path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73069645/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum... | code |
73069645/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum... | code |
73069645/cell_32 | [
"text_plain_output_1.png"
] | import imageio
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'r... | code |
73069645/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews... | code |
73069645/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)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum() | code |
73069645/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum... | code |
73069645/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum... | code |
73069645/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum... | code |
73069645/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum... | code |
73069645/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.head(3) | code |
73069645/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews... | code |
73069645/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.head() | code |
90147386/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split... | code |
90147386/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image
from wordcloud import WordCloud,STOPWORDS,ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['... | code |
90147386/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
plt.figure(figsize=(8, 5))
comm... | code |
90147386/cell_25 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['t... | code |
90147386/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts() | code |
90147386/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.la... | code |
90147386/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
print(analysis_df.shape)
analysis_df.head(5) | code |
90147386/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import ... | code |
90147386/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.spl... | code |
90147386/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment... | code |
90147386/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment... | code |
90147386/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
plt.figure(figsize=(10, 10))
sns.countplot(df.label, palette='mako') | code |
90147386/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from wordcloud import WordCloud,STOPWORDS,ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['... | code |
90147386/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment... | code |
90147386/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df | code |
90147386/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split... | code |
90147386/cell_24 | [
"text_html_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['t... | code |
90147386/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from PIL import Image
from wordcloud import WordCloud,STOPWORDS,ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['... | code |
90147386/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split... | code |
90147386/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
plt.figure(figsize=(10, 5))
sn... | code |
90147386/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.spl... | code |
90147386/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
df.info() | code |
74067974/cell_21 | [
"text_plain_output_1.png"
] | from prophet import Prophet
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitco... | code |
74067974/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.info() | code |
74067974/cell_25 | [
"text_plain_output_1.png"
] | from prophet import Prophet
from prophet.plot import plot_yearly
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_hal... | code |
74067974/cell_23 | [
"text_html_output_1.png"
] | from prophet import Prophet
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitco... | code |
74067974/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.head() | code |
74067974/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum() | code |
74067974/cell_19 | [
"text_plain_output_1.png"
] | from prophet import Prophet
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitco... | code |
74067974/cell_1 | [
"text_plain_output_1.png"
] | !pip install prophet | code |
74067974/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.tail() | code |
74067974/cell_28 | [
"image_output_1.png"
] | from prophet import Prophet
from prophet.plot import plot_plotly, plot_components_plotly
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Cl... | code |
74067974/cell_8 | [
"text_html_output_2.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.describe() | code |
74067974/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from prophet import Prophet
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitco... | code |
74067974/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
74067974/cell_27 | [
"text_plain_output_1.png"
] | from prophet import Prophet
from prophet.plot import plot_plotly, plot_components_plotly
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Cl... | code |
74067974/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna() | code |
90106473/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
label_names = df_train.columns[-1]
fi... | code |
90106473/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
df_test.describe() | code |
90106473/cell_19 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exp... | code |
90106473/cell_28 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for... | code |
90106473/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
df_train.describe() | code |
90106473/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
label_na... | code |
90106473/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for... | code |
90106473/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
print(f'Features: {feature_names}')
label_names = df_train.co... | code |
90106473/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
label_names = df_train.columns[-1]
fi... | code |
90106473/cell_5 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73078642/cell_19 | [
"image_png_output_1.png"
] | from IPython.display import display
import ipywidgets as widgets
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree... | code |
73078642/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from ipywidgets import HBox, Output... | code |
73078642/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from ipywidgets import HBox, Output... | code |
73078642/cell_22 | [
"image_output_1.png"
] | from IPython.display import display
import ipywidgets as widgets
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree... | code |
73078642/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from ipywidgets import HBox, Output... | code |
33103199/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.rea... | code |
