path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130004668/cell_29 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeClassifier
param_grid = [{'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random']}]
grid = GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), param_grid=param_grid, cv=5)
grid.fit(X_train, y_t... | code |
130004668/cell_26 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from tqdm import tqdm
import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train... | code |
130004668/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_untrimmed_sample = df_reviews_raw.groupby... | code |
130004668/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import warnings
import nltk
import string
import re
import sklearn
import pandas as pd
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from sklearn.cluster import... | code |
130004668/cell_18 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from tqdm import tqdm
import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unn... | code |
130004668/cell_32 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeClassifier
param_grid = [{'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random']}]
grid = GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), param_grid=param_grid, cv=5)
grid.fit(X_train, y_t... | code |
130004668/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler, OrdinalEncoder
import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_review... | code |
130004668/cell_35 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from sklearn.tree import DecisionTreeClassifier
from tqdm import tqdm
import pandas as pd
df_re... | code |
130004668/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeClassifier
param_grid = [{'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random']}]
grid = GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), param_grid=param_grid, cv=5)
grid.fit(X_train, y_t... | code |
130004668/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_untrimmed_sample = df_reviews_raw.groupby... | code |
130004668/cell_37 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from sklearn.tree import DecisionTreeClassifier
from tqdm import tqdm
import pandas as pd
df_re... | code |
130004668/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_untrimmed_sample = df_reviews_raw.groupby... | code |
130004668/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum() | code |
130004668/cell_36 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from sklearn.tree import DecisionTreeClassifier
from tqdm import tqdm
import pandas as pd
df_re... | code |
105200358/cell_4 | [
"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/penguins/penguins.csv')
df.describe() | code |
105200358/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | print(data) | code |
105200358/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 |
105200358/cell_3 | [
"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/penguins/penguins.csv')
df.head() | code |
105200358/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 = pd.read_csv('../input/penguins/penguins.csv')
df.info() | code |
106196903/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
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 re
import ... | code |
106196903/cell_13 | [
"text_html_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)
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/bbc-text.csv')
df
df.isna().sum()
x_train, x_test = train_test_split(df, random_state=1, test_size=0.2)
(x_train.... | code |
106196903/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/b... | code |
106196903/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/bbc-text.csv')
df
df.isna().sum() | code |
106196903/cell_6 | [
"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
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/bbc-text.csv')
df
df.isna().sum()
sns.countplot(df['c... | code |
106196903/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/bbc-text.csv')
df
df.isna().sum()
x_train, x_test = train_test_sp... | code |
106196903/cell_29 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.model_selection import train_test_split
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas ... | code |
106196903/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/bbc-text.csv')
df
df.isna().sum()
x_train, x_test = train_test_sp... | code |
106196903/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import numpy as np
import numpy ... | code |
106196903/cell_2 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import nltk
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
import plotly_express as px
from sklearn.model_selection import train_test_split
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from war... | code |
106196903/cell_19 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
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 re
import seaborn as sns
import string
df = pd.read_csv('/kaggle/input/b... | code |
106196903/cell_7 | [
"text_plain_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)
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/bbc-text.csv')
df
df.isna().sum()
x_train, x_test = train_test_split(df, random_state=1, test_size=0.2)
(x_train.... | code |
106196903/cell_49 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import numpy as np
import numpy ... | code |
106196903/cell_18 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
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 re
import seaborn as sns
import string
df = pd.read_csv('/kaggle/input/b... | code |
106196903/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/b... | code |
106196903/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import seaborn as sns
import string
df = pd.read_csv('/kaggle/input/b... | code |
106196903/cell_16 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
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 re
import seaborn as sns
import string
df = pd.read_csv('/kaggle/input/b... | code |
106196903/cell_47 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import numpy as np
import numpy ... | code |
106196903/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)
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/bbc-text.csv')
df | code |
106196903/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import seaborn as sns
import string
df = pd.read_csv('/kaggle/input/b... | code |
106196903/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
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 re
import ... | code |
106196903/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
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 re
import ... | code |
106196903/cell_27 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.model_selection import train_test_split
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas ... | code |
106196903/cell_12 | [
"application_vnd.jupyter.stderr_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)
df = pd.read_csv('/kaggle/input/bbc-fulltext-and-category/bbc-text.csv')
df
df.isna().sum()
x_train, x_test = train_test_split(df, random_state=1, test_size=0.2)
(x_train.... | code |
73064882/cell_4 | [
"text_html_output_2.png"
] | import pandas as pd
import plotly.express as px
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
fig = px.scatter(glass, x='Mg', y='Fe', color='Type', color_continuous_scale='portland')
fig.show() | code |
73064882/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
glass | code |
73064882/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.subplots import make_subplots
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
glass = pd.read_csv('/kaggle/input/glass/glass.csv')
fig = px.scatter(glass, x="Mg", y="Fe", color='Type',
... | code |
73089893/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_44 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import seaborn as sns
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplo... | code |
73089893/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_39 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import seaborn as sns
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplo... | code |
