Naïve Bayes – Python

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Naïve Bayes: In the continuation of Naïve Bayes algorithm, let us look into the basic codes of Python to implement Naïve Bayes. We will start with installation of libraries required for Naïve Bayes then move onto the commands required for the implementation of algorithm.

We will try to predict probability of default/Non-Default using Naïve Bayes algorithm. Please download the data provided in the data repository section.

Step 1: Import the Libraries. We would use three libraries for this analysis:

  • numpy
  • pyplot
  • pandas

Use below command to import the libraries.

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Step 2: Import the data set.

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Step 3: Splitting the data set into the Training set and Test set. Provided data set has 300 observations, we would divide it into 75%-25% for training and testing purpose.

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Step 4: Feature Scaling: Since range of two of our predictor variables is very different, while Age varies from 18-60 whereas salary varies from 1127-142293. We need to do feature scaling so that both of the variables have same proportional impact on the target variable. In the following command we are excluding the target variable from feature scaling which is at position 3.

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Step 5: Fitting Naive Bayes to the Training set

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Step 6: Predicting the Test set results

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Step 7: Checking the accuracy of data model using confusion matrix

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