Contains 5th Semester AIML Lab programs

A collection of data science concepts, datasets, industry-applications, walk-through's & notes. See ISSUES tab for in-depth studies by topics.

A scalable, explainable Java Naive Bayes Classifier that works either in memory or on persistent fast key-value store (MapDB, RocksDB or LevelDB)

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.

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.

Code templates for data prep and different ML algorithms in Python.

Predicting whether an SMS (text message) is spam using natural language processing (NLP), naive Bayes classifier and cross validation (in Python)

This is a simple python program to train a classification model using decision tree, random forest and Naive Bayes algorithms

Native_Bayes_Sentimientos Básico

The aim of the iris flower classification is to predict flowers based on their specific features.

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

This project aims to address the challenge of predicting whether it will rain or snow in Hungary based on various meteorological variables.

Personality test which classifies in four personality types. For the classification is used the natural language processing classification algorithm - Multinomial Naive-Bayes.

Created an api using flask to take user input statement, clean the dataset, model the data, and classify the statement as positive, negative or neutral.

The purpose of this analysis is to build a prediction model to predict whether a review on the restaurant is positive or negative