Data set: The Abalone Data Set
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
Implement a Naïve Bayesian classifier to predict the age of abalone in Python from scratch.
(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