path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72121245/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols ... | code |
72121245/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test.isna().sum()[test.isna().sum() > 0] | code |
72121245/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
print(f'Train Shape: {train.shape}\nTest Shape: {test.shape}') | code |
72121245/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
sample_submission.head() | code |
72121245/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
... | code |
72121245/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols ... | code |
72121245/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
... | code |
72121245/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols | code |
1002958/cell_21 | [
"image_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1002958/cell_13 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='... | code |
1002958/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePr... | code |
1002958/cell_23 | [
"image_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1002958/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | cor_dict = corr['SalePrice'].to_dict()
del cor_dict['SalePrice']
print('List the numerical features decendingly by their correlation with Sale Price:\n')
for ele in sorted(cor_dict.items(), key=lambda x: -abs(x[1])):
print('{0}: \t{1}'.format(*ele)) | code |
1002958/cell_39 | [
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], te... | code |
1002958/cell_41 | [
"text_html_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1002958/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe() | code |
1002958/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1002958/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train.head() | code |
1002958/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePr... | code |
1002958/cell_38 | [
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], te... | code |
1002958/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1002958/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePr... | code |
1002958/cell_14 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
sns.distplot(train['SalePrice']) | code |
1002958/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')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe() | code |
1002958/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')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
print('Skewness: %f' % train['SalePrice'].skew())
print('Kurtosis: %f' % train['SalePrice'].kurt()) | code |
1002958/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePr... | code |
121148265/cell_9 | [
"text_plain_output_1.png"
] | from transformers import BartTokenizer, BartModel, BartForConditionalGeneration
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large... | code |
121148265/cell_7 | [
"text_plain_output_1.png"
] | from transformers import BartTokenizer, BartModel, BartForConditionalGeneration
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large... | code |
121148265/cell_8 | [
"text_plain_output_1.png"
] | from transformers import BartTokenizer, BartModel, BartForConditionalGeneration
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large... | code |
121148265/cell_3 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large') | code |
121148265/cell_10 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from transformers import BartTokenizer, BartModel, BartForConditionalGeneration
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large... | code |
129035267/cell_6 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
path = '/kaggle/input/sports-image-dataset/data/badminton/00000052.jpg'
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.axis('off')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.imshow(gray)
plt.axis('off')
plt.show() | code |
129035267/cell_7 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
path = '/kaggle/input/sports-image-dataset/data/badminton/00000052.jpg'
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.axis('off')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.axis('off')
canny = cv2.Canny(gray, 100, 200)
plt.imshow(canny)
plt.ax... | code |
129035267/cell_8 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
path = '/kaggle/input/sports-image-dataset/data/badminton/00000052.jpg'
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.axis('off')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.axis('off')
canny = cv2.Canny(gray, 100, 200)
plt.... | code |
129035267/cell_5 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
path = '/kaggle/input/sports-image-dataset/data/badminton/00000052.jpg'
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.axis('off')
plt.show() | code |
129023258/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-b... | code |
129023258/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-b... | code |
129023258/cell_19 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-... | code |
129023258/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-b... | code |
129023258/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-b... | code |
129023258/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-... | code |
129023258/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-... | code |
129023258/cell_17 | [
"text_html_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-... | code |
129023258/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-b... | code |
106205317/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapda... | code |
106205317/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
mapdata1.info() | code |
106205317/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapda... | code |
106205317/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapda... | code |
106205317/cell_44 | [
"text_plain_output_1.png"
] | from sklearn import tree
model = tree.DecisionTreeClassifier(criterion='entropy', random_state=0)
model.fit(x_train, y_train) | code |
106205317/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapda... | code |
106205317/cell_40 | [
"text_plain_output_1.png"
] | """confusion = metrics.confusion_matrix(x_test, y_test)
confusion""" | code |
106205317/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapda... | code |
106205317/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)
predict = knn.predict(x_test)
predict
knn.predict_proba(x_test) | code |
106205317/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapda... | code |
106205317/cell_19 | [
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapda... | code |
106205317/cell_45 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)
predict = knn.predict(x_test)
predict
model = tree.DecisionTreeClassifier(criterion='entropy', random_state=0)
