Client is a U.S. Healthcare provider which specializes in hospital services, mental health services, women’s health services, specialty services, and substance abuse work in the community to deliver a healthcare experience designed around individual needs.
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They were looking to revolutionize their performance evaluation system and build a solution that could analyze employee data, predict employee performance and offer unbiased decision-making capabilities & recommendations to improve employee performance.
Key Challenges
- The healthcare client wanted to overcome issues found in their current evaluation system as it was dealt with by humans
- Humans default to their emotions, biases, prejudices, etc., which could negatively impact the organization’s growth and lead to inefficient decisions
- 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 that lower employee morale with a relatively easy-to-employ framework
Solution
- Sparity developed and implemented predictive models for performance evaluation using machine learning algorithms
- Employed Hybrid techniques and developed and trained two predictive models, Data clustering and Decision Tree Classifier algorithms
- Applied data clustering for evaluating the employee’s performance and decision-making process and imported python libraries — NumPy and pandas
- 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
- Used the Label encoder utility class from scikit-learn and transformed those diverse categorical values into numerical
- Leveraged Decision tree classifier to help visualize and analyze the situation better and evaluate the employee’s performance as well as in the decision-making process
Benefits
- Attained maximum productivity by reducing manual workload by more than 35%
- Empowered client to identify over and underutilized resources and design relevant training courses for a specific period of time
- Minimized the bias that’s inherited in the old-school method of performance management
- Predicts the employees’ performance for the following year and enabled easy advancement and promotion determinations
