Objective Recap

• To determine key predictors that influence the number of employees in firms as per the ‘Census’ dataset.

• Data Summary:

  • Our target variable is OWNPDEMP, which represents the number of employees.
    
  • After checking the documentation, we wrangled the dataset and filtered out outliers to improve the modeling process and identify effective predictors.

• Dataset Details:

  • Length of Dataset (rows): 135,152
    
  • Columns in API: 28 variables

• Feature Importance Analysis Using H2O: “Leveraged H2O’s automated machine learning framework to compute variable importance, identifying the most influential predictors for employee count.”

• Key Insights from H2O Analysis: “H2O’s feature importance analysis highlighted [mention top 2-3 variables, e.g., ‘industry type,’ ‘firm size,’ and ‘region’] as the most impactful factors influencing workforce size.”

Industry Visualization

• This horizontal bar chart illustrates the top 10 industries by the average number of employees.
• The “Professional, Scientific” sector ranks the highest with the largest workforce, followed by industries such as Construction and Retail Trade. Other significant sectors include Health Care and Social Assistance, Accommodation and Food Services, and Ambulatory Health Care.
• The chart provides a clear comparison of workforce distribution across various industries, highlighting the prominence of service-oriented sectors. The data emphasizes workforce concentration, useful for economic and employment trend analysis.

Modeling with h2o:

Graph 1 – Actual vs. Predicted Values

• Description: This scatter plot compares the actual values of OWNPDEMP to the predicted values from the model, evaluating its performance.
• Key Points:
  1. The red dashed line represents the ideal case where predictions perfectly match the actual number of employer firms (y = x).
     
  2. Deviations from the line indicate prediction errors, with larger gaps showing higher residuals.

Modeling with h2o:

Graph 2 – Residual Plot

• Description: The residual plot visualizes the differences between actual and predicted values of OWNPDEMP across the dataset.
• Key Points:
  1. The red dashed line at zero represents no prediction error.
     
  2. Higher residual dispersion at larger actual values suggests the model struggles with predicting large firm counts accurately.

Variable Importance Table

• Description: The table shows the relative importance of variables in the predictive model.
• Key Points:
  • “Period” is the most critical predictor, showing a significant impact on model performance.
    
  • Industry classifications like “NAICS2017.23” (Construction) and “NAICS2017.54” (Professional Services) are highly influential, reflecting sector-specific characteristics.

Key Takeaway from Variable Importance

1> The scatter plot (Graph 1) reveals that the model performs well for smaller firms, with actual values closely aligning with predicted values. However, variance increases for larger firms, suggesting potential room for improvement in handling outliers or extreme values.

2> The residual plot (Graph 2) highlights issues with heteroscedasticity, where prediction errors are more pronounced for firms with higher employee counts. This suggests the need for additional feature engineering or the application of advanced algorithms to address variability in large firms. Insights from Variable Importance:

3> The analysis shows that “Period” and specific industry categories (e.g., NAICS2017.23 for Construction) play a significant role in predicting employer firm counts, indicating temporal trends and industry-specific growth patterns are key factors.

4> Demographic variables (e.g., ownership characteristics like gender or ethnicity) show lower importance in the model, suggesting that industry and time-based factors dominate the prediction landscape.

Data Visualizations considering Peer Comments:

Areas for Improvement for Policymakers:

• Handling Skew: The presence of highly skewed industries in Graph 1 and the residual dispersion in Graph 2 indicates the need for better handling of non-normal data distributions.

• Model Refinement: Incorporating interaction terms or non-linear models could improve accuracy for larger firms, as their prediction errors disproportionately affect model performance. Practical Implications:

• Policymakers and businesses should focus on high-impact variables like industry type and temporal changes to understand business growth patterns better.

• Supporting small firms in skewed industries may help balance disparities, as indicated by the residual and variable importance analysis.

Thank You..