path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128030195/cell_14 | [
"text_plain_output_1.png"
] | from keras.models import Model, load_model
from keras.utils.vis_utils import plot_model
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout
from tensorflow.keras.layers imp... | code |
128030195/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.models import Model, load_model
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.models import Model
import tensorflow as tf
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_di... | code |
128030195/cell_5 | [
"text_plain_output_35.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_40.png",
"text_plain_output_31.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_... | import tensorflow as tf
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation... | code |
90116663/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
d = np.array([1, 2, 3])
e = np.array([4, 5, 6])
f = np.concatenate([d, e])
g = np.array([7, 8, 9])
h = np.concatenate([f, g])
i = np.concatenate([d, e, g])
j =... | code |
90116663/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
d = np.array([1, 2, 3])
e = np.array([4, 5, 6])
f = np.concatenate([d, e])
print('f : ', f) | code |
90116663/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
print('a : ', a.ndim)
print('b : ', b.ndim) | code |
90116663/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
print('a : ', a.size)
print('b : ', b.size) | code |
90116663/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
print(np.random.randint(10, size=7)) | code |
90116663/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
d = np.array([1, 2, 3])
e = np.array([4, 5, 6])
f = np.concatenate([d, e])
g = np.array([7, 8, 9])
h = np.concatenate([f, g])
i = np.concatenate([d, e, g])
j =... | code |
90116663/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
print(a.dtype)
print(b.dtype) | code |
90116663/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
d = np.array([1, 2, 3])
e = np.array([4, 5, 6])
f = np.concatenate([d, e])
g = np.array([7, 8, 9])
h = np.concatenate([f, g])
i = np.concatenate([d, e, g])
j =... | code |
90116663/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
print('c : ', c)
print()
print('3x3 matris hali : \n\n', c.reshape(3, 3)) | code |
90116663/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
d = np.array([1, 2, 3])
e = np.array([4, 5, 6])
f = np.concatenate([d, e])
g = np.array([7, 8, 9])
h = np.concatenate([f, g])
i = np.concatenate([d, e, g])
j =... | code |
90116663/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
d = np.array([1, 2, 3])
e = np.array([4, 5, 6])
f = np.concatenate([d, e])
g = np.array([7, 8, 9])
h = np.concatenate([f, g])
i = np.concatenate([d, e, g])
j =... | code |
90116663/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
d = np.array([1, 2, 3])
e = np.array([4, 5, 6])
f = np.concatenate([d, e])
g = np.array([7, 8, 9])
h = np.concatenate([f, g])
i = np.concatenate([d, e, g])
j =... | code |
90116663/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
d = np.array([1, 2, 3])
e = np.array([4, 5, 6])
f = np.concatenate([d, e])
g = np.array([7, 8, 9])
h = np.concatenate([f, g])
i = np.concatenate([d, e, g])
j =... | code |
90116663/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
c = np.random.randint(7, size=9, dtype=int)
d = np.array([1, 2, 3])
e = np.array([4, 5, 6])
f = np.concatenate([d, e])
g = np.array([7, 8, 9])
h = np.concatenate([f, g])
print(h)
print('------------------... | code |
90116663/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.random.randint(10, size=7)
b = np.random.randint(10, size=(3, 5))
print('a : ', a.shape)
print('b : ', b.shape) | code |
74042282/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from google.colab import drive
from tqdm import tqdm
import glob
import json
import numpy as np
import os
import os
def is_in_ipython():
"""Is the code running in the ipython environment (jupyter including)"""
program_name = os.path.basename(os.getenv('_', ''))
if 'jupyter-notebook' in program_name or... | code |
74042282/cell_8 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
from google.colab import drive
from tqdm import tqdm
import glob
import json
import numpy as np
import os
import random
import os
def is_in_ipython():
"""Is the code running in the ipython environment (jupyter including)"""
program_name = os.path.basename(os.getenv('_', ... | code |
74042282/cell_3 | [
"text_plain_output_1.png"
] | #為確保安裝最新版
!pip uninstall tridentx -y
!pip install ../input/trident/tridentx-0.7.3.20-py3-none-any.whl --upgrade
import re
import pandas
import json
import copy
import numpy as np
#調用trident api
import random
from tqdm import tqdm
import scipy
import time
import glob
import trident as T
from trident import *
from tride... | code |
74042282/cell_10 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from trident.models import rfbnet
new_rfbmodel = rfbnet.RfbNet(pretrianed=False, num_classes=5, num_regressors=4)
new_rfbmodel.model.trainable = True
new_rfbmodel.summary() | code |
74042282/cell_5 | [
"image_output_1.png"
] | from google.colab import drive
from tqdm import tqdm
import glob
import json
import numpy as np
import os
import os
def is_in_ipython():
"""Is the code running in the ipython environment (jupyter including)"""
program_name = os.path.basename(os.getenv('_', ''))
if 'jupyter-notebook' in program_name or... | code |
17113265/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix,classification_report
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
plt.... | code |
17113265/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
plt.style.use('ggplot')
zoo = pd.read_csv('../input/zoo.csv')
data = zoo.copy()
data.drop('animal_name', axis=1, inplace=True)
x = data... | code |
17113265/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
plt.style.use('ggplot')
zoo = pd.read_csv('../input/zoo.csv')
print(zoo.class_type.value_counts())
plt.figure(figsize=(10, 8))
sns.countplot(zoo.class_type)
plt.show() | code |
