path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90109221/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_tra... | code |
90109221/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
90109221/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
mnist_train.head() | code |
90109221/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
90109221/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
90109221/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_trai... | code |
90109221/cell_10 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda... | code |
90109221/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_t... | code |
90109221/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_test = pd.read_csv('../input/mnist-in-csv/mnist_test.csv')
X_train = mnist_train.iloc[:, 1:785]
y_train = mnist_train.iloc[:, 0]
lda = LDA(n_components=9)
X_train_r2 = lda.fit(X_train, y_train)
X_train_r2 | code |
129010847/cell_9 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import transformers
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to... | code |
129010847/cell_4 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import transformers
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to... | code |
129010847/cell_8 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import transformers
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to... | code |
129010847/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import transformers
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to... | code |
129010847/cell_5 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import transformers
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to... | code |
33106189/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors ... | code |
33106189/cell_4 | [
"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
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from... | code |
33106189/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder, StandardScaler
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
import numpy as np
import pandas as pd
import mat... | code |
33106189/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 |
33106189/cell_3 | [
"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
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from... | code |
33106189/cell_5 | [
"text_html_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
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklear... | code |
90154899/cell_25 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.svm import SVC
f... | code |
90154899/cell_20 | [
"image_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
impo... | code |
90154899/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/personal-key-indicators-of-heart-disease/heart_2020_cleaned.csv')
data.info() | code |
90154899/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.model_selection import StratifiedKFold
fro... | code |
90154899/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/personal-key-indicators-of-heart-disease/heart_2020_cleaned.csv')
data.describe().T | code |
90154899/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.model_selection import StratifiedKFold
fro... | code |
90154899/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.model_selection import StratifiedKFold
fro... | code |
90154899/cell_5 | [
"image_output_11.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import pandas as pd
data = pd.read_csv('../input/personal-key-indicators-of-heart-disease/heart_2020_cleaned.csv')
data.head() | code |
104124170/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
a = data['Description'].str.split()
data['Type'] = a.apply(lambda x: ' '.join(x[-2:]))
Rev_Coustomer = data.groupby('CustomerID')... | code |
104124170/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
Rev_Coustomer = data.groupby('CustomerID')['Revenue'].sum().sort_values(ascending=False).reset_index()[:15]
Rev_Country = data.gr... | code |
104124170/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
data['Type'].nunique() | code |
104124170/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
a = data['Description'].str.split()
data['Type'] = a.apply(lambda x: ' '.join(x[-2:]))
Rev_Coustomer = data.groupby('CustomerID')... | code |
104124170/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data.info() | code |
104124170/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
a = data['Description'].str.split()
data['Type'] = a.apply(lambda x: ' '.join(x[-2:]))
Rev_Coustomer = data.groupby('CustomerID')... | code |
104124170/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
Rev_Coustomer = data.groupby('CustomerID')['Revenue'].sum().sort_values(ascending=False).reset_index()[:15]
plt.figure(figsize=(12... | code |
104124170/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
a = data['Description'].str.split()
data['Type'] = a.apply(lambda x: ' '.join(x[-2:]))
Rev_Coustomer = data.groupby('CustomerID')... | code |
104124170/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
Rev_Coustomer = data.groupby('CustomerID')['Revenue'].sum().sort_values(ascending=False).reset_index()[:15]
Rev_Country = data.gr... | code |
104124170/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data.head() | code |
104124170/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
a = data['Description'].str.split()
data['Type'] = a.apply(lambda x: ' '.join(x[-2:]))
Rev_Coustomer = data.groupby('CustomerID')... | code |
104124170/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
a = data['Description'].str.split()
data['Type'] = a.apply(lambda x: ' '.join(x[-2:]))
Rev_Coustomer = data.groupby('CustomerID')... | code |
104124170/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
Rev_Coustomer = data.groupby('CustomerID')['Revenue'].sum().sort_values(ascending=False).reset_index()[:15]
Rev_Country = data.gr... | code |
104124170/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
a = data['Description'].str.split()
data['Type'] = a.apply(lambda x: ' '.join(x[-2:]))
