path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
74063930/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data.income = [1 if each == '>50K' else 0 for each in income_data.income]
y = income_data.income
y
dup = income_data.duplicated().any()
income_data = income_data... | code |
74063930/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data.income = [1 if each == '>50K' else 0 for each in income_data.income]
y = income_data.income
y
dup = income_data.duplicated().any()
print('Gibt es doppelte We... | code |
74063930/cell_25 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, r2_score
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data['workclass'] = income_data['w... | code |
74063930/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.describe() | code |
74063930/cell_23 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
x_train.shape
y_train.shape
knn_model = KNeighborsRegressor(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)
model = knn_model.fit(x_train, y_train)
y_pred = model.predict(x_test)
y_pred[0:... | code |
74063930/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum() | code |
74063930/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsRegressor
x_train.shape
y_train.shape
knn = KNeighborsRegressor(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)
knn = KNeighborsClassifier()
knn.fit... | code |
74063930/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, r2_score
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data['workclass'] = income_data['w... | code |
74063930/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.head() | code |
74063930/cell_19 | [
"text_plain_output_1.png"
] | y_train.shape | code |
74063930/cell_18 | [
"text_plain_output_1.png"
] | y_test | code |
74063930/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']... | code |
74063930/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data.income = [1 if each == '>50K' else 0 for each in income_data.income]
y = income_data.income
y | code |
74063930/cell_16 | [
"text_html_output_1.png"
] | x_test | code |
74063930/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['... | code |
74063930/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.info() | code |
74063930/cell_17 | [
"text_plain_output_1.png"
] | x_train.shape | code |
74063930/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']... | code |
74063930/cell_24 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, r2_score
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data['workclass'] = income_data['w... | code |
74063930/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, r2_score
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data['workclass'] = income_data['w... | code |
74063930/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, r2_score
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data['workclass'] = income_data['w... | code |
74063930/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data.income = [1 if each == '>50K' else 0 for each in income_data.income]
y = income_data.income
y
dup = income_data.duplicated().any()
income_data = income_data... | code |
74063930/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum() | code |
74063930/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, r2_score
from sklearn.neighbors import KNeighborsRegressor
import numpy as np
import pandas as pd
income_data = pd.read_csv('../input/adult-income-dataset/adult.csv')
income_data.isnull().sum()
income_data.isin(['?']).sum()
income_data['workclass'] = income_data['w... | code |
128034546/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
plt.figure(figsize=(20, 15))
sns.heatmap(train.corr(), annot=True, cmap='coolwarm')
plt.show() | code |
128034546/cell_6 | [
"text_plain_output_1.png"
] | from scipy.spatial.distance import mahalanobis
from scipy.stats import chi2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
feat... | code |
128034546/cell_2 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train.head() | code |
128034546/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
features = ['clonesize', 'honeybee', 'bumbles', 'andrena', 'osmia', 'AverageRainingDays', 'fruitset', ... | code |
128034546/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline... | code |
128034546/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.spatial.distance import mahalanobis
from scipy.stats import chi2
from scipy.stats import shapiro
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playgro... | code |
128034546/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
train.describe(include='all')
test.describe(include='all') | code |
128034546/cell_10 | [
"text_html_output_1.png"
] | from scipy.spatial.distance import mahalanobis
from scipy.stats import chi2
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
import numpy as np
import p... | code |
73079465/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw = pd.read_csv('../input/ratings.csv')
raw.drop_duplicates(inplace=True)
raw.describe() | code |
73079465/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import surprise
import surprise #Scikit-Learn library for recommender systems.
raw = pd.read_csv('../input/ratings.csv')
raw.drop_duplicates(inplace=True)
raw = raw[['user_id', 'book_id... | code |
73079465/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import surprise | code |
73079465/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw = pd.read_csv('../input/ratings.csv')
raw.describe() | code |
73079465/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import surprise
import surprise #Scikit-Learn library for recommender systems.
raw = pd.read_csv('../input/ratings.csv')
raw.drop_duplicates(inplace=True)
raw = raw[['user_id', 'book_id', 'rating']]
reader = surprise.Reade... | code |
73079465/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import surprise
import surprise #Scikit-Learn library for recommender systems.
raw = pd.read_csv('../input/ratings.csv')
raw.drop_duplicates(inplace=True)
raw = raw[['user_id', 'book_id... | code |
73079465/cell_22 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import surprise
import surprise #Scikit-Learn library for recommender systems.
