The Importance of Data Preprocessing in Machine Learning
Let’s start this post with a question – Is it sufficient for your business to merely collect data with the aim of analyzing it? As they say, there’s many a slip between the cup and the lip. The same is true with data analytics.
While analyzing data, you must ensure that it has no errors, inconsistencies, duplicates, or missing values. All of these may otherwise give a false impression of the overall statistics of the data.
Inconsistencies and outliers can also disrupt the model’s learning, resulting in inaccurate predictions.
So, between gathering information (data) and analyzing it, there are some more steps that must be undertaken (processing) in the interest of accuracy and getting the best actionable insights. More so when you are about to ask an algorithm-run machine to do so.
Data processing stands for the entire gamut of things: starting with the collection of data (input), its transformation into usable information, to the actual processing by the machine learning algorithm.
Where and How Does it all Start?
Data comes in all types and forms. The process starts with taking the raw data and converting it eventually into a more understandable format for the machine, which employees can easily interpret in an enterprise (output).
The very first step in this process is data preprocessing. It is a technique that is also used to convert the initial data into a standardized format. “Noisy” data needs to be cleaned and standardized for the next course of action. The aim is to make clean and formatted data available for building AI/ML models.
The words “Preprocessing” and “Processing” may sound interchangeable, but there’s a fine line dividing them. Data preprocessing is nothing but a subset of the overall data processing technique.
If you do not apply the right data processing techniques, your model will not be able to turn out meaningful or accurate information from your data analytics.
For this article, you will limit ourselves to the topic of data preprocessing. There are many data preprocessing methods and steps to go about it, but not all of them are effective.
In the next post, let’s look at the overall aspect of data processing.
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What Really is Data Preparation?
It wouldn’t be an exaggeration to say that data preprocessing/preparation is a crucial and a “must-have” step in any machine learning project. Data analysis and interpretation is an essential part of almost any field of study. When working with data, it is crucial to understand how to prepare it properly for analysis.
This can involve various tasks, including cleaning, transforming, and aggregation.
Preprocessing is significant because it helps you to focus your analysis. Without it, you may lose sight of what you’re actually trying to learn from your data. In most operational environments, the preprocessing will run as an Extract Transfer Load (ETL) job for batch processing, or, in the case of “live” data, it could be part of the streaming process.
In machine learning, preprocessing involves transforming a raw dataset so the model can use it. This is necessary for reducing the dimension, identifying relevant data, and increasing the performance of some machine learning models. It involves transforming or encoding data so that a computer can quickly parse it.
What’s more, predictions made by a model should be accurate and precise because the algorithm should be able to interpret the data easily.
Here’s an analogy to help you understand better: Imagine you’re a patient afflicted recently with a virus. Your doctor tries to figure out what’s wrong with you, obviously based on the symptoms you exhibit.
But before recommending a line of treatment, the doctor also wants to know your medical history, maybe your travel history, and other related information like age, etc. (inputs).
All of it in the correct, recognized way (properly format). If you are vague in describing, say, your symptoms, it can be a problem in the ultimate diagnosis. Even more crucial is that before diagnosis, the doctor must also be aware of all the possible symptoms and severity of the disease.
This is necessary to compare it to the symptoms you are exhibiting now. Otherwise, the diagnosis could be limited, thus negatively impacting the treatment (output).
Data processing is like the initial flow of information such as symptoms, etc. It helps distinguish between relevant and irrelevant information and weed out the unwanted. It can be used to filter out trivia or information like typos or unwanted decimal places that don’t matter for the analysis.
Furthermore, it can also be used to transform one data set into another, which is often necessary for analysis. Some common tasks involving data preprocessing that need to be undertaken, but more on that, later.
So, now you know that preprocessing is part of the larger data processing technique; one of the very first steps from the time of data collection to its analysis. It also includes data standardization and data normalization. While we all know what standardization is about, “normalization” refers to a broader set of procedures to eliminate errors.
Normalization techniques help ensure the table has data directly related to the primary key, and each data field contains only a single data element. It helps to delete duplicate and unwanted data.
What are the Major Steps of Data Preprocessing?
Major steps of Data Preprocessing are:
- Data Acquisition
- Data Normalization/Cleaning
- Data Formatting
- Data Sampling
- Data Scaling
Manipulating data is often the most time-consuming part of data science. So much so that in many enterprises, data analysts spend much of their valuable time preparing the data rather than drawing insights from it, which is the main task.
