The healthcare client wanted to revitalize their traditional review methods and chose Sparity as their technological partner for developing a predictive performance evaluation model for assessment of Staff Performance.

The client specialize in hospital services, testing services, mental health services, women’s health services, specialty services, and substance abuse work in the community deliver a healthcare experience designed around your individual needs. The healthcare client offers comprehensive, state-of-the-art medical care and is committed to safety and delivering patient care excellence.


The healthcare client wanted to revolutionize their performance evaluation system and needed a solution that could analyze employee data & predict employee performance, offer unbiased decision-making capabilities and recommendations to improve the employee performance. Sparity quickly developed and trained two predictive models that can predict employee performance based on various factors as inputs and help make meaningful decisions for employees.


The healthcare client needed to overcome most of the issues found in traditional review methods as their current evaluation system was dealt with by humans. Sometimes humans default to their emotions, biases, prejudices, etc., that could negatively impact the organization’s growth and lead to inefficient decisions making. Additionally, managing and analyzing vast quantities of HR data manually is time-consuming and prone to errors. The client needed a solution to help them eliminate inefficiencies/ negatives that lower employee morale with a relatively easy to employ framework.


Applied data clustering for evaluating the employee’s performance and decision making process and imported python libraries — NumPy and pandas
As there are numerous categorical values,
used the Label encoder utility class from scikit-learn and transformed those values into numerical.

Built and trained two predictive models – Random Forest Classifier and decision tree Classifier algorithm that predicts employee’s performance based on various input factors

Decision tree classifier help to better analyze,
visualize and interpret the situation better by
taking different variables into account


Sparity developed and implemented a predictive performance evaluation model leveraging machine learning algorithms, i.e., clustering algorithm and decision tree of data mining technique to evaluate the employee performance and the decision-making process. The predictive model considered different performance evaluation factors like personality, punctuality, tact, oral expression, Quality of Work, perseverance, public relations, observance of security measures, capacity to guide & train subordinates, attitude towards superiors, moral integrity and more to provide actionable insights and predict and evaluate the performance of the employees.

Key Deliverables


Reduced employee attrition rates with proper assessment and recognition of employees and maximized productivity

Better features

Built model that predicts the employees’ performance for the following year and enabled easy advancement and promotion determinations


Capable of measuring over and underutilized resources and also able to identify relevant training needs for a specific period of time


Minimized the bias that’s inherited in the old-school method of performance management and enhanced assessment approach

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