Implement a perceptron from scratch
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Updated
May 12, 2024 - Jupyter Notebook
Implement a perceptron from scratch
Performing classification tasks with the LibSVM toolkit on four different datasets: Iris, News, Abalone, and Income.
Implemented K-Nearest Neighbors (KNN) Algorithm on the given Abalone Dataset using Python Language
Sklearn-like python package with class implementations of different ML algorithms
Implemented K-Means Clustering on the given Abalone Dataset using Python Language
Taken dataset from UC for above task used Linear Ridge Regression for Performing it. Normalisation, Debugging, Plotting Graphs .
This repository contains a Jupyter notebook that implements and optimizes several machine learning models on a dataset
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 …
performance of naïve Bayes and k nearest neighbors on the Connect-4 dataset
Regression with an Abalone Dataset - Kaggle
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, …
Contains ML projects
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