📊 HR Analytics: Job Change Prediction

📋 Project Overview

This project addresses a critical challenge in the HR and talent acquisition domain, specifically for a company specializing in Big Data and Data Science training and recruitment. The company provides specialized training programs as part of their recruitment pipeline but faces significant resource allocation challenges due to uncertainty about candidates' post-training intentions.

The project leverages machine learning techniques to analyze candidate data collected during the signup and enrollment processes. By examining various factors including demographics, educational background, work experience, and training participation, we've developed predictive models to identify candidates genuinely interested in joining the company post-training versus those likely to seek employment elsewhere.

Key challenges addressed in this project include:

• Managing an imbalanced dataset reflecting real-world recruitment patterns
• Handling multiple categorical variables with high cardinality
• Processing missing data across several critical features
• Developing a robust pipeline for automated data preprocessing and model training

🎯 Project Objectives

• Predict the likelihood of candidates changing jobs after training
• Identify key factors influencing job change decisions
• Optimize training resource allocation
• Reduce recruitment costs and time
• Improve candidate categorization

🔍 Dataset Description

Data Source

• Training set: 19,158 records
• Test set: 2,129 records
• Target variable is binary (0: No job change, 1: Will change job)
• Dataset on Kaggle

Features

Feature	Description
enrollee_id	Unique ID for candidate
city	City code
city_development_index	Development index of the city (scaled)
gender	Gender of candidate
relevant_experience	Relevant experience of candidate
enrolled_university	Type of University course enrolled if any
education_level	Education level of candidate
major_discipline	Education major discipline of candidate
experience	Candidate total experience in years
company_size	No of employees in current employer's company
company_type	Type of current employer
lastnewjob	Difference in years between previous job and current job
training_hours	Training hours completed
target	Looking for job change (0: No, 1: Yes)

🛠 Methodology

Data Preprocessing

• Handled missing values
• Implemented encoding techniques
• Applied feature scaling
• Reduced dimensions using PCA
• Created automated pipelines for preprocessing steps

Machine Learning Models

Explored multiple classification algorithms:

• Stochastic Gradient Descent
• Logistic Regression
• Linear SVC
• Random Forest Classifier
• Extra Trees Classifier
• Voting Classifier (Ensemble of Logistic Regression, Random Forest, and ExtraTrees)

Model Evaluation

• Primary metric: ROC-AUC Score (due to class imbalance)
• Secondary metric: Accuracy

📊 Results

Best Model: Soft Voting Classifier

Validation Set Performance:

• Accuracy: 91.01%
• ROC-AUC Score: 93.4%

Test Set Performance:

• Accuracy: 73%
• ROC-AUC Score: 71.49%

🚀 Installation

Prerequisites

• Python 3.8+
• Required libraries:

  pandas
  numpy
  scikit-learn
  matplotlib
  seaborn
  pickle
  

Setup

git clone https://github.com/odabashi/HR-Analytics.git
cd HR-Analytics

📈 Future Improvements

• Implementation of more sophisticated anomaly detection methods
• Feature engineering to improve model performance
• Exploration of deep learning approaches
• Addressing class imbalance by applying over- & undersampling techniques

👥 Team Members

Team Member
Baraa ALSALEH
Abdurrahman ODABAŞI
Muhammed ŞİHEBİ

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