path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104124186/cell_8 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
plt.scatter(a, b)
plt.scatter(c, d)
plt.grid()
plt.show() | code |
104124186/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
x = df.YearsExperience.values.reshape(-1, 1)
y = df.Salary.values.reshape(-1, 1)
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regre... | code |
104124186/cell_3 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df | code |
104124186/cell_24 | [
"text_html_output_1.png"
] | YearsExperience = float(input('please enter the years expercience: '))
Salary = 26986.69131674 + 9379.71049195 * YearsExperience
print(Salary) | code |
104124186/cell_14 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_tes... | code |
104124186/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/salary/Salary_Data.csv')
data
df = pd.DataFrame(data)
df
x = df.YearsExperience.values.reshape(-1, 1)
y = df.Salary.values.reshape(-1, 1)
regressor = LinearRegression()
regressor.fit(... | code |
104124186/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
print(regressor.intercept_)
print(regressor.coef_) | code |
104124186/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)
y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
a = x_test
b = y_test
c = x_test
d = y_pred
plt.scatter(y_test, y_pred)
plt.show() | code |
128000238/cell_2 | [
"text_plain_output_1.png"
] | !pip install azure-ai-textanalytics --pre | code |
128000238/cell_7 | [
"text_plain_output_1.png"
] | def create_twitter_url():
handle = 'nasi goreng'
max_results = 10
mrf = 'max_results={}'.format(max_results)
q = 'query={}'.format(handle)
url = 'https://api.twitter.com/2/tweets/search/recent?{}&{}'.format(mrf, q)
return url
create_twitter_url() | code |
128000238/cell_16 | [
"text_plain_output_1.png"
] | from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
from kaggle_secrets import UserSecretsClient
import ast
import json
import requests
import requests
import json
import ast
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics ... | code |
17136911/cell_9 | [
"text_plain_output_1.png"
] | from keras import layers
from keras.optimizers import Adam
from keras.layers import Input, Dense, Activation, BatchNormalization, Flatten
from keras.layers import Dropout
from keras.models import Model
from keras.preprocessing import image
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
... | code |
17136911/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Bidirectional
from keras.layers import Input, Dense, Activation, BatchNormalization, Flatten
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import S... | code |
17136911/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.... | code |
17136911/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
le = LabelEncoder()
oh = OneHotEncoder(sparse=False)
df = pd.rea... | code |
17136911/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Bidirectional
from keras.layers import Input, Dense, Activation, BatchNormalization, Flatten
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import S... | code |
17136911/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Bidirectional
from keras.layers import Input, Dense, Activation, BatchNormalization, Flatten
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import S... | code |
16118732/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from PIL import Image
import cv2
import glob
import glob
import os
import os
import os
import os
import pydicom
import pydicom
import pydicom
import numpy as np
import pandas as pd
import os
import cv2
import os
import pydicom
inputdir = '../input/sample images/'
outdir = './'
test_li... | code |
16118732/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import os
import os
import pydicom
import numpy as np
import pandas as pd
import os
import cv2
import os
import pydicom
inputdir = '../input/sample images/'
outdir = './'
test_list = [os.path.basename(x) for x in glob.glob(inputdir + './*.dcm')]
for f in test_list:
ds = pydicom.read_file(inputdir + ... | code |
16118732/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input/sample images')) | code |
323056/cell_4 | [
"text_plain_output_1.png"
] | imgs.keys()
(imgs[1].shape, masks[1].shape) | code |
323056/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import glob
import numpy as np
def load_cv2_images(folder):
imgs, masks, img_ids = ({}, {}, {})
for i in range(47):
imgs[i + 1] = []
masks[i + 1] = []
img_ids[i + 1] = []
paths = glob.glob(folder + '*.tif')
paths = [p for p in paths if 'mask' not in p]
for p in ... | code |
323056/cell_7 | [
"text_plain_output_1.png"
] | import cv2
import glob
import numpy as np
def load_cv2_images(folder):
imgs, masks, img_ids = ({}, {}, {})
for i in range(47):
imgs[i + 1] = []
masks[i + 1] = []
img_ids[i + 1] = []
paths = glob.glob(folder + '*.tif')
paths = [p for p in paths if 'mask' not in p]
for p in ... | code |
323056/cell_3 | [
"text_plain_output_1.png"
] | imgs.keys() | code |
323056/cell_5 | [
"text_plain_output_1.png"
] | import cv2
import glob
import numpy as np
def load_cv2_images(folder):
