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Linear regression models exploring the abalone dataset

A GitHub repository showcasing ML projects on abalone age prediction with XAI and Twitter topic modeling using NMF.

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Nonlinear least squares estimates of abalone mortality and some SIP models

Neural network model for Abalone biology dataset

Abalone Age Prediction Model Using Machine Learning

Bayesian Structural Equation Model (SEM) is applied to a real data set called Abalone.

Spatially explicit individual-based model for Ezo abalone

The goal is to predict the age of abalones using machine learning models. Instead of predicting raw age in years, the final goal will be to predict "young","medium","old"-defined as grouping ring classes 1-8, 9-10, and 11 on.

Machine Learning models to predict the age of Abalones

Prediction Abalone's 'Age' with linear modeling. in Stacking Regressor

This project analyzes the Abalone dataset and builds predictive models to estimate the age of abalones based on their physical measurements.

R code for creating classification model with UCI Machine Learning Repository 'Abalone' Dataset

Analysis and Machine Learning model to predict the age of abalone using multiple regression.

Machine Learning project: Construct a linear model which predicts the age of an Abalone.

Linear Regression model done for Kaggle competition(late submission) using the Regression with Abalone dataset.

Predict the age of abalone based on physical characteristics. The data is highly correlated, and I think a single feature captures the data well. We model the data using a spline as well as with Poisson regression.

Finding the RMSLE on the Abalone data set by building an Elastic Net model that combines Ridge regression and Lasso Regression.

This repository contains a Jupyter Notebook that analyzes the characteristics of abalones and uses a machine learning model to predict age based on input data.

A non-linearity regression is conducted using Generalized Additive Model (GAM) by using the Abalone data sets from Kaggle,com and results submitted into Kaggle late submission.

A general function that uses K-Fold validation for Neural Network modelling for either classification or regression problem. The function is also demonstrated with the Red Wine and Abalone dataset.

🐚 A comprehensive analysis of the Abalone dataset using R — includes EDA, multiple regression models, PCA for dimensionality reduction, clustering with K-Prototypes, and statistical testing to predict age and uncover patterns.

We implemented three layer neural network classifier using Back-propagation learning algorithm. We use Abalone Data Set from UCI as training and testing dataset. we divided the dataset in three groups: training, validation and testing. Optimized the weights using training data and optimized the model parameters using the validation data and calculated performance using the test data.

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