path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
105197097/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1]
train_df.values[:, 1:]
X = train_df.values[:, 1:]
y = train_df.values[:, :1]
X.shape
X_scale = X / 255.0
dim = int(np.sqrt(X_scale.shape[1]))
dim
N = X_scale.shape[0]
N | code |
105197097/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.info() | code |
105197097/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1] | code |
105197097/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1]
train_df.values[:, 1:]
X = train_df.values[:, 1:]
y = train_df.values[:, :1]
X.shape
X_scale = X / 255.0
dim = int(np.sqrt(X_scale.shape[1]))
dim | code |
105197097/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105197097/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum() | code |
105197097/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1]
train_df.values[:, 1:]
X = train_df.values[:, 1:]
y = train_df.values[:, :1]
X.shape
X_scale = X / 255.0
print(X_scale.shape[1])
print(np.sqrt(X_scale.shape[1]))
print(int(np.sqrt(X_scale.shape[1]))) | code |
105197097/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
plt.figure(figsize=(8, 7))
sns.countplot(x='label', data=train_df)
plt.title('Label distribution')
plt.show() | code |
105197097/cell_15 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1]
train_df.values[:, 1:]
X = train_df.values[:, 1:]
y = train_df.values[:, :1]
y.shape | code |
105197097/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1]
train_df.values[:, 1:]
X = train_df.values[:, 1:]
y = train_df.values[:, :1]
X.shape
X_scale = X / 255.0
print('Max value: ', X_scale.max())
print('Max value: ', X_scale.min()) | code |
105197097/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1]
train_df.values[:, 1:]
X = train_df.values[:, 1:]
y = train_df.values[:, :1]
X.shape | code |
105197097/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1]
train_df.values[:, 1:]
X = train_df.values[:, 1:]
y = train_df.values[:, :1]
X.shape
X_scale = X / 255.0
dim = int(np.sqrt(X_scale.shape[1]))
dim
N = X_scale.shape[0]
N
X_scale = X_scale.reshape((N, dim, dim, 1))
X_scale | code |
105197097/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0] | code |
105197097/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1]
train_df.values[:, 1:] | code |
105197097/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape | code |
2015893/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
train_y = train_y.reshape((1080,))
test_y = test_y.reshape((120,))
Y_train = np.zeros([1080, 6])
count = 0
for i in train_y:
Y_train[count, i] = 1
count = count + 1
Y_test = np.zeros([120, 6])
count = 0
for i in test_y:
Y_test[count, i] = 1
count = count + 1
train_y | code |
2015893/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
train_y = train_y.reshape((1080,))
test_y = test_y.reshape((120,))
Y_train = np.zeros([1080, 6])
count = 0
for i in train_y:
Y_train[count, i] = 1
count = count + 1
Y_test = np.zeros([120, 6])
count = 0
for i in test_y:
Y_test[count, i] = 1
count = count + 1
def plain_layer(X, n_c):
X_in = X
X = Conv2D(n_c, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(2, 2))(X)
return X
def identity_block(X, F):
X_in = X
F1, F2, F3 = F
X = Conv2D(F1, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F2, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F3, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Add()([X, X_in])
X = Activation('relu')(X)
return X
def Resnet(input_shape=(64, 64, 3), classes=6):
X_in = Input(input_shape)
X = plain_layer(X_in, 32)
F = [16, 32, 32]
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = plain_layer(X, 16)
X = Flatten()(X)
X = Dense(512, activation='relu')(X)
X = Dense(128, activation='relu')(X)
X = Dense(classes, activation='softmax')(X)
model = Model(inputs=X_in, outputs=X, name='Resnet')
return model
train_x = train_x / 255
test_x = test_x / 255
my_model = Resnet()
my_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
my_model.fit(x=train_x, y=Y_train, epochs=10, batch_size=32)
my_model.evaluate(train_x, Y_train)
pred = my_model.predict(train_x)
pred | code |
2015893/cell_23 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
train_y = train_y.reshape((1080,))
test_y = test_y.reshape((120,))
Y_train = np.zeros([1080, 6])
count = 0
for i in train_y:
Y_train[count, i] = 1
count = count + 1
Y_test = np.zeros([120, 6])
count = 0
for i in test_y:
Y_test[count, i] = 1
count = count + 1
def plain_layer(X, n_c):
X_in = X
X = Conv2D(n_c, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(2, 2))(X)
return X
def identity_block(X, F):
X_in = X
F1, F2, F3 = F
X = Conv2D(F1, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F2, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F3, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Add()([X, X_in])
X = Activation('relu')(X)
return X
def Resnet(input_shape=(64, 64, 3), classes=6):
X_in = Input(input_shape)
X = plain_layer(X_in, 32)
F = [16, 32, 32]
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = plain_layer(X, 16)
X = Flatten()(X)
X = Dense(512, activation='relu')(X)
X = Dense(128, activation='relu')(X)
X = Dense(classes, activation='softmax')(X)
model = Model(inputs=X_in, outputs=X, name='Resnet')
return model
train_x = train_x / 255
test_x = test_x / 255
my_model = Resnet()
my_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
my_model.fit(x=train_x, y=Y_train, epochs=10, batch_size=32)
my_model.evaluate(train_x, Y_train)
pred = my_model.predict(train_x)
my_model.evaluate(test_x, Y_test) | code |
2015893/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
train_y = train_y.reshape((1080,))
test_y = test_y.reshape((120,))
Y_train = np.zeros([1080, 6])
count = 0
for i in train_y:
Y_train[count, i] = 1
count = count + 1
Y_test = np.zeros([120, 6])
count = 0
for i in test_y:
Y_test[count, i] = 1
count = count + 1
def plain_layer(X, n_c):
X_in = X
X = Conv2D(n_c, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(2, 2))(X)
return X
def identity_block(X, F):
X_in = X
F1, F2, F3 = F
X = Conv2D(F1, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F2, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F3, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Add()([X, X_in])
X = Activation('relu')(X)
return X
def Resnet(input_shape=(64, 64, 3), classes=6):
X_in = Input(input_shape)
X = plain_layer(X_in, 32)
F = [16, 32, 32]
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = plain_layer(X, 16)
X = Flatten()(X)
X = Dense(512, activation='relu')(X)
X = Dense(128, activation='relu')(X)
X = Dense(classes, activation='softmax')(X)
model = Model(inputs=X_in, outputs=X, name='Resnet')
return model
train_x = train_x / 255
test_x = test_x / 255
my_model = Resnet()
my_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
my_model.fit(x=train_x, y=Y_train, epochs=10, batch_size=32)
my_model.evaluate(train_x, Y_train)
pred = my_model.predict(train_x)
predlabel = np.argmax(pred, axis=1)
np.sum(predlabel == train_y) / 1080 | code |
2015893/cell_11 | [
"text_plain_output_1.png"
] | import keras.backend as K
from keras import layers
from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import preprocess_input
from keras.initializers import glorot_uniform
