diff --git a/Digit Recognizer.py b/Digit Recognizer.py
new file mode 100644
index 0000000..abe0df4
--- /dev/null
+++ b/Digit Recognizer.py	
@@ -0,0 +1,133 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from keras.utils.np_utils import to_categorical
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
+from keras.optimizers import RMSprop
+from keras.preprocessing.image import ImageDataGenerator
+from keras.callbacks import ReduceLROnPlateau, CSVLogger
+
+TRAINING_LOGS_FILE = "training_logs11.csv"
+MODEL_SUMMARY_FILE = "model_summary11.txt"
+MODEL_FILE = "model11.h5"
+TRAINING_PLOT_FILE = "training11.png"
+VALIDATION_PLOT_FILE = "validation11.png"
+KAGGLE_SUBMISSION_FILE = "kaggle_submission_final2.csv"
+
+
+
+VERBOSITY = 1
+EPOCHS = 100
+BATCH_SIZE = 512
+CLASSES = 10
+CHANNELS = 1
+IMAGE_SIZE = 28
+IMAGE_WIDTH, IMAGE_HEIGHT = IMAGE_SIZE, IMAGE_SIZE
+VALIDATION_RATIO = 0.1
+
+# Load the data (https://www.kaggle.com/c/digit-recognizer/data)
+
+train = pd.read_csv("train.csv")
+test = pd.read_csv("test.csv")
+
+y = train["label"]
+x = train.drop(labels = ["label"], axis = 1) 
+
+# Reshape data
+x = x.values.reshape(-1, IMAGE_WIDTH, IMAGE_HEIGHT, CHANNELS)
+test = test.values.reshape(-1, IMAGE_WIDTH, IMAGE_HEIGHT, CHANNELS)
+
+# One-Hot encoding
+y = to_categorical(y, num_classes=CLASSES)
+
+# Prepare training/validation sets
+x_training, x_validation, y_training, y_validation = train_test_split(x,
+                                                                      y,
+                                                                      test_size=VALIDATION_RATIO,
+                                                                      shuffle = True)
+
+# Model (0.99471)
+model = Sequential()
+
+model.add(Conv2D(filters=32,
+                 kernel_size=(5,5),
+                 padding='Same', 
+                 activation='relu',
+                 input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, CHANNELS)))
+model.add(Conv2D(filters=32,
+                 kernel_size=(5,5),
+                 padding='Same', 
+                 activation='relu'))
+model.add(MaxPool2D(pool_size=(2,2)))
+model.add(Dropout(0.3))
+
+model.add(Conv2D(filters=64, kernel_size=(3,3),padding='Same', 
+                 activation='relu'))
+model.add(Conv2D(filters=64, kernel_size=(3,3),padding='Same', 
+                 activation='relu'))
+model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
+model.add(Dropout(0.3))
+
+model.add(Flatten())
+model.add(Dense(8192, activation='relu'))
+model.add(Dropout(0.3))
+
+model.add(Dense(2048, activation='relu'))
+model.add(Dropout(0.5))
+
+model.add(Dense(CLASSES, activation="softmax"))
+
+model.compile(optimizer=RMSprop(lr=0.0001,
+                                rho=0.9,
+                                epsilon=1e-08,
+                                decay=0.00001),
+              loss="categorical_crossentropy",
+              metrics=["accuracy"])
+
+with open(MODEL_SUMMARY_FILE,"w") as fh:
+    model.summary(print_fn=lambda line: fh.write(line + "\n"))
+
+# Data augmentation
+data_generator = ImageDataGenerator(rescale=1./255,
+                                    rotation_range=1,
+                                    zoom_range=0.1, 
+                                    width_shift_range=0.05,
+                                    height_shift_range=0.05)
+data_generator.fit(x_training)
+
+# Training
+history = model.fit_generator(data_generator.flow(x_training,
+                                                  y_training,
+                                                  batch_size=BATCH_SIZE),
+                              epochs=EPOCHS,
+                              validation_data=(x_validation, y_validation),
+                              verbose=VERBOSITY,
+                              steps_per_epoch=x_training.shape[0] // BATCH_SIZE)
+model.save_weights(MODEL_FILE)
+
+# Testing
+predictions = model.predict_classes(test, verbose=1)
+pd.DataFrame({"ImageId":list(range(1,len(predictions)+1)),
+              "Label":predictions}).to_csv(KAGGLE_SUBMISSION_FILE,
+                                           index=False,
+                                           header=True)
+
+# Drawing plots
+epochs = [i for i in range(1, len(history.history['loss'])+1)]
+
+plt.plot(epochs, history.history['loss'], color='blue', label="training_loss")
+plt.plot(epochs, history.history['val_loss'], color='red', label="validation_loss")
+plt.legend(loc='best')
+plt.title('training')
+plt.xlabel('epoch')
+plt.savefig(TRAINING_PLOT_FILE, bbox_inches='tight')
+plt.close()
+
+plt.plot(epochs, history.history['acc'], color='blue', label="training_accuracy")
+plt.plot(epochs, history.history['val_acc'], color='red',label="validation_accuracy")
+plt.legend(loc='best')
+plt.title('validation')
+plt.xlabel('epoch')
+plt.savefig(VALIDATION_PLOT_FILE, bbox_inches='tight')
+plt.close()