Browsing by Author "Khan, Shaista Agha (14CO02)"

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    Extracting distinctive ingredients from cuisines using data analytic techniques
    (AIKTC, 2018-05) Bodke, Kalpana R.; Khan, Shaista Agha (14CO02); Momin, Saniya (14CO04); Shivankar, Sneha (14CO08)
    Data analysis is a process which is required to analyze a given set of data. This process is very much needed today where information is extracted and transformed into a format which is easy to process and interpret. In this paper, we consider techniques for identifying and classifying ingredients and aim to explore what are the essential ingredients that are required for a given cuisine. Each nation has its own unique ingredients that make their cuisine iconic. If we look at a particular cuisine, estimating its ingredients even if there is sufficient data, is a tedious task as the ingredients of the same cuisine differ from region to region and according to the culture. The problems faced by the users in the existing search system is getting just the recipe and ingredients. It does not classify the essential ingredients required to cook a particular dish. So, we propose a project that will extract the ingredients of the given cuisine from various websites on the web with the help of web scraping tools or techniques, then display the most essential ingredients amongst them by using data mining techniques and sorting as well as filtering algorithms. Web scraping is a process of extracting data from the web world through various methods. It involves fetching a web page and extracting data from it. Data mining techniques will help predict knowledge-driven decisions. This will include performing analyses on different data sets extracted and the genuine ingredients of every individual country, identifying them and then display them after filtration. Thousands of recipes which represent different national cuisines will be analyzed so that a better output can be generated which distinguishes the common and distinctive ingredients of each nation. Only those ingredients will be displayed which are distinct and do not belong to the users location.