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Kenil Vasani
Kenil Vasani

Kenil Vasani

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Asked: 2020-12-14T21:08:20+00:00 In:

ValueError: could not convert string to float:

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I am following a this tutorial to write a Naive Bayes Classifier:
http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/

I keep getting this error:

dataset[i] = [float(x) for x in dataset[i]]
ValueError: could not convert string to float:

Here is the part of my code where the error occurs: 

def loadDatasetNB(filename):
    lines = csv.reader(open(filename, "rt"))
    dataset = list(lines)
    for i in range(len(dataset)):
        dataset[i] = [float(x) for x in dataset[i]]
    return dataset

And here is how the file is called:

def NB_Analysis():
    filename = 'fvectors.csv'
    splitRatio = 0.67
    dataset = loadDatasetNB(filename)
    trainingSet, testSet = splitDatasetNB(dataset, splitRatio)
    print('Split {0} rows into train={1} and test={2} rows').format(len(dataset), len(trainingSet), len(testSet))
    # prepare model
    summaries = summarizeByClassNB(trainingSet)
    # test model
    predictions = getPredictionsNB(summaries, testSet)
    accuracy = getAccuracyNB(testSet, predictionsNB)
    print('Accuracy: {0}%').format(accuracy)

NB_Analysis()

My file fvectors.csv looks like this

What is going wrong here and how do I fix it?

csvmachine-learningpythonscikit-learn
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    1 Answer

    1. Kenil Vasani

      Kenil Vasani

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      Best Answer

      Try to skip a header, an empty header in the first column is causing the issue.

      >>> float(' ')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: could not convert string to float:

      If you want to skip the header you can achieve it with:

      def loadDatasetNB(filename):
    lines = csv.reader(open(filename, "rt"))
    next(reader, None)  # <<- skip the headers
    dataset = list(lines)
    for i in range(len(dataset)):
        dataset[i] = [float(x) for x in dataset[i]]
    return dataset

      (2) Or you can just ignore the exception:

      try:
    float(element)
except ValueError:
    pass

      If you decide to go with option (2), make sure that you skip only first row or only rows that contain text and you know it for sure.

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