Data set: The Abalone Data Set

(Source: https://archive.ics.uci.edu/ml/datasets/abalone)

Data set information

These data consisted of 4,177 observations of 9 attributes, detailed as follows.

Name / Data Type / Measurement Unit / Description

—————————–

Sex / nominal / — / M, F, and I (infant)

Length / continuous / mm / Longest shell measurement

Diameter / continuous / mm / perpendicular to length

Height / continuous / mm / with meat in shell

Whole weight / continuous / grams / whole abalone

Shucked weight / continuous / grams / weight of meat

Viscera weight / continuous / grams / gut weight (after bleeding)

Shell weight / continuous / grams / after being dried

Rings / integer / — / +1.5 gives the age in years

Objective

Implement a Naïve Bayesian classifier to predict the age of abalone in Python from scratch.

Task requirements

(1) Randomly separate the data into two subsets: ~70% for training and ~30% for test.

(2) The Naïve Bayesian classifier must implements techniques to overcome the numerical underflows and zero counts.

(3) No ML library can be used in this task. The implementation must be developed from scratch. However, scientific computing libraries such as NumPy and SciPy are allow