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Now-a-days customers prefer online shopping rather than offline shopping. The crucial challenge of online shopping is to analyze the behavior of the customers. Many of the people are visiting the online shopping sites and spending their time by surfing either to buy or for window shopping. Customers behavior varies from person to person based on their buying behavior patterns. Our main aim is to analyze the customer behavior like who buys what. The result of this analysis is suggesting some techniques for improving the sales. The success of business is to know the requirement of the customer and providing the good offers in right time. Data mining is used to extract the important information from the bulk of data to save it and summarize it in effective manner. Different approaches for customer behavior analysis in data mining are: Classification. We use one of these appropriate Data Mining techniques for behavioral analysis of customers, therefore to optimize the business outcome in online shopping.


Online shopping is the easy solution for busy life in today’s world. Earlier, consumers buy the goods and products by physically at the stores. But now-a-days, the people want to save the time for their professional or personal sake, so that they are willing to go for online shopping. This online shopping saves crucial time for modern people. In the 21st century, trade and commerce have been so diversified that multichannel has taken place and online shopping has increased significantly throughout the world. Unlike physical store, all the goods in the online stores were described through text, with photos, with multimedia files. The online stores also provide the links for much detailed information of the product. The online consumers are adventurous explorer, shopping lover and some are technology muddler, hate waiting for the product to ship. The online consumer behavior like the action during searching, buying and using products became a contemporary research area for an increasing number of researchers to understand this unique nature of online shopping. Thus the purpose of this study is to understand the consumer behavior towards online shopping, their liking, disliking and satisfaction level. This system uses classification or association techniques of data mining to analyze the behavior of customer.


Data mining is a process of extracting and discovering patterns in large dataset involving methods at the intersection of machine learning, statistics and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

The term “data mining” is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate.

The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.

The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data; in contrast, data mining uses machine learning and statistical models to uncover cl and estine or hidden patterns in a large volume of data.

The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

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