path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
74070897/cell_11 | [
"text_plain_output_1.png"
] | from configparser import ConfigParser
from dateutil import parser
from pyproj import Geod
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import os
import... | code |
32062775/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
sex_and_embark_train = pd.get_dummies(t... | code |
32062775/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
gender_submission | code |
32062775/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test | code |
32062775/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
sex_and_embark_train = pd.get_dummies(t... | code |
32062775/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
sex_and_embark_train = pd.get_dummies(t... | code |
32062775/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 |
32062775/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
sex_and_embark_train = pd.get_dummies(t... | code |
32062775/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
sex_and_embark_train = pd.get_dummies(t... | code |
32062775/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
sex_and_embark_train = pd.get_dummies(t... | code |
32062775/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CS... | code |
32062775/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CS... | code |
32062775/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train | code |
1008349/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
gTemp = pd.read_csv('../input/GlobalTemperatures.csv')
gTempCountry = pd.read_csv('../input/GlobalLandTemperaturesByCountry.csv')
gTempState = pd.read_csv('../input/GlobalLandTemperaturesByState.csv')
gTempMajorCity = pd.read_csv('../input/GlobalLandTemperaturesByMajorCity.csv')
gTempCity = pd.read... | code |
1008349/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
gTemp = pd.read_csv('../input/GlobalTemperatures.csv')
gTempCountry = pd.read_csv('../input/GlobalLandTemperaturesByCountry.csv')
gTempState = pd.read_csv('../input/GlobalLandTemperaturesByState.csv')
gTempMajorCity = pd.read_csv('../input/GlobalLandTemperaturesByMajorCity.csv')
gTempCity = pd.read... | code |
320942/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv', header=0)
df[df['Age'] > 60][['Sex', 'Pclass', 'Age', 'Survived']] | code |
320942/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv', header=0)
df['Gender'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)
df.head(3) | code |
320942/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv', header=0)
df.head(3) | code |
320942/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
320942/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv', header=0)
df['Gender'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)
df.head(3) | code |
320942/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv', header=0)
df.info() | code |
320942/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pylab as P
df = pd.read_csv('../input/train.csv', header=0)
import pylab as P
df['Age'].hist()
P.show() | code |
50223292/cell_9 | [
"text_plain_output_100.png",
"text_plain_output_334.png",
"text_plain_output_770.png",
"text_plain_output_743.png",
"text_plain_output_673.png",
"text_plain_output_445.png",
"text_plain_output_640.png",
"text_plain_output_201.png",
"text_plain_output_586.png",
"text_plain_output_261.png",
"text_... | from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.callbacks import EarlyStopping
import tensorflow as tf
from tensorflow.keras.applications.inception_v3 import InceptionV3
pretrained_base = InceptionV3(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
pretrained... | code |
50223292/cell_4 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import cv2
image = cv2.imread('../input/cassava-leaf-disease-classification/test_images/2216849948.jpg')
plt.figure(figsize=(20, 10))
plt.imshow(image)
plt.axis('off')
plt.show() | code |
50223292/cell_6 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
num_test_images = len(os.listdir('../input/cassava-leaf-disease-classification/test_images'))
num_test_images
num_train_images = len(os.listdir('../input/cassava-leaf-disease-classification/train_images'))
num_train_images
import cv2
image = cv2.imread('../input... | code |
50223292/cell_2 | [
"image_output_1.png"
] | import os
num_test_images = len(os.listdir('../input/cassava-leaf-disease-classification/test_images'))
num_test_images | code |
50223292/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
import pandas as pd
import tensorflow as tf
sub = pd.read_csv('../input/cassava-leaf-disease-classification/sample_sub... | code |
50223292/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import InceptionV3
pretrained_base = InceptionV3(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
pretrained_base.trainable = False | code |
50223292/cell_3 | [
"text_html_output_1.png"
] | import os
num_test_images = len(os.listdir('../input/cassava-leaf-disease-classification/test_images'))
num_test_images
num_train_images = len(os.listdir('../input/cassava-leaf-disease-classification/train_images'))
num_train_images | code |
50223292/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
import pandas as pd
sub = pd.read_csv('../input/cassava-leaf-disease-classification/sample_submission.csv')
train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
train['label'] = train['label'].astype('str')
train_da... | code |
