path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128048094/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
model_logistic = LogisticRegression()
model_logistic.fit(X_train, y_train)
print('Training accuracy: ', model_logistic.score(X_train, y_train))
print('Testing accuracy: ', model_logistic.score(X_test, y_test)) | code |
128048094/cell_40 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
impor... | code |
128048094/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
model_knn = KNeighborsClassifier()
model_knn.fit(X_train, y_train)
model_tree = DecisionTree... | code |
128048094/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
impor... | code |
128048094/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data = data.dropna()
data_encoded = pd.get_dummies(data, columns=['Property_Area'])
data_encoded.head(10) | code |
128048094/cell_18 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
model_tree = DecisionTreeClassifier()
model_tree.fit(X_train, y_train)
print('Training accuracy: ', model_tree.score(X_train, y_train))
print('Testing accuracy: ', model_tree.score(X_test, y_test)) | code |
128048094/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
model... | code |
128048094/cell_16 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
model_knn = KNeighborsClassifier()
model_knn.fit(X_train, y_train)
print('Training accuracy: ', model_knn.score(X_train, y_train))
print('Testing accuracy: ', model_knn.score(X_test, y_test)) | code |
128048094/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
impor... | code |
128048094/cell_17 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.svm import LinearSVC
model_svm = LinearSVC()
model_svm.fit(X_train, y_train)
print('Training accuracy: ', model_svm.score(X_train, y_train))
print('Testing accuracy: ', model_svm.score(X_test, y_test)) | code |
128048094/cell_35 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
impor... | code |
128048094/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
impor... | code |
128048094/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
impor... | code |
128048094/cell_24 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
model_knn = KNeighborsClassifier()
model_knn.fit(X_train, y_train)
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, clas... | code |
128048094/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
model_knn = KNeighborsClassifier()
model_knn.fit(X_train, ... | code |
128048094/cell_37 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
impor... | code |
128048094/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/loan-data-set/loan_data_set.csv')
data.head(10) | code |
128048094/cell_36 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
impor... | code |
17098311/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_4 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_6 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17098311/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas_profiling as pp
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(colu... | code |
17098311/cell_15 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
17098311/cell_5 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error,mean_absolute_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def Model_Comparision_Train_Test(AllModels, x_train, y_train, x_test, y_test):
return_df = pd.DataFrame(columns=['Model', 'MSE', 'RMSE', 'M... | code |
50237900/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
NUM_FOLDS = 5
import pandas as pd
from tqdm import tqdm
data_dir = '../input/ranzcr-clip-catheter-line-classification/'
target_col = ['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal', 'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged', 'NGT - Normal', 'CVC - Abnormal', 'CVC - Bor... | code |
50237900/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
NUM_FOLDS = 5
import pandas as pd
from tqdm import tqdm
data_dir = '../input/ranzcr-clip-catheter-line-classification/'
target_col = ['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal', 'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged', 'NGT - Normal', 'CVC - Abnormal', 'CVC - Bor... | code |
50237900/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
NUM_FOLDS = 5
import pandas as pd
from tqdm import tqdm
data_dir = '../input/ranzcr-clip-catheter-line-classification/'
target_col = ['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal', 'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged', 'NGT - Normal', 'CVC - Abnormal', 'CVC - Bor... | code |
50237900/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import pandas as pd
NUM_FOLDS = 5
import pandas as pd
from tqdm import tqdm
data_dir = '../input/ranzcr-clip-catheter-line-classification/'
target_col = ['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal', 'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged', 'NGT - Normal', 'CVC ... | code |
50237900/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
NUM_FOLDS = 5
import pandas as pd
from tqdm import tqdm
data_dir = '../input/ranzcr-clip-catheter-line-classification/'
target_col = ['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal', 'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged', 'NGT - Normal', 'CVC - Abnormal', 'CVC - Bor... | code |
50237900/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
NUM_FOLDS = 5
import pandas as pd
from tqdm import tqdm
data_dir = '../input/ranzcr-clip-catheter-line-classification/'
target_col = ['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal', 'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged', 'NGT - Normal', 'CVC - Abnormal', 'CVC - Bor... | code |
50237900/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
NUM_FOLDS = 5
import pandas as pd
from tqdm import tqdm
data_dir = '../input/ranzcr-clip-catheter-line-classification/'
target_col = ['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal', 'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged', 'NGT - Normal', 'CVC - Abnormal', 'CVC - Bor... | code |
17109703/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import os
from random import shuffle
from tqdm import tqdm
from PIL import Image
import warnin... | code |
17109703/cell_4 | [
"image_output_1.png"
] | from PIL import Image
Image.open('../input/training_set/training_set/cats/cat.1.jpg')
Image.open('../input/training_set/training_set/dogs/dog.1.jpg') | code |
17109703/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import os
from random import shuffle
from tqdm import tqdm
from PIL import Image
import warnings
warnings.filterwarnings('ignore')
import os
print(os.listdir('../input')) | code |
17109703/cell_8 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import os
from random import shuffle
from tqdm import tqdm
from PIL import Image
import warnin... | code |
17109703/cell_3 | [
"image_output_1.png"
] | from PIL import Image
Image.open('../input/training_set/training_set/cats/cat.1.jpg') | code |
17109703/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import os
from random import shuffle
from tqdm import tqdm
from PIL import Image
import warnin... | code |
32068734/cell_23 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import csv
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.optimize as optim
