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import pandas as pd
import pickle
from keras.api.models import Sequential
from keras.api.layers import Dense, Dropout, BatchNormalization
from sklearn.preprocessing import StandardScaler
# Load the data
data = pd.read_csv("diabetes.csv")
data = data[data.BMI >= 5] # Filter out data with BMI less than 5
X = data.drop("Outcome", axis=1) # Features (independent variables)
y = data["Outcome"] # Target variable (dependent variable)
# Preprocessing step
sc = StandardScaler() # Initialize the StandardScaler
X = sc.fit_transform(X) # Fit and transform the feature data
# Create the model
model = Sequential() # Initialize a Sequential model
model.add(Dense(120, activation="relu", input_shape=(X.shape[1],))) # Add the input layer with ReLU activation
model.add(BatchNormalization()) # Add Batch Normalization layer
model.add(Dropout(0.3)) # Add Dropout layer with a rate of 0.3
model.add(Dense(64, activation="relu")) # Add a hidden layer with ReLU activation
model.add(BatchNormalization()) # Add Batch Normalization layer
model.add(Dropout(0.3)) # Add Dropout layer with a rate of 0.3
model.add(Dense(1, activation="sigmoid")) # Add the output layer with sigmoid activation
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]) # Compile the model
# Train the model
model.fit(X, y,
epochs=10, # Number of epochs
batch_size=32, # Size of each batch
validation_split=0.2, # Split 20% of data for validation
verbose=1) # Verbosity mode
model.save("model.keras") # Save the model
joblib.dump(sc, "diabetes_scaler.pkl") # Save the scaler |