Data Mining: Bookstore Recommendation Project

Source: Slideplayer
I wrote a brief introduction about this project here, be sure to check it out. Today, I will be sharing the steps we took in solving this team project, the algorithm we used and finally the outcome of the project. I love to keep my articles short, simple and straight to the point so if there are few gaps or questions. You can drop them in the comment section or contact me. I will try to provide a solution if I can. Otherwise let's get right into it.

The task was to recommend set of books to promote together for a specific customer group. Also, to give recommendations to the CEO so that he/she can make the right decision with the aim to improve decision making and stay profitable. The good thing about this project is that the sample dataset was given to us so all we needed to do was work with the data and provide the necessary recommendations. 

WEKA was the recommended software we were asked to use to solve this problem. I will be breaking down the steps we used to solve this problem in WEKA  accompanied with screenshots of our outputs.

1) We launched WEKA application 
2) We imported the given dataset. Note: The dataset we were given was in ARFF format
3) The first thing to note in this kind of problem is that, it is a recommendation problem which obviously streamlined our decision on the algorithm to use.
4) We decided to use Association mining rule. We applied the Apriori Algorithm. Why? Simply because this rule/algorithm are known to be used for finding relationships between frequent itemsets, correlations and associations. 
5) After deciding on the rule that was applicable to our project, we needed to be sure there were no missing data in our dataset and also know the type of data that we were given which is called pre-processing stage.

A. Pre-processing Stage
1) Our data had 599 instances with 12 attributes. 
2) The data was initially a numeric (REAL) data type. However, in order to be able to apply Apriori algorithm, the data type was converted to nominal using 'NumericToNominal' unsupervised filter in WEKA.

Raw Data Visualization in WEKA
Data after Conversion in WEKA
3) The 'ID Transaction' attribute was removed simply because it does not add any value to the data mining approach. 
4) At the end of the pre-processing stage, the dataset consisted of  11 attributes with either 0s or 1s. Where 0 indicated that the item was not bought and 1 indicated that an item was bought.

B. Analysis Stage
1) In this stage, the Apriori algorithm was applied on the dataset. However, we discovered that WEKA built the model based on only unpurchased items ๐Ÿ˜• which was not our intention. Our aim is to give recommendations based on purchased items or 1s. How then we do we move forward from here๐Ÿ˜–
2) WEKA of course has a feature to solve this which was what we applied and voila we got some juicy outputs to work with๐Ÿ˜‹. What then is this feature?
3) Well, in the Apriori algorithm settings there is a feature called "treatZeroAsMissing" which by default is set to "False" so we set this feature to "True" and yes ๐Ÿ’ช that was it.


4) We reran the algorithm but no best rules were found at the default 'minMetric' of 0.9, which indicated that no best rules were found at a 90% confidence.


5) However, we reduced the 'minMetric' to 0.8, 0.7 and even 0.6.and we were able to get some really good combinations which we used for our recommendation/ solution to the problem.

Output at 0.8 confidence level (3 best rules found)
Output at 0.7 confidence level (10 best rules found)

Output at 0.6 confidence level (10 best rules found)

C. Analysis of Results
1) Each of the rules that we found contained ‘A=>C’ which means that if a set of antecedents (A) are purchased, then there is a probability that Consequent (C) will also be purchased. For example, for the output at 0.8 confidence level, 78 transactions contained purchase of a Youthbook and a Cookbook (A). Out of those transactions, 67 instances contained a Childbook (C). The latter part is referred to as ‘Support’ for the Consequent. The Confidence score shows how confident the association rule is, given the dataset. It is calculated as: C/A: 67 / 78 = 0.86.

2) Another interesting parameter is the ‘Lift’ which is defined as how likely it is to have all antecedents and consequent in one single transaction in comparison to the entire transaction dataset. Basically the larger the lift ratio, the more significant the association of the itemset. In order to calculate Lift, first we needed to figure out the ‘Expected Confidence’ which is the probability of the purchase of consequent regardless of the antecedents. As an example, looking at the first rule (0.8 confidence), the total number of transactions containing Childbook (250) divided by the total number of transactions (599): 250/ 599 = 0.417362270 (approximately 0.42)

3) After calculating the Expected Confidence, the Lift can then be calculated. This is the ratio of the confidence and the expected confidence: 0.86 / 0.417362270 = 2.06

4)With the confidence score of 86% and the lift score of 2.06, this rule can be considered as a strong association. That is just the analysis of one rule. I wouldn't be going through all the analysis of all the rules in this article.

5) After building the model, these three best rules were found by Weka:
  • If a Youthbook and a Cookbook are purchased in one transaction, there is 86% confidence that a Childbook will be purchased
  • If a Cookbook and a Refbook are purchased in one transaction, there is 83% confidence that Childbook will be purchased
  • If a Cookbook and a Geogbook are purchased in one transaction, there is 82% confidence that Childbook will be purchase

D. Our Recommendation
Based on our analysis and the results from WEKA, the decision/business model that we would recommend is that: since Childbook has a relatively high correlation with Youthbooks, Cookbooks, Refbooks and GeogBooks, then they can be promoted together.


