A Visual Introduction to Machine Learning

A Visual Introduction to Machine Learning

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A visual introduction to Machine Learning is a very important concept in learning machine learning. Machine Learning is a unique way to identify various patterns in data automatically. These patterns are identified by the aid of statistical approaches and techniques. Therefore, the statistical approaches are helpful to finalize the predication in an accurate manner.

The theme behind Visual Introduction to Machine Learning:

For the sake of understanding, we have declared a task to make the topic more clear. We have added Amazon product reviews data that was extracted from the Amazon website. The task is to identify the parameters that assist to identify the defective or non-defective products. On the basis of extracted reviews, each review is labeled is defective or critical on the rating given by the buyer. Some of the books from where machine learning is discussed in great detail. The list of top machine learning books in 2021 is very interesting.

Using NLP and sentiment analysis dictionaries, different features are computed.  NLP and sentiment analysis is a must for the visual introduction of machine learning. A brief feature engineering is performed to get realistic results. Out of all computed features, the most outperformed features are selected for the Machine Learning model. The outperformed features are computed using various techniques that include information gain, gain ratio, and correlation score. The normalization process is completed using the “min, max” approach for outperforming features. Therefore, the value of each record against each feature is between 0 – 1. You can learn more about Visual description of machine Learning.

How Visual Machine Learning Seems?

  1. Predictive Performance:

Famous Machine Learning models are used for testing and training. Based on the selected features 10-fold cross-validation is used to generate the results. These models are beneficial to find out which feature is most helpful for finding defective products. All the features are concluded from the product reviews stated by various users on the Amazon platform. Some people mix both the concepts of artificial learning and machine learning but these are different from our point of view.

    2. Features Importance:

We all know that features are derived from the extracted data of amazon reviews. So, finding the most suitable feature from the extracted data is a significant way of segregating defective products. This approach will be helpful for product manufactures to distinguish between defective and non-defective products. Applying such a technique will surely enhance the quality. Moreover, adopting this study will help organizations to generate an excellent reputation against the competitors. Organizations having a good reputation in the market produces high revenue. The below-given figure illustrates the contribution of each feature for finding the defective product.

Significance of Visualization in Machine Learning:

Visualization is one of the most important domains in the field of Machine Learning. Visualization aids to summarize the whole study or research. Visualization is a kind of blessing for the professionals and key stakeholders of the organizations. As it is time-saving for the viewers to grab useful insights rather than reading papers and digital libraries. One of the most important things regarding visualization in Machine Learning is that it can be adopted easily. Plenty of software and tools are available to support this class of Machine Learning.

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