Predicting performance analysis of garments women working status in Bangladesh using machine learning approaches

Abstract

In Bangladesh, the garment industry has played an important role in economically uplifting a diverse community of poor and marginalized people. There are now 4,825 garment factories that employ more than three million people. Completely 85% of these employees are female. But most of the female workers work to support their family and also contribute his family to lead a minimum life. In this paper, we try to find out relation between their health status, their family earning, their family member information, their working time or how many year they work in this sector and how many time they want to work. The dataset is collected from the Ashulia and Gazipur area garments of Bangladesh. This research work has observed that most of the female workers work at finishing, swing, helper, and cleaner sector. In this sector they cannot get huge salary that's why their income is limited and the range of their salaries is very low. It has also been found that, some women manage their whole family with their own income. Besides they are feeling bored with the same work. Nowadays machine learning and data mining tools play a vital role in finding the measurement of some important factors. This paper analyses the women working performance based on their previous activity and use some machine learning algorithms likely: Decision Tree Classifier(DTC), Logistic Regression(LR), Random Forest Classifier(RFC), and Stochastic Gradient Descent(SGD) we get the best result from Logistic Regression(LR) and it is 69%.

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