50223292/cell_5 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.pn... | import pandas as pd
sub = pd.read_csv('../input/cassava-leaf-disease-classification/sample_submission.csv')
train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
train['label'] = train['label'].astype('str')
train.head() | code |
73095834/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
df = pd.read_csv('G:\\MS Avishkara\\winequality-red.csv', sep=';')
df.head() | code |
16166947/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.api as sm
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.head(10)[['damageDealt'... | code |
16166947/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from scipy import stats
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.head(10)[['damageDealt', 'winPlacePerc']]
import mat... | code |
16166947/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.head(10)[['damageDealt', 'winPlacePerc']]
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='dar... | code |
16166947/cell_25 | [
"text_plain_output_1.png"
] | from scipy import stats
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.api as sm
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../i... | code |
16166947/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.api as sm
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.head(10)[['damageDealt'... | code |
16166947/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.api as sm
train_data = pd.read_csv('../i... | code |
16166947/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16166947/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.head(10)[['damageDealt', 'winPlacePerc']]
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='dar... | code |
16166947/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.api as sm
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.head(10)[['damageDealt'... | code |
16166947/cell_1 | [
"text_plain_output_1.png"
] | # https://stackoverflow.com/questions/56283294/importerror-cannot-import-name-factorial
!pip install statsmodels==0.10.0rc2 | code |
16166947/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.head(10)[['damageDealt', 'winPlacePerc']] | code |
16166947/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.api as sm
train_data = pd.read_csv('../i... | code |
16166947/cell_15 | [
"text_html_output_1.png"
] | from scipy import stats
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.head(10)[['damageDealt', 'winPlacePerc']]
import mat... | code |
16166947/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.head(10)[['damageDealt', 'winPlacePerc']]
import mat... | code |
16166947/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train_V2.csv')
test_data = pd.read_csv('../input/test_V2.csv')
train_data.info()
train_data.head() | code |
74041294/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from mlxtend.frequent_patterns import fpgrowth, association_rules # MBA
import numpy as np
import os
import pandas as pd
def preDot(text):
return text.rsplit('.', 1)[0]
np.random.seed(73)
pd.options.mode.chained_assignment = None
dataDict = {}
for dirname, _, filenames in os.walk('/kaggle/input'):
for file... | code |
74041294/cell_26 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from mlxtend.frequent_patterns import fpgrowth, association_rules # MBA
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
def preDot(text):
return text.rsplit('.', 1)[0]
np.random.seed(73)
pd.options.mode.chained_assignment = None
dataDict = {}
for dirname, _, filenames in os.wa... | code |
74041294/cell_16 | [
"text_html_output_1.png"
] | from mlxtend.frequent_patterns import fpgrowth, association_rules # MBA
import numpy as np
import os
import pandas as pd
def preDot(text):
return text.rsplit('.', 1)[0]
np.random.seed(73)
pd.options.mode.chained_assignment = None
dataDict = {}
for dirname, _, filenames in os.walk('/kaggle/input'):
for file... | code |
74041294/cell_3 | [
"image_output_1.png"
] | import numpy as np
import os
import pandas as pd
def preDot(text):
return text.rsplit('.', 1)[0]
np.random.seed(73)
pd.options.mode.chained_assignment = None
dataDict = {}
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
... | code |
74041294/cell_12 | [
"text_plain_output_1.png"
] | from mlxtend.frequent_patterns import fpgrowth, association_rules # MBA
import numpy as np
import os
import pandas as pd
def preDot(text):
return text.rsplit('.', 1)[0]
np.random.seed(73)
pd.options.mode.chained_assignment = None
dataDict = {}
for dirname, _, filenames in os.walk('/kaggle/input'):
for file... | code |
106206626/cell_21 | [
"text_html_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(color='red')
y_train = train['register__sales_... | code |
106206626/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(color='red')
y_train = train['register__sales_dollar_amt_this_hour... | code |
106206626/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(color='red')
print(train['register__sales_dollar_amt_this_hour'].s... | code |
106206626/cell_23 | [
"text_html_output_1.png",
"image_output_1.png"
] | from flaml import AutoML
from flaml import AutoML
import numpy as np
import pandas as pd
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.den... | code |
106206626/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(color='red')
y_train = train['register__sales_dollar_amt_this_hour... | code |
106206626/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(color='red')
