Predictive Analytics is , “When you use your historical data with statistical techniques and Machine Learning to make predictions “ . Predictive Analytics looks like a technological magic and If you want to learn how to do this Magic . Just give a quick read to the this Article – “What is Predictive Analytics : A Complete Guide for beginners” . Believe me Predictive Analytics is not that harder as you think .There are so many use cases for Predictive Analytics .
Specially you must be aware of Predictive Analytics use case in eCommerce Industry .When you browse any eCommerce website like Amazon or eBay , It makes a trace of your product choices in your cache of browser . So the next time when you login that website , It uses the historical data to predict products . It does not only predict the same product but the related product as well . I mean it use own predictive model which is already trained by consume choices . For example if you are searching for Tooth brush , it may show you some tooth paste . This recommendation feature gives too much customer satisfaction .
What is Predictive Analytics (Introduction)-
Lets talk little generic ! Do you make predictions ? Obviously you do . I mean Everybody do it . Prediction could be small and big , simpler an complex but they need some basic steps for fulfillment . In this Introduction section I am starting our Prective Analytics journey with the most simplest one . Just you need to understand the steps . Now Rewind you day and think about the last Prediction you made on today .
If I rewind my day . My last Prediction for today is to guess the number of visitors in my blog for today . Lets understand Predictive analytics at ground level with the simple level of prediction that I made today . I predict on the basis of historical data of visitor in last two months . .So I simply check numbers of visitors in last five weeks on Monday ( Assuming today is Monday ) . Once I have the data . I simply take the average of numbers of visitors in last five Mondays . What ever number I get as an average I simply say it is my Predictive value .
I guess you must be thinking that the above example is very simple . As you know about Predictive Analytics , It involves much of statistical and mathematics . How is the above example appropriate for Predictive Analytics ? Obviously It is appropriate , Only you need to understand the steps . Later you can replace these step with complex one .
Predictive analytics Example ( break down into simple steps ) –
- First you define the problem . In our case we need to decide the predictive value for visitor . There can be different target values or events like you can predict temperature or collection of Movies etc .
- Second step is to collect the data , In our case we manually collect the data but you can write script for this . There could be any source for data like PDF , Web , CSV , Text File Etc.
- After collecting the data , You need to clean o require . In our example we do not need this step . The data we used it was already clean . Cleaning is mostly require in raw data like text .
- Use Predictive technique , In our case we choose the simplest one which is Simple Average . You cab choose other like Regression etc .
Key Terms Related to Predictive Analytics –
See Guys , I always try to use simple example to make you understand . I hope now , you are clear about what is predictive analytics ? Lets understand the other aspect of predictive analytics –
1. Training Data –
predictive Model needs Training data as an Ingredient . Lets start from the basic definition of Machine Learning , Any model which can learn from the past experiences and Improve itself with more data is a machine learning model . So here , The past experience is nothing but training data .
2. Feature Vector-
From the raw data , we choose those factors which should go with model for any decision making . In the other word you can say them features .
3. Machine Learning Algorithms-
There are so many machine learning algorithm in which we have to choose which provide best results .Here is the list for Top Machine Learning Algorithms. When your data is preprocessed and clean .
4. Cross Validation & Performance Evaluation Matrix-
In this step , we divide our dataset in two parts . Usually 70% data is kept for training and rest 30 % for testing . This percentage distribution could be different. Ideally 80:20 and 70:30 is considered best . Once the model is trained , we test our model performance on different evaluation matrixes .
- Confusion Matrices
- ROC Curve
- AUC (Area under the curve)
5. Predictive Analytics Project Life cycle –
Predictive Analytics Project is typically data science project . Hence it follows the same life cycle steps . Here are the steps –
- Business Understanding.
- Extract the business problems ( Hidden or known )
- (Raw)Data Acquisition
- Data preparation
- (Training) Data Modeling
- Evaluation and Interpretation
This article ,” What is Predictive Analytics : An Overview for beginners ” gives a walk through on predictive analytics . We have not explored the topic in details .You may take it as Introductory article . If you like this article and do not want to miss the future article on this area . Please Subscribe Data Science Learner .
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