Data Preparation for Data Mining Simplified 101

Kavya Tolety • Last Modified: December 29th, 2022

data preparation for data mining: FI

In Data Preparation, you can construct a dataset from one or more data sources for exploration, visualizing, and modeling.

Data Mining has several benefits; it helps discover various insights and detect any possible data quality issues or vulnerabilities in your dataset.

Data Preparation is frequently a time-consuming and error-prone procedure. The saying “garbage in, garbage out” is true in Data Science initiatives; when multiple incorrect, out-of-range, and missing results are collected, the output can also be messy. Analyzing data that hasn’t been thoroughly checked for these issues might lead to inaccurate conclusions. Therefore, the success of Data Science projects heavily depends on the quality of data preparation during data mining.

This article talks about Data Preparation for Data Mining in detail. In addition to that, it also explains Data Preparation and Data Mining.

Table Of Contents


Understand the Data Quality issues

What is Data Preparation?

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Data Preparation is a process where the appropriate data is collected, cleaned, and organized according to the business requirements; it usually begins after the data understanding phase of Data Mining. It’s often the case that the data isn’t clean and unfit for examination. Since data comes from different sources, sometimes data, in particular, might be inadequate, incorrect, or inconsistent.

In the real world, almost every dataset has flaws. This is why Data Preparation for Data Mining process is crucial. In a broader sense, Data Preparation also includes determining the best data-gathering technique. And these techniques take up the majority of the Data Mining time.

For instance, data may be spread throughout several tables, and values may be stored at a granularity that is inconvenient for the business. Individual sales transactions, for example, are contained in point-of-sale data, but the business purpose is to do product profitability analysis. To make goods the focus of the study, data must be restructured.

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Key Benefits of Data Preparation

Data Scientists frequently complain that instead of evaluating data, they spend most of their time obtaining, purifying, and organizing it. One of the most significant advantages of exemplary data preparation for data mining process is that it allows them and other end-users to concentrate more on data analysis and model building, thereby providing more value.

For instance, if Data Preparation is carried out appropriately, other following steps, including prediction with machine learning models, can be streamlined for consistency.

Data Preparation benefits an organization in achieving the following goals:

  • Data used in analytics applications generate reliable results. 
  • Discover and solve data issues that would otherwise go undetected. 
  • Enable better-informed decision-making by business leaders and operational employees. 
  • Lower data management and analytics expenses. 
  • Minimize duplication of effort in preparing data for use in different applications. 
  • Receive a more significant ROI from BI and analytics investments.

Adequate Data Preparation is essential in big data environments. A mix of structured, semistructured, and unstructured data is stored, frequently in raw form, until it’s needed for specific analytical purposes. Data Analytics, Data Mining, Machine Learning (ML), and other sophisticated analytics generally require vast quality data to generate desired outcomes.

What is Data Mining?

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Data is unquestionably valuable. However, analyzing it is not easy. With the exponential expansion of data, a technique to extract relevant information that leads to usable insights is required. This is where Data Mining comes into place. Data Mining acts as the backbone for Business Intelligence and Data Analytics.

Data Mining can be defined as the process of analyzing large volumes of data to derive useful insights from it that can help businesses solve problems, seize new opportunities, and mitigate risks. It can be leveraged to answer business questions that were traditionally considered to be too time-consuming to resolve manually

It is the process of finding patterns in large volumes of data to translate them into valuable information. Data Mining Tools help you get comprehensive Business Intelligence, plan company decisions, and substantially reduce expenses. 

Due to the expanding significance of Data Mining in a wide range of industries, new tools, and software improvements are constantly being introduced to the market. As a result, selecting the appropriate Data Mining Tool becomes a challenging and time-consuming procedure. So, before making any hasty judgments, it’s critical to think about the company or research needs.

By using a range of statistical techniques to analyze data in different ways, businesses can seamlessly identify patterns, relationships, and trends. For example, the world’s most popular streaming platform, Netflix, has approximately 93 million active users per month. The data pipeline of Netflix captures more than 500 billion user events per day.

