Applying unsupervised learning algoriths on online shoppers intention data and model building
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Updated
Dec 8, 2019 - HTML
Applying unsupervised learning algoriths on online shoppers intention data and model building
Information Retrieval Course - Assignment - Naïve Bayes Classifier
Some nice code scripts in R depectinve various ML models
In this program, you’ll master valuable machine learning skills that are in demand across countless industries. Investment levels in this space continue to rise, thousands of highly-valued startups have entered the field, and demand for machine learning talent shows no signs of leveling. Program graduates emerge uniquely prepared to excel in mac…
Implementación del algoritmo propuesto en http://proceedings.mlr.press/v72/rivas18a/rivas18a.pdf
Data preprocessing and classification for the detection of fraudulent transactions
Introduction to Computer Science & Engg. course projects
In this repo, I have stored all the files which I used to deploy my naive bayes classifier model on google cloud platform
Loan Binary Classification Task
Spam SMS Detection model is a powerful solution built to identify and classify spam messages using the Naive Bayes algorithm. The accompanying Flask interface provides users with a seamless experience for submitting SMS entries, tracking usage, and receiving real-time classification results.
Preparing floras for morphological parsing and integration
Classification of imdb movie reviews using naive bayes. Performance of any movie can be analysed using this pre- trained model.
An application to classify a news as "FAKE" or "REAL".
Data Science/machine learning methods performed on the Titanic dataset
A machine learning model to predict potential donors for charity.
Simple Naive Bayes Spam Classifier
Sentiment Analysis Webapp
A machine learning and NLP based web Application to detect SMS as Spam or Ham built with Flask and deployed using Heroku.
The Spotify Cross-Border Hit Analysis project was conducted as part of the Introduction to Data Analysis course at Drexel University in Philadelphia. The project aimed to investigate the factors that contribute to a song becoming a cross-border hit. The top fifty songs from the ten leading streaming countries were acquired using the Spotify API.
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