Salary Prediction: Harnessing Data for Informed Compensation Insights

Salary Prediction" offers a deep dive into leveraging data analytics to predict salaries accurately. This presentation showcases methodologies such as machine learning algorithms and statistical models that analyze factors influencing compensation trends. Ideal for project presentations, this topic explores how businesses can use predictive analytics to optimize budgeting, attract talent, and ensure fair compensation practices in dynamic industries for more details visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. Salary Range Prediction

2. Agenda INTRODUCTION Data Description Data Cleaning & Preprocessing Feature Engineering Model Development Model Evaluation and Validation Deployment Success Evaluation Conclusion and Future Work

3. Introduction The project aimed to address the challenge of predicting accurate salary ranges for job postings, a significant aspect of talent management and recruitment. This enhances strategic HR planning and budgeting.

4. Data Description The dataset was sourced from partner organizations and included various features related to job postings. Initial observations noted challenges such as high dimensionality and missing values, which were addressed during preprocessing.

5. Data Cleaning & Preprocessing Data cleaning involved handling missing values through imputation and removing outliers using the IQR method. Data transformations such as log transformations were applied to normalize the data.

6. Feature Engineering New features were engineered to enhance model performance, and features were selected based on their predictive power and relevance. Encoding and scaling techniques were used to prepare the data for modeling.

7. Model Development Various models were tested, with Gradient Boosting and Random Forest performing the best. Model selection was based on a combination of performance metrics and computational efficiency.

8. Model Evaluation and Validation Models were evaluated using MAE, MSE, and R² metrics. Validation techniques such as cross-validation were used to assess model robustness.

9. Deployment The model was deployed on a cloud-based platform, facilitating real-time salary prediction. An interactive dashboard was developed to allow HR personnel to use the model easily.

10. Success Evaluation The project's success was evaluated based on accuracy thresholds and user satisfaction, with positive feedback from stakeholders.

11. Conclusion and Future Work The project successfully met its objectives, with potential future enhancements including the integration of additional data sources and the implementation of more advanced machine learning techniques to further improve accuracy.

12. Questions ?

13. Thank You!