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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(booster, figsize):
fig, ax = plt.subplots(1,1,figsize=figsize)
return plot_importance(booster=booster, ax=ax)
plot_features(model1, (10, 14)) | 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 = df_test['Date'].max()
test_date_min - train_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_train['Date'], format='%Y-%m-%d')
df_test['Date'] = pd.to_datetime(df_test['Date'], format='%Y-%m-%d')
def categoricalToInteger(df):
df.Province_State.fillna('NaN', inplace=True)
oe = OrdinalEncoder()
df[['Province_State', 'Country_Region']] = oe.fit_transform(df.loc[:, ['Province_State', 'Country_Region']])
return df
add_datepart(df_train, 'Date', drop=False)
df_train.drop('Elapsed', axis=1, inplace=True)
df_train = categoricalToInteger(df_train)
def lag_feature(df, lags, col):
tmp = df[['Dayofyear', 'Country_Region', 'Province_State', col]]
for i in lags:
shifted = tmp.copy()
shifted.columns = ['Dayofyear', 'Country_Region', 'Province_State', col + '_lag_' + str(i)]
shifted['Dayofyear'] += i
df = pd.merge(df, shifted, on=['Dayofyear', 'Country_Region', 'Province_State'], how='left')
return df
df_train = lag_feature(df_train, [1, 2, 3, 6, 11], 'ConfirmedCases')
df_train = lag_feature(df_train, [1, 2, 3, 6, 11], 'Fatalities')
df_train.columns
lags = [1, 2, 3, 6, 11]
features = ['ConfirmedCases', 'Fatalities', 'Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start', 'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']
for lag in lags:
features.append('ConfirmedCases_lag_' + str(lag))
features.append('Fatalities_lag_' + str(lag))
corr_matrix = df_train[features].corr()
corr_matrix['ConfirmedCases'].sort_values(ascending=False)
corr_matrix['Fatalities'].sort_values(ascending=False) | 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(booster, figsize):
fig, ax = plt.subplots(1,1,figsize=figsize)
return plot_importance(booster=booster, ax=ax)
plot_features(model2, (10, 14)) | 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_train['Date'], format='%Y-%m-%d')
df_test['Date'] = pd.to_datetime(df_test['Date'], format='%Y-%m-%d')
def categoricalToInteger(df):
df.Province_State.fillna('NaN', inplace=True)
oe = OrdinalEncoder()
df[['Province_State', 'Country_Region']] = oe.fit_transform(df.loc[:, ['Province_State', 'Country_Region']])
return df
add_datepart(df_train, 'Date', drop=False)
df_train.drop('Elapsed', axis=1, inplace=True)
df_train = categoricalToInteger(df_train)
def lag_feature(df, lags, col):
tmp = df[['Dayofyear', 'Country_Region', 'Province_State', col]]
for i in lags:
shifted = tmp.copy()
shifted.columns = ['Dayofyear', 'Country_Region', 'Province_State', col + '_lag_' + str(i)]
shifted['Dayofyear'] += i
df = pd.merge(df, shifted, on=['Dayofyear', 'Country_Region', 'Province_State'], how='left')
return df
df_train = lag_feature(df_train, [1, 2, 3, 6, 11], 'ConfirmedCases')
df_train = lag_feature(df_train, [1, 2, 3, 6, 11], 'Fatalities')
df_train.columns
lags = [1, 2, 3, 6, 11]
features = ['ConfirmedCases', 'Fatalities', 'Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start', 'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']
for lag in lags:
features.append('ConfirmedCases_lag_' + str(lag))
features.append('Fatalities_lag_' + str(lag))
corr_matrix = df_train[features].corr()
corr_matrix['ConfirmedCases'].sort_values(ascending=False) | 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_min))
print('Maximum date from test set: {}'.format(test_date_max)) | 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.replace(('cp', 'im', 'pp', 'imU', 'om', 'omL', 'imL', 'imS'), (1, 0, 0, 0, 0, 0, 0, 0), inplace=True)
dataset = dataframe.values
DataX = np.array(dataset[:, 0:7])
DataY = np.transpose([dataset[:, 7]])
X_train, X_test, Y_train, Y_test = train_test_split(DataX, DataY, test_size=0.2)
print(X_train.shape, Y_train.shape)
print(X_test.shape, Y_test.shape) | 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'), (1, 0, 0, 0, 0, 0, 0, 0), inplace=True)
dataset = dataframe.values
DataX = np.array(dataset[:, 0:7])
DataY = np.transpose([dataset[:, 7]])
def intialize_parameters(n_x, n_h, n_y):
np.random.seed(4)
W1 = np.random.randn(n_h, n_x)
W2 = np.random.randn(n_y, n_h)
parameters = {'W1': W1, 'W2': W2}
return parameters
intialize_parameters(5, 4, 3) | 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'), (1, 0, 0, 0, 0, 0, 0, 0), inplace=True)