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
import keras.backend as K
K.set_image_data_format('channels_last') | code |
2015893/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape) | code |
2015893/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
train_y = train_y.reshape((1080,))
test_y = test_y.reshape((120,))
Y_train = np.zeros([1080, 6])
count = 0
for i in train_y:
Y_train[count, i] = 1
count = count + 1
Y_test = np.zeros([120, 6])
count = 0
for i in test_y:
Y_test[count, i] = 1
count = count + 1
def plain_layer(X, n_c):
X_in = X
X = Conv2D(n_c, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(2, 2))(X)
return X
def identity_block(X, F):
X_in = X
F1, F2, F3 = F
X = Conv2D(F1, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F2, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F3, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Add()([X, X_in])
X = Activation('relu')(X)
return X
def Resnet(input_shape=(64, 64, 3), classes=6):
X_in = Input(input_shape)
X = plain_layer(X_in, 32)
F = [16, 32, 32]
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = plain_layer(X, 16)
X = Flatten()(X)
X = Dense(512, activation='relu')(X)
X = Dense(128, activation='relu')(X)
X = Dense(classes, activation='softmax')(X)
model = Model(inputs=X_in, outputs=X, name='Resnet')
return model
train_x = train_x / 255
test_x = test_x / 255
my_model = Resnet()
my_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
my_model.fit(x=train_x, y=Y_train, epochs=10, batch_size=32)
my_model.evaluate(train_x, Y_train) | code |
2015893/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import h5py
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2015893/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
train_y = train_y.reshape((1080,))
test_y = test_y.reshape((120,))
Y_train = np.zeros([1080, 6])
count = 0
for i in train_y:
Y_train[count, i] = 1
count = count + 1
Y_test = np.zeros([120, 6])
count = 0
for i in test_y:
Y_test[count, i] = 1
count = count + 1
def plain_layer(X, n_c):
X_in = X
X = Conv2D(n_c, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(2, 2))(X)
return X
def identity_block(X, F):
X_in = X
F1, F2, F3 = F
X = Conv2D(F1, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F2, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F3, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Add()([X, X_in])
X = Activation('relu')(X)
return X
def Resnet(input_shape=(64, 64, 3), classes=6):
X_in = Input(input_shape)
X = plain_layer(X_in, 32)
F = [16, 32, 32]
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = plain_layer(X, 16)
X = Flatten()(X)
X = Dense(512, activation='relu')(X)
X = Dense(128, activation='relu')(X)
X = Dense(classes, activation='softmax')(X)
model = Model(inputs=X_in, outputs=X, name='Resnet')
return model
train_x = train_x / 255
test_x = test_x / 255
my_model = Resnet()
my_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
my_model.fit(x=train_x, y=Y_train, epochs=10, batch_size=32) | code |
2015893/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
train_y = train_y.reshape((1080,))
test_y = test_y.reshape((120,))
Y_train = np.zeros([1080, 6])
count = 0
for i in train_y:
Y_train[count, i] = 1
count = count + 1
Y_test = np.zeros([120, 6])
count = 0
for i in test_y:
Y_test[count, i] = 1
count = count + 1
def plain_layer(X, n_c):
X_in = X
X = Conv2D(n_c, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(2, 2))(X)
return X
def identity_block(X, F):
X_in = X
F1, F2, F3 = F
X = Conv2D(F1, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F2, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F3, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Add()([X, X_in])
X = Activation('relu')(X)
return X
def Resnet(input_shape=(64, 64, 3), classes=6):
X_in = Input(input_shape)
X = plain_layer(X_in, 32)
F = [16, 32, 32]
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = plain_layer(X, 16)
X = Flatten()(X)
X = Dense(512, activation='relu')(X)
X = Dense(128, activation='relu')(X)
X = Dense(classes, activation='softmax')(X)
model = Model(inputs=X_in, outputs=X, name='Resnet')
return model
train_x = train_x / 255
test_x = test_x / 255
my_model = Resnet()
my_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
my_model.fit(x=train_x, y=Y_train, epochs=10, batch_size=32)
my_model.evaluate(train_x, Y_train)
pred = my_model.predict(train_x)
predlabel = np.argmax(pred, axis=1)
np.sum(predlabel == train_y) / 1080
np.sum(predlabel) | code |
2015893/cell_10 | [
"text_plain_output_1.png"
] | import h5py
import matplotlib.pyplot as plt
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
import matplotlib.pyplot as plt
plt.subplots(2, 2)
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.1, right=0.95, hspace=0.45, wspace=0.45)
plt.subplot(2, 2, 1)
plt.title('train_x[5] label : 4')
plt.imshow(train_x[5])
plt.subplot(2, 2, 2)
plt.title('train_x[10] label : 2')
plt.imshow(train_x[10])
plt.subplot(2, 2, 3)
plt.title('test_x[5] label : 0')
plt.imshow(test_x[5])
plt.subplot(2, 2, 4)
plt.title('test_x[10] label : 5')
plt.imshow(test_x[10]) | code |
2015893/cell_27 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.layers import Input,Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
import h5py
import numpy as np # linear algebra
def load_dataset():
train_dataset = h5py.File('../input/hand-sign/train_signs.h5', 'r')
train_set_x_orig = np.array(train_dataset['train_set_x'][:])
train_set_y_orig = np.array(train_dataset['train_set_y'][:])
test_dataset = h5py.File('../input/hand-sign-test/test_signs.h5', 'r')
test_set_x_orig = np.array(test_dataset['test_set_x'][:])
test_set_y_orig = np.array(test_dataset['test_set_y'][:])
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return (train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig)
train_x, train_y, test_x, test_y = load_dataset()
(train_x.shape, train_y.shape, test_x.shape, test_y.shape)
train_y = train_y.reshape((1080,))
test_y = test_y.reshape((120,))
Y_train = np.zeros([1080, 6])
count = 0
for i in train_y:
Y_train[count, i] = 1
count = count + 1
Y_test = np.zeros([120, 6])
count = 0
for i in test_y:
Y_test[count, i] = 1
count = count + 1
def plain_layer(X, n_c):
X_in = X
X = Conv2D(n_c, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = MaxPooling2D(pool_size=(2, 2))(X)
return X
def identity_block(X, F):
X_in = X
F1, F2, F3 = F
X = Conv2D(F1, kernel_size=(3, 3), padding='same')(X_in)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F2, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(F3, kernel_size=(3, 3), padding='same')(X)
X = BatchNormalization()(X)
X = Add()([X, X_in])
X = Activation('relu')(X)
return X
def Resnet(input_shape=(64, 64, 3), classes=6):
X_in = Input(input_shape)
X = plain_layer(X_in, 32)
F = [16, 32, 32]
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = identity_block(X, F)
X = MaxPooling2D(pool_size=(2, 2))(X)
X = plain_layer(X, 16)
X = Flatten()(X)
X = Dense(512, activation='relu')(X)
X = Dense(128, activation='relu')(X)
X = Dense(classes, activation='softmax')(X)
model = Model(inputs=X_in, outputs=X, name='Resnet')
return model
train_x = train_x / 255
test_x = test_x / 255
my_model = Resnet()
my_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
