E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations' influence on customer clicks and buys, three target areas-customer behavior, data collection, user-interface-will be explored for possible sources of erroneous data. Varied customer behavior misrepresents the recommendations' true influence on a customer due to the presence of B2B interactions and outlier customers. Non-parametric statistical procedures for outlier removal are delineated and other strategies are investigated to account for the effect of a large percentage of new customers or high bounce rates. Subsequently, in data collection we identify probable misleading interactions in the raw data, propose a robust method of tracking unique visitors, and accurately attributing the buy influence for combo products. Lastly, user-interface issues discuss the possible problems caused due to the recommendation widget's positioning on the e-commerce website and the stringent conditions that should be imposed when utilizing data from the product listing page. This collective methodology results in an exact and valid estimation of the customer's interactions influenced by the recommendation model in the context of standard industry metrics, such as Click-through rates, Buy-through rates, and Conversion revenue.

E-commerce businesses employ recommender models to assist in identifying a personalized set of products for each visitor. To accurately assess the recommendations’ influence on customer clicks and buys, three target areas—customer behavior, data collection, user-interface —will be explored for possible sources of erroneous data. Varied customer behavior misrepresents the recommendations’ true influence on a customer due to the presence of B2B interactions and outlier customers. Non-parametric statistical procedures for outlier removal are delineated and other strategies are investigated to account for the effect of a large percentage of new customers or high bounce rates. Subsequently, in data collection we identify probable misleading interactions in the raw data, propose a robust method of tracking unique visitors, and accurately attributing the buy influence for combo products. Lastly, user-interface issues discuss the possible problems caused due to the recommendation widget’s positioning on the e-commerce website and the stringent conditions that should be imposed when utilizing data from the product listing page. This collective methodology results in an exact and valid estimation of the customer’s interactions influenced by the recommendation model in the context of standard industry metrics such as Click-through rates, Buy-through rates, and Conversion revenue.

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Thyroid gland is vital gland. It produces Thyroxine T3 and Tri-Idothyronine T4 hormones which are needed for the growth and development. Iodine is needed for the formation of T3 and T4 hormones. Iodine is found in different food such as sea food, vegetables produced in Iodine rich soil and table salt. The associated problems of thyroid gland are hypothyroidism and hyperthyroidism. The objective of this study was to find out the prevalence of thyroid problems attending at Siddharthanagar City Hospital. It was hospital based retrospective study. Data related to thyroid problems T3,T4 and TSH was collected from hospital laboratory reports . The tests were done using Semi Auto Clia Plate Analyzer of TOSOH India Company. The total number of lab reports was 299 taken for study. Among them the percentage of male and female was 21.7 and 78.3 respectively. The overall prevalence of thyroid problems was 5.9% among general public. The overall prevalence of hypothyroidism was 9.69% and hyperthyroidism was 2.17% respectively. The prevalence of thyroid problems was seen more in female. This study showed that thyroid problem including hyperthyroidism and hypothyroidism is common. Female population and 21-40 years age group are highly suffered in comparison to male population.

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