Update app.py

app.py

@@ -1,91 +1,79 @@
-import pandas as pd
-import numpy as np
-import os
-import matplotlib.pyplot as plt
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score, classification_report
-from imblearn.over_sampling import SMOTE
-from joblib import dump
-
-# ==========================
-# Load dataset
-# ==========================
-data = pd.read_csv("dia.csv")
-
-# Features & Target
-X = data.drop(columns=["Outcome"])
-y = data["Outcome"]
-
-# Replace zero values in certain columns (except Pregnancies & Outcome)
-cols_with_zero = ["Glucose", "BloodPressure",
-                  "SkinThickness", "Insulin", "BMI"]
-X[cols_with_zero] = X[cols_with_zero].replace(0, np.nan)
-
-# Fill missing with median
-X = X.fillna(X.median())
-
-# Train-Test Split
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, test_size=0.2, random_state=42, stratify=y
-)
-
-# Scale
-scaler = StandardScaler()
-X_train_scaled = scaler.fit_transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-
-# Balance dataset with SMOTE
-sm = SMOTE(random_state=42)
-X_train_bal, y_train_bal = sm.fit_resample(X_train_scaled, y_train)
-
-# ==========================
-# Train Random Forest
-# ==========================
-model = RandomForestClassifier(n_estimators=200, random_state=42)
-model.fit(X_train_bal, y_train_bal)
-
-# ==========================
-# Evaluation @ Default Threshold 0.5
-# ==========================
-y_pred = model.predict(X_test_scaled)
-acc = accuracy_score(y_test, y_pred)
-print("✅ Default Threshold Accuracy:", acc)
-print("\nClassification Report (Threshold=0.5):\n",
-      classification_report(y_test, y_pred))
-
-# ==========================
-# Threshold Tuning
-# ==========================
-print("\n🔎 Threshold Tuning Results")
-y_proba = model.predict_proba(X_test_scaled)[:, 1]
-
-for thresh in [0.3, 0.4, 0.5, 0.6]:
-    y_pred_thresh = (y_proba >= thresh).astype(int)
-    acc_thresh = accuracy_score(y_test, y_pred_thresh)
-    print(f"\nThreshold = {thresh}")
-    print("Accuracy:", acc_thresh)
-    print(classification_report(y_test, y_pred_thresh, digits=3))
-
-# ==========================
-# Feature Importance
-# ==========================
-importances = model.feature_importances_
-features = X.columns
-sorted_idx = np.argsort(importances)[::-1]
-
-plt.figure(figsize=(8, 5))
-plt.bar(range(len(importances)), importances[sorted_idx], color="skyblue")
-plt.xticks(range(len(importances)), features[sorted_idx], rotation=45)
-plt.title("Feature Importance (RandomForest)")
-plt.tight_layout()
-plt.show()
-
-# ==========================
-# Save model & scaler
-# ==========================
-os.makedirs("models", exist_ok=True)
-dump(model, "models/diabetes.sav")
-dump(scaler, "models/scaler.sav")
-print("✅ Final Model and Scaler saved in 'models/' folder.")
+import pandas as pd
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report
+from imblearn.over_sampling import SMOTE
+from joblib import dump
+
+# Load dataset
+data = pd.read_csv("dia.csv")
+
+# Features & Target
+X = data.drop(columns=["Outcome"])
+y = data["Outcome"]
+
+# Replace zero values in certain columns (except Pregnancies & Outcome)
+cols_with_zero = ["Glucose", "BloodPressure",
+                  "SkinThickness", "Insulin", "BMI"]
+X[cols_with_zero] = X[cols_with_zero].replace(0, np.nan)
+
+# Fill missing with median
+X = X.fillna(X.median())
+
+# Train-Test Split
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42, stratify=y
+)
+
+# Scale
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# Balance dataset with SMOTE
+sm = SMOTE(random_state=42)
+X_train_bal, y_train_bal = sm.fit_resample(X_train_scaled, y_train)
+
+# Train Random Forest
+model = RandomForestClassifier(n_estimators=200, random_state=42)
+model.fit(X_train_bal, y_train_bal)
+
+# Evaluation @ Default Threshold 0.5
+y_pred = model.predict(X_test_scaled)
+acc = accuracy_score(y_test, y_pred)
+print(" Default Threshold Accuracy:", acc)
+print("\nClassification Report (Threshold=0.5):\n",
+      classification_report(y_test, y_pred))
+
+# Threshold Tuning
+print("\n Threshold Tuning Results")
+y_proba = model.predict_proba(X_test_scaled)[:, 1]
+
+for thresh in [0.3, 0.4, 0.5, 0.6]:
+    y_pred_thresh = (y_proba >= thresh).astype(int)
+    acc_thresh = accuracy_score(y_test, y_pred_thresh)
+    print(f"\nThreshold = {thresh}")
+    print("Accuracy:", acc_thresh)
+    print(classification_report(y_test, y_pred_thresh, digits=3))
+
+# Feature Importance
+importances = model.feature_importances_
+features = X.columns
+sorted_idx = np.argsort(importances)[::-1]
+
+plt.figure(figsize=(8, 5))
+plt.bar(range(len(importances)), importances[sorted_idx], color="skyblue")
+plt.xticks(range(len(importances)), features[sorted_idx], rotation=45)
+plt.title("Feature Importance (RandomForest)")
+plt.tight_layout()
+plt.show()
+
+# Save model & scaler
+os.makedirs("models", exist_ok=True)
+dump(model, "models/diabetes.sav")
+dump(scaler, "models/scaler.sav")
+print(" Final Model and Scaler saved in 'models/' folder.")