Abalone's Age Predictor

background

Here, we predict age of marine snails, Abalones. Traditional way to determine age of Abalone is to cut their shell through the cone, marking it and counting the number of rings using microscope. The number of rings corresponds to their age. Since we don’t have the microscope and we can predict age of Abalone by using Machine Learning as well, we are using Linear Regression Alogrithm written in C# to achieve the goal.

Full code for algorithm can be downloaded from :
https://github.com/ImShiven/LinearRegression

From GitHub

Dataset


View Dataset

DEMO


Length (in mm, <1 mm) Diameter (in mm , <1 mm) Height (in mm , <1.5 mm) Whole Weight (in grams, <3 grams)
Age is : years.

Description

Steps are very straight forward.We have dataset containing physical measurement of Abalones. We will train our model to find some co-relation between these measurements and ages of Abalones.
Algorithm is implemented in C# and requires basic knowledge of Linear Algebra.Some of matrix operations which are used listed below:

The predictor variables are usually called the independent variables. When there’s just a single predictor variable , the technique is sometimes called simple linear regression. When there are two or more predictor variables (as in this scenario), the technique is generally called multiple, or multivariate, linear regression.
Below steps represent how we are approaching the problem, training our model and finding Beta Co-efficients :

  1. Identify Predictors & Dependent Variable
  2. Data Preparation
  3. Data Normalization
  4. Build Model
  5. Train Model (Find Beta Co-efficients)
  6. Test Model

Age prediction of Abalones is one of the simplest example just to get an idea how we can leverage the power of Machine Learning in other real time problems that we encounter in our daily lives.Predictive Analysis , Error Reduction , User experience ,For example,are the most common fields where machine learning can be used to get useful results.