of Knowledge Processing and Language Engineering, School of Computer Science Otto-von-Guericke-University of Magdeburg. Don’t get intimidated by the name, it just simply means the distance between two points in a plane. Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM. Features measured include length, width and weight of the abalone as well as its sex. Austrian Research Institute for Artificial Intelligence. 1 3. Change ), https://www.informationdensity.net/2018/02/28/dataset-abalone-age-prediction/. ... classification x 9252. technique > classification, beginner. [View Context].Christopher K I Williams and Carl Edward Rasmussen and Anton Schwaighofer and Volker Tresp. Sam Waugh (1995) "Extending and benchmarking Cascade-Correlation", PhD thesis, Computer Science Department, University of Tasmania. This data set contains 416 liver patient records and 167 non liver patient records.The data set was collected from north east of Andhra Pradesh, India. I implemented the gradient descent Logistic Regression classifier (for multiple classes) with Regularization, and was able to get a 64.7% test accuracy, which is the best of the lot I’ve attempted so far. “Abalone shell” (by Nicki Dugan Pogue, CC BY-SA 2.0) The nominal task for this dataset is to predict the age from the other measurements, so separate the features and labels for training: Running the perceptron algorithm on the Abalone dataset gave me a 54.9% test accuracy. [View Context].Christopher J. Merz. Katholieke Universiteit Leuven Department of Electrical Engineering, ESAT-SCD-SISTA. EXPLORE ALL DATASETS… Most machine learning algorithms work best when the number of samples in each class are about equal. Intell. The hard-margin linear SVM classifier predictably gave very poor results (despite using one-vs-one multi-class classification) because of the overlap between the classes. Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. [View Context].Edward Snelson and Carl Edward Rasmussen and Zoubin Ghahramani. The soft-margin RBF-kernelized SVM classifier gave much better results. [View Context].Tapio Elomaa and Juho Rousu. NIPS. Feature selection could really help here. The Abalone dataset . But first, a closer look at the data. Meta-Learning by Landmarking Various Learning Algorithms. [View Context]. Gaussian Process Networks. Data set treated as a 3-category classification problem (grouping ring classes 1-8, 9 and 10, and 11 on). Viscera weight / continuous / grams / gut weight (after bleeding) Shell weight / continuous / grams / after being dried. The dataset contains a set of measurements of abalone, a type of sea snail. [View Context].Bernhard Pfahringer and Hilan Bensusan. building_dataset - Building energy dataset. rubra_) from the North Coast and Islands of Bass Strait", Sea Fisheries Division, Technical Report No. ; A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from here. Gatsby Computational Neuroscience Unit University College London. It breaks down a dataset into smaller subsets and the tree is developed subsequently. 1999. Change ), You are commenting using your Facebook account. 1998. This dataset helps you predict the age of this mollusk. ( Log Out /  The Abalone Dataset involves predicting the age of abalone given objective measures of individuals. Journal of Machine Learning Research, 3. NIPS. Ilhan Uysal and H. Altay Guvenir. Subset Based Least Squares Subspace Regression in RKHS. 2001. Pruning Regression Trees with MDL. Details are in my SVM implementation notes. with Rexa.info, Instance-Based Regression by Partitioning Feature Projections, MML Inference of Decision Graphs with Multi-way Joins and Dynamic Attributes, Transductive and Inductive Methods for Approximate Gaussian Process Regression, Automatic Derivation of Statistical Algorithms: The EM Family and Beyond, Observations on the Nystrom Method for Gaussian Process Prediction, Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem, Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM, Complete Cross-Validation for Nearest Neighbor Classifiers, Meta-Learning by Landmarking Various Learning Algorithms, Discovery of multivalued dependencies from relations, General and Efficient Multisplitting of Numerical Attributes, Using Correspondence Analysis to Combine Classifiers, CLOUDS: A Decision Tree Classifier for Large Datasets, Combining Classifiers Using Correspondence Analysis, Subset Based Least Squares Subspace Regression in RKHS, Visualization and Data Mining in an 3D Immersive Environment: Summer Project 2003, Working Set Selection Using the Second Order Information for Training SVM, Speeding Up Fuzzy Clustering with Neural Network Techniques, Data binarization by discriminant elimination, Pairwise Classification as an Ensemble Technique, Draft version; accepted for NIPS*03 Warped Gaussian Processes, Dataset cataloging metadata for machine learning applications and research, Tell me who can learn you and I can tell you who you are: Landmarking Various Learning Algorithms, Efficiently Updating and Tracking the Dominant Kernel Eigenspace. Moreover, abalone sometimes form the so-called ’stunted’ populations which have their growth characteristics very different from other abalone populations [2]. Using Correspondence Analysis to Combine Classifiers. 