Volume-07 , Special Issue-14 , May - 2019 Go Back

Globally retail industries are growing day by day, because retail industries give more profit in less time duration. In 2017 USD 23,460 billion was the value of global retail industry, it is expected to increase 5.3% during the forecast period (2018-2023) reaching to USD 31,880.8 by 2023 [11]. Every business revolves around one word – Profit! Every business man wants to increase the profit of his business, no one wants to loose. The best way to increase the profit is by extracting knowledge about the business and transforming that knowledge into right predictions. In retail sales business, prediction of future sales is very much essential to improve the business operation and to increase profit. Manually analysing large amount of data for predicting future sales may lead to less accurate results. Statistical techniques were used initially to forecast future sales, later Datamining techniques were inculcated into the process of prediction. Only Datamining techniques were not sufficient to accurately predict the sales, so Artificial Intelligence (AI) domain is chosen by software professionals for prediction. Machine Learning (ML) is an application of AI and Deep Learning (DL) is an upgradation of ML especially Artificial Neural Network (ANN). Various ML and DL prediction models are gaining more attention in recent days [12]. The models can be chosen based on the type of data that is being analysed and the response time of prediction models. This paper provides review of various prediction models used for sales prediction in retail industries based on data features and models.

Data Mining, Deep Learning, Machine Learning and Retail sales prediction

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Surendra Babu K N, Mallikarjun M Kodabagi, "Machine Learning based Models used for Sales Prediction in Retail Shops: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.516-521, 2019.

As of late, we have seen a twist of audit sites. It displays an incredible chance to share our point of view for different items we buy. In any case we face the data over-burdening issue. The most effective method to mine significant data from audits to comprehend a client`s inclinations and make an exact suggestion is vital. Conventional recommender frameworks (RS) think about certain components. Furthermore, we consider a client`s own nostalgic characteristics as well as mull over relational wistful impact. At that point Finally, we intertwine three variables client conclusion closeness, interpersonal sentimental impact, and thing`s notoriety likeness into our recommender framework to make a precise rating forecast. We direct an act assessment of 3 wistful elements gathered from Yelp. The trial output demonstrate the assumption will clearly describe client inclinations, that help to enhance the proposal execution.

Recommendation System; Sentiment Analysis; Machine Learning; Social Networks

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D Siva Kumar, Borra L S Chaitanya Reddy, C Naveen Sai, G Nandini, Raghavendra Reddy, "A Novel Approach to Recommendation System by Using User Trust and Item Ratings", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.522-527, 2019.