dataset = dataframe.values
DataX = np.array(dataset[:, 0:7])
DataY = np.transpose([dataset[:, 7]])
def intialize_parameters(n_x, n_h, n_y):
np.random.seed(4)
W1 = np.random.randn(n_h, n_x)
W2 = np.random.randn(n_y, n_h)
parameters = {'W1': W1, 'W2': W2}
return parameters
def intialize_parameters_deep(layer_dims):
np.random.seed(4)
L = len(layer_dims)
parameters = {}
for l in range(1, L):
parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l - 1])
return parameters
parameters = intialize_parameters_deep([5, 4, 3])
print(parameters) | 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'), (1, 0, 0, 0, 0, 0, 0, 0), inplace=True)
dataset = dataframe.values
DataX = np.array(dataset[:, 0:7])
print(DataX.shape)
DataY = np.transpose([dataset[:, 7]])
print(DataY.shape) | 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')])
model.summary() | 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(['Time'], axis=1)
x = data.iloc[:, data.columns != 'Class']
y = data.iloc[:, data.columns == 'Class']
y.head() | 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_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, activation='relu'), Dense(units=24, activation='relu'), Dropout(0.5), Dense(20, activation='relu'), Dense(24, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=15, epochs=5)
score = model.evaluate(x_test, y_test)
y_pred = model.predict(x_test)
y_pred.shape
y_test.shape
y_test = pd.DataFrame(y_test)
cnf_matrix = confusion_matrix(y_test, y_pred.round())
print(cnf_matrix) | 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 (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(['Time'], axis=1)
x = data.iloc[:, data.columns != 'Class']
y = data.iloc[:, data.columns == 'Class']
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, activation='relu'), Dense(units=24, activation='relu'), Dropout(0.5), Dense(20, activation='relu'), Dense(24, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=15, epochs=5)
score = model.evaluate(x_test, y_test)
def plot_confusion_matrix(cm, classes, normalize=False, title='None', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
plt.text(j, i, format(cm[i, j], fmt), ha='center', va='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
y_pred = model.predict(x_test)
y_pred.shape
y_test.shape
y_test = pd.DataFrame(y_test)
y_pred1 = model.predict(x)
y_actual = pd.DataFrame(y)
cnf_matrix1 = confusion_matrix(y_actual, y_pred1.round())
plot_confusion_matrix(cnf_matrix1, classes=[0, 1]) | 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(y_test)
y_test.head() | 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/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)
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')])
model.summary()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=15, epochs=5)
score = model.evaluate(x_test, y_test)
def plot_confusion_matrix(cm, classes, normalize=False, title='None', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
plt.text(j, i, format(cm[i, j], fmt), ha='center', va='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
y_pred = model.predict(x_test)
y_pred.shape
y_test.shape
y_test = pd.DataFrame(y_test)
cnf_matrix = confusion_matrix(y_test, y_pred.round())
plot_confusion_matrix(cnf_matrix, classes=[0, 1]) | 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, activation='relu'), Dense(units=24, activation='relu'), Dropout(0.5), Dense(20, activation='relu'), Dense(24, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=15, epochs=5)
score = model.evaluate(x_test, y_test)
print(score) | 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, activation='relu'), Dense(units=24, activation='relu'), Dropout(0.5), Dense(20, activation='relu'), Dense(24, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=15, epochs=5) | 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(['Time'], axis=1)
data.head() | 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, activation='relu'), Dense(units=24, activation='relu'), Dropout(0.5), Dense(20, activation='relu'), Dense(24, activation='relu'), Dense(1, activation='sigmoid')])
model.summary()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=15, epochs=5)
score = model.evaluate(x_test, y_test)
y_pred = model.predict(x_test)
y_pred.shape | 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(['Time'], axis=1)
x = data.iloc[:, data.columns != 'Class']
y = data.iloc[:, data.columns == 'Class']
