path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
129014537/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 |
129014537/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
RANDOM_STATE = 12... | code |
129014537/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
RANDOM_STATE = 12... | code |
129014537/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
RANDOM_STATE = 12... | code |
33102708/cell_4 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
def print_files():
for dirname,_,filname in os.walk('..../kaggle/input'):
for filename in filenames:
print(os.path.join(dirname,filename))
PATH=('../kaggle/input/mp/architecture/MPLA A... | code |
33102708/cell_2 | [
"text_plain_output_1.png"
] | request = 'request.get(http://raw.githubusercontent.com/CSSEGIS.SandData/COVID-19/master/cssc_COVID-19/confirmed.csv)'
request = 'download'
download = '....../input/http://raw.githubusercontent.com/CSSEGIS.SandData/COVID-19/master/cssc_COVID-19/confirmed.csv'
df = 'download'
print(df)
request = 'request.get(http://raw.... | code |
33102708/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.signal import find_peaks
import matplotlib.pyplot as plt
import cmath
import os.path
import scipy as integrate
import numpy as np
import pandas as pd
from pandas import DataFrame as df
import pywaffle
import joypy
from dateutil.parser import parse | code |
33102708/cell_3 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
def print_files():
for dirname, _, filname in os.walk('..../kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
PATH = '../kaggle/input/mp/architecture/MPLA Architecture_png'
i... | code |
33102708/cell_5 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
def print_files():
for dirname,_,filname in os.walk('..../kaggle/input'):
for filename in filenames:
print(os.path.join(dirname,filename))
PATH=('../ka... | code |
2025278/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
LR = LinearRegression()
y = Housetrain2.SalePrice
X = Housetrain2.drop('SalePrice', axis=1)
LR.fit(X, y) | code |
2025278/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Housetrain = pd.read_csv('../input/train.csv')
Housetrain.isnull().sum(axis=0)
Housetrain1 = Housetrain.dropna(axis=1, how='any')
Housetrain1 | code |
2025278/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
LR = LinearRegression()
y = Housetrain2.SalePrice
X = Housetrain2.drop('SalePrice', axis=1)
LR.fit(X, y)
LR.score(X, y)
LR | code |
2025278/cell_20 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Housetrain = pd.read_csv('../input/train.csv')
Housetrain.isnull().sum(axis=0)
Housetrain1 = Housetrain.dropna(axis=1, how='any')
y = Housetrain2.SalePrice
X = Housetrain2.drop... | code |
2025278/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Housetrain = pd.read_csv('../input/train.csv')
Housetest = pd.read_csv('../input/test.csv')
Housetest.head() | code |
2025278/cell_2 | [
"text_html_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 |
2025278/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Housetrain = pd.read_csv('../input/train.csv')
Housetrain.isnull().sum(axis=0) | code |
2025278/cell_16 | [
"text_plain_output_1.png"
] | y = Housetrain2.SalePrice
X = Housetrain2.drop('SalePrice', axis=1) | code |
2025278/cell_22 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
LR = LinearRegression()
y = Housetrain2.SalePrice
X = Housetrain2.drop('SalePrice', axis=1)
LR.fit(X, y)
LR.score(X, y) | code |
2025278/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Housetrain = pd.read_csv('../input/train.csv')
Housetrain.head() | code |
34129676/cell_21 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.models import Sequential
img_width, img_height = (204, 204)
batch_size = 64
num_classes = 2
input_shape = (img_width, img_height, 3)
EPOCHS = 10
model = Sequential()
model.add(Conv2D(32, kernel_size... | code |
34129676/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data_dir = '/kaggle/input/lego-vs-unknown-cropped/VIA_dataset_cropped/train/'
data_dir_train_lego = '/kaggle/inpu... | code |
34129676/cell_34 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selectio... | code |
34129676/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
data_dir = '/kaggle/input/lego-vs-unknown-cropped/VIA_dataset_cropped/train/'
data_dir_train_lego = '/kaggle/input//lego-vs-unknown-cropped/VIA_dataset_cropped/train/LEGO'
test_lego = ['LEGO/' + f for f in os.listdir(data_dir_train_lego)]
data_dir_test_Unknown = '/kag... | code |
34129676/cell_39 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selectio... | code |
34129676/cell_11 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data_dir = '/kaggle/input/lego-vs-unknown-cropped/VIA_dataset_cropped/train/'
data_dir_train_lego = '/kaggle/inpu... | code |
34129676/cell_7 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data_dir = '/kaggle/input/lego-vs-unknown-cropped/VIA_dataset_cropped/train/'
data_dir_train_lego = '/kaggle/input//lego-vs-unknown-cropped/VIA_dataset_cropped/train/L... | code |
34129676/cell_18 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data_dir = '/kaggle/input/lego-vs-unknown-cropped/VIA_dataset_cropped/train/'
data_dir_train_lego = '/kaggle/inpu... | code |
34129676/cell_15 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data_dir = '/kaggle/input/lego-vs-unknown-cropped/VIA_dataset_cropped/train/'
data_dir_train_lego = '/kaggle/inpu... | code |
34129676/cell_38 | [
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selectio... | code |
34129676/cell_3 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.layers import Conv2D, MaxPooling2D
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from sklearn.model_selection imp... | code |
34129676/cell_17 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data_dir = '/kaggle/input/lego-vs-unknown-cropped/VIA_dataset_cropped/train/'
data_dir_train_lego = '/kaggle/inpu... | code |
34129676/cell_31 | [
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selectio... | code |
34129676/cell_14 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data_dir = '/kaggle/input/lego-vs-unknown-cropped/VIA_dataset_cropped/train/'
data_dir_train_lego = '/kaggle/inpu... | code |
34129676/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
data_dir = '/kaggle/input/lego-vs-unknown-cropped/VIA_dataset_cropped/train/'
data_dir_train_lego = '/kaggle/inpu... | code |
34129676/cell_27 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selectio... | code |
34129676/cell_37 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, load_img
from sklearn.model_selectio... | code |
129013037/cell_42 | [
"image_output_1.png"
] | from mlxtend.plotting import plot_decision_regions
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas a... | code |
129013037/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.... | code |
129013037/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.isnull().sum()
Q1 = train['Protein_(g)'].quantile(0.25)
Q3 = train['Protein_(g)'].quantile(0... | code |
