path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2017333/cell_17 | [
"text_html_output_1.png"
] | from sklearn import svm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['Sex'] = train['Sex'].apply(lambda x: 1 if x == 'male' else 0)
train['Age'] = train['Age'].fill... | code |
2017333/cell_14 | [
"text_html_output_1.png"
] | from sklearn import svm
from sklearn import svm
clf = svm.SVC()
clf.fit(X_train, Y_train)
clf.score(X_train, Y_train)
clf.fit(X_test, Y_test) | code |
2017333/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
train['Sex'] = train['Sex'].apply(lambda x: 1 if x == 'male' else 0)
train = train[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']]
X ... | code |
2017333/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn import svm
clf = svm.SVC()
clf.fit(X_train, Y_train) | code |
2017333/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('../input/train.csv')
train['Sex'] = train['Sex'].apply(lambda x: 1 if x == 'male' else 0)
train.head() | code |
2017393/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv'... | code |
2017393/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import missingno as msno # check for missing values
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv... | code |
2017393/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_29 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv'... | code |
2017393/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_28 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
2017393/cell_10 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
air_reserve = pd.read_csv('../input/air_reserve.csv')
hpg_reserve = pd.read_csv('../input/hpg_reserve.csv')
visits = pd.read_csv('../input/air_visit_data.csv')
dates = pd.read_csv('... | code |
74054476/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
plt.scatter(X_train.iloc[:20], y_train.iloc[:20], color='Red')
plt.title('Training Data')
plt.xlabel('x')
plt.ylabel('y')
plt.show() | code |
74054476/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
plt.scatter(X_test, y_test, color='Red')
plt.plot(X_t... | code |
74054476/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 |
74054476/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
plt.scatter(X_train, y_train, color='Red')
plt.plot(X... | code |
74054476/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
df_test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
df_train.head() | code |
74054476/cell_12 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train) | code |
74054476/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/random-linear-regression/train.csv')
df_test = pd.read_csv('/kaggle/input/random-linear-regression/test.csv')
print(df_train.isnull().sum())
print(df_test.isnull().sum())
df_train.dropna(inplace=True)
df_test.... | code |
49122760/cell_21 | [
"text_plain_output_1.png"
] | from keras import layers
from keras import models
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tensorflow as tf
proj_dir = '../input/cassava-... | code |
49122760/cell_20 | [
"text_plain_output_1.png"
] | from keras import layers
from keras import models
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tensorflow as tf
proj_dir = '../input/cassava-leaf-disease-classification/train_ima... | code |
49122760/cell_19 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
proj_dir = '../input/cassava-leaf-disease-classification/train_images/'
train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
train.loc[:, 'labe... | code |
49122760/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
proj_dir = '../input/cassava-leaf-disease-classification/train_images/'
train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
train.loc[:, 'labe... | code |
49122760/cell_5 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
conv_base = tf.keras.models.load_model('../input/pretrained-models/vgg16')
conv_base.summary() | code |
33122543/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
name = 'Raghu'
from datetime imp... | code |
33122543/cell_6 | [
"text_plain_output_1.png"
] | from datetime import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
name = 'Raghu'
from datetime import datetime
started_at = datetime.now().strftime('%H:%M:%S')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
def ... | code |
33122543/cell_2 | [
"text_plain_output_1.png"
] | from datetime import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
name = 'Raghu'
from datetime import datetime
started_at = datetime.now().strftime('%H:%M:%S')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
print... | code |
33122543/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 |
33122543/cell_8 | [
"text_plain_output_1.png"
] | from datetime import datetime
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
name = 'Raghu'
from datetime import datetime
started_at = datetime.now().strftime('%H:%M:%S')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data... | code |
33122543/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from datetime import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
name = 'Raghu'
from datetime import datetime
started_at = datetime.now().strftime('%H:%M:%S')
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
def ... | code |
122263777/cell_6 | [
"text_html_output_1.png"
] | !pip install recipe-scrapers # Insalling scraping lib | code |
122263777/cell_19 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
from recipe_scrapers import scrape_me
import pandas as pd
import re
URLs = ['https://www.allrecipes.com/recipes/721/world-cuisine/european/french/', 'https://www.allrecipes.com/recipes/16126/world-cuisine/european/french/french-bread/', 'https://www.allrecipes.com/recipes/17138/world-c... | code |
122263777/cell_12 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