my_model.fit(x=train_x, y=Y_train, epochs=10, batch_size=32)
my_model.evaluate(train_x, Y_train)
pred = my_model.predict(train_x)
predlabel = np.argmax(pred, axis=1)
np.sum(predlabel == train_y) / 1080
np.sum(predlabel)
pred = cnn(Variable(torch.Tensor(test_x.reshape(120, 3, 64, 64).astype(float))))
pred_np = pred.data.numpy()
pred_label = np.argmax(pred_np, axis=1)
pred_label.shape
target = np.squeeze(test_y)
np.sum(pred_label == target) / 120 | code |
106196793/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pokemon = pd.read_csv('../input/pokemon/Pokemon.csv')
pokemon['Type'] = np.where(pokemon['Type 2'].notnull(), pokemon['Type 1'] + '/' + pokemon['Type 2'], pokemon['Type 1'])
pokemon_new = pokemon.drop(['Type 1', 'Type 2'], axis=1)
print(pokemon['Type'].unique())
print(pokemon_new.info())
print(pokemon_new.describe()) | code |
106196793/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
pokemon = pd.read_csv('../input/pokemon/Pokemon.csv')
print(pokemon.info())
print(pokemon.describe()) | code |
129007439/cell_21 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
df2 = df['Gender']
df2
x = df1['Annual Income (k$)']
y = df1['Spending Score (1-100)']
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.cluster_centers_
clusters = kmeans.predict(df1)
clusters[50:60]
x = df1['Annual Income (k$)']
y = df1['Spending Score (1-100)']
plt.figure(figsize=(20, 9))
x = df1['Annual Income (k$)']
y = df1['Spending Score (1-100)']
plt.subplot(321)
plt.title('K-means Predictions')
sns.scatterplot(data=df, x=x, y=y, hue=clusters)
plt.subplot(322)
plt.title('Actual Clusters')
sns.scatterplot(data=df, x=x, y=y, hue=df2)
x = df1['Age']
y = df1['Spending Score (1-100)']
plt.subplot(323)
plt.title('K-means Predictions')
sns.scatterplot(data=df, x=x, y=y, hue=clusters)
plt.subplot(324)
plt.title('Actual Clusters')
sns.scatterplot(data=df, x=x, y=y, hue=df2)
x = df1['Age']
y = df1['Annual Income (k$)']
plt.subplot(325)
plt.title('K-means Predictions')
sns.scatterplot(data=df, x=x, y=y, hue=clusters)
plt.subplot(326)
plt.title('Actual Clusters')
sns.scatterplot(data=df, x=x, y=y, hue=df2)
plt.tight_layout() | code |
129007439/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1 | code |
129007439/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum() | code |
129007439/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.cluster_centers_
clusters = kmeans.predict(df1)
clusters[50:60]
kmeans.inertia_
sse = {}
for k in range(1, 10):
kmeans = KMeans(n_clusters=k, max_iter=1000).fit(df1)
sse[k] = kmeans.inertia_
sse | code |
129007439/cell_20 | [
"text_html_output_2.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
fig = px.scatter(df, x="Annual Income (k$)", y="Spending Score (1-100)", color='Gender')
fig.show()
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.cluster_centers_
clusters = kmeans.predict(df1)
clusters[50:60]
fig = px.scatter(df1, x='Annual Income (k$)', y='Spending Score (1-100)', color=clusters)
fig.show() | code |
129007439/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
plt.figure(figsize=(12, 6))
sns.scatterplot(data=df, x=df['Annual Income (k$)'], y=df['Spending Score (1-100)'], hue=df['Gender']) | code |
129007439/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.cluster_centers_
clusters = kmeans.predict(df1)
clusters[50:60]
kmeans.inertia_
sse = {}
for k in range(1, 10):
kmeans = KMeans(n_clusters=k, max_iter=1000).fit(df1)
sse[k] = kmeans.inertia_
sse
for n_cluster in range(2, 11):
kmeans = KMeans(n_clusters=n_cluster).fit(df1)
label = kmeans.labels_
sil_coeff = silhouette_score(df1, label, metric='euclidean')
print('For n_clusters={}, The Silhouette Coefficient is {}'.format(n_cluster, sil_coeff)) | code |
129007439/cell_26 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
model = KMeans(n_clusters=4, max_iter=1000)
model.fit(df1) | code |
129007439/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df | code |
129007439/cell_19 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
x = df1['Annual Income (k$)']
y = df1['Spending Score (1-100)']
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.cluster_centers_
clusters = kmeans.predict(df1)
clusters[50:60]
x = df1['Annual Income (k$)']
y = df1['Spending Score (1-100)']
plt.scatter(x, y, c=clusters) | code |
129007439/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129007439/cell_18 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.cluster_centers_
clusters = kmeans.predict(df1)
clusters[50:60] | code |
129007439/cell_28 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
model = KMeans(n_clusters=4, max_iter=1000)
model.fit(df1)
model.predict([[31, 17, 40]])
from sklearn.metrics import silhouette_score
score = silhouette_score(df1, model.labels_)
score | code |
129007439/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df_copy | code |
129007439/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
x = df1['Annual Income (k$)']
y = df1['Spending Score (1-100)']
plt.scatter(x, y) | code |
129007439/cell_16 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1) | code |
129007439/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.info() | code |
129007439/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.cluster_centers_ | code |
129007439/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
df2 = df['Gender']
df2
x = df1['Annual Income (k$)']
y = df1['Spending Score (1-100)']
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.cluster_centers_
clusters = kmeans.predict(df1)
clusters[50:60]
x = df1['Annual Income (k$)']
y = df1['Spending Score (1-100)']
x = df1['Annual Income (k$)']
y = df1['Spending Score (1-100)']
x = df1['Age']
y = df1['Spending Score (1-100)']
x = df1['Age']
y = df1['Annual Income (k$)']
plt.tight_layout()
kmeans.inertia_
sse = {}
for k in range(1, 10):
kmeans = KMeans(n_clusters=k, max_iter=1000).fit(df1)
sse[k] = kmeans.inertia_
sse
plt.plot(list(sse.keys()), list(sse.values()))
plt.xlabel('Number of cluster')
plt.ylabel('SSE') | code |
129007439/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
fig = px.scatter(df, x='Annual Income (k$)', y='Spending Score (1-100)', color='Gender')
fig.show() | code |
129007439/cell_22 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
kmeans = KMeans(n_clusters=4, max_iter=1000)
kmeans.fit(df1)
kmeans.cluster_centers_
clusters = kmeans.predict(df1)
clusters[50:60]
kmeans.inertia_ | code |
129007439/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
df2 = df['Gender']
df2 | code |
129007439/cell_27 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df_copy = df.copy()
df1 = df.drop(['CustomerID', 'Gender'], axis=1)
df1
model = KMeans(n_clusters=4, max_iter=1000)
model.fit(df1)
model.predict([[31, 17, 40]]) | code |
129007439/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df
df.isna().sum()
df.describe() | code |
33099181/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
from sklearn.preprocessing import LabelEncoder
train_data['MSZoning'] = LabelEncoder().fit_transform(train_data['MSZoning'])
train_data['Street'] = LabelEncoder().fit_transform(train_data['Street'])
train_data['Alley'] = train_data['Alley'].fillna('Not')
train_data['Alley'] = LabelEncoder().fit_transform(train_data['Alley'])
train_data['LotShape'] = LabelEncoder().fit_transform(train_data['LotShape'])
train_data['LandContour'] = LabelEncoder().fit_transform(train_data['LandContour'])
train_data['Utilities'] = LabelEncoder().fit_transform(train_data['Utilities'])
train_data['LotConfig'] = LabelEncoder().fit_transform(train_data['LotConfig'])
train_data['LandSlope'] = LabelEncoder().fit_transform(train_data['LandSlope'])
train_data['Neighborhood'] = LabelEncoder().fit_transform(train_data['Neighborhood'])
train_data['Condition1'] = LabelEncoder().fit_transform(train_data['Condition1'])
train_data['Condition2'] = LabelEncoder().fit_transform(train_data['Condition2'])
train_data['BldgType'] = LabelEncoder().fit_transform(train_data['BldgType'])
train_data['HouseStyle'] = LabelEncoder().fit_transform(train_data['HouseStyle'])
train_data['RoofStyle'] = LabelEncoder().fit_transform(train_data['RoofStyle'])
train_data['RoofMatl'] = LabelEncoder().fit_transform(train_data['RoofMatl'])
train_data['Exterior1st'] = LabelEncoder().fit_transform(train_data['Exterior1st'])
train_data['Exterior2nd'] = LabelEncoder().fit_transform(train_data['Exterior2nd'])
train_data['MasVnrType'] = train_data['MasVnrType'].fillna('Not')
train_data['MasVnrType'] = LabelEncoder().fit_transform(train_data['MasVnrType'])
train_data['ExterQual'] = LabelEncoder().fit_transform(train_data['ExterQual'])
train_data['ExterCond'] = LabelEncoder().fit_transform(train_data['ExterCond'])
train_data['Foundation'] = LabelEncoder().fit_transform(train_data['Foundation'])
train_data['BsmtQual'] = train_data['BsmtQual'].fillna('Not')
train_data['BsmtQual'] = LabelEncoder().fit_transform(train_data['BsmtQual'])
train_data['BsmtCond'] = train_data['BsmtCond'].fillna('Not')
train_data['BsmtCond'] = LabelEncoder().fit_transform(train_data['BsmtCond'])
train_data['BsmtExposure'] = train_data['BsmtExposure'].fillna('Not')
train_data['BsmtExposure'] = LabelEncoder().fit_transform(train_data['BsmtExposure'])
train_data['BsmtFinType1'] = train_data['BsmtFinType1'].fillna('Not')
train_data['BsmtFinType1'] = LabelEncoder().fit_transform(train_data['BsmtFinType1'])
train_data['BsmtFinType2'] = train_data['BsmtFinType2'].fillna('Not')
train_data['BsmtFinType2'] = LabelEncoder().fit_transform(train_data['BsmtFinType2'])
train_data['Heating'] = LabelEncoder().fit_transform(train_data['Heating'])
train_data['HeatingQC'] = LabelEncoder().fit_transform(train_data['HeatingQC'])
train_data['CentralAir'] = LabelEncoder().fit_transform(train_data['CentralAir'])
train_data['Electrical'] = train_data['Electrical'].fillna('SBrkr')
train_data['Electrical'] = LabelEncoder().fit_transform(train_data['Electrical'])
train_data['KitchenQual'] = LabelEncoder().fit_transform(train_data['KitchenQual'])
train_data['Functional'] = LabelEncoder().fit_transform(train_data['Functional'])
train_data['FireplaceQu'] = train_data['FireplaceQu'].fillna('Not')
train_data['FireplaceQu'] = LabelEncoder().fit_transform(train_data['FireplaceQu'])
train_data['GarageType'] = train_data['GarageType'].fillna('Not')
train_data['GarageType'] = LabelEncoder().fit_transform(train_data['GarageType'])
train_data['GarageFinish'] = train_data['GarageFinish'].fillna('Not')
train_data['GarageFinish'] = LabelEncoder().fit_transform(train_data['GarageFinish'])
train_data['GarageQual'] = train_data['GarageQual'].fillna('Not')
train_data['GarageQual'] = LabelEncoder().fit_transform(train_data['GarageQual'])
train_data['GarageCond'] = train_data['GarageCond'].fillna('Not')
train_data['GarageCond'] = LabelEncoder().fit_transform(train_data['GarageCond'])
train_data['PavedDrive'] = LabelEncoder().fit_transform(train_data['PavedDrive'])
train_data['PoolQC'] = train_data['PoolQC'].fillna('Not')
train_data['PoolQC'] = LabelEncoder().fit_transform(train_data['PoolQC'])
train_data['Fence'] = train_data['Fence'].fillna('Not')
train_data['Fence'] = LabelEncoder().fit_transform(train_data['Fence'])
train_data['MiscFeature'] = train_data['MiscFeature'].fillna('Not')
train_data['MiscFeature'] = LabelEncoder().fit_transform(train_data['MiscFeature'])
train_data['SaleType'] = LabelEncoder().fit_transform(train_data['SaleType'])
train_data['SaleCondition'] = LabelEncoder().fit_transform(train_data['SaleCondition'])
train_data['LotFrontage'] = train_data['LotFrontage'].fillna(np.mean(train_data['LotFrontage']))
train_data['MasVnrArea'] = train_data['MasVnrArea'].fillna(np.mean(train_data['MasVnrArea']))
train_data['GarageYrBlt'] = train_data['GarageYrBlt'].fillna(np.mean(train_data['GarageYrBlt']))
test_data['Street'] = LabelEncoder().fit_transform(test_data['Street'])
test_data['Alley'] = test_data['Alley'].fillna('Not')
test_data['Alley'] = LabelEncoder().fit_transform(test_data['Alley'])
test_data['LotShape'] = LabelEncoder().fit_transform(test_data['LotShape'])
test_data['LandContour'] = LabelEncoder().fit_transform(test_data['LandContour'])
test_data['LotConfig'] = LabelEncoder().fit_transform(test_data['LotConfig'])
test_data['LandSlope'] = LabelEncoder().fit_transform(test_data['LandSlope'])
test_data['Neighborhood'] = LabelEncoder().fit_transform(test_data['Neighborhood'])
test_data['Condition1'] = LabelEncoder().fit_transform(test_data['Condition1'])
test_data['Condition2'] = LabelEncoder().fit_transform(test_data['Condition2'])
test_data['BldgType'] = LabelEncoder().fit_transform(test_data['BldgType'])
test_data['HouseStyle'] = LabelEncoder().fit_transform(test_data['HouseStyle'])
test_data['RoofStyle'] = LabelEncoder().fit_transform(test_data['RoofStyle'])
test_data['RoofMatl'] = LabelEncoder().fit_transform(test_data['RoofMatl'])
test_data['MasVnrType'] = test_data['MasVnrType'].fillna('Not')
test_data['MasVnrType'] = LabelEncoder().fit_transform(test_data['MasVnrType'])
test_data['ExterQual'] = LabelEncoder().fit_transform(test_data['ExterQual'])
test_data['ExterCond'] = LabelEncoder().fit_transform(test_data['ExterCond'])
test_data['Foundation'] = LabelEncoder().fit_transform(test_data['Foundation'])
test_data['BsmtQual'] = test_data['BsmtQual'].fillna('Not')
test_data['BsmtQual'] = LabelEncoder().fit_transform(test_data['BsmtQual'])
test_data['BsmtCond'] = test_data['BsmtCond'].fillna('Not')
test_data['BsmtCond'] = LabelEncoder().fit_transform(test_data['BsmtCond'])
test_data['BsmtExposure'] = test_data['BsmtExposure'].fillna('Not')
test_data['BsmtExposure'] = LabelEncoder().fit_transform(test_data['BsmtExposure'])
test_data['BsmtFinType1'] = test_data['BsmtFinType1'].fillna('Not')
test_data['BsmtFinType1'] = LabelEncoder().fit_transform(test_data['BsmtFinType1'])
test_data['BsmtFinType2'] = test_data['BsmtFinType2'].fillna('Not')
test_data['BsmtFinType2'] = LabelEncoder().fit_transform(test_data['BsmtFinType2'])
test_data['Heating'] = LabelEncoder().fit_transform(test_data['Heating'])
test_data['HeatingQC'] = LabelEncoder().fit_transform(test_data['HeatingQC'])
test_data['CentralAir'] = LabelEncoder().fit_transform(test_data['CentralAir'])
test_data['Electrical'] = test_data['Electrical'].fillna('SBrkr')
test_data['Electrical'] = LabelEncoder().fit_transform(test_data['Electrical'])
test_data['FireplaceQu'] = test_data['FireplaceQu'].fillna('Not')
test_data['FireplaceQu'] = LabelEncoder().fit_transform(test_data['FireplaceQu'])
test_data['GarageType'] = test_data['GarageType'].fillna('Not')
test_data['GarageType'] = LabelEncoder().fit_transform(test_data['GarageType'])
test_data['GarageFinish'] = test_data['GarageFinish'].fillna('Not')
test_data['GarageFinish'] = LabelEncoder().fit_transform(test_data['GarageFinish'])
test_data['GarageQual'] = test_data['GarageQual'].fillna('Not')
test_data['GarageQual'] = LabelEncoder().fit_transform(test_data['GarageQual'])
test_data['GarageCond'] = test_data['GarageCond'].fillna('Not')
test_data['GarageCond'] = LabelEncoder().fit_transform(test_data['GarageCond'])
test_data['PavedDrive'] = LabelEncoder().fit_transform(test_data['PavedDrive'])
test_data['PoolQC'] = test_data['PoolQC'].fillna('Not')
test_data['PoolQC'] = LabelEncoder().fit_transform(test_data['PoolQC'])
test_data['Fence'] = test_data['Fence'].fillna('Not')
test_data['Fence'] = LabelEncoder().fit_transform(test_data['Fence'])
test_data['MiscFeature'] = test_data['MiscFeature'].fillna('Not')
test_data['MiscFeature'] = LabelEncoder().fit_transform(test_data['MiscFeature'])
test_data['SaleCondition'] = LabelEncoder().fit_transform(test_data['SaleCondition'])
test_data['LotFrontage'] = test_data['LotFrontage'].fillna(np.mean(test_data['LotFrontage']))
test_data['MasVnrArea'] = test_data['MasVnrArea'].fillna(np.mean(test_data['MasVnrArea']))
test_data['GarageYrBlt'] = test_data['GarageYrBlt'].fillna(np.mean(test_data['GarageYrBlt']))
print(test_data.head(10)) | code |
33099181/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
print(train_data.head())
test_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
print(test_data.head()) | code |
33099181/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33099181/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
from sklearn.preprocessing import LabelEncoder
train_data['MSZoning'] = LabelEncoder().fit_transform(train_data['MSZoning'])
train_data['Street'] = LabelEncoder().fit_transform(train_data['Street'])
train_data['Alley'] = train_data['Alley'].fillna('Not')
train_data['Alley'] = LabelEncoder().fit_transform(train_data['Alley'])
train_data['LotShape'] = LabelEncoder().fit_transform(train_data['LotShape'])
train_data['LandContour'] = LabelEncoder().fit_transform(train_data['LandContour'])
train_data['Utilities'] = LabelEncoder().fit_transform(train_data['Utilities'])
train_data['LotConfig'] = LabelEncoder().fit_transform(train_data['LotConfig'])
train_data['LandSlope'] = LabelEncoder().fit_transform(train_data['LandSlope'])
train_data['Neighborhood'] = LabelEncoder().fit_transform(train_data['Neighborhood'])
train_data['Condition1'] = LabelEncoder().fit_transform(train_data['Condition1'])
train_data['Condition2'] = LabelEncoder().fit_transform(train_data['Condition2'])
train_data['BldgType'] = LabelEncoder().fit_transform(train_data['BldgType'])
train_data['HouseStyle'] = LabelEncoder().fit_transform(train_data['HouseStyle'])
train_data['RoofStyle'] = LabelEncoder().fit_transform(train_data['RoofStyle'])
train_data['RoofMatl'] = LabelEncoder().fit_transform(train_data['RoofMatl'])
train_data['Exterior1st'] = LabelEncoder().fit_transform(train_data['Exterior1st'])
train_data['Exterior2nd'] = LabelEncoder().fit_transform(train_data['Exterior2nd'])
train_data['MasVnrType'] = train_data['MasVnrType'].fillna('Not')
train_data['MasVnrType'] = LabelEncoder().fit_transform(train_data['MasVnrType'])
train_data['ExterQual'] = LabelEncoder().fit_transform(train_data['ExterQual'])
train_data['ExterCond'] = LabelEncoder().fit_transform(train_data['ExterCond'])
train_data['Foundation'] = LabelEncoder().fit_transform(train_data['Foundation'])
train_data['BsmtQual'] = train_data['BsmtQual'].fillna('Not')
train_data['BsmtQual'] = LabelEncoder().fit_transform(train_data['BsmtQual'])
train_data['BsmtCond'] = train_data['BsmtCond'].fillna('Not')
train_data['BsmtCond'] = LabelEncoder().fit_transform(train_data['BsmtCond'])
train_data['BsmtExposure'] = train_data['BsmtExposure'].fillna('Not')
train_data['BsmtExposure'] = LabelEncoder().fit_transform(train_data['BsmtExposure'])
train_data['BsmtFinType1'] = train_data['BsmtFinType1'].fillna('Not')
train_data['BsmtFinType1'] = LabelEncoder().fit_transform(train_data['BsmtFinType1'])
train_data['BsmtFinType2'] = train_data['BsmtFinType2'].fillna('Not')
train_data['BsmtFinType2'] = LabelEncoder().fit_transform(train_data['BsmtFinType2'])
train_data['Heating'] = LabelEncoder().fit_transform(train_data['Heating'])
train_data['HeatingQC'] = LabelEncoder().fit_transform(train_data['HeatingQC'])
train_data['CentralAir'] = LabelEncoder().fit_transform(train_data['CentralAir'])
train_data['Electrical'] = train_data['Electrical'].fillna('SBrkr')
train_data['Electrical'] = LabelEncoder().fit_transform(train_data['Electrical'])
train_data['KitchenQual'] = LabelEncoder().fit_transform(train_data['KitchenQual'])
train_data['Functional'] = LabelEncoder().fit_transform(train_data['Functional'])
train_data['FireplaceQu'] = train_data['FireplaceQu'].fillna('Not')
train_data['FireplaceQu'] = LabelEncoder().fit_transform(train_data['FireplaceQu'])
train_data['GarageType'] = train_data['GarageType'].fillna('Not')
train_data['GarageType'] = LabelEncoder().fit_transform(train_data['GarageType'])
train_data['GarageFinish'] = train_data['GarageFinish'].fillna('Not')
train_data['GarageFinish'] = LabelEncoder().fit_transform(train_data['GarageFinish'])
train_data['GarageQual'] = train_data['GarageQual'].fillna('Not')
train_data['GarageQual'] = LabelEncoder().fit_transform(train_data['GarageQual'])
train_data['GarageCond'] = train_data['GarageCond'].fillna('Not')
train_data['GarageCond'] = LabelEncoder().fit_transform(train_data['GarageCond'])
train_data['PavedDrive'] = LabelEncoder().fit_transform(train_data['PavedDrive'])
train_data['PoolQC'] = train_data['PoolQC'].fillna('Not')
train_data['PoolQC'] = LabelEncoder().fit_transform(train_data['PoolQC'])
train_data['Fence'] = train_data['Fence'].fillna('Not')
train_data['Fence'] = LabelEncoder().fit_transform(train_data['Fence'])
train_data['MiscFeature'] = train_data['MiscFeature'].fillna('Not')
train_data['MiscFeature'] = LabelEncoder().fit_transform(train_data['MiscFeature'])
train_data['SaleType'] = LabelEncoder().fit_transform(train_data['SaleType'])
train_data['SaleCondition'] = LabelEncoder().fit_transform(train_data['SaleCondition'])
train_data['LotFrontage'] = train_data['LotFrontage'].fillna(np.mean(train_data['LotFrontage']))
train_data['MasVnrArea'] = train_data['MasVnrArea'].fillna(np.mean(train_data['MasVnrArea']))
train_data['GarageYrBlt'] = train_data['GarageYrBlt'].fillna(np.mean(train_data['GarageYrBlt']))
print(train_data.head(10)) | code |
33099181/cell_5 | [
"text_plain_output_1.png"
] | code |
|
89127515/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89127515/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from urllib.request import urlopen
from PIL import Image
from math import sin,cos,pi
import catboost as cb
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
import shap
from pycaret.regression import *
!pip install pycaret | code |
