There are 0 repository under gaussian-naive-bayes topic.
This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
Elixir library for machine learning
Implements Naive Bayes and Gaussian Naive Bayes Machine learning Classification algorithms from scratch in Python.
Linear discriminant Analysis(LDA) for Wine Dataset of Machine Learning
PCA(Principle Component Analysis) For Seed Dataset in Machine Learning
This research constitutes an attempt to assess the dry spell patterns in the northern region of Ghana, near Burkina Faso. We aim to develop a model which by exploiting satellite products overcomes the poor temporal and spatial coverage of existing ground precipitation measurements. For this purpose 14 meteorological stations featuring different temporal coverage are used together with satellite-based precipitation products. Conventional machine-learning and deep-learning algorithms were compared in an attempt to establish a link between satellite products and field rainfall data for dry spell assessment.
PCA(Principle Component Analysis) For Wine dataset in ML
LDA(Linear Discriminant Analysis) for Seed Dataset
A prediction model that uses genetic data for disease classification. Data is extracted from a DNA microarray which measures the expression levels of large numbers of genes simultaneously.
To Detect Sepsis Disease using six Classifiers on clinical data
A tool for Automatic Question Generation tool in python using Natural Language Processing. Scanned images of notes are to be uploaded from which questions have to be extracted from. These scanned images converted to ‘.txt’ file. Question are generated and saved in a '.txt' file using NLP and Gaussian Naive Bayes classification
According to the World Health Organization, depression is the leading cause of disability worldwide. Globally, more than 300 million people of all ages suffer from the disorder. And the incidence of the disorder is increasing everywhere. Depression is a complex condition, involving many systems of the body
Machine Learning Algorithms
Kmeans and HCA clustering Visualization for WINE dataset in machine learning.
Kmeans and Hierarchical clustering for Seed-dataset in Machine Learning
Linear discriminant Analysis clustering Visualization for IRIS dataset
LDA(Linear discriminant Analysis) for Wine Dataset in machine learning
Principle Component Analysis Clustering Visualization for Iris dataset
Stacking Classifier with parallel computing architecture based on Message Passing Interface.
League of Legends Game Data Analysis (Random Forest, KNN, SVM, XGB / Kaggle game data 2017)
This project was made as a report for a Big Data Challenge at Satria Data 2020 by IPB University.
Implementation of Gaussian Naive Bayes classification algorithm in Python using Pandas, NumPy and Scikit-Learn
If you miss payments or you don't pay the right amount, your creditor may send you a default notice, also known as a notice of default. If the default is applied it'll be recorded in your credit file and can affect your credit rating. An account defaults when you break the terms of the credit agreement.
A machine learning model to predict the survived passengers from the titanic disaster.
Intro to Machine Learning Final Project
Deployed a classification model to predict coronary heart risk of a person
Repository for "Optimization of a Double Wishbone Suspension Geometry for Off-road Vehicles using Genetic Algorithm and Machine Learning" (ICMAE 2022)
Predict which user is going to buy a product displayed on a social network advertisement using different classifier.
4 classifier models viz. k-NN classifier, Naive Bayes classifier, Decision Tree and Logistic Regression classifier predicts the outcome of Loan Appliction Status.
Disease Prediction Model using KNN, Gaussian Naive Bayes Classifier along with various performance metrics
A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomialNB & GaussianNB to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer. It is implemented using LSTM and Word Embeddings to gain accuracy of 97.70% .
This project focuses on using Gaussian Naive Bayes, a probabilistic algorithm, to filter spam emails effectively. By leveraging its simplicity and efficiency, the goal is to enhance email security by accurately classifying emails as either spam or legitimate.
Algorithmic Trading in Python
Simple machine learning model using scikit-learn