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https://github.com/vvipjain/employee-analysis
Employee Data Analysis
https://github.com/vvipjain/employee-analysis
numpy numpy-arrays numpy-library pandas pandas-dataframe pandas-library pandas-python plotly plotly-express plotly-python python python3
Last synced: 11 days ago
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Employee Data Analysis
- Host: GitHub
- URL: https://github.com/vvipjain/employee-analysis
- Owner: VVipJain
- Created: 2024-08-08T09:37:52.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-08-08T09:45:54.000Z (7 months ago)
- Last Synced: 2024-12-18T00:14:16.331Z (2 months ago)
- Topics: numpy, numpy-arrays, numpy-library, pandas, pandas-dataframe, pandas-library, pandas-python, plotly, plotly-express, plotly-python, python, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 182 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Employee-Analysis
This repository contains a comprehensive analysis of employee data using Python, Pandas, Numpy and Plotly. The project aims to provide insights into employee demographics, performance, retention, and other key metrics through interactive visualizations.
INTRODUCTION -> In this project, we analyze employee data to uncover patterns and trends related to demographics, performance, retention, and other key aspects of the workforce. By leveraging the data manipulation capabilities of Pandas and the interactive visualization features of Plotly, we aim to provide valuable insights that can help organizations make informed HR decisions. This project is ideal for HR professionals, data analysts, and business managers interested in workforce analytics.
DATASET -> The dataset used in this analysis contains detailed information about employees, including personal details, job-related information, and performance metrics. The data is stored in a CSV file named employee_data.csv.
ANALYSIS -> The analysis is divided into several sections:
* Loading and Cleaning Data: Importing the dataset and performing initial cleaning operations such as handling missing values and converting data types.
* Demographic Analysis: Exploring the demographic composition of the workforce, including age, gender, and education.
* Performance Analysis: Analyzing employee performance scores across different departments and job titles.
* Salary Analysis: Examining salary distribution and identifying trends related to compensation.
* Retention Analysis: Investigating employee retention rates and identifying factors that may influence turnover.
* Tenure Analysis: Understanding how the number of years at the company relates to other metrics like performance and salary.
VISUALISATION -> We use Plotly to create interactive visualizations. Some of the key visualizations are as follows:
