path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16161701/cell_13 | [
"text_plain_output_1.png"
] | from collections import Counter
from sklearn.metrics import accuracy_score
import numpy as np
import numpy as np # linear algebra
def predict(x_train, y_train, x_test, k):
distances = []
targets = []
for i in range(len(x_train)):
distance = np.sqrt(np.sum(np.square(x_test - x_train.values[i, :])... | code |
16161701/cell_20 | [
"text_plain_output_1.png"
] | from collections import Counter
from math import sqrt
from sklearn import neighbors
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
def predict(x_trai... | code |
16161701/cell_29 | [
"text_plain_output_1.png"
] | from collections import Counter
from math import sqrt
from sklearn import neighbors
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
def predict(x_trai... | code |
16161701/cell_11 | [
"text_plain_output_1.png"
] | from collections import Counter
from sklearn.metrics import accuracy_score
import numpy as np
import numpy as np # linear algebra
def predict(x_train, y_train, x_test, k):
distances = []
targets = []
for i in range(len(x_train)):
distance = np.sqrt(np.sum(np.square(x_test - x_train.values[i, :])... | code |
16161701/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16161701/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
from math import sqrt
from sklearn import neighbors
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
... | code |
16161701/cell_28 | [
"image_output_1.png"
] | from collections import Counter
from math import sqrt
from sklearn import neighbors
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
import numpy as np
import numpy as np # linear algebra
def predict(x_train, y_train, x_test, k):
distances = []
targets = []
fo... | code |
16161701/cell_15 | [
"text_plain_output_1.png"
] | from collections import Counter
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
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 nu... | code |
16161701/cell_16 | [
"text_plain_output_1.png"
] | from collections import Counter
from math import sqrt
from sklearn import neighbors
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
import numpy as np
import numpy as np # linear algebra
def predict(x_train, y_train, x_test, k):
distances = []
targets = []
fo... | code |
16161701/cell_3 | [
"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 math
import operator
df = pd.read_csv('../input/Iris.csv')
print(df.head())
df.shape
from collections import Counter | code |
16161701/cell_17 | [
"text_plain_output_1.png"
] | from collections import Counter
from math import sqrt
from sklearn import neighbors
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
import numpy as np
import numpy as np # linear algebra
def predict(x_train, y_train, x_test, k):
distances = []
targets = []
fo... | code |
16161701/cell_24 | [
"text_plain_output_1.png"
] | from collections import Counter
from math import sqrt
from sklearn import neighbors
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
import numpy as np
import numpy as np # linear algebra
def predict(x_train, y_train, x_test, k):
distances = []
targets = []
fo... | code |
16161701/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
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 math
import operator
df = pd.read_csv('../input/Iris.csv')
df.shape
from collections import Counter
from sklearn.model_selecti... | code |
74058414/cell_21 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax.plot(x,f(x))
if ... | code |
74058414/cell_13 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax.plot(x,f(x))
if ... | code |
74058414/cell_25 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax.plot(x,f(x))
if ... | code |
74058414/cell_40 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax... | code |
74058414/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
table = pd.DataFrame({'Heads': [0.15, 0.9], 'Tails': [3.32, 0.1]}, index=['S(x)', 'P(x)'])
table
Entropy = table.loc['P(x)', 'Heads'] * table.loc['S(x)', 'Heads'] + table.loc['P(x)', 'Tails'] * table.loc['S(x)', 'Heads']
Entropy | code |
74058414/cell_11 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax.plot(x,f(x))
if ... | code |
74058414/cell_19 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax.plot(x,f(x))
if ... | code |
74058414/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
table = pd.DataFrame({'Heads': [0.15, 0.9], 'Tails': [3.32, 0.1]}, index=['S(x)', 'P(x)'])
table | code |
74058414/cell_15 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax.plot(x,f(x))
if ... | code |
74058414/cell_38 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax... | code |
74058414/cell_31 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax... | code |
74058414/cell_10 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax.plot(x,f(x))
if ... | code |
74058414/cell_36 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Code to plot a function. Borrowed from fastai library.
