There are 3 repositories under naive-bayes-algorithm topic.
NaiveBayes classifier for JavaScript
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch
Naive Bayes with support for categorical and continuous data
Gauss Naive Bayes in Python From Scratch.
A Python implementation of Naive Bayes from scratch.
An interactive approach to understanding Machine Learning using scikit-learn
Simple naive bayes implementation for weather prediction in python
Gaussian Naive Bayes (GaussianNB) classifier
All exercises for the course Elements of AI - Building AI
Examples of all Machine Learning Algorithm in Apache Spark
Library PHP untuk klasifikasi teks Bahasa Indonesia menggunakan algoritma Naive Bayes Classifier (NBC)
This Telegram-Bot answers python questions by using stackoverflow subjects.
Fake News Detection-Naive Bayes Model
Stock Market Price Prediction: Used machine learning algorithms such as Linear Regression, Logistics Regression, Naive Bayes, K Nearest Neighbor, Support Vector Machine, Decision Tree, and Random Forest to identify which algorithm gives better results. Used Neural Networks such as Auto ARIMA, Prophet(Time-Series), and LSTM(Long Term-Short Memory) then compare make Inferences about the model.
Crop Prediction using Artificial Intelligence (AI) CSP Algorithm
Driver drowsiness is one of the causes of traffic accidents. According to the statistics; highway road crashes hold 11.09% of the total number of accidents. There are several reasons of drowsy driving such as: a lack of quality of sleep, may be overnight driving or having sleep disorders e.g. sleep apnea. However; all people should know that: People can not fight against to sleep. Using Image Processing and both classical and new-brand Machine Learning techniques, we are trying to know beforehand the driver's drowsiness and warning him/her with an alert before any crash happened.
In simpler words we tell whether a user on Social Networking site after clicking the ad’s displayed on the website,end’s up buying the product or not. This could be really helpful for the company selling the product. Lets say that its a car company which has paid the social networking site(For simplicity we’ll assume its Facebook from now on)to display ads of its newly launched car.Now since the company relies heavily on the success of its newly launched car it would leave no stone unturned while trying to advertise the car. Well then whats better than advertising it on the most popular platform right now.But what if we only advertise it to the correct crowd.
Naive Bayes Classifier with stop words | Naive Bayes Classifier without stop words | Binary Naive Bayes Classifier
"100-days-of-machine-learning" is a repository containing a comprehensive guide to Machine Learning, covering the basics and advanced topics, with daily exercises and real-world examples for 100 days.
I implemented a Naive Bayes classifier form scratch and applied it on MNIST dataset.
A scalable, explainable Java Naive Bayes Classifier that works either in memory or on persistent fast key-value store (MapDB, RocksDB or LevelDB)
DL, ML, and Rule based approaches for Sentiment analysis
Estudos sobre mineração de emoção em textos em Python
URL classification by Naive Bayes algorithm
A Sentiment Analyzer for a set of Hotel Reviews using Naive Bayes Algorithm
JavaFX application detecting whether files are spam or not using Naive Bayes filtering
Loan Prediction using Classification Techniques
Predicting the champion of the 2023 Cricket World Cup through the implementation of the Random Forest algorithm.
Building a Spam Filter using Naive Bayes algorithm from scratch.
Heart disease is a major global health concern that affects millions of people around the world. Early detection and accurate prediction of heart disease can help to prevent the progression of the disease and save lives. In this project, we aim to develop a predictive model for heart disease using various machine learning algorithms.
Our project utilizes the advanced algos such as Random Forest, Decision Tree, Gaussian NB,KNN,SVM to analyze various factors and attributes of wine samples, enabling us to predict their quality with remarkable precision.Whether you're a wine enthusiast or a producer looking to optimize quality control, this project is your go-to resource.