Business Intelligence Project: Using Predictive Analytics to Improve A Business
Data mining is a powerful method of extracting knowledge/information from large amount of data. It has received great attention in recent years because of growing amount of data and the persistent need of turning the huge data into information. It is widely adapted in many fields like bioinformatics, business analytics, marketing, security and many more. For large scale companies Predictive Analysis, Frequent Pattern Mining algorithms and various data mining techniques have proved very effective techniques for improving their sales. One such algorithm Apriori Algorithm can be used for the coffee shop for improving the sale. Apriori algorithm can be used efficiently to train a program with historical data of frequently bought together items and it can predict or suggest other related items to Customers. Such a system when integrated with any business can make it easier for Customers to find the items they may be looking for.
Background and Aim:
Predictive Analytics has proven extremely effective in recent times for huge companies with large data, this is true for companies such as “Google” and “Amazon” whereby in the case of amazon for the year 2020, 35% of their sales came from the suggested items in the “people who have bought this item also got..”, this would be roughly 56$ Billion from a total of 163$ Billion in Amazon’s online stores Revenue for 2020.
This increase proved to be a lucrative technology and we believe that today, even small businesses should start looking at such methods. The three things that we will be focusing on in this project will be; Inventory, customer analysis and finally sales.
We are aiming to be able to raise awareness on how current technological advances can help small businesses thrive in such uncertain times, given the severe hit taken by small businesses (50% of restaurants in New York are out of business due to COVID-19 pandemic).
We will be teaming with a local small coffee shop owner to get real data of inventory, sales and customer data. We will create a python program that will help create a model that can be applied to improve sales, customer journey and inventory tracking. We will then use our finding and apply it in the coffee shop and determine the rate of success of our model on the business.
Big data, Predictive analysis, Apriori algorithm, Frequent pattern, Data mining
Data mining is a powerful method of extracting knowledge/information from large amount of data. It has great attention in recent years because of growing amount of data and the persistent need of turning the huge data into information. It is widely adapted in many fields like bioinformatics, business analytics, marketing, security and many more.
An important task of data mining is discovering frequent patterns that play an important role in clustering, associations mining, correlations, etc. Frequent patterns are patterns that appear in a data set frequently, such as itemsets, subsequences, or substructures. An itemset A (or subsequence, or substructure) is said to be frequent if it satisfies the predetermined minimum support count, where
Predictive Analytics is the process of transforming data into future insights. The backbone of predictive analytics are models, this is done by looking at both historical and current data to provide actionable insights.
Many current studies have been conducted in the use of predictive analytics and they have been translated in applications (usually in beta stages) that are slowly emerging in the market but have still not been accepted by the general public. A few examples of these great softwares are “RapidMiner” and “IBM SPSS Modeler”, whereby these apps unify data science lifecycles from data preprocessing to machine learning and predictive analytics.
Time series and sequential data mining is one of the most important problems from 10 challenges identified in paper . Clustering, classification, and trend prediction of these data is an important open research topic. Another problem identified in  is mining complex data in the form of graphs.
Mining frequent tree pattern is an important open research area. Previous research studies highly suggest the pattern growth method for efficient pattern mining. Authors of  have developed a pattern growth method for mining frequent tree patterns. Two algorithms, Chopper and XSpanner, have been devised, from which XSpanner algorithm is faster than Chopper. These two algorithms perform better than TreeMinerV of M.J.Zaki, Effciently mining frequent trees in a forest of KDD02 . In that, Chopper consists of two separate phases (i) mining sequential patterns and (ii) the extraction of frequent tree patterns. It generates and tests all possible tree patterns of the database. XSpanner algorithm combines these two phases of Chopper algorithm.
Table 1: Comaprison of various Association Rule Mining Algorithms
|AIS||Focuses on improving the quality of database and process the dicision support queries.||Candidate set generated on the fly.||Not frequently used, but when used is used for small problems.|
|Easy to use.||Size of candidate set large.|
|Better than STEM.
|Requires multiple scans on whole database.|
|Needs more memory.|
|STEM||Seperates generation from counting.||Very large execution time.||Not frequently used.|
|Size of candidate set is large.|
|Apriori||Fast, more efficient than AIS||Takes a lot of memory.
|Best for closed itemsets.|
|Less candidate sets. Generates candidate sets on the from only those items that were found large.|
|Apriori TID||Doesn’t use whole database to count candidate sets.||–||Used for smaller problems.|
|Better than STEM and it is fast.|
|Apriori Hybrid||Better than Aprior and Apriori TID||Used where Apriori and Ariori TID can be used.|
|FP-Growth||Only 2-passes of dataset||Using tree structure creates complexity||Used in cases of large problems as it doesn’t require generation of candidate sets.|
|Compresses dataset||Not suitable for incremental mining.|
|No candidate set generation required.|
|Rapid Association rule mining||Avoids candidate generation process||Requires more memory|
|Faster than FP-Tree algorithm|
In the best case scenario we would like to empower small businesses around the world by giving them more comfort and security when having to make business decisions, this will be done by helping them understand their business in ways that were previously not possible nor feasible.
We will be using methods such as Apriori Algorithm to help us understand customers’ buying patterns to be able to use this to our advantage in a way that we can bundle and group these items with a discount and theoretically this would reflect in higher sales for the business.
We would like to contribute to the global knowledge of small business operations the few techniques that we will be developing in hopes to empower these businesses to survive and prosper in a rapidly changing marketplace.
Apriori algorithm is given by R. Agrawal and R. Srikant in 1994 for finding frequent itemsets in a dataset for boolean association rule. Name of the algorithm is Apriori since it uses prior knowledge of frequent itemset properties. We will use an iterative method identify where k-frequent itemsets are used to find k+1 itemsets.
To improve the efficiency of iterative generation of frequent itemsets, an important property is used called Apriori property which helps by reducing the search space.
All non-empty subset of frequent itemset must be frequent. Apriori algorithm assumes that “All subsets of a frequent itemset must be frequent (Apriori property). If an itemset is infrequent, all its supersets will be infrequent.”
Step-1: Determine the support of itemsets from a database (CSV file) of transactions, and select the minimum support and confidence.
Step-2: Include all supports in the transaction with support above the configured minimum support value.
Step-3: Find all the rules of these subsets that have higher confidence value than the threshold or minimum confidence.
Step-4: Sort the rules as the decreasing order of lift.
Consider the following dataset and we will find frequent itemsets and generate association rules for them.
|Items bought together|
|I1, I2, I5|
|I1, I2, I4|
|I1, I2, I3, I5|
|I1, I2, I3|
minimum support count is 2
(I) Create a table of itemsets and support counts of each item present in dataset – Call it candidate set C1
(II) Compare item support count for items in candidate set C1 and remove entries which has support count less than the minimum support count (i.e. less than 2). The resultant set is Itemset L1.
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