path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128020060/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
seasons_stats[season... | code |
128020060/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
players = pd.read_csv('/kaggle/input/nba-players-stats/Players.csv')
seasons_stats = pd.read_csv('/kaggle/input/nba-players-stats/Seasons_Stats.csv')
player_data = pd.read_csv('/kaggle/input/nba-players-stats/player_data.csv')
players.isnull().sum... | code |
129002034/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)
df = pd.read_csv('/kaggle/input/loan-eligible-dataset/loan-train.csv')
print(df) | code |
129002034/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 |
129002034/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
models = {'Logistic Regression': LogisticRegression(), 'Decision Tree': D... | code |
18118023/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
from pathlib import Path
path = Path('../input')
pd.read_csv(path / 'sample_submission_v2.csv').head(5)
df_tags = pd.read_csv(path / 'trai... | code |
18118023/cell_4 | [
"image_output_1.png"
] | from pathlib import Path
import os
import os
import numpy as np
import pandas as pd
import os
import os
from pathlib import Path
path = Path('../input')
an_image_path = os.listdir(path / 'train-tif-v2')[1]
an_image_path | code |
18118023/cell_6 | [
"text_plain_output_1.png"
] | from PIL import Image
from pathlib import Path
import os
import os
import numpy as np
import pandas as pd
import os
import os
from pathlib import Path
path = Path('../input')
from PIL import Image
Image.open(path / 'train-tif-v2' / 'train_0.tif') | code |
18118023/cell_2 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import os
import numpy as np
import pandas as pd
import os
import os
from pathlib import Path
path = Path('../input')
print(os.listdir(path)) | code |
18118023/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18118023/cell_8 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
from pathlib import Path
path = Path('../input')
pd.read_csv(path / 'sample_submission_v2.csv').head(5)
df_tags = pd.read_csv(path / 'trai... | code |
18118023/cell_3 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
from pathlib import Path
path = Path('../input')
pd.read_csv(path / 'sample_submission_v2.csv').head(5) | code |
18118023/cell_10 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
from pathlib import Path
path = Path('../input')
pd.read_csv(path / 'sample_submission_v2.csv').head(5)
df_tags = pd.read_csv(path / 'trai... | code |
18118023/cell_12 | [
"text_html_output_1.png"
] | from pathlib import Path
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import os
import os
from pathlib import Path
path = Path('../input')
np.random.seed(42)
src = ImageFileList.from_folder(path).label_from_csv('train_v2.csv', sep=' ', folder='train-jpg', suffix='... | code |
18118023/cell_5 | [
"text_html_output_1.png"
] | from pathlib import Path
import os
import os
import numpy as np
import pandas as pd
import os
import os
from pathlib import Path
path = Path('../input')
an_image_path = os.listdir(path / 'train-tif-v2')[1]
an_image_path
an_image_path | code |
1006327/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu_data = pd.read_csv('../input/menu.csv')
menu_data.shape
type(menu_data['Item'][0]) | code |
1006327/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu_data = pd.read_csv('../input/menu.csv')
menu_data.head() | code |
1006327/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu_data = pd.read_csv('../input/menu.csv')
menu_data.shape | code |
1006327/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1006327/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu_data = pd.read_csv('../input/menu.csv')
menu_data.shape
menu_data.describe() | code |
1006327/cell_8 | [
"text_html_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
menu_data = pd.read_csv('../input/menu.csv')
menu_data.shape
plt.figure(figsize=(13, 5))
sns.countplot(data=menu_data, x='Category') | code |
1006327/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
menu_data = pd.read_csv('../input/menu.csv')
menu_data.shape
Item_data = pd.DataFrame(menu_data['Item'], index=range(len(menu_data['Item'])))
Item_data | code |
1006327/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import jieba | code |
1006327/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
menu_data = pd.read_csv('../input/menu.csv')
menu_data.shape
y1 = menu_data['Calories'].tolist()
y2 = menu_data['Calories from Fat'].tolist()
y3 = menu_data[... | code |
50212750/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', columns='Sur... | code |
50212750/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t[['Fare', 'Survived']].groupby(['Fare'], as_index=False).mean().sort_values(by=['Survived'], ascending=False) | code |
50212750/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean() | code |
50212750/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', columns='Sur... | code |
50212750/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t.describe() | code |
50212750/cell_56 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', columns='Sur... | code |
50212750/cell_44 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t.describe(include=['O']) | code |
50212750/cell_40 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', columns='Sur... | code |
50212750/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', columns='Sur... | code |
50212750/cell_65 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_48 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_41 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_61 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t | code |
50212750/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by=['Survived'], ascending=False) | code |
50212750/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', columns='Sur... | code |
50212750/cell_50 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_52 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True) | code |
50212750/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', columns='Sur... | code |
50212750/cell_62 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_59 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean() | code |
50212750/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t[['Parch', 'Survived']].groupby(['Parch'], as_index=False).sum().sort_values(by=['Parch'], ascending=False) | code |
50212750/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t[['SibSp', 'Parch', 'Survived']].groupby(['SibSp', 'Parch'], as_index=False).count().sort_values(by=['SibSp', 'Parch', 'Survived'], ascending=True) | code |
50212750/cell_47 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True) | code |
50212750/cell_35 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
guess_ages = np.zeros((2, 3))
guess_ages | code |
50212750/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).sum().sort_values(by=['Survived'], ascending=False) | code |
50212750/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', columns='Sur... | code |
50212750/cell_53 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean() | code |
