) the absenteeism of individuals. Examples: • Binomial: Individuals with more than X hours in total (based on ’Absenteeism time in hours’ predictor), or less than that.

The project will address Absenteeism at a company during work time. It will work on predicting Absenteeism from work, whether or not an employee will be absent for a specific number of hours on a work day. It will explore whether a person presenting certain characteristics is expected to be away from work at some point in time or not.