path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32063423/cell_23 | [
"text_plain_output_1.png"
] | from xgboost import XGBRegressor
from xgboost import plot_importance
from xgboost import plot_importance, plot_tree
import matplotlib.pyplot as plt
model1 = XGBRegressor(n_estimators=1000)
model1.fit(X_train, y_train[:, 0])
from xgboost import plot_importance
import matplotlib.pyplot as plt
def plot_features(boos... | code |
32063423/cell_20 | [
"text_plain_output_1.png"
] | from xgboost import XGBRegressor
model1 = XGBRegressor(n_estimators=1000)
model1.fit(X_train, y_train[:, 0]) | code |
32063423/cell_6 | [
"text_plain_output_1.png",
"image_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')
train_date_min = df_train['Date'].min()
train_date_max = df_train['Date'].max()
test_date_min = df_test['Date'].min()
test_date_max... | code |
32063423/cell_15 | [
"text_plain_output_1.png"
] | from fastai.tabular import add_datepart
from sklearn.preprocessing import OrdinalEncoder
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_train['Date'] = pd.to_datetime(df_trai... | code |
32063423/cell_24 | [
"text_plain_output_1.png"
] | from xgboost import XGBRegressor
from xgboost import plot_importance
from xgboost import plot_importance, plot_tree
import matplotlib.pyplot as plt
model2 = XGBRegressor(n_estimators=1000)
model2.fit(X_train, y_train[:, 1])
from xgboost import plot_importance
import matplotlib.pyplot as plt
def plot_features(boos... | code |
32063423/cell_14 | [
"text_plain_output_1.png"
] | from fastai.tabular import add_datepart
from sklearn.preprocessing import OrdinalEncoder
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_train['Date'] = pd.to_datetime(df_trai... | code |
32063423/cell_5 | [
"text_plain_output_1.png",
"image_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')
test_date_min = df_test['Date'].min()
test_date_max = df_test['Date'].max()
print('Minimum date from test set: {}'.format(test_date_... | code |
2029174/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.rep... | code |
2029174/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.replace(('cp', 'im', 'pp', 'imU', 'om', 'omL', 'imL', 'imS... | code |
2029174/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.replace(('cp', 'im', 'pp', 'imU', 'om', 'omL', 'imL', 'imS... | code |
2029174/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas
dataframe = pandas.read_csv('../input/ecoli.csv', delim_whitespace=True)
dataframe.columns = ['seq_name', 'mcg', 'gvh', 'lip', 'chg', 'aac', 'alm1', 'alm2', 'site']
dataframe = dataframe.drop('seq_name', axis=1)
dataframe.site.replace(('cp', 'im', 'pp', 'imU', 'om', 'omL', 'imL', 'imS... | code |
2029174/cell_10 | [
"text_plain_output_1.png"
] | A_prev, W, b = linear_activation_forward_test_case() | code |
17098287/cell_21 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
model = Sequential([Dense(units=16, input_dim=29, activation='relu'), Dense(units=24, activation='relu'), Dropout(0.5), Dense(20, activation='relu'), Dense(24, activation='relu'), Dense(1, activation='sigmoid')])
mode... | code |
17098287/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Amount'], axis=1)
data = data.drop([... | code |
17098287/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Amount'], axis=1)
data.head() | code |
17098287/cell_4 | [
"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/creditcard.csv')
data['Amount'].values | code |
17098287/cell_30 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.metrics import confusion_matrix
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
np.array(x_trai... | code |
17098287/cell_33 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | code |
17098287/cell_20 | [
"text_html_output_1.png"
] | from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout | code |
17098287/cell_6 | [
"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/creditcard.csv')
data['Amount'].values.shape[-1] | code |
17098287/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
np.array(x_train)
x_train = np.array(x_train)
x_test = np.array(x_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
y_test.shape
y_test = pd.DataFrame(... | code |
17098287/cell_2 | [
"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/creditcard.csv')
data.head(10) | code |
17098287/cell_19 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
np.array(x_train)
x_train = np.array(x_train)
x_test = np.array(x_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
x_train | code |
17098287/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
print(os.listdir('../input'))
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import accuracy_score | code |
17098287/cell_7 | [
