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William-Hill merged 1 commit into from
Feb 18, 2026
Merged

ci: add GitHub Actions for Next.js build and Python lint #51

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85 changes: 42 additions & 43 deletions ai_model/complete_ml_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,10 @@
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import (
accuracy_score, precision_score, recall_score, f1_score,
roc_auc_score, confusion_matrix, classification_report,
mean_squared_error, mean_absolute_error, r2_score
roc_auc_score, confusion_matrix, mean_squared_error, mean_absolute_error, r2_score
)
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
import xgboost as xgb
Expand All @@ -27,17 +26,17 @@
warnings.filterwarnings('ignore')

# Database utilities
import sys
import os
import sys # noqa: E402
import os # noqa: E402
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from operations.db_utils import (
save_dataframe_to_db,
save_model_performance,
from operations.db_utils import ( # noqa: E402
save_dataframe_to_db,
save_model_performance,
create_model_performance_table,
test_connection
)
from operations.db_config import TABLES, DB_CONFIG
from operations.db_config import TABLES, DB_CONFIG # noqa: E402

# Get the project root directory
PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
Expand Down Expand Up @@ -178,7 +177,7 @@ def assign_credential_type(row):

df['target_credential_type'] = df.apply(assign_credential_type, axis=1)

print(f"Created target variables:")
print("Created target variables:")
print(f" - Retention: {df['target_retention'].value_counts().to_dict()}")
print(f" - At Risk: {df['target_at_risk'].value_counts().to_dict()}")
print(f" - Credential Type: {df['target_credential_type'].value_counts().to_dict()}")
Expand Down Expand Up @@ -285,8 +284,8 @@ def preprocess_features(df, feature_list):
print("TESTING MULTIPLE MODELS WITH CROSS-VALIDATION")
print("-" * 80)

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression # noqa: E402
from sklearn.model_selection import StratifiedKFold # noqa: E402

models_to_test = {
'Logistic Regression': LogisticRegression(
Expand Down Expand Up @@ -349,11 +348,11 @@ def preprocess_features(df, feature_list):
print(f" Gap: {gap:.4f} ({gap*100:.2f}%)")

if gap < 0.05:
print(f" ✓ No overfitting (gap < 5%)")
print(" ✓ No overfitting (gap < 5%)")
elif gap < 0.10:
print(f" ⚠ Minimal overfitting (gap < 10%)")
print(" ⚠ Minimal overfitting (gap < 10%)")
else:
print(f" ✗ Overfitting detected (gap > 10%)")
print(" ✗ Overfitting detected (gap > 10%)")

model_comparison.append({
'Model': model_name,
Expand Down Expand Up @@ -413,8 +412,8 @@ def preprocess_features(df, feature_list):

print("\nConfusion Matrix:")
cm = confusion_matrix(y_test, y_pred)
print(f" Predicted")
print(f" Not Ret Retained")
print(" Predicted")
print(" Not Ret Retained")
print(f"Actual Not {cm[0,0]:6d} {cm[0,1]:6d}")
print(f" Ret {cm[1,0]:6d} {cm[1,1]:6d}")

Expand Down Expand Up @@ -545,8 +544,8 @@ def assign_alert_level(risk_score):
low_retention_low_risk = df[(df['retention_probability'] < 0.3) & (df['at_risk_alert'] == 'LOW')]
print(f"Students with <30% retention flagged as LOW: {len(low_retention_low_risk)} (should be very few)")

print(f"\nEarly warning system aligned with retention predictions")
print(f"\nAlert distribution:")
print("\nEarly warning system aligned with retention predictions")
print("\nAlert distribution:")
print(df['at_risk_alert'].value_counts().sort_index())

# ============================================================================
Expand Down Expand Up @@ -611,7 +610,7 @@ def assign_alert_level(risk_score):
df['predicted_time_to_credential'] = time_model.predict(X_full_retention)
df['predicted_graduation_year'] = df['Cohort'].str[:4].astype(float) + df['predicted_time_to_credential']

print(f"Time predictions generated")
print("Time predictions generated")
else:
print("Warning: Insufficient data for time-to-credential model")
df['predicted_time_to_credential'] = np.nan
Expand All @@ -630,7 +629,7 @@ def assign_alert_level(risk_score):
y_credential = y_credential[valid_idx]

