path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106201316/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(rc={'figure.figsize': (30, 18)})
import matplotlib.pyplot as plt
import os
train = pd.read_csv('../input/titani... | code |
106201316/cell_43 | [
"text_html_output_1.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/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_14 | [
"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/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_10 | [
"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/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_27 | [
"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/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_36 | [
"text_html_output_1.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/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
32068083/cell_9 | [
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_1.png",
"text_html_output_8.png",
"text_html_output_3.png",
"text_html_output_7.png"
] | import datetime
import datetime
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_... | code |
32068083/cell_4 | [
"text_html_output_1.png"
] | import datetime
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.io as pio
import plotly.offline as py
import numpy as np
import pandas as pd
from plotly import tools, subplots
import plotly.offline as py
py.init_notebook_mode(connected=Tru... | code |
32068083/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission_df = pd.read_csv('/kaggle/input/covid19-global-forecas... | code |
32068083/cell_11 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | import datetime
import datetime
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.io as pio
import plotly.offline as py
import numpy as np
import pandas as pd
from plotly import tools, subplots
import plotly.offline as py
py.init_notebook_m... | code |
32068083/cell_1 | [
"text_plain_output_1.png"
] | import os
import plotly.io as pio
import plotly.offline as py
import numpy as np
import pandas as pd
from plotly import tools, subplots
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.... | code |
32068083/cell_7 | [
"text_plain_output_1.png"
] | import datetime
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.io as pio
import plotly.offline as py
import numpy as np
import pandas as pd
from plotly import tools, subplots
import plotly.offline as py
py.init_notebook_mode(connected=Tru... | code |
32068083/cell_8 | [
"text_html_output_29.png",
"text_html_output_27.png",
"text_html_output_28.png",
"text_html_output_10.png",
"text_html_output_22.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_26.png",
"text_html_output_2.png",
"text_html_output_15.png",
... | import tensorflow as tf
import datetime
from numpy import array
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
print(tf.__version__) | code |
32068083/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import datetime
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.io as pio
import plotly.offline as py
import numpy as np
import pandas as pd
from plotly import tools, subplots
import plotly.offline as py
py.init_notebook_mode(connected=Tru... | code |
32068083/cell_14 | [
"text_html_output_29.png",
"text_html_output_27.png",
"text_html_output_28.png",
"text_html_output_10.png",
"text_html_output_22.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_26.png",
"text_html_output_2.png",
"text_html_output_15.png",
... | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy import array
import datetime
import datetime
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotl... | code |
32068083/cell_12 | [
"text_html_output_4.png",
"text_html_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy import array
import datetime
import datetime
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotl... | code |
32068083/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import datetime
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.io as pio
import plotly.offline as py
import numpy as np
import pandas as pd
from plotly import tools, subplots
import plotly.offline as py
py.init_notebook_mode(connected=Tru... | code |
104117830/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application... | code |
104117830/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/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 |
104117830/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
104117830/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
application_record = pd.read_csv('../input/creditcard/application_record.csv')
credit_record = pd.read_csv('../input/creditcard/credit_record.csv')
application_record = pd.read_csv('../input/creditcard/app... | code |
1005954/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/database.csv', low_memory=False)
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(13, 15))
crims_by_relationship = data[['Relationship', 'Record ID']].groupby('Relationship').... | code |
1005954/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/database.csv', low_memory=False)
data.head() | code |
1005954/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1005954/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/database.csv', low_memory=False)
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(13, 15))
crims_by_relationship = data[['Relationship', 'Record ID']].groupby('Relationship').c... | code |
74043801/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import numpy as np
import pandas as pd
import os
df = pd.DataFrame()
import random
random.seed(0)
for file in random.sample(filenames, 20):
if df.empty:
df = pd.read_csv(os.path.join(dirname, file))
else:
... | code |
311500/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df[df.data_field == 'confirmed_male'].value.plot()
df[df.data_field == 'confirmed_female'].value.plot(... | code |
311500/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.data_field.unique() | code |
311500/cell_2 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.head(3) | code |
311500/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sbn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
311500/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.data_field.unique()
