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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.

Absenteesim at work . Prediction using Machine learning classifier models.

Created a dashboard using Tableau to analyze the various factors resulting to absenteeism of an employee for more than 3 hours during work. The data was preprocessed and cleaned using Python and SQL with help of pymysql library and MySQL Work Bench, then a logistic regression model was trained to predict the probability. Finally, Tableau was used to visualize the factors versus probability.

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