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- What is Marketing Analytics & Why It Matters?
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Big Data & Analytics
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- The Journey to Becoming a Data Analyst: A Step-by-Step Guide
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- The Ethical Dilemma: Exploring the Implications of Data Analytics
- Data Analytics Case Studies: Real-World Examples of Business Insights and Success
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Interview Questions on Data Science
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Recomender System Interview questions and Answers
Meet the Author : Mr. Bharani Kumar
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
In the context of Recommender systems. When customer data is about the Rating matrix for the various products, for similarity measure between the customers, it is required to ignore products where one of the pair of the customers has not rated the product. What aspect of Data preprocessing is being addressed by this?
- a) Pair wise deletion
- b) Mode imputation
- c) Hot deck Imputation
- d) Case wise deletion
Answer - a) Pair wise deletion
Pairwise deletion or Avalablbe case analysis is a method of keeping the missingness as is, till the point where the computation/comparison is not possible and only for those cases the missingness is deleted along with the complementing record(the other record with which the record with missingness is being compared/studied/distance computed) In a database with user ratings of products/services there bound to be a large number of missing values and absence of rating does not imply a '0' rating. therefore the pair-wise deletion is used wherein when a pair is being considered for distance/similarity measure only those products (columns) that have non-nan values for both the pairs are included in the measure and the rest are excluded.