print(f"\nDataset size: {len(X_cred):,} students")
print(f"Credential type distribution:")
print("Credential type distribution:")
cred_labels = {0: 'No Credential', 1: 'Certificate', 2: 'Associate', 3: 'Bachelor'}
for k, v in y_credential.value_counts().sort_index().items():
print(f" {cred_labels.get(k, k)}: {v:,} ({v/len(y_credential)*100:.1f}%)")
Expand Down Expand Up @@ -677,7 +676,7 @@ def assign_alert_level(risk_score):
model_name='Credential Type Prediction',
model_type='classification',
metrics={'accuracy': cred_accuracy, 'f1': cred_f1},
notes=f'Random Forest Classifier - 4 classes (No Credential, Certificate, Associate, Bachelor)'
notes='Random Forest Classifier - 4 classes (No Credential, Certificate, Associate, Bachelor)'
)

# Generate predictions for all students
Expand All @@ -699,7 +698,7 @@ def assign_alert_level(risk_score):
if class_idx < len(prob_labels):
df[prob_labels[int(class_idx)]] = proba[:, i]

print(f"Credential type predictions generated")
print("Credential type predictions generated")

# ============================================================================
# STEP 8: MODEL 5 - GATEWAY MATH SUCCESS PREDICTION
Expand Down Expand Up @@ -784,8 +783,8 @@ def assign_alert_level(risk_score):

print("\nConfusion Matrix:")
cm = confusion_matrix(y_test, y_pred)
print(f" Predicted")
print(f" No Pass Pass")
print(" Predicted")
print(" No Pass Pass")
print(f"Actual No {cm[0,0]:6d} {cm[0,1]:6d}")
print(f" Pass {cm[1,0]:6d} {cm[1,1]:6d}")

Expand All @@ -795,7 +794,7 @@ def assign_alert_level(risk_score):
model_name='Gateway Math Success Prediction',
model_type='classification',
metrics={'accuracy': math_accuracy, 'auc_roc': math_auc, 'precision': math_precision, 'recall': math_recall, 'f1_score': math_f1},
notes=f'XGBoost - Predicts gateway math completion Year 1'
notes='XGBoost - Predicts gateway math completion Year 1'
)

# Generate predictions for all students
Expand All @@ -810,7 +809,7 @@ def assign_alert_level(risk_score):
labels=['High Risk', 'Moderate Risk', 'Likely Pass', 'Very Likely Pass']
)

print(f"Gateway math predictions generated")
print("Gateway math predictions generated")

# ============================================================================
# STEP 9: MODEL 6 - GATEWAY ENGLISH SUCCESS PREDICTION (NEW!)
Expand Down Expand Up @@ -895,8 +894,8 @@ def assign_alert_level(risk_score):

print("\nConfusion Matrix:")
cm = confusion_matrix(y_test, y_pred)
print(f" Predicted")
print(f" No Pass Pass")
print(" Predicted")
print(" No Pass Pass")
print(f"Actual No {cm[0,0]:6d} {cm[0,1]:6d}")
print(f" Pass {cm[1,0]:6d} {cm[1,1]:6d}")

Expand All @@ -906,7 +905,7 @@ def assign_alert_level(risk_score):
model_name='Gateway English Success Prediction',
model_type='classification',
metrics={'accuracy': english_accuracy, 'auc_roc': english_auc, 'precision': english_precision, 'recall': english_recall, 'f1_score': english_f1},
notes=f'XGBoost - Predicts gateway English completion Year 1'
notes='XGBoost - Predicts gateway English completion Year 1'
)

# Generate predictions for all students
Expand All @@ -921,7 +920,7 @@ def assign_alert_level(risk_score):
labels=['High Risk', 'Moderate Risk', 'Likely Pass', 'Very Likely Pass']
)

print(f"Gateway English predictions generated")
print("Gateway English predictions generated")

# ============================================================================
# STEP 10: MODEL 7 - FIRST-SEMESTER GPA < 2.0 PREDICTION (NEW! - FIXED DATA LEAKAGE)
Expand Down Expand Up @@ -1009,8 +1008,8 @@ def assign_alert_level(risk_score):

print("\nConfusion Matrix:")
cm = confusion_matrix(y_test, y_pred)
print(f" Predicted")
print(f" GPA>=2.0 GPA<2.0")
print(" Predicted")
print(" GPA>=2.0 GPA<2.0")
print(f"Actual >=2.0 {cm[0,0]:6d} {cm[0,1]:6d}")
print(f" <2.0 {cm[1,0]:6d} {cm[1,1]:6d}")

