path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17141241/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.isnull().sum()
test.head() | code |
17141241/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.isnull().sum()
train['Embarked'].value_counts() | code |
74058807/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
plt.figure(figsize=(24, 18))
for i in range(0, 32):
plt.subplot(8, 8, 2 * i + 1)
plt.imshow(images_gray[i], cmap='gray')
plt.title(f'Input {i + 1}')
plt.axis('off')
plt.subplot(8, 8, 2 * i + 2)
plt.imshow(images_col[i])
plt.ti... | code |
74058807/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | img_paths = []
for r, d, f in os.walk(DIR_PATH):
for file in f:
img_paths.append(os.path.join(r, file))
np.random.shuffle(img_paths) | code |
74058807/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, MaxPooling2D, BatchNormalization, UpSampling2D
from keras.models import Sequential, Model
import tensorflow as tf
SEED = 42
INPUT_DIM = (144, 144, 1)
BATCH_SIZE = 128
EPOCHS = 100
LOSS = 'mse'
METRICS = ['accuracy']
OPTI... | code |
74058807/cell_11 | [
"text_plain_output_1.png"
] | images_col = []
images_gray = []
for i, img_path in tqdm(enumerate(img_paths)):
img = np.asarray(Image.open(img_path))
if img.shape == (150, 150, 3):
resized_image = tf.image.resize(img, [144, 144])
images_col.append(resized_image)
images_gray.append(tf.image.rgb_to_grayscale(resized_ima... | code |
74058807/cell_28 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, MaxPooling2D, BatchNormalization, UpSampling2D
from keras.models import Sequential, Model
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import tensorflow as tf
SEED = 42
for i in range(0, 32):
plt... | code |
74058807/cell_24 | [
"image_output_1.png"
] | from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, MaxPooling2D, BatchNormalization, UpSampling2D
from keras.models import Sequential, Model
INPUT_DIM = (144, 144, 1)
BATCH_SIZE = 128
EPOCHS = 100
LOSS = 'mse'
METRICS = ['accuracy']
OPTIMIZER = 'adam'
def Colorize():
... | code |
74058807/cell_10 | [
"text_plain_output_1.png"
] | len(img_paths) | code |
74058807/cell_27 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, MaxPooling2D, BatchNormalization, UpSampling2D
from keras.models import Sequential, Model
import tensorflow as tf
SEED = 42
INPUT_DIM = (144, 144, 1)
BATCH_SIZE = 128
EPOCHS = 100
LOSS = 'mse'
METRICS = ['accuracy']
OPTI... | code |
74058807/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | print(images_gray.shape)
print(images_col.shape) | code |
2038627/cell_42 | [
"text_html_output_1.png"
] | test_mean.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts(... | code |
2038627/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts(... | code |
2038627/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum() | code |
2038627/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts(... | code |
2038627/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
print(train['Outlet_Establishment_Year'].max())
print('\n')
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts() | code |
2038627/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
tes... | code |
2038627/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | train_median.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[tr... | code |
2038627/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.head() | code |
2038627/cell_40 | [
"text_html_output_1.png"
] | train_mean.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[tr... | code |
2038627/cell_39 | [
"text_html_output_1.png"
] | train.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_41 | [
"text_html_output_1.png"
] | test.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
print(train.shape) | code |
2038627/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts(... | code |
2038627/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[tr... | code |
2038627/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
print(test['Outlet_Establishment_Year'].max())
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Ye... | code |
2038627/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test_mode.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[tr... | code |
2038627/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
tes... | code |
2038627/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts(... | code |
2038627/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().head() | code |
2038627/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[tr... | code |
2038627/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
tes... | code |
2038627/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.head() | code |
2038627/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
tes... | code |
2038627/cell_43 | [
"text_html_output_1.png"
] | train_mode.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
tes... | code |
2038627/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test_median.Item_Weight.plot(kind='hist', color='white', edgecolor='black', facecolor='blue', figsize=(12, 6), title='Item Weight Histogram') | code |
2038627/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts(... | code |
2038627/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()] | code |
2038627/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts(... | code |
2038627/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train.describe(include='all') | code |
2038627/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
train.isnull().sum()
train[train.Item_Weight.isnull()]
train[tr... | code |
2038627/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
train.Outlet_Establishment_Year = 2013 - train.Outlet_Establishment_Year
train['Outlet_Establishment_Year'].value_counts()
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
tes... | code |
2038627/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape
test.Outlet_Establishment_Year = 2013 - test.Outlet_Establishment_Year
test.Outlet_Establishment_Year.value_counts(... | code |
