path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105192343/cell_12 | [
"text_plain_output_1.png"
] | total_apple = 5890
no_of_people = 70
no_of_apple_to_each = total_apple / no_of_people
total_apple = 5890
no_of_people = 70
no_of_apple_reminded = total_apple % no_of_people
print('no of apple reminded is', no_of_apple_reminded) | code |
17098574/cell_4 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
X = df[['OverallQual']].values
y = df['SalePrice'].values
slr = LinearRegression()
slr.fit(X, y) | code |
17098574/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_test[['Id', 'SalePrice']].head() | code |
17098574/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import seaborn as sns
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt | code |
17098574/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_test.head() | code |
17098574/cell_8 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
X = df[['OverallQual']].values
y = df['SalePrice'].values
slr = LinearRegression()
slr.fit(X, y)
df_test = pd.read_csv('../... | code |
17098574/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.head() | code |
17098574/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_test.head() | code |
17098574/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
X = df[['OverallQual']].values
y = df['SalePrice'].values
slr = LinearRegression()
slr.fit(X, y)
plt.scatter(X, y)
plt.plot... | code |
327983/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grou... | code |
327983/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_... | code |
327983/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.head() | code |
327983/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.count()
test_df = test_df.dropna()
test_d... | code |
327983/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean() | code |
327983/cell_29 | [
"text_html_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
import sklearn.ensemble as ske
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby(... | code |
327983/cell_26 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grou... | code |
327983/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.count() | code |
327983/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grou... | code |
327983/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import random
import numpy as np
import pandas as pd
from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import sklearn.ensemble as ske
import tensorflow as tf
from tensorflow.contrib import skflow | code |
327983/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
print(class_sex_grouping['Survi... | code |
327983/cell_28 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
import sklearn.ensemble as ske
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby(... | code |
327983/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
class_sex_grouping['Survived']... | code |
327983/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_... | code |
327983/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.head() | code |
327983/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
test_df.count()
test_df = test_df.dropna()
test_df.count() | code |
327983/cell_31 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
from tensorflow.contrib import skflow
import numpy as np
import pandas as pd
import tensorflow as tf
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': n... | code |
327983/cell_24 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grou... | code |
327983/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_... | code |
327983/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import numpy as np
import pandas as pd
import sklearn.ensemble as ske
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df.groupby(... | code |
327983/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
titanic_df['Survived'].mean() | code |
50242244/cell_13 | [
"text_plain_output_1.png"
] | from lightfm import LightFM
from lightfm.datasets import fetch_movielens
import numpy as np
import numpy as np # linear algebra
data = fetch_movielens(min_rating=3.0)
model_0 = LightFM(loss='warp')
model_0.fit(data['train'], epochs=70, num_threads=4)
def recommendation(model, data, ids):
n_users, n_items = da... | code |
50242244/cell_9 | [
"text_plain_output_1.png"
] | from lightfm import LightFM
from lightfm.datasets import fetch_movielens
data = fetch_movielens(min_rating=3.0)
model_1 = LightFM(loss='bpr')
model_1.fit(data['train'], epochs=70, num_threads=4) | code |
50242244/cell_6 | [
"text_plain_output_1.png"
] | from lightfm.datasets import fetch_movielens
data = fetch_movielens(min_rating=3.0)
print(repr(data['train']))
print(repr(data['test'])) | code |
50242244/cell_8 | [
"text_plain_output_1.png"
] | from lightfm import LightFM
from lightfm.datasets import fetch_movielens
data = fetch_movielens(min_rating=3.0)
model_0 = LightFM(loss='warp')
model_0.fit(data['train'], epochs=70, num_threads=4) | code |
50242244/cell_10 | [
"text_plain_output_1.png"
] | from lightfm import LightFM
from lightfm.datasets import fetch_movielens
from lightfm.evaluation import precision_at_k,auc_score
data = fetch_movielens(min_rating=3.0)
model_0 = LightFM(loss='warp')
model_0.fit(data['train'], epochs=70, num_threads=4)
model_1 = LightFM(loss='bpr')
model_1.fit(data['train'], epochs... | code |
50242244/cell_5 | [
"text_plain_output_1.png"
] | from lightfm.datasets import fetch_movielens
data = fetch_movielens(min_rating=3.0)
data | code |
17121162/cell_13 | [
"text_plain_output_1.png"
] | param = {'lr': (0.1, 10, 10), 'batch_size': [32, 64, 128, 256, 512], 'epochs': [10, 20, 50], 'validation_split': [0.1, 0.2, 0.5], 'dropout': [0.1, 0.25, 0.5, 0.8], 'optimizer': [Adam, Nadam], 'loss': ['categorical_crossentropy'], 'last_activation': ['softmax'], 'weight_regulizer': [None]} | code |
17121162/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 3303, 5160]
