path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2008232/cell_11 | [
"image_output_1.png"
] | from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import sqlite3
input = sqlite3.connect('../input/FPA_FOD_20170508.sqlite')
df = pd.read_sql_query("SELECT * FROM 'Fires'", input)
epoch = pd.to_datetime(0, unit='s').t... | code |
2008232/cell_7 | [
"image_output_1.png"
] | from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import sqlite3
input = sqlite3.connect('../input/FPA_FOD_20170508.sqlite')
df = pd.read_sql_query("SELECT * FROM 'Fires'", input)
epoch = pd.to_datetime(0, unit='s').t... | code |
2008232/cell_16 | [
"image_output_1.png"
] | from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import sqlite3
input = sqlite3.connect('../input/FPA_FOD_20170508.sqlite')
df = pd.read_sql_query("SELECT * FROM 'Fires'", input)
epoch = pd.to_datetime(0, unit='s').t... | code |
2008232/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import pandas as pd
import sqlite3
input = sqlite3.connect('../input/FPA_FOD_20170508.sqlite')
df = pd.read_sql_query("SELECT * FROM 'Fires'", input)
epoch = pd.to_datetime(0, unit='s').to_julian_date()
df.DISCOVERY_DATE = pd.to_datetime(df.DISCOVER... | code |
16164281/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
dataset = pd.read_csv('../input/predictnav-beta/dataset_beta.csv')
dataset.d... | code |
16164281/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.sv... | code |
16164281/cell_11 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing imp... | code |
16164281/cell_19 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import se... | code |
16164281/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline... | code |
16164281/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm impor... | code |
16164281/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import se... | code |
16164281/cell_15 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import xgboost as xgb
dataset = pd.read_csv('../input/predictnav-beta/datas... | code |
16164281/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
import xgboost as xgb
dataset = pd.read_csv('../input/predictnav-beta/datas... | code |
16164281/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
pipeline_1 = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(ran... | code |
16164281/cell_14 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
dataset = pd.read_csv('... | code |
16164281/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
import pandas as pd # data proc... | code |
16164281/cell_12 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection impor... | code |
16164281/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
pipeline_1 = Pipeline([('scl', Standard... | code |
73067458/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.cs... | code |
73067458/cell_9 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_25 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_23 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_20 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.cs... | code |
73067458/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.info()
p... | code |
73067458/cell_29 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_11 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_19 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_32 | [
"text_plain_output_1.png"
] | rans = 42
def log_transform(x):
return np.log(x + 1)
transformer = FunctionTransformer(log_transform)
numerical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='mean')), ('scaler', StandardScaler())])
categorical_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='most_frequent')), ('... | code |
73067458/cell_28 | [
"image_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_15 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.cs... | code |
73067458/cell_3 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
print('Tensor Flow:', tf.__version__)
print('SciKit Learn:', sk.__version__)
print('Pandas:', pd.__version__)
print('Numpy:', np.__version__)
print('Seaborn:', ... | code |
73067458/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.cs... | code |
73067458/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.cs... | code |
73067458/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_37 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
result_df = pd.... | code |
73067458/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isnull()... | code |
73067458/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
import tensorflow as tf
import xgboost as xgb
X_full = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.head() | code |
73067458/cell_36 | [
"text_html_output_1.png"
] | clf = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])
final_model = clf.fit(X_train, y_train)
preds_valid = final_model.predict(X_valid)
print('MAE:', mean_absolute_error(y_valid, preds_valid))
print('RMSE:', mean_squared_error(y_valid, preds_valid, squared=False)) | code |
32062582/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_test.head() | code |
32062582/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fa... | code |
32062582/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
print('Minimum date from test set: {}'.format(df_test['Date'].min()))
print('Maximum date from test set: {}'.format(df_test['Date'].... | code |
32062582/cell_26 | [
"text_html_output_1.png"
] | from xgboost import XGBRegressor
import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[... | code |
32062582/cell_19 | [
"text_html_output_2.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fa... | code |
32062582/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
display(df_train.head())
display(df_train.describe())
display(df_train.info()) | code |
32062582/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fa... | code |
32062582/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fa... | code |
32062582/cell_10 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objects as go
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc... | code |
32062582/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def add_daily_measures(df):
df.loc[0, 'Daily Cases'] = df.loc[0, 'ConfirmedCases']
df.loc[0, 'Daily Deaths'] = df.loc[0, 'Fa... | code |
32062582/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
print('Minimum date from training set: {}'.format(df_train['Date'].min()))