x.head() | 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(['Time'], axis=1)
data.head() | 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/creditcard.csv')
data['normalized_amt'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Amount'], axis=1)
data = data.drop(['Time'], axis=1)
x = data.iloc[:, data.columns != 'Class']
y = data.iloc[:, data.columns == 'Class']
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)
def plot_confusion_matrix(cm, classes, normalize=False, title='None', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
plt.text(j, i, format(cm[i, j], fmt), ha='center', va='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
cnf_matrix = confusion_matrix(y_test, y_pred)
y_pred1 = random_forest.predict(x)
y_pred1
y_pred1.round()
cnf_matrix = confusion_matrix(y, y_pred1)
plot_confusion_matrix(cnf_matrix, classes=[0, 1]) | 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 = data.drop(['Amount'], axis=1)
data = data.drop(['Time'], axis=1)
x = data.iloc[:, data.columns != 'Class']
y = data.iloc[:, data.columns == 'Class']
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_pred1 = random_forest.predict(x)
y_pred1
y_pred1.round() | 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 RandomForestClassifier
print(os.listdir('../input')) | 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 = data.drop(['Amount'], axis=1)
data = data.drop(['Time'], axis=1)
x = data.iloc[:, data.columns != 'Class']
y = data.iloc[:, data.columns == 'Class']
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_pred1 = random_forest.predict(x)
y_pred1 | 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_test)
cnf_matrix = confusion_matrix(y_test, y_pred)
print(cnf_matrix) | 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_pred = random_forest.predict(x_test)
random_forest.score(x_test, y_test)
def plot_confusion_matrix(cm, classes, normalize=False, title='None', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
plt.text(j, i, format(cm[i, j], fmt), ha='center', va='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
cnf_matrix = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(cnf_matrix, classes=[0, 1]) | 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 Regression ', yhat) | 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=90, end=110)
model = ARMA(ydata, order=(0, 1))
model_fit = model.fit(disp=False)
yhat = model_fit.predict(start=90, end=110)
print('Predicted value for Moving Average 0,1 ', yhat) | 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=90, end=110)
model = ARMA(ydata, order=(0, 1))
model_fit = model.fit(disp=False)
yhat = model_fit.predict(start=90, end=110)
model_fit = model.fit(disp=False)
yhat = model_fit.predict(start=90, end=110)
print('Predicted value for Moving Average 2,1 ', yhat) | 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)
model_fit = model.fit()
yhat = model_fit.predict(start=90, end=110)
model = ARMA(ydata, order=(0, 1))
model_fit = model.fit(disp=False)
yhat = model_fit.predict(start=90, end=110)
model_fit = model.fit(disp=False)
yhat = model_fit.predict(start=90, end=110)
data = []
for i in range(100):
v1 = i + random()
v2 = v1 + random()
row = [v1, v2]
data.append(row)
print(data)
model = VAR(data)
model_fit = model.fit()
yhat = model_fit.forecast(model_fit.y, steps=1)
print('Predicted value using VAR ', yhat) | 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', 'KG_CAP values'] *= conversion_factor
meat
grouped_data = meat.groupby('subject')['KG_CAP values'].agg(['sum', 'mean'])
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10))
colors = ['blue', 'green', 'orange', 'red']
ax1.bar(grouped_data.index, grouped_data['sum'], color=colors)
ax1.set_ylabel('Total mean consumed')
ax1.set_xticklabels(grouped_data.index, rotation=45, ha='right')
for i, v in enumerate(grouped_data['sum']):
ax1.text(i, v, str(v), ha='center', va='bottom')
ax2.bar(grouped_data.index, grouped_data['mean'], color=colors)
ax2.set_ylabel('Mean consumption of meat')
ax2.set_xticklabels(grouped_data.index, rotation=45, ha='right')
for i, v in enumerate(grouped_data['mean']):
ax2.text(i, v, str(v), ha='center', va='bottom')
plt.xlabel('Type of meat')
plt.suptitle('Total and mean consumption of meat')
plt.tight_layout()
plt.show() | 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', 'KG_CAP values'] *= conversion_factor
meat
meat | 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', 'KG_CAP values'] *= conversion_factor
meat