333462/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_train.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_test = pd.read_csv('../input/act_test.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10, 5), label='Train')
act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10, 5), label='Test')
positive_counts = pd.DataFrame({'positive_counts': act_train[act_train['outcome'] == 1].groupby('people_id', as_index=True).size()}).reset_index()
negative_counts = pd.DataFrame({'negative_counts': act_train[act_train['outcome'] == 0].groupby('people_id', as_index=True).size()}).reset_index()
hstry = positive_counts.merge(negative_counts, on='people_id', how='outer')
hstry['positive_counts'] = hstry['positive_counts'].fillna('0').astype(np.int64)
hstry['negative_counts'] = hstry['negative_counts'].fillna('0').astype(np.int64)
hstry['profit'] = hstry['positive_counts'] - hstry['negative_counts']
hstry.sort_values(by='positive_counts', ascending=False).head(10) | code |
333462/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
333462/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_train.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_test = pd.read_csv('../input/act_test.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10, 5), label='Train')
act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10, 5), label='Test')
plt.legend()
plt.show() | code |
333462/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_train.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_test = pd.read_csv('../input/act_test.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10, 5), label='Train')
act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10, 5), label='Test')
positive_counts = pd.DataFrame({'positive_counts': act_train[act_train['outcome'] == 1].groupby('people_id', as_index=True).size()}).reset_index()
negative_counts = pd.DataFrame({'negative_counts': act_train[act_train['outcome'] == 0].groupby('people_id', as_index=True).size()}).reset_index()
hstry = positive_counts.merge(negative_counts, on='people_id', how='outer')
hstry['positive_counts'] = hstry['positive_counts'].fillna('0').astype(np.int64)
hstry['negative_counts'] = hstry['negative_counts'].fillna('0').astype(np.int64)
hstry['profit'] = hstry['positive_counts'] - hstry['negative_counts']
hstry.sort_values(by='positive_counts', ascending=False).head(10)
hstry.sort_values(by='negative_counts', ascending=False).head(10)
hstry['prof_label'] = pd.to_numeric(hstry['profit'] < -5).astype(int) * 4 + pd.to_numeric(hstry['profit'].isin(range(-5, 1))).astype(int) * 3 + pd.to_numeric(hstry['profit'].isin(range(1, 6))).astype(int) * 2 + pd.to_numeric(hstry['profit'] > 5).astype(int) * 1
people2 = pd.merge(people, hstry, on='people_id', how='inner')
people2['positive_counts'] = people2['positive_counts'].fillna('0').astype(np.int64)
people2['negative_counts'] = people2['negative_counts'].fillna('0').astype(np.int64)
people2['profit'] = people2['profit'].fillna('0').astype(np.int64)
xfeats = list(people2.columns)
xfeats.remove('people_id')
xfeats.remove('profit')
xfeats.remove('prof_label')
xfeats.remove('positive_counts')
xfeats.remove('negative_counts')
print(xfeats)
X, Y = (people2[xfeats], people2['prof_label']) | code |
333462/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_train.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_test = pd.read_csv('../input/act_test.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10, 5), label='Train')
act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10, 5), label='Test')
goods = act_train[act_train['outcome'] == 1]
bads = act_train[act_train['outcome'] == 0]
goods['date'].groupby(goods.date.dt.date).count().plot(figsize=(10, 5), label='Good')
bads['date'].groupby(bads.date.dt.date).count().plot(figsize=(10, 5), c='r', label='Bad')
positive_counts = pd.DataFrame({'positive_counts': act_train[act_train['outcome'] == 1].groupby('people_id', as_index=True).size()}).reset_index()
negative_counts = pd.DataFrame({'negative_counts': act_train[act_train['outcome'] == 0].groupby('people_id', as_index=True).size()}).reset_index()
hstry = positive_counts.merge(negative_counts, on='people_id', how='outer')
hstry['positive_counts'] = hstry['positive_counts'].fillna('0').astype(np.int64)
hstry['negative_counts'] = hstry['negative_counts'].fillna('0').astype(np.int64)
hstry['profit'] = hstry['positive_counts'] - hstry['negative_counts']
hstry.sort_values(by='positive_counts', ascending=False).head(10)
hstry.sort_values(by='negative_counts', ascending=False).head(10)
hstry['prof_label'] = pd.to_numeric(hstry['profit'] < -5).astype(int) * 4 + pd.to_numeric(hstry['profit'].isin(range(-5, 1))).astype(int) * 3 + pd.to_numeric(hstry['profit'].isin(range(1, 6))).astype(int) * 2 + pd.to_numeric(hstry['profit'] > 5).astype(int) * 1
people2 = pd.merge(people, hstry, on='people_id', how='inner')
people2['positive_counts'] = people2['positive_counts'].fillna('0').astype(np.int64)
people2['negative_counts'] = people2['negative_counts'].fillna('0').astype(np.int64)
people2['profit'] = people2['profit'].fillna('0').astype(np.int64)
obs = ['group_1']
for i in range(1, 10):
obs.append('char_' + str(i))
for x in obs:
people2[x] = people2[x].fillna('type 0')
people2[x] = people2[x].str.split(' ').str[1]
bools = []
for i in range(10, 38):
bools.append('char_' + str(i))
for x in list(set(obs).union(set(bools))):
people2[x] = pd.to_numeric(people2[x]).astype(int)
people2['date'] = pd.to_numeric(people2['date']).astype(int)
for x in bools:
plt.figure()
fig, ax = plt.subplots()
ax.set_xticks([1.5, 2.5, 3.5, 4.5])
ax.set_xticklabels(('Very\nGood', 'Good', 'Bad', 'Very\nBad'))
fig.suptitle(x, fontsize=15)
neg = people2[people2[x] == 0]
pos = people2[people2[x] == 1]
plt.hist([pos['prof_label'], neg['prof_label']], 4, range=(1, 5), normed=True, stacked=True, label=['Has Trait', 'No Trait'])
plt.legend()
plt.show() | code |
333462/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_train.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_test = pd.read_csv('../input/act_test.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10, 5), label='Train')
act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10, 5), label='Test')
goods = act_train[act_train['outcome'] == 1]
bads = act_train[act_train['outcome'] == 0]
goods['date'].groupby(goods.date.dt.date).count().plot(figsize=(10, 5), label='Good')
bads['date'].groupby(bads.date.dt.date).count().plot(figsize=(10, 5), c='r', label='Bad')
plt.legend()
plt.show() | code |
333462/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
print(zip(clf.predict(X_test), y_test)) | code |
333462/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_train.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_test = pd.read_csv('../input/act_test.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10, 5), label='Train')
act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10, 5), label='Test')
positive_counts = pd.DataFrame({'positive_counts': act_train[act_train['outcome'] == 1].groupby('people_id', as_index=True).size()}).reset_index()
negative_counts = pd.DataFrame({'negative_counts': act_train[act_train['outcome'] == 0].groupby('people_id', as_index=True).size()}).reset_index()
hstry = positive_counts.merge(negative_counts, on='people_id', how='outer')