Rev_Coustomer = data.groupby('CustomerID')... | code |
104124170/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding='cp1252', header=0)
data = data.dropna()
Rev_Coustomer = data.groupby('CustomerID')['Revenue'].sum().sort_values(ascending=False).reset_index()[:15]
Rev_Country = data.gr... | code |
34120972/cell_21 | [
"text_plain_output_1.png"
] | import albumentations
import ast
import cv2
import matplotlib.patches as patches
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection'
WORK_DIR = '/kaggle/working'
np.random.seed(1996)
train_df = pd.... | code |
34120972/cell_4 | [
"image_output_1.png"
] | import ast
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection'
WORK_DIR = '/kaggle/working'
np.random.seed(1996)
train_df = pd.read_csv(os.path.join(BASE_DIR, 'train.csv'))
train_df[['x_min', 'y_min',... | code |
34120972/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import ast
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection'
WORK_DIR = '/kaggle/working'
np.random.seed(1996)
train_df = pd.read_csv(os.path.join(BASE_DIR, 'train.csv'))
train_df[['x_min', 'y_min',... | code |
34120972/cell_29 | [
"text_plain_output_1.png"
] | from albumentations.augmentations.bbox_utils import denormalize_bbox, normalize_bbox
from albumentations.core.transforms_interface import DualTransform
from collections import namedtuple
import albumentations
import ast
import cv2
import matplotlib.patches as patches
import numpy as np # linear algebra
import o... | code |
34120972/cell_32 | [
"image_output_1.png"
] | from albumentations.augmentations.bbox_utils import denormalize_bbox, normalize_bbox
from albumentations.core.transforms_interface import DualTransform
from collections import namedtuple
from tqdm import tqdm
import albumentations
import ast
import cv2
import matplotlib.patches as patches
import numpy as np # l... | code |
34120972/cell_8 | [
"text_html_output_1.png"
] | import ast
import cv2
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection'
WORK_DIR = '/kaggle/working'
np.random.seed(1996)
train_df = pd.read_csv(os.path.join(BASE_DIR, 'train.csv'))
train_df[['x_mi... | code |
34120972/cell_16 | [
"text_html_output_1.png"
] | import albumentations
import ast
import cv2
import matplotlib.patches as patches
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection'
WORK_DIR = '/kaggle/working'
np.random.seed(1996)
train_df = pd.... | code |
34120972/cell_5 | [
"image_output_1.png"
] | import ast
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
BASE_DIR = '/kaggle/input/global-wheat-detection'
WORK_DIR = '/kaggle/working'
np.random.seed(1996)
train_df = pd.read_csv(os.path.join(BASE_DIR, 'train.csv'))
train_df[['x_min', 'y_min',... | code |
90118148/cell_4 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', parse_dates=['time'])
train['hour'] = train['time'].dt.hour
train['minute'] = train['time'].dt.minute
submission_in = pd.read_csv('../input/tabular-playground-march-2022-04/... | code |
90118148/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv', parse_dates=['time'])
train['hour'] = train['time'].dt.hour
train['minute'] = train['time'].dt.minute
submission_in = pd.read_csv('../input/tabular-playground-march-2022-04/... | code |
73090666/cell_4 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
categorical = [col for col in train.columns if train[col].dtype == 'object']
encoder = La... | code |
73090666/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_error
from xgboost import XGBRegressor
model = XGBRegressor(n_estimators=1000, learning_rate=0.05, gamma=0.2)
model.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_test, y_test)], verbose=False)
y_predict = model.predict(X_test)
print('MSE', me... | code |
73090666/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
train.head() | code |
73090666/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
... | code |
73090666/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-m... | code |
73090666/cell_3 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
categorical = [col for col in train.columns if train[col].dtype == 'object']
encoder = La... | code |
18116693/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vocab = pd.read_csv('../input/vocabulary.csv')
Vertical1 = vocab.groupby('Vertical1')
vocab.dtypes | code |
18116693/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vocab = pd.read_csv('../input/vocabulary.csv')
sample_sub = pd.read_csv('../input/sample_submission.csv')
print(sample_sub.head()) | code |
18116693/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vocab = pd.read_csv('../input/vocabulary.csv')
print('Total number of names :', vocab['Name'].nunique()) | code |
18116693/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vocab = pd.read_csv('../input/vocabulary.csv')
Vertical1 = vocab.groupby('Vertical1')
vocab.dtypes
text = ' '.join((WD for WD in vocab.WikiDescription))
print('There are {} words in the combination of all review.'.format(len(text))) | code |
18116693/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18116693/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vocab = pd.read_csv('../input/vocabulary.csv')
Vertical1 = vocab.groupby('Vertical1')
print(Vertical1.describe().head()) | code |
18116693/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vocab = pd.read_csv('../input/vocabulary.csv')
Vertical1 = vocab.groupby('Vertical1')
print(vocab.WikiDescription.head(10)) | code |
18116693/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vocab = pd.read_csv('../input/vocabulary.csv')
print(vocab.head()) | code |
18116693/cell_12 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vocab = pd.read_csv('../input/vocabulary.csv')
Vertical1 = vocab.groupby('Vertical1')
vocab.dtypes
text = ' '.join((WD for WD in vocab.WikiDescript... | code |
18116693/cell_5 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input/frame-sample/frame/')) | code |
17120711/cell_6 | [
"text_plain_output_1.png"
] | from hyperopt import hp, fmin, tpe, space_eval, STATUS_OK, Trials
