MIDEVAL_CODE + ASSIGNMENT1 + ASSIGNMENT3 + ASSIGNMENT4 :- RUDRAKUMAR

Assignment 3/Assignment3_rudrakumar/requirements.txt

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+tensorflow
+numpy
+matplotlib
+scikit-learn
+panda
+zipfile

Assignment 3/Assignment3_rudrakumar/rudrakumar_Assignment3.py

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+#library import
+
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Sequential 
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from sklearn.metrics import confusion_matrix
+import numpy as np
+import matplotlib.pyplot as plt
+from zipfile import ZipFile
+
+#extracted zip for dataset
+
+zip_path = 'dataset.zip'
+extract_path = 'dataset/'
+with ZipFile(zip_path, 'r') as zip_ref:
+    zip_ref.extractall(extract_path)
+
+#data preparation 
+
+trainpath = 'dataset/Train'
+testpath = 'dataset/Test'
+
+imgsize = 160
+batchsize = 32
+epoch = 25
+
+traindata = ImageDataGenerator(rescale=1./255,
+                               rotation_range=15,
+                               zoom_range=0.15,
+                               width_shift_range=0.1,
+                               height_shift_range=0.1,
+                               horizontal_flip=True).flow_from_directory(trainpath,
+                                                                         target_size=(imgsize, imgsize),
+                                                                            batch_size=batchsize,
+                                                                            class_mode='binary')
+
+testdata = ImageDataGenerator(rescale=1./255).flow_from_directory(testpath,
+                                                                      target_size=(imgsize, imgsize),
+                                                                      batch_size=batchsize,
+                                                                      class_mode='binary',
+                                                                      shuffle=False)
+
+#model creation 
+
+model = Sequential([
+    Conv2D(32, (3, 3), activation='relu', input_shape=(imgsize, imgsize, 3)),
+    MaxPooling2D(2, 2),
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPooling2D(2, 2),
+    Conv2D(128, (3, 3), activation='relu'),
+    MaxPooling2D(2, 2),
+    Flatten(),
+    Dense(128, activation='relu'),
+    Dropout(0.5),
+    Dense(64, activation='relu'),
+    Dropout(0.3),
+    Dense(1, activation='sigmoid')
+])
+
+model.compile(
+    optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
+    loss='binary_crossentropy',
+    metrics=['accuracy']
+)
+
+#model training
+
+history = model.fit(
+    traindata,
+    validation_data=testdata,
+    epochs=epoch,
+    verbose=1
+)
+
+#plot wala part
+
+plt.plot(history.history['accuracy'], label='train accuracy')
+plt.plot(history.history['val_accuracy'], label='test accuracy')
+plt.title('Model Accuracy')
+plt.legend(['Train', 'Test'])
+plt.show()
+
+plt.plot(history.history['loss'], label='train loss')
+plt.plot(history.history['val_loss'], label='test loss')
+plt.title('Model Loss')
+plt.legend(['Train', 'Test'])
+plt.show()
+
+y_pred_prob = model.predict(testdata, verbose=1)
+y_pred = (y_pred_prob > 0.5).astype(int)
+y_true = testdata.classes
+cm = confusion_matrix(y_true, y_pred)
+plt.imshow(cm, cmap="Blues")
+plt.title("Confusion Matrix")
+plt.colorbar()
+plt.xlabel("Predicted Labels")
+plt.ylabel("True Labels")
+for i in range (2):
+    for j in range (2):
+        plt.text(j, i, cm[i, j], ha='center', va='center')
+plt.show()

Assignment 4/Assignment4_rudrakumar/requirements.txt

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+numpy
+pandas
+yfinance
+mplfinance
+tensorflow
+scikit-learn
+matplotlib
+seaborn