"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/creditcard.csv')
data['Amount'].values.reshape(-1, 1).shape | code |
17098287/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
np.array(x_train)
x_train = np.array(x_train)
x_test = np.array(x_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
y_test.shape | code |
17098287/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data.head() | code |
17098287/cell_15 | [
"text_plain_output_1.png"
] | for i in [x_train, x_test, y_train, y_test]:
print(i.shape) | code |
17098287/cell_16 | [
"text_plain_output_1.png"
] | x_train.head() | code |
17098287/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['Amount'] | code |
17098287/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
np.array(x_train) | code |
17098287/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.metrics import confusion_matrix
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)
data = pd.read_csv('../input/... | code |
17098287/cell_24 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
import numpy as np # linear algebra
np.array(x_train)
x_train = np.array(x_train)
x_test = np.array(x_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
model = Sequential([Dense(units=16, input_dim=29, ac... | code |
17098287/cell_22 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
import numpy as np # linear algebra
np.array(x_train)
x_train = np.array(x_train)
x_test = np.array(x_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
model = Sequential([Dense(units=16, input_dim=29, ac... | code |
17098287/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Amount'], axis=1)
data = data.drop([... | code |
17098287/cell_27 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
import numpy as np # linear algebra
np.array(x_train)
x_train = np.array(x_train)
x_test = np.array(x_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
model = Sequential([Dense(units=16, input_dim=29, ac... | code |
17098287/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Amount'], axis=1)
data = data.drop([... | code |
17098287/cell_5 | [
"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/creditcard.csv')
data['Amount'].values.shape | code |
17101142/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | x_train.head() | code |
17101142/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Amount'], axis=1)
data = data.drop(['... | code |
17101142/cell_20 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
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)
data = pd.read_csv('../input/... | code |
17101142/cell_2 | [
"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/creditcard.csv')
data.head(10) | code |
17101142/cell_11 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
y_train.values
y_train.values.ravel()
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(x_train, y_train.values.ravel())
y_pred = random_forest.predict(x_test)
random_forest.score(x_test, y_test) | code |
17101142/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data... | code |
17101142/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomFores... | code |
17101142/cell_7 | [
"text_plain_output_1.png"
] | y_train.values | code |
17101142/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data... | code |
17101142/cell_8 | [
"text_plain_output_1.png"
] | y_train.values
y_train.values.ravel() | code |
17101142/cell_15 | [
"text_plain_output_1.png"
] | y_test.head() | code |
17101142/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
y_train.values
y_train.values.ravel()
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(x_train, y_train.values.ravel())
y_pred = random_forest.predict(x_test)
random_forest.score(x_test, y_tes... | code |
17101142/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Amount'], axis=1)
data.head() | code |
17101142/cell_17 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np # linear algebra
y_train.values
y_train.values.ravel()
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(x_train, y_train.values.ravel())
y_... | code |
17101142/cell_14 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
y_train.values
y_train.values.ravel()
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(x_train, y_train.values.ravel())
y_pred = random_forest.predict(x_test)
random_forest.score(x_test, y_test)
y_pred | code |
17101142/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
y_train.values
y_train.values.ravel()
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(x_train, y_train.values.ravel()) | code |
74044330/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from random import random
from statsmodels.tsa.ar_model import AR
import matplotlib.pyplot as plt
xdata = range(1, 100)
ydata = [x + 3 * random() for x in xdata]