imgs, masks, img_ids = ({}, {}, {})
for i in range(47):
imgs[i + 1] = []
masks[i + 1] = []
img_ids[i + 1] = []
paths = glob.glob(folder + '*.tif')
paths = [p for p in paths if 'mask' not in p]
for p in ... | code |
72071704/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Coun... | code |
72071704/cell_9 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Country')['PercentOfBaseline'].me... | code |
72071704/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 = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.head() | code |
72071704/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum() | code |
72071704/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Country')['PercentOfBaseline'].me... | code |
72071704/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 |
72071704/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df['Country'].unique() | code |
72071704/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Country')['PercentOfBaseline'].me... | code |
72071704/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.isnull().sum()
df1 = df.groupby('Country')['PercentOfBaseline'].me... | code |
72071704/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/covid19s-impact-on-airport-traffic/covid_impact_on_airport_traffic.csv')
df.info() | code |
1004678/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
houseprice_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
houseprice_df.head() | code |
1004678/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 |
1004678/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
houseprice_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
houseprice_df.info()
print('----------------------------')
test_df.info() | code |
1004678/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
houseprice_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
houseprice_df = houseprice_df.drop(['Alley', 'PoolQC', 'Fence'], axis=1)
test_df = test_df.drop(['Alley', 'PoolQC', 'Fence'], axis=1)
print(pd.value_coun... | code |
89138107/cell_21 | [
"text_plain_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_13 | [
"text_plain_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_9 | [
"text_plain_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_23 | [
"text_plain_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_20 | [
"image_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_11 | [
"text_plain_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_19 | [
"text_plain_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/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 |
89138107/cell_18 | [
"image_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_15 | [
"text_plain_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_16 | [
"image_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_17 | [
"text_plain_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_14 | [
"image_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_22 | [
"image_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_10 | [
"image_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
89138107/cell_12 | [
"image_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
import warnings
warnings.simplefilter('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
train_transaction = pd... | code |
32073488/cell_21 | [
"text_plain_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
maxlen = 130
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
for i in x_train[0:10]:
print(len(i)) | code |
32073488/cell_13 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
print(type(word_index))
print(len(word_index)) | code |
32073488/cell_9 | [
"image_output_1.png"
] | d = x_train[0]
print(len(d)) | code |
32073488/cell_25 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_... | code |
32073488/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
unique, counts = np.unique(y_train, return_counts=True)
print('Y Train distrubution:', dict(zip(unique, counts))) | code |
32073488/cell_23 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_... | code |
32073488/cell_20 | [
"image_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
maxlen = 130
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
print(x_train[5]) | code |
32073488/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
plt.figure()
sns.countplot(y_train)
plt.xlabel('Classes')
plt.ylabel('Freq')
plt.title('y train')
plt.show() | code |
32073488/cell_26 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
import matplotlib.pyplot as plt
import seaborn as sns
(x_train, y_train), (x_test, y_test)... | code |
32073488/cell_2 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3) | code |
32073488/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
review_len_train = []
review_len_test = []
for i, ii in zip(x_train, x_test):
review_len_train.append(len(i))
review_len_test.append(len(ii))
sns.distplot(review_len_train, hist_kws={'alpha': 0.3})
sns.distplot(review_len_test, hist_kws={'alpha': 0.3})
pl... | code |
32073488/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers import SimpleRNN, Dense, Activation | code |
