Strategies for Employee Retention: Building a Resilient Workforce

In this insightful presentation, we explore effective strategies for enhancing employee retention in today’s competitive job market. Discover key factors that influence employee satisfaction, engagement, and loyalty, and learn how to implement practical solutions tailored to your organization's unique culture. From fostering a positive workplace environment to offering growth opportunities, this project presentation provides actionable insights and data-driven approaches to help you build a resilient workforce. Perfect for HR professionals, team leaders, and organizational decision-makers looking to reduce turnover and cultivate lasting employee relationships. for more information visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. EMPLOYEE RETENTION

2. Introduction Title: Predicting Employee Behavior using Machine Learning Objective: Build a machine learning model to predict a specific outcome related to employees or job seekers (e.g., job attrition or job-seeking behavior). Use various features such as demographics, work experience, education, and job-related data to make accurate predictions.

3. Dataset Overview Key Steps : Missing Values : Detected and handled missing values for variables like gender, major_discipline , and company_size . Encoding: Categorical variables such as gender and education_level were converted into numerical values using Encoding techniques like label encoding. Feature Engineering :Derived new features like cleaned_experience to better capture a person's work history. Data Splitting : The dataset was split into training (to train the model) and testing (to evaluate performance) sets.

4. Exploratory Data Analysis (EDA) Key Insights : Distribution of Features : Analyzed distributions of key variables like education_level , gender , and company_size . Correlation Analysis : Investigated correlations between different features and the target variable to understand which factors most affect predictions Visualizations: Used plots (e.g., bar charts, histograms) to visualize distributions and relationships between features. For example, checking how training_hours varies for different education_levels .

5. Heat Map

6. Model Selection Title : Random Forest Classifier Why Random Forest : A robust ensemble learning method that builds multiple decision trees and combines their results for more accurate predictions. Handles both categorical and numerical data effectively, making it suitable for your dataset. It also helps in handling missing data and mitigating overfitting, which makes it a reliable choice for this kind of classification task.

7. Model Training and Evaluation Steps: Splitting Data : Divided the data into training and testing sets. Training : The model was trained using the training set. Evaluation : After training, the model was evaluated on the test data using performance metrics such as: Confusion Matrix : To measure how many predictions were correct versus incorrect. Accuracy Score : The percentage of correct predictions out of total predictions.

8. Model Results Title : Performance Metrics Confusion Matrix : Demonstrates the number of correct and incorrect classifications for each class (e.g., how well the model predicted those who would leave versus those who wouldn’t). Accuracy : Report the accuracy score. The accuracy score represents the proportion of correct predictions (true positives and true negatives) out of all predictions.

9. Error Encountered and Debugging Title : Model Debugging Issue : During the prediction step, a TypeError occurred where the RandomForestClassifier was treated as a callable function. Solution: Instead of directly calling the model, use the .predict() method to generate predictions.

10. Title : Challenges and Key Learnings Challenges : Handling missing values and dealing with categorical data. Model debugging and fixing errors. Learnings : Improved understanding of machine learning workflows, including data preprocessing, model training, and evaluation. Experience with error handling and debugging in machine learning. Challenges and Learnings

11. Summary : Successfully built a machine learning model that predicts outcomes based on job and demographic data. The model achieved reasonable accuracy, with the potential for improvement through further feature engineering and hyperparameter tuning. Future Work : Try different models (e. g. SVM ) to improve performance. Perform more hyperparameter tuning on the Random Forest to optimize its performance. Implement cross-validation to better assess the model's generalization ability. Conclusion and Future Work

12. Questions ?

13. Thank You!