path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
48165025/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../inpu... | code |
48165025/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../inpu... | code |
48165025/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../inpu... | code |
48165025/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import cufflinks as cf
cf.go_offline()
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, roc_curve, roc_auc_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree im... | code |
48165025/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../input/titanic/test.csv', index_col='PassengerId')
train.inf... | code |
48165025/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../input/titanic/test.csv', index_col='PassengerId')
train.isn... | code |
48165025/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../input/titanic/test.csv', index_col='PassengerId')
train.hea... | code |
48165025/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../input/titanic/test.csv', index_col='PassengerId')
test.head... | code |
48165025/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../input/titanic/test.csv', index_col='PassengerId')
test.desc... | code |
48165025/cell_31 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../inpu... | code |
48165025/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../inpu... | code |
128030873/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
dataset.info(verbose=True) | code |
128030873/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
dataset.describe().T | code |
128030873/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import seaborn as sns
import warnings
from mlxtend.plotting import plot_decision_regions
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import warnings
warnings.filterwarnings('ignore') | code |
128030873/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 |
128030873/cell_7 | [
"text_plain_output_1.png"
] | 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)
dataset = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
dataset.describe().T
dataset_copy = dataset.copy(deep=True)
dataset_copy[['Glucose', ... | code |
128030873/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
dataset.head() | code |
128030873/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
dataset.describe() | code |
128047807/cell_2 | [
"text_plain_output_1.png"
] | import os
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from torchvision.transforms.transforms import Resize
import timm | code |
128047807/cell_1 | [
"text_plain_output_1.png"
] | !pip install torch torchvision
!pip install timm
!pip install scikit-learn | code |
128047807/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
test_labels = pd.read_csv('/kaggle/input/plant-pathology-2020-fgvc7/test.csv')
test_image_ids = test_labels['image_id'].values
train_labels = pd.read_csv('/kaggle/input/plant-pathology-2020-fgvc7/train.csv')
train_labels = train_labels[['healthy', 'multiple_diseases', 'rust', '... | code |
128047807/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import os
import pandas as pd
image_dir = '/kaggle/input/plant-pathology-2020-fgvc7/images'
image_files = os.listdir(image_dir)
image_paths = [os.path.join(image_dir, filename) for filename in image_files]
import pandas as pd
test_labels = pd.read_csv('/kaggle/in... | code |
128047807/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset
from torch.utils.data import Dataset, DataLoader
import os
import pandas as pd
import timm
import timm
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms a... | code |
74041348/cell_13 | [
"text_plain_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_raw.shape
winners_raw.isnull().sum()
winners = winners... | code |
74041348/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_raw.shape
winners_raw.isnull().sum()
winners = winners... | code |
74041348/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes | code |
74041348/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.head(10) | code |
74041348/cell_11 | [
"text_html_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_raw.shape
winners_raw.isnull().sum()
winners = winners... | code |
74041348/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 |
74041348/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_raw.shape
winners_raw.isnull().sum() | code |
74041348/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as ps
import seaborn as sn
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_r... | code |
74041348/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as ps
import seaborn as sn
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_r... | code |
74041348/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as ps
import seaborn as sn
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_r... | code |
74041348/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.describe() | code |
74041348/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as ps
import seaborn as sn
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_r... | code |
74041348/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as ps
import seaborn as sn
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_r... | code |
74041348/cell_10 | [
"text_html_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_raw.shape
winners_raw.isnull().sum()
winners = winners... | code |
74041348/cell_12 | [
"text_plain_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_raw.shape
winners_raw.isnull().sum()
winners = winners... | code |
74041348/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
import pandas as ps