raw = pd.read_csv('../input/ratings.csv')
raw.drop_duplicates(inplace=True)
raw = raw[['user_id', 'book_id... | code |
73079465/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw = pd.read_csv('../input/ratings.csv')
raw.drop_duplicates(inplace=True)
print(f'Existem {raw.shape[0]} avalações.')
print('Usuários únicos:', len(raw.user_id.unique()))
print('Livros únicos:', len(raw.book_id.unique()))
p... | code |
1002832/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | df = pd.read_csv('../input/cameras.csv', encoding='utf-8')
print('The shape:')
print(df.shape)
print('\nThe information:')
print(df.info())
print('\nNAs:')
print(np.sum(df.isnull()))
print(np.sum(df.isnull()) / len(df) * 100)
print('\nStart and End:')
print(df['DATE'][[0, df.shape[0] - 1]])
print('\nDifferent cameras:'... | code |
50237715/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_33 | [
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_contain... | code |
50237715/cell_39 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_contain... | code |
50237715/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import riiideducation
import seaborn as sns
import matplotlib.pyplot as plt
import gc
import os
import warnings
warnings.filterwarnings('ignore')
for dirname, _, filenames in os.walk('/kaggle/input/riiid-test-answer-prediction'):
for filename in fil... | code |
50237715/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_24 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int... | code |
50237715/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
50237715/cell_36 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv', low_memory=False, nrows=10 ** 6, dtype={'row_id': 'int64', 'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'task_container_id': 'int16', 'user_answer': 'int8', 'answered_correc... | code |
74060518/cell_7 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import glob
import os
INPUT_PATH = '../input/cityscapes-image-pairs/cityscapes_data'
train_files = glob.glob(os.path.join(INPUT_PATH + '/train', '*jpg'))
val_files = glob.glob(os.path.join(INPUT_PATH + '/val', '*jpg'))
print('Total train images:', len(train_files))
print('Total validation images:', len(val_files)) | code |
74060518/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import cv2
import glob
import matplotlib.pyplot as plt
import numpy as np
import os
INPUT_PATH = '../input/cityscapes-image-pairs/cityscapes_data'
BATCH_SIZE = 4
INPUT_IMG_SIZE = 256
OUTPUT_CLASSES = 10
LEARINING_RATE = 0.001
train_files = glob.glob(os.path.join(INPUT_PATH + '... | code |
74060518/cell_8 | [
"image_output_1.png"
] | import cv2
import glob
import matplotlib.pyplot as plt
import os
INPUT_PATH = '../input/cityscapes-image-pairs/cityscapes_data'
train_files = glob.glob(os.path.join(INPUT_PATH + '/train', '*jpg'))
val_files = glob.glob(os.path.join(INPUT_PATH + '/val', '*jpg'))
fig, axes = plt.subplots(1, 2, figsize=(20, 5))
for ... | code |
74060518/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import cv2
import glob
import matplotlib.pyplot as plt
import numpy as np
import os
INPUT_PATH = '../input/cityscapes-image-pairs/cityscapes_data'
BATCH_SIZE = 4
INPUT_IMG_SIZE = 256
OUTPUT_CLASSES = 10
LEARINING_RATE = 0.001
train_files = glob.glob(os.path.join(INPUT_PATH + '... | code |
74060518/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import cv2
import glob
import matplotlib.pyplot as plt
import numpy as np
import os
INPUT_PATH = '../input/cityscapes-image-pairs/cityscapes_data'
BATCH_SIZE = 4
INPUT_IMG_SIZE = 256
OUTPUT_CLASSES = 10
LEARINING_RATE = 0.001
train_files = glob.glob(os.path.join(INPUT_PATH + '... | code |
74060518/cell_14 | [
"image_output_1.png"
] | import cv2
import glob
import matplotlib.pyplot as plt
import numpy as np
import os
INPUT_PATH = '../input/cityscapes-image-pairs/cityscapes_data'
BATCH_SIZE = 4
INPUT_IMG_SIZE = 256
OUTPUT_CLASSES = 10
LEARINING_RATE = 0.001
train_files = glob.glob(os.path.join(INPUT_PATH + '/train', '*jpg'))
val_files = glob.g... | code |
74060518/cell_10 | [
"text_plain_output_1.png"
] | import cv2
import glob
import matplotlib.pyplot as plt
import os
INPUT_PATH = '../input/cityscapes-image-pairs/cityscapes_data'