Expand All @@ -1020,7 +1019,7 @@ def assign_alert_level(risk_score):
model_name='First-Semester Low GPA Prediction',
model_type='classification',
metrics={'accuracy': gpa_accuracy, 'auc_roc': gpa_auc, 'precision': gpa_precision, 'recall': gpa_recall, 'f1_score': gpa_f1},
notes=f'XGBoost - Predicts GPA < 2.0 risk (NO DATA LEAKAGE)'
notes='XGBoost - Predicts GPA < 2.0 risk (NO DATA LEAKAGE)'
)

# Generate predictions for all students
Expand All @@ -1033,7 +1032,7 @@ def assign_alert_level(risk_score):
labels=['Low Risk', 'Moderate Risk', 'High Risk', 'Critical Risk']
)

print(f"Low GPA predictions generated")
print("Low GPA predictions generated")

# ============================================================================
# STEP 11: SAVE PREDICTIONS TO STUDENT-LEVEL FILE
Expand Down Expand Up @@ -1066,7 +1065,7 @@ def assign_alert_level(risk_score):
if_exists='replace'
)
if success:
print(f"✓ Student-level predictions saved to database")
print("✓ Student-level predictions saved to database")
print(f" Table: {TABLES['student_predictions']}")
print(f" Records: {len(df):,}")
print(f" Columns: {len(df.columns)}")
Expand All @@ -1077,7 +1076,7 @@ def assign_alert_level(risk_score):
# Always save CSV files for backup and local analysis
output_file = os.path.join(DATA_DIR, 'kctcs_student_level_with_predictions.csv')
df.to_csv(output_file, index=False)
print(f"\n✓ Saved student-level predictions to CSV:")
print("\n✓ Saved student-level predictions to CSV:")
print(f" File: {output_file}")
print(f" Records: {len(df):,}")
print(f" Columns: {len(df.columns)}")
Expand Down Expand Up @@ -1117,15 +1116,15 @@ def assign_alert_level(risk_score):
if_exists='replace'
)
if success:
print(f"✓ Course-level predictions saved to database")
print("✓ Course-level predictions saved to database")
print(f" Table: {TABLES['course_predictions']}")
print(f" Records: {len(merged_with_predictions):,}")
print(f" Columns: {len(merged_with_predictions.columns)}")

# Always save CSV files for backup and local analysis
output_file = os.path.join(DATA_DIR, 'kctcs_merged_with_predictions.csv')
merged_with_predictions.to_csv(output_file, index=False)
print(f"\n✓ Saved course-level predictions to CSV:")
print("\n✓ Saved course-level predictions to CSV:")
print(f" File: {output_file}")
print(f" Records: {len(merged_with_predictions):,}")
print(f" Columns: {len(merged_with_predictions.columns)}")
Expand Down Expand Up @@ -1165,7 +1164,7 @@ def assign_alert_level(risk_score):
pct = count / len(df) * 100
summary_report += f" {cat:20s} {count:6,} ({pct:5.1f}%)\n"

summary_report += f"""
summary_report += """
2. EARLY WARNING SYSTEM
Algorithm: Composite Risk Score (Retention + Performance Metrics)
Approach: Aligned with retention predictions to eliminate contradictions
Expand Down Expand Up @@ -1331,14 +1330,14 @@ def assign_alert_level(risk_score):
print(" ✗ Database connection failed - used CSV fallback")

# Record counts loaded to database
print(f"\nRecords Loaded to Database:")
print("\nRecords Loaded to Database:")
if db_connected == 1:
print(f" - student_predictions table: {len(df):,} records")
print(f" - course_predictions table: {len(merged_with_predictions):,} records")
print(f" - ml_model_performance table: 4 model records")
print(" - ml_model_performance table: 4 model records")
print(f"\n Total records saved: {len(df) + len(merged_with_predictions) + 4:,}")
else:
print(f" - No records loaded to database (CSV fallback used)")
print(" - No records loaded to database (CSV fallback used)")
print(f" - student_predictions.csv: {len(df):,} records")
print(f" - course_predictions.csv: {len(merged_with_predictions):,} records")

Expand Down
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