Learn the introduction to predictive modelling or predictive Analytics, Basics of predictive modelling. 

you can here this word when you begin your career in artificial intelligence. If you are a beginner this topic will confuse you. I try to explain in a simple way.

Okay, let’s come to the reality, did you ever notice that when you entered into your online shopping account you will receive the list of products you’re interested in.  You can notice that website’s advertisements are relevant to their contents only. An online job portal sends your job recommendations to your account accurately! Predictive modeling in health care is a new trend now.

Likes dislikes are not common to all the people. It differs from person to person. But how they are sending correct information to their customers as per their interest? Here Predictive Analytics comes to the party! This all works are done by using the technology of Predictive Analytics.

Many companies are using predictive analytics to improve their customer satisfaction. They are doing very good in their target. They are improving profit margins by showing relevant products to their customer’s interest.

So what is Predictive modeling?

Gartner says that

Predictive analytics uses your past data and applies them to the future. For example, you’re purchasing a brand new smart phone from an ecommerce site. After your purchase, ecommerce site shows some product (Accessories) recommendations relating to your brand new phones. You will definitely purchase relevant accessories to your phone (99% of chances are there to buy new accessories) am I right?

Here you can see that predictive analytics uses your data and it will predict your future. In this example, it uses your smart phone purchase as your current data and it converts into the future. You can think like it’s so simple. But imagine you have to predict for millions of people with millions of product categories with multiple options. So here predictive analytics is most important.

First step in predictive model is to collect the data from various sources. In this case data is all about your product purchased in the past, pages you visited, name, age, address etc., sometimes data may be wrong. We have to clean the data as much as possible. If data cleaning is not done properly then prediction accuracy may be decrease.

There must be sync between various business hypotheses. For example particular age or gender group may have higher likelihood to purchase certain products. Age and gender needs to be attributed at customer level. In this case output is category called as classification problem. The algorithm does the learning. It finds category relationships. Now the model is ready.

We can use our learned model on new data for which we don’t know the output.
I will explain about algorithms in future posts. Comment your opinions!!!