y_train = train['register__sales_... | code |
106206626/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib import colors
import xgboost as xgb
import os
import sys
from datetime import datetime, timedelta
from time import time
from uuid import uuid4
from scipy.ndimage import convolve1d
from sklearn.metric... | code |
106206626/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(c... | code |
106206626/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(c... | code |
106206626/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(color='red') | code |
106206626/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import seaborn as sns
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(c... | code |
106206626/cell_24 | [
"text_html_output_1.png",
"image_output_1.png"
] | from flaml import AutoML
from flaml import AutoML
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-globa... | code |
106206626/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/juniper-networks-global-ai-challenge/training_dataset.csv')
test = pd.read_csv('../input/juniper-networks-global-ai-challenge/test_dataset.csv')
train.register__sales_dollar_amt_this_hour.plot.density(color='red')
y_train = train['register__sales_... | code |
17131450/cell_4 | [
"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)
structures = pd.read_csv('../input/structures.csv')
structures = structures.head(n=100)
structures.head(n=10) | code |
17131450/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.metrics import pairwise_distances
from sklearn.neighbors import NearestNeighbors
import os
import warnings
print(os.listdir('../input')) | code |
17131450/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | warnings.filterwarnings('ignore')
structures['nearestn'] = structures.groupby('molecule_name')['x'].transform(nn_features)
structures.head(n=10) | code |
17131450/cell_10 | [
"text_plain_output_1.png"
] | structures['nn_1'] = structures['nearestn'].apply(lambda x: x[0])
structures['nn_2'] = structures['nearestn'].apply(lambda x: x[1])
structures['nn_3'] = structures['nearestn'].apply(lambda x: x[2])
structures['nn_4'] = structures['nearestn'].apply(lambda x: x[3])
structures['nn_1_dist'] = structures['nearestn'].apply(l... | code |
16154605/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import torch.nn as nn
import torch.nn as nn
import torchvision.models as models
model_v = models.resnet18()
model_c = models.resnet18()
model_c.fc = nn.Linear(512, 10, bias=True)
model_v.fc = nn.Linear(512, 10, bias=True)
print(model_c) | code |
16154605/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from torch.utils.data import DataLoader, Dataset, random_split
import os
import os
import torch
import torch
import torchvision.transforms as transforms
import torchvision.transforms as transforms
import numpy as np
import pandas as pd
import os
import os
from PIL import Image
import matp... | code |
16154605/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from torch.utils.data import DataLoader, Dataset, random_split
import os
import os
import torch
import torch
import torchvision.transforms as transforms
import torchvision.transforms as transforms
import numpy as np
import pandas as pd
import os
import os
from PIL import Image
import matp... | code |
16154605/cell_19 | [
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_18.png",
"text_plain_output_3... | from PIL import Image
from torch.utils.data import DataLoader, Dataset, random_split
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import torch
import torch
import torch.nn as nn
impor... | code |
16154605/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16154605/cell_18 | [
"text_plain_output_1.png"
] | from PIL import Image
from torch.utils.data import DataLoader, Dataset, random_split
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import torch
import torch
import torch.nn as nn
impor... | code |
16154605/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from torch.utils.data import DataLoader, Dataset, random_split
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import torch
import torch
import torchvision.transforms as transforms
import torchvis... | code |
16154605/cell_15 | [
"text_plain_output_1.png"
] | from PIL import Image
from torch.utils.data import DataLoader, Dataset, random_split
import os
import os
import torch
import torch
import torchvision.transforms as transforms
import torchvision.transforms as transforms
import numpy as np
import pandas as pd
import os
import os
from PIL import Image
import matp... | code |
16154605/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
from torch.utils.data import DataLoader, Dataset, random_split
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import torch
import torch
import torch.nn as nn
impor... | code |
16154605/cell_17 | [
"text_plain_output_1.png"
] | print(net) | code |
16154605/cell_12 | [
"text_plain_output_1.png"
] | import torchvision.models as models
model_v = models.resnet18()
model_c = models.resnet18()
print(model_c) | code |
73059749/cell_30 | [
"image_output_1.png"
] | from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
checkpoint_callback = ModelCheckpoint(monitor='val_loss', filename='model-{epoch:02d}-{val_loss:.2f}', save_top_k=1, mode='min')
early_stop_callback = EarlyStopping(monitor='val_loss', patience... | code |
73059749/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from pathlib import Path
