This project involves analyzing a dataset to understand factors associated with the productivity of garment manufacturing workers. The data comes from the University of California – Irvine’s Machine Learning Repository.
Cleaning and transforming the dataset, including creating new variables for better analysis.
Conducting a detailed analysis of the data, including creating histograms and box plots, and performing t-tests to understand the distribution and impact of different variables on productivity.
Building an ordinary least square regression model to predict productivity based on various factors.
- Understanding of the key factors that impact worker productivity in garment manufacturing.
- Statistical models that can predict productivity based on various inputs.
- Practical suggestions based on data analysis for improving productivity in the garment manufacturing industry.