50212750/cell_37 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
pd.pivot_table(t, index='SibSp', columns='Parch', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='SibSp', values='Survived', aggfunc='count', margins=True)
pd.pivot_table(t, index='... | code |
50212750/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by=['Survived'], ascending=False) | code |
50212750/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
t = pd.read_csv('../input/titanic/train.csv')
t
t.info() | code |
90116924/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-2021/train.csv')
sample_submission = pd.read_csv('../input/feedback-prize-2021/sample_submission.csv')
train.shape
print(f'Training data consists of {train.shape[0]} annotaions') | code |
90116924/cell_25 | [
"text_html_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/feedback-prize-2021/train.csv')
sample_submission = pd.read_csv('../input/feedback-prize-2021/sample_submission.csv')
train.shape
raw_text_files = os.listdir('/kaggle/input/feedback-prize-2021/train')
train[train['id'] == '423A1CA112E2'] | code |
90116924/cell_28 | [
"text_html_output_1.png"
] | texts_df.head() | code |
90116924/cell_16 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/feedback-prize-2021/train.csv')
sample_submission = pd.read_csv('../input/feedback-prize-2021/sample_submission.csv')
train.shape
raw_text_files = os.listdir('/kaggle/input/feedback-prize-2021/train')
print(f'Training data consists of {len(raw_text_files)}... | code |
90116924/cell_24 | [
"text_plain_output_1.png"
] | with open('../input/feedback-prize-2021/train/423A1CA112E2.txt', 'r') as file:
first_txt = file.read()
print(first_txt) | code |
90116924/cell_22 | [
"text_plain_output_1.png"
] | from glob import glob
train_txt = glob('../input/feedback-prize-2021/train/*.txt')
test_txt = glob('../input/feedback-prize-2021/test/*.txt')
train_txt | code |
90116924/cell_27 | [
"text_plain_output_1.png"
] | texts = []
for file in raw_text_files:
with open(f'/kaggle/input/feedback-prize-2021/train/{file}') as f:
texts.append({'id': file[:-4], 'text': f.read()})
texts_df = pd.DataFrame(texts) | code |
90116924/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/feedback-prize-2021/train.csv')
sample_submission = pd.read_csv('../input/feedback-prize-2021/sample_submission.csv')
train.shape | code |
128018806/cell_57 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from tqdm import notebook
import nump... | code |
128018806/cell_56 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from tqdm import notebook
import nump... | code |
128018806/cell_39 | [
"text_plain_output_1.png"
] | from tqdm import notebook
import numpy as np
import pandas as pd
import torch
import transformers as ppb
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
try:
data = pd.read_csv('https://code.s3.yandex.net/datasets/toxic_comments.csv')
except:
data = pd.read_csv('/kaggle/input/toxic... | code |
128018806/cell_61 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
lgr = LGBMClassifier()
param_dist = {'learning_rate': [0.1], 'num_leaves': [100], 'n_estimators': [300], 'device': ['gpu']}
LGBM_model = GridSearchCV(lgr, param_grid=param_dist, cv=3, scoring='f1', ... | code |
128018806/cell_72 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from tqdm import notebook
import numpy as np
import pandas as pd
import torch
import tr... | code |
128018806/cell_50 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from tqdm import notebook
import numpy as np
import pandas as pd
import torch
import tr... | code |
128018806/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import torch
import re
import transformers as ppb
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.metrics import f1_score
from tqdm import notebook
from sklearn.linear_model import... | code |
128018806/cell_62 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
lgr = LGBMClassifier()
param_dist = {'learning_rate': [0.1], 'num_leaves': [100], 'n_estimators': [300], 'device': ['gpu']}
LGBM_model = GridSearchCV(lgr, param_grid=param_dist, cv=3, scoring='f1', ... | code |
128018806/cell_28 | [
"text_plain_output_1.png"
] | import torch
import transformers as ppb
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model_class, tokenizer_class, pretrained_weights = (ppb.DistilBertModel, ppb.DistilBertTokenizer, 'distilbert-base-uncased')
tokenizer = tokenizer_class.from_pretrained(pretrained_weights)
model = model_cl... | code |
128018806/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
try:
data = pd.read_csv('https://code.s3.yandex.net/datasets/toxic_comments.csv')
except:
data = pd.read_csv('/kaggle/input/toxic-commentscsv/toxic_comments.csv')
data.info() | code |
128018806/cell_66 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.svm import LinearSVC
fro... | code |
128018806/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import notebook
import numpy as np
import pandas as pd
import torch
import transformers as ppb
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
try:
data = pd.read_csv('https://code.s3.yandex.net/datasets/toxic_comments.csv')
except:
data = pd.read_csv('/kaggle/input/toxic... | code |
106201316/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
train.head(3) | code |
106201316/cell_23 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_33 | [
"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)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_44 | [
"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/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
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 seaborn as sns
sns.set(rc={'figure.figsize': (30, 18)})
import matplotlib.pyplot as plt
import os
train = pd.read_csv('../input/titani... | code |
106201316/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_40 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_29 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_39 | [
"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)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_19 | [
"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/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_50 | [
"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)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_52 | [
"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)
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_1 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(rc={'figure.figsize': (30, 18)})
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106201316/cell_32 | [
"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/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_8 | [
"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/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
all_data = [train, test]
submition_id = test['PassengerId']
def check_missing(data, dtype='object'):
if dtype == 'object':
nulls = data.s... | code |
106201316/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
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 seaborn as sns
sns.set(rc={'figure.figsize': (30, 18)})
import matplotlib.pyplot as plt
import os
train = pd.read_csv('../input/titani... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.