This project aims to discover the factors that affect employee performance, to train a model to accurately predict the employee's performance rating, to analyze the data to provide recommendations for improving performance, and to learn from the analysis.
data: Folder containing the raw data.data_eda: Folder containing the data after exploratory analysis.data_exploratory_analysis[2].ipynb: Jupyter Notebook for exploratory data analysis.data_processing[1].ipynb: Jupyter Notebook for data preprocessing.emp_rating_model: Folder containing the trained model to predict the employee's performance rating.inx_emp.xls: Excel file containing the raw data.predict_model[4].ipynb: Jupyter Notebook for model prediction.train_model[3].ipynb: Jupyter Notebook for model training.visualization [5].ipynb: Jupyter Notebook for data visualization.x_test: Folder containing the test data for the model.y_test: Folder containing the test results for the model.
- Start with the exploratory data analysis in
data_exploratory_analysis[2].ipynb. - Continue with data preprocessing in
data_processing[1].ipynb. - Train the model with
train_model[3].ipynb. - Make predictions with
predict_model[4].ipynb. - Visualize the results with
visualization [5].ipynb.
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
- xgboost