def plot_func(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
x = np.linspace(min,max)
fig,ax = plt.subplots(figsize=figsize)
ax... | code |
72081821/cell_25 | [
"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('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
a = [1, 2, 3, 4]
b = ['a', 'b', 'c', 'd']
pd.Series(dict... | code |
72081821/cell_34 | [
"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 re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_44 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_6 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
os.getcwd() | code |
72081821/cell_29 | [
"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 re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_19 | [
"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 re
df = pd.read_csv('../input/titanic/train.csv')
column_text = 'PassengerId => 乘客ID\n Survived => 是否幸存\n Pclass => 乘客等级(1/2/3等舱位)\n Name => 乘客姓名\n Sex => 性别\... | code |
72081821/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 |
72081821/cell_7 | [
"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('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
df2.head() | code |
72081821/cell_45 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/titanic/train.csv')
column_text = 'PassengerId => 乘客ID\n Survived => 是否幸存\n Pclass => 乘客等级(1/2/3等舱位)\n Name => 乘客姓名\n Sex => 性别\... | code |
72081821/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)
import re
df = pd.read_csv('../input/titanic/train.csv')
column_text = 'PassengerId => 乘客ID\n Survived => 是否幸存\n Pclass => 乘客等级(1/2/3等舱位)\n Name => 乘客姓名\n Sex => 性别\... | code |
72081821/cell_35 | [
"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 re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_46 | [
"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 re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/cell_24 | [
"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('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
a = [1, 2, 3, 4]
b = ['a', 'b', 'c', 'd']
pd.Series(dict... | code |
72081821/cell_14 | [
"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 re
df = pd.read_csv('../input/titanic/train.csv')
column_text = 'PassengerId => 乘客ID\n Survived => 是否幸存\n Pclass => 乘客等级(1/2/3等舱位)\n Name => 乘客姓名\n Sex => 性别\... | code |
72081821/cell_27 | [
"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('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
a = [1, 2, 3, 4]
b = ['a', 'b', 'c', 'd']
pd.Series(dict... | code |
72081821/cell_37 | [
"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 re
df = pd.read_csv('../input/titanic/train.csv')
df2 = pd.read_csv('/kaggle/input/titanic/train.csv')
chunker = pd.read_csv('../input/titanic/train.csv', chunksize=1000)
column_text = 'PassengerId => 乘客ID\n ... | code |
72081821/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)
import re
df = pd.read_csv('../input/titanic/train.csv')
column_text = 'PassengerId => 乘客ID\n Survived => 是否幸存\n Pclass => 乘客等级(1/2/3等舱位)\n Name => 乘客姓名\n Sex => 性别\... | code |
72081821/cell_5 | [
"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('../input/titanic/train.csv')
df.head() | code |
2024516/cell_6 | [
"image_output_1.png"
] | from random import sample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
airvisit = pd.read_csv('../input/air_visit_data.csv')
ids = airvisit.air_store_id.unique()
mindate = airvisit.visit_date.min()
maxdate = airvisit.visit_date.max()
skeleton = pd.DataFrame({})
dates = pd.date_range(minda... | code |
2024516/cell_1 | [
"text_plain_output_1.png"
] | from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt | code |
2024516/cell_8 | [
"image_output_1.png"
] | from random import sample
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
airvisit = pd.read_csv('../input/air_visit_data.csv')
ids = airvisit.air_store_id.unique()
mindate = airvisit.visit_date.min()
maxdate = airvisit.visit_date.max()
skeleton = pd.DataFrame({})
dates = pd.date_range(minda... | code |
32062338/cell_4 | [
"text_plain_output_1.png"
] | from copy import deepcopy
from tqdm import tqdm
import json
import os
import pandas as pd
import os
import json
from copy import deepcopy
from tqdm import tqdm
import pandas as pd
def format_name(author):
middle_name = ' '.join(author['middle'])
if author['middle']:
return ' '.join([author['first']... | code |