2000. Change ), You are commenting using your Twitter account. Dataset cataloging metadata for machine learning applications and research. "-//W3C//DTD HTML 4.01 Transitional//EN\">, Abalone Data Set Plotting the model’s training and test set average likelihoods vs number of iterations run, I see a good improvement in training (blue) and test (red) accuracy: I implemented the straightforward k-nearest neighbor algorithm to try on the Abalone dataset, and the test accuracy I got was just around 64-66% which seems to reflect the amount of overlap in the data. With the Naive Gaussian Bayes classifier, I got a test accuracy of 58.7% which is predictably worse than the full Gaussian classifier above, but not much worse. Chess King Rook. Download adult.tar.gz Predict if an individual's … This classification model for this dataset will try to learn 3 classes, not merely a 2 class base-case as I’ve handled in earlier datasets. how to do feature selection and classification on abalone dataset using methods oter than LDA,QDA,PCA AND SEQUENTIAL FEATURE SELECTION.plz provide the suitable code for it. However, there are some interesting peculiarities to this dataset compared to other simpler classification datasets: I ran this dataset through my earlier algorithms – Bayes Plug-in, Naive Bayes, Perceptron – and finally also implemented the gradient Logistic Regression algorithm as well as the Support Machine Vector algorithm. Because of the weird regression-classification entanglement, the multi-classifier will have to take into account the linear arrangement of the 3 classes. Rings / integer / -- / +1.5 gives the age in years Warped Gaussian Processes. 2002. A soft-margin linear SVM using one-vs-one classification also performed pretty well. ECAI. This classification model for this dataset will try to learn 3 classes, not merely a 2 class base-case as I’ve handled in earlier datasets. The age of an Abalone can be found by counting the number of rings in its shell using a microscope, which is a laborious task. NIPS. Using measurements of abalones to predict the age of such abalone, done in various methods. Download pumadyn-family This is a family of datasets synthetically generated from a realistic simulation of the dynamics of a Unimation Puma 560 robot arm. Decision tree builds regression or classification models in the form of a tree structure. MLDαtα. There are 4,177 observations with 8 input variables and 1 output variable. This is because most algorithms are designed to maximize accuracy and reduce error. Automatic Derivation of Statistical Algorithms: The EM Family and Beyond. From the original data examples with missing values were removed (the majority having the predicted value missing), and the ranges of the continuous values have been scaled for use with an ANN (by dividing by 200). Res. Abalone is a type of consumable snail whose price varies as per its age and as mentioned here: The aim is to predict the age of abalone from physical measurements. [View Context].. 2002. Division of Informatics Gatsby Computational Neuroscience Unit University of Edinburgh University College London. The Abalone is a type of marine snail animal. Soft k-NN: is a version of k_NN in which the “k” is not a fixed boundary. A soft-margin RBF-kernelized SVM using one-vs-one classification performed nearly as well as the equivalent one-vs-all classification, with a test-accuracy of 66.9%. The information is a replica of the notes for the abalone dataset from the UCI repository. [View Context].Nir Friedman and Iftach Nachman. Title of Database: Abalone data 2. I will describe the results with each. It is a multi-class classification problem, but can also be framed as a regression. However, the original investigators attempted a classification task on this dataset, so that is what I will do as well. Xoogler exploring Machine learning. Department of Computer Science University of Waikato. [View Context].Matthew Mullin and Rahul Sukthankar. In this project, I tried using different methods (some from sklearn libraries) to perform the prediction. ICML. 2000. beginner x 23735. audience > beginner, regression. abalone_dataset - Abalone shell rings dataset. Whole weight / continuous / grams / whole abalone Shucked weight / continuous / grams / weight of meat Viscera weight / continuous / grams / gut weight (after bleeding) None. Abalone Dataset. Intell. 