hstry['positive_counts'] = hstry['positive_counts'].fillna('0').astype(np.int64)
hstry['negative_counts'] = hstry['negative_counts'].fillna('0').astype(np.int64)
hstry['profit'] = hstry['positive_counts'] - hstry['negative_counts']
hstry.sort_values(by='positive_counts', ascending=False).head(10)
hstry.sort_values(by='negative_counts', ascending=False).head(10)
hstry['prof_label'] = pd.to_numeric(hstry['profit'] < -5).astype(int) * 4 + pd.to_numeric(hstry['profit'].isin(range(-5, 1))).astype(int) * 3 + pd.to_numeric(hstry['profit'].isin(range(1, 6))).astype(int) * 2 + pd.to_numeric(hstry['profit'] > 5).astype(int) * 1
people2 = pd.merge(people, hstry, on='people_id', how='inner')
people2['positive_counts'] = people2['positive_counts'].fillna('0').astype(np.int64)
people2['negative_counts'] = people2['negative_counts'].fillna('0').astype(np.int64)
people2['profit'] = people2['profit'].fillna('0').astype(np.int64)
xfeats = list(people2.columns)
xfeats.remove('people_id')
xfeats.remove('profit')
xfeats.remove('prof_label')
xfeats.remove('positive_counts')
xfeats.remove('negative_counts')
X, Y = (people2[xfeats], people2['prof_label'])
people2[['prof_label', 'pred']].sample(20) | code |
333462/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_train.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_test = pd.read_csv('../input/act_test.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10, 5), label='Train')
act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10, 5), label='Test')
goods = act_train[act_train['outcome'] == 1]
bads = act_train[act_train['outcome'] == 0]
goods['date'].groupby(goods.date.dt.date).count().plot(figsize=(10, 5), label='Good')
bads['date'].groupby(bads.date.dt.date).count().plot(figsize=(10, 5), c='r', label='Bad')
positive_counts = pd.DataFrame({'positive_counts': act_train[act_train['outcome'] == 1].groupby('people_id', as_index=True).size()}).reset_index()
negative_counts = pd.DataFrame({'negative_counts': act_train[act_train['outcome'] == 0].groupby('people_id', as_index=True).size()}).reset_index()
hstry = positive_counts.merge(negative_counts, on='people_id', how='outer')
hstry['positive_counts'] = hstry['positive_counts'].fillna('0').astype(np.int64)
hstry['negative_counts'] = hstry['negative_counts'].fillna('0').astype(np.int64)
hstry['profit'] = hstry['positive_counts'] - hstry['negative_counts']
hstry.sort_values(by='positive_counts', ascending=False).head(10)
hstry.sort_values(by='negative_counts', ascending=False).head(10)
plt.figure()
plt.hist(hstry['prof_label'], 4, range=(1, 5))
plt.show() | code |
333462/cell_24 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"image_output_17.png",
"text_plain_output_9.png",
"image_output_14.png",
"image_output_28.png",
"text_plain_output_20.png",
"image_output_23.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_14.png",
"image_output_18.png",
"image_output_21.png",
"text_plain_output_27.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_24.png",
"text_plain_output_21.png",
"text_plain_output_25.png",
"image_output_20.png",
"text_plain_output_18.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_22.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_16.png",
"image_output_16.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"image_output_27.png",
"image_output_6.png",
"text_plain_output_23.png",
"image_output_12.png",
"text_plain_output_28.png",
"image_output_22.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"text_plain_output_19.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"image_output_15.png",
"image_output_9.png",
"image_output_19.png",
"image_output_26.png"
] | from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
print(clf.feature_importances_) | code |
333462/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import auc
from sklearn.cross_validation import train_test_split, cross_val_score | code |
333462/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_train.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_test = pd.read_csv('../input/act_test.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10, 5), label='Train')
act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10, 5), label='Test')
positive_counts = pd.DataFrame({'positive_counts': act_train[act_train['outcome'] == 1].groupby('people_id', as_index=True).size()}).reset_index()
negative_counts = pd.DataFrame({'negative_counts': act_train[act_train['outcome'] == 0].groupby('people_id', as_index=True).size()}).reset_index()
hstry = positive_counts.merge(negative_counts, on='people_id', how='outer')
hstry['positive_counts'] = hstry['positive_counts'].fillna('0').astype(np.int64)
hstry['negative_counts'] = hstry['negative_counts'].fillna('0').astype(np.int64)
hstry['profit'] = hstry['positive_counts'] - hstry['negative_counts']
hstry.sort_values(by='positive_counts', ascending=False).head(10)
hstry.sort_values(by='negative_counts', ascending=False).head(10) | code |
333462/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_train.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_test = pd.read_csv('../input/act_test.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'otcome': np.int8}, parse_dates=['date'])
act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10, 5), label='Train')
act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10, 5), label='Test')
positive_counts = pd.DataFrame({'positive_counts': act_train[act_train['outcome'] == 1].groupby('people_id', as_index=True).size()}).reset_index()
negative_counts = pd.DataFrame({'negative_counts': act_train[act_train['outcome'] == 0].groupby('people_id', as_index=True).size()}).reset_index()
hstry = positive_counts.merge(negative_counts, on='people_id', how='outer')
hstry['positive_counts'] = hstry['positive_counts'].fillna('0').astype(np.int64)
hstry['negative_counts'] = hstry['negative_counts'].fillna('0').astype(np.int64)
hstry['profit'] = hstry['positive_counts'] - hstry['negative_counts']
hstry.sort_values(by='positive_counts', ascending=False).head(10)
hstry.sort_values(by='negative_counts', ascending=False).head(10)
hstry['profit'].describe() | code |
128030251/cell_13 | [
"text_html_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
X.drop(list(vif_data[vif_data['VIF'] > vif_data['VIF'].mean()]['feature']), axis=1, inplace=True)
X
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
X | code |
128030251/cell_9 | [
"text_plain_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
X.drop(list(vif_data[vif_data['VIF'] > vif_data['VIF'].mean()]['feature']), axis=1, inplace=True)
X | code |
128030251/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data | code |
128030251/cell_34 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.neural_network import MLPClassifier
import seaborn as sns
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
sns.set(rc={'figure.figsize': (5, 3)})
clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, 3200, 1600, 800, 400, 200, 100, 50), max_iter=3000, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
sns.set(rc={'figure.figsize': (5, 3)})
clf = RandomForestClassifier(max_depth=2000)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('Confusion Matrix :')
