import pandas as pd

import matplotlib.pyplot as plt

import pickle

# Import necessary modules

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

# Keras specific

import keras

from keras.models import Sequential

from keras.layers import Dense

df = pd.read_csv('cancer patient data sets.csv')

level_mapping = {'Low': float(0), 'Medium': float(1.0), 'High': float(2.0)}

df['Level'] = df['Level'].replace(level_mapping)

df.to_csv('cancer patient data sets.csv', index=False)

X = df.iloc[:, 2:-1].values  # Select all rows and columns from index 2 (excluding Level and index, Patient Id) up to the last column

y = df.iloc[:, -1].values

scaler = StandardScaler()

X = scaler.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)

model = Sequential()

model.add(Dense(32, activation='relu', input_dim=23))

model.add(Dense(16, activation='relu'))

model.add(Dense(3, activation='sigmoid'))

# Compile the model

optimizer = keras.optimizers.Adam(learning_rate=0.001)

model.compile(optimizer=optimizer,

              loss='binary_crossentropy',

              metrics=['accuracy'])

# Train the model

history = model.fit(X_train, keras.utils.to_categorical(y_train, 3), epochs=10, batch_size=32, validation_data=(X_test, keras.utils.to_categorical(y_test, 3)), verbose=2)

# Evaluate the model on test data

loss, accuracy = model.evaluate(X_test, keras.utils.to_categorical(y_test, 3))

print('Test accuracy:', round(accuracy*100, 2), '%')

# Plot the training and validation loss and accuracy

plt.plot(history.history['accuracy'])

plt.plot(history.history['val_accuracy'])

plt.title('Model Accuracy')

plt.ylabel('Accuracy')

plt.xlabel('Epoch')

plt.legend(['Train', 'Validation'], loc='upper left')

plt.show()

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'])

plt.title('Model Loss')

plt.ylabel('Loss')

plt.xlabel('Epoch')

plt.legend(['Train', 'Validation'], loc='upper left')

plt.show()

# Save the model

model.save('my_model.h5')

# Save the scaler object to a file

filename = 'scaler.pkl'

with open(filename, 'wb') as f:

    pickle.dump(scaler, f)