129013037/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
from sklearn.inspection import DecisionBoundaryDisplay
disp = DecisionBoundaryDisplay.from_estimator(clf, X_test, response_method=... | code |
129013037/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.isnull().sum()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
data =... | code |
129013037/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.isnull().sum()
Q1 = train['Protein_(g)'].quantile(0.25)
Q3 = train['Pro... | code |
129013037/cell_30 | [
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = KNeighborsClassifier(2)
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129013037/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = KNeighborsClassifier(2)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
scores = []
for k in ... | code |
129013037/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.isnull().sum()
import matplotlib.pyplot as plt
import numpy as np
impor... | code |
129013037/cell_40 | [
"text_plain_output_1.png"
] | from mlxtend.plotting import plot_decision_regions
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
fr... | code |
129013037/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = KNeighborsClassifier(2)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129013037/cell_48 | [
"image_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
params = {'n_neighbors': range(1, 30), 'metric': ['l1', 'l2']}
best_clf = GridSearchCV(estimator=KNeighborsClassifier(), param_grid=params)
best_clf.fit(X_train, y_train)
best_clf.score(X_test, y_test) | code |
129013037/cell_41 | [
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = KNeighborsClassifier(2)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
scores = []
for k in ... | code |
129013037/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.describe() | code |
129013037/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
params = {'n_neighbors': range(1, 30), 'metric': ['l1', 'l2']}
best_clf = GridSearchCV(estimator=KNeighborsClassifier(), param_grid=params)
best_clf.fit(X_train, y_tr... | code |
129013037/cell_1 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train | code |
129013037/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.isnull().sum()
Q1 = train['Protein_(g)'].quantile(0.25)
Q3 = train['Protein_(g)'].quantile(0.75)
IQR = Q3 - Q1
train = train[(train['Protein... | code |
129013037/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
params = {'n_neighbors': range(1, 30), 'metric': ['l1', 'l2']}
best_clf = GridSearchCV(estimator=KNeighborsClassifier(), param_grid=params)
best_clf.fit(X_train, y_train)
best_clf.score(X_test, y_test)
best_clf.best_... | code |
129013037/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.... | code |
129013037/cell_51 | [
"image_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = KNeighborsClassifier(2)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf = KNeighborsClassifier()
clf.fit(X_train, y_trai... | code |
129013037/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from mlxtend.plotting import plot_decision_regions
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
fr... | code |
129013037/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.isnull().sum()
Q1 = train['Protein_(g)'].quantile(0.25)
Q3 = train['Protein_(g)'].quantile(0... | code |
129013037/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.isnull().sum()
Q1 = train['Protein_(g)'].quantile(0.25)
Q3 = train['Pro... | code |
129013037/cell_38 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.... | code |
129013037/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.isnull().sum() | code |
129013037/cell_31 | [
"image_output_1.png"
] | from mlxtend.plotting import plot_decision_regions
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
fr... | code |
129013037/cell_22 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()
clf.fit(X_train, y_train)
clf.score(X_test, y_test) | code |
129013037/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.xlsx')
train
train.isnull().sum()
Q1 = train['Protein_(g)'].quantile(0.25)
Q3 = train['Protein_(g)'].quantile(0... | code |
129013037/cell_36 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_excel('/kaggle/input/products/ABBREV_with_CLASS.... | code |
129024934/cell_21 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/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)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(d)
ser1 = pd.Series([1, 2, 3, 4... | code |
129024934/cell_25 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_4 | [
"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)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data) | code |
129024934/cell_57 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_56 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_30 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_33 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_44 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_55 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_6 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(d) | code |
129024934/cell_39 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_26 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_48 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_41 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_54 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_11 | [
"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)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(d)
ser1 = pd.Series([1, 2, 3, 4... | code |
129024934/cell_19 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_50 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_7 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
d | code |
129024934/cell_45 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_18 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_32 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_51 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_59 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_58 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_28 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_16 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_38 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_47 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_17 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_35 | [
"text_html_output_1.png"
] | states = 'CA NY WY OR'.split()
states | code |
129024934/cell_43 | [
"text_html_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_31 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
129024934/cell_46 | [
"text_plain_output_1.png"
] | from numpy.random import randn
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = ['a', 'b', 'c']
my_data = [10, 20, 30]
arr = np.array(my_data)
d = {'a': 10, 'b': 20, 'c': 30}
pd.Series(data=my_data)
pd.Series(data=my_data, index=labels)
pd.Series(... | code |
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