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# Splitting Features and Label | |
y = train['Survived'] | |
X = train.drop(['Survived'],1) | |
#Using Train Test Split from Sklearn to Split Our Train Dataset into Train and Testing Datasets | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) |
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from sklearn.ensemble import GradientBoostingClassifier | |
model = GradientBoostingClassifier(learning_rate=0.1,max_depth=3) | |
model.fit(X_train, y_train) | |
predictions = model.predict(X_test) |
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from sklearn.metrics import confusion_matrix, classification_report | |
print(confusion_matrix(y_test, predictions)) | |
print(classification_report(y_test,predictions)) |
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test['Age'].fillna(test['Age'].median(),inplace=True) # Age | |
test['Fare'].fillna(test['Fare'].median(),inplace=True) # Fare | |
d = {1:'1st',2:'2nd',3:'3rd'} #Pclass | |
test['Pclass'] = test['Pclass'].map(d) | |
test['Embarked'].fillna(test['Embarked'].value_counts().index[0], inplace=True) # Embarked | |
ids = test[['PassengerId']]# Passenger Ids | |
test.drop(['PassengerId','Name','Ticket','Cabin'],1,inplace=True)# Drop Unnecessary Columns | |
categorical_vars = test[['Pclass','Sex','Embarked']]# Get Dummies of Categorical Variables | |
dummies = pd.get_dummies(categorical_vars,drop_first=True) | |
test = test.drop(['Pclass','Sex','Embarked'],axis=1)#Drop the Original Categorical Variables |
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preds = model.predict(test) | |
results = ids.assign(Survived=preds) | |
results.to_csv('titanic_submission.csv',index=False) |
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import tensorflow as tf | |
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() |
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import matplotlib.pyplot as plt | |
%matplotlib inline # Only use this if using iPython | |
image_index = 7777 # You may select anything up to 60,000 | |
print(y_train[image_index]) # The label is 8 | |
plt.imshow(x_train[image_index], cmap='Greys') |
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x_train.shape |
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# Reshaping the array to 4-dims so that it can work with the Keras API | |
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) | |
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1) | |
input_shape = (28, 28, 1) | |
# Making sure that the values are float so that we can get decimal points after division | |
x_train = x_train.astype('float32') | |
x_test = x_test.astype('float32') | |
# Normalizing the RGB codes by dividing it to the max RGB value. | |
x_train /= 255 | |
x_test /= 255 |
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# Importing the required Keras modules containing model and layers | |
from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D | |
# Creating a Sequential Model and adding the layers | |
model = Sequential() | |
model.add(Conv2D(28, kernel_size=(3,3), input_shape=input_shape)) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Flatten()) # Flattening the 2D arrays for fully connected layers | |
model.add(Dense(128, activation=tf.nn.relu)) | |
model.add(Dropout(0.2)) |