Year = [2014, 2015, 2016, 2017, 2018, 2019]
list_of_tuples = list(zip(Year, Accepted, Submitted))
df = pd.DataFrame... | code |
17121162/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
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 pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 3303, 5160]
Year = [2014, 2015, 2016, 2017,... | code |
17121162/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 3303, 5160]
Year = [2014, 2015, 2016, 2017, 2018, 2019]
list_of_tuples = list(zip(Year, Accepted, Submitted))
df = pd.DataFrame... | code |
17121162/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
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 pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 3303, 5160]
Year = [2014, 2015, 2016, 2017,... | code |
17121162/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
from sklearn.utils import class_weight
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 pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 330... | code |
17121162/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17121162/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
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 pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 3303, 5160]
Year = [2014, 2015, 2016, 2017,... | code |
17121162/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
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 pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 3303, 5160]
Year = [2014, 2015, 2016, 2017,... | code |
17121162/cell_15 | [
"text_plain_output_1.png"
] | from talos import Reporting
from talos import Reporting
r = Reporting('../input/resnet50-talos-score/resnet50_talos_score.csv')
r.data.sort_values(['val_acc'], ascending=False)
r.best_params()[0] | code |
17121162/cell_16 | [
"text_plain_output_1.png"
] | from talos import Reporting
from talos import Reporting
r = Reporting('../input/resnet50-talos-score/resnet50_talos_score.csv')
r.data.sort_values(['val_acc'], ascending=False)
r.best_params()[0]
r.correlate('val_loss') | code |
17121162/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 3303, 5160]
Year = [2014, 2015, 2016, 2017, 2018, 2019]
list_of_tuples = list(zip(Year, Accept... | code |
17121162/cell_17 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dropout())
model.add(Dense(units=10, activation='softmax'))
model.compile(loss='categorical_... | code |
17121162/cell_14 | [
"text_plain_output_1.png"
] | from talos import Reporting
from talos import Reporting
r = Reporting('../input/resnet50-talos-score/resnet50_talos_score.csv')
r.data.sort_values(['val_acc'], ascending=False) | code |
17121162/cell_10 | [
"text_html_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
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 pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 3303, 5160]
Year = [2014, 2015, 2016, 2017,... | code |
17121162/cell_12 | [
"text_plain_output_1.png"
] | !pip install talos | code |
17121162/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
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 pandas as pd
Accepted = [540, 602, 643, 783, 979, 1300]
Submitted = [1807, 2123, 2145, 2620, 3303, 5160]
Year = [2014, 2015, 2016, 2017,... | code |
18120034/cell_13 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('openaq', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref = dataset_ref.table('global_air_quality')
table = client... | code |
18120034/cell_9 | [
"text_html_output_1.png"
] | query = "\n SELECT city\n FROM `bigquery-public-data.openaq.global_air_quality`\n WHERE country = 'US'\n"
query | code |
18120034/cell_4 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('openaq', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
for table in tables:
print(table.table_id) | code |
18120034/cell_6 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('openaq', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref = dataset_ref.table('global_air_quality')
table = client... | code |
18120034/cell_2 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('openaq', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref) | code |
18120034/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18120034/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from google.cloud import bigquery
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('openaq', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref = dataset_ref.table('global_air_quality')
table = client... | code |
18120034/cell_15 | [
"text_html_output_1.png"
] | from google.cloud import bigquery
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('openaq', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref = dataset_ref.table('global_air_quality')
table = client... | code |
18120034/cell_14 | [
"text_plain_output_1.png"
] | query_1 = "\n SELECT city, country, source_name\n FROM `bigquery-public-data.openaq.global_air_quality`\n WHERE country = 'US'\n"
query_job_1 = client.query(query_1)
df_1 = query_job_1.to_dataframe()
df_1.head() | code |
18120034/cell_12 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('openaq', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref = dataset_ref.table('global_air_quality')
table = client... | code |
16163769/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df
simple_feature_cutting_df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)
simple_feature_cutting_df = simple_feature_cutting_df.dropna()
simple_feature_cutting_df = pd.get_dummies... | code |
16163769/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df
simple_feature_cutting_df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)
simple_feature_cutting_df = simple_feature_cutting_d... | code |
16163769/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df | code |
16163769/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16163769/cell_7 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df
