Udacity MWS Nanodegree project 3
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Updated
Nov 25, 2018 - JavaScript
Udacity MWS Nanodegree project 3
OpenTable reviews sentiment extractor
This project focuses on classifying restaurant reviews to evaluate customer feedback, helping businesses enhance customer service and address negative reviews to influence potential consumers positively.
An architectural kata on a restaurant review system
🍽️ 🌟MWS-Restaurant-Reviews🌟. Nanodegree Mobile Web Specilist.
Simple hypothesis testing and sentimental analysis for beginners
Authentic Restaurant Design Website
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".
Java Client for using Yelp Fusion API
Mobile UI Testing Activities and Code Reviewing in NoCountry 2nd Project MVP Delivery - Open to updates in Automation Testing
With the rapid growth of online platforms for sharing opinions and reviews, restaurants often rely on the customer feedback to improve their services and attract new customers. Analyzing the sentiment of these reviews can provide valuable insights into customer satisfaction.
Udacity Restaurant Reviews Project
Restro Finder is an application that helps users discover the best restaurants and cuisines around them
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.
Created a fully functional restaurant reviews app with React, allowing users to search for restaurants in their area using Google places API for live data and real time reviews. Users are also able to add new restaurants, add new full review feedback.
A repo to query Zomato API
Sentiment Analysis on restaurant reviews using NLP & ML models to help businesses improve customer experience.
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…
Recommendation engine on restaurant reviews
🍽️ Predict sentiment(+ve / -ve) of Restaurant Reviews using NLP
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