32062338/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from copy import deepcopy
from tqdm import tqdm
import json
import os
import pandas as pd
import pandas as pd
import os
import json
from copy import deepcopy
from tqdm import tqdm
import pandas as pd
def format_name(author):
middle_name = ' '.join(author['middle'])
if author['middle']:
return ' '.... | code |
32062338/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | code | |
32062338/cell_3 | [
"text_html_output_1.png"
] | from copy import deepcopy
from tqdm import tqdm
import json
import os
import pandas as pd
import os
import json
from copy import deepcopy
from tqdm import tqdm
import pandas as pd
def format_name(author):
middle_name = ' '.join(author['middle'])
if author['middle']:
return ' '.join([author['first']... | code |
32062338/cell_14 | [
"text_plain_output_1.png"
] | from copy import deepcopy
from datetime import datetime
from tqdm import tqdm
import json
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pandas as pd
import os
import json
from copy import deepcopy
from tqdm import tqdm
import pandas a... | code |
32062338/cell_5 | [
"text_html_output_1.png"
] | from copy import deepcopy
from tqdm import tqdm
import json
import os
import pandas as pd
import os
import json
from copy import deepcopy
from tqdm import tqdm
import pandas as pd
def format_name(author):
middle_name = ' '.join(author['middle'])
if author['middle']:
return ' '.join([author['first']... | code |
2009978/cell_4 | [
"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/spam.csv', encoding='latin-1')
df.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
df = df.rename(columns={'v1': 'class', 'v2': 'text'})
df.head() | code |
2009978/cell_6 | [
"text_plain_output_1.png"
] | from nltk.tokenize import WhitespaceTokenizer
from subprocess import check_output
import numpy as np # linear algebra
import operator
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_... | code |
2009978/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/spam.csv', encoding='latin-1')
df.head() | code |
2009978/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np # linear algebra
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
np.set_printoptions(threshold=np.inf)
i... | code |
104116934/cell_21 | [
"text_html_output_1.png"
] | from keras.layers import Dense, LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
104116934/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gdz22-datathon/train.csv')
trafo = pd.read_csv('/kaggle/input/gdz22-datathon/trafo.csv')
bulutluluk_orani = pd.read_csv('/kaggle/input/gdz22-dat... | code |
104116934/cell_25 | [
"text_html_output_1.png"
] | from keras.layers import Dense, LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read... | code |
104116934/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gdz22-datathon/train.csv')
trafo = pd.read_csv('/kaggle/input/gdz22-datathon/trafo.csv')
bulutluluk_orani = pd.read_csv('/kaggle/input/gdz22-dat... | code |
104116934/cell_20 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.cs... | code |
104116934/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gdz22-datathon/train.csv')
trafo = pd.read_csv('/kaggle/input/gdz22-datathon/trafo.csv')
bulutluluk_orani = pd.read_csv('/kaggle/input/gdz22-dat... | code |
104116934/cell_26 | [
"image_output_1.png"
] | from keras.layers import Dense, LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pyplot as plt
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
... | code |
104116934/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 |
104116934/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gdz22-datathon/train.csv')
trafo = pd.read_csv('/kaggle/input/gdz22-datathon/trafo.csv')
bulutluluk_orani = pd.read_csv('/kaggle/input/gdz22-dat... | code |
104116934/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import warnings
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gd... | code |
104116934/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gdz22-datathon/train.csv')
trafo = pd.read_csv('/kaggle/input/gdz22-datathon/trafo.csv')
bulutluluk_orani = pd.read_csv('/kaggle/input/gdz22-dat... | code |
104116934/cell_17 | [
"text_html_output_1.png"
] | import math
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gdz22-datathon/train.csv')
trafo = pd.read_csv('/kaggle/input/gdz22-datathon/trafo.csv')
... | code |
