Employee Appraisal Prediction ML Model Implementation Using Neural Network

Overview

An academic research institution was grappling with a steady rise in employee attrition. Internal assessments revealed that one of the core issues was compensation misalignment. Salaries did not reflect current market trends or individual employee value, leading to dissatisfaction and increased turnover. The organization needed a scalable, data-driven solution to redefine its compensation strategy and retain top talent. In response, we designed and implemented a Machine Learning (ML) powered and AI-based employee performance prediction system capable of predicting fair and competitive salary benchmarks. The model leveraged key employee attributes such as skills, years of experience, job role, and geographic location to deliver salary recommendations that aligned with both internal equity and external market conditions.

Solution Implemented

We developed a predictive model using machine learning for employee evaluation. The model supports compensation planning and ensures equitable salary distribution. The model evaluated each employee’s profile and generated benchmark salary recommendations.

• Multiple algorithms were tested, including Random Forest and Linear Regression, to compare accuracy and performance.
• XGBoost emerged as the most suitable model due to its ability to handle structured data efficiently and deliver accurate employee performance prediction using AI.
• The final model predicted fair compensation ranges and highlighted discrepancies between an employee’s current salary and their predicted benchmark.

This approach enabled workforce performance optimization using AI and delivered insights for targeted pay adjustments.

Why XGBoost?

Among all tested models, XGBoost provided superior performance:

• Handled high-dimensional structured data with minimal preprocessing.
• Demonstrated better generalization across diverse employee profiles.
• Outperformed other models in predictive accuracy and deep learning for employee assessment, particularly when benchmarked against Random Forest and Linear Regression.
• Required less tuning while maintaining robustness and speed.

Its ability to handle complex feature interactions and prevent overfitting made it ideal for this application.

Data Considered

A broad range of employee and market variables was evaluated. While not all were retained in the final model, they played a role in exploratory analysis and model training iterations.

These variables were essential in refining the model and enhancing its ability to deliver AI-based KPI assessment system development for transparent performance reviews.

Implementation Process

Visualization of Findings

To enhance understanding and facilitate strategic discussions, we created a suite of visualizations:

• Salary distribution curves by job role and experience level.
• Market Percentile comparisons showing how individual salaries stacked against the market.
• Gap analysis dashboards identifying underpaid and overpaid roles.
• Location-based salary maps to assess regional pay competitiveness.

These insights helped the client quickly identify compensation inefficiencies and take targeted action.

Key Outcomes

• Delivered a market-aligned, evidence-based salary structure tailored to individual roles and geographies.
• Supported the reduction of attrition through competitive and transparent pay frameworks.
• Uncovered actionable insights to support compensation adjustments based on skill sets, experience, and job market conditions.
• Enabled more accurate appraisals and budgeting with employee appraisal prediction model capabilities.

Impact on Client's Business

The implementation had a measurable impact on the client’s HR and compensation functions:

• 
  
  Improved employee retention

  by ensuring salaries aligned with market expectations and internal equity



• 
  
  Enabled data-driven decisions

  in performance appraisals, salary negotiations, and budgeting



• 
  
  Positioned the client competitively

  in the talent market by aligning compensation strategies with industry benchmarks