age_groups = ('confirmed_age_under_1', 'confirmed_age_1-4', 'confirmed_age_5-9... | code |
311500/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))
plt.title('Number of locations repor... | code |
17133658/cell_9 | [
"text_html_output_1.png"
] | train_data = train_data.sample(n=5000)
train_data.shape
train_data.head() | code |
17133658/cell_34 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
import matplotlib.pyplot as plt
import pandas as pd
import xgboost as xgb
xgb = xgb.XGBRFRegressor()
tree = ensemble.RandomForestRegressor()
ada = ensemble.AdaBoostRegressor()
grad = ensemble.GradientBoostingRegressor()
clf = ensemble.AdaBoostRegressor(n_estimators=500, learning_rate=0... | code |
17133658/cell_29 | [
"text_html_output_1.png"
] | from sklearn import ensemble
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.pipeline import Pipeline
import scipy as sp
import warnings
import xgboost as xgb
xgb = xgb.XGBRFRegressor()
tree = ensemble.RandomForestRegressor()
ada = ensemble.AdaBoostRegressor()
grad = ensemble.Gra... | code |
17133658/cell_39 | [
"image_output_1.png"
] | from sklearn import ensemble
from sklearn.metrics import roc_auc_score
import matplotlib.pyplot as plt
import pandas as pd
import xgboost as xgb
xgb = xgb.XGBRFRegressor()
tree = ensemble.RandomForestRegressor()
ada = ensemble.AdaBoostRegressor()
grad = ensemble.GradientBoostingRegressor()
clf = ensemble.AdaBoost... | code |
17133658/cell_41 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
from sklearn.metrics import roc_auc_score
import matplotlib.pyplot as plt
import pandas as pd
import xgboost as xgb
xgb = xgb.XGBRFRegressor()
tree = ensemble.RandomForestRegressor()
ada = ensemble.AdaBoostRegressor()
grad = ensemble.GradientBoostingRegressor()
clf = ensemble.AdaBoost... | code |
17133658/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | train_data = train_data.sample(n=5000)
train_data.shape
train_data.corr()
train_data.TARGET.value_counts() | code |
17133658/cell_7 | [
"text_plain_output_1.png"
] | train_data = train_data.sample(n=5000)
train_data.shape | code |
17133658/cell_32 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
import matplotlib.pyplot as plt
import pandas as pd
import xgboost as xgb
xgb = xgb.XGBRFRegressor()
tree = ensemble.RandomForestRegressor()
ada = ensemble.AdaBoostRegressor()
grad = ensemble.GradientBoostingRegressor()
clf = ensemble.AdaBoostRegressor(n_estimators=500, learning_rate=0... | code |
17133658/cell_16 | [
"text_plain_output_1.png"
] | columns_to_drop = []
columns = train_data.columns
for i in range(len(columns) - 1):
column_to_check = train_data[columns[i]]
for c in range(i + 1, len(columns)):
if np.array_equal(column_to_check, train_data[columns[c]].values):
columns_to_drop.append(columns[c])
train_data.drop(columns_to_d... | code |
17133658/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import ensemble
from sklearn import neighbors
from sklearn import linear_model
import xgboost as xgb
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.model_selection import Gr... | code |
17133658/cell_17 | [
"text_html_output_1.png"
] | train_data = train_data.sample(n=5000)
train_data.shape
train_data.corr() | code |
17133658/cell_24 | [
"text_plain_output_1.png"
] | X_train = df_train.drop(['ID', 'TARGET'], axis=1)
y_train = df_train.TARGET
X_test = df_test.drop(['ID', 'TARGET'], axis=1)
y_test = df_test.TARGET
X_valid = df_valid.drop(['ID', 'TARGET'], axis=1)
y_valid = df_valid.TARGET
data_for_sub = test_data.drop(['ID'], axis=1) | code |
17133658/cell_14 | [
"text_plain_output_1.png"
] | dropable_cols = []
for i in train_data.columns:
if (train_data[i] == 0).all():
dropable_cols.append(i)
train_data.drop(dropable_cols, axis=1, inplace=True)
test_data.drop(dropable_cols, axis=1, inplace=True)
print('Data shape after droping rows: ')
print('Train data shape: ', train_data.shape, 'Test data sh... | code |
17133658/cell_22 | [
"text_plain_output_1.png"
] | df_train = train_data[:3000]
df_test = train_data[3000:4000]
df_valid = train_data[4000:]
print(df_train.shape, df_test.shape, df_valid.shape) | code |
17133658/cell_37 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.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",
"text_plain_output_3.png",
"text_plain_output_7.png",
"tex... | from sklearn import ensemble
import matplotlib.pyplot as plt
import pandas as pd
import xgboost as xgb
train_data = train_data.sample(n=5000)
train_data.shape
train_data.corr()
train_data.TARGET.value_counts()
xgb = xgb.XGBRFRegressor()
tree = ensemble.RandomForestRegressor()
ada = ensemble.AdaBoostRegressor()
g... | code |
17133658/cell_12 | [
"text_plain_output_1.png"
] | train_data.isnull().sum().any() > 0 | code |
17133658/cell_5 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | test_data = pd.read_csv('../input/test.csv')
train_data = pd.read_csv('../input/train.csv') | code |
33096285/cell_34 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn import preprocessing
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
df_cred = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df_cred.shape
plt.style.use('ggplot') # Using ggplot2 style visu... | code |
33096285/cell_33 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
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
df_cred = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df_cred.shape
plt.style.use('ggplot') # Using ggplot2 style visu... | code |
33096285/cell_44 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping ,ReduceLROnPlateau
from keras.callbacks import ModelCheckpoint, TensorBoard
from keras.layers import Input, Dense
from keras.models import Model, load_model
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as ... | code |
33096285/cell_6 | [
"text_html_output_1.png"
] | from contextlib import contextmanager
import plotly.offline as py
import warnings
from scipy.stats import randint as sp_randint
from scipy.stats import uniform as sp_uniform
import warnings
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
imp... | code |
33096285/cell_29 | [
"image_output_1.png"
] | from sklearn import preprocessing
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
df_cred = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df_cred.shape
plt.style.use('ggplot') # Using ggplot2 style visu... | code |
