A scalable, explainable Java Naive Bayes Classifier that works either in memory or on persistent fast key-value store (MapDB, RocksDB or LevelDB)
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Updated
Jun 14, 2023 - Java
A scalable, explainable Java Naive Bayes Classifier that works either in memory or on persistent fast key-value store (MapDB, RocksDB or LevelDB)
End-to-end implementation and deployment of Machine Learning Restaurant Reviews Sentiment Analysis using python, flask, gunicorn, scikit-Learn, nltk, etc. on the Heroku web application platform.
Contains 5th Semester AIML Lab programs
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The purpose of this analysis is to build a prediction model to predict whether a review on the restaurant is positive or negative
WEB AND SOCIAL MEDIA ANALYSIS
Naïve Bayes Algorithm is implemented from scratch in order to classify spam and not spam emails.
Aims to build and test classification models to predict salaries from the text contained in the job description.
This is a simple python program to train a classification model using decision tree, random forest and Naive Bayes algorithms
Implementation of natural language processing, supervised and unsupervised machine learning methods for classifying events around the US to automate a travel start-up's recommendation pipeline. Includes interactive command line tool.
Interactive Streamlit Visualisation for a Naive Bayes Classfier
A generic Naive Bayes Classifier from Scratch in Python 2 with the following principles: OOP, GUI,Files I/O (csv and txt), design patterns(observer-observable and MVC), the project is OS independent.
The aim of the iris flower classification is to predict flowers based on their specific features.
Code templates for data prep and different ML algorithms in Python.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Klasifikasi Berita dengan Naive Bayes
A simple implementation of the Naive Bayes Algorithm to understand its inner workings.
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