Team Members

Problem Description

We will investigate the estimated compliance costs associated with U.S. federal regulations. Specifically, we aim to categorize regulatory compliance burden into low and high cost tiers using a combination of regulatory restrictiveness, complexity, and industry relevance.

Why It’s Interesting

Understanding compliance costs is crucial for businesses, policymakers, and economists alike. High compliance costs can influence a company’s profitability, affect the speed of innovation, and even shape industry trends. Despite the significance, compliance costs are rarely quantified in an accessible way, making it challenging for businesses to prepare and allocate resources effectively. By developing a predictive model to categorize compliance costs, this project offers a data-driven approach to identify the most burdensome regulations, helping stakeholders make informed decisions about compliance strategies and potential regulatory reforms.

Analytics plan: How will you analyze your data? What methods or tools will you use?

For this project, the analytics plan will outline how the RegData U.S. 5.0 dataset will be processed, analyzed, and modeled to predict and categorize compliance costs.

Analytics Plan

  1. Data Preprocessing
    • Feature Engineering: Create a compliance cost proxy variable based on regulatory restrictiveness, complexity metrics, industry relevance, and word count. Engineer additional predictors such as “restrictions per word” or “acronyms per 100 sentences.”
    • Standardization/Normalization: Scale features to ensure consistency and improve model performance.
    • Categorical Encoding: One-hot encode categorical variables like agency and department to incorporate organizational context.
  2. Exploratory Data Analysis (EDA)
    • Descriptive Statistics: Summarize data to understand the distribution of variables like restrictions, complexity, and industry relevance.
    • Visualizations: Use visualizations to inspect correlations between variables, distributions across compliance cost categories, and to identify potential outliers or imbalances in the dataset.
    • Correlation Analysis: Assess feature correlations to understand relationships between complexity, restrictiveness, and compliance costs.
  3. Classification Modeling
    • Machine Learning Framework: Use H2O or Keras as specified, focusing on a deep learning-based classification model.
    • Model Selection: Build a multi-class classification model (e.g., using H2O’s Deep Learning or a Keras neural network) to categorize compliance cost into low, moderate, and high tiers.
    • Hyperparameter Tuning: Optimize model parameters (e.g., layers, nodes, learning rate) to improve classification accuracy and generalizability.
  4. Evaluation
    • Model Metrics: Evaluate model performance using metrics like accuracy, precision, recall, and F1-score to assess the model’s ability to classify each compliance cost tier.
    • Confusion Matrix: Analyze the matrix to understand misclassification rates between low, moderate, and high compliance cost tiers, refining the model as needed.
    • Feature Importance: For interpretability, assess feature importance or contribution (e.g., using permutation importance for H2O models) to identify key drivers of compliance costs.
  5. Deployment and Interpretation
    • Compliance Cost Predictions: Use the trained model to predict compliance cost categories on new or unseen data.
    • Business Insights: Provide insights on regulatory burden across different industries or agencies, highlighting which factors most influence compliance cost levels.
    • Documentation: Summarize the findings, model performance, and key insights in a report to present actionable results.

Tools

  • Libraries: H2O or Keras for model building, pandas for data manipulation, and matplotlib/seaborn for data visualization.
  • Environment: R environment for code execution and model training.

Here’s a structured evaluation plan to assess the effectiveness and accuracy of the compliance cost estimation model:

Evaluation Plan

  1. Model Performance Metrics
    • Accuracy: Measures the proportion of correct predictions across all compliance cost categories (low, moderate, high). While useful as a general indicator, accuracy alone may not be sufficient if there is class imbalance.
    • Precision, Recall, and F1-Score: These metrics will be evaluated for each compliance cost category:
      • Precision: The proportion of true positives out of all predictions for a given class (e.g., the proportion of correctly classified high-cost regulations out of all regulations classified as high-cost).
      • Recall: The proportion of true positives out of all actual instances of a given class, which helps understand how well the model captures all instances of each compliance cost level.
      • F1-Score: The harmonic mean of precision and recall for each class, providing a balanced measure for classes with potential imbalance.
  2. Confusion Matrix
    • A confusion matrix will show the distribution of true vs. predicted categories, allowing for detailed analysis of misclassifications between low, moderate, and high compliance cost categories. This will highlight if certain categories are being confused, which could indicate areas for model improvement.
  3. ROC-AUC and Precision-Recall Curves
    • ROC-AUC (Receiver Operating Characteristic - Area Under Curve): For multiclass problems, this metric helps assess the model’s ability to differentiate between the classes.
    • Precision-Recall Curve: Useful if the classes are imbalanced, as it focuses on the model’s performance in identifying the high-cost category accurately.
  4. Cross-Validation
    • Implement k-fold cross-validation to ensure the model’s performance generalizes across various subsets of the data, reducing overfitting and providing a more robust measure of the model’s predictive power.
  5. Feature Importance Analysis
    • For interpretability, assess which features most strongly impact the model’s predictions. In H2O, permutation importance can provide insights into which features drive compliance cost predictions, helping validate the choice of features and the model’s logic.
  6. Error Analysis
    • Examine cases where the model misclassifies compliance costs. Investigate if specific agencies, departments, or regulation types are consistently misclassified, which could suggest biases or areas where feature engineering might improve results.
  7. Model Comparison (if applicable)
    • If multiple models (e.g., H2O vs. Keras) are trained, compare them based on these evaluation metrics to select the best-performing model for compliance cost estimation.

Summary of Evaluation Process

By using a combination of classification metrics, confusion matrix analysis, cross-validation, and feature importance interpretation, this evaluation plan ensures that the model’s predictions are both accurate and meaningful, providing insights into regulatory compliance costs across different categories. This plan also allows for iterating on the model based on misclassification patterns or feature impact, leading to continuous improvement.

Data Source

Data load using API

library(httr)
library(readr)
library(knitr)

# Define the API endpoint and API key
url <- "https://h50pmkmeb6.execute-api.us-east-1.amazonaws.com/dev/quantgov/"
api_key <- "5ntHOMzpYo5T8FoXe7GIq8g9Qx51awhV1tJ2zOPH"

# Make the GET request with the API key
response <- GET(url, add_headers(`X-Api-Key` = api_key), verbose())

# Check if the request was successful
if (status_code(response) == 200) {
  # Read the content as text, assuming it's a CSV
  csv_content <- content(response, as = "text")
  
  # Parse the CSV content into a data frame
  df <- suppressWarnings(read_csv(csv_content))
  kable(View(df))
  print(head(df))
} else {
  print(paste("Request failed with status:", status_code(response)))
  print(content(response, as = "text"))
}
## No encoding supplied: defaulting to UTF-8.
## Rows: 26701 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): usregdata5
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 6 × 1
##   usregdata5                                                                    
##   <chr>                                                                         
## 1 "document_id,year,document_reference,title,part,agency_parent_name,agency_nam…
## 2 "19100000001,2022,\"Title 1, Part 1\",1,1,administrative committee of federal…
## 3 "19100000015,2022,\"Title 1, Part 19\",1,19,administrative committee of feder…
## 4 "19100000030,2022,\"Title 1, Part 602\",1,602,national capital planning commi…
## 5 "19100000041,2022,\"Title 2, Part 200\",2,200,executive office of the preside…
## 6 "19100000052,2022,\"Title 2, Part 600\",2,600,department of state,department …