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Vesireddy, Priyanka (2020) Prediction Rating of Best Cuisines in Country Capitals Using Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.
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Abstract
The exciting thing for the people who loves food (foodies) what makes them happier is food wanted to see, enjoy the flavour of the dish, wants to get engaged with the new varieties of platters across global cites. As this is the inspiration from Zomato API it is the exploration for the individuals who wanted to try different varieties of platters within their convincing finance. It’s study also relates the individuals who are looking for the point that has wide range of outlets which supplies different varieties of platters which they exactly desire. This Research study suggests a platform, procedure for studying the features which are static and real-time and wanted to use some models on Zomato recommendations on the prediction ‘aggregate rating’ and analyse its features to show the kind of cuisine is served, customer recommendations and worked on the real time data from the Zomato API blog where the data engineers can follow procedure to study the features and use in there deployment purpose as they do not have the readily available code for their production. The basic novelty is that with the serving of proper cuisines and considering the consumer priorities personalized recommendations of dining rooms are brought for where ML techniques is used like analysing and adding the new features. The research study is worked on the data file from Zomato API blog in the configuration of raw. json folder. It uses set of methodologies and algorithms which is CRISP-DM, Feature engineering on analysis of currency from different origins and conversion, Exploratory data analysis on rating, Regression, Random forest, Decision tree, Gradient Boosting, XG Boost. The obtained results are good enough for the used procedures in this work. The overall used models give their better contribution of accuracy. The future scope is discussed.
Keywords: Rf model, testing features, EDA, XG Boost, Regression Analysis, Boosting
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Hospitality Industry |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Dan English |
| Date Deposited: | 18 Jun 2020 15:11 |
| Last Modified: | 18 Jun 2020 15:11 |
| URI: | https://norma.ncirl.ie/id/eprint/4308 |
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