2038627/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train/Train.csv', header=0)
test = pd.read_csv('../input/testpr-a102/Test.csv', header=0)
test.shape | code |
73094936/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
numImages = 16
fig = plt.figure(figsize=(7, 7))
imgData = np.zeros(shape=(numImages, 36963))
for i in range(1, numImages + 1):
filename = '../input/foodpics/pics/Picture' + str(i) + '.jpg'
img = mpimg.imread(filename)
ax = f... | code |
73094936/cell_21 | [
"text_html_output_1.png"
] | import io
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', ... | code |
73094936/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_9 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_25 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import io
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', ... | code |
73094936/cell_23 | [
"text_plain_output_1.png"
] | import io
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', ... | code |
73094936/cell_33 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import io
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.... | code |
73094936/cell_40 | [
"text_html_output_1.png"
] | import io
import numpy as np
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniform... | code |
73094936/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | data2 = data.drop(['Class'], axis=1)
data2['Bare Nuclei'] = pd.to_numeric(data2['Bare Nuclei'])
data2.boxplot(figsize=(20, 3)) | code |
73094936/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_7 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_38 | [
"text_html_output_1.png"
] | import io
import numpy as np
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniform... | code |
73094936/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single... | code |
73094936/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import io
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import requests
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-... | code |
73094936/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_5 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
73094936/cell_36 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)
data.columns = ['Sample code', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Margina... | code |
89132155/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 |
2022597/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)... | code |
2022597/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.describe() | code |
2022597/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)... | code |
2022597/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.info() | code |
2022597/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
f, ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax) | code |
2022597/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_livin... | code |
2022597/cell_3 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import scale
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
scale = StandardScaler() | code |
2022597/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
import statsmodels.api as sm
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([... | code |
2022597/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)... | code |
2022597/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)... | code |
2022597/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)... | code |
2022597/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.head() | code |
1009893/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from glob import glob
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files... | code |
1009893/cell_6 | [
"text_plain_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*... | code |
1009893/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*... | code |
1009893/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1009893/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files = glob(os.path.join(TRAIN_DATA, 'Type_3', '*... | code |
1009893/cell_17 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_2_files = glob(os.path.join(TRAIN_DATA, 'Type_2', '*.jpg'))
type_3_files... | code |
90154229/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.... | code |
90154229/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.target[-20:]
model = linear_mode... | code |
90154229/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, linear_model
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.target[-20:]
model = linear_mode... | code |
90154229/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, linear_model
diabetes = datasets.load_diabetes()
print(diabetes.keys()) | code |
90154229/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_t... | code |
90154229/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
import numpy as np
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-30]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-30]
diabetes_y_test = diabetes.... | code |
106191525/cell_21 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import LocalOutlierFactor
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
C = data['diagnosis'].value_counts()... | code |
106191525/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
C = data['diagnosis'].value_counts()
corr = data.corr()
top_feature = corr.index[abs(corr['diagnosis']) > 0.5]
print(top_feature.values)
Im... | code |
106191525/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
plt.title('Diagnostic Distribution')
C = data['diagnosis'].value_counts()
C.plot(kind='pie')
print(C) | code |
106191525/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.neighbors import LocalOutlierFactor
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
data = pd.read_csv('../input/breast-cancer/breast-cancer - breast-cancer.csv')
C = data['diagnosis'].value_counts()
corr = data.corr()
top_feature = corr.index[abs(corr... | code |
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