Intelligent Decision Analytical System requires integration of decision analysis and predictions. Most of the business organizations heavily depend on a knowledge base and demand prediction of sales trends. The accuracy in sales forecast provides a big impact in business. Data mining techniques are very effective tools in extracting hidden knowledge from an enormous dataset to enhance accuracy and efficiency of forecasting. The detailed study and analysis of comprehensible predictive models to improve future sales predictions are carried out in this research. Traditional forecast systems are difficult to deal with the big data and accuracy of sales forecasting. These issues could be overcome by using various data mining techniques. In this paper, we briefly analyzed the concept of sales data and sales forecast. The various techniques and measures for sales predictions are described in the later part of the research work. On the basis of a performance evaluation, a best suited predictive model is suggested for the sales trend forecast. The results are summarized in terms of reliability and accuracy of efficient techniques taken for prediction and forecasting. The studies found that the best fit model is Gradient Boost Algorithm, which shows maximum accuracy in forecasting and future sales prediction.

Intelligent Sales Prediction Using Machine Learning Techniques

Clustering divides data into meaningful, useful groups known as clusters without any prior knowledge about the data. One of the drawbacks of K-Means clustering is the estimation of initial centroids which influence the performance of the algorithm. To overcome this issue, optimization algorithms like Bat and Firefly are executed as pre-processing step. These algorithms return optimal centroids which is given as input to the K-Means algorithm. Clustering is carried out on large data sets, therefore Apache Spark, an open source software framework is used. The performance of the optimization algorithms is evaluated and the best algorithm is determined.

Performance Analysis of Parallel K-Means with Optimization Algorithms for Clustering on Spark

Sales Prediction Using Machine Learning Techniques

In recent years, clustering analysis for interval data has attracted the attention of many researchers. Nevertheless, an algorithm that can automatically determine the number of clusters, and can e...

An efficient robust automatic clustering algorithm for interval data

/pdf/sales-forecasting-using-machine-learning-techniques-2kqgfhdd.pdf

Sales forecasting using machine learning techniques

Big data analysis is a hot topic in the IT field now. Spark is a high-reliability and high-performance distributed parallel computing framework for big data sets. K-means algorithm is one of the classical partition methods in clustering algorithm. In this paper, we study the k-means clustering algorithm on Spark. Firstly, the principle of the algorithm is analyzed, and then the clustering analysis is carried out on the supermarket customers through the experiment to find out the different shopping patterns. At the same time, this paper proposes the parallelization of k-means algorithm and the distributed computing framework of Spark, and gives the concrete design scheme and implementation scheme. This paper uses the two-year sales data of a supermarket to validate the proposed clustering algorithm and achieve the goal of subdividing customers, and then analyze the clustering results to help enterprises to take different marketing strategies for different customer groups to improve sales performance.

Research on retailer data clustering algorithm based on Spark

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Research on retailer data clustering algorithm based on Spark