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from sklearn.model_selection import train_test_split
from torch import nn
from torch.utils.data import Dataset, DataLoader, random_split
from... | code |
73059749/cell_20 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader, random_split
from torchvision import transforms
import matplotlib.pyplot as plt
import pandas as pd
import pytorch_lightning as pl
RANDOM_STATE = 42
NUM_CLASSES =... | code |
73059749/cell_6 | [
"text_plain_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import wandb
from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
wandb_api = user_secrets.get_secret('wandb-key')
wandb.login(key=wandb_api) | code |
73059749/cell_40 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from sklearn import metrics
from sklearn.model_selection import train_test_split
from torch import nn
from torch.utils.data import Dataset, DataLoader, random_split
from torchmetrics.functional import accuracy, auroc
from torchmetrics.functional import mean_absolute... | code |
73059749/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from pathlib import Path
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from sklearn.model_selection import train_test_split
from torch import nn
from torch.utils.data import Dataset, DataLoader, random_split
from... | code |
73059749/cell_2 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install -q wandb | code |
73059749/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from kaggle_secrets import UserSecretsClient
from pytorch_lightning.loggers import WandbLogger
import wandb
from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
wandb_api = user_secrets.get_secret('wandb-key')
wandb.login(key=wandb_api)
wandb.init(project='count-the-green-boxes')
wandb_lo... | code |
73059749/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from PIL import Image
from kaggle_secrets import UserSecretsClient
from pathlib import Path
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.loggers import WandbLogger
from sklearn.model_selection import train_test_... | code |
73059749/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from pathlib import Path
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader, random_split
from torchvision import transforms
import matplotlib.pyplot as plt
import pandas as pd
import pytorch_lightning as pl
RANDOM_STATE = 42
NUM_CLASSES =... | code |
73059749/cell_10 | [
"text_plain_output_1.png"
] | import pytorch_lightning as pl
RANDOM_STATE = 42
NUM_CLASSES = 98
MAX_HOURS = 90
BATCH_SIZE = 128
pl.seed_everything(RANDOM_STATE) | code |
73059749/cell_37 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
from sklearn import metrics
from sklearn.model_selection import train_test_split
from torch import nn
from torch.utils.data import Dataset, DataLoader, random_split
from torchmetrics.functional import accuracy, auroc
from torchmetrics.functional import mean_absolute... | code |
72101516/cell_21 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot, plot
import pandas as pd
import plotly.graph_objs as go
medal = pd.read_excel('../input/2021-olympics-in-tokyo/Medals.xlsx', index_col=0)
athlete = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx', index_col=0)
gender = pd.read_excel('../input/2021-o... | code |
72101516/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
medal = pd.read_excel('../input/2021-olympics-in-tokyo/Medals.xlsx', index_col=0)
athlete = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx', index_col=0)
gender = pd.read_excel('../input/2021-olympics-in-tokyo/EntriesGender.xlsx', index_col=0)
team = pd.read_excel('../input/2021-olymp... | code |
72101516/cell_2 | [
"text_html_output_1.png"
] | !pip install openpyxl # Solving Kaggle error while importing the data | code |
72101516/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
medal = pd.read_excel('../input/2021-olympics-in-tokyo/Medals.xlsx', index_col=0)
athlete = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx', index_col=0)
gender = pd.read_excel('../input/2021-olympics-in-tokyo/EntriesGender.xlsx', index_col=0)
team = pd.read_excel('../input/2021-olymp... | code |
72101516/cell_1 | [
"text_plain_output_1.png"
] | import nltk
import os
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression
import seaborn as sns
sns.set()
from sklearn.cluster import KMeans
from mpl_toolkits.mplot3d import Axes3D
import tensorflo... | code |
72101516/cell_7 | [
"image_output_1.png"
] | import pandas as pd
medal = pd.read_excel('../input/2021-olympics-in-tokyo/Medals.xlsx', index_col=0)
athlete = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx', index_col=0)
gender = pd.read_excel('../input/2021-olympics-in-tokyo/EntriesGender.xlsx', index_col=0)
team = pd.read_excel('../input/2021-olymp... | code |
72101516/cell_8 | [
"text_html_output_2.png"
] | import pandas as pd
medal = pd.read_excel('../input/2021-olympics-in-tokyo/Medals.xlsx', index_col=0)
athlete = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx', index_col=0)
gender = pd.read_excel('../input/2021-olympics-in-tokyo/EntriesGender.xlsx', index_col=0)
team = pd.read_excel('../input/2021-olymp... | code |
72101516/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import nltk
import os
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression
import seaborn as sns
sns.set()
from sklearn.cluster import KMeans
fr... | code |
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