In conclusion, when trying to solve a data mining problem. There are several ways to go about it. There are also several ways to interpret your results after your analysis. However, I would recommend that you understand the basis behind WEKA if you are not familiar with it. This will give you a better understanding of whatever project you are given and diferent ways to go about it.

I hope this article helps someone out there trying to get a hang of a similar project. Below are the list of some useful links that were very useful for us while we were solving this problem.
1) Building a market basket model
2) Market Basket Analysis with Association Rule Learning
3) Lift in Association Rule 


Hey before you go, if you like this article, consider buying me a coffee by clicking here. Until next time...๐Ÿ’‹



24 comments

  1. Really nice and interesting post. I was looking for this kind of information and enjoyed reading this one. Keep posting. Thanks for sharing.
    ExcelR Data Analytics Course
    Data Science Interview Questions
    ExcelR Data Science Course

    ReplyDelete
  2. I just got to this amazing site not long ago. I was actually captured with the piece of resources you have got here. Big thumbs up for making such wonderful blog page!

    business analytics course

    data analytics courses

    data science interview questions

    data science course in mumbai


    For more info :

    ExcelR - Data Science, Data Analytics, Business Analytics Course Training in Mumbai

    304, 3rd Floor, Pratibha Building. Three Petrol pump, Opposite Manas Tower, LBS Rd, Pakhdi, Thane West, Thane, Maharashtra 400602
    18002122120

    ReplyDelete
  3. It's really nice and meanful. it's really cool blog. Linking is very useful thing.you have really helped lots of people who visit blog and provide them usefull information.
    Data Science Institute in Bangalore

    ReplyDelete
  4. I have express a few of the articles on your website now, and I really like your style of blogging. I added it to my favorite’s blog site list and will be checking back soon…
    Data Scientist Courses This is a great inspiring article.I am pretty much pleased with your good work.You put really very helpful information...

    ReplyDelete
  5. I am really enjoying reading your well written articles. It looks like you spend a lot of effort and time on your blog. I have bookmarked it and I am looking forward to reading new articles. Keep up the good work.
    360DigiTMG

    ReplyDelete
  6. I wanted to leave a little comment to support you and wish you a good continuation. Wishing you the best of luck for all your blogging efforts.
    Data Analytics course PuneI am a new user of this site so here i saw multiple articles and posts posted by this site,I curious more interest in some of them hope you will give more information on this topics in your next articles.

    ReplyDelete
  7. It’s very informative and you are obviously very knowledgeable in this area. You have opened my eyes to varying views on this topic with interesting and solid content.
    Data Analyst Course

    ReplyDelete

  8. Thank you for helping people get the information they need. Great stuff as usual. Keep up the great work!!!
    360digitmg

    ReplyDelete
  9. Really exciting to see this blog. I would like to appreciate you for the efforts you had performed in writing this impressive article.
    advantages of ai
    applications of net
    what is hadoop
    list of devops tools
    selenium interview questions and answers for experienced

    ReplyDelete
  10. This detailed explanation of a data mining project using WEKA is exceptionally insightful. The clear step-by-step breakdown, coupled with screenshots, enhances understanding. Kudos on a well-documented and informative post!
    Is iim skills fake?

    ReplyDelete
  11. Great read! Learned a lot about how they suggest books online. It's like a book genie behind the screen!
    Data Analytics Courses In Gujarat

    ReplyDelete
  12. This post opened a treasure chest of data secrets, guiding me through the labyrinth of book recommendations. I feel like a digital detective solving reading mysteries!
    Data Analytics Courses In Gujarat

    ReplyDelete
  13. good blog
    Data Analytics Courses In Vadodara

    ReplyDelete
  14. Data mining is the process of discovering patterns and valuable information from large datasets, a crucial component of data analytics that drives insights and informed decision-making. For those interested in pursuing a career in data analytics, London offers top-notch Data Analytics courses that provide the skills and knowledge needed to excel in this data-driven age. Please also Digital Marketing Courses in London .

    ReplyDelete
  15. Your examination of the subject is comprehensive and presented in a clear and understandable manner. Anticipating further contributions from you.
    data Analytics courses in leeds

    ReplyDelete
  16. The article is truly exceptional, offering an abundance of information.
    Data Analytics Courses in Leeds

    ReplyDelete
  17. the post was great and the information was really helpful
    Data Analytics With Python

    ReplyDelete
  18. Thanks for sharing valuable and detailed explanation on Data Mining Project.
    data analyst courses in limerick

    ReplyDelete
  19. Thank you for sharing in depth knowledge and explanation on Data Mining Project.
    Adwords marketing

    ReplyDelete
  20. Fantastic breakdown of the Data Mining project! Your clear steps, insightful analysis, and detailed recommendations make it an excellent guide for others. Thanks for sharing your expertise.

    How Digital marketing is changing business

    ReplyDelete
  21. I love how you break down complex topics into easy-to-understand points. Your writing style is engaging and makes learning enjoyable

    investment banking courses in India

    ReplyDelete