Effective data mining aids in various aspects of business strategy planning and operations management. Marketing, advertising, sales, and customer service are examples of customer-facing functions, as well as manufacturing, supply chain management, finance, and human resources. Fraud Detection, Risk Management, Cybersecurity Planning, and many other critical business use cases are all aided by Data Mining. Healthcare, government, scientific research, mathematics, sports, and other fields all benefit from it.

The storage of this data requires approximately a storage space of 1.3 Petabytes (1 Petabyte = 1,000,000 Gigabytes) per day. The advantages of having such high volumes of data are as follows:

  • It allows Netflix to plan its future releases by analyzing the kind of content viewers like.
  • It allows Netflix to understand how they can make the user experience on their website and Android/iOS applications better by analyzing user behavior on these services.

To learn more about Data Mining, visit here.

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Every two years, the amount of data produced doubles. 90% of the digital universe is made up of unstructured data. However, having more information does not always imply having more knowledge.

You can use Data Mining to:

  • Sift through your data to find all of the random and repetitive noise.
  • Understand what’s important, and then use that knowledge to predict what will happen.
  • Increase the speed with which you can make well-informed decisions.
  • Telecom, Media & Technology: In a crowded market with intense competition, the answers are frequently found in your customer data. Analytic models can help telecommunications, media, and technology companies make sense of mountains of customer data, allowing them to predict customer behavior and deliver highly targeted and relevant campaigns.
  • Education: Educators can predict student performance before they enter the classroom using unified, data-driven views of their progress, and develop intervention strategies to keep them on track. Data Mining allows educators to gain access to student data, predict achievement levels, and identify students or groups of students who require additional support.
  • Insurance: Insurance companies can solve complex problems like Fraud, Compliance, Risk Management, and Customer Attrition using analytic expertise. Companies have used Data Mining techniques to better price products across business lines and discover new ways to offer competitive products to their existing customer base.
  • Manufacturing: It is critical to align supply plans with demand forecasts, as well as to detect problems early, ensure quality, and invest in brand equity. Manufacturers can predict asset wear and maintenance, allowing them to maximize uptime and keep the production line on schedule.
  • Banking: Banks can use automated algorithms to better understand their customers as well as the billions of transactions that make up the financial system. Financial services companies can use Data Mining to gain a better understanding of market risks, detect fraud more quickly, manage regulations, and compliance obligations, and maximize the return on their marketing investments.
  • Retail: Customer insight hidden in large customer databases can help you improve relationships, optimize marketing campaigns, and forecast sales. Retailers can offer more targeted campaigns and find the offer that has the greatest impact on customers by using more accurate data models.

Key Benefits of Data Mining

  • Pattern Discovery: Automatic pattern discovery is a strategic advantage, and this technique helps in modeling and predicting future behavior.
  • Trend Analysis: Understanding trends keeps you up-to-date with current developments in the industry, and helps reduce costs and timeliness to market.
  • Fraud Detection: Data Mining techniques help in fraud detection by discovering anomalies in datasets. This is used to detect which insurance claims, credit card purchases, etc., are likely to be fraudulent.
  • Forecasting in Financial Markets: Data Mining techniques are extensively used to model financial markets and predict likely outcomes.

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  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Steps in Data Mining 

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Data Mining is the act of finding patterns and other important information from massive data sets. Given the advancement of Data Warehousing Technologies and the rise of Big Data, Data Mining techniques have exploded in recent decades, supporting businesses in turning raw data into valuable knowledge.

There are four steps of Data Mining:

  • Establish Business Objectives: This might be the most challenging element of the Data Mining process, and many companies overlook this crucial stage. To identify the business challenges, Data Scientists and business stakeholders must work together, which will guide the data queries and parameters for a specific project. Analysts may need to conduct more studies to comprehend the business environment fully.
  • Data Preparation: Once the scope of the problem is determined, Data Scientists may more easily choose which collection of data will assist them in answering the essential business questions. After collecting it, they will clean the data, eliminating any noise such as duplicates, missing numbers, and outliers. An extra step may be required, depending on the dataset, to decrease the number of dimensions, as having too many features might slow down any subsequent calculation.
  • Data Modelling and Pattern Mining: Data Scientists may look at any intriguing data linkages, based on the type of research. While high-frequency patterns offer wider uses, data variations can often be more fascinating insights.
  • Results evaluation and knowledge application: The results must be evaluated and understood after the data has been compiled. Valid, unique, relevant, and precise results should be finalized. Companies may utilize this information to develop new strategies and achieve their goals when this criterion is satisfied.

