HR Analytics: Job Change Prediction for Data Scientists

A detailed exploratory data analysis (EDA) on an HR dataset aimed at understanding the key factors that influence data science professionals to change jobs. This analysis provides business insights for HR departments and talent management teams to improve employee retention and talent acquisition strategies.

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Project Overview

This project explores and visualizes the "HR Analytics: Job Change of Data Scientists" dataset from Kaggle. The goal is to extract actionable insights by analyzing patterns in education, experience, company attributes, and training that influence an individual’s likelihood to switch jobs.

Dataset Source: Kaggle - Job Change of Data Scientists

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View Notebook on Kaggle

You can also explore this project directly on Kaggle:

HR Analytics – Job Change Prediction on Kaggle

Objectives

• Understand the distribution and quality of features.
• Handle missing values and inconsistent data entries.
• Explore correlations between features and job change.
• Visualize the behavioral trends of professionals considering a career move.
• Provide business-ready insights based on data.

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Tools & Technologies

Category	Tools & Libraries
Language	Python
Data Handling	pandas, numpy
Visualization	seaborn, matplotlib, plotly
Notebook Environment	Jupyter Notebooks

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Dataset Structure

The dataset includes the following key features:

• gender
• education_level
• major_discipline
• relevent_experience
• experience
• company_size
• company_type
• training_hours
• city_development_index
• target (Whether the candidate is looking for a new job)

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Analysis Highlights

• Distribution analysis of categorical and numerical features
• Comparison between job seekers and non-job seekers
• Correlation heatmaps and feature relationships
• Interactive Plotly visualizations
• Handling of missing values and potential feature engineering steps

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Key Insights

• Candidates with low experience or high training hours are more likely to consider job changes.
• Company size and company type have a strong influence on retention.
• City Development Index shows positive correlation with career moves.
• Professionals without relevant experience are less likely to move unless supported with training.

Insights are summarized at the end of the notebook.

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Connect

If you have any questions, feedback, or collaboration ideas, feel free to reach out:

• Email: mohamedmahmoud2682003@gmail.com
• WhatsApp: +20 109 643 3244
• Kaggle: https://www.kaggle.com/mohamedmahmoud111
• Linkedin: https://www.linkedin.com/in/mohamed-mahmoud26/

Getting Started

1. Clone the repository:

git clone https://github.com/mohamedmahmoud26/HR_Analytics.git
cd HR_Analytics