This repository contains exploratory data analysis (EDA) on team performance data. The analysis is based on a dataset with relevant columns, including team number, workforce details, productivity metrics, and various factors influencing performance. The EDA is performed in Python, providing insights derived solely from the given dataset.
https://www.kaggle.com/datasets/gauravduttakiit/employee-performance-prediction
- Explore team composition, size, and their impact on productivity
- Analyze productivity metrics, including Standard Minute Value (SMV) and targeted productivity
- Examine temporal trends with day, month, and quarters breakdown
- Investigate the impact of idle time, idle men, style changes, work in progress, overtime, and incentives
- Gain insights into the actual productivity achieved by the teams
data: Contains the dataset used for analysis. Code: .ipynb code for the exploratory data analysis. images: Visualizations and plots generated during the analysis. LICENSE: Details about the license under which this repository is shared.
- Clone the repository to your local machine
- Navigate to the Code directory
- Open and run the each line of code in sequential order in either Jupyter notebook or Google Colab
- Python 3.x
- Pandas
- Matplotlib
- Seaborn
- IPython
- Jupyter Notebooks/Google Colab
MIT License
Copyright (c) 2024 VarshaSrinivasan