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from sklearn.metrics import mean_squared_log_error
import numpy as np
rmsle = np.sqrt(mean_squared_log_error(y_actual, y_hat))

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from sklearn.metrics import mean_squared_error
import numpy as np
rmse = np.sqrt(mean_squared_error(y_actual, y_hat))

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from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_actual, y_hat)

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# Set number of splits
NO_SPLITS = fulldata.shape[0]

# Create KFold object with number of splits
kf = KFold(n_splits=NO_SPLITS)

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import pandas as pd

from sklearn import datasets
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression

# Import breast cancer (dataset) object from sklearn library
breast_cancer = datasets.load_breast_cancer()

# Define features need to be extracted from breast cancer (dataset) object

socratesk

import pandas as pd

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Import breast cancer (dataset) object from sklearn library
breast_cancer = datasets.load_breast_cancer()

# Define features need to be extracted from breast cancer (dataset) object

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# import libraries
import numpy as np
import pandas as pd
import eli5

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from eli5.sklearn import PermutationImportance

# load data file

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# Initalize webcam. 0 starts built-in camera
cap = cv2.VideoCapture(0)

# Specify HSV range of Tennis Ball
ballHSVLower = np.array([25, 75, 85])
ballHSVUpper = np.array([50, 220, 255])

while True:
    # Read captured webcam frame
    _, frame = cap.read()

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import pandas as pd

incExpDF = pd.DataFrame({'id' : [101, 102, 103, 104, 105],
                         'familyCnt' : [2, 4, 3, 3, 5],
                         'totalInc' : [68000, 72000, 34000, 44000, 52000],
                         'totalExp' : [48000, 66000, 33000, 41000, 50000]})

incExpDF['incPerPerson'] = incExpDF['totalInc'] / incExpDF['familyCnt']
incExpDF['expPerPerson'] = incExpDF['totalExp'] / incExpDF['familyCnt']
incExpDF['savingsPerPerson'] = incExpDF['incPerPerson'] - incExpDF['expPerPerson']

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import pandas as pd

gadgetDF = pd.DataFrame({'gadgetId' : [101, 102, 103, 104, 105], 
                           'gadgetName' : ['Apple_iPhone_6', 
                                           'Apple_iPad_3', 
                                           'Samsung_Galaxy_S8', 
                                           'Samsung_Galaxy_S9', 
                                           'Google_Pixel_3']})

dummyDF = gadgetDF['gadgetName'].apply(lambda x: pd.Series(x.split('_')))