https://github.com/shwetajanwekar/hr-data-analytics
Analyze the HR data through exploratory data analysis, we identified key factors influencing employee attrition, satisfaction levels, and performance. Machine learning models enabled us to predict employee churn and classify potential candidates for promotion.
https://github.com/shwetajanwekar/hr-data-analytics
matplotlib numpy pandas seaborn
Last synced: 2 months ago
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Analyze the HR data through exploratory data analysis, we identified key factors influencing employee attrition, satisfaction levels, and performance. Machine learning models enabled us to predict employee churn and classify potential candidates for promotion.
- Host: GitHub
- URL: https://github.com/shwetajanwekar/hr-data-analytics
- Owner: shwetajanwekar
- Created: 2025-01-23T11:01:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-23T11:43:01.000Z (over 1 year ago)
- Last Synced: 2025-07-10T05:16:23.389Z (12 months ago)
- Topics: matplotlib, numpy, pandas, seaborn
- Homepage:
- Size: 1.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# HR-Data-Analytics
The primary aim of this project is to harness the power of Python libraries to analyze HR data comprehensively.
By employing statistical techniques, data visualization, and machine learning algorithms, we aim to uncover patterns, trends, and correlations within the dataset.
Our focus lies in gaining actionable insights that can inform decision-making processes in the realm of human resource management.
This project focuses on HR analytics conducted in Python, utilizing a specific library. The dataset utilized in this project was collected from Kaggle, a renowned platform for data science enthusiasts and professionals.
These are some of the essential libraries utilized in Python for HR analytics projects, providing functionalities for data manipulation (Pandas), numerical computing (NumPy), data visualization (Matplotlib), and enhanced visualizations (Seaborn).
These libraries offer a wide range of functionalities for data manipulation, visualization, statistical analysis, machine learning, and deep learning, making them essential tools for HR analytics projects conducted in Python.