import matplotlib.pyplot as plt
import seaborn as sn
from pandas_profiling import ProfileReport
winners_raw = ps.read_csv('/kaggle/input/nobel-prize-winners-19002020/nobel_prize_by_winner.csv')
winners_raw.dtypes
winners_raw.shape | code |
17132106/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size()
dataset.hist(edgecolor='black', linewidth=2) | code |
17132106/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size()
plt.figure(figsize=(20, 8))
dataset.plot(kind='box', sharex=False, sharey=False) | code |
17132106/cell_9 | [
"image_output_1.png"
] | import pandas as pd
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size() | code |
17132106/cell_4 | [
"image_output_1.png"
] | import pandas as pd
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset.head(5) | code |
17132106/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size()
sns.set(style='darkgrid', palette='deep')
sns.set(style='darkgrid', palette='deep')
sns.set(style='darkgrid', pa... | code |
17132106/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.head(5) | code |
17132106/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size()
sns.set(style='darkgrid', palette='deep')
sns.set(style='darkgrid', palette='deep')
sns.set(style='darkgrid', pa... | code |
17132106/cell_8 | [
"image_output_1.png"
] | import pandas as pd
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.info() | code |
17132106/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size()
sns.set(style='darkgrid', palette='deep')
plt.figure(figsize=(12, 6))
plt.title('Compare the distribution of Sepal... | code |
17132106/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size()
sns.set(style='darkgrid', palette='deep')
sns.set(style='darkgrid', palette='deep')
plt.figure(figsize=(12, 6))
p... | code |
17132106/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size()
sns.set(style='darkgrid', palette='deep')
sns.set(style='darkgrid', palette='deep')
sns.set(style='darkgrid', pa... | code |
17132106/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size()
dataset.boxplot(by='Species', figsize=(12, 8)) | code |
17132106/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data = '../input/Iris.csv'
dataset = pd.read_csv(data)
dataset = dataset.drop('Id', axis=1)
dataset.groupby('Species').size()
dataset.describe() | code |
73075982/cell_21 | [
"text_plain_output_1.png"
] | import tensorflow as tf
print('Num GPUs Available: ', len(tf.config.list_physical_devices('GPU'))) | code |
73075982/cell_26 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.decomposition import TruncatedSVD
from sklearn.metrics import confusion_matrix,jaccard_score,f1_score,recall_score
from sklearn.metrics import precision_score,roc_auc_score,log_loss
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preproc... | code |
73075982/cell_32 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from lime import lime_text
from nltk.corpus import stopwords
from sklearn.decomposition import TruncatedSVD
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix,jaccard_score,f1_score,recall_score
from sklearn.metrics import plot_confusion_matrix,plot_roc_curve,brier_sco... | code |
73075982/cell_28 | [
"text_html_output_1.png"
] | from lime import lime_text
from nltk.corpus import stopwords
from sklearn.decomposition import TruncatedSVD
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix,jaccard_score,f1_score,recall_score
from sklearn.metrics import precision_score,roc_auc_score,log_loss
from s... | code |
73075982/cell_31 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from lime import lime_text
from nltk.corpus import stopwords
from sklearn.decomposition import TruncatedSVD
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix,jaccard_score,f1_score,recall_score
from sklearn.metrics import plot_confusion_matrix,plot_roc_curve,brier_sco... | code |
73075982/cell_24 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.decomposition import TruncatedSVD
from sklearn.metrics import confusion_matrix,jaccard_score,f1_score,recall_score
from sklearn.metrics import precision_score,roc_auc_score,log_loss
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preproc... | code |
73075982/cell_22 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.decomposition import TruncatedSVD
from sklearn.metrics import confusion_matrix,jaccard_score,f1_score,recall_score
from sklearn.metrics import precision_score,roc_auc_score,log_loss
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preproc... | code |
16148304/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from sklearn.model_selection import train_test_split
from sklearn import svm
from keras.models import Sequential
from keras.layers import Dense, Conv2DTranspose, Conv2D, MaxPooling2D
from keras.layers import Dropout,... | code |
16148304/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
Y_train = train['label']
X_train = train.drop(labels=['label'], axis=1)
X_train = X_train / 255.0
test = test / 255.0
X_train = X_train.values.reshape(-1, 28, 28, 1)
test = test.val... | code |
16148304/cell_17 | [
"image_output_1.png"
] | from keras.layers import Dense, Conv2DTranspose,Conv2D, MaxPooling2D
from keras.layers import Dropout, Flatten
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical
import pandas as pd
train = pd.read_csv('../input/train.csv')
... | code |
129027504/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()] | code |
129027504/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.head() | code |
129027504/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
import string
import nltk
import pickle
from nltk import word_tokenize
from nltk.stem import PorterStemmer
from tqdm import tqdm
