Case Study of Amazon Review System
Data Science

Case Study of Amazon Review System

So, First of all we need to understand the ‘ Traditional programming ’ and ‘Machine Learning Programming’.

What is Machine Learning Programming and Traditional Programming ?

Lets talk about first :-

1.Traditional Programming:-

  • In simple way I just want to say , In Traditional Programming all work is doing in manual form.
  • In traditional work, Men work will be more, It will be costly as well as lengthy.
  • Due to increasing of data day-by-day ,this programming work will not work.
  • That’s why, Machine Learning programming has started to be used.

2. Machine Learning Programming:-

  • Machine learning programming is nothing but it is a prediction machine, In which you feed input data to train the algorithm and that algorithm make a model.
  • In that model, We feed our output to know the accuracy of our model.

“ In short, Machine Learning is a combination of human brain, ideas and It’s own strategy to predict the data.”

How to Predict the data by using Machine learning algorithm.?

  • Lets take a example :-
  • “Amazon Review System”
  • As we already know Traditional Programming and Machine learning programming.
  • First of all , We have to know the Problem Statement of this case study.
  • We want the best product in-front of the page in Amazon platform.
  • There are so many products in Amazon , But we have to take a example of ‘T-shirts’ only. In daily life ,we can see that there are lots of people buy those products and gives their reviews.

Why is Amazon review analysis important.?

  • Reviews tell you, what products are trending and what’s in demand.
  • It is totally based on the customer-decision making process before purchase.
  • That’s why it is important.

Fig:- (2) Amazon Review Analysis


  • Let’s take a sample data From Amazon , We just split the data into training data and testing data.

Fig:-(3) Amazon Sample Data

Why we need splitting of data.?

  • Because, You can’t evaluate the predictive performance of a model by using same data that you’ve use for training.
  • To know the accuracy of model we need some fresh data.
  • That’s why we splitting data.
  • So, we splitting the data into 80% of ‘Training Data’ and 20% of ‘Testing Data’.

Fig:-(4) Splitting of Data Set

  • First of all talk about Training Data, Training data is nothing but the sample of that data we can use to train the machine learning algorithm to make a model.
  • Lets take a example of cooking, In cooking — The ingredients and the procedure of cooking is our training data.
  • After the working on training data or we can say working on our cooking , Before serving the dish to guest we will taste that dish as a ‘Actual Testing Data’ and then serve the guest. At the end compare your opinion about your dish with guest opinions and then you know the accuracy of your dish.
  • Like that, We take X-Train Data as a Input and Y-Train Data as a Output. In that Input is called as ‘Features’, ’Content’ or ’Review’ and Output is called as ‘Class’ or ‘Label’.
  • So, After that we feed the Training input data as a ‘Review’ and output data as a ‘Decision’ according to fig:-(1). To train the machine learning algorithm to make a Model or we can say Function .

Fig:-(5) Working on Training Data

  • After that process, We feed the testing input data (X-test)in model, Our actual test output data(Y-test) not be available to my function.

Fig:-(6) Testing Model (When Function Is Ready)

  • After that whole process we compared the Actual Data(Y-actual data) with Predicted data(Y-Predicted data) To know the accuracy of our Model

Fig:-(7) Comparison Chart

  • In that we found only two matching output , it means the accuracy of our model is 40%.
  • In this way we can see the accuracy of our model.

Why we doing this whole process.?

  • To make sure that our model is working perfectly in the real data of real world.
  • Machine Learning never give 100% result, In that we only want a prediction
  • So , In this way we know the accuracy and find out the prediction of our sample data to make sure that our model is working exactly in real world data.

Thank You!…..

  • Sneha Gaupale
  • Dec, 27 2022

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