meat['time'] = pd.to_datetime(meat['time'], format='%Y')
meat
grouped_data = meat.groupby('subject')['KG_CAP values'].agg(['sum', 'mean'])
# Create a figure with two subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10))
# Plot the sum of KG_CAP values
colors = ['blue', 'green', 'orange', 'red']
ax1.bar(grouped_data.index, grouped_data['sum'], color=colors)
ax1.set_ylabel('Total mean consumed')
ax1.set_xticklabels(grouped_data.index, rotation=45, ha='right')
# Add exact values on top of each bar in the sum plot
for i, v in enumerate(grouped_data['sum']):
ax1.text(i, v, str(v), ha='center', va='bottom')
# Plot the mean of KG_CAP values
ax2.bar(grouped_data.index, grouped_data['mean'], color=colors)
ax2.set_ylabel('Mean consumption of meat')
ax2.set_xticklabels(grouped_data.index, rotation=45, ha='right')
# Add exact values on top of each bar in the mean plot
for i, v in enumerate(grouped_data['mean']):
ax2.text(i, v, str(v), ha='center', va='bottom')
# Set x-axis label and title for the entire figure
plt.xlabel('Type of meat')
plt.suptitle('Total and mean consumption of meat')
# Adjust spacing between subplots
plt.tight_layout()
# Display the plot
plt.show()
meat['time'] = pd.to_datetime(meat['time'], format='%Y')
subjects = ['BEEF', 'PIG', 'POULTRY', 'SHEEP']
filtered_data = meat[meat['subject'].isin(subjects)]
time_intervals = [
(pd.Timestamp('1990-01-01'), pd.Timestamp('1997-01-01')),
(pd.Timestamp('1997-01-01'), pd.Timestamp('2004-01-01')),
(pd.Timestamp('2004-01-01'), pd.Timestamp('2010-01-01')),
(pd.Timestamp('2010-01-01'), pd.Timestamp('2016-01-01')),
(pd.Timestamp('2016-01-01'), pd.Timestamp('2022-01-01')),
(pd.Timestamp('2022-01-01'), pd.Timestamp('2029-01-01'))
]
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()
for i, subject in enumerate(subjects):
ax = axes[i]
ax.set_title(subject)
subject_data = filtered_data[filtered_data['subject'] == subject]
x_labels = []
x_ticks = []
for j, (start_date, end_date) in enumerate(time_intervals):
interval_data = subject_data[(subject_data['time'] >= start_date) & (subject_data['time'] < end_date)]
total_count = interval_data['KG_CAP values'].sum()
ax.bar(j, total_count)
x_label = f'{start_date.year}-{end_date.year}'
x_labels.append(x_label)
x_ticks.append(j)
ax.set_xticks(x_ticks)
ax.set_xticklabels(x_labels, rotation='vertical')
ax.set_ylabel('Total Count of KG_CAP values')
plt.tight_layout()
plt.show()
grouped_data = filtered_data.groupby('location')['KG_CAP values'].mean()
top_locations = grouped_data.nlargest(15)
fig, ax = plt.subplots(figsize=(10, 8))
colors = plt.cm.Set3(range(len(top_locations)))
ax.bar(top_locations.index, top_locations.values, color=colors)
ax.set_xticklabels(top_locations.index, rotation=45, ha='right')
ax.set_xlabel('Location')
ax.set_ylabel('Mean KG_CAP values')
ax.set_title('Top 15 Locations with Highest Mean KG_CAP Values')
plt.tight_layout()
plt.show() | 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', 'KG_CAP values'] *= conversion_factor
meat
meat['time'] = pd.to_datetime(meat['time'], format='%Y')
meat
grouped_data = meat.groupby('subject')['KG_CAP values'].agg(['sum', 'mean'])
# Create a figure with two subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10))
# Plot the sum of KG_CAP values
colors = ['blue', 'green', 'orange', 'red']
ax1.bar(grouped_data.index, grouped_data['sum'], color=colors)
ax1.set_ylabel('Total mean consumed')
ax1.set_xticklabels(grouped_data.index, rotation=45, ha='right')
# Add exact values on top of each bar in the sum plot
for i, v in enumerate(grouped_data['sum']):
ax1.text(i, v, str(v), ha='center', va='bottom')
# Plot the mean of KG_CAP values
ax2.bar(grouped_data.index, grouped_data['mean'], color=colors)
ax2.set_ylabel('Mean consumption of meat')
ax2.set_xticklabels(grouped_data.index, rotation=45, ha='right')
# Add exact values on top of each bar in the mean plot
for i, v in enumerate(grouped_data['mean']):
ax2.text(i, v, str(v), ha='center', va='bottom')
# Set x-axis label and title for the entire figure
plt.xlabel('Type of meat')
plt.suptitle('Total and mean consumption of meat')
# Adjust spacing between subplots
plt.tight_layout()
# Display the plot
plt.show()
meat['time'] = pd.to_datetime(meat['time'], format='%Y')
subjects = ['BEEF', 'PIG', 'POULTRY', 'SHEEP']
filtered_data = meat[meat['subject'].isin(subjects)]
