merishnaSuwal

import numpy as np  # linear algebra
import cv2 # opencv
import matplotlib.pyplot as plt # image plotting
# keras
from keras import Sequential
from keras.layers import Flatten, Dense
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input
from keras.preprocessing.image import ImageDataGenerator

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# Save the trained model 
model.save('saved_model.h5')

# Load the model
model = keras.models.load_model('saved_model.h5')

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# summarize the result and plot the training and test loss
plt.plot(result.history['loss'])
plt.plot(result.history['val_loss'])

# Set the parameters
plt.title('Deep learning model loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'test'], loc='upper right')

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# Get the loss and accuracy of the model by evaluation
loss, acc = model.evaluate(X_test, y_test)

# Print the loss and accuracy score for the model
print("%s: %.2f%%" % (model.metrics_names[0], loss*100))
print("%s: %.2f%%" % (model.metrics_names[1], acc*100))

# Predicting the output predictions
y_pred = model.predict(X_test).round()

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# Input
model = Sequential()
# Hidden layer
model.add(Dense(64, kernel_initializer='uniform', input_dim=24, activation='relu'))
# Output layer
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))

# Compiling the model with 'adam' optimizer and loss function as 'binary_crossentropy'
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

merishnaSuwal

from sklearn.preprocessing import StandardScaler

# Initialization of the class
scaler = StandardScaler()

# Applying the scaler on test and train data
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

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from sklearn.model_selection import train_test_split

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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# Features
X = dataset.drop('target', axis = 1) # selecting all columns except the target
# Target variable
y = dataset['target']

# Print the shape
print(X.shape, y.shape)

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# Replacing null values by mean 
dataset['Age'].fillna(dataset['Age'].mean(), inplace = True)
dataset['T4U'].fillna(dataset['T4U'].mean(), inplace = True)

# Replacing null values by median
dataset['TSH'].fillna(dataset['TSH'].mean(), inplace = True)
dataset['T3'].fillna(dataset['T3'].median(), inplace = True)
dataset['TT4'].fillna(dataset['TT4'].median(), inplace = True)
dataset['FTI'].fillna(dataset['FTI'].median(), inplace = True)

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# Plot the histogram of different features
dataset.hist(figsize = (20,20));