HR Analysis Repository Description
This repository hosts data and analysis related to Human Resources (HR) tasks. The dataset provided contains information about employee performance and various attributes such as satisfactory level, last evaluation, number of projects, average monthly hours, etc. The analysis aims to delve into employee behavior, identify factors influencing employee retention, and potentially predict employee attrition.
Contents:
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Data: Contains the raw dataset (
people.csv) used for analysis. -
Analysis Notebook: The Jupyter notebook (
HR_Analysis.ipynb) contains the code for exploratory data analysis (EDA), predictive modeling, and generating insights from the HR dataset.
Analysis Overview:
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Exploratory Data Analysis (EDA): Investigates the distribution, relationships, and summary statistics of various HR attributes.
-
Predictive Modeling: Utilizes machine learning techniques to forecast employee attrition and identify key predictors influencing retention.
Usage:
- Clone the repository to your local machine.
- Open and run the Jupyter notebook (
HR_Analysis.ipynb) to view the analysis. - Ensure you have Python and required libraries installed.
- Follow the instructions within the notebook to execute the analysis and generate insights.
Dependencies:
The analysis code requires the following Python libraries:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
Feedback:
Feedback and contributions are welcomed! Feel free to submit pull requests or open issues for any suggestions, improvements, or collaborations.
Author:
This analysis is authored by Rakhi Tulaskar. Contact [email protected] for any inquiries or collaborations.