17113265/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix,classification_report
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | code |
17113265/cell_4 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import os
print(os.listdir('../input')) | code |
17113265/cell_30 | [
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix,classification_report
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np # linear algebra... | code |
17113265/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix,classification_report
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
plt.style.use('ggplot')
zoo = pd.read_cs... | code |
17113265/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
zoo = pd.read_csv('../input/zoo.csv')
zoo.head() | code |
17113265/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix,classification_report
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | code |
17113265/cell_19 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
plt.style.use('ggplot')
zoo = pd.read_csv('../input/zoo.csv')
data = zoo.copy()
data.drop('animal_name', ax... | code |
17113265/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
zoo = pd.read_csv('../input/zoo.csv')
zoo.info() | code |
17113265/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix,classification_report
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np # linear algebra... | code |
17113265/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix,classification_report
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import numpy as np # linear algebra... | code |
17113265/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix,classification_report
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | code |
105212440/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
import cv2
import os | code |
105212440/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
train_cats = '../input/cat-and-dogs/dataset/training_set/cats'
train_dogs = '../input/cat-and-dogs/dataset/training_set/dogs'
test_cats = '../input/cat-and-dogs/dataset/training_set/cats'
test_dogs = '../input/cat-and-dogs/data... | code |
104126200/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import random
import torch
def set_seed(seed=0):
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
set_seed()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device | code |
104126200/cell_29 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import numpy as np
import random
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torchvision
def set_seed(seed=0):
np.random.seed(seed)
random.seed(s... | code |
104126200/cell_26 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import numpy as np
import random
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torchvision
def set_seed(seed=0):
np.random.seed(seed)
random.seed(s... | code |
104126200/cell_14 | [
"text_plain_output_1.png"
] | from torchvision import transforms
import torchvision
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, transform=transform, download=True)
test_dataset = torchvision.datasets.CIFAR10... | code |
104126200/cell_22 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import numpy as np
import random
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torchvision
def set_seed(seed=0):
np.random.seed(seed)
random.seed(s... | code |
73073677/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar = ... | code |
73073677/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar = calendar[['listing_id', 'date', '... | code |
73073677/cell_25 | [
"image_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
reviews.shape
reviews.head() | code |
73073677/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar.info() | code |
73073677/cell_23 | [
"image_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
reviews.shape | code |
73073677/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
reviews.shape
reviews = reviews[['listing_i... | code |
73073677/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar = ... | code |
73073677/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
reviews.shape
reviews = reviews[['listing_i... | code |
73073677/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar = ... | code |
73073677/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar = calendar[['listing_id', 'date', '... | code |
73073677/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar = calendar[['listing_id', 'date', '... | code |
73073677/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape) | code |
73073677/cell_24 | [
"image_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
reviews.shape
reviews.info() | code |
73073677/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar = calendar[['listing_id', 'date', '... | code |
73073677/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
listings.isnull().sum() | code |
73073677/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar = calendar[['listing_id', 'date', '... | code |
73073677/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
calendar.head() | code |
73073677/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
calendar = pd.read_csv('../input/prague-airbnb/Prague_calendar.csv')
listings = pd.read_csv('../input/prague-airbnb/Prague_listings.csv')
reviews = pd.read_csv('../input/prague-airbnb/Prague_reviews.csv')
(calendar.shape, listings.shape, reviews.shape)
listings.info() | code |
129031792/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import json
import numpy as np
import numpy as np
import tensorflow as tf... | code |
129031792/cell_7 | [
"text_plain_output_1.png"
] | from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import numpy as np
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
import urllib
import urllib
import zipfile
def solution_mod... | code |
129031792/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
def solution_model():
xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)
ys = np.array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0], dtype=float)
model = tf.keras.Sequentia... | code |