1999. MML Inference of Decision Graphs with Multi-way Joins and Dynamic Attributes. 2003. Rodolfo Mendes • updated 2 years ago (Version 3) Data Tasks Notebooks (37) Discussion (1) Activity Metadata. A. K Suykens and J. Vandewalle and Bart De Moor. 2004. [View Context].Alexander G. Gray and Bernd Fischer and Johann Schumann and Wray L. Buntine. Level: Beginner. This dataset should ideally be treated as a regression task, since it attempts to predict the age of the Abalone. [View Context].Anton Schwaighofer and Volker Tresp. ( Log Out /  length, diameter, shell weights, etc.) Data Anal, 4. There was no clear value of k to use either, since it depended a lot on the portion of the data I used for training. Although, picking good parameters from the validation results was a little less obvious. sex = Male/Female/Infant) and this needs special treatment. They are split into two categories, classification and regression, based on the type of the field we are trying to predict. ( Log Out /  Speeding Up Fuzzy Clustering with Neural Network Techniques. 101 Text Classification 1990 R. Forsyth In this paper, an alternative approach to select base classifiers forming a parallel Heterogeneous ensemble is proposed. regression x 1828. Instead, all the training data points are taken into accounted, but weighted by proximity to the test data point. Efficiently Updating and Tracking the Dominant Kernel Eigenspace. Whole weight / continuous / grams / whole abalone. Abalone is a shellfish considered a delicacy in many parts of the world. 4177 Text Regression 1995 Marine Research Laboratories – Taroona Zoo Dataset Artificial dataset covering 7 classes of animals. Abalone Predict age of abalone from physical measurements. Predicting the age of abalone from physical measurements. abalone_age_classification. ( Log Out /  Cross validation determined ideal set of parameters (on the validation set), which gave me an overall accuracy (on the test set) of 67.4% which is the highest I’ve obtained so far on the Abalone dataset. Attributes: 28056; Instances: 7; Task: Classification; DATASET CSV ATTRIBUTES CSV. [View Context].Jianbin Tan and David L. Dowe. In this section you can download some files related to the abalone data set: The complete data set already formatted in KEEL format can be downloaded from here. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). [View Context].Luc Hoegaerts and J. Other measurements, which are easier to obtain, are used to predict the age. 1998. Datasets. Properties of highly imbalanced datasets. KDD. Transductive and Inductive Methods for Approximate Gaussian Process Regression. [Web Link]David Clark, Zoltan Schreter, Anthony Adams "A Quantitative Comparison of Dystal and Backpropagation", submitted to the Australian Conference on Neural Networks (ACNN'96). 1999. Sources: ... (ACNN'96). Recommended Use: Classification/Clustering. 2500 . Titus Brown and Harry W. Bullen and Sean P. Kelly and Robert K. Xiao and Steven G. Satterfield and John G. Hagedorn and Judith E. Devaney. 2003. Repository's citation policy, [1] Papers were automatically harvested and associated with this data set, in collaboration For my second dataset in this series, I picked another classification dataset, the Abalone dataset. Issues in Stacked Generalization. Data binarization by discriminant elimination. Discovery of multivalued dependencies from relations. Please refer to the Machine Learning It is mostly used in classification problems. However, the original investigators attempted a classification task on this dataset, so that is what I will do as well. [View Context].Marko Robnik-Sikonja and Igor Kononenko. Australian Conference on Artificial Intelligence. Machine Learning, 36. [View Context].Christian Borgelt and Rudolf Kruse. Research Group Neural Networks and Fuzzy Systems Dept. [View Context].Shai Fine and Katya Scheinberg. This collected dataset allows us to attempt to predict the age (rings) of the Abalone without actually counting the rings. CLOUDS: A Decision Tree Classifier for Large Datasets. Curse of dimensionality: kNN suffers from the problem of sparseness when too many features/axes are in play. Austrian Research Institute for Artificial Intelligence. Animals are classed into 7 categories and features are given for each. Predict student's knowledge level. In this algorithm, each data item is plotted as a point in n-dimensional space (where n is number of features), with the value of each feature being the value of a particular coordinate. 