!pip install gradio
import gradio as gr
ps = PorterStemmer() # stemming
stopword... | code |
129027504/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_24 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum()
data[data.question1.isnull()]
data[data.... | code |
129027504/cell_10 | [
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_48.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output_40.png",
"text_plain_output... | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
print(data['similar'].value_counts())
print(data['similar'].va... | code |
129027504/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.rename(columns={'is_duplicate': 'similar'}, inplace=True)
data.drop(['id', 'qid1', 'qid2'], axis=1, inplace=True)
data.isnull().sum() | code |
129027504/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/quora-question-pairs/train.csv.zip')
pd.set_option('display.max_colwidth', 100)
data.head() | code |
73083395/cell_25 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | preds = aml.predict(test_hf)
preds_df = h2o.as_list(preds)
preds_df | code |
73083395/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | ypred = automl.predict(test_df.values) | code |
73083395/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | train_hf = h2o.H2OFrame(train_df.copy())
test_hf = h2o.H2OFrame(test_df.copy()) | code |
73083395/cell_33 | [
"text_plain_output_1.png"
] | print('Best ML leaner:', automl.best_estimator)
print('Best hyperparmeter config:', automl.best_config)
print('Best accuracy on validation data: {0:.4g}'.format(1 - automl.best_loss))
print('Training duration of best run: {0:.4g} s'.format(automl.best_config_train_time)) | code |
73083395/cell_29 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | !pip install -U flaml | code |
73083395/cell_11 | [
"text_plain_output_1.png"
] | from lightautoml.automl.presets.tabular_presets import TabularAutoML, TabularUtilizedAutoML
from lightautoml.tasks import Task
import torch | code |
73083395/cell_32 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | automl = AutoML()
automl_settings = {'time_budget': 1200, 'metric': 'rmse', 'task': 'regression', 'seed': 2021, 'log_file_name': 'tpsaug21log.log'}
automl.fit(X_train=X, y_train=y, **automl_settings) | code |
73083395/cell_16 | [
"text_plain_output_1.png"
] | automl = TabularAutoML(task=task, timeout=TIMEOUT, cpu_limit=N_THREADS, reader_params={'n_jobs': N_THREADS, 'cv': N_FOLDS, 'random_state': RANDOM_STATE}, general_params={'use_algos': [['linear_l2', 'cb', 'lgb', 'lgb_tuned']]}, lgb_params={'default_params': lgb_params, 'freeze_defaults': True}, cb_params={'default_param... | code |
73083395/cell_3 | [
"text_plain_output_1.png"
] | !pip install scikit-learn --upgrade
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler | code |
73083395/cell_17 | [
"text_plain_output_1.png"
] | oof_pred = automl.fit_predict(train_df, roles=roles)
test_pred = automl.predict(test_df) | code |
73083395/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | ypred | code |
73083395/cell_24 | [
"text_plain_output_1.png"
] | aml = H2OAutoML(seed=2021, max_runtime_secs=1200, sort_metric='RMSE')
aml.train(x=train_hf.columns, y='loss', training_frame=train_hf)
lb = aml.leaderboard
lb.head(rows=lb.nrows) | code |
73083395/cell_22 | [
"text_plain_output_4.png",
"text_plain_output_6.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"
] | import h2o
h2o.init() | code |
73083395/cell_10 | [
"text_plain_output_1.png"
] | !pip install -U lightautoml | code |
90124933/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_orig = pd.read_csv('/kaggle/input/daily-min-temperatures/daily-min-temperatures.csv')
data_orig['Date'] = pd.to_datetime(data_orig['Date'])
ax = data_orig.plot(x='Date', y='Temp', figsize=(12, 6)) | code |
90124933/cell_6 | [
"image_output_1.png"
] | from statsmodels.tsa.seasonal import seasonal_decompose
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_orig = pd.read_csv('/kaggle/input/daily-min-temperatures/daily-min-temperatures.csv')
data_orig['Date'] = pd.to_datetime(data_orig['Date']) # convert date column to datetime
ax = data_o... | code |
90124933/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_orig = pd.read_csv('/kaggle/input/daily-min-temperatures/daily-min-temperatures.csv')
print(data_orig.count)
data_orig.head() | code |
90124933/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90124933/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_orig = pd.read_csv('/kaggle/input/daily-min-temperatures/daily-min-temperatures.csv')
data_orig['Temp'].isnull().values.any() | code |
90124933/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_orig = pd.read_csv('/kaggle/input/daily-min-temperatures/daily-min-temperatures.csv')
data_orig['Date'] = pd.to_datetime(data_orig['Date']) # convert date column to datetime
ax = data_orig.plot(x='Date', y='Temp', figsize=(12,6))
ax = data_o... | code |
32062359/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from langdetect import detect
from nltk.tokenize import sent_tokenize,word_tokenize
from sklearn.model_selection import train_test_split
from snorkel.labeling import PandasLFApplier,LFAnalysis,LabelingFunction
from snorkel.labeling.model.label_model import LabelModel
from tqdm import tqdm
import pandas as pd
imp... | code |
32062359/cell_2 | [
"text_plain_output_1.png"
] | #!pip install -U git+https://github.com/dgunning/cord19.git
!pip install -U git+https://github.com/dgunning/cord19.git@5b68c9a807f74f529b34d959f584712520da2f03
!pip install langdetect
!pip install pandas
!pip install tqdm
!pip install snorkel | code |
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