In this project I built a basic recommendation system for Zomato restaurants in Bangalore, IN. Although the main task is to analyze the data and gain strong insights which will help Zomato for decision making. This analysis will also help those people who want start their restaurant/cafe/food chain in Bangalore, IN. This notebook is actually from my my Kaggle. I used the data from Kaggle itself which is in the form of .csv.
Zomato is an Indian multinational restaurant aggregator and food delivery company founded by Deepinder Goyal and Pankaj Chaddah in 2008. Zomato provides information, menus and user-reviews of restaurants as well as food delivery options from partner restaurants in select cities.
The rapid growth of data collection has led to a new era of information. Data is being used to create more efficient systems and this is where Recommendation Systems come into play. Recommendation Systems are a type of information filtering systems as they improve the quality of search results and provides items that are more relevant to the search item or are realted to the search history of the user. They are active information filtering systems which personalize the information coming to a user based on his interests, relevance of the information etc. Recommender systems are used widely for recommending movies, articles, restaurants, places to visit, items to buy etc.
There are basically three types of recommender systems:-
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Demographic Filtering- They offer generalized recommendations to every user, based on movie popularity and/or genre. The System recommends the same movies to users with similar demographic features.
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Content Based Filtering- They suggest similar items based on a particular item. This system uses item metadata, such as genre, director, description, actors, etc. for movies, to make these recommendations.
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Collaborative Filtering- This system matches persons with similar interests and provides recommendations based on this matching. Collaborative filters do not require item metadata like its content-based counterparts.
Here I will be using Content Based Filtering
Content-Based Filtering: This method uses only information about the description and attributes of the items users has previously consumed to model user's preferences. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past (or is examining in the present). In particular, various candidate items are compared with items previously rated by the user and the best-matching items are recommended.
This data set consists of restaurants of Bangalore,India collected from Zomato.
My aim is to create a content based recommender system in which when I will write a restaurant name, Recommender system will look at the reviews of other restaurants, and System will recommend us other restaurants with similar reviews and sort them from the highest rated.
Dynamic visualization is not supported by GitHub(if you know then please let me know) so here is the link of the notebook.
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