sns.set(rc={'figure.figsize': (5, 3)})
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True) | code |
128030251/cell_23 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
print(clf.score(X_test, y_test)) | code |
128030251/cell_33 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, 3200, 1600, 800, 400, 200, 100, 50), max_iter=3000, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = RandomForestClassifier(max_depth=2000)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
print(clf.score(X_test, y_test)) | code |
128030251/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy | code |
128030251/cell_29 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.neural_network import MLPClassifier
import seaborn as sns
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
sns.set(rc={'figure.figsize': (5, 3)})
clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, 3200, 1600, 800, 400, 200, 100, 50), max_iter=3000, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('Confusion Matrix :')
sns.set(rc={'figure.figsize': (5, 3)})
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True) | code |
128030251/cell_2 | [
"text_plain_output_1.png"
] | pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html | code |
128030251/cell_11 | [
"text_plain_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
X.drop(list(vif_data[vif_data['VIF'] > vif_data['VIF'].mean()]['feature']), axis=1, inplace=True)
X
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data | code |
128030251/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import os
import networkx as nx
import numpy as np
import pandas as pd
import networkx as nx
import gensim
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
from statsmodels.stats.outliers_influence import variance_inflation_factor
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import pickle
from sklearn.metrics import classification_report
from sklearn import tree
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from tpot import TPOTClassifier
from sklearn.ensemble import ExtraTreesClassifier
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import seaborn as sns
from imblearn.over_sampling import SMOTE | code |
128030251/cell_7 | [
"text_plain_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data | code |
128030251/cell_18 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
X.drop(list(vif_data[vif_data['VIF'] > vif_data['VIF'].mean()]['feature']), axis=1, inplace=True)
X
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
sc = StandardScaler()
X = sc.fit_transform(X)
pca = PCA(n_components=182)
X_pca = pca.fit_transform(X)
explained_variance = pca.explained_variance_ratio_
explained_variance
pca = PCA(n_components=170)
X_pca = pca.fit_transform(X)
comp = []
for i in range(1, 171):
comp.append('comp' + str(i))
comp
data_preprocessed = pd.DataFrame(data=X_pca, columns=comp)
data_preprocessed.shape | code |
128030251/cell_32 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, 3200, 1600, 800, 400, 200, 100, 50), max_iter=3000, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = RandomForestClassifier(max_depth=2000)
clf.fit(X_train, y_train) | code |
128030251/cell_28 | [
"image_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, 3200, 1600, 800, 400, 200, 100, 50), max_iter=3000, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
print(clf.score(X_test, y_test)) | code |
128030251/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
vif_data[vif_data['VIF'] > vif_data['VIF'].mean()] | code |
128030251/cell_15 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
X.drop(list(vif_data[vif_data['VIF'] > vif_data['VIF'].mean()]['feature']), axis=1, inplace=True)
X
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
sc = StandardScaler()
X = sc.fit_transform(X)
pca = PCA(n_components=182)
X_pca = pca.fit_transform(X)
explained_variance = pca.explained_variance_ratio_
explained_variance
plt.rcParams['figure.figsize'] = (12, 6)
fig, ax = plt.subplots()
xi = np.arange(0, 182, step=1)
y = np.cumsum(explained_variance)
plt.ylim(0.0, 1.1)
plt.plot(xi, y, marker='o', linestyle='--', color='b')
plt.xlabel('Number of Components')
plt.xticks(np.arange(0, 182, step=5))
plt.ylabel('Cumulative variance (%)')
plt.title('The number of components needed to explain variance')
plt.axhline(y=0.98, color='r', linestyle='-')
plt.text(0.5, 0.85, '98% cut-off threshold', color='red', fontsize=16)
ax.grid(axis='x')
plt.show() | code |
128030251/cell_38 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, 3200, 1600, 800, 400, 200, 100, 50), max_iter=3000, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = RandomForestClassifier(max_depth=2000)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = GradientBoostingClassifier(n_estimators=300, max_depth=300, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
print(clf.score(X_test, y_test)) | code |
128030251/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns | code |
128030251/cell_17 | [
"text_html_output_1.png"
] | comp = []
for i in range(1, 171):
comp.append('comp' + str(i))
comp | code |
128030251/cell_24 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('Confusion Matrix :')
sns.set(rc={'figure.figsize': (5, 3)})
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True) | code |
128030251/cell_14 | [
"text_html_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
X.drop(list(vif_data[vif_data['VIF'] > vif_data['VIF'].mean()]['feature']), axis=1, inplace=True)
X
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
sc = StandardScaler()
X = sc.fit_transform(X)
pca = PCA(n_components=182)
X_pca = pca.fit_transform(X)
explained_variance = pca.explained_variance_ratio_
explained_variance | code |
128030251/cell_22 | [
"text_html_output_1.png"
] | from sklearn import tree
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train) | code |
128030251/cell_10 | [
"text_plain_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
list(vif_data[vif_data['VIF'] > vif_data['VIF'].mean()]['feature']) | code |
128030251/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
clf = tree.DecisionTreeClassifier(max_depth=400, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
clf = MLPClassifier(hidden_layer_sizes=(50, 100, 200, 400, 800, 1600, 3200, 1600, 800, 400, 200, 100, 50), max_iter=3000, random_state=42)
clf.fit(X_train, y_train) | code |
128030251/cell_12 | [
"text_html_output_1.png"
] | from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy
final_copy.drop(['lncRNA', 'miRNA'], axis=1, inplace=True)
final_copy
X = final_copy.drop('label', axis=1)
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
X.drop(list(vif_data[vif_data['VIF'] > vif_data['VIF'].mean()]['feature']), axis=1, inplace=True)
X
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]
vif_data
vif_data[vif_data['VIF'] > 20] | code |
128030251/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
final_data = pd.read_csv('/kaggle/input/new-01-05-2023-update-1/new_final_updated_dataset.csv')
final_data.columns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
final_data.drop('Unnamed: 0', axis=1, inplace=True)
final_data
final_copy = final_data.copy(deep=True)
final_copy | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.