time_intervals = [(pd.Timestamp('1990-01-01'), pd.Timestamp('1997-01-01')), (pd.Timestamp('1997-01-01'), pd.Timestamp('2004-01-01')), (pd.Timestamp('2004-01-01'), pd.Timestamp('2010-01-01')), (pd.Timestamp('2010-01-01'), pd.Timestamp('2016-01-01')), (pd.Timestamp('2016-01-01'), pd.Timestamp('2022-01-01')), (pd.Timestamp('2022-01-01'), pd.Timestamp('2029-01-01'))]
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
axes = axes.flatten()
for i, subject in enumerate(subjects):
ax = axes[i]
ax.set_title(subject)
subject_data = filtered_data[filtered_data['subject'] == subject]
x_labels = []
x_ticks = []
for j, (start_date, end_date) in enumerate(time_intervals):
interval_data = subject_data[(subject_data['time'] >= start_date) & (subject_data['time'] < end_date)]
total_count = interval_data['KG_CAP values'].sum()
ax.bar(j, total_count)
x_label = f'{start_date.year}-{end_date.year}'
x_labels.append(x_label)
x_ticks.append(j)
ax.set_xticks(x_ticks)
ax.set_xticklabels(x_labels, rotation='vertical')
ax.set_ylabel('Total Count of KG_CAP values')
plt.tight_layout()
plt.show() | 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', 'KG_CAP values'] *= conversion_factor
meat
meat.describe() | 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', 'KG_CAP values'] *= conversion_factor
meat
meat.info() | 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', 'KG_CAP values'] *= conversion_factor
meat | 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', 'KG_CAP values'] *= conversion_factor
meat
meat['time'] = pd.to_datetime(meat['time'], format='%Y')
meat | 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.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')
import imageio
import matplotlib.pyplot as plt
img = imageio.imread('../input/digital-image/tanjiro.png')
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
imh | 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.imshow(img) | 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.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.imread('../input/digital-image/tanjiro.png')
import imageio
import matplotlib.pyplot as plt
img = imageio.imread('../input/digital-image/tanjiro.png')
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
imh
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 1)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 1)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
pl.title('DEMONS SLAYER')
pl.subplot(2, 2, 1)
pl.imshow(imh)
pl.subplot(2, 2, 4)
pl.imshow(imh) | 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 plt
import matplotlib.pyplot as mpimg
img = mpimg.imread('../input/digital-image/tanjiro.png')
import imageio
import matplotlib.pyplot as plt
img = imageio.imread('../input/digital-image/tanjiro.png')
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
pl.imshow(img) | 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-image/tanjiro.png')
import matplotlib.pyplot as plt
import matplotlib.pyplot as mpimg
img = mpimg.imread('../input/digital-image/tanjiro.png')
import imageio
import matplotlib.pyplot as plt
img = imageio.imread('../input/digital-image/tanjiro.png')
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
pl.imshow(imh) | 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
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')
import imageio
import matplotlib.pyplot as plt
img = imageio.imread('../input/digital-image/tanjiro.png')
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
imh
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 1)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
pl.title('DEMONS SLAYER')
pl.imshow(imh) | 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
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 plt
import matplotlib.pyplot as mpimg
img = mpimg.imread('../input/digital-image/tanjiro.png')
import imageio
import matplotlib.pyplot as plt
img = imageio.imread('../input/digital-image/tanjiro.png')
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
imh
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 1)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 1)
imh = cv.cvtColor(img, cv.COLOR_BGR2RGB)
import cv2 as cv
import matplotlib.pyplot as pl
img = cv.imread('../input/digital-image/tanjiro.png', 0)
print('Image Dimension:', img.shape) | 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.imread('../input/digital-image/tanjiro.png')
import imageio
import matplotlib.pyplot as plt
img = imageio.imread('../input/digital-image/tanjiro.png')
plt.imshow(img) | 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', ascending=False).head(10).set_index('title')
