GitHub topics: abalone-dataset
jessieli-avellar/ml-classification-abalone
📊🐚 a comparison of machine learning binary classification models
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msikorski93/Abalone-Dataset
Estimating abalone rings (age) based on their physical characteristics, such as gender, length, height, diameter, weight, etc.
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Kimiarfaie/Age-Classification-Abalone Fork of poirot-en-bardo/data-science
Age classification of Abalone dataset. Assignment for Data Science course in UGR.
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matin-ghorbani/perceptron
Implement a perceptron from scratch
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chikeorah/ML
This repository contains a Jupyter notebook that implements and optimizes several machine learning models on a dataset
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willrleao/Regression-with-an-Abalone-Dataset
Regression with an Abalone Dataset - Kaggle
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Jarvis017/Naive-Bayes-and-KNN-on-Abalone
performance of naïve Bayes and k nearest neighbors on the Connect-4 dataset
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sid230798/Predict_Age_Of_Abalone_Linear_Regression
Taken dataset from UC for above task used Linear Ridge Regression for Performing it. Normalisation, Debugging, Plotting Graphs .
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Aishu-ai/Machine-Learning
Contains ML projects
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shubov/machine-learning-lab-ss-21
Sklearn-like python package with class implementations of different ML algorithms
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SameetAsadullah/K-Means-Clustering-on-Abalone-Dataset
Implemented K-Means Clustering on the given Abalone Dataset using Python Language
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SameetAsadullah/KNN-Algorithm-on-Abalone-Dataset
Implemented K-Nearest Neighbors (KNN) Algorithm on the given Abalone Dataset using Python Language
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ablanco1950/ABALONE_NAIVEBAYES_WEIGHTED_ADABOOST
ABALONE_NAIVEBAYES_WEIGHTED_ADABOOST: Two procedures are attached that use the Abalone file as test and training (https://archive.ics.uci.edu/ml/datasets/abalone). Both start from a treatment of the training part calculating the frequencies corresponding to each value of each field and applying a Naive Bayes probability calculation. In a second step, one of the procedures takes advantage of the previous result to apply weights based on each field to the wrong or true records. The other procedure uses Adaboost, using the adaboost routine published at https://github.com/jaimeps/adaboost-implementation (Jaime Pastor). A hit rate of around 58% is obtained, that is, in the low range of the existing procedures to treat this multiclass file, which are detailed in the documentation to download from https://archive.ics.uci.edu/ml/ datasets / abalone
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ablanco1950/ABALONE_DECISIONTREE_C4-5
ABALONE_DECISIONTREE_C4-5: A procedure is attached that uses the Abalone file (https://archive.ics.uci.edu/ml/datasets/abalone) as test and training . After evaluating the entropy of each field, a tree has been built with the nodes corresponding to fields 0, 7 and 4 and branch values ??in each node: 1 for the root node corresponding to field 0, 29 for the next node in the hierarchy corresponding to field 7, and 33 in the last node corresponding to field 4. The values ??of each field have been associated with indices, which can encompass several real values. the values ??of these indices are those that have been considered for the calculation of entropies and for making a branching of values ??at each node. A hit rate of around 58% is obtained, that is, in the low range of the existing procedures to treat this multiclass file, which are detailed in the documentation to download from https://archive.ics.uci.edu/ml/ datasets / abalone The depth of the tree has been increased without obtaining significant improvements. Nor has it been significantly improved by applying adaboost. Resources: Spyder 4 On the c: drive there should be the abalone-1.data file downloaded from https://archive.ics.uci.edu/ml/datasets/abalone Functioning: From Spyder run: AbaloneDecisionTree_C4-5-ThreeLevels.py The screen indicates the number of hits and failures and in the file C:\AbaloneCorrected.txt the records of the test file (records 3133 to 4177 of abalone-1.data) with an indication of whether their predicted class values ??coincide with the reals, the predicted class value and the order number of the record in abalone-1.data The following programs are also attached: AbaloneDecisionTree_ID3.py and AbaloneDecisionTree_C4-5_parameters.py that have served to calculate the necessary parameters to build the tree. Cite this software as: ** Alfonso Blanco García ** ABALONE_DECISIONTREE_C4-5 References: https://archive.ics.uci.edu/ml/datasets/abalone
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andi611/LibSVM-Classification
Performing classification tasks with the LibSVM toolkit on four different datasets: Iris, News, Abalone, and Income.
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