plt.xlim(0, 100)
plt.ylim(0, 100)
plt.scatter(xdata, ydata, s=10)
plt.show()
model = AR(ydata)
model_fit = model.fit() | code |
74044330/cell_6 | [
"text_plain_output_1.png"
] | from random import random
from statsmodels.tsa.ar_model import AR
import matplotlib.pyplot as plt
xdata = range(1, 100)
ydata = [x + 3 * random() for x in xdata]
plt.xlim(0, 100)
plt.ylim(0, 100)
model = AR(ydata)
model_fit = model.fit()
yhat = model_fit.predict(start=90, end=110)
print('Predicted value for Auto Re... | code |
74044330/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from random import random
from statsmodels.tsa.ar_model import AR
from statsmodels.tsa.arima_model import ARMA
import matplotlib.pyplot as plt
xdata = range(1, 100)
ydata = [x + 3 * random() for x in xdata]
plt.xlim(0, 100)
plt.ylim(0, 100)
model = AR(ydata)
model_fit = model.fit()
yhat = model_fit.predict(start=9... | code |
74044330/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from random import random
from statsmodels.tsa.ar_model import AR
from statsmodels.tsa.arima_model import ARMA
import matplotlib.pyplot as plt
xdata = range(1, 100)
ydata = [x + 3 * random() for x in xdata]
plt.xlim(0, 100)
plt.ylim(0, 100)
model = AR(ydata)
model_fit = model.fit()
yhat = model_fit.predict(start=9... | code |
74044330/cell_12 | [
"text_plain_output_1.png"
] | from random import random
from statsmodels.tsa.ar_model import AR
from statsmodels.tsa.arima_model import ARMA
from statsmodels.tsa.vector_ar.var_model import VAR
import matplotlib.pyplot as plt
xdata = range(1, 100)
ydata = [x + 3 * random() for x in xdata]
plt.xlim(0, 100)
plt.ylim(0, 100)
model = AR(ydata)
mode... | code |
128011291/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.drop(['frequency', 'indicator'], axis=1, inplace=True)
meat | code |
128011291/cell_9 | [
"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)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat['subject'].unique() | code |
128011291/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.info() | code |
128011291/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.drop(['frequency', 'indicator'], axis=1, inplace=True)
meat
meat['KG_CAP values'] = meat['value']
conversion_factor = 1000
meat.loc[meat['measure'] == 'THND_TONNE... | code |
128011291/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.drop(['frequency', 'indicator'], axis=1, inplace=True)
meat
meat['KG_CAP values'] = meat['value']
conversion_factor = 1000
meat.loc[meat['measure'] == 'THND_TONNE... | code |
128011291/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.describe() | code |
128011291/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.drop(['frequency', 'indicator'], axis=1, inplace=True)
meat
meat['KG_CAP values'] = meat['value']
conversion_factor = 1000
meat.loc[meat['measure'] == 'THND_TONNE... | code |
128011291/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.drop(['frequency', 'indicator'], axis=1, inplace=True)
meat
meat['KG_CAP values'] = meat['value']
conversion_factor = 1000
meat.loc[meat['measure'] == 'THND_TONNE... | code |
128011291/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat['frequency'].unique() | code |
128011291/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.drop(['frequency', 'indicator'], axis=1, inplace=True)
meat
meat['KG_CAP values'] = meat['value']
conversion_factor = 1000
meat.loc[meat['measure'] == 'THND_TONNE... | code |
128011291/cell_7 | [
"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)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat['location'].unique() | code |
128011291/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.drop(['frequency', 'indicator'], axis=1, inplace=True)
meat
meat['KG_CAP values'] = meat['value']
conversion_factor = 1000
meat.loc[meat['measure'] == 'THND_TONNE... | code |
128011291/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat['indicator'].unique() | code |
128011291/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.drop(['frequency', 'indicator'], axis=1, inplace=True)
meat
meat['KG_CAP values'] = meat['value']
conversion_factor = 1000
meat.loc[meat['measure'] == 'THND_TONNE... | code |
128011291/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat | code |
128011291/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat.drop(['frequency', 'indicator'], axis=1, inplace=True)
meat
meat['KG_CAP values'] = meat['value']
conversion_factor = 1000
meat.loc[meat['measure'] == 'THND_TONNE... | code |
128011291/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
meat = pd.read_csv('/kaggle/input/meat-consumption/meat_consumption.csv')
meat
meat['measure'].unique() | code |
72074669/cell_9 | [
"text_plain_output_1.png"
] | import cv2 as cv
