path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90118648/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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test['Embarked'] = [1 if l == 'S' else 2 if l == 'C' else 3 for l in test['Embarked']]
test['Embarked'].value_counts() | code |
90118648/cell_27 | [
"text_plain_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp',... | code |
90118648/cell_37 | [
"text_html_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.impute import KNNImputer
from sklearn.preprocessing import PowerTransformer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/in... | code |
90118648/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.info() | code |
90118648/cell_36 | [
"text_html_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.impute import KNNImputer
from sklearn.preprocessing import PowerTransformer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
pre... | code |
18118296/cell_4 | [
"text_html_output_1.png"
] | import os
data_dir = '../input'
os.listdir(f'{data_dir}') | code |
18118296/cell_6 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10) | code |
18118296/cell_29 | [
"text_html_output_1.png"
] | from bisect import bisect
from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier,
from sklearn.model_selection import KFold
import numpy as np
import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test... | code |
18118296/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10)
train_df_raw.describe(include='all').T | code |
18118296/cell_32 | [
"text_html_output_1.png"
] | from bisect import bisect
from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier,
from sklearn.model_selection import KFold
import numpy as np
import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test... | code |
18118296/cell_8 | [
"text_html_output_1.png"
] | import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10)
test_df_raw.describe(include='all').T | code |
18118296/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10)
train_df_raw.describe(include='all').T
test_df_raw.desc... | code |
106211827/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import tensorflow as tf
from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
from transformers import WEIGHTS_NAME, CONFIG_NAME
import os | code |
106211827/cell_23 | [
"text_plain_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
import os
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import os
data_location = 'data'
if not os.path.exists(data_location):
os.makedirs(data_location)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
configur... | code |
106211827/cell_11 | [
"text_plain_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2') | code |
106211827/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
import tensorflow as tf
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
configuration = GPT2Config(vocab_size=tokenizer.vocab_size, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id)
model = TFGPT2LMHeadModel(configuration... | code |
106211827/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 |
106211827/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | !pip install tokenizer
!pip install transformers | code |
106211827/cell_22 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
import tensorflow as tf
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
configuration = GPT2Config(vocab_size=tokenizer.vocab_size, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id)
model = TFGPT2LMHeadModel(configuration... | code |
106211827/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
configuration = GPT2Config(vocab_size=tokenizer.vocab_size, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id)
model = TFGPT2LMHeadModel(configuration) | code |
128015730/cell_7 | [
"text_plain_output_1.png"
] | !python /kaggle/input/iot23bymyself/creatDataset.py | code |
128015730/cell_8 | [
"text_plain_output_1.png"
] | !zip -r /kaggle/working/dataImage imagesData | code |
16147726/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorfl... | code |
16147726/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import pickle
import pickle
with open('../input/X.pickle', 'rb') as fp:
X_feature = pickle.load(fp)
with open('../input/Y.pickle', 'rb')... | code |
1010749/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_nul... | code |
1010749/cell_9 | [
"image_output_11.png",
"image_output_239.png",
"image_output_536.png",
"image_output_98.png",
"image_output_573.png",
"image_output_477.png",
"image_output_538.png",
"image_output_337.png",
"image_output_416.png",
"image_output_452.png",
"image_output_508.png",
"image_output_121.png",
"image... | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_nul... | code |
1010749/cell_33 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f]... | code |
1010749/cell_40 | [
"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_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13... | from statsmodels.graphics.factorplots import interaction_plot
from statsmodels.stats.weightstats import ztest
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
column... | code |
1010749/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_nul... | code |
1010749/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_nul... | code |
1010749/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_nul... | code |
1010749/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_nul... | code |
1010749/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f]... | code |
1010749/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_nul... | code |
1010749/cell_37 | [
"text_plain_output_1.png"
] | from statsmodels.stats.weightstats import ztest
import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str... | code |
1010749/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
column_full_names = {'MSSubClass': 'The Building Class', 'MSZoning': 'The General Zoning Classification', 'SalePrice': 'Sale Price', 'LotFrontage': 'Linear feet of street connected to property', 'LotArea': 'Lot size in square feet', ... | code |
1010749/cell_5 | [
"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_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13... | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
print(columns)
print('Number of Features: %s' % len(columns)) | code |
50220357/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
plt.figure(figsize=(10, 10))
sn.heatmap(train.isnull(), yticklabels=False, cbar=False) | code |
50220357/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12,... | code |
50220357/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.describe(include='all') | code |
50220357/cell_44 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12,... | code |
