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Wrangling

This Report insights can help reducing employee turnover?

📊 Over all

I am working for the human capital department of a large corporation. The Board is worried about the relatively high turnover, and my team must look into ways to reduce the number of employees leaving the company.

The team needs to understand better the situation, which employees are more likely to leave, and why? Once it is clear what variables impact employee churn, you can present your findings along with your ideas on how to attack the problem.

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📰 The data set

The department has assembled data on almost 10,000 employees. The team used information from exit interviews, performance reviews, and employee records.

  • "department" - the department the employee belongs to.
  • "promoted" - 1 if the employee was promoted in the previous 24 months, 0 otherwise.
  • "review" - the composite score the employee received in their last evaluation.
  • "projects" - how many projects the employee is involved in.
  • "salary" - for confidentiality reasons, salary comes in three tiers: low, medium, high.
  • "tenure" - how many years the employee has been at the company.
  • "satisfaction" - a measure of employee satisfaction from surveys.
  • "avg_hrs_month" - the average hours the employee worked in a month.
  • "left" - "yes" if the employee ended up leaving, "no" otherwise.

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💪 Competition challenge

Create a report that covers the following:

  1. Which department has the highest employee turnover? Which one has the lowest?
  2. Investigate which variables seem to be better predictors of employee departure.
  3. What recommendations would you make regarding ways to reduce employee turnover?

🧑‍⚖️ Judging criteria

CATEGORYWEIGHTINGDETAILS
Recommendations35%
  • Clarity of recommendations - how clear and well presented the recommendation is.
  • Quality of recommendations - are appropriate analytical techniques used & are the conclusions valid?
  • Number of relevant insights found for the target audience.
Storytelling35%
  • How well the data and insights are connected to the recommendation.
  • How the narrative and whole report connects together.
  • Balancing making the report in-depth enough but also concise.
Visualizations20%
  • Appropriateness of visualization used.
  • Clarity of insight from visualization.
Votes10%
  • Up voting - most upvoted entries get the most points.

✅ Checklist before publishing into the competition

  • Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
  • Remove redundant cells like the judging criteria, so the workbook is focused on your story.
  • Make sure the workbook reads well and explains how you found your insights.
  • Check that all the cells run without error.

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Data Wrangling section.

# importing the libs and load data set
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('./data/employee_churn_data.csv')
df.head()
#Check if there are any nulls or duplicated values
print('checking the number of nulls in columns',df.isnull().sum())
print('dublicated=',df.duplicated().sum())


#Check  if there are any columns not in the right type
df.info()
# so I will make some  format changes here! 
df['review'] = pd.Series([round(val, 2) for val in df['review']], index = df.index)
df['satisfaction'] = pd.Series([round(val, 2) for val in df['satisfaction']], index = df.index)

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