Implements Naive Bayes and Gaussian Naive Bayes Machine learning Classification algorithms from scratch in Python.
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Updated
Jun 22, 2022 - Python
Implements Naive Bayes and Gaussian Naive Bayes Machine learning Classification algorithms from scratch in Python.
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Machine Learning Algorithms
Dog Race Classifier by Image
Stacking Classifier with parallel computing architecture based on Message Passing Interface.
Optimising parameters for multiple machine learning algorithms using grid search cv
Assignments of the course
Using data mining techniques to predict if the organization is prone to bankruptcy using the data with 250 records and 6 nominal attributes per record. Machine learning techniques used: Linear and non-linear SVM, Decision Tree Classifier, Gaussian Naive Bayes.
Implementation of a multi class Gaussian Naive Bayes classifier in python from scratch.
This repository contains 10+ machine learning models built from scratch in python using NumPy module.
Simple machine learning model using scikit-learn
I developed a Leaf Disease Detection system using image processing techniques and tried to improve its performance using a MPI Cluster by using 2 Virtual Machines. In this project a performance analysis is also done to know about how much the speedup takes place when the system is run on a single node (1 VM) and on a 2-node cluster (2 VMs). I ha…
Prediction of the outcome of football matches of European teams using various ML models. The features include many in-game statistics and team ratings to predict the result.
This repository provides examples of image feature vector classification using PySpark and scikit-learn, offering flexibility in choosing between the two popular machine learning libraries.
Gaussian Naive Bayes and logistic regression for bank note authentication
A Multivariate Gaussian Bayes classifier written using JAX
Build a model based on Gaussian Naive Model from scratch(without using any library), that classify the rice samples based on their quality and then compare the model's accuracy with the model created using Sklearn library. .
The visualization, analysis and machine learning of Adults dataset
This project consists of Two models that classifies the potability of water .
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