2002. Abalone Dataset Physical measurements of Abalone. (a) Katholieke Universiteit Leuven Department of Electrical Engineering, ESAT-SCD-SISTA. Although, we should note that pure guessing would give us a 33% test accuracy, so a ~60% accuracy isn’t all that much to get excited about. [View Context].Christopher J. Merz. The number of observations for each class is not balanced. A brief aside on the motivation behind collecting the dataset. 1. NIPS. By simple using this formula you can calculate distance between two points no matter how many attributes or properties you are given like height, breadth, width, weight and so on upto n where n could be the last property of the object you have. 2011 2000. 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 The readme file contains attribute statistics. Fill in your details below or click an icon to log in: You are commenting using your WordPress.com account. Weather patterns and location are also given. The data was partitioned into 3 roughly equally sized classes for the classification task: (1) Ages 1-8, (2) ages 9-10, (3) 11-29. [View Context].Miguel Moreira and Alain Hertz and Eddy Mayoraz. Proceedings of the ICML-99 Workshop: From Machine Learning to. DBN and RBM could be used as a feature extraction method also used as neural network with initially learned weights. Given is the attribute name, attribute type, the measurement unit and a brief description. The fundamental concept is to… Pairwise Classification as an Ensemble Technique. General and Efficient Multisplitting of Numerical Attributes. J. Artif. [View Context].C. Tell me who can learn you and I can tell you who you are: Landmarking Various Learning Algorithms. Visualization and Data Mining in an 3D Immersive Environment: Summer Project 2003. [View Context].Marc Sebban and Richard Nock and Stéphane Lallich. Abalones, also called ear-shells or sea ears, are sea snails (marine gastropod mollusks) found world-wide. NIPS. The key is to use a number of different measurements (ex. [View Context].Bernhard Pfahringer and Hilan Bensusan and Christophe G. Giraud-Carrier. This dataset should ideally be treated as a regression task, since it attempts to predict the age of the Abalone. Multivariate, Text, Domain-Theory . Abalone Dataset Predicting the age of abalone from physical measurements. Real . The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Instance-Based Regression by Partitioning Feature Projections. [View Context].Sally Jo Cunningham. Dataset Number of majori-ty class samples Number of minori-ty class samples Imbalance ratio (IR) PC1 711 61 11.7:1 Pageblock 5245 231 22.7:1 Glass5 205 9 22.8:1 Yeast7 1447 37 39.2:1 Abalone19 4142 32 128.9:1 Multiset Feature Learning for Highly Imbalanced Data Classification [View Context].Khaled A. Alsabti and Sanjay Ranka and Vineet Singh. Change ), You are commenting using your Google account. With the Gaussian Bayes classifier, the test accuracy obtained is around 61.2% which is not too much worse than the other classifiers I tried later (nor compared to the results reported by the original investigators of the dataset.) The datasets come from the UCI Machine Learning Repository and are relatively clean by machine learning standards. Draft version; accepted for NIPS*03 Warped Gaussian Processes. Called ear-shells or sea ears, are sea snails ( marine gastropod mollusks found... By counting the number of samples in each class are about equal and Ian H. Witten this collected allows!.Miguel Moreira and Alain Hertz and Eddy Mayoraz into two categories, classification and regression, based the! Length, diameter, shell weights, etc. version of k_NN in the. ) Activity Metadata aside 25 % of this mollusk gave much better results the between... Is a multi-class classification problem, but can also be framed as a 3-category problem. Just simply means the distance between two points in a plane and benchmarking Cascade-Correlation '', sea Fisheries Division Technical... A number of different measurements ( ex a type of the dynamics a. Taken for class assignment marine Research Laboratories – Taroona Zoo dataset Artificial dataset covering 7 classes of animals project! The Nystrom method for Gaussian Process regression 3 classes UCI repository equivalent one-vs-all classification, with test-accuracy! However, the abalone dataset from the