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WeCare Attrition Analysis

Data Analyst

Introduction

This is a Power Bi Project for a Company in the retails industry. WeCare is a large organization with over a thousand employees working in various departments and roles. WeCare is looking to identity factors influencing attrition is their company.

Disclaimer: All datasets and reports do not represent any company, institution or country. All info are dummy dataset to demonstrate my capabilities in Power Bi.

Problem Statement

  1. Analyze the dataset to find out factors influencing Employee Attrition in the company
  2. Is there a relationship between attrition and monthly income and monthly rate?
  3. How do factors like job satisfaction, marital status, gender, job role department, work life balance etc affects attrition?

Skills & Concepts demonstrated

  • Understanding the data was the first thing then the scenario. Identified general factors before we explore the data.
  • To better understand attrition behaviour we deployed Power Bi to further analyze factors.
  • Using Analytical approach like Data Cleasing, Uni & Multivariate Analysis, DAX etc & Visualization approach like Scatter plot, Gauge, Decomposition tree, Bar chart.
  • Identification of patterns and trends to get informed insights and data-driven recommendation.

Modelling

Automatically derived relationship are adjusted to remove and replace relationships with required informations.

Data Analyst

Visualization

This report contains

  • Scatter Plot
  • Decomposition Tree
  • Key Influencers
  • Donut Chart
  • Clustered Bar Chart
  • Table

Analysis

Scatter Plot:

Every point on this chart represent each worker in the company and amount they earn. The Chart further gives insight that workers earning between $0 - $5,000 populates high percentage of the attrition.

Data Analyst

Decomposition Tree:

This chart looked at the gender of workers, marital and age. The insights from this further shows that Single, male workers between 18years to 22 years also pupolates the attrition affection the company.

Data Analyst

Key Influencers:

This chart dig very dip comparing many variables against eachother so we can truly identify the main reason for this attrition. Insights from this chart shows us that OverTime is a major contributor to attrition while we also have Job Roles & Worklife Balance.

Data Analyst

Conclusion and Recommendations:

01 - OVERTIME

I recommend the company to recognize employees who consistently take up overtime through rewards, such as gift cards, bonuses, rewarding pay and extra time off. ⌚

02- KEY DEPARTMENTS

Sales, HR, and Laboratory Technicians, bear the brunt of attrition, accounting for 40%, 24%, and 23% respectively. improving employee satisfaction and retention in these key departments, like providing more training and support, creating a more positive and supportive work environment, and offering competitive compensation and benefits. 🏦

03 - IMBALANCE WORKLIFE

Implement measures to improve work-life balance, such as flexible scheduling, wellness programs, and clear boundaries between work and personal time. ⚖

04 - LOW INCOME $0 - $5,000

Improving the work-life balance and compensation for younger employees. They should also provide them with opportunities for professional development and advancement, consider providing financial assistance to employees who are struggling to make ends meet. 💸

Thank You

For more info you can Email me

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WeCare Attrition Project | Power BI

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