path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17115723/cell_4 | [
"text_plain_output_1.png"
] | from time import time
import cv2 as cv
import imageio as io
import numpy as np # linear algebra
import os
list_train_img = []
a = 0
timea = time()
print('Converting training images to a numpy array...')
for im in os.listdir('../input/train_images'):
uri = '../input/train_images/' + im
image = io.imread(uri... | code |
17115723/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
from time import time
import numpy as np
import pandas as pd
import imageio as io
import cv2 as cv
import matplotlib.pyplot as plt
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
print('Setup complete!') | code |
17115723/cell_3 | [
"text_plain_output_1.png"
] | import os
print('Number of images in the training set:', len(os.listdir('../input/train_images')))
print('Number of images in the test set:', len(os.listdir('../input/test_images'))) | code |
32069310/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching.head() | code |
32069310/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_2 | [
"text_html_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 |
32069310/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
32069310/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pitching = pd.read_csv('/kaggle/input/baseball-databank/Pitching.csv')
pitching = pitching[(pitching['yearID'] >= 1990) & (pitching['GS'] >= 26) & (pitching['G'] == pitching['GS'])].iloc[:, :20]
pitching = pitching.drop(['stint', 'W', 'L', 'CG', '... | code |
105179030/cell_9 | [
"text_plain_output_1.png"
] | i = 0
while i <= 10:
i = i + 1
j = 0
for i in range(1, 21):
j = j + i
print(j) | code |
105179030/cell_6 | [
"text_plain_output_1.png"
] | i = 0
while i <= 10:
i = i + 1
student = ['adnan', 'saad', 'zaheeb']
for i in student:
print(i) | code |
105179030/cell_7 | [
"text_plain_output_1.png"
] | i = 0
while i <= 10:
i = i + 1
a = range(10)
for i in a:
if i % 2 == 1:
print(i) | code |
105179030/cell_3 | [
"text_plain_output_1.png"
] | i = 0
while i <= 10:
if i % 2 == 0:
print(i)
i = i + 1 | code |
105179030/cell_5 | [
"text_plain_output_1.png"
] | i = 0
while i <= 10:
i = i + 1
name = 'Adnan'
for i in name:
print(i) | code |
105174093/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
import os
kaggle_survey_2019 = pd.read_csv('../input/kaggle-survey-2019/multip... | code |
105174093/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
import os
kaggle_survey_2019 = pd.read_csv('../input/kaggle-survey-2019/multip... | code |
105174093/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
import os
kaggle_survey_2019 = pd.read_csv('../input/kaggle-survey-2019/multip... | code |
105174093/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
import os
kaggle_survey_2019 = pd.read_csv('../input/kaggle-survey-2019/multip... | code |
105174093/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
import os
kaggle_survey_2019 = pd.read_csv('../input/kaggle-survey-2019/multip... | code |
2013637/cell_4 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
train.head() | code |
2013637/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data = all_data.dro... | code |
2013637/cell_5 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data = all_data.drop(['Name'], axis=1)
all_data = pd.get_dummies(all... | code |
17096261/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import torchvision.transforms as transforms
import os
import random
df = pd.read_csv('../input/train.csv')
exps = df['experiment'].unique()
exps = [exp.spl... | code |
17096261/cell_7 | [
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torchvision.transforms as transforms
import numpy as np
import pandas as pd
import matplotlib.pyplot as... | code |
17096261/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torchvision.transforms as transforms
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import... | code |
17096261/cell_5 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torchvision.transforms as transforms
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import... | code |
49123700/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import matplotlib.pyplot a... | code |
49123700/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import matplotlib.pyplot a... | code |
49123700/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import... | code |
49123700/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import... | code |
49123700/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import matplotlib.pyplot a... | code |
49123700/cell_18 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import... | code |
