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c4fc2e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import os
import sys
import joblib
# Add project root to sys.path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import path_utils
def run_preprocessing():
"""Performs feature engineering, scaling, and splitting."""
# 1. Load Data
csv_file = path_utils.get_data_path("raw", "diabetes_binary_health_indicators_BRFSS2015.csv")
df = pd.read_csv(csv_file)
print(f"Initial Dataset: {df.shape[0]} rows, {df.shape[1]} columns")
# 2. Clinical Feature Engineering
print("Engineering Clinical Features...")
# BMI_OBESE = 1 if BMI >= 30 else 0
df['BMI_OBESE'] = (df['BMI'] >= 30).astype(int)
# HIGH_RISK_COMBO = 1 if HighBP == 1 AND HighChol == 1 else 0
df['HIGH_RISK_COMBO'] = ((df['HighBP'] == 1) & (df['HighChol'] == 1)).astype(int)
# POOR_HEALTH_SCORE = GenHlth + DiffWalk + PhysHlth_flag
# where PhysHlth_flag = 1 if PhysHlth > 14
df['PhysHlth_flag'] = (df['PhysHlth'] > 14).astype(int)
df['POOR_HEALTH_SCORE'] = df['GenHlth'] + df['DiffWalk'] + df['PhysHlth_flag']
# Drop intermediate flag
df.drop(columns=['PhysHlth_flag'], inplace=True)
print(f"Features Engineered. Total columns: {df.shape[1]}")
# 3. Features and Target
X = df.drop(columns=['Diabetes_binary'])
y = df['Diabetes_binary']
# 4. Train-Test Split (80/20, stratified)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
print(f"Splits Created: Train={X_train.shape[0]}, Test={X_test.shape[0]}")
# 5. Scaling
print("Scaling Features...")
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Save Scaler for later use in app.py
os.makedirs(path_utils.get_models_path(), exist_ok=True)
joblib.dump(scaler, path_utils.get_models_path("scaler.pkl"))
# 6. Save Processed Data
os.makedirs(path_utils.get_data_path("processed"), exist_ok=True)
# Convert scaled back to DataFrame to preserve feature names for training script
X_train_final = pd.DataFrame(X_train_scaled, columns=X.columns)
X_test_final = pd.DataFrame(X_test_scaled, columns=X.columns)
X_train_final.to_csv(path_utils.get_data_path("processed", "X_train.csv"), index=False)
X_test_final.to_csv(path_utils.get_data_path("processed", "X_test.csv"), index=False)
y_train.to_csv(path_utils.get_data_path("processed", "y_train.csv"), index=False)
y_test.to_csv(path_utils.get_data_path("processed", "y_test.csv"), index=False)
print("Preprocessing Complete. Data and Scaler saved.")
if __name__ == "__main__":
run_preprocessing()
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