32073488/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
plt.figure()
sns.countplot(y_test)
plt.xlabel('Classes')
plt.ylabel('Freq')
plt.title('y test')
plt.show() | code |
32073488/cell_8 | [
"image_output_1.png"
] | d = x_train[0]
print(x_train[0]) | code |
32073488/cell_15 | [
"image_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
for keys, values in word_index.items():
if values == 4:
print(keys) | code |
32073488/cell_16 | [
"image_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
for keys, values in word_index.items():
if values == 123:
print(keys) | code |
32073488/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
print('Y Train Values:', np.unique(y_train))
print('Y Test Values:', np.unique(y_test)) | code |
32073488/cell_17 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
def whatItSay(index=9):
reverse_index = dict([(value, key) for key, value in word_in... | code |
32073488/cell_24 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_... | code |
32073488/cell_14 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
for keys, values in word_index.items():
if values == 1:
print(keys) | code |
32073488/cell_22 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path='imdb.npz', num_words=None, skip_top=0, maxlen=None, seed=113, start_char=1, oov_char=2, index_from=3)
word_index = imdb.get_word_index()
def whatItSay(index=9):
reverse_index = dict([(value, key) for key, value in word_in... | code |
32073488/cell_27 | [
"text_plain_output_1.png"
] | from keras.datasets import imdb
from keras.layers import SimpleRNN, Dense, Activation
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
import matplotlib.pyplot as plt
import seaborn as sns
(x_train, y_train), (x_test, y_test)... | code |
32073488/cell_12 | [
"text_plain_output_1.png"
] | from scipy import stats
import numpy as np
unique, counts = np.unique(y_train, return_counts=True)
unique, counts = np.unique(y_test, return_counts=True)
review_len_train = []
review_len_test = []
for i, ii in zip(x_train, x_test):
review_len_train.append(len(i))
review_len_test.append(len(ii))
print('Trai... | code |
32073488/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
unique, counts = np.unique(y_train, return_counts=True)
unique, counts = np.unique(y_test, return_counts=True)
print('Y Test distrubution:', dict(zip(unique, counts))) | code |
73071444/cell_42 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
combine = [train_data, test_data]
for dataset in combine:
... | code |
73071444/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['Pclass', 'Survived']].groupby('Pclass', as_index=False).mean() | code |
73071444/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.describe(include=['O']) | code |
73071444/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20)
class_age_plt = sns.FacetGrid(train_data, c... | code |
73071444/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.head() | code |
73071444/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
combine = [train_data, test_data]
for dataset in combine:
... | code |
73071444/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20)
class_age_plt = sns.FacetGrid(train_data, c... | code |
73071444/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
test_data.info() | code |
73071444/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
train_data = train_data.drop(['Name'], axis=1)
test_data = t... | code |
73071444/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20) | code |
73071444/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
(train_data['Ticket'].unique().shape, test_data['Ticket'].unique().shape) | code |
73071444/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.describe() | code |
73071444/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['SibSp', 'Survived']].groupby('SibSp', as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
73071444/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['Parch', 'Survived']].groupby('Parch', as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
73071444/cell_38 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
train_data[['Title', 'Survived']].groupby('Title', as_index=... | code |
73071444/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data = train_data.drop(['Cabin', 'Ticket', 'PassengerId'], axis=1)
test_data = test_data.drop(['Cabin', 'Ticket'], axis=1)
combine = [train_data, test_data]
for dataset in combine:
... | code |
73071444/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20)
class_age_plt = sns.FacetGrid(train_data, c... | code |
73071444/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['Embarked', 'Survived']].groupby('Embarked', as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
73071444/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
age_plt = sns.FacetGrid(train_data, col='Survived')
age_plt.map(plt.hist, 'Age', bins=20)
class_age_plt = sns.FacetGrid(train_data, c... | code |
73071444/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean() | code |
73071444/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.info() | code |
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