plt.figure(figsize=(15, 10))
sns.barplot(highest_rated['ratings_count'], highest_rated.index, palette='deep') | 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_count, y=author_count.index, palette='rocket')
plt.title('Top 10 authors with most number of books')
plt.xlabel('Number of Books Written') | 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 = df.sort_values('ratings_count', ascending=False).head(10).set_index('title')
lowest_rated = df.sort_values('ratings_count', ascending=True).head(10).set_index('title')
from subprocess import check_output
from wordcloud import WordCloud
wordcloud = (WordCloud(width=1440, height=1080, relative_scaling=0.5).generate_from_frequencies(df['language_code'].value_counts()))
fig = plt.figure(1,figsize=(15, 15))
plt.imshow(wordcloud)
plt.axis('off')
plt.show()
df_cdb = df[df['language_code'] == 'fre']
df_cdb.head(5)
plt.figure(figsize=(15, 10))
locs = df_cdb['authors'].value_counts()[:10]
sns.barplot(x=locs, y=locs.index, palette='Set3')
plt.title('Top 10 Authors with most number of books in French Books')
plt.xlabel('Number of Books') | 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'].value_counts()[:10]
highest_rated = df.sort_values('ratings_count', ascending=False).head(10).set_index('title')
lowest_rated = df.sort_values('ratings_count', ascending=True).head(10).set_index('title')
from subprocess import check_output
from wordcloud import WordCloud
wordcloud = WordCloud(width=1440, height=1080, relative_scaling=0.5).generate_from_frequencies(df['language_code'].value_counts())
fig = plt.figure(1, figsize=(15, 15))
plt.imshow(wordcloud)
plt.axis('off')
plt.show() | 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', ascending=False).head(10).set_index('title')
lowest_rated = df.sort_values('ratings_count', ascending=True).head(10).set_index('title')
plt.figure(figsize=(5, 10))
sns.barplot(lowest_rated['ratings_count'].notnull(), lowest_rated.index, palette='Set3') | 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/melbourne-housing-snapshot/melb_data.csv')
y = data.Price
melb_predictors = data.drop(['Price'], axis=1)
X = melb_predictors.select_dtypes(exclude=['object'])
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
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)
model.fit(X_train, y_train)
preds = model.predict(X_valid)
return mean_absolute_error(y_valid, preds)
cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_valid = X_valid.drop(cols_with_missing, axis=1)
from sklearn.impute import SimpleImputer
my_imputer = SimpleImputer()
imputed_X_train = pd.DataFrame(my_imputer.fit_transform(X_train))
imputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid))
imputed_X_train.columns = X_train.columns
imputed_X_valid.columns = X_valid.columns
X_train_plus = X_train.copy()
X_valid_plus = X_valid.copy()
for col in cols_with_missing:
X_train_plus[col + '_was_missing'] = X_train_plus[col].isnull()
X_valid_plus[col + '_was_missing'] = X_valid_plus[col].isnull()
my_imputer = SimpleImputer()
imputed_X_train_plus = pd.DataFrame(my_imputer.fit_transform(X_train_plus))
imputed_X_valid_plus = pd.DataFrame(my_imputer.transform(X_valid_plus))
imputed_X_train_plus.columns = X_train_plus.columns
imputed_X_valid_plus.columns = X_valid_plus.columns
print('MAE from Approach 3 (An Extension to Imputation):')
print(score_dataset(imputed_X_train_plus, imputed_X_valid_plus, y_train, y_valid)) | 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_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
y = data.Price
melb_predictors = data.drop(['Price'], axis=1)
X = melb_predictors.select_dtypes(exclude=['object'])
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
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)
model.fit(X_train, y_train)
preds = model.predict(X_valid)
return mean_absolute_error(y_valid, preds)
cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_valid = X_valid.drop(cols_with_missing, axis=1)
from sklearn.impute import SimpleImputer
my_imputer = SimpleImputer()
imputed_X_train = pd.DataFrame(my_imputer.fit_transform(X_train))
imputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid))
imputed_X_train.columns = X_train.columns
imputed_X_valid.columns = X_valid.columns
X_train_plus = X_train.copy()
X_valid_plus = X_valid.copy()
for col in cols_with_missing:
X_train_plus[col + '_was_missing'] = X_train_plus[col].isnull()