33103199/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)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.info() | code |
33103199/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = tra... | code |
33103199/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-tec... | code |
33103199/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, cross_val_predict, StratifiedKFold
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
f... | code |
33103199/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = tra... | code |
33103199/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
train = pd.read_csv('/kaggle/input/... | code |
33103199/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape | code |
33103199/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
train = pd.read_csv('/kaggle/input/... | code |
33103199/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = tra... | code |
33103199/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.rea... | code |
33103199/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)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum() | code |
129020971/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pickle
import torch
import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from tqdm import tqdm
import pickle
device = 'cuda' if torch.cuda.is_av... | code |
129020971/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pickle
import torch
import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from tqdm import tqdm
import pickle
de... | code |
129020971/cell_1 | [
"text_plain_output_1.png"
] | import torch
import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from tqdm import tqdm
import pickle
device = 'cuda' if torch.cuda.is_available() else 'cpu'
import pandas a... | code |
129020971/cell_15 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
import pandas as pd
import pickle
valid_ratio = 0.2
X = pickle.load(open('/kaggle/input/embedder/train_embedding', 'rb'))
y = pd.read_csv('/kaggle/input/embedder/preprocessed_train.csv').target_relabeled.values
valid_size = int(y.shape[0] * valid_ratio)
in_features = X.shap... | code |
129020971/cell_14 | [
"image_output_1.png"
] | from catboost import CatBoostClassifier
import pandas as pd
import pickle
valid_ratio = 0.2
X = pickle.load(open('/kaggle/input/embedder/train_embedding', 'rb'))
y = pd.read_csv('/kaggle/input/embedder/preprocessed_train.csv').target_relabeled.values
valid_size = int(y.shape[0] * valid_ratio)
in_features = X.shap... | code |
129020971/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pickle
import torch
import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from tqdm import tqdm
import pickle
device = 'cuda' if torch.cuda.is_av... | code |
128009107/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import os
train_images_dir = 'train_images'
train_maps_dir = 'train_labels'
val_images_dir = 'valid_images'
val_maps_dir = 'valid_labels'
test_images_dir = 'test_images'
test_maps_dir = 'test_labels'
train_images = np.array(os.listdir(train_images_dir), dtype=object)
train_maps = np.arra... | code |
128009107/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import os
train_images_dir = 'train_images'
train_maps_dir = 'train_labels'
val_images_dir = 'valid_images'
val_maps_dir = 'valid_labels'
test_images_dir = 'test_images'
test_maps_dir = 'test_labels'
train_images = np.array(os.listdir(train_images_dir), dtype=object)
train_maps = np.arra... | code |
128009107/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | """
counter = 1
for backbone in BACKBONES:
for loss in losses:
for freeze_boolean, freeze_tag in zip([True,False], ['frozen','nonFrozen']):
for pretrained_state, pretrained_tag in zip(['imagenet',None], ['pretrained','nonPretrained']):
model.tag = backbone + '_' + loss.name + '_'... | code |
128009107/cell_11 | [
"text_plain_output_1.png"
] | import cv2
from PIL import Image
import albumentations as A
import matplotlib.pyplot as plt
from tensorflow.keras.utils import Sequence | code |
128009107/cell_1 | [
"text_plain_output_1.png"
] | !pip install segmentation_models
!yes | pip install tensorflow==2.10
!yes | apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2 | code |
128009107/cell_18 | [
"text_plain_output_1.png"
] | import segmentation_models as sm
import tensorflow as tf
import tensorflow.keras as K | code |
128009107/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import os
train_images_dir = 'train_images'
train_maps_dir = 'train_labels'
val_images_dir = 'valid_images'
val_maps_dir = 'valid_labels'
test_images_dir = 'test_images'
test_maps_dir = 'test_labels'
train_images = np.array(os.listdir(train_images_dir), d... | code |
128009107/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !cp -r /kaggle/input/crochet-samples-v3 /kaggle/working
os.chdir('/kaggle/working/crochet-samples-v3')
# train_images = np.array(os.listdir(train_images_dir), dtype = object)
retval = os.getcwd()
print("Current working directory %s" % retval) | code |
105178983/cell_2 | [
"text_plain_output_1.png"
] | def my_first_function():
print('success')
my_first_function() | code |
105178983/cell_11 | [
"text_plain_output_1.png"
] | def calci(a, b, c):
"""
fdgfd
"""
if c == '+':
return a + b
elif c == '-':
return a - b
elif c == '/':
return a / b
elif c == '*':
return a * b
cal = calci(5, 6, '/')
print(cal) | code |
105178983/cell_18 | [
"text_plain_output_1.png"
] | def upper_count(name):
count = 0
for i in name:
if i.isupper():
count = count + 1
return count
def avg(*marks):
count, total = (0, 0)
for i in marks:
total = total + i
count = count + 1
return total / count
a = avg(45, 43, 35, 67)
print(a) | code |
105178983/cell_8 | [
"text_plain_output_1.png"
] | def upper_count(name):
count = 0
for i in name:
if i.isupper():
count = count + 1
return count
upper_count('My Name Is Adnan') | code |
105178983/cell_15 | [
"text_plain_output_1.png"
] | def my_salary(weekly_hrs, week, pay_per_hour=500):
salary = weekly_hrs * week * pay_per_hour
return salary
salary = my_salary(pay_per_hour=600, week=6, weekly_hrs=40)
salary = my_salary(4, 5)
print(salary) | code |
105178983/cell_14 | [
"text_plain_output_1.png"
] | def my_salary(weekly_hrs, week, pay_per_hour=500):
salary = weekly_hrs * week * pay_per_hour
return salary
salary = my_salary(pay_per_hour=600, week=6, weekly_hrs=40)
print(salary) | code |
105178983/cell_5 | [
"text_plain_output_1.png"
] | def add(a, b):
return a + b
result = add(4, 5)
print(result) | code |
106210927/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_train['sample'] = 1
df_test['sam... | code |
106210927/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_test.info() | code |
106210927/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
sample_submission.head() | code |
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