Create app.py

app.py

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+import pandas as pd
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import MinMaxScaler
+
+# Load data
+df = pd.read_csv("diabetes.csv")
+
+# Replace 0s with NaN (Glucose, BP, etc.)
+cols = ["Glucose", "BloodPressure", "SkinThickness", "Insulin", "BMI"]
+df[cols] = df[cols].replace(0, float('nan'))
+
+# Impute missing values with mean
+imputer = SimpleImputer(strategy="mean")
+df[cols] = imputer.fit_transform(df[cols])
+
+# Remove outliers using IQR
+Q1 = df.quantile(0.25)
+Q3 = df.quantile(0.75)
+IQR = Q3 - Q1
+df = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]
+
+# Feature selection (keep: Pregnancies, Glucose, Insulin, BMI, Age)
+X = df[["Pregnancies", "Glucose", "Insulin", "BMI", "Age"]]
+y = df["Outcome"]
+
+# Normalize to [0, 1]
+scaler = MinMaxScaler()
+X = scaler.fit_transform(X)
+
+
+
+
+# Machine Learning Models (DT, KNN, RF, NB, AB, LR, SVM)
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import GaussianNB
+
+# Split data (85% train, 15% test)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)
+
+# Initialize models
+models = {
+    "DT": DecisionTreeClassifier(),
+    "KNN": KNeighborsClassifier(n_neighbors=7),
+    "RF": RandomForestClassifier(),
+    "NB": GaussianNB(),
+    "AB": AdaBoostClassifier(),
+    "LR": LogisticRegression(),
+    "SVM": SVC()
+}
+
+# Evaluate via k-fold CV (k=10)
+for name, model in models.items():
+    scores = cross_val_score(model, X, y, cv=10, scoring="accuracy")
+    print(f"{name} CV Accuracy: {scores.mean():.2%}")
+
+# Evaluate via train-test split
+for name, model in models.items():
+    model.fit(X_train, y_train)
+    acc = model.score(X_test, y_test)
+    print(f"{name} Test Accuracy: {acc:.2%}")
+
+
+#Neural Network (Keras)
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import SGD
+
+# NN with 2 hidden layers (architecture from paper)
+model = Sequential([
+    Dense(26, activation="relu", input_shape=(5,)),
+    Dense(5, activation="relu"),
+    Dense(1, activation="sigmoid")
+])
+
+# Compile with SGD (lr=0.01)
+model.compile(optimizer=SGD(learning_rate=0.01), 
+              loss="binary_crossentropy", 
+              metrics=["accuracy"])
+
+# Train for 400 epochs
+history = model.fit(X_train, y_train, epochs=400, batch_size=32, 
+                    validation_data=(X_test, y_test), verbose=0)