simple_feature_cutting_df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)
simple_feature_... | code |
16163769/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df
simple_feature_cutting_df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)
simple_feature_... | code |
16163769/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df
import matplotlib.pyplot as plt
df['Age'].hist(bins=20) | code |
72069261/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/breast-c... | code |
72069261/cell_9 | [
"text_plain_output_1.png",
"image_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/breast-cancer-csv/breastCancer.csv')
df
df.shape
df.columns
print(df['class'].value_counts() / 6.99)
df['class'].value_counts() | code |
72069261/cell_4 | [
"text_plain_output_1.png",
"image_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/breast-cancer-csv/breastCancer.csv')
df
df.head(10) | code |
72069261/cell_33 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
i... | code |
72069261/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/breast-cancer-csv/breastCancer.csv')
df
df.shape
df.describe() | code |
72069261/cell_29 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, ... | code |
72069261/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/breast-cancer-csv/breastCancer.csv')
df
df.shape
df.columns
... | code |
72069261/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 |
72069261/cell_7 | [
"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/breast-cancer-csv/breastCancer.csv')
df
df.shape
df.info() | code |
72069261/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/breast-cancer-csv/breastCancer.csv')
df
df.shape
df.columns
... | code |
72069261/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data proce... | code |
72069261/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)
df = pd.read_csv('/kaggle/input/breast-cancer-csv/breastCancer.csv')
df
df.shape
df.columns | code |
72069261/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-csv/breastCancer.csv')
df
df.shape
df.columns
c = {col: df[df[col] == '?'].shape[0] for col in df.col... | code |
72069261/cell_3 | [
"text_plain_output_1.png",
"image_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/breast-cancer-csv/breastCancer.csv')
df | code |
72069261/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/breast-cancer-csv/breastCancer.csv')
df
df.shape
df.columns
c = {col: df[df[col] == '?'].shape[0] for col in df.col... | code |
72069261/cell_24 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seab... | code |
72069261/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data proce... | code |
72069261/cell_12 | [
"text_html_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/breast-cancer-csv/breastCancer.csv')
df
df.shape
df.columns
c = {col: df[df[col] == '?'].shape[0] for col in df.columns}
c | code |
72069261/cell_5 | [
"text_plain_output_1.png",
"image_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/breast-cancer-csv/breastCancer.csv')
df
df.shape | code |
128033738/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
path = '/kaggle/input/news-headlines/news_summary.csv'
df = pd.read_csv(path)
df.head() | code |
128033738/cell_24 | [
"text_html_output_82.png",
"text_html_output_255.png",
"text_html_output_149.png",
"text_html_output_277.png",
"text_html_output_338.png",
"text_html_output_282.png",
"text_html_output_219.png",
"text_html_output_130.png",
"text_html_output_320.png",
"text_html_output_155.png",
"text_html_output... | from rich import box
from rich.console import Console
from rich.table import Column, Table
from torch import cuda
from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler
from transformers import T5Tokenizer, T5ForConditionalGeneration
import numpy as np
import os
import os
import pan... | code |
128033738/cell_22 | [
"text_plain_output_1.png"
] | from rich import box
from rich.console import Console
from rich.table import Column, Table
from torch import cuda
from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler
from transformers import T5Tokenizer, T5ForConditionalGeneration
import numpy as np
import os
import os
import pan... | code |
128033738/cell_10 | [
"text_html_output_1.png"
] | from torch import cuda
from torch import cuda
device = 'cuda' if cuda.is_available() else 'cpu'
device | code |
16168139/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
structures = pd.read_csv('../input/structures.csv')
M = 8000
fig, ax = plt.subplots(1,3,figsize=(20,5))
colors = ["darkred", "dodgerblue", "mediumseagreen", "gold", "purple"]
atoms = structures.atom.unique()
for ... | code |
16168139/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
structures = pd.read_csv('../input/structures.csv')
structures.head() | code |
16168139/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import os
print(os.listdir('../input')) | code |
16168139/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
structures = pd.read_csv('../input/structures.csv')
M = 8000
fig, ax = plt.subplots(1, 3, figsize=(20, 5))
colors = ['darkred', 'dodgerblue', 'mediumseagreen', 'gold', 'purple']
atoms = structures.atom.unique()
for... | code |
106196484/cell_13 | [
"text_plain_output_1.png"
] | from pipelines import pipeline
nlp = pipeline('multitask-qa-qg') | code |
106196484/cell_2 | [
"text_plain_output_1.png"
] | !pip install Wikipedia-API
import wikipediaapi
wiki_wiki = wikipediaapi.Wikipedia('en')
ml_art = wiki_wiki.page('Machine_Learning')
print("Page - Exists: %s" % ml_art.exists())
print("Page - Title: %s" % ml_art.title)
print("Page - Summary: %s" % ml_art.summary[0:60])
print(ml_art.fullurl)
ml_ftxt = ml_art.text | code |
106196484/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install git+https://github.com/boudinfl/pke.git
import pke | code |
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