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diff --git a/Day 3/Issue 14/Saga/image_recognition.py b/Day 3/Issue 14/Saga/image_recognition.py
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+++ b/Day 3/Issue 14/Saga/image_recognition.py	
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+import cv2 as cv
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras import datasets, layers, models
+
+
+def load_and_preprocess_data():
+    (training_images, training_labels), (testing_images, testing_labels) = datasets.cifar10.load_data()
+    training_images, testing_images = training_images / 255.0, testing_images / 255.0
+    return (training_images, training_labels), (testing_images, testing_labels)
+
+
+def display_sample_images(training_images, training_labels):
+    class_names = ['Plane', 'Automobile', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']
+
+    plt.figure(figsize=(10, 10))
+    for i in range(16):
+        plt.subplot(4, 4, i + 1)
+        plt.xticks([])
+        plt.yticks([])
+        plt.grid(False)
+        plt.imshow(training_images[i], cmap=plt.cm.binary)
+        plt.xlabel(class_names[training_labels[i][0]])
+
+    plt.show()
+
+
+def create_model():
+    model = models.Sequential()
+    model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)))
+    model.add(layers.MaxPooling2D((2,2)))
+    model.add(layers.Conv2D(64, (3,3), activation='relu'))
+    model.add(layers.MaxPooling2D((2,2)))
+    model.add(layers.Conv2D(64, (3,3), activation='relu'))
+    model.add(layers.Flatten())
+    model.add(layers.Dense(64, activation='relu'))
+    model.add(layers.Dense(10, activation='softmax'))
+    return model
+
+
+def compile_and_train_model(model, training_images, training_labels, testing_images, testing_labels):
+    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+    model.fit(training_images, training_labels, epochs=20, validation_data=(testing_images, testing_labels))
+    return model
+
+
+def evaluate_model(model, testing_images, testing_labels):
+    loss, accuracy = model.evaluate(testing_images, testing_labels)
+    print(f"Loss: {loss}")
+    print(f"Accuracy: {accuracy}")
+
+
+def load_model():
+    model = models.load_model('image_classifier.model')
+    return model
+
+
+def predict(model):
+    img = cv.imread('<enter file name>')
+    img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
+
+    plt.imshow(img, cmap = plt.cm.binary)
+
+    prediction = model.predict(np.array([img])/255)
+    index = np.argmax(prediction)
+
+    print(f"Prediction is: {class_names[index]}")
+
+    plt.show()
+
+
+def main():
+    (training_images, training_labels), (testing_images, testing_labels) = load_and_preprocess_data()
+    display_sample_images(training_images, training_labels)
+    
+    model = create_model()
+    model = compile_and_train_model(model, training_images, training_labels, testing_images, testing_labels)
+    evaluate_model(model, testing_images, testing_labels)
+
+    model.save("image_classifier.model")
+
+
+if __name__ == "__main__":
+    main()