from keras.layers import Dense, Activation, Dropout, Conv2D, MaxPooling2D, Flatten, BatchNormalization
from keras.models import Sequential, model_from_json
from sklearn.preprocessing import MinMaxScaler
import keras
import matplotlib.pyplot as plt
... | code |
17120711/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential, model_from_json
from keras.layers import Dense, Activation, Dropout, Conv2D, MaxPooling2D, Flatten, BatchNormalization
from sklearn.preprocessing import MinMaxScaler
from hyperopt import hp... | code |
17120711/cell_7 | [
"image_output_1.png"
] | from hyperopt import hp, fmin, tpe, space_eval, STATUS_OK, Trials
from keras.layers import Dense, Activation, Dropout, Conv2D, MaxPooling2D, Flatten, BatchNormalization
from keras.models import Sequential, model_from_json
from sklearn.preprocessing import MinMaxScaler
import keras
import matplotlib.pyplot as plt
... | code |
17120711/cell_18 | [
"text_plain_output_1.png"
] | from hyperopt import hp, fmin, tpe, space_eval, STATUS_OK, Trials
from keras.layers import Dense, Activation, Dropout, Conv2D, MaxPooling2D, Flatten, BatchNormalization
from keras.models import Sequential, model_from_json
from sklearn.preprocessing import MinMaxScaler
import keras
import matplotlib.pyplot as plt
... | code |
17120711/cell_16 | [
"image_output_1.png"
] | from hyperopt import hp, fmin, tpe, space_eval, STATUS_OK, Trials
from keras.layers import Dense, Activation, Dropout, Conv2D, MaxPooling2D, Flatten, BatchNormalization
from keras.models import Sequential, model_from_json
from sklearn.preprocessing import MinMaxScaler
import keras
import matplotlib.pyplot as plt
... | code |
17120711/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from hyperopt import hp, fmin, tpe, space_eval, STATUS_OK, Trials
from keras.layers import Dense, Activation, Dropout, Conv2D, MaxPooling2D, Flatten, BatchNormalization
from keras.models import Sequential, model_from_json
from sklearn.preprocessing import MinMaxScaler
import keras
import matplotlib.pyplot as plt
... | code |
17120711/cell_12 | [
"text_plain_output_1.png"
] | from hyperopt import hp, fmin, tpe, space_eval, STATUS_OK, Trials
from keras.layers import Dense, Activation, Dropout, Conv2D, MaxPooling2D, Flatten, BatchNormalization
from keras.models import Sequential, model_from_json
from sklearn.preprocessing import MinMaxScaler
import keras
import matplotlib.pyplot as plt
... | code |
73061688/cell_21 | [
"text_plain_output_1.png"
] | from keras.applications import InceptionV3
IncV3 = InceptionV3(include_top=False, weights='imagenet', input_shape=(224, 224, 3)) | code |
73061688/cell_13 | [
"image_output_2.png",
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
import matplotlib.pyplot as plt
train_path = '../input/100-bird-species/285 birds/train'
test_path = '../input/100-bird-species/285 birds/test'
validation_path = '../input/100-bird-species/285 birds/valid'
img = load_img(train_path + '/... | code |
73061688/cell_26 | [
"text_plain_output_1.png"
] | from glob import glob
from keras.applications import InceptionV3
from keras.layers import Dense, Flatten, BatchNormalization, Dropout
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
train_path = '../input/100-bird-species/285 birds/train'
test_pa... | code |
73061688/cell_19 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
train_path = '../input/100-bird-species/285 birds/train'
test_path = '../input/100-bird-species/285 birds/test'
validation_path = '../input/100-bird-species/285 birds/valid'
train_datagen = ImageDataGenerator(rescale=1 / 255)
validation_... | code |
73061688/cell_28 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from glob import glob
from keras.applications import InceptionV3
from keras.layers import Dense, Flatten, BatchNormalization, Dropout
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
import matplotlib.pyplot as plt
train_path = '../input/100-bird... | code |
73061688/cell_16 | [
"text_plain_output_1.png"
] | from glob import glob
train_path = '../input/100-bird-species/285 birds/train'
test_path = '../input/100-bird-species/285 birds/test'
validation_path = '../input/100-bird-species/285 birds/valid'
className = glob(train_path + '/*')
NumberofClass = len(className)
print('NumberofClass:', NumberofClass) | code |
73061688/cell_14 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
import matplotlib.pyplot as plt
train_path = '../input/100-bird-species/285 birds/train'
test_path = '../input/100-bird-species/285 birds/test'
validation_path = '../input/100-bird-species/285 birds/valid'
img = load_img(train_path + '/... | code |
105209340/cell_1 | [
"text_plain_output_1.png"
] | import gc
import os
import cv2
import zipfile
import rasterio
import numpy as np
import pandas as pd
from PIL import Image
import tifffile
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
from rasterio.windows import Window
from torch.utils.data import Dataset
!pip install staintools
!pip install spams | code |
105209340/cell_8 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import staintools
imaging_measurements = {'hpa': {'pixel_size': {'kidney': 0.4, 'prostate': 0.4, 'largeintestine': 0.4, 'spleen': 0.4, 'lung': 0.4}, 'tissue_thickness': {'kidney': 4, 'prostate': 4, 'largeintestine': 4, 'sp... | code |
72120119/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_33 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_20.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png"... | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/c... | code |
72120119/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
72120119/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/c... | code |
72120119/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
72120119/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
submission = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/sample_submission.csv')
items_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_catego... | code |
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