--- a
+++ b/model.py
@@ -0,0 +1,71 @@
+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)