The Garment Industry is one of the key examples of the industrial globalization of this modern era. It is a highly labor-intensive industry with lots of manual processes. Satisfying the huge global demand for garment products is mostly dependent on the production and delivery performance of the employees in the garment manufacturing companies. So, it is highly desirable among the decision makers in the garments industry to track, analyze and predict the productivity performance of the working teams in their factories. This dataset can be used for regression purpose by predicting the productivity range (0-1) or for classification purpose by transforming the productivity range (0-1) into different classes.

Column Information:

quarter: A portion of the month. A month was divided into four quarters

department: Associated department with the instance

day: Day of the Week

team: Associated team number with the instance

no_of_workers: Number of workers in each team

no_of_style_change: Number of changes in the style of a particular product

targeted_productivity: Targeted productivity set by the Authority for each team for each day.

smv: Standard Minute Value, it is the allocated time for a task

wip: Work in progress. Includes the number of unfinished items for products

over_time: Represents the amount of overtime by each team in minutes

incentive: Represents the amount of financial incentive (in BDT) that enables or motivates a particular course of action.

idle_time: The amount of time when the production was interrupted due to several reasons

idle_men: The number of workers who were idle due to production interruption

actual_productivity: The actual % of productivity that was delivered by the workers. It ranges from 0-1

Column 14 is our target. For this lab, please do these following

Import the data into your Google Colab runtime (25%)

Determine the type of each column (class or numeric not the data types that are imported by pandas). Note that some columns may appear to be number, but they are classes. Please focus on the fact that whether the values of the columns are meaningful as numbers or not, i.e. whether adding or comparing them are meaningful. (25%)

Draw histograms for all the numeric columns, discuss any issues that should be addressed (25%)

Draw bar charts for all the class columns, discuss any issues that should be addressed (25%)

Note: for Q3 and Q4, you just need to discuss the issue that you think are there. You do not have to write any code to fix them as it is module 4 task. Also, please use text cells in Google Colab for questions that need writing answers.

Please submit the Jupyter notebook file with all the codes and outputs.

Do not hesitate to contact me if you have any questions.