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Yelp_​ETL_​data_​analysis

QUERY 4Using the Rule-based Row Filter (Dictionary) to display reviews having positivewords such as Great, Good, Nice, Like and Love. Then counting restaurantswhich are open and visualizing them based on location. QUERY 2Filtering 4 star and 5 start reviews and sorting them indescending order. Visualizing the output through ahistogram. QUERY 1Using GroupBy filter to get the unique count ofrestaurants across the available locations. Visualizingthe output through a donut chart. QUERY 5Using a two-pronged approach through the use ofreview_id and user_id columns to identify anyduplicate reviews. The 'duplicate' column at theend denotes all rows as "unique". Hence, noduplicates. QUERY 3Using the 'Top k Selector' node to sort 25 restaurants indescending order of 'useful' reviews. All operations pertaining to Yelp Business Converting 'Business' and 'Review' data of Yelp from JSON to table format.Renaming the columns appropriately and joining both tables. QUERY QUESTIONIdea is to pin down a restaurent based on the rating and reviews, there may be mutiple reviews by same customer so to remove the bias limited to 1 review for customer Node 1BusinessReviewNode 4Node 5Node 6Node 7Node 8Node 9Node 10Node 11Node 12Node 13Node 27Node 28Node 29Node 31Node 32Node 33Node 34Node 35Node 36Node 43 MongoDB Connector MongoDB Reader MongoDB Reader MongoDB Writer MongoDB Connector MongoDB Writer JSON to Table MongoDB Reader MongoDB Reader JSON to Table Joiner Row Filter CSV Writer Pie/Donut Chart Sorter Row Filter GroupBy Histogram Top k Selector Rule-basedRow Filter DuplicateRow Filter Column Filter Pie/Donut Chart QUERY 4Using the Rule-based Row Filter (Dictionary) to display reviews having positivewords such as Great, Good, Nice, Like and Love. Then counting restaurantswhich are open and visualizing them based on location. QUERY 2Filtering 4 star and 5 start reviews and sorting them indescending order. Visualizing the output through ahistogram. QUERY 1Using GroupBy filter to get the unique count ofrestaurants across the available locations. Visualizingthe output through a donut chart. QUERY 5Using a two-pronged approach through the use ofreview_id and user_id columns to identify anyduplicate reviews. The 'duplicate' column at theend denotes all rows as "unique". Hence, noduplicates. QUERY 3Using the 'Top k Selector' node to sort 25 restaurants indescending order of 'useful' reviews. All operations pertaining to Yelp Business Converting 'Business' and 'Review' data of Yelp from JSON to table format.Renaming the columns appropriately and joining both tables. QUERY QUESTIONIdea is to pin down a restaurent based on the rating and reviews, there may be mutiple reviews by same customer so to remove the bias limited to 1 review for customer Node 1BusinessReviewNode 4Node 5Node 6Node 7Node 8Node 9Node 10Node 11Node 12Node 13Node 27Node 28Node 29Node 31Node 32Node 33Node 34Node 35Node 36Node 43MongoDB Connector MongoDB Reader MongoDB Reader MongoDB Writer MongoDB Connector MongoDB Writer JSON to Table MongoDB Reader MongoDB Reader JSON to Table Joiner Row Filter CSV Writer Pie/Donut Chart Sorter Row Filter GroupBy Histogram Top k Selector Rule-basedRow Filter DuplicateRow Filter Column Filter Pie/Donut Chart

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Created by: gamanash
Created at: 2022-12-02
On NodePit since: 2023-03-14
Last update: 2024-04-28
Created with KNIME version: v4.6.3

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