train_data = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_data = train_data.replace(np.nan, '', regex=True)
test_data ... | code |
32068734/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 |
32068734/cell_18 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_data = train_data.replace(np.nan, '', regex=True)
test_data = pd.read_csv('../input/covid19-global-forec... | code |
32068734/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_data = train_data.replace(np.nan, '', regex=True)
test_data = pd.read_csv('../input/covid19-global-forec... | code |
32068734/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_data = train_data.replace(np.nan, '', regex=True)
test_data = pd.read_csv('../input/covid19-global-forec... | code |
32068734/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy import stats
import csv
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.optimize as optim
train_data = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_data = train_data.replace(np.nan, '... | code |
32068734/cell_12 | [
"text_plain_output_1.png"
] | import csv
population_reader = csv.reader(open('../input/population/population.csv', 'r'))
population_dict = {}
next(population_reader)
for row in population_reader:
k, v = row
population_dict[k] = int(v)
pop_by_region_reader = csv.reader(open('../input/populationbycity/populationbycity.csv', 'r'))
population... | code |
106192404/cell_1 | [
"text_plain_output_1.png"
] | !pip install open_clip_torch | code |
106192404/cell_7 | [
"text_plain_output_1.png"
] | import torch
model = MyModel()
model.eval()
x = torch.rand(1, 3, 336, 336, device='cuda')
model(x).shape
saved_model = torch.jit.script(model)
saved_model.save('saved_model.pt')
saved_model(x).shape | code |
106192404/cell_5 | [
"text_plain_output_1.png"
] | import torch
model = MyModel()
model.eval()
x = torch.rand(1, 3, 336, 336, device='cuda')
model(x).shape | code |
18120849/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
c[3:5] = (300, 400)
c | code |
18120849/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
print(f'Numpy Array b {b}')
print(f'Type of Numpy Array b {type(b)}')
print(f'Elements Type of Numpy Array b {b.dtype}')
print(f'Size of Numpy Array b {b.size}')
print(f'Dimensions of Numpy Array b {b.ndim}')
print(f'Shape o... | code |
18120849/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v | code |
18120849/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
y = np.array([1, 2])
y
u = np.array([1, 2])
u
v = np.array([3, 1])
v | code |
18120849/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
y = np.array([1, 2])
y | code |
18120849/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
y = np.array([1, 2])
y
u = np.array([1, 2])
u | code |
18120849/cell_44 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
d = c[1:4]
d
d.size | code |
18120849/cell_40 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_39 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z | code |
18120849/cell_48 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
for index, element in enumerate(a):
print(f'index {index} element {element}') | code |
18120849/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
d = c[1:4]
d | code |
18120849/cell_50 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_52 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
print(f'Numpy Array a \n{a}')
print(f'Type of Numpy Array a {type(a)}')
print(f'Elements Type of Numpy Array a {a.dtype}')
print(f'Size of Numpy Array a {a.size}')
print(f'Dimensions of Numpy Array a {a.ndim}')
print(f'Shape of Numpy Array a {a.shape}') | code |
18120849/cell_49 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_51 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z | code |
18120849/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c | code |
18120849/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
c[0] = 100
c | code |
18120849/cell_38 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
y = np.array([1, 2])
y
u = np.array([1, 2])
u
u.T | code |
18120849/cell_47 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
c[4] = 0
c | code |
18120849/cell_43 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z | code |
18120849/cell_46 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u | code |
18120849/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
for index, element in enumerate(b):
print(f'index {index} element {element}') | code |
18120849/cell_53 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z
u.T
z = np.dot(u,... | code |
18120849/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
a[0] | code |
18120849/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
type(z) | code |
18120849/cell_36 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([0, 1, 2, 3, 4])
b = np.array([3.1, 11.02, 6.2, 231.2, 5.2])
c = np.array([20, 1, 2, 3, 4])
c
u = np.array([1, 0])
u
v = np.array([0, 1])
v
z = u + v
z
z = u - v
z
y = np.array([1, 2])
y
z = 2 * y
z
u = np.array([1, 2])
u
v = np.array([3, 1])
v
z = u * v
z | code |
129023470/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns | code |
129023470/cell_30 | [
"text_html_output_1.png"
] | from sklearn.utils import resample
import pandas as pd
df = pd.read_csv('/kaggle/input/credit-card-fraud-detection/creditcard.csv')
df_train = pd.concat([X_train, y_train], axis='columns')
df_not_fraud = df_train[df_train['Class'] == 0]
df_fraud = df_train[df_train['Class'] == 1]
df_not_fraud_downsample = resample... | code |
129023470/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/credit-card-fraud-detection/creditcard.csv')
df.head() | code |
129023470/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/credit-card-fraud-detection/creditcard.csv')
df.describe() | code |
129023470/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/credit-card-fraud-detection/creditcard.csv')
plt.figure(figsize=(12, 8))
sns.countplot(x=df['Class'], data=df)
plt.show() | code |
129023470/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/credit-card-fraud-detection/creditcard.csv')
df['Class'].value_counts() | code |
34130031/cell_21 | [
"text_html_output_1.png"
] | import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
samp_sub = pd.read_csv('/kaggle/input/iwildcam-2020-fgvc7/sample_submissio... | code |
34130031/cell_13 | [
"text_plain_output_1.png"
] | import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
samp_sub = pd.read_csv('/kaggle/input/iwildcam-2020-fgvc7/sample_submissio... | code |
34130031/cell_9 | [
"text_html_output_1.png"
] | import json
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_test_information.json', 'r', errors='ignore') as f:
test_information = json.load(f)
... | code |
34130031/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
samp_sub = pd.read_csv('/kaggle/input/iwildcam-2020-fgvc7/sample_submission.csv')
samp_sub | code |
34130031/cell_11 | [
"text_html_output_1.png"
] | import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
samp_sub = pd.read_csv('/kaggle/input/iwildcam-2020-fgvc7/sample_submissio... | code |
34130031/cell_19 | [
"text_html_output_1.png"
] | import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
samp_sub = pd.read_csv('/kaggle/input/iwildcam-2020-fgvc7/sample_submissio... | code |
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