path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
1009871/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('train.csv')
train.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
train.head() | code |
105176805/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
import openpyxl
import yfinance as yf
import datetime
import time
import requests
import io
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90128404/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import matp... | code |
90128404/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevs... | code |
90128404/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data... | code |
90128404/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
data.head() | code |
90128404/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import nump... | code |
90128404/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty(... | code |
90128404/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
import pandas as pd
data = pd.read_csv('../input/hardfakevsrealfaces/data.csv')
height, width = (128, 128)
X = np.empty((data.shape[0], height, width, 3))
for i in range(data.shape[0]):
img = load_img('../input/hardfakevs... | code |
90128404/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import nump... | code |
90128404/cell_10 | [
"text_html_output_1.png"
] | from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Conv2D(64, kernel_size=3, activation='relu', input_shape=(height, width, 3)))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(32, kernel_size=3, ac... | code |
90128404/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.utils import to_categorical
import matp... | code |
1003217/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
print('Skewness: %f' % train['SalePrice'].skew())
print('Kurtosis: %f' % train['SalePrice'].kurt()) | code |
1003217/cell_33 | [
"text_html_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1003217/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePr... | code |
1003217/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe() | code |
1003217/cell_28 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePr... | code |
1003217/cell_15 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
sns.distplot(train['SalePrice']) | code |
1003217/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePr... | code |
1003217/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrL... | code |
1003217/cell_31 | [
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], te... | code |
1003217/cell_14 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='... | code |
1003217/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train.head() | code |
1003217/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePr... | code |
1003217/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe() | code |
1009496/cell_9 | [
"image_output_1.png"
] | from glob import glob
import cv2
import matplotlib.pylab as plt
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s... | code |
1009496/cell_2 | [
"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 |
1009496/cell_8 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files ... | code |
1009496/cell_3 | [
"text_plain_output_1.png"
] | from glob import glob
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files = glob(os.pa... | code |
1009496/cell_5 | [
"text_plain_output_1.png"
] | from glob import glob
import cv2
import numpy as np # linear algebra
import os
import os
from glob import glob
TRAIN_DATA = '../input/train'
type_1_files = glob(os.path.join(TRAIN_DATA, 'Type_1', '*.jpg'))
type_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, 'Type_1')) + 1:-4] for s in type_1_files])
type_2_files ... | code |
50230145/cell_30 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
import numpy as np
import pandas as pd
import requests
titles = []
years = []
urls = []
ranks = [i for i in range(1, 1001)]
def JazzStandardsTable(url):
r = requests.get(url)
soup = BeautifulSoup(r.content, 'html.parser')
for i in range(25, 125):
titles.append(soup... | code |
50230145/cell_14 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
titles = []
years = []
urls = []
ranks = [i for i in range(1, 1001)]
def JazzStandardsTable(url):
r = requests.get(url)
soup = BeautifulSoup(r.content, 'html.parser')
for i in range(25, 125):
titles.append(soup.find_all('a')[i].ge... | code |
73079773/cell_30 | [
"image_output_11.png",
"image_output_24.png",
"image_output_46.png",
"image_output_25.png",
"image_output_47.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13... | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Mo... | code |
73079773/cell_2 | [
"image_output_2.png",
"image_output_1.png"
] | code | |
73079773/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Mo... | code |
73079773/cell_16 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Mo... | code |
73079773/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Mo... | code |
73079773/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.backend import clear_session
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import SGD
from tens... | code |
73079773/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import zipfile
import zipfile
input_path = '/kaggle/input/dogs-vs-cats'
work_path = '/kaggle/working/data'
train_path = os.path.join(input_path, 'train.zip')
test_path = os.path.join(input_path, 'test1.zip')
with zipfile.ZipFile(... | code |
73079773/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.backend import clear_session
from tensorflow.keras.layers import Convolution2D,MaxPooling2D, Dense, Flatten, InputLayer
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.preprocessing.image import ImageDataGenera... | code |
128008433/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv... | code |
128008433/cell_13 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train,... | code |
128008433/cell_23 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClass... | code |
128008433/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncode... | code |
128008433/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncode... | code |
128008433/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv... | code |
128008433/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
data | code |
128008433/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv... | code |
128008433/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
da... | code |
128008433/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape | code |
128008433/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
da... | code |
128008433/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, in... | code |
128008433/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncode... | code |
128008433/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifie... | code |
128008433/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape
data = data.dropna()
data.shape
X = data.drop('Loan_Status', axis=1)
y = data['Loan_Status']
X_train, X_test, y_train,... | code |
128008433/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.drop('Loan_ID', axis=1, inplace=True)
data.shape | code |
128020267/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data = data.drop({'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Time', 'Summary'}, axis=1)
data.Score = ['positive' if i >= 4 else 'negative' for i in data.Score]
