Preparing Your Dataset for Machine Learning on Data Warehouse 

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Data preparation for machine learning is non-negotiable, especially in today’s world where virtually all business operations are data-driven. According to a recent IDC market research report, the volume of data collected in the next three years will be more than what businesses collected in the last three decades!
With massive amounts of data generated today, maintaining data quality is no easy task. However, it doesn’t have to be. In this eye-opening guide, we will walk you through how to prepare data for machine learning, as early as now before your data sets become overwhelming. Read on!

What is Data Preparation for Machine Learning?

Data preparation or data pre-processing is the process of gathering and combining raw data before structuring and organizing it for business analysts to run it through machine learning algorithms. Data preparation is the most basic step when a business is trying to solve real-world challenges faced by consumers through data engineering and machine learning applications.

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Preparing data for machine learning is important because:

ML Algorithms Work with Numbers
A typical data set is usually presented in numerous tables featuring rows and columns, although every type of data might have different variables. For instance, some data types may have numeric variables, such as integers, percentages, rates, or even ranks. Other prevalent variables used in data presentation include names and categories, or binary options such as true or false.

However, machine learning algorithms only work with numeric data. Technically, these algorithms take numerical inputs and give predictions (output) in numbers. That’s why data scientists usually view ML data as vectors and matrices.

Businesses Must Meet the Requirements of ML Algorithms
Businesses have a plethora of options when it comes to choosing a machine learning algorithm, depending on the foregoing predictive modeling project. That said, these algorithms have distinct requirements, as well as expectations when it comes to data input.

For instance, an algorithm, such as a linear machine model might require a specific probability distribution (Gaussian) for each input and target variable. In that case, machine learning data preparation will help change the input variables to match Gaussian probability distribution, or change the ML algorithm altogether to reconfigure data input expectations.

Machine Learning Definition, Goals, and Types

Machine learning, popularly abbreviated as ML is a special artificial intelligence (AI) tech that empowers software applications to give nearly accurate predictive outcomes, without necessarily programming them. The goal of this tech is to optimize computer systems to become smarter and more intelligent with little to zero human interference. Typically, this entails building programs that can handle specific practical learning tasks. Another goal for ML is to come up with elaborate computations of human learning processes and perform programmed simulations based on them.

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There are three types of machine learning, including:

Supervised Learning

According to Gartner, supervised learning will probably be the most prevalent machine learning among enterprise IT leaders throughout 2022 and beyond. As the name suggests, the machine is supervised while learning as the data scientists feed in the algorithm information.

Supervised learning works by feeding pairs of historical input and output data to ML algorithms, which creates an output that is nearly as accurate as the desired outcome. Prevalent algorithms used in supervised learning ML include neural networks and linear regression.

This type of ML is used in various real-world use cases, such as:

  • Determination of low-risk and high-risk loan applicants
  • Prediction of future real estate prices
  • Determination of disease risk factors
  • Prediction of failures in a system’s mechanical parts
  • Revealing fraudulent bank transactions
Unsupervised Learning

Unsupervised learning is common in ML applications that seek to identify various data patterns in a set and draw conclusive insights from them. Unlike supervised learning, this ML doesn’t require constant human intervention to learn. Instead, it automatically detects less obvious patterns in a data set using a host of algorithms, such as Hidden Markov models, hierarchical clustering, or even k-means.

Unsupervised learning ML is instrumental in creating predictive models. Examples of its uses cases in real-world scenarios include:

  • Inventory clustering based on manufacturing or sales metrics
  • Customer grouping based on purchase history and trends
  • Segmenting correlations in customer data
Reinforced Learning

Reinforced learning is probably the closest ML that mimics how humans learn. Typically, the leveraged algorithm learns through direct interactions with the environment in question, to give a positive or negative reward. Prevalent algorithms used in reinforced learning include Q-learning, temporal difference, or even deep adversarial networks.

However, reinforced learning isn’t a go-to ML application for many organizations because it requires enormous computation power to execute. But at the same time reinforced learning requires less human supervision, making it ideal when working on unlabeled data sets.

Although real-world use cases for reinforced learning are still a work in progress, some examples include:

  • Teaching cars to drive or park autonomously
  • Dynamic traffic lights control to ease jam congestion
  • Robotics training using raw video images for systems to simulate what they see

How to Prepare Data for Machine Learning – Best Practices

Data preparation for machine learning can be an in-house DIY task or an outsourced service, depending on the company policy and the amount of data that you are dealing with. Nonetheless, you can prepare data for machine learning in the following simple steps:

Problem Formulation
Which problem is your business trying to solve? Getting an answer to this question will not only help you prepare data the right way but also build a successful ML model by understanding what and how to do it.

You can do this by going back to the basics, away from data. Spend quality time with the professionals within the domain in question to get a better understanding of the problems being solved. After that, use your findings to formulate a hypothesis of the factors and forces in play to determine which type of data you are going to capture or focus on. This will help you come up with a practical machine learning problem to be solved.