Data preprocessing is where you start to “prepare” the data for the machine learning algorithm.
There are a few different types of preprocessing that you can do. you can, for example, filter the data to remove any invalid entries. You can also reduce the size of the dataset to make it easier to process. You can also normalize the data to make it more consistent.
Here are the some of the major steps in Data preprocessing :
Step 1: Data Acquisition
This is probably the most important step in the preprocessing process. The data you will be working with will almost certainly come from somewhere. In the case of machine learning, it’s usually a spreadsheet application (Excel, Google Sheets, Etc.) that is manipulated by someone else.
In the best case, it’s a tool like R or Python that you can use to grab the data and perform some basic manipulations easily.
There are a few things to note here. First, the data you’ll be working with might be in a format that is not directly usable by the machine learning algorithm. For example, if you’re trying to load data from an SPSS file, you’ll need to do some cleaning to get the data into a valid format.
Second, the tools you mentioned can also do quite a bit of cleaning, but sometimes it’s more explicit data processing that you’re looking for.
Before you take the next step, you will need to import all the libraries like Python for the preprocessing tasks. You may also use the Python programming language and its built-in data library to perform more sophisticated data processing.
The three core Python libraries for this purpose are Pandas, NumPy, and Matplotlib to easily manipulate your data in several ways.
Step 2: Data Normalization/Cleaning
Here, you delete the unwanted data and fix the instances of missing data by removing them. The term “data cleaning” is a little misleading because it makes it sound like you’re just trying to fix the data. In reality, you’re trying to eliminate errors and inconsistencies so that our data is as consistent as possible.
This means removing any invalid or erroneous values. There are a number of things you can do here. You can make sure that each data item is unique and standardize various properties of the data, such as their unit of measurement. Ensure that each data point has a uniquely determined value.
This means no duplicates and no missing values.
Step 3: Data Formatting
Data formatting begins once you have clean data. It helps convert the data into a more usable format by machine learning algorithms. Data can be available in different forms, including proprietary and Parquet formats, among others. Learning models can work effectively with data when its formatting is appropriate.
You can use several different formats, and each has its own benefits.
One popular option these days goes under the brand name TensorFlow or TFRecords, enabling us to establish one unified set of labeled training records across different models within MLflow for flexible model auditing.
Step 4: Data Sampling
You need to ensure that the data samples represent the population from which they came, for this is where bias and variance can come into play. Bias is the tendency for data to exhibit patterns that are not representative of the population from which it came.
One of the most important things you can do when working with data is to ensure you’re sampling it properly. This means that you’re taking a representative sample of the data rather than just grabbing whatever data is available. Instead of picking the entire dataset, you can use a smaller sample of the whole, thus saving time and memory space.
This is also important because it ensures you get a fair data representation. You’ll get questionable results with biases if you’re sampling too heavily in one direction.
Also, you need to split the dataset into two – for training and test purposes. Training sets are subsets of datasets used for training machine learning models. The output is something you already know. In contrast, a test set is a subset of the dataset useful for testing the machine learning model. To predict outcomes, the ML model uses the test set.
A 70:30 or 80:20 ratio is usually used for the dataset, i.e. you take either 70% or 80% of the data for training the model while leaving out the rest 30% or 20% for testing. What guides this decision is the form and size of the dataset in question.
Step 5: Data Scaling
Data Scaling is the standardization of independent variables within a range. To put it another way, feature scaling limits the range of variables so that their comparison is fair.
Standardizing the features of a dataset reduces the variability within a dataset so that its comparison and analysis become easier. Like 0-100 or 0-1. It helps ensure that the data you’ve received has similar properties.
There are several different ways that you can standardize the data. For example, you can use standard deviation to reduce the variance within a dataset.
Once the preprocessing steps are done, you need to undertake the rest of the data processing steps like data transformation before loading the data into the machine learning algorithm and training the algorithm. This is, essentially, a process of “teaching” the machine learning algorithm how to recognize and understand the patterns in our data.
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Overall, machine learning algorithms are of two types:
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
Supervised learning algorithms learn from a set of training data. The training data is usually paired with corresponding feedback data, which helps the machine learning algorithm learn the correct associations between the different features of the data.
Unsupervised learning algorithms don’t require any corresponding feedback data. Instead, it is in their design to learn from data on their own.
That said about preprocessing of data, in Part 2, you will be looking at the overall aspect of data processing.
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