Overview / Usage

Employee attrition is a problem that is faced by organizations across various industries. When an employee leaves, it can cost the company a lot of money, time, and productivity. Price and Waters, a boutique data science consulting firm, is looking to build a machine learning model to predict employee attrition. The model will help companies identify whether an employee is likely to leave and take appropriate actions to mitigate such issues.

The company has collected employee performance data for some of the months randomly chosen for each employee to understand it in the context of attrition. The consulting firm intends to analyze this data to identify specific features that are highly indicative of attrition. These features will provide insights into the reasons why employees leave and help the company take appropriate actions to retain employees.

To build the machine learning model, the company will use a classification approach to predict whether an employee is likely to quit soon. They will explore the data and preprocess it, including removing unnecessary columns, filling in missing data, and converting data into appropriate changes. Additionally, they will perform feature engineering, using the existing columns to create new columns.

The model building process will use the F1 score as the evaluation metric to balance both classes. The company will use SMOTE to oversample the training data since the target variable is highly imbalanced.

Overall, Price and Waters' machine learning model will provide a comprehensive HR solution that helps companies make informed decisions about recruitment and employee retention. By predicting employee attrition, the model will enable companies to intervene and retain valuable employees, thereby reducing the costs associated with turnover. This will allow companies to retain their valuable workforce and ensure maximum productivity.

Methodology / Approach

Methodology:

Intel one-API can be effectively used in an employee attrition system in machine learning by leveraging its performance optimization capabilities and heterogeneous computing support. Here are the key steps to follow:

1. Data Preparation: Gather historical employee data, including attributes like job role, performance ratings, salary, years of experience, etc., along with attrition labels (whether an employee left or not). Clean and preprocess the data, handling missing values and categorical variables appropriately.
2. Feature Engineering: Extract meaningful features from the data that can help predict employee attrition. This can involve techniques like one-hot encoding, scaling numerical features, creating interaction terms, and more.
3. Model Selection: Choose an appropriate machine learning model for predicting employee attrition. Common models for this task include logistic regression, decision trees, random forests, and gradient boosting algorithms. Consider the trade-offs between model complexity, interpretability, and accuracy.
4. Training and Optimization: Use Intel one-API tools, such as the Intel one-API Base Toolkit and Intel one-API Data Analytics Library (one-DAL), to optimize the training process and accelerate computations on various hardware architectures, including CPUs, GPUs, and FPGAs. Utilize parallel processing, vectorization, and threading techniques to improve performance.
5. Model Evaluation: Split the data into training and testing sets. Train the model using the training set and evaluate its performance using appropriate evaluation metrics like accuracy, precision, recall, or F1 score. Adjust the model and hyperparameters as needed to achieve satisfactory results.
6. Deployment and Monitoring: Once the model performs well on the test set, deploy it in a production environment. Monitor its performance and retrain periodically with new data to keep it up-to-date.

Additionally, Intel one-API provides optimization tools like Intel V-Tune Profiler, which can help analyze and optimize the performance of your code, ensuring efficient execution and resource utilization.

Technologies Used

scikit-learn-intelex

geopy==2.3.0

numpy==1.22.2

pandas==1.4.1

PyPDF2==3.0.1

pyresumeparser

spacy==2.3.5

scikit-learn==0.15.2

Python==3.9

one-API

Repository

https://github.com/Ahamedthaiyub/Workfoce-360-based-on-intelex-