path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18149558/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
print('Items set shape', items.shape) | code |
18149558/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
print('Shops set shape', shops.shape) | code |
18149558/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test) | code |
18149558/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test) | code |
18149558/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
train.columns.values | code |
18149558/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train) | code |
18149558/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
print('Testing set shape', test.shape) | code |
121151039/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train) | code |
121151039/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
data = pd.read_csv('/kaggle/input/wind-turbine-scada-dataset/T1.csv')
data = data[data['LV ActivePower (kW)'] > 0]
data = data.dropna()
data = pd.get_dummies(data)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data[['Wi... | code |
121151039/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/wind-turbine-scada-dataset/T1.csv')
print(data.head())
print(data.describe())
print(data.info()) | code |
88085617/cell_21 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris... | code |
88085617/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x... | code |
88085617/cell_23 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris... | code |
88085617/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.... | code |
88085617/cell_7 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df | code |
88085617/cell_18 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.... | code |
88085617/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.... | code |
88085617/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.... | code |
88085617/cell_17 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.... | code |
88085617/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x... | code |
88085617/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris... | code |
88085617/cell_10 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df | code |
88085617/cell_12 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x... | code |
33116172/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
nRowsRead = 1000
df2 = pd.read_csv('/kaggle/input/column_3C.csv', delimiter=',', nrows=nRo... | code |
33116172/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
df1.head(5) | code |
33116172/cell_25 | [
"text_plain_output_1.png"
] | (y_test.shape, X_test.shape)
X_test.min() | code |
33116172/cell_23 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
import xgboost as xgb
import xgboost as xgb
model = xgb.XGBClassifier()
model.fit(X_train, y_train)
model.save_model('model.bst')
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
print('Accuracy: ... | code |
33116172/cell_30 | [
"text_plain_output_1.png"
] | from platform import python_version
from platform import python_version
print(python_version()) | code |
33116172/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
nRowsRead = 1000
df1 = pd.read_... | code |
33116172/cell_26 | [
"text_plain_output_1.png"
] | (y_test.shape, X_test.shape)
X_test.min()
X_test.max() | code |
33116172/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
nRowsRead = 1000
df2 = pd.read_csv('/kaggle/input/column_3C.csv', delimiter=',', nrows=nRo... | code |
33116172/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
print(f'There are {nRow} rows and {nCol} columns') | code |
33116172/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
nRowsRead = 1000
df1 = pd.read_... | code |
33116172/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
nRowsRead = 1000
df2 = pd.read_csv('/kaggle/input/column_3C.csv', d... | code |
33116172/cell_24 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | (y_test.shape, X_test.shape) | code |
33116172/cell_27 | [
"text_plain_output_1.png"
] | (y_test.shape, X_test.shape)
X_test.min()
X_test.max()
X_test.mean() | code |
72075480/cell_42 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('disp... | code |
72075480/cell_21 | [
"text_html_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '... | code |
72075480/cell_9 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../inpu... | code |
72075480/cell_34 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('d... | code |
72075480/cell_30 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '... | code |
72075480/cell_40 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from nltk.sentiment import SentimentIntensityAnalyzer
from textblob import Word, TextBlob
from warnings import filterwarnings
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_... | code |
72075480/cell_29 | [
"text_html_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '... | code |
72075480/cell_39 | [
"image_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('disp... | code |
72075480/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 |
72075480/cell_7 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../inpu... | code |
72075480/cell_32 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '... | code |
72075480/cell_28 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '... | code |
72075480/cell_8 | [
"text_html_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../inpu... | code |
72075480/cell_38 | [
"text_html_output_1.png"
] | from nltk.sentiment import SentimentIntensityAnalyzer
sia = SentimentIntensityAnalyzer()
sia.polarity_scores('The food was awesome') | code |
72075480/cell_3 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from PIL import Image
from nltk.corpus import stopwords
from nltk.sentiment import SentimentIntensityAnalyzer
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression... | code |
72075480/cell_31 | [
"text_html_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '... | code |
72075480/cell_24 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../inpu... | code |
72075480/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../inpu... | code |
72075480/cell_36 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('disp... | code |
2014797/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True) | code |
2014797/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train.head(5) | code |
2014797/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']],... | code |
2014797/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how=... | code |
2014797/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how=... | code |
2014797/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = trai... | code |
2014797/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv') | code |
2014797/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = trai... | code |
2014797/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how=... | code |
2014797/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
train.head() | code |
2014797/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how=... | code |
2014797/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train.info() | code |
2014797/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how=... | code |
17131726/cell_9 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: n... | code |
17131726/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: name.strip(), names_col)
train_df.columns = new_names_col
list(train_df.columns.values) | code |
17131726/cell_6 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: name.strip(), names_col)
train_df.columns = new_names_col
list(train_d... | code |
17131726/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
train_df.head() | code |
17131726/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
17131726/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: name.strip(), names_col)
train_df.columns = new_names_col
list(train_d... | code |
17131726/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: name.strip(), names_col)
train_df.columns = new_names_col
list(train_df.columns.values)
train_df.isna().sum() | code |
128022699/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
df = pd.read_csv('/content/drive/MyDrive/PRML LABs/PRML Major Project/audio_dataset.csv')
df | code |
128022699/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import os
import re
import pandas as pd
import librosa
import numpy as np
from sklearn.tree import DecisionTreeClassifier... | code |
90151187/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_d... | code |
90151187/cell_4 | [
"text_html_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_d... | code |
90151187/cell_6 | [
"text_plain_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_d... | code |
90151187/cell_8 | [
"text_html_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_d... | code |
90151187/cell_15 | [
"text_plain_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_d... | code |
90151187/cell_17 | [
"text_html_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_d... | code |
72120060/cell_40 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor
from xgboost import XGBRegressor
im... | code |
72120060/cell_39 | [
"text_plain_output_1.png"
] | results_xgb = skopt.forest_minimize(objective, search_space_xgb, **HPO_params) | code |
72120060/cell_41 | [
"text_plain_output_1.png"
] | import skopt
search_space = [skopt.space.Integer(4, 12, name='max_depth'), skopt.space.Integer(50, 200, name='n_estimators'), skopt.space.Integer(17, 24, name='max_features'), skopt.space.Real(0.0, 1.0, name='min_impurity_decrease'), skopt.space.Categorical(categories=[True, False], name='bootstrap')]
def to_named_pa... | code |
72120060/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 |
72120060/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from warnings import filterwarnings
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import Gradi... | code |
72120060/cell_16 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import ML... | code |
72120060/cell_27 | [
"image_output_1.png"
] | code | |
2007618/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
def priceOverTime(data, label):
"""Plot price over time"""
priceOverTime(newdf3, 'California')
priceOverTime(newdf4, 'Colorado')
priceOverTime(newdf5, 'Michigan')
def priceOverTime2(data, label):
data.groupby(data.Date.dt.year)['MedianSoldPrice_AllHomes'].mean().plot(kind='bar'... | code |
2007618/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
def priceOverTime(data, label):
"""Plot price over time"""
data.groupby(newdf.Date.dt.year)['MedianSoldPrice_AllHomes'].mean().plot(kind='bar', figsize=(10, 6), color='grey', edgecolor='black', linewidth=2)
plt.suptitle(label, fontsize=12)
plt.ylabel('MedianSoldPrice_All... | code |
2007618/cell_3 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
state_data = '../input/State_time_series.csv'
df = pd.read_csv(state_data)
city_data = '../input/City_time_series.csv'
dfCity = pd.read_csv(city_data)
State_house = pd.read_csv('../input/State_time_serie... | code |
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