33096285/cell_50 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping ,ReduceLROnPlateau
from keras.callbacks import ModelCheckpoint, TensorBoard
from keras.layers import Input, Dense
from keras.models import Model, load_model
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as ... | code |
33096285/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 |
33096285/cell_7 | [
"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
df_cred = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df_cred.shape
plt.style.use('ggplot')
f, ax = plt.subplots(figsize=(11, 15))
ax.set_facecolor('#faf... | code |
33096285/cell_8 | [
"image_output_1.png"
] | from contextlib import contextmanager
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 plotly.graph_objs as go
import plotly.offline as py
import seaborn as sns
import warnings
df_cred = pd.read_csv('/kaggle/input/creditcardfraud/c... | code |
33096285/cell_38 | [
"text_plain_output_1.png"
] | from keras.models import Model, load_model
from keras.layers import Input, Dense
from keras.callbacks import ModelCheckpoint, TensorBoard
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from keras.optimizers import Adam | code |
33096285/cell_43 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping ,ReduceLROnPlateau
from keras.callbacks import ModelCheckpoint, TensorBoard
from keras.layers import Input, Dense
from keras.models import Model, load_model
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as ... | code |
33096285/cell_46 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping ,ReduceLROnPlateau
from keras.callbacks import ModelCheckpoint, TensorBoard
from keras.layers import Input, Dense
from keras.models import Model, load_model
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as ... | code |
33096285/cell_12 | [
"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
df_cred = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df_cred.shape
plt.style.use('ggplot') # Using ggplot2 style visuals
f, ax = plt.subplots(figsize=... | code |
33096285/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_cred = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df_cred.shape | code |
33096285/cell_36 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.model_selection import train_test_split
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
df_cred = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df_cred.sha... | code |
18138191/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from IPython.display import clear_output
from time import sleep
import os
os.listdir('../input')
train_data = pd.read_csv('../input/training/training.csv')
test_data = pd.read_csv('../input/test/test.csv')
lookid_data... | code |
18138191/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from IPython.display import clear_output
from time import sleep
import os
os.listdir('../input')
train_data = pd.read_csv('../input/training/training.csv')
test_dat... | code |
18138191/cell_2 | [
"text_html_output_1.png"
] | import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from IPython.display import clear_output
from time import sleep
import os
os.listdir('../input')
train_data = pd.read_csv('../input/training/training.csv')
test_data = pd.read_csv('../input/test/test.csv')
lookid_data... | code |
18138191/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Conv2D,Dropout,Dense,Flatten
from keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from IPython.display import clear_output
from time import sleep
import o... | code |
2035149/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
from vgg16 import vgg16
import numpy as np
import os
from datalab import DataLabTrain | code |
2035149/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datalab import DataLabTrain
from vgg16 import vgg16
import tensorflow as tf
def train(n_iters):
model, params = vgg16(fine_tune_last=True, n_classes=2)
X = model['input']
Z = model['out']
Y = tf.placeholder(dtype=tf.float32, shape=[None, 2])
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_... | code |
2035149/cell_10 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from datalab import DataLabTrain
from make_file import make_sub
from vgg16 import vgg16
import numpy as np
import tensorflow as tf
def train(n_iters):
model, params = vgg16(fine_tune_last=True, n_classes=2)
X = model['input']
Z = model['out']
Y = tf.placeholder(dtype=tf.float32, shape=[None, 2])
... | code |
88075491/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/customer-personality-analysis/marketing_campaign.csv', delimiter='\\t', engine='python')
df_mod = df
df_mod['Age'] = max(df_mod.Dt_Customer.dt.year) - df['Year_Birth']
df_mod['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'], format='%d-%m-%Y')
df_mod = df.rename(columns... | code |
90107080/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)
test = test.drop('U... | code |
90107080/cell_33 | [
"text_plain_output_1.png"
] | from joblib import dump, load
import pandas as pd
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/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index... | code |
90107080/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)
train = train.drop(... | code |
90107080/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklear... | code |
90107080/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression , Ridge , LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
train... | code |
90107080/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import warnings
import pandas as pd
impo... | code |
90107080/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 |
90107080/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklear... | code |
90107080/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import warnings
import pandas as pd
import numpy as np
import seaborn as sn... | code |
90107080/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/train.csv', index_col=None)
test = pd.read_csv('/kaggle/input/airline-passenger-satisfaction/test.csv', index_col=None)
train = train.drop(... | code |
90107080/cell_35 | [
"text_plain_output_1.png"
] | from joblib import dump, load
from sklearn.linear_model import LinearRegression , Ridge , LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas... | code |
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