Data Mining And Machine Learning

DATA MINING AND MACHINE LEARNING

Exploring the available datasets to find patterns and anomalies is known as data mining. The technique of learning from heterogeneous data in a way that may predict or forecast unknown / future values is known as machine learning. Together, these two ideas make it possible to depict historical data and anticipate future data.

Data Mining

The purpose of data mining is to identify patterns in data.

1)  Categorising data

We categorise data every day. For example, when we split groceries into oils, ingredients, spices, cleaning essentials etc. Sorting gets significantly more difficult when dealing with huge data. For instance, consumers are divided into “churners” and “not-churners” after integrating data on their demographics, past billings, payment histories and other details.

2)  Detecting interdependency

The different features are either independent or dependent in various extents among themselves. For example, consider a shopping mall where thousands of customers are shopping. Once data is collected, it would probably be revealed that people are buying certain set of commodities together. Sometimes associations are completely bizarre and beyond any anticipation.

3)  Identifying outliers and anomalies

Finding odd or unexpected facts can be really helpful. A fraud detection system used by a credit card firm would serve as an illustration. if suddenly high-value items are purchased from a person’s account outside his or her regular buying range or country of residence, the system will isolate the incident and sound a virtual alarm to indicate something unusual is happening and alert the customer by way of phone calls or sms on immediate freezing the operation as a precautionary measure.

4)  Grouping data

Cluster analysis groups items together based on shared properties. The groups are heterogeneous, but members of a group are homogeneous. For example, an e-commerce company segment customers depending on their purchasing behaviours for promotional strategies.

5)  Measuring feature importance

Various features are sorted according to their impact on the target variable. The effects may be negative or positive in case of value prediction. Whereas the magnitude is important in case of label prediction.

Finding patterns in data is the ultimate goal of all data mining techniques, regardless of the form.

The process of generating tools that may be used to analyse new data using the findings of data mining is known as machine learning.

Machine Learning

The main purpose of machine learning is to develop algorithms that can “learn” from data. A machine learning algorithm’s performance will be impacted by each piece of data it processes.

For instance, if the algorithm is performed on the information of one cancer patient, the machine will learn what traits a cancer patient has. The algorithm will have been exposed to several characteristics that cancer patients typically have if hundreds of cancer patient details are run through it. The objective of machine learning is to create an algorithm that can work independently and be used with fresh data. In this case, it would be an algorithm that could correctly identify if a patient had cancer or not. The same logic applies to value prediction as well.

Accurately characterised data is split into “training” and “test” sets in supervised learning. Typically, training sets make up around 80% of the data, and test sets make up the remaining 20%. In our example, we have patients that are classified as “cancer” or “not cancer” by human experts. The training set, which consists of some of the patients who have previously been identified, is used to construct the machine learning algorithm. After the entire training set has processed and the optimised algorithm has been created, the algorithm is tested against the test set to ascertain its accuracy. The number of times the algorithm characterises the test set data correctly serves as a measure of accuracy. Generally speaking, a classification accuracy of over 90% is regarded as satisfactory.

The classes in unsupervised learning are unknown. Based on input comparisons and the clustering of the data into several groups, the machine learning system would deduce patterns and properties.

Data Scientists are the decision makers

 Data scientists are not only interested in categorizing current data, even if that is a substantial aspect of their job. They are equally interested in precisely characterizing unknown data as well as projecting future data.

Data scientists must use both data mining and machine learning in order to conduct their tasks. To characterise the data, they must use data mining, and to generate predictions, they must incorporate machine learning techniques.

these two procedures involve a significant amount of programming. Thus, data scientists should be proficient in programming languages like R and Python in addition to having a solid understanding of statistics.