Udacity MWS Nanodegree project 3
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Updated
Nov 25, 2018 - JavaScript
Udacity MWS Nanodegree project 3
An architectural kata on a restaurant review system
A Twitter application which crawls twitter to fetch and extract restaurants from tweets made about restaurants and rate each restaurant according to the tweet
Restaurant Review Analysis. "Thrilled to unveil my latest project on Streamlit - a cutting-edge restaurant review ML model 🍽️🔍 Using innovative AI technology, share your feedback and predict to model. Join me on LinkedIn to explore the future of culinary exploration! #AI #MachineLearning #Streamlit #RestaurantReviews" .
A repo to query Zomato API
Dependency parsing was used to extract relevant information from a review in order to predict the sentiment of a given aspect term. Different machine learning models such as Naïve Bayes, Logistic Regression, Support Vector Classifier and Neural Networks were used to make predictions. A maximum accuracy score of 0.74 on the test dataset was achie…
Udacity Restaurant Reviews Project
Restaurant Review System is a Flask-based web application that allows restaurant owners to analyze and visualize customer reviews. This platform leverages natural language processing (NLP) techniques using the Natural Language Toolkit (NLTK) and TextBlob in Python.
Restaurant Review Analysis. "Thrilled to unveil my latest project on Streamlit - a cutting-edge restaurant review ML model 🍽️🔍 Using innovative AI technology, share your feedback and predict to model. Join me on LinkedIn to explore the future of culinary exploration! #AI #MachineLearning #Streamlit #RestaurantReviews".
Mobile Web Specialist Restaurant Reviews App: Stage 3
Docker supported restaurant crawlers.
Java Client for using Yelp Fusion API
NLP - NLTK - Restaurant review analysis
Mobile UI Testing Activities and Code Reviewing in NoCountry 2nd Project MVP Delivery - Open to updates in Automation Testing
🍽️ 🌟MWS-Restaurant-Reviews🌟. Nanodegree Mobile Web Specilist.
Project done under the course Machine Learning A-Z™: Hands-On Python & R In Data Science. This project reads a TSV file, cleans the restaurant reviews, generates a bag-of-words model and uses a classifier which tells whether the review is a positive one or a negative one.
Simple hypothesis testing and sentimental analysis for beginners
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