73089893/cell_41 | [
"text_plain_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_2 | [
"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 |
73089893/cell_32 | [
"text_html_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
impo... | code |
73089893/cell_36 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.io as pio
import seaborn as sns
import warnings
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import matplo... | code |
17102352/cell_13 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import os
import pandas as pd
import random # Ra... | code |
17102352/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import random
from keras.preprocessing.image import load_img
from PIL import Image
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
from keras.optimizers import ... | code |
17102352/cell_7 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
from keras.models import Sequential
dropout_rate = 0.25
fc_units_1 = 512
fc_units_2 = 256
output_units = 2
epochs = 50
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=3, strides=1, padding='same', input_shape=(ima... | code |
17102352/cell_3 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
import matplotlib.pyplot as plt
import os
import pandas as pd
import random # Randomly select a filename for viewing
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import random
from keras.preprocessing.image import load_img
from PIL im... | code |
17102352/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.preprocessing.ima... | code |
17102352/cell_12 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.preprocessing.ima... | code |
17102352/cell_5 | [
"image_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import os
import pandas as pd
import random # Randomly select a filename for viewing
import pandas as pd
import numpy as np
im... | code |
2037446/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Imputer
num_pipeline = Pipeline([('imputer', Imputer(strategy='medi... | code |
2037446/cell_13 | [
"text_html_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
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)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
2037446/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing['ocean_proximity'].value_counts() | code |
2037446/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.pipeline import FeatureUnion
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
from sklearn.preprocessing... | code |
2037446/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
housing.hist(bins=50, figsize=(20, 15))
plt.show() | code |
2037446/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing.head() | code |
2037446/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
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)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
2037446/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV ... | code |
2037446/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 |
2037446/cell_7 | [
"application_vnd.jupyter.stderr_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)
housing = pd.read_csv('../input/housing.csv')
from sklearn.model_selection import train_test_split
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
print(len(trai... | code |
2037446/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
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)
housing = pd.read_csv... | code |
2037446/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing['income_cat'].where(housing['inc... | code |
2037446/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
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)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
2037446/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing.info() | code |
2037446/cell_17 | [
"text_html_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] =... | code |
2037446/cell_14 | [
"image_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
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)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
2037446/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.pipeline import FeatureUnion
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing ... | code |
2037446/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
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)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
2037446/cell_12 | [
"text_plain_output_1.png"
] | from pandas.tools.plotting import scatter_matrix
from sklearn.model_selection import StratifiedShuffleSplit
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)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat']... | code |
2037446/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing.describe() | code |
17097145/cell_13 | [
"text_plain_output_1.png"
] | from PIL import Image
import cv2 as cv
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 torch
import torchvision.transforms as transforms
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
t... | code |
17097145/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from sklearn.metrics import accuracy_score
import cv2 as cv
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 torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.sampler as... | code |
17097145/cell_4 | [
"text_plain_output_1.png"
] | from PIL import Image
import cv2 as cv
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 torch
import torchvision.transforms as transforms
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
t... | code |
17097145/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import cv2 as cv
import os
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
import torch.utils.data.dataloader as DataLoader
import torch.utils.data.sampler as sampler
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim ... | code |
17097145/cell_8 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.metrics import accuracy_score
import cv2 as cv
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 torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.sampler as... | code |
17097145/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.metrics import accuracy_score
import cv2 as cv
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 torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data.sampler as... | code |
17097145/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
import cv2 as cv
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 torch
import torch.utils.data.sampler as sampler
import torchvision.transforms as transforms
train_df = pd.read_csv('../input/train.csv')
... | code |
306027/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import sqlite3
import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
scripts = pd.read_sql_query('\n\nSELECT s.Id,\n\n cv.Title,\n\n COUNT(DISTINCT vo.Id) NumVotes,\n\n COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END) Num... | code |
306027/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import sqlite3
import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
scripts = pd.read_sql_query('\nSELECT s.Id,\n cv.Title,\n COUNT(DISTINCT vo.Id) NumVotes,\n COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END) NumNonSelfV... | code |
306027/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline, FeatureUnion
import pandas as pd
import sqlite3
import pandas as pd
import sqlite3
con = sqlite3.connect('../input/database.sqlite')
scripts = pd.read_sql_query('\n\nSELE... | code |
90133854/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge
x = 0
for i in ge.columns:
print(f'{i},"coulmn_value_count"' + f'num_of_coulmn=\t{x}' + f'\tnum_of_items_in_each_coulmn\t{len(ge[i].value_counts())})')
print(ge[i].value_counts().to_frame)
x = x ... | code |
90133854/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge
x = 0
for i in ge.columns:
x = x + 1
ge.describe().round(2).T
ge.isnull().sum()
ge1 = ge.copy()
cat_ge = list(ge.select_dtypes(exclude='float64... | code |
90133854/cell_6 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge
x = 0
for i in ge.columns:
x = x + 1
ge.describe().round(2).T
ge.info() | code |
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