50220357/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sn.barplot(x='SibSp', y='Survived', data=train)
print('Percentage of SibSp = 0 who survived:', train['Survived'][train['SibSp'] == 0].value_count... | code |
50220357/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12,... | code |
50220357/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12,... | code |
50220357/cell_48 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12,... | code |
50220357/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum() | code |
50220357/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
print(train.columns.values) | code |
50220357/cell_45 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12,... | code |
50220357/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.head(10) | code |
50220357/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.drop(['Cabin'], axis=1, inplace=True)
test.drop(['Cabin'], axis=1, inplace=True)
train.drop(['Ticket'], axis=1, inplace=True)
test.drop(['T... | code |
50220357/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sn.barplot(x='Sex', y='Survived', data=train)
print('Percentage of females who survived:', train['Survived'][train['Sex'] == 'female'].value_coun... | code |
50220357/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12,... | code |
50220357/cell_53 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12,... | code |
50220357/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe(include='all') | code |
74052542/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74052542/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74052542/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74052542/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 |
74052542/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74052542/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.... | code |
74052542/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74052542/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74052542/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input... | code |
74052542/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
74052542/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-20... | code |
34122127/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cols_list = []
for j in range(8):
for i in range(8):
cols_list.append(f'S{i}R{j}')
cols_list.append('target')
df = pd.read_csv('/kaggle/input/emg-4/0.csv', header=None)
df.columns = cols_list
df | code |
34122127/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 |
34122127/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cols_list = []
for j in range(8):
for i in range(8):
cols_list.append(f'S{i}R{j}')
cols_list.append('target')
df = pd.read_csv('/kaggle/input/emg-4/0.csv', header=None)
df.columns = cols_list
df
pd.wide_to_long(df.reset_index(), ['S1',... | code |
106209373/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(13, 8))
sns.countplot(x='arrival_date_month', ... | code |
106209373/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['is_canceled'].value_counts() | code |
106209373/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
hotel['arrival_date_year'].unique() | code |
106209373/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['country'].value_counts(normalize=True) | code |
106209373/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(13, 8))
sns.countplot(x='market_segment', data... | code |
106209373/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['arrival_date_year'].unique() | code |
106209373/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel.head() | code |
106209373/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
confirmed_bookings['arrival_date_year'] = hotel['arrival_date_year']
Last = confirmed_bookings['arrival_date_year'].value_... | code |
106209373/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['arrival_date_month'].value_counts() | code |
106209373/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(10, 8))
sns.countplot(x='deposit_type', data=h... | code |
106209373/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(18, 9))
sns.lineplot(data=hotel, x='arrival_da... | code |
106209373/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['market_segment'].value_counts(normalize=True) | code |
106209373/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['customer_type'].value_counts() | code |
106209373/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(10, 8))
sns.countplot(data=hotel, x='total_of_... | code |
106209373/cell_47 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(7, 8))
sns.countplot(x='reservation_status', d... | code |
106209373/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel.info() | code |
106209373/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['reservation_status'].value_counts(normalize=True) | code |
106209373/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['meal'].value_counts().unique | code |
106209373/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
print(round(100 * (hotel.isnull().sum() / len(hotel.index)), 2)) | code |
106209373/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(8, 8))
sns.countplot(data=hotel, x='hotel', hu... | code |
2032996/cell_13 | [
"text_plain_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import Ridge
from sklearn.preprocessing import LabelEncoder... | code |
2032996/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.sparse import csr_matrix, hstack
import time
import re
import math
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler, LabelBinarizer
from sklearn.cross_validation import train_test_split
fr... | code |
2032996/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import Ridge
from sklearn.preprocessing import LabelEncoder... | code |
2032996/cell_7 | [
"text_plain_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import pandas as pd
import time
Time_0 = time.time()
train = pd.read_csv('../input/train.tsv', sep='\t... | code |
129023624/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'],... | code |
129023624/cell_56 | [
"text_html_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict, KFold, GridSearchCV
from tra... | code |
129023624/cell_34 | [
"text_plain_output_1.png"
] | from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import ... | code |
129023624/cell_44 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
MODEL_TYPE = 'xlm-roberta-la... | code |
129023624/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.head() | code |
129023624/cell_29 | [
"image_output_1.png"
] | !pip install transformers | code |
129023624/cell_2 | [
"text_plain_output_1.png"
] | !pip install contractions | code |
129023624/cell_54 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict, KFold, GridSearchCV
from tra... | code |
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