import cv2 as cv
import cv2 as cv
import imageio
import matplotlib.pyplot as mpimg
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.py... | code |
72074669/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as mpimg
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
nezuko = plt.imread('../input/digital-image/tanjiro.png')
import matplotlib.pyplot as plt
import matplotlib.pyplot as mpimg
img = mpimg.imread('../input/digital-image/tanjiro.png')
plt.i... | code |
72074669/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)
import pandas as pd
df = pd.read_csv('../input/coviddatasety/phsm-severity-data.csv')
df.head(20) | code |
72074669/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from PIL import Image
tanjiro = Image.open('../input/digital-image/tanjiro.png')
tanjiro | code |
72074669/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2 as cv
import cv2 as cv
import cv2 as cv
import cv2 as cv
import cv2 as cv
import imageio
import matplotlib.pyplot as mpimg
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.p... | code |
72074669/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 |
72074669/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2 as cv
import imageio
import matplotlib.pyplot as mpimg
import matplotlib.pyplot as pl
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
nezuko = plt.imread('../input/digital-image/tanjiro.png')
import matplotlib.pyplot as p... | code |
72074669/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2 as cv
import cv2 as cv
import imageio
import matplotlib.pyplot as mpimg
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
nezuko = plt.imread('../input/digital-... | code |
72074669/cell_3 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
nezuko = plt.imread('../input/digital-image/tanjiro.png')
plt.imshow(nezuko) | code |
72074669/cell_10 | [
"image_output_1.png"
] | import cv2 as cv
import cv2 as cv
import cv2 as cv
import cv2 as cv
import imageio
import matplotlib.pyplot as mpimg
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
imp... | code |
72074669/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2 as cv
import cv2 as cv
import cv2 as cv
import cv2 as cv
import cv2 as cv
import cv2 as cv
import imageio
import matplotlib.pyplot as mpimg
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
import matplotlib.pyplot as pl
i... | code |
72074669/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import imageio
import matplotlib.pyplot as mpimg
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
nezuko = plt.imread('../input/digital-image/tanjiro.png')
import matplotlib.pyplot as plt
import matplotlib.pyplot as mpimg
img = mpimg.i... | code |
18116987/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/books.csv', error_bad_lines=False)
df.shape
author_count = df['authors'].value_counts()[:10]
highest_rated = df.sort_values('ratings_count', a... | code |
18116987/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/books.csv', error_bad_lines=False)
df.shape
df.head(5) | code |
18116987/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18116987/cell_7 | [
"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/books.csv', error_bad_lines=False)
df.shape
plt.figure(figsize=(10, 7))
author_count = df['authors'].value_counts()[:10]
sns.barplot(x=author_c... | code |
18116987/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from wordcloud import WordCloud
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/books.csv', error_bad_lines=False)
df.shape
author_count = df['authors'].value_counts()[:10]
highest_rated = ... | code |
18116987/cell_14 | [
"text_html_output_1.png"
] | from subprocess import check_output
from wordcloud import WordCloud
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/books.csv', error_bad_lines=False)
df.shape
author_count = df['authors'].... | code |
18116987/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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/books.csv', error_bad_lines=False)
df.shape
author_count = df['authors'].value_counts()[:10]
highest_rated = df.sort_values('ratings_count', a... | code |
18116987/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/books.csv', error_bad_lines=False)
df.shape | code |
18141020/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/... | code |
18141020/cell_19 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.model_sel... | code |
18141020/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
def score_dataset(X_train, X_valid, y_train, y_valid):
model = RandomForestRegressor(n_estimators=10, random_state=0... | code |
18141020/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.model_sel... | code |
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