104116934/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gdz22-datathon/train.csv')
trafo = pd.read_csv('/kaggle/input/gdz22-datathon/trafo.csv')
bulutluluk_orani = pd.read_csv('/kaggle/input/gdz22-dat... | code |
104116934/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gdz22-datathon/train.csv')
trafo = pd.read_csv('/kaggle/input/gdz22-datathon/trafo.csv')
bulutluluk_orani = pd.read_csv('/kaggle/input/gdz22-dat... | code |
104116934/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/gdz22-datathon/submission.csv')
df = pd.read_csv('/kaggle/input/gdz22-datathon/train.csv')
trafo = pd.read_csv('/kaggle/input/gdz22-datathon/trafo.csv')
bulutluluk_orani = pd.read_csv('/kaggle/input/gdz22-dat... | code |
122263700/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
sns.set_style('whitegrid')
plt.axis('equal')
# Categoric... | code |
122263700/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
sns.set_style('whitegrid')
plt.axis('equal')
plt.figure(figsize=(10, 4))
sns... | code |
122263700/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
traindf.shape
traindf.columns
traindf['Expenses_group'] = np.nan
traindf.loc[traindf['totalExpenses'] == 0, 'Ex... | code |
122263700/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
traindf.shape
traindf.columns
traindf['Expenses_group'] = np.nan
traindf.loc[traindf['totalExpenses'] == 0, 'Ex... | code |
122263700/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
testdf.head() | code |
122263700/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
traindf.shape
traindf.columns
traindf['Expenses_group'] = np.nan
traindf.loc[traindf['totalExpenses'] == 0, 'Ex... | code |
122263700/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
sns.set_style('whitegrid')
plt.axis('equal')
categorical_feats = ['HomePlane... | code |
122263700/cell_19 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
traindf.shape
traindf.columns
traindf['Expenses_group'] = np.nan
traindf.loc[traindf['totalExpenses'] == 0, 'Ex... | code |
122263700/cell_18 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
traindf.shape
traindf.columns
traindf['Expenses_group'] = np.nan
traindf.loc[traindf['totalExpenses'] == 0, 'Ex... | code |
122263700/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
plt.figure(figsize=(6, 6))
sns.set_style('whitegrid')
plt.pie(traindf['Transp... | code |
122263700/cell_15 | [
"image_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
traindf.shape
traindf.columns | code |
122263700/cell_31 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
sns.set_style('whitegrid')
plt.axis('equal')
# Categoric... | code |
122263700/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
sns.set_style('whitegrid')
plt.axis('equal')
# Categoric... | code |
122263700/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
traindf.shape | code |
122263700/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
traindf = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
testdf = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
data_cleaner = [traindf, testdf]
traindf.head() | code |
122262215/cell_42 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
import pandas as pd
import pandas as pd
import re
import re
email = 'We are goind USA to meet on saturday or sunday on 09:30 PM or 1... | code |
122262215/cell_21 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
import pandas as pd
import requests
ps = soup.find_all('p', {'class': 'sentence-item__text'})
df = pd.DataFrame(columns=['text', 'label'])
days = 'Monday Tuesday Wednesday Thursday Friday Saturday Sunday'.lower().split()
days
days = 'Monday Tuesday Wednesday Thursday Friday Saturday ... | code |
122262215/cell_13 | [
"text_plain_output_1.png"
] | days = 'Monday Tuesday Wednesday Thursday Friday Saturday Sunday'.lower().split()
days | code |
122262215/cell_25 | [
"text_plain_output_1.png"
] | import nltk
import nltk
nltk.download('omw-1.4') | code |
122262215/cell_6 | [
"text_plain_output_1.png"
] | import re
email = 'We are goind USA to meet on saturday or sunday on 09:30 PM or 10:00 am ok on january good ? in Cairo or Giza ?'
email = email.lower()
re.findall('saturday|sunday|monday|wednesday', email) | code |
122262215/cell_40 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
model = MultinomialNB()
model.fit(X_train, y_train) | code |
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