train_files = glob.glob(os.path.join(INPUT_PATH + '/train', '*jpg'))
val_files = glob.glob(os.path.join(INPUT_PATH + '/val', '*jpg'))
fig, axes = plt.subplots(1,2, figsize = (20,5))
for ... | code |
50242460/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/credit-card-customers/BankChurners.csv', dtype={'Income_Category': 'str'}).iloc[:, :-2]
y = df['Attrition_Flag']
df.drop(['Attrition_Flag'], axis=1, inplace=True)
df.head() | code |
50242460/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
plt.figure(1, figsize=(4, 3), dpi=100)
axes = plt.gca()
axes.set_ylim([0.5, 0.9])
plt.text(x=0.0, y=0.75, s='16%', fontsize=60, color='#ae012e', fontweight='medium')
plt.text(x=0.0, y=0.63, s='of total data points have \ncustomers who will attrit', fontsize=20, color='gray')
plt.axis('o... | code |
73077959/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from tensorflow.keras.models import load_model
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV f... | code |
73077959/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 |
73077959/cell_7 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import load_model
import numpy as np
import numpy as np # linear algebra
import os
import os
import random
import tensorflow as tf
import numpy as np
import pandas as pd
import os
model = load_model('../input/efficientnetv2transfer/model-18-fine_after.h5')
def set_seed(seed=200):
... | code |
73077959/cell_3 | [
"text_plain_output_1.png"
] | !pip install -U git+https://github.com/GdoongMathew/EfficientNetV2 --no-deps | code |
1009415/cell_1 | [
"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 |
1009415/cell_7 | [
"text_plain_output_1.png"
] | import glob
import glob
train_image_filenames = sorted(glob.glob('../input/train.7z'))
print('Found images:')
print(train_image_filenames) | code |
1009415/cell_5 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
33109223/cell_21 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
data_normal = norm.rvs(size=10000, loc=0, scale=1)
stats.kstest(data_normal, 'norm') | code |
33109223/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import gamma
from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
n = 10000
start = 10
width = 20
data_uniform = uniform.rvs(size=n, loc=start, scale=width)
ax = sns.distplot(data_uniform,
bins=100,
kde=True,
color... | code |
33109223/cell_54 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import bernoulli
from scipy.stats import binom
from scipy.stats import expon
from scipy.stats import gamma
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
import seaborn as sns
n = 10000
start = 10
width = 20
data_uniform = uniform.rvs(size=n, loc=st... | code |
33109223/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
n = 10000
start = 10
width = 20
data_uniform = uniform.rvs(size=n, loc=start, scale=width)
ax = sns.distplot(data_uniform,
bins=100,
kde=True,
color='skyblue',
h... | code |
33109223/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import binom
from scipy.stats import expon
from scipy.stats import gamma
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
import seaborn as sns
n = 10000
start = 10
width = 20
data_uniform = uniform.rvs(size=n, loc=start, scale=width)
ax = sns.distplo... | code |
33109223/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import expon
from scipy.stats import gamma
from scipy.stats import norm
from scipy.stats import poisson
from scipy.stats import uniform
import seaborn as sns
n = 10000
start = 10
width = 20
data_uniform = uniform.rvs(size=n, loc=start, scale=width)
ax = sns.distplot(data_uniform,
... | code |
33109223/cell_22 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
data_normal = norm.rvs(size=10000, loc=0, scale=1)
stats.kstest(data_normal, 'norm')
stats.anderson(data_normal, dist='norm') | code |
33109223/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import uniform
import seaborn as sns
n = 10000
start = 10
width = 20
data_uniform = uniform.rvs(size=n, loc=start, scale=width)
ax = sns.distplot(data_uniform, bins=100, kde=True, color='skyblue', hist_kws={'linewidth': 15, 'alpha': 1})
ax.set(xlabel='Uniform Distribution ', ylabel='Frequency') | code |
33109223/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import expon
from scipy.stats import gamma
