Predictor of employee elegible to promotion and Web Application
GOAL
HR analytics is revolutionising the way human resources departments operate, leading to higher efficiency and better results overall. Human resources has been using analytics for years; however, the collection, processing, and analysis of data has been largely manual. Given the nature of human resources dynamics and HR KPIs, the manual approach has been constraining HR. Therefore, it is surprising that HR departments opened up to the utility of machine learning so late in the game. Here is an opportunity to use predictive analytics in identifying the employees most likely to be promoted through the use of an app fed with the relevant HR information.
BUILDING AND TRAINING MODEL PREDICTOR
The client is a large MNC with 9 broad verticals across the organisation. The client faces a challenge in identifying the right people for promotion and preparing them in time. Under the current process, the final promotions are only announced after a first round of training and evaluation; and this leads to delay in employees' transition to their new roles. Hence, the company needs help in identifying the best candidates at an earlier juncture, in order to expedite the entire promotion cycle.
The company has provided the following employee datasets:
Emp_ID: Unique ID for each employee
Department: The department in which the employee works
No of Region: Region of employment (unordered)
Level of Education: Education Level
Gender: Gender of employee
Recruitment_channel: Channel through which employee was recruited
No_of_trainings_completed: Number of other training sessions completed in previous year on soft skills, technical skills, etc.; but after which promotion did not occur.
Age: Age of employee
Performance score: Employee rating for the previous year
Length_of_service: Length of service (in years)
High KPIS: If percentage of KPIs > 80%, then 1; else 0.
Awards won: If awards won during previous year, then 1; else 0.
Average score evaluation: Average score in current training evaluations
Is_promoted: Employee was actually promoted (this is our target variable). 1 if promoted; else 0.
Data cleaning and Data Preprocessing
The "level of education" and "performance score" columns contained null values, which were addressed.
Column labels were renamed for ease of understanding.
Exploratory analysis was performed on the data, creating several charts to visualize the important trends. To see the Exploratory Data Analysis, click here
Extract and create new features, also the reason behind them
Building the model
Label Encoded all categorical features
Create the target vector
Split the data into training and testing with optional portion
Apply "Standart ScaLer" from the sklearn preprocessing library to scale X_train dataset
Explored the model performance of different ML classification algorithms. ( Logistic Regression, Random Forest Classifier , Gaussian Naive Bayes)
Analysis of various metrics to determine the best algorithm, The metric used to decide the best algorithm was accuracy.
The most promising algorithm (RFC) was chosen, and adjusted as necessary.
The characteristics that have the greatest impact on the prediction were calculated.
WEB APPLICATION TO PREDICT EMPLOYEE PROMOTION
By integrating innovative components from Streamlit's documentation, such as the user registration feature, into an application, have significantly elevated its functionality and user engagement potential. This incorporation not only demonstrates Streamlit's adaptability but also enriches the user experience, offering a personalized and interactive interface. Deploying such a refined application in Streamlit for evaluating prediction models becomes even more impactful due to its user-friendly nature, making complex model evaluation accessible and enabling swift iteration and adaptation based on user feedback or model adjustments.
In the following application, HR personnel will have the opportunity to input employee information. By clicking the prediction button, they'll determine if an employee is likely to be promoted or not. Additionally, there's an option to visualize data for all employees, providing a comprehensive view of the workforce within the tool.
Project Description
Data cleaning and feature engineering
Exploratory Data Analysis
Build and training model predictor
Web Application