-
Goal:The goal of this project is to conduct an Exploratory Data Analysis (EDA) using Python to gain valuable insights into employee attrition within the HR department of a company. By analyzing a comprehensive dataset containing relevant employee attributes, the project aims to identify key factors that contribute to attrition. -
Purpose:Provide valuable insights to HR professionals, helping them understand and address employee attrition, leading to improved retention, job satisfaction, and optimized workforce management.
-
High employee attrition negatively impacts business performance by causing disruptions in workflow, loss of institutional knowledge, and increased recruitment and training costs.
-
Conducting an in-depth analysis of HR data allows businesses to identify the key factors contributing to employee attrition. These factors may include low job satisfaction, inadequate work-life balance, limited career growth opportunities, or disparities in compensation. Understanding these drivers empowers companies to implement targeted retention strategies that address specific pain points, boost employee engagement, and create a supportive work environment.
-
By proactively addressing attrition factors, businesses can build a more resilient and motivated workforce, reduce turnover rates, and ultimately enhance productivity and profitability. Moreover, fostering a positive company culture and prioritizing employee well-being can attract top talent, strengthen the organization's reputation, and create a competitive edge in the market.
-
Importing Libraries: - Import all the essentials libraries for Data Manipulation,Visualization & Data Analysis.
-
Loading Dataset: - Load the dataset into a suitable data structure using pandas.
-
Data Wrangling: - To clean, transform, and restructure the data in order to make it suitable for analysis and derive meaningful insights.
-
Exploatory Data Analysis: - To gain insights, discover patterns, and understand the characteristics of the data before applying further analysis.
-
Statistical Analysis: - To assess the significance and impact of different features on the target variable, identify the most important variables.
-
Conclusion: - Conclude the project by summarizing the key findings and limitations related to employee attrition.
- 💻 Python
- 💻 HTML
- 🐼 Pandas
- 📊 Matplotlib
- 📈 Seaborn
- 📈 Statistics
- 📓 Jupyter Notebook
- 🔗 GitHub
- 📊 Power BI
- The project has reached completion, successfully meeting the predefined goals and purposes.
- All project objectives have been accomplished, including end-to-end execution from data collection and preprocessing to model development and evaluation.
Contributions are welcome! If you have any suggestions, bug fixes, or feature additions, please open an issue or submit a pull request.
For any questions or inquiries, please contact kumod.aws@gmail.com or you can contact me on LinkedIn.
Thank you for checking out my repository! I hope you find the projects and code provided helpful and informative. If you have any questions or suggestions, please feel free to reach out.😊