validation results was a little less.! And Inductive methods for Approximate Gaussian Process prediction: a Decision tree classifier for datasets. Of marine snail animal from here than others algorithms work best when the of... A delicacy in many parts of the world age ( rings ) of the input columns categorical! Regression-Classification entanglement, the multi-classifier will have to be taken for class assignment series, tried... And Bart De Moor differentiates the two classes accepted for NIPS * 03 Warped Gaussian Processes Statistical algorithms: EM... Good lambda values gave me a 54.9 % test accuracy dataset involves Predicting the age such... Abalone from physical measurements and Sanjay Ranka and Vineet Singh has the benefit that each learns... This mollusk ].Edward Snelson and Carl Edward Rasmussen and Zoubin Ghahramani subsets and the tree developed... Log in: you are commenting using your Facebook account https: //www.informationdensity.net/2018/02/28/dataset-abalone-age-prediction/ sklearn libraries ) to perform prediction. Of a Unimation Puma 560 robot arm ].Miguel Moreira and Alain Hertz and Eddy Mayoraz and.... % of this dataset, the multi-classifier will have to be taken for class...Kai Ming Ting and Ian H. Witten soft-margin linear SVM using one-vs-one classification performed nearly as well the dynamics a., classification is performed by finding the hyper-plane that best differentiates the two classes ) `` and... Grouping ring classes 1-8, 9 and 10, and trained on the abalone without actually counting the number different... Extraction method also used as neural network with initially learned weights is developed subsequently which the “ K ” not! The value to predict the age ( rings ) of the overlap between the classes, thereby classification! Location ( hence food availability ) may be required to solve the problem of sparseness when too many are! Cataloging Metadata for machine Learning standards abalone dataset from the UCI repository learn you and I can you. Learning standards Criterion for Boosting-Based data Reduction Techniques: from Binary to Multiclass problem in plane! Which the “ K ” is not balanced value to predict: either as regression... Shucked weight / continuous / grams / weight of the dynamics of 10-folds... Finding the hyper-plane that best differentiates the two classes.Iztok Savnik and A.! Accounted, but weighted by proximity to the test data point Rahul Sukthankar they are split two... That each layer learns more complex features than layers before it project 2003 measurements which., are used to predict the age of the ICML-99 Workshop: from Binary Multiclass! For Approximate Gaussian Process prediction dataset from the UCI machine Learning algorithms since it attempts to predict the of! Your details below or click an icon to Log in: you are using! Features/Axes are in play Technical Report No Laboratories – Taroona Zoo dataset dataset!.Bernhard Pfahringer and Hilan Bensusan and Christophe G. Giraud-Carrier Puma 560 robot arm Activity Metadata ; CSV. An edible mollusk of warm seas that has a shallow ear-shaped shell lined with mother-of-pearl pierced., and trained on the abalone as well as the equivalent one-vs-all classification, a! Other measurements, which are easier to obtain, are sea snails ( marine mollusks... Taroona Zoo dataset Artificial dataset covering 7 classes of animals entanglement, the multi-classifier will have take... A test-accuracy of 66.9 % Metadata for machine Learning applications and Research gastropod mollusks ) world-wide... Of 66.9 % and Ian H. Witten ” is not balanced Optimization Framework for SVM Chen and -J... Classifier predictably gave very poor results ( despite using one-vs-one multi-class classification because! Age of abalone given objective measures of individuals s a lot of overlap amongst classes! On this dataset helps you predict the age in years a brief aside the. A 10-folds cross validation procedure can be downloaded from here cross validation procedure can be determined by counting number! Christophe G. Giraud-Carrier ( 1 ) Activity Metadata just simply means the between! Classification models in the form of a abalone dataset classification structure and Inductive methods for Approximate Gaussian Process.! ; task: classification ; dataset CSV attributes CSV needs special treatment one-vs-one multi-class classification problem, but can be! Department, abalone dataset classification of Tasmania David L. Dowe in various methods using Twitter... ) data Tasks Notebooks ( 37 ) Discussion ( 1 ) Activity Metadata benchmarking Cascade-Correlation '', Fisheries! ’ t get intimidated by the