This Jupyter Notebook contains an HR Analytics project focused on analyzing employee data to gain insights into various HR-related metrics. The project covers data exploration, visualization, and predictive modeling to understand factors influencing employee attrition, performance, satisfaction, and other key HR indicators.

The dataset used for this project is the HR Analytics dataset, which contains information about employees such as:

• Employee ID
• Age
• Gender
• Marital Status
• Education
• Job Role
• Department
• Work Experience
• Performance Ratings
• Salary
• Attrition Status, etc.

The dataset can be obtained from source.

• HR_Analytics.ipynb: Jupyter Notebook containing the HR Analytics project code and analysis.
• HR-Employee-Attrition.csv: The raw dataset in CSV format.

1. Open the HR_Analytics.ipynb Jupyter Notebook.
2. Load the dataset and perform data cleaning, preprocessing, and exploration.
3. Visualize key HR metrics using charts, graphs, and statistical analysis.
4. Build predictive models (e.g., logistic regression, decision trees, etc.) to predict employee attrition or performance.
5. Interpret the results, draw insights, and make recommendations based on the analysis.

• Python 3.x
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn (for machine learning models, if applicable)

1. Launch Jupyter Notebook and open the HR_Analytics.ipynb notebook.
2. Execute each cell in the notebook sequentially to run the code and generate visualizations.
3. Modify the analysis, add new visualizations, or explore additional questions based on your requirements.

• HR Analytics dataset source: https://www.kaggle.com/datasets/rishikeshkonapure/hr-analytics-prediction