Gaussian Naive Bayes in Android : Iris Classification Problem

This project aims to create a Gaussian Naive Bayes classifier in Android and use it on the famous Iris dataset. Gaussian Naive Bayes is a different version of commonly used Naive Bayes classifier, as it deals with numerical features.

We assume that each numerical feature in our dataset, follows a Gaussian distribution, with the mean and standard deviation calculated from that feature itself.

The app uses Opencsv to parse the dataset in CSV format ( from the app's assets folder ).

Use your own dataset

You may use your dataset in the app. Keep the following points in mind, so as to provide a clean dataset to the algorithm,

1. The dataset should be in the CSV format and should be placed in the app's assets folder.
2. The first row should contain only names of the columns. Like in this case of the Iris dataset, the first row contains sepal_length,sepal_width,petal_length,petal_width,species.
3. The last column in the CSV file should correspond to the labels column, just as we have the species column in the Iris dataset.
4. The labels column should contain all labels as Strings only. Like in the Iris dataset, the species colun contains three distinct Strings ( classes ), setosa, versicolor, virginica. All other columns, except the labels column should contain only numerical features. ( Just as the Iris dataset has sepal_length,sepal_width,petal_length,petal_width columns ).
5. Clean the dataset if it has null values in any of the columns. Null/blank values in any of the columns could cause an error.

Follow the same format as the Iris dataset.

License

MIT License

Copyright (c) 2021 Shubham Panchal

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