Data mining is a concept that has been around for over a century but came into public existence in the late 1990s. The first instance took place in 1936 when Alan Turing introduced the idea of a universal machine capable of performing computations like our modern-day computers. Then, in the 1990s, the word “Data Mining” appeared in the database community. Since then, we have come a long way since businesses are now harnessing machine learning and data mining to improve their financials.

So, what exactly is Data Mining, why are we using it, and how does it work? Let’s read on!

**What is Data Mining?**

Data mining is a process of uncovering patterns and other valuable information from a large piece of data set. It is also termed as knowledge discovery in data (KDD). In simple words, Data mining is a process used by companies to turn the raw data into useful information.

With the rapid growth of Big Data and the evolution of data warehousing, the adoption of data mining techniques has seen growth over the last couple of years, assisting companies in churning their raw data into knowledgeable pieces of content. However, data mining depends on computer processing, data warehousing, and effective data collection.

**Why Do We Use Data Mining?**

Data mining has helped organizations in improving decision-making techniques through insightful data analysis. The volume of data is increasing rapidly, in fact doubling every second year. With this pace, the unstructured data will make up to 90% of the digital universe data. But more information does not mean more knowledge, and this is where Data mining comes in place.

With the use of Data mining, you can solve any business problems that involve data:

- Understand the relevancy of data, and make the best use of that information
- Accelerate the pace of making informed decisions
- Shift through the chaos and noise of data
- Acquiring new customers
- Understanding customers preference
- Improving up-selling and cross-selling
- Increasing customer loyalty
- Increasing revenue
- Identifying theft and credit risks

**Who Uses Data Mining?**

Data mining is being used everywhere and in every industry. So it’s not wrong to say that Data mining is at the heart of analytics across all businesses.

Here are some of the industries using Data mining:

- Insurance
- Banking
- Telecom
- Retail
- Media & Technology

**How Does Data Mining Work?**

The data mining process involves multiple steps:

- Asking the right question from businesses.
- Collecting data, visualization.
- Extracting valuable information from large data sets.

Finally, data mining techniques are used to generate descriptions and predictions about a large set of data.

Data mining consists of four major steps:

- Setting the business objectives
- Data preparation
- Model building
- Evaluating results

**Setting the Business Objectives:**

This is the initial step of Data mining and can be the hardest part. Not all organizations spend much time on this little information Business Stakeholders, and Data Scientists need to work together to define the business problems and objectives

**Data Preparation:**

Once you have the problem defined, it is easier for Data Scientists to identify which set of data will answer the questions of the business. Once the relevant data is collected, the data will be cleaned, removing any duplicates and missing values. Depending on the data set, the Data Scientists will retain the most important predictors to ensure optimal accuracy.

**Model Building:**

Data Scientists might investigate any data relationships such as sequential patterns, correlation, association rules depending upon the data analysis.

Deep learning algorithms may be applied to classify a data set depending upon available data sets. First, the data can be labeled and unlabelled (i.e., supervised and unsupervised learning). When the data is labeled, a classification model is used to categorize the data. When the data isn’t labeled, the individual points in the training sets are compared with each other to discover similarities and cluster them based on several characteristics.

**Evaluating Results:**

Once the data is segregated, the results need to be interpreted and evaluated. The data should be useful, valid, and understandable while finalizing the results. Further organizations can use this knowledge to achieve their targets, objectives and implement new strategies.

**Data Mining Techniques**

So far, we know that Data mining works by using various techniques and algorithms to turn large sets of data into useful information. Here are some of the common Data mining techniques:

- K- nearest neighbor (KNN)
- Neural Network
- Association Rule
- Decision Tree

**K- nearest neighbor (KNN)**

K-nearest neighbor (KNN) algorithm for machine learning is one of the simplest algorithms based on supervised learning. It can be used to solve both classification and regression problems. Moreover, it is easy to understand and implement; this algorithm assumes that similar data points can be found near each other, then calculates the distance between data points.

**Neural Network**

A Neural network is a set of algorithms modeled closely after human brains, designed to recognize patterns. Here, neural networks process the data by imitating the connectivity of human brains through a layer of nodes. Each node is made up of weights, a bias, inputs, and an output. If that output exceeds the given threshold, the node gets activated.

**Association Rule**

It is a rule-based method for finding relationships between variables in a given set of data. For example, this method is used for market basket analysis, which helps companies understand the relation between different products.

**Decision Tree**

The Decision Tree algorithm is a part of the supervised learning algorithms. This technique uses regressions or classification methods to classify or predict potential outcomes based on a set of decisions.

**Getting Started with Data Mining**

As we have understood, Data mining has been an essential part of every organization for decades. And as businesses tend to grow, the data will increase rapidly, and the demand for certified Data Scientists and Data Analysts to analyze and extract the data is going to rise. So, if you are also looking forward to step-up into the world of Data, then the Python Data Science Course offered by Simplilearn is the right course for you. The course provides a complete overview of Python, Data Analytics tools, and techniques, and it helps you gain the required skills and expertise that will help you stand out from the crowd.

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