Restaurant Sales Prediction Using Machine Learning

In general, the revenue forecast, offer information, and the weather gauge setting will record an accurate estimate of any restaurant's future revenue. The turnover is significantly focused on the need of the customers. Either way, the performance has transformed over the past couple of years with the presentation of huge amounts of information and calculations during the time taken to gain the upper hand. It is fundamental to learn and understand the importance of the information that will be used in any business process. Again, climate forecasting can be done alongside business expectations with the organization.

ANDROID SALES PREDICTION DURING PANDEMIC USING NAÏVE BAYES AND K-NN METHODS BASED ON PARTICLE SWARM OPTIMIZATION

During the pandemic, most schools, campuses, and places of education conducted online teaching and learning activities. Many teaching and learning activities are carried out using the Zoom, Google, WebEx, or Microsoft Teams applications. All of this can be done through a PC or laptop, or using a cellphone, so the need for PCs and cellphones increases, both new and used goods. Even though during the pandemic the economic situation was declining, many companies suffered losses, resulting in a reduction in employees and causing a high unemployment rate, the need for Android phones remains high. In addition to online distance learning facilities, Android phones can also be used for online sales through e-commerce, market places, social media, and other digital platforms. Currently, Android phones have many choices and according to the funds we have, with various brands and specifications. Many brands issue android cellphone products with pretty good specifications and affordable prices, so that even though purchasing power has decreased due to the pandemic, sales of android cellphones are still high. In this study, the author predicts the highest sales of android cellphones using the Naïve Bayes method and the K-Nearest Neighbor method based on Particle Swarm Optimization accuracy of 81.33%.

Hybrid Customer-Centric Sales Forecasting Model Using AI ML Approaches

Business Intelligence is a process of preparing, analyzing, presenting, and maintaining the data to gain insights for the decision-makers to make informed decisions. While there are many approaches to predict the growth based on the sales figures a very few consider the influence of customer data on the forecasting and the relevance of the same while making the predictions. So, in this study, we will look at some of the existing techniques used so far to make predictions and studies used to understand the customer data. With the analysis, we shall try to devise a hybrid approach to the traditional sales prediction, which would include a customer-centric data analysis. We shall look at some of the techniques which are traditionally used in Market Basket analysis and at the same time look at the techniques like classification, segmentation, regression, etc. to get a perception of the impact of customer data on sales forecasting. We shall highlight all the pros and cons of the algorithms and try to come up with an intelligent approach that would give accurate results

Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning

Online business has grown exponentially during the last decade, and the industries are focusing on online business more than before. However, just setting up an online store and starting selling might not work. Different machine learning and data mining techniques are needed to know the users’ preferences and know what would be best for business. According to the decision-making needs of online product sales, combined with the influencing factors of online product sales in various industries and the advantages of deep learning algorithm, this paper constructs a sales prediction model suitable for online products and focuses on evaluating the adaptability of the model in different types of online products. In the research process, the full connection model is compared with the training results of CNN, which proves the accuracy and generalization ability of CNN model. By selecting the non-deep learning model as the comparison baseline, the performance advantages of CNN model under different categories of products are proved. In addition, the experiment concludes that the unsupervised pretrained CNN model is more effective and adaptable in sales forecasting.

Social Media Marketing in the Sales Volume Prediction for the Lolita Fashion Brand

Despite extensively investigating the impact of social media on fashion products’ marketing, little evidence is available on how the platforms influence sales prediction. Focusing on Lolita fashion, this study investigates the impact of social media marketing on the sales volume prediction of fashion products. Essentially, we analyzed marketing data, including comments, likes, and shares from the Weibo social platform, to forecast future sales, examine how to enhance profit performance, and make production decisions. Using a quantitative approach, we tested three different prediction models, including multiple regression, decision tree, and XGBoost. The results revealed that increasing comments and decreasing the number of likes could significantly improve the sales volumes of Lolita products. In contrast, shares exerted a less significant impact on sales. Regarding prediction models, XGBoost was found to be the best method. In the fashion industry, social media is a useful tool for forecasting market trend. A limitation of this study is that only one social media platform was used to extract data, which might limit the generalization of the findings.

Sistem Informasi PT Bintang Sidoraya Dengan Peramalan Penjualan Menggunakan Metode Statistical Parabolic Projection

PT Bintang Sidoraya Information Systems with Sales Forecasting Using Statistical Parabolic Projection Method. The problem that often occurs in companies is the sales prediction in the future period based on data and information in the previous period. These predictions will affect the decisions taken by management for stock availability for the coming period. Due to the demand for goods shipping from around all major cities in Indonesia, sufficient stock availability is needed to minimize the possibility of losing customers. This research was conducted to build an information system application to record data and accompanied by forecasting features using the Statistical Parabolic Projection method. The result of this research is an information system that successfully predicts sales that can facilitate the stock availability calculation for the future period.Keywords: PT Bintang Sidoraya, information systems, Statistical Parabolic Projection Abstrak. Permasalahan yang sering terjadi pada perusahaan ialah prediksi penjualan di periode yang akan datang berdasarkan data dan informasi pada periode sebelumnya. Prediksi tersebut akan berpengaruh terhadap keputusan yang diambil oleh manajemen untuk berapa persediaan stok periode yang akan datang. Karena permintaan pengiriman barang yang hampir mencakupi seluruh kota besar di Indonesia, diperlukan persediaan stok yang cukup untuk meminimalkan terjadinya potensi kehilangan pelanggan. Penelitian ini dilakukan untuk membangun aplikasi sistem informasi untuk melakukan perekapan data dan disertai fitur peramalan menggunakan metode Statistical Parabolic Projection. Hasil dari penelitian ini ialah sebuah sistem informasi yang berhasil melakukan prediksi penjualan yang dapat mempermudah penentuan jumlah stok pada periode mendatang.Kata kunci: PT Bintang Sidoraya, information systems, Statistical Parabolic Projection