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 humansโโโโ
- Humans default to their emotions, biases, prejudices, etc., which could negatively impact the organization’s growth and lead to inefficient decisions โ
- 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 โ
- Applied data clustering for evaluating the employeeโs performance and decision-making process and imported python libraries โ 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 โ โ
- Used the Label encoder utility class from scikit-learn and transformed those diverse categorical values into numerical โ
- 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 โ โ
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 timeโ
- Minimized the bias that’s inherited in the old-school method of performance management
- Predicts the employeesโ performance for the following year and enabled easy advancement and promotion determinations