data.he... | code |
128020267/cell_2 | [
"text_html_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 |
128020267/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data.head() | code |
128020267/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data.describe() | code |
128020267/cell_16 | [
"text_html_output_1.png"
] | !pip install gensim pandas
import pandas as pd
import gensim | code |
128020267/cell_17 | [
"text_html_output_1.png"
] | import gensim
import pandas as pd
import pandas as pd
import re
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data = data.drop({'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Time', 'Summary'}, axis=1)
data.Score = ['positive' if i >=... | code |
128020267/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import re
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data = data.drop({'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Time', 'Summary'}, axis=1)
data.Score = ['positive' if i >= 4 else 'negative' for i in data.Sco... | code |
128020267/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/amazon-fine-food-reviews/Reviews.csv', index_col='Id')
data = data.drop({'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator', 'HelpfulnessDenominator', 'Time', 'Summary'}, axis=1)
data.head() | code |
129012188/cell_20 | [
"text_plain_output_1.png"
] | from copy import deepcopy
from copy import deepcopy
from datasets import list_metrics,load_metric
from random import randint
from random import randint,shuffle
from sentence_transformers import SentenceTransformer, util
from sklearn.metrics import confusion_matrix
import numpy as np
import pandas as pd
import ... | code |
129012188/cell_6 | [
"text_plain_output_100.png",
"text_plain_output_201.png",
"text_plain_output_261.png",
"text_plain_output_84.png",
"text_plain_output_322.png",
"text_plain_output_205.png",
"text_plain_output_271.png",
"text_plain_output_56.png",
"text_plain_output_158.png",
"text_plain_output_223.png",
"text_pl... | !pip install -U sentence-transformers
!pip install openpyxl | code |
50237666/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import os
def mkdir(p):
if not os.path.exists(p):
os.mkdir(p)
def link(src, dst):
if not os.path.exists(dst):
os.symlink(src, dst, target_is_directory=True)
os.mkdir('../input/fruits/fruits-360/smallImages')
classes = ['Bana... | code |
50237666/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import os
def mkdir(p):
if not os.path.exists(p):
os.mkdir(p)
def link(src, dst):
if not os.path.exists(dst):
os.symlink(src, dst, target_is_directory=True)
os.mkdir('../input/fruits/fruits-360/smallImages')
classes = ['Bana... | code |
50237666/cell_5 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from glob import glob
from keras.applications.vgg16 import VGG16
from keras.layers import Input, Lambda, Dense, Flatten
from keras.models import Model
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix
from utils import plot... | code |
128006002/cell_42 | [
"text_html_output_2.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_score
import xgboost as xgb
def build_random_forest(x_train, y_train, x_test, y_test... | code |
128006002/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)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('e... | code |
128006002/cell_4 | [
"text_html_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)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data | code |
128006002/cell_6 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"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)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
diferencias = all_data['employee_residence'].compare(all_data['company_location'])
print(diferencias) | code |
128006002/cell_29 | [
"text_html_output_1.png"
] | from matplotlib import pyplot
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.metrics import r2_score
from sklearn.tree import DecisionTreeRegressor
import xgboost as xgb
from sklearn.tr... | code |
128006002/cell_26 | [
"text_html_output_1.png"
] | from matplotlib import pyplot
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.tree import... | code |
128006002/cell_41 | [
"text_html_output_1.png"
] | print(x_train.shape, x_test.shape, y_train.shape, y_test.shape) | code |
128006002/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 |
128006002/cell_18 | [
"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)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('e... | code |
128006002/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salar... | code |
128006002/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)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('e... | code |
128006002/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)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('e... | code |
128006002/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)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('e... | code |
128006002/cell_38 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from collections import Counter
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_da... | code |
128006002/cell_35 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from collections import Counter
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_da... | code |
128006002/cell_31 | [
"text_html_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)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('e... | code |
128006002/cell_24 | [
"text_plain_output_1.png"
] | print(x_train.shape, x_test.shape, y_train.shape, y_test.shape) | code |
128006002/cell_22 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salar... | code |
128006002/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import missingno as msno
import missingno as msno
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_... | code |
128006002/cell_12 | [
"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)
all_data = pd.read_csv('/kaggle/input/data-science-salaries-2023/ds_salaries.csv')
all_data = all_data.drop('salary', axis=1)
all_data = all_data.drop('salary_currency', axis=1)
all_data = all_data.drop('e... | code |
32068608/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Death... | code |
32068608/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Death... | code |
32068608/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.info() | code |
32068608/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data_tests | code |
32068608/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32068608/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Death... | code |
32068608/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Death... | code |
32068608/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Death... | code |
32068608/cell_15 | [
"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)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', '... | code |
32068608/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', '... | code |
32068608/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_tests = pd.read_csv('../input/covid19-in-turkey/test_numbers.csv')
data = pd.read_csv('../input/covid19-in-turkey/covid_19_data_tr.csv')
data.rename(columns={'Country/Region': 'Ülke', 'Last_Update': 'Tarih', 'Confirmed': 'Vaka_Sayisi', 'Death... | code |
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