Data Collection and Discovery
Your data science team will proceed to collect and discover various data sets after establishing the real problem to be solved. This phase includes capturing various data sources from within the enterprise and third parties as well. An important factor, this process shouldn’t only focus on what the data ought to represent. Instead, it should also extend to reveal what the data might mean, especially when leveraged in different contexts. This is not to forget any factor that might have biased the data.

Determining any bias, and its extent at data collection points will help mitigate biases in the ML in the long haul. Let’s assume you want to create a machine learning model that predicts consumer behavior. In that case, you can investigate bias by establishing whether the data was collected from diverse customer bases, perspectives, as well as geographical locations.

Data Cleansing and Validation
After investigating bias, it’s time to determine whether you have clean data that will give you the highest quality information to drive key decisions in your organization. Innovative data cleansing and validation tools, as well as techniques, can help you spot outliers, anomalies, inconsistencies, or even missing sets of data altogether. This will in turn help you to factor in missing values as neutrals or mitigate their impact on the final ML model.

Raw Uncompressed Data Backup
Raw uncompressed data is just as important as structured data since it might contain vital information about your brand. In that case, you would want to back it up before sorting and structuring. Moreover, raw data is the foundation of any downstream analysis when it comes to implementing machine learning models in your organization.

Also, it’s worth noting that some variables in raw uncompressed data such as time points in interviews are unique and nigh impossible to reproduce. With this in mind, you’d want to back it up as well.

Data Structuring
Once you are satisfied with the type and volume of data, it will now help if you structure it before employing preferred ML algorithms. Typically any ML algorithm will work better and effectively if your data is structured into various categories, as opposed to simply uploading it in raw numbers. Prevalent effective practices, but often overlooked when preparing data for machine learning are data smoothing and binning continuous features.

Smoothing as a continuous feature enhances denoising raw data by imposing casual assumptions in data extractions processes. This practice points out relationships in ordered data sets to give an easy-to-follow and understand order among data sets. Binning on the other hand structures data sets into bins using equi-statistical methods.

Other practices for data structuring in preparation for ML application include:

  • Data reduction
  • Data normalization
  • Data segmentation, based on training and testing ML models

Feature Engineering and Selection
This is the last stage in data preprocessing before delving deeper into building an effective machine learning model. Feature engineering entails creating or topping up new variables to enhance the ML model’s output. For instance, a data scientist may extract, aggregate, or even decompose various variables from a data set before transforming the features depending on probability distributions.

Feature selection in this case entails pinpointing the relevant features to focus on and doing away with the non-essential ones. Inasmuch as a feature might look promising, it’s your responsibility to ensure that it doesn’t bring model training and over-lifting challenges when analyzing new data.

Sum Up

Machine learning data preparation will help you build a successful ML model to drive key decisions in your organization. This guide explains the practices in a basic, layman’s language However, in the real sense, it takes an experienced data scientist or even a team of experts to do it effectively. That said, never hesitate to seek professional help when preparing data for machine learning. Contact us today and find out how our experts can be of help.

FAQs on Dataset for Machine Learning on Data Warehouse

You can leverage various libraries to prepare a dataset for machine learning in Python, such as NumPy, pandas, and sci-kit learn. The process is as follows:

  • Collect the data set
  • Handle any missing data
  • Encode categorical data
  • Categorize the data into training and testing sets
  • Perform feature engineering and selection

Data preparation for ML is important because machine learning algorithms can only be effective when data is formatted in a specific way.

You can prepare data for a model in the following steps:

  • Formulate problems to be solved
  • Collect data sets
  • Cleanse and validate data sets
  • Backup and structure the raw data

The type of data used for machine learning is usually raw, which can be categorized into different types, such as numerical, categorical, text, and time series data sets.

Cross Industry Standard Process for Data Mining (CRISP-DM)

The CRISP-DM process serves as the foundation for nearly all data science processes, and comprises of six sequential steps, including:

Business understanding
This phase entails understanding particular business objectives before determining and setting up data mining goals. You’ll also determine whether the needed resources are available to meet the set project requirements, as well as perform a cost-benefit analysis on the whole project plan.

cross-industry-standard-process

Data understanding
After understanding the business needs, you’ll need to determine and analyze the data sets to be mined, in line with the project goals. This would mean describing data in terms of format and field identities, exploring data through visualization, and verifying the same to enhance quality consistency.

Data preparation
Data preparation, also known as data munging in the CRISP-DM process follows these steps:

  • Data selection
  • Data cleaning
  • Data construction
  • Data integration
  • Data formatting

Modeling
This phase entails building and assessing multiple data models. It include four steps:

  • Model technique selection based on neural net or regression algorithms
  • Test design generation by splitting data into training, test, and validation sets
  • Model development using a preferred code language
  • Model assessment based on domain knowledge

Evaluation
This phase evaluates whether the constructed model is in line with the forgoing business needs and requirements. Besides evaluating the results in the previous phase, you’ll also need to review the entire process and ensure that they were correctly executed. After that, you’ll be in a better position to determine which next steps to follow, whether its deployment, further iteration or even start an entirely new project altogether.

Deployment
Deployment depends on the prevailing business requirements. It can be as simple as coming up with a generalized report or as complex as initiating multiple data mining processes. Either way, you’ll need to plan, monitor, review, and offer ongoing maintenance.

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