print('Maximum date from training set: {}'.format(df_trai... | code |
128019479/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_test.shape
Data_train.columns
Data_test.columns
... | code |
128019479/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_test.shape
Data_test.columns
Data_test.isnull().sum().sum() | code |
128019479/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.describe() | code |
128019479/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_test.shape
Data_train.columns
Data_test.columns
... | code |
128019479/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_test.shape
Data_train.columns
Data_test.columns
Data_train.isnull().... | code |
128019479/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_test.shape | code |
128019479/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegre... | code |
128019479/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns | code |
128019479/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_test.shape
Data_test.columns | code |
128019479/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum()
Data_train.corr() | code |
128019479/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum()
Data_train.corr()
sns.heatmap(Data_train.corr(), cmap='... | code |
128019479/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum()
Data_train.corr()
feat... | code |
128019479/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum()
Data_train.hist(figsize=(30, 30)) | code |
128019479/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_test.shape
Data_train.columns
Data_test.columns
Data_train.isnull().... | code |
128019479/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_test.shape
Data_test.columns
Data_test.describe() | code |
128019479/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape
Data_train.columns
Data_train.isnull().sum().sum() | code |
128019479/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
Data_train = pd.read_csv('/kaggle/input/playground-series-s3e1/train.csv')
Data_test = pd.read_csv('/kaggle/input/playground-series-s3e1/test.csv')
Data_train.shape | code |
89140329/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../inp... | code |
89140329/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-sp... | code |
89140329/cell_25 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
len(STOPWORDS) | code |
89140329/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/... | code |
89140329/cell_34 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset... | code |
89140329/cell_29 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset... | code |
89140329/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../inp... | code |
89140329/cell_41 | [
"text_html_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/... | code |
89140329/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
print('#----------------------------------------------------#')
print(df1.head(3))
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset... | code |
89140329/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../inp... | code |
89140329/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirna... | code |
89140329/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_... | code |
89140329/cell_16 | [
"text_plain_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)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../inp... | code |
89140329/cell_38 | [
"image_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 =... | code |
89140329/cell_35 | [
"text_plain_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset... | code |
89140329/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset... | code |
89140329/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'L... | code |
89140329/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-sp... | code |
89140329/cell_37 | [
"image_output_1.png"
] | from nltk.stem import WordNetLemmatizer
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 =... | code |
89140329/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import re
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../inp... | code |
89140329/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/email-spam-dataset/completeSpamAssassin.csv')
df1 = df1[['Body', 'Label']]
df2 = pd.read_csv('../input/email-spam-dataset/enronSpamSubset.csv')
df2 = df2[['Body', 'Label']]
df3 = pd.read_csv('../input/email-spam-dataset/... | code |
17135990/cell_13 | [
"text_html_output_1.png"
] | from IPython.display import display
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
df_raw.SalePrice = np.log(df_ra... | code |
17135990/cell_20 | [
"text_plain_output_1.png"
] | from IPython.display import display
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
df_raw = pd.read_feather('tmp/bulldozers-raw') | code |
17135990/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17135990/cell_11 | [
"text_plain_output_1.png"
] | from IPython.display import display
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
display_all(df_raw.describe(include='all').T) | code |
17135990/cell_1 | [
"text_plain_output_1.png"
] | !pip install fastai==0.7.0
!pip install torchtext==0.2.3 | code |
17135990/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !ls {PATH} | code |
17135990/cell_18 | [
"text_plain_output_1.png"
] | from IPython.display import display
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
df_raw.SalePrice = np.log(df_ra... | code |
17135990/cell_15 | [
"text_html_output_1.png"
] | from IPython.display import display
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
df_raw.SalePrice = np.log(df_ra... | code |
17135990/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
m = RandomForestRegressor(n_jobs=-1)
m.fit(df, y)
m.score(df, y) | code |
17135990/cell_10 | [
"text_plain_output_1.png"
] | from IPython.display import display
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/'
df_raw = pd.read_csv(f'{PATH}train/Train.csv', low_memory=False, parse_dates=['saledate'])
def display_all(df):
pass
display_all(df_raw.tail().T) | code |
121150522/cell_4 | [
"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/spaceship-titanic/train.csv')
df.info() | code |
121150522/cell_23 | [
"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/spaceship-titanic/train.csv')
df.pivot_table(index='CryoSleep', columns='Transported', aggfunc={'Transported': 'count'})
df_count = df[['Age']... | code |
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