129031792/cell_5 | [
"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
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
def solution_model():
xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)
ys = np.array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0], dtype=float)
model = tf.keras.Sequentia... | code |
50219444/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sarcasm_df = pd.read_csv('../input/sarcasm/train-balanced-sarcasm.csv')
sarcasm_df.dropna(subset=['comment'], inplace=True)
sarcasm_df['comment'] = sarcasm_df['comment'].... | code |
50219444/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sarcasm_df = pd.read_csv('../input/sarcasm/train-balanced-sarcasm.csv')
sarcasm_df.dropna(subset=['comment'], inplace=True)
sarcasm_df['comment'] = sarcasm_df['comment'].... | code |
50219444/cell_23 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sarcasm_df = pd.read_csv('../input/sarcasm/train-balanced-sarcasm.csv'... | code |
50219444/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sarcasm_df = pd.read_csv('../input/sarcasm/train-balanced-sarcasm.csv')
sarcasm_df.dropna(subset=['comment'], inplace=True)
sarcasm_df['comment'] = sarcasm_df['comment'].str.lower()
sarcasm_df['comment'] = sarcasm_df['comment'... | code |
50219444/cell_19 | [
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sarcasm_df = pd.read_csv('../input/sarcasm/train-balanced-sarcasm.csv'... | code |
50219444/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 |
50219444/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sarcasm_df = pd.read_csv('../input/sarcasm/train-balanced-sarcasm.csv')
sarcasm_df.dropna(subset=['comment'], inp... | code |
50219444/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sarcasm_df = pd.read_csv('../input/sarcasm/train-balanced-sarcasm.csv')
sarcasm_df.dropna(subset=['comment'], inplace=True)
sarcasm_df['comment'] = sarcasm_df['comment'].... | code |
89135965/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/titanic/train.csv')
data_test = pd.read_csv('../input/titanic/test.csv')
X = data[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Cabin']]
y = data.Survived
X.isnull().sum()
def impute_age(X):
mc_... | code |
89135965/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/titanic/train.csv')
data_test = pd.read_csv('../input/titanic/test.csv')
X = data[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Cabin']]
y = data.Survived
X.isnull().sum() | code |
89135965/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/titanic/train.csv')
data_test = pd.read_csv('../input/titanic/test.csv')
data.head() | code |
89135965/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/titanic/train.csv')
data_test = pd.read_csv('../input/titanic/test.csv')
X = data[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Cabin']]
y = data.Survived
X.isnull().sum()
def impute_age(X):
mc_... | code |
89135965/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score, train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/titanic/train.csv')
data_test = pd.read_csv('../input/titanic/test.csv')
X = data[['Pclass', '... | code |
73079027/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.head() | code |
73079027/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train.shape | code |
105201347/cell_13 | [
"text_plain_output_1.png"
] | import lazypredict
from lazypredict.Supervised import LazyClassifier | code |
105201347/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indian-liver-patient-records/indian_liver_patient.csv')
data
data.describe() | code |
105201347/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indian-liver-patient-records/indian_liver_patient.csv')
data | code |
105201347/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/indian-liver-patient-records/indian_liver_patient.csv')
data
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
data = data.apply(le.fit_transform)
... | code |
105201347/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/indian-liver-patient-records/indian_liver_patient.csv')
data
from sklearn.preprocessing import LabelEncoder
le = Labe... | code |
105201347/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indian-liver-patient-records/indian_liver_patient.csv')
data
data.info() | code |
105201347/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/indian-liver-patient-records/indian_liver_patient.csv')
data
from sklearn.preprocessing import LabelEncoder
le = Labe... | code |
105201347/cell_5 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/indian-liver-patient-records/indian_liver_patient.csv')
data
plt.figure(figsize=(10, 6))
sns.heatmap(data.corr(), annot=True) | code |
33104556/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
train.target.value_counts()
train.isnull().sum()
tes... | code |
33104556/cell_9 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
test.isnull().sum() | code |
33104556/cell_4 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train.head(10) | code |
33104556/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
sns.countplot(x='target', data=train) | code |
33104556/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
train.target.value_counts()
train.isnull().sum()
tes... | code |
33104556/cell_7 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
train.target.value_counts() | code |
33104556/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
train.target.value_counts()
train.isnull().sum() | code |
33104556/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
train.target.value_counts()
train.isnull().sum()
tes... | code |
33104556/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
train.target.value_counts()
train.isnull().sum()
tes... | code |
33104556/cell_3 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
print('Train : ', train.shape)
print('*' * 10)
print('Test : ', test.shape) | code |
33104556/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
train.target.value_counts()
train.isnull().sum()
tes... | code |
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