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Analysis of historical customer data can highlight if a certain combination of products purchased makes an additional purchase more likely. This is called market basket analysis (also called as MBA). It is a widely used technique to identify the best possible mix of frequently bought products or services. This is also called product association analysis. The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases. Market Basket Analysis creates If-Then scenario rules, for example, if item A is purchased then item B is likely to be purchased. The rules are probabilistic in nature or, in other words, they are derived from the frequencies of co-occurrence in the observations. Market Basket analysis is particularly useful for physical retail stores as it can help in planning floor space and product placement amongst many other benefits.
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.
The goal of this data science project is to take an image of a handwritten single digit, and determine what that digit is.
Build a predictive model to correctly classify products between 9 product categories (fashion, electronics, etc.) using the Otto Group dataset.