A collection of data science concepts, datasets, industry-applications, walk-through's & notes. See ISSUES tab for in-depth studies by topics.
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Updated
Oct 30, 2021 - Jupyter Notebook
A collection of data science concepts, datasets, industry-applications, walk-through's & notes. See ISSUES tab for in-depth studies by topics.
Contains 5th Semester AIML Lab programs
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.
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.
Aims to build and test classification models to predict salaries from the text contained in the job description.
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
The aim of the iris flower classification is to predict flowers based on their specific features.
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.
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
Sentiment analysis with spark
WEB AND SOCIAL MEDIA ANALYSIS
Models to detect real pictures from fake photoshoped pictures
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
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