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Data Scientist: Employee Productivity

Kedeisha June 7, 2023

Business Overview

The dataset provided contains daily data from a garment manufacturing company. This company operates in a highly competitive industry where efficiency and productivity are key to maintaining profitability and a competitive edge. The data includes information about the date, day of the week, team, targeted productivity, actual productivity, and other related factors.

The business problem we can address with this dataset is to understand the factors affecting worker productivity and how to improve it. This is a critical issue for any manufacturing company, as increased productivity can lead to higher output, lower costs, and increased profitability.

Productivity in a garment factory can be affected by a wide range of factors. Some of these are included in the dataset, such as the number of workers, the department, and the day of the week. Other potential factors, not included in the dataset, could include worker skill and experience, the complexity of the garments being produced, and external factors like temperature and humidity.

The company’s management is interested in understanding these factors in more detail, with the goal of identifying strategies to improve productivity. For example, if productivity is found to be lower on certain days of the week, they might consider changing their work schedules or staffing levels. If certain teams or departments are more productive than others, they might look into the reasons for this and see if they can be applied elsewhere in the company.

In addition to understanding the current factors affecting productivity, the company is also interested in predicting future productivity. Accurate forecasts could help with planning and resource allocation, and could also be used to set more realistic productivity targets.

Potential questions that the company might be interested in include:

  • What are the factors that most strongly affect productivity?
  • How does productivity vary by day of the week or team?
  • Can we predict future productivity based on past data?

This project will involve a combination of exploratory data analysis, data visualization, hypothesis testing, and machine learning. The goal is to provide a comprehensive analysis of the factors affecting productivity and to develop predictive models that can be used to forecast future productivity. The insights and models generated by this project could have a significant impact on the company’s operations and profitability.


But, your manager would like a predictive model to help predict productivity and also a slide deck to present your analysis and recommendations to the VP of Operations.

The slide deck can be done in Google Slides, PowerPoint, or any other tool. Just save it as a PDF.

Get the Data

Download the data here.

Guiding Questions

Exploratory Data Analysis

  1. What is the distribution of actual productivity? Does it follow a normal distribution or is it skewed?
    • This is important to understand the general trend of productivity in the garment factory. It can help identify if there are any systemic issues affecting productivity.
  2. Are there any outliers in the dataset? How might these outliers affect our analysis?
    • Outliers can skew our analysis and make our models less accurate. Identifying and handling outliers is an important step in data preprocessing.
  3. What is the average, median, and range of productivity by day of the week? By team?
    • This can help us understand how productivity varies. For example, if productivity is consistently lower on certain days or for certain teams, we might want to investigate why.

Feature Engineering

  1. Can we create a new binary feature that represents whether the actual productivity met the target (1 if actual productivity >= target productivity, 0 otherwise)?
    • This feature could be useful in a classification model that predicts whether a given day/team will meet its productivity target.
  2. Can we create new features from the date, such as the month, the quarter, or the day of the month? How might these features be related to productivity?
    • Time-related features can often reveal trends or patterns in the data. For example, productivity might be higher or lower at certain times of the year.

Data Visualization

  1. Can we visualize the trend of productivity over time? Are there any noticeable patterns or anomalies?
    • Visualizing data can often reveal patterns or trends that are not immediately obvious from the raw data.
  2. Can we create a heatmap to show the correlation between different features? What features are most strongly correlated with productivity?
    • A heatmap can help us understand the relationships between different features. This can guide our feature selection for machine learning models.
  3. Can we visualize the difference between targeted and actual productivity over time?
    • This can help us understand how well the company is doing at meeting its productivity targets.
  4. Can we create a boxplot to compare the distributions of actual productivity across different departments?
    • This can help us identify if there are differences in productivity between departments.

Hypothesis Testing

  1. Is there a significant difference in productivity between different teams and departments? Use a suitable statistical test to answer this question.
    • This can help us understand whether team membership has a significant effect on productivity.
  2. Does the day of the week have a significant effect on productivity? Use a suitable statistical test to answer this question.
    • This can help us understand whether certain days of the week are more productive than others.
  3. Does the quarter of the year have a significant effect on productivity?
    • This can help us understand whether there are seasonal effects on productivity.

Machine Learning

  1. Can we build a regression model to predict productivity based on the other features in the dataset? What is the performance of this model?
    • A regression model can help us understand the relationships between the features and the target variable, and can also be used to predict future productivity.
  1. What features are most important in our model? How do these important features affect productivity?
    • Understanding feature importance can help us interpret our model and understand which factors are most important in determining productivity.
  1. Can we improve the performance of our model by tuning its hyperparameters?
    • Hyperparameter tuning is an important step in machine learning that can help improve model performance.
  2. Can we use a time series model to forecast future productivity?
    • Time series forecasting is a specialized type of machine learning that can be very useful for predicting future trends.