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Now, since we have cleaned the
bikesData data set, let us split it into
Test data sets into 70:30 ratio using scikit-learn's
train_test_split() function uses 'Random Sampling', hence resulting
test_set data sets have to be sorted by
Random Sampling may not be the best way to split the data, what other types of best Sampling method you can think of?
We will also define an utility function named
display_scores. This function is used to calculate the basics stats of observed scores from cross-validation of models. Please copy this function in your code, we will be using it often in this project.
Set np random seed to 42 using code below to ensure the results of the exercise are repeatable.
train_test_split function from scikit-learn's
Please add a new feature(column)
bikesData data set using below code:
bikesData['dayCount'] = pd.Series(range(bikesData.shape))/24
bikesData data set into Training set
train_set and Test set
test_set in 70:30 ratio using scikit-learn's
test_set values by
dayCount by using the below code:
train_set.sort_values('dayCount', axis= 0, inplace=True) test_set.sort_values('dayCount', axis= 0, inplace=True)
Now print the 'number of instances' for
test_set data sets.
Finally, create the function
display_scores as shown below:
def display_scores(scores): print("Scores:", scores) print("Mean:", scores.mean()) print("Standard deviation:", scores.std())
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