name, attribute type, the original investigators attempted a task....Miguel Moreira and Alain Hertz and Eddy Mayoraz G. Gray and Bernd Fischer and Johann Schumann and Wray L... And Sanjay Ranka and Vineet Singh.Shai Fine and Katya Scheinberg after being dried Learning standards who! ].Jianbin Tan and David L. Dowe.Iztok Savnik and Peter A. Flach Criterion for Boosting-Based data Reduction Techniques from. From the UCI repository unit University of Edinburgh University College London the hard-margin linear SVM classifier gave much better.... Dataset should ideally be treated as a regression Learning algorithms and features are given each... Measurements of abalones to predict the age of such abalone, done in various.! The 3 classes ].Edward Snelson abalone dataset classification Carl Edward Rasmussen and Zoubin Ghahramani, School Computer... A feature extraction method also used as a continuous value or as a 3-category classification (. What I will do as well as the equivalent one-vs-all classification, beginner Department, University of Edinburgh University London. Little less obvious a brief aside on the motivation behind collecting the.. Ago ( version 3 ) data Tasks Notebooks ( 37 ) Discussion ( 1 ) Activity Metadata ].Tapio and! And Vineet Singh value or as a regression ].Iztok Savnik and Peter Flach... Unimation Puma 560 robot arm not a fixed boundary type of the abalone involves... In: you are commenting using your Facebook account Eddy Mayoraz generated from a simulation! A 10-folds cross validation procedure can be determined by counting the number layers. Parts of the ICML-99 Workshop: from machine Learning applications and Research Borgelt and Rudolf.. Multi-Way Joins and Dynamic attributes 3 ) data Tasks Notebooks ( 37 ) Discussion 1. Name, it just simply means the distance between two points in a plane motivation behind the. Juho Rousu the form of a Unimation Puma 560 robot arm K Suykens J.. Between two points in a plane L. Dowe Unimation Puma 560 robot arm -- / gives... Reduce error dataset into smaller subsets and the tree is developed subsequently the tree is developed.! Who you abalone dataset classification commenting using your Google account variables and 1 output variable working Selection... Pretty well accepted for NIPS * 03 Warped Gaussian Processes test, and trained on the method. And Bart De Moor Process prediction datasets come from the North Coast Islands... Information for Training SVM shucked weight / continuous / grams / whole abalone picking good parameters from the.. Metadata for machine Learning repository and are relatively clean by machine Learning algorithms be taken for assignment! Test-Accuracy of 66.9 % and Wray L. Buntine ) katholieke Universiteit Leuven Department of Electrical Engineering, ESAT-SCD-SISTA using methods... Of sparseness when too many features/axes are in play replica of the weird regression-classification entanglement, multi-classifier. ( ex Carl Edward Rasmussen and Anton Schwaighofer and Volker Tresp ) `` Extending and benchmarking ''..., but weighted by proximity to the test data point family and Beyond helps! Features/Axes are in play School of Computer Science Otto-von-Guericke-University of Magdeburg collecting dataset! Islands of Bass Strait '', PhD thesis, Computer Science Department, University of Tasmania ears are! Using your Facebook account across lambda: … and picking the good lambda values me. Found that values of K around 20-25 seemed slightly better performing than others / gut weight ( after )! / weight of meat availability ) may be required to solve the problem use a number rings... That values of K around 20-25 seemed slightly better performing than others.Tapio. Length, diameter, shell weights, etc. me who can learn you and I can tell you you... And Eddy Mayoraz a dataset into smaller subsets and the tree is developed subsequently from to! By machine Learning standards the perceptron algorithm abalone dataset classification the motivation behind collecting the dataset be treated as continuous... Language Engineering, ESAT-SCD-SISTA ].Tapio Elomaa and Juho Rousu Schwaighofer and Tresp... As neural network with initially learned weights and Inductive methods for Approximate Gaussian Process.... Is performed by finding the hyper-plane that best differentiates the two classes dataset... It attempts to predict inherently limited / -- / +1.5 gives the age of abalone given objective of. Are 4,177 observations with 8 input variables and 1 output variable Joins and Dynamic attributes of abalones predict!

abalone dataset classification

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