path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130013718/cell_12 | [
"text_html_output_1.png"
] | from PIL import Image
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
Id = []
impo... | code |
130013718/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import os
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5]
Id = []
import numpy as np
import pandas as pd
import os
fo... | code |
2001102/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}... | code |
2001102/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}... | code |
2001102/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition... | code |
2001102/cell_18 | [
"image_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}... | code |
2001102/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}... | code |
2001102/cell_31 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
random_forest_model = RandomForestRegressor(n_jobs=-1, min_samples_leaf=3, n_estimators=200)
random_forest_model.fit(features_rdf, target_rdf)
random_forest_model.score(... | code |
2001102/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}... | code |
2001102/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}... | code |
2001102/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}... | code |
2001102/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}... | code |
1006233/cell_4 | [
"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/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features... | code |
1006233/cell_6 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
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('../... | code |
1006233/cell_2 | [
"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/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features... | code |
1006233/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1006233/cell_3 | [
"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/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features... | code |
1006233/cell_5 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
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('../... | code |
1008540/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import seaborn as sns
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Activation, Flatten, Dense
from keras.optimizers... | code |
18104686/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survi... | code |
18104686/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
print(os.listdir('../input'))
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.head() | code |
18104686/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survi... | code |
130008236/cell_21 | [
"text_html_output_1.png"
] | 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
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rena... | code |
130008236/cell_13 | [
"text_plain_output_1.png"
] | 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 pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(... | code |
130008236/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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 pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(... | code |
130008236/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)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(f... | code |
130008236/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
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df.head() | code |
130008236/cell_11 | [
"text_html_output_1.png"
] | 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 pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(... | code |
130008236/cell_19 | [
"text_html_output_1.png"
] | 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
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rena... | code |
130008236/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 |
130008236/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | 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 pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(... | code |
130008236/cell_18 | [
"text_plain_output_1.png"
] | 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
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rena... | code |
130008236/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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 re
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={... | code |
130008236/cell_16 | [
"text_html_output_1.png"
] | 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
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rena... | code |
130008236/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)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(f... | code |
130008236/cell_14 | [
"text_plain_output_1.png"
] | 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
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rena... | code |
130008236/cell_10 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | 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 re
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={... | code |
130008236/cell_12 | [
"text_plain_output_1.png"
] | 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 pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(... | code |
130008236/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)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(f... | code |
88079729/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 |
90146618/cell_9 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.models import load_model
from keras.models import load_model
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_mode... | code |
90146618/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_model
from PIL import Image
from sklearn.model_selection import train_test_split
import os
print(os.listdir('/')) | code |
90146618/cell_11 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.models import load_model
from keras.models import load_model
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_mode... | code |
90146618/cell_8 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.models import load_model
from keras.models import load_model
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_mode... | code |
90146618/cell_3 | [
"text_plain_output_1.png"
] | from keras.models import load_model
from keras.models import load_model
model = load_model('../input/facenet/keras-facenet/model/facenet_keras.h5')
print('Loaded Model') | code |
90146618/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.models import load_model
from keras.models import load_model
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_mode... | code |
32068320/cell_13 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.model_selection import ... | code |
32068320/cell_9 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.model_selection import ... | code |
32068320/cell_2 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from keras.models import Sequential
from... | code |
32068320/cell_19 | [
"image_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.metrics import mean_squ... | code |
32068320/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 |
32068320/cell_8 | [
"text_html_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.model_selection import ... | code |
32068320/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_df = train_df.fillna('No State')
train_df | code |
32068320/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.model_selection import ... | code |
32068320/cell_12 | [
"text_html_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.metrics import mean_squ... | code |
122258225/cell_21 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/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)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.info() | code |
122258225/cell_23 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/cell_33 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
df_gender_subm... | code |
122258225/cell_20 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/cell_26 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/cell_11 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/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 |
122258225/cell_8 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/cell_17 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
... | code |
122258225/cell_24 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/cell_10 | [
"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_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabi... | code |
122258225/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_test.info() | code |
2035583/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import AdaBoostClassifier, AdaBoostRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsOneClassifier
from sklearn.m... | code |
2035583/cell_4 | [
"text_html_output_1.png"
] | X.head() | code |
2035583/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 |
2035583/cell_5 | [
"text_plain_output_1.png"
] | print(X.isnull().any()) | code |
17135521/cell_34 | [
"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')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontag... | code |
17135521/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fe... | code |
17135521/cell_30 | [
"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')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontag... | code |
17135521/cell_33 | [
"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')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontag... | code |
17135521/cell_26 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
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)
import seaborn as sns
df = pd.read_csv('../input/train.csv')
corrmat = ... | code |
17135521/cell_11 | [
"text_html_output_1.png"
] | 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)
import seaborn as sns
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
k = 10
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(df[cols].va... | code |
17135521/cell_19 | [
"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')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontag... | code |
17135521/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')
len(df) | code |
17135521/cell_8 | [
"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.describe() | code |
17135521/cell_16 | [
"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')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontag... | code |
17135521/cell_3 | [
"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 |
17135521/cell_17 | [
"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')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontag... | code |
17135521/cell_31 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
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)
import seaborn as sns
df = pd.read... | code |
17135521/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing'... | code |
17135521/cell_14 | [
"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')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15) | code |
17135521/cell_10 | [
"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')
corrmat = df.corr()
corrmat.head() | code |
17135521/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
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)
import seaborn as sns
df = pd.read... | code |
17135521/cell_5 | [
"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.head() | code |
104131002/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
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
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("###########... | code |
104131002/cell_25 | [
"text_plain_output_1.png"
] | from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
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
import seaborn as sns
import sea... | code |
104131002/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
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
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
... | code |
104131002/cell_6 | [
"text_html_output_1.png"
] | !pip install xgboost
!pip install lightgbm
!pip install catboost
import numpy as np
import datetime as dt
import pandas as pd
import seaborn as sns
from scipy import stats
from sklearn.cluster import AgglomerativeClustering
from sklearn.linear_model import Ridge, Lasso, ElasticNet
from sklearn.metrics import mean_squar... | code |
104131002/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
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
import seaborn as sns
import seaborn as sns
import seabo... | code |
104131002/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
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
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("###########... | code |
104131002/cell_8 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
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
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("###########... | code |
104131002/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
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
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("###########... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.