X_valid_plus[col + '_was_missing'] = X_valid_plus[col].isnull()
my_imputer = SimpleImputer()
imputed_X_train_plus = pd.DataFrame(my_imputer.fit_transform(X_train_plus))
imputed_X_valid_plus = pd.DataFrame(my_imputer.transform(X_valid_plus))
imputed_X_train_plus.columns = X_train_plus.columns
imputed_X_valid_plus.columns = X_valid_plus.columns
missing_val_count_by_column = X_train.isnull().sum()
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
y = data.Price
X = data.drop(['Price'], axis=1)
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
cols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()]
X_train_full.drop(cols_with_missing, axis=1, inplace=True)
X_valid_full.drop(cols_with_missing, axis=1, inplace=True)
low_cardinality_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
s = X_train.dtypes == 'object'
object_cols = list(s[s].index)
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=100, random_state=0)
model.fit(X_train, y_train)
preds = model.predict(X_valid)
return mean_absolute_error(y_valid, preds)
drop_X_train = X_train.select_dtypes(exclude=['object'])
drop_X_valid = X_valid.select_dtypes(exclude=['object'])
from sklearn.preprocessing import LabelEncoder
label_X_train = X_train.copy()
label_X_valid = X_valid.copy()
label_encoder = LabelEncoder()
for col in object_cols:
label_X_train[col] = label_encoder.fit_transform(X_train[col])
label_X_valid[col] = label_encoder.transform(X_valid[col])
print('MAE from Approach 2 (Label Encoding):')
print(score_dataset(label_X_train, label_X_valid, y_train, y_valid)) | 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)
model.fit(X_train, y_train)
preds = model.predict(X_valid)
return mean_absolute_error(y_valid, preds)
cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_valid = X_valid.drop(cols_with_missing, axis=1)
print('MAE from Approach 1 (Drop columns with missing values):')
print(score_dataset(reduced_X_train, reduced_X_valid, y_train, y_valid)) | 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_selection import train_test_split
import pandas as pd
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
y = data.Price
melb_predictors = data.drop(['Price'], axis=1)
X = melb_predictors.select_dtypes(exclude=['object'])
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
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)
model.fit(X_train, y_train)
preds = model.predict(X_valid)
return mean_absolute_error(y_valid, preds)
cols_with_missing = [col for col in X_train.columns if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_valid = X_valid.drop(cols_with_missing, axis=1)
from sklearn.impute import SimpleImputer
my_imputer = SimpleImputer()
imputed_X_train = pd.DataFrame(my_imputer.fit_transform(X_train))
imputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid))
imputed_X_train.columns = X_train.columns
imputed_X_valid.columns = X_valid.columns
X_train_plus = X_train.copy()
X_valid_plus = X_valid.copy()
for col in cols_with_missing:
X_train_plus[col + '_was_missing'] = X_train_plus[col].isnull()
X_valid_plus[col + '_was_missing'] = X_valid_plus[col].isnull()
my_imputer = SimpleImputer()
imputed_X_train_plus = pd.DataFrame(my_imputer.fit_transform(X_train_plus))
imputed_X_valid_plus = pd.DataFrame(my_imputer.transform(X_valid_plus))
imputed_X_train_plus.columns = X_train_plus.columns
imputed_X_valid_plus.columns = X_valid_plus.columns
missing_val_count_by_column = X_train.isnull().sum()
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')
y = data.Price
X = data.drop(['Price'], axis=1)
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
cols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()]
X_train_full.drop(cols_with_missing, axis=1, inplace=True)
X_valid_full.drop(cols_with_missing, axis=1, inplace=True)
low_cardinality_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = low_cardinality_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
s = X_train.dtypes == 'object'
object_cols = list(s[s].index)
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=100, random_state=0)
model.fit(X_train, y_train)
preds = model.predict(X_valid)
return mean_absolute_error(y_valid, preds)
drop_X_train = X_train.select_dtypes(exclude=['object'])
drop_X_valid = X_valid.select_dtypes(exclude=['object'])
print('MAE from Approach 1 (Drop categorical variables):')
print(score_dataset(drop_X_train, drop_X_valid, y_train, y_valid)) | code |
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