49123700/cell_32 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import... | code |
49123700/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import matplotlib.pyplot a... | code |
49123700/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import matplotlib.pyplot a... | code |
49123700/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
imp... | code |
49123700/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import six
import warnings
import warnings
warnings.filterwarnings('ignore')
import sys, joblib
import six
sys.modules['sklearn.externals.six'] = six
sys.modules['sklearn.externals.joblib'] = joblib
import numpy as np
import pandas as pd
import re
import matplotlib.pyplot a... | code |
72098069/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import roc_auc_score
from xgboost import XGBClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/dont-overfit-ii/train.csv')
df_test = pd.read_csv('../input/dont-overfit-ii/test.csv')
labels = df_train.columns.drop(['id', 'target'])
t... | code |
72098069/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(x_train, y_train)
preds = model.predict(x_test)
from sklearn.metrics import roc_auc_score
print('auc_train:', roc_auc_score(y_train,... | code |
72098069/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/dont-overfit-ii/train.csv')
df_test = pd.read_csv('../input/dont-overfit-ii/test.csv')
labels = df_train.columns.drop(['id', 'target'])
target = df_train['target']
ide = df_test['id']
df_test = df_test.drop('id', ax... | code |
72098069/cell_6 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/dont-overfit-ii/train.csv')
df_test = pd.read_csv('../input/dont-overfit-ii/test.csv')
labels = df_train.columns.drop(['id', 'target'])
target = df_train['target']
ide = df_test['id']
df_te... | code |
72098069/cell_11 | [
"text_plain_output_1.png"
] | from xgboost import XGBClassifier
from xgboost import XGBClassifier
m = XGBClassifier(max_depth=2, gamma=11, eta=0.8, reg_alpha=0.7, reg_lambda=0.9, eval_metric=None)
m.fit(x_train, y_train)
pred = m.predict(x_test) | code |
72098069/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import missingno as msno
from sklearn.model_selection import train_test_split
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
72098069/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/dont-overfit-ii/train.csv')
df_test = pd.read_csv('../input/dont-overfit-ii/test.csv')
labels = df_train.columns.drop(['id', 'target'])
target = df_train['target']
ide = df_test['id']
df_test = df_test.drop('id', ax... | code |
72098069/cell_12 | [
"text_html_output_1.png"
] | from sklearn.metrics import roc_auc_score
from xgboost import XGBClassifier
from xgboost import XGBClassifier
m = XGBClassifier(max_depth=2, gamma=11, eta=0.8, reg_alpha=0.7, reg_lambda=0.9, eval_metric=None)
m.fit(x_train, y_train)
pred = m.predict(x_test)
print('auc_train:', roc_auc_score(y_train, m.predict(x_trai... | code |
33103605/cell_9 | [
"text_plain_output_1.png"
] | from ktrain import text
import ktrain
import pandas as pd
import pathlib
train_path = '../input/sentimentdatasets/testStackOverFlow.csv'
tr_path = pathlib.Path(train_path)
if tr_path.exists():
train_df = pd.read_csv(train_path, encoding='utf-16', sep=';', header=None)
else:
raise SystemExit('Train path does... | code |
33103605/cell_4 | [
"text_plain_output_1.png"
] | from ktrain import text
import pandas as pd
import pathlib
train_path = '../input/sentimentdatasets/testStackOverFlow.csv'
tr_path = pathlib.Path(train_path)
if tr_path.exists():
train_df = pd.read_csv(train_path, encoding='utf-16', sep=';', header=None)
else:
raise SystemExit('Train path does not exist.')
m... | code |
33103605/cell_6 | [
"text_plain_output_100.png",
"text_plain_output_84.png",
"text_plain_output_56.png",
"text_plain_output_158.png",
"text_plain_output_181.png",
"text_plain_output_137.png",
"text_plain_output_139.png",
"text_plain_output_35.png",
"text_plain_output_130.png",
"text_plain_output_117.png",
"text_pla... | from ktrain import text
import ktrain
import pandas as pd
import pathlib
train_path = '../input/sentimentdatasets/testStackOverFlow.csv'
tr_path = pathlib.Path(train_path)
if tr_path.exists():
train_df = pd.read_csv(train_path, encoding='utf-16', sep=';', header=None)
else:
raise SystemExit('Train path does... | code |
33103605/cell_2 | [
"text_plain_output_1.png"
] | !pip install ktrain
import ktrain
from ktrain import text | code |
33103605/cell_3 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pathlib
train_path = '../input/sentimentdatasets/testStackOverFlow.csv'
tr_path = pathlib.Path(train_path)
if tr_path.exists():
train_df = pd.read_csv(train_path, encoding='utf-16', sep=';', header=None)
print('Train path set.')
