Purpose

This section documents the data cleaning and preparation phase for the logistic regression model predicting test preparation completion. Perfect data retention indicates no observations were excluded during preprocessing, which is critical for maintaining statistical power and representativeness when identifying student characteristics that predict completion behavior.

Key Findings

Retention Rate: 100% (1,000 of 1,000 rows retained) - No observations were removed during cleaning, suggesting either minimal data quality issues or that missing values were handled through imputation rather than deletion
Rows Removed: 0 - The dataset required no exclusions, contrasting with the metadata note that "missing values removed" yet maintaining full row count
Train/Test Split: Not documented - The absence of explicit split information suggests the model may use the full dataset or employ alternative validation methods not captured here

Interpretation

The perfect retention rate supports the model's ability to leverage the complete 1,000-student sample for logistic regression estimation. This maximizes statistical power for detecting significant predictors of test preparation completion. However, the discrepancy between "missing values removed" and zero rows removed suggests preprocessing may have occurred at the column level rather than row level, potentially through imputation or feature engineering that isn't explicitly documented here.

Context

The lack of train/test split documentation limits visibility into how model performance metrics (AUC=0.8, Accuracy=