from scipy.stats import norm
from scipy.stats import uniform
import seaborn as sns
n = 10000
start = 10
width = 20
data_uniform = uniform.rvs(size=n, loc=start, scale=width)
ax = sns.distplot(data_uniform,
bins=100,
kd... | code |
32068746/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('... | code |
32068746/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 |
32068746/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
model = LinearRegression()
model.fit(x_train, y_train)
preds = model.predict(x_valid)
print(mean_absolute_error(preds, y_valid)) | code |
128001996/cell_9 | [
"text_plain_output_100.png",
"text_plain_output_334.png",
"application_vnd.jupyter.stderr_output_145.png",
"application_vnd.jupyter.stderr_output_289.png",
"application_vnd.jupyter.stderr_output_313.png",
"application_vnd.jupyter.stderr_output_373.png",
"text_plain_output_84.png",
"text_plain_output_3... | import pandas as pd
import seaborn as sns
target = 'yield'
df1 = pd.read_csv(TRAIN_CSV)
df1.rename({'Id': 'id'}, axis=1, inplace=True)
df1['test'] = 0
df1['gen'] = 1
df2 = pd.read_csv(TEST_CSV)
df2.rename({'Id': 'id'}, axis=1, inplace=True)
df2['test'] = 1
df2['gen'] = 1
df3 = pd.read_csv(EXTERNAL_CSV)
df3.rename({'R... | code |
128001996/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
target = 'yield'
df1 = pd.read_csv(TRAIN_CSV)
df1.rename({'Id': 'id'}, axis=1, inplace=True)
df1['test'] = 0
df1['gen'] = 1
df2 = pd.read_csv(TEST_CSV)
df2.rename({'Id': 'id'}, axis=1, inplace=True)
df2['test'] = 1
df2['gen'] = 1
df3 = pd.read_csv(EXTERNAL_CSV)
df3.rename({'Row#': 'id'}, axis=1, in... | code |
128001996/cell_23 | [
"image_output_1.png"
] | from sklearn.metrics import roc_auc_score, mean_absolute_error
from sklearn.model_selection import train_test_split, RepeatedKFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor, XGBClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as p... | code |
128001996/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
target = 'yield'
df1 = pd.read_csv(TRAIN_CSV)
df1.rename({'Id': 'id'}, axis=1, inplace=True)
df1['test'] = 0
df1['gen'] = 1
df2 = pd.read_csv(TEST_CSV)
df2.rename({'Id': 'id'}, axis=1, inplace=True)
df2['test'] = 1
df2['gen'] = 1
df3 = pd.read_csv(EXTERNAL_CSV)
df3.rename({'Row#': 'id'}, axis=1, in... | code |
128001996/cell_2 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn
from sklearn.model_selection import train_test_split, RepeatedKFold, StratifiedKFold
from sklearn.metrics import roc_auc_score, mean_absolute_error
from sklearn.preprocessing import StandardScaler
import optuna
f... | code |
128001996/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso, Ridge, LogisticRegression
from sklearn.metrics import roc_auc_score, mean_absolute_error
from sklearn.model_selection import train_test_split, RepeatedKFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
... | code |
128001996/cell_24 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor, CatBoostClassifier
from sklearn.metrics import roc_auc_score, mean_absolute_error
from sklearn.model_selection import train_test_split, RepeatedKFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import ... | code |
128001996/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
target = 'yield'
df1 = pd.read_csv(TRAIN_CSV)
df1.rename({'Id': 'id'}, axis=1, inplace=True)
df1['test'] = 0
df1['gen'] = 1
df2 = pd.read_csv(TEST_CSV)
df2.rename({'Id': 'id'}, axis=1, inplace=True)
df2['test'] = 1
df2['gen'] = 1
df3 = pd.read_csv(EXTERNAL_CSV)
df3.rename({'Row#': 'id'}, axis=1, in... | code |
128001996/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.metrics import roc_auc_score, mean_absolute_error
from sklearn.model_selection import train_test_split, RepeatedKFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import nump... | code |
128001996/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
target = 'yield'
df1 = pd.read_csv(TRAIN_CSV)
df1.rename({'Id': 'id'}, axis=1, inplace=True)
df1['test'] = 0
df1['gen'] = 1
df2 = pd.read_csv(TEST_CSV)
df2.rename({'Id': 'id'}, axis=1, inplace=True)
df2['test'] = 1
df2['gen'] = 1
df3 = pd.read_csv(EXTERNAL_CSV)
df3.rename({'Row#': 'id'}, axis=1, in... | code |
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