else:
raise SystemExit('Train path does not exist.... | code |
33103605/cell_10 | [
"text_plain_output_1.png"
] | from ktrain import text
import ktrain
import pandas as pd
import pathlib
train_path = '../input/sentimentdatasets/testStackOverFlow.csv'
tr_path = pathlib.Path(train_path)
if tr_path.exists():
train_df = pd.read_csv(train_path, encoding='utf-16', sep=';', header=None)
else:
raise SystemExit('Train path does... | code |
33103605/cell_5 | [
"text_plain_output_1.png"
] | from ktrain import text
import ktrain
import pandas as pd
import pathlib
train_path = '../input/sentimentdatasets/testStackOverFlow.csv'
tr_path = pathlib.Path(train_path)
if tr_path.exists():
train_df = pd.read_csv(train_path, encoding='utf-16', sep=';', header=None)
else:
raise SystemExit('Train path does... | code |
130011822/cell_13 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
vectorizer = CountVectorizer()
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)
from sklearn.preprocessing import ... | code |
130011822/cell_6 | [
"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/unlock-the-power-of-english-asl-with-aslg-pc12-c/train.csv')
df.columns
df.head() | code |
130011822/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
vectorizer = CountVectorizer()
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)
from sklearn.preprocessing import ... | code |
130011822/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 |
130011822/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression | code |
130011822/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
vectorizer = CountVectorizer()
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)
from sklearn.preprocessing import ... | code |
130011822/cell_5 | [
"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/unlock-the-power-of-english-asl-with-aslg-pc12-c/train.csv')
df.columns | code |
122263749/cell_13 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from os import listdir
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import cv2
import numpy as np
INIT_LR = 0.001
BS = 32
default_image_size = tuple((72, 72))
image_size = 72
directory_root = '../input/brain-tumor-classif... | code |
122263749/cell_9 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from os import listdir
import cv2
import numpy as np
INIT_LR = 0.001
BS = 32
default_image_size = tuple((72, 72))
image_size = 72
directory_root = '../input/brain-tumor-classification-mri/Training'
width = 256
height = 256
depth = 3
def convert_image_to_array(image_dir):
t... | code |
122263749/cell_20 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.preprocessing import image
from os import listdir
from tensorflow import keras
from tensorflow.keras import layers
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
... | code |
122263749/cell_11 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from os import listdir
from sklearn.preprocessing import LabelEncoder
import cv2
import numpy as np
INIT_LR = 0.001
BS = 32
default_image_size = tuple((72, 72))
image_size = 72
directory_root = '../input/brain-tumor-classification-mri/Training'
width = 256
height = 256
depth =... | code |
122263749/cell_15 | [
"text_plain_output_1.png"
] | print(f'x_train shape: {x_train.shape} - y_train shape: {y_train.shape}')
print(f'x_test shape: {x_test.shape} - y_test shape: {y_test.shape}') | code |
122263749/cell_16 | [
"image_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.preprocessing import image
from os import listdir
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
INIT_LR = 0.001
BS = 32
default_image_size = tuple((72, 72))
image_size = 72
dir... | code |
122263749/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.preprocessing import image
from os import listdir
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
INIT_LR = 0.001
BS = 32
default_image_size = tuple((72, 72))
image_size = 72
directory_root = '../input/brain-tumor-classification-mri/T... | code |
122263749/cell_12 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from os import listdir
from sklearn.preprocessing import LabelEncoder
import cv2
import numpy as np
INIT_LR = 0.001
BS = 32
default_image_size = tuple((72, 72))
image_size = 72
directory_root = '../input/brain-tumor-classification-mri/Training'
width = 256
height = 256
depth =... | code |
122263749/cell_5 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | pip install -U tensorflow-addons | code |
90118648/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train['Embarked'] = [1 if l == 'S' else 2 if l == 'C' else 3 for l in train['Embarked']]
train['Embarked'].value_counts() | code |
90118648/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.info() | code |
90118648/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.preprocessing import PowerTransformer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', '... | code |
90118648/cell_33 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.preprocessing import PowerTransformer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', '... | code |
90118648/cell_20 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.describe(include='all') | code |
90118648/cell_19 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/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 |
90118648/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train['Sex'] = [0 if l == 'male' else 1 for l in train['Sex']]
train['Sex'].value_counts() | code |
90118648/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_32 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.preprocessing import PowerTransformer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', '... | code |
90118648/cell_28 | [
"text_plain_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp',... | code |
90118648/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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test['Sex'] = [0 if l == 'male' else 1 for l in test['Sex']]
test['Sex'].value_counts() | code |
90118648/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_35 | [
"text_html_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.impute import KNNImputer
from sklearn.preprocessing import PowerTransformer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
pre... | code |
90118648/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.preprocessing import PowerTransformer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', '... | code |
90118648/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_14 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
90118648/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predict... | code |
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