I have written this 2 part blog to articulate the technical aspects of machine learning in layman’s terms. For part 2 of this series click here
It is now the age of data. For several years we humans have been collecting and collating data for various purposes. When I was a kid, I loved borrowing books from the neighborhood local lending library in Chennai, India. It’s a tiny place stacked with aisles of books, I got access to my first Harry Potter book there. Every time I borrow a book, the librarian used to pull out a heavy bundle of papers, go through them to locate my library number and once he pulls out my sheet he would jot down the title of my book and the date. I used to wonder back then (keep in mind this was the 90s) how the poor librarian would go through all those papers and find how many customers need to be paying late fees. It must have been a nightmare having to go through all those papers one by one and see if the due date of the a book has passed.
Gone are the 90s, we now have computers in the 00s. I visit the same library and see the stacks of papers replaced by a bulky white computer. I borrow a book and the librarian now enters the book ID and customer ID on the computer and then it is magic! All late payments are tracked and everything is perfect now. The librarian doesn’t have to go through stacks and stacks of data in the weekends. The librarian is happy.
Say hello to the 2010s. What now? There are other libraries popping up closer to his. Imagine these are not council libraries lending books for free and these are all privately owned libraries making good profit by lending books to people. Having new libraries popping up means there is competition for your business. With the advent of Kindle very few people prefer to own and read physical books and this means that few will visit his library. What is the use of the computer now when things are getting more digital. What does the librarian have that will draw in the right customers to his library?
The answer lies in data. Imagine, he has access to all his previous and current customer’s data. He has collected information on his customers’ profile (like age, gender, address, education, qualification) etc. He also has information on each book’s profile. Has a particular book been quite popular than others? Does age impact the genre of the book a customer chooses?
He can answer several other questions like these using his data. What makes a customer visit his library regularly? Is it the customer’s location, age or gender? Is it the librarian’s books that influence him? If only he could build a machine that would take into account every single instance that would impact the customer and somehow learn what it is that makes him/her stay? It would be useful if the machine could just predict if a new customer will stay or leave.
The answer lies in machine learning. What is machine learning anyway? It is the process of analysing vast amounts of information (or data), look into several variables (instances like customer information, etc) and predict if a future customer will stay or leave.
The librarian can just use a type of marketing medium to gain more publicity. Imagine a new situation, replace the library to a bigger organisation. This time there are several more problems to address. We might have several more customers to target. They are scattered everywhere around the country. We have several more variables. We are faced in a situation where we need something more than just advertising now. An important point to note is that machine learning is not a replacement to all your current operations. It is only a complement / an add-on bonus.
For part 2 of this series click here