--- a
+++ b/diabetes-api/train_model.py
@@ -0,0 +1,53 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import accuracy_score
+import joblib
+
+# Load dataset
+df = pd.read_csv('diabetes.csv')
+
+# Handle missing values (replace zeros with NaN for specific columns)
+columns_to_replace = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']
+df[columns_to_replace] = df[columns_to_replace].replace(0, pd.NA)
+
+# Fill missing values with the median
+df.fillna(df.median(), inplace=True)
+
+# Separate features and labels
+X = df.drop('Outcome', axis=1)
+y = df['Outcome']
+
+# Train/test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create a pipeline with scaling and Random Forest
+pipeline = Pipeline([
+    ('scaler', StandardScaler()),
+    ('classifier', RandomForestClassifier(random_state=42))
+])
+
+# Hyperparameter tuning
+param_grid = {
+    'classifier__n_estimators': [100, 200, 300],
+    'classifier__max_depth': [5, 10, 15],
+    'classifier__min_samples_split': [2, 5, 10],
+    'classifier__min_samples_leaf': [1, 2, 4]
+}
+
+grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1)
+grid_search.fit(X_train, y_train)
+
+# Best model
+best_model = grid_search.best_estimator_
+
+# Evaluate
+y_pred = best_model.predict(X_test)
+acc = accuracy_score(y_test, y_pred)
+print(f"Accuracy: {acc * 100:.2f}%")
+
+# Save the model
+joblib.dump(best_model, 'diabetes_model.pkl')
+print("Model saved as diabetes_model.pkl")