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# Tag Info

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In case that the number of items is quite small, turning the problem into a classification problem will be the most convenient solution. Use each item as a feature, the class as a concept. Now you can apply the methods that @OvisAmmon recommended on or other classification methods. However, usually in market basket analysis there are many items and each ...

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I've done this (using anaconda) with the following libraries. from mlxtend.frequent_patterns import apriori from mlxtend.frequent_patterns import association_rules Have a look here.

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No it is not important and highly recommended to remove the duplicate items and sort the items in lexicographical order in each transaction. This is to improve the performance. In association rule mining, an item is frequent iff it is repeated in multiple transactions not in a single transaction. This is why you don't need to have duplicate items in a each ...

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Question I feel that the question needs to be reformulated for clarity to: Given a fixed set of purchased products $S=\{s_i\}$ with associated prices $p_i$, find the greatest number of disjoint subsets $S_1, S_2, etc$ such that the number of reward points attained in each subset equals the number of points attained for the whole. A reward point is ...

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a) The items that are frequent are $\{1,2,3,4,5,\dots,20\}$ because these all appear in at least 5 baskets. b) (1) $\{5, 7\} → 2$. Then $Confidence=\frac{1}{2}$ hence 5 and 7 will appear together in basket no. 35 and 70 and 2 will appear along with 7 and 5 in basket no. 70 so:  Confidence = \frac{support(\{5,7\}\cup\{2\})}{support(\{5,7\})}= \frac{1}{...

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We can see Web Analytics like an application of Data Science concepts. Almost a field of it. Usually it's very close to Business Intelligence for marketing aims and the main goal is to analyse users information. So it's about marketing.

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You are looking into s classification task where text in the left (like Student etc.) is the target class, while list of shoppings is an almost ready-to-go list of features - variables on which you will be learning a classification model. While you should looking into classification algorithm family in general, really most basic algorithms you may use are: ...

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Actually, I'd propose to tackle it from a different angle - assuming you want to predict product categories, and those are usually few, you can see it as a regular classification problem. Every product group is a category, which you will try to predict. So you can use simple models, which are easy to train (logistic regression, decision trees etc.), easy to ...

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That is called a "recommender system" and is related to the associative rules/market basket stuff you said you tried. There is a package for it in R called recommenderlab. Here is a nice walk-through: http://bigdata-doctor.com/recommender-systems-101-practical-example-in-r/

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Association rule mining is nearly always done with the apriori or eclat algorithms. There are a few papers which have utilized these in the time series context. This paper details a thorough implementation, though this is done with relation to identifying gene patterns, rather than customer purchases. The idea is still the same though, only there is slightly ...

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Business Analyst A business analyst is one who understands the specific domain of the project (ex. retail, merchandising to be specific, supply chain etc.). His/her role is to understand the business problem, analyze the current state and capture requirements using various tools like surveys, interviews, group discussions and then provide recommendations ...

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Considering data stored in CSV format like below without headers you can use below R code to plot simple bar chart. It will plot occurrences of transactions grouped by unique transactions. product1,product2,product3 product1,product4 product1,product2 product4,product3,product1,product2 product1,product2,product3 product1,product4 product1,product4 R Code -...

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Let's start with your problem definition: "a good strategy make the relationships first and then count the occurrences". That is, roughly, the basic strategy that market basket analysis algorithms use. However, algorithms like Apriori or FPGrowth are specially designed to analyze such datasets (at scale) and infer the inherent association rules between ...

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