model.fit(x_train, y_train)
predict = model.predict(x_test... | code |
106205317/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapda... | code |
106205317/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)
predict = knn.predict(x_test)
predict | code |
106205317/cell_17 | [
"image_output_11.png",
"image_output_24.png",
"image_output_46.png",
"image_output_25.png",
"image_output_47.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_59.png",
"image_output_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapda... | code |
106205317/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapda... | code |
106205317/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
mapdata1.head() | code |
106205317/cell_22 | [
"text_plain_output_1.png"
] | """for col in mapdata1:
if mapdata1.values.type == boolean:
mapdata1.drop(axis=1, inplace=True)""" | code |
106205317/cell_27 | [
"text_plain_output_1.png"
] | """def correlation(x_data, threshold):
col_corr = set()
corr_matrix = x_data.corr()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if abs(corr_matrix.iloc[i, j] > threshold:
colname = corr_matrix.columns[i]
col_corr.add(colname... | code |
106205317/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train) | code |
106205317/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape | code |
328714/cell_2 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_8.png",
"application_vnd.jupyter.stderr_output_10.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"te... | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 1000]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return ... | code |
328714/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 1000]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return ... | code |
50234024/cell_4 | [
"text_plain_output_1.png"
] | WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
print('Train images: %d' % len(os.listdir(os.path.join(WORK_DIR, 'train')))) | code |
50234024/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstance... | code |
50234024/cell_2 | [
"image_output_1.png"
] | import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print(f'Running on TPU {tpu.master()}')
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimenta... | code |
50234024/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstance... | code |
50234024/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models, layers
from tensorflow.keras.applications import Xception
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except V... | code |
50234024/cell_3 | [
"image_output_1.png"
] | WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR) | code |
50234024/cell_17 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, '... | code |
50234024/cell_14 | [
"text_plain_output_1.png"
] | from tensorflow.keras import models, layers
from tensorflow.keras.applications import Xception
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except V... | code |
50234024/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
str... | code |
50234024/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, '... | code |
333909/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers.core import Dense, Dropout, Activation, Flatten, MaxoutDense
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop, Adadelta, SGD
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np
import pandas... | code |
333909/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, ZeroPadding2D
from keras.optimizers import Adam, RMSprop, Adadelta, SGD
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlySt... | code |
333909/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers.core import Dense, Dropout, Activation, Flatten, MaxoutDense
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop, Adadelta, SGD
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np
import pandas... | code |
129009155/cell_42 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content... | code |
129009155/cell_21 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
from gensim.models import KeyedVectors
wv = KeyedVectors.load('/content/drive/MyDrive/HMD_project/glove-twitter-200')
w = wv['hate']
print(w)
print('\n\nlength of word vector', len(w))
print('\n\n type of word vector model ', type(wv))
print('\n\n word vector type', type(w)) | code |
129009155/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
print(df_train.isna().sum())
print('\n\n', df_dev.isna().s... | code |
129009155/cell_25 | [
"text_plain_output_1.png"
] | import spacy
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm') | code |
129009155/cell_29 | [
"text_plain_output_1.png"
] | import spacy
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
def preprocess(text):
doc = nlp(text)
filtered_token = []
for token in doc:
if token.is_punct or token.is_space or token.is_bracket or token.is_stop:
continue
else:
... | code |
129009155/cell_48 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
from sklearn.decomposition import NMF
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import numpy as np
import pandas as pd
import spa... | code |
129009155/cell_11 | [
"text_plain_output_1.png"
] | with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
print(type(file_names[0])) | code |
129009155/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
print('success') | code |
129009155/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
df_train['label'].value_counts().plot(kind='bar', figsize=... | code |
129009155/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import gensim.downloader
print(list(gensim.downloader.info()['models'].keys())) | code |
129009155/cell_38 | [
"text_html_output_1.png"
] | from gensim.models import KeyedVectors
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read... | code |
129009155/cell_3 | [
"text_plain_output_1.png"
] | from google.colab import drive
from google.colab import drive
drive.mount('/content/drive') | code |
129009155/cell_35 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read... | code |
129009155/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_projec... | code |
129009155/cell_46 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive... | code |
129009155/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_project/new/text_aug... | code |
129009155/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
print(df_dev.tail()) | code |
129009155/cell_36 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read... | code |
48165025/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../input/titanic/test.csv', index_col='PassengerId')
train.des... | code |
48165025/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../inpu... | code |
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