Data Preparation for Data Mining Steps

Pattern Recognition, Information Retrieval, Machine Learning, Data Mining, and Web intelligence all require the pre-processing of raw data. Data Cleaning and preparation account for around 80% of the overall data engineering labor. It is essential to master this step in the entire Data Preparation for Data Mining process.

If Data Preparation for Data Mining isn’t carried out appropriately, there can be issues with the results that are to be predicted. There are seven essential steps to preparing the data, and we will talk about each of these in detail.

Data Preparation for Data Mining: Accuracy of Data

  • You must collect accurate data from sources you can trust. Even the most powerful machine learning algorithms will fail if there is insufficient data. 
  • Make sure that the data is free of human errors. Test a portion of your data that has been collected or labeled by individuals to discover how frequently mistakes occur.
  • Check to see if there were any data transmission issues. Due to server failure and storage disaster, for example, similar documents may have been duplicated. Examine the impact these events have on your data.
  • Examine the data to see if there are any missing values. There can be several methods to handle missing data, like incorporating null values or ignoring them.
  • Check if the collected information is sufficient to perform the task. For example, what if you want to figure out if you’re selling phones in Europe with the customer data of a mobile phone company manufactured in the United States.
  • Check if the data in the system is unbalanced. Assume you’re attempting to limit supply chain risks by filtering out suppliers that you believe are untrustworthy. You’re using several characteristics (e.g., location, size, rating, etc.). Suppose your labeled dataset includes 1,500 items classified as trustworthy and only 30 entries labeled as unreliable. In that case, the model won’t be able to learn about the unreliable ones since there aren’t enough samples.

Data Preparation for Data Mining: Data Consistency

  • Data Formatting refers to converting data to the required format you want to use. It’s also not difficult to convert a dataset into the file format that your machine learning system prefers.
  • If you’re merging data from several sources or many people have manually updated your dataset, double-check that all variables inside a particular attribute are written consistently.
  • Formats for dates, money (4.03, $4.03, or even $4.03), addresses, and so on. Across the dataset, the input format should be constant.

Data Preparation for Data Mining: Amount of Data

  • You can reduce data by aggregating it into more enormous records by separating attribute data into various groups and drawing a number for each group.
  • Combine them into weekly or monthly ratings instead of looking at the most popular goods on any particular day over five years. This will help reduce data quantity and computation time without causing any discernible prediction losses.
  • Record sampling is another technique. To increase prediction accuracy, you delete records (objects) with missing, erroneous, or less representative data.
  • Later on, when you need a model prototype to verify if a machine learning approach you’ve chosen achieves the expected results and evaluate the ROI of your Data Mining project, you may use the methodology.

Data Preparation for Data Mining: Data Cleaning

Cleaning your data is one of the most important steps. You can clean it in several ways; choosing the best strategy is also influenced by the data and domain you have:

  • Substitute missing data, such as n/a for null categories or 0 for numerical values.
  • Substitute average numbers for the missing numerical values.
  • You may also utilize the most common things to fill in for category values.

Data Cleaning may be automated if you utilize machine learning as a service platform. For example, Azure Machine Learning lets you pick from various methodologies, whereas Amazon Machine Learning does it automatically.

Data Preparation for Data Mining: Make New Features

Because some of the numbers in your data collection are likely to be complex, breaking them down into smaller chunks will allow you to capture more specific correlations. This method is the total opposite of data reduction in that it requires you to create new attributes based on current ones.

Data Preparation for Data Mining: Data Rescaling

Data Rescaling is a Data Normalization method that aims to improve the quality of a dataset by lowering the number of dimensions and avoiding situations where specific values outnumber others.

Data Preparation for Data Mining: Data Storage

After the data has been prepared, it may be kept or sent into a third-party program, such as a Business Intelligence tool, allowing for processing and analysis.


This article explains Data Preparation for Data Mining extensively. It also gives a brief introduction to Data Preparation and Data mining.

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