URLs = ['https://www.allrecipes.com/recipes/721/world-cuisine/european/french/', 'https://www.allrecipes.com/recipes/16126/world-cuisine/european/french/french-bread/', 'https://www.allrecipes.com/recipes/17138/world-cuisine/european/french/main-dishes/', 'https://www.allrecipes.com/recip... | code |
72081028/cell_13 | [
"text_plain_output_1.png"
] | from numpy import array, argmax, random, take
import matplotlib.pyplot as plt
import pandas as pd
def read_text(filename):
file = open(filename, mode='rt', encoding='utf-8')
text = file.read()
file.close()
return text
def to_lines(text):
sents = text.strip().split('\n')
sents = [i.split('\t... | code |
72081028/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from numpy import array, argmax, random, take
def read_text(filename):
file = open(filename, mode='rt', encoding='utf-8')
text = file.read()
file.close()
return text
def to_lines(text):
sents = text.strip().split('\n')
sents = [i.split('\t') for i in sents]
return sents
data = read_text(... | code |
72081028/cell_34 | [
"image_output_1.png"
] | from numpy import array, argmax, random, take
from keras import optimizers
from keras.callbacks import ModelCheckpoint
from keras.layers import Dense, LSTM, Embedding, Bidirectional, RepeatVector, TimeDistributed
from keras.models import Sequential
from keras.models import load_model
from keras.preprocessing.seq... | code |
72081028/cell_30 | [
"text_plain_output_1.png"
] | from numpy import array, argmax, random, take
from keras import optimizers
from keras.callbacks import ModelCheckpoint
from keras.layers import Dense, LSTM, Embedding, Bidirectional, RepeatVector, TimeDistributed
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from kera... | code |
72081028/cell_32 | [
"text_plain_output_1.png"
] | from numpy import array, argmax, random, take
from keras import optimizers
from keras.callbacks import ModelCheckpoint
from keras.layers import Dense, LSTM, Embedding, Bidirectional, RepeatVector, TimeDistributed
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from kera... | code |
72081028/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from numpy import array, argmax, random, take
from keras.preprocessing.text import Tokenizer
def read_text(filename):
file = open(filename, mode='rt', encoding='utf-8')
text = file.read()
file.close()
return text
def to_lines(text):
sents = text.strip().split('\n')
sents = [i.split('\t') for... | code |
72081028/cell_17 | [
"text_plain_output_1.png"
] | from numpy import array, argmax, random, take
from keras.preprocessing.text import Tokenizer
def read_text(filename):
file = open(filename, mode='rt', encoding='utf-8')
text = file.read()
file.close()
return text
def to_lines(text):
sents = text.strip().split('\n')
sents = [i.split('\t') for... | code |
2002850/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.sparse as sparse
books = pd.read_csv('../input/Books.csv', encoding='ISO 8859-1')
users = pd.read_csv('../input/Users.csv', encoding='ISO 8859-1')
book_ratings = pd.read_csv('../input/BookRatings.c... | code |
2002850/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
books = pd.read_csv('../input/Books.csv', encoding='ISO 8859-1')
users = pd.read_csv('../input/Users.csv', encoding='ISO 8859-1')
book_ratings = pd.read_csv('../input/BookRatings.csv', encoding='ISO 8859-1', low_memory=False)
book_ratings['UserID'... | code |
2002850/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 |
2002850/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.sparse as sparse
books = pd.read_csv('../input/Books.csv', encoding='ISO 8859-1')
users = pd.read_csv('../input/Users.csv', encoding='ISO 8859-1')
book_ratings = pd.read_csv('../input/BookRatings.c... | code |
88100945/cell_4 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
TRAIN_PATH = '../input/tabular-playground-series-feb-2022/train.csv'
TEST_PATH = '../input/tabular-playground-series-feb-2022/test.csv'
PSEUDO_PATH = '../input/automl-tps-02-22-flaml-prediction/submission.csv'
ID = 'row_id'
TARGET = 'target'
train = pd.read_csv(TRAIN_PATH)
print('train size = ', l... | code |
88100945/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
TRAIN_PATH = '../input/tabular-playground-series-feb-2022/train.csv'
TEST_PATH = '../input/tabular-playground-series-feb-2022/test.csv'
PSEUDO_PATH = '../input/automl-tps-02-22-flaml-prediction/submission.csv'
ID = 'row_id'
TARGET = 'target'
train = pd.read_csv(TRAIN_PATH)
test = pd.read_csv(TEST... | code |
88100945/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
TRAIN_PATH = '../input/tabular-playground-series-feb-2022/train.csv'
TEST_PATH = '../input/tabular-playground-series-feb-2022/test.csv'
PSEUDO_PATH = '../input/automl-tps-02-22-flaml-prediction/submission.csv'
ID = 'row_id'
TARGET = 'target'
train = pd.read_csv(TRAIN_PATH)
test = pd.read_csv(TEST... | code |
18161210/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
y_train = train.y
train = train.iloc[:, 1:15]
test = test.iloc[:, 1:15]
des = pd.concat((train, test))
des.head() | code |
18161210/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print('train:', train.shape, 'test:', test.shape, sep='\n') | code |
18161210/cell_20 | [
"text_html_output_1.png"
] | from sklearn.model_selection import cross_val_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
y_train = train.y
train = train.iloc[:, 1:15]
test = test.iloc[:, 1:15]
des = pd.concat((train... | code |
18161210/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
y_train = train.y
train = train.iloc[:, 1:15]
test = test.iloc[:, 1:15]
des = pd.concat((train, test))
des = pd.get_dummies(des)
X_train = des.iloc[0:32967, :]
X_... | code |
18161210/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18161210/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
y_train = train.y
