Price Suggestion and Price Prediction Algorithms

Abstract

 In the current scenario with the advancement of technology, the online sales are dominating offline sales. Among 10 people, nearly 6 people are opting for online purchase. In this process, there is a lot of increase in category of price points which has both pros and cons. The advantage is that the customer has more number of choices to select but this leads to a dilemma in choosing a product with optimal price. This paper uses fast and high-performance decision tree algorithm called Light GBM for price suggestion which can be a solution to such kind of issues. In another scenario the purchase in the stock market takes place with the help of a trading agency. The customer wants to know the future trends of the company or entity so that he/she can decide whether to buy it or not. The development of the stock price prediction model helped the customer to check the required information on their own. This paper analyses various price prediction algorithms namely Auto ARIMA, Prophet and LSTM. The analysis shows that Auto ARIMA is the most suitable algorithm for stock price prediction problem among chosen algorithms.