train = train.iloc[:, 1:15]
test = test.iloc[:, 1:15]
des =... | code |
18161210/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
18161210/cell_17 | [
"text_html_output_1.png"
] | from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
y_train = train.y
train = train.iloc[:, 1:15]
test = test.iloc[:, 1:15]
des =... | code |
18161210/cell_22 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
y_train = train.y
train = train.iloc[:, 1:15]
test = test.iloc[:, 1:15]
des = pd.concat((train, test))
des = pd.get_dummies(des)
X_train = des.ilo... | code |
88101554/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
tempdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/... | code |
88101554/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape
tempdf = pd.read_pickle('../input/recommendationsyetem... | code |
88101554/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape
tempdf = pd.read_pickle('../input/recommendationsyetem... | code |
88101554/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.head(2) | code |
88101554/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape
tempdf = pd.read_pickle('../input/recommendationsyetem... | code |
88101554/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 tensorflow.keras.layers import Activation
from tensorflow.keras import backend as K
from sklearn.model_selection import train_test_split
import tensorflow.keras as keras
import tensorflow as tf
print(tf.__version__)
from t... | code |
88101554/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
tempdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/... | code |
88101554/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape | code |
88101554/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape
tempdf = pd.read_pickle('../input/recommendationsyetem... | code |
88101554/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
tempdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/... | code |
88101554/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape
tempdf = pd.read_pickle('../input/recommendationsyetem... | code |
88101554/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape
tempdf = pd.read_pickle('../input/recommendationsyetem... | code |
88101554/cell_31 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_coun... | code |
88101554/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape
tempdf = pd.read_pickle('../input/recommendationsyetem... | code |
88101554/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape
tempdf = pd.read_pickle('../input/recommendationsyetem... | code |
88101554/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
restdf.shape
tempdf = pd.read_pickle('../input/recommendationsyetem... | code |
88101554/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
restdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/restdf.pkl')
restdf = restdf.drop(columns=['address', 'city', 'state', 'postal_code', 'latitude', 'longitude', 'review_count', 'is_open', 'attributes', 'hours'])
tempdf = pd.read_pickle('../input/recommendationsyetemforrestraunts/... | code |
89143029/cell_4 | [
"text_html_output_1.png"
] | from pandas import read_csv
from pandas import read_csv
train = read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
sample = read_csv('/kaggle/input/tabular-playground-series-mar-2022/sample_submission.csv')
train.memory_us... | code |
89143029/cell_3 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from pandas import read_csv
from pandas import read_csv
train = read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
sample = read_csv('/kaggle/input/tabular-playground-series-mar-2022/sample_submission.csv')
train.memory_us... | code |
89143029/cell_5 | [
"text_html_output_1.png"
] | from pandas import read_csv
from pandas import read_csv
train = read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
sample = read_csv('/kaggle/input/tabular-playground-series-mar-2022/sample_submission.csv')
train.memory_us... | code |
128009742/cell_13 | [
"text_html_output_1.png"
] | """plt.figure(figsize = (8, 4), dpi = 300)
sns.barplot(data = mae_list.reindex((mae_list).mean().sort_values().index, axis = 1), palette = 'viridis', orient = 'h')
plt.title('MAE Comparison', weight = 'bold', size = 20)
plt.show()
""" | code |
128009742/cell_23 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error, roc_auc_score, roc_curve
from sklearn.model_selection import RepeatedStratifiedKFold,... | code |
128009742/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor
... | code |
128009742/cell_16 | [
"text_plain_output_1.png"
] | """for (label, model) in models:
mae_list[label] = cross_val_score(
Pipeline([('fe1', FE1()), (label, model)]),
label = label
)
""" | code |
128009742/cell_17 | [
"text_plain_output_1.png"
] | """plt.figure(figsize = (8, 4), dpi = 300)
sns.barplot(data = mae_list.reindex((mae_list).mean().sort_values().index, axis = 1), palette = 'viridis', orient = 'h')
plt.title('MAE Comparison', weight = 'bold', size = 20)
plt.show()""" | code |
128009742/cell_22 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error, roc_auc_score, roc_curve
from sklearn.model_selection import RepeatedStratifiedKFold,... | code |
128009742/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor
... | code |
130015002/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df.info() | code |
130015002/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns | code |
130015002/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df = train_df.drop(['AveRooms'], ... | code |
130015002/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
test_df = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
train_df.shape
train_df.columns
train_df.isnull().sum()
train_df = train_df.drop(['AveRooms'], ... | code |
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