Data Preprocessing techniques of Machine Learning that will help you to live 100
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Gone are the days, when people used to do the work manually. But now, In the era of automation, machine learning and Artificial intelligence exist where everything will be done via robots
Nowadays without data, there are no facts and figures, isn’t. Data is actually considered one of the important resources in today’s world. As per the Economic Forum, by 2025 we'll be generating about 463 Exabytes of knowledge globally per day! But is all this data fit enough to be employed by machine learning algorithms? How can we decide that?
In this article, we'll explore the subject of some preprocessing techniques which would be — transforming the information so that it becomes machine-readable…
Before proceeding with the topic, let’s understand what actually machine learning is…
Machine learning is described as the method of knowledge analysis that automates analytical model building. It's a branch of AI-supported the thought that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Today, samples of machine learning are all around us. Digital assistants search online and play music in response to our voice commands. Websites recommend products and films and songs that supported what we bought, watched, or listened to before. Robots vacuum our floors while we do . . . something better with our time. Medical image analysis systems help doctors spot tumors they could have missed and therefore the first self-driving cars are hitting the road. On the contrary, robots are doing everything and the machine understands the whole command
“That’s the power of automation”
Now, let’s come to our topic, breaking down
What is Data Pre-Processing?
When we mention data, we usually consider some large datasets with a huge number of rows and columns. While that's a possible scenario, it's not always the case — data might be in numerous different forms: Structured Tables, Images, Audio files, Videos, etc.
Machines don’t understand free text, image, or video data because it is, they understand 1s and 0s. So it probably won’t be ok if we placed a slideshow of all our images and expect our machine learning model to urge trained just by that. The machine understands the command and data which gives are going to be machine friendly.
In the machine learning process, data pre-processing basically is the step in which the data gets encoded and transformed in such a way that the whole machine can retrieve it easily.
Some prerequisites of data pre-processing of machine learning
- A dataset is often viewed as a set of knowledge objects, which are often also called a record, points, vectors, patterns, events, cases, samples, observations, or entities.
- Data objects are described by a variety of features, that capture the essential characteristics of an object, like the mass of an object or the time at which an occasion occurred, etc. Features are often called variables, characteristics, fields, attributes, or dimensions.
- For instance, color, mileage, and power are often considered features of a car. There are different types of features that we will encounter once we affect data.
Now that we've gone over the fundamentals, allow us to begin with the techniques of data preprocessing of machine learning. Remember, not all the techniques are applicable for every problem, it's highly hooked into the info we are working with, so maybe only a couple of steps could be required together with your dataset. Generally, these are as follows:-
1. Data Quality Assessment
Because data is usually taken from multiple sources which are normally not too reliable which too in several formats, quite half our time is consumed in handling data quality issues when performing on a machine learning problem. it's simply unrealistic to expect that the info is going to be perfect. There could also be problems thanks to human error, limitations of measuring devices, or flaws within the data collection process.
Some problems might be caused by:-
- Missing values - These can be solved by eliminating rows, estimating and figuring out the missing and empty values
- Inconsistent values:- These can be solved with the help of data quality assessment
2. Feature Aggregation
Feature Aggregations are performed so on taking the aggregated values so as to place the info from a better perspective and consider transactional data, suppose we've day-to-day transactions of a product from recording the daily sales of that product in various store locations over the year. Aggregating the transactions to single-store-wide monthly or yearly transactions will help us reduce hundreds or potentially thousands of transactions that occur daily at a selected store, thereby reducing the number of knowledge objects.
- This leads to a decrease in memory consumption and time interval
- Aggregations provide us with a high-level view of the info because the behavior of groups or aggregates is more stable than individual data objects
3. Feature Sampling
Sampling may be a quite common method for choosing a subset of the dataset that we are analyzing. In most cases, working with the entire dataset can end up being too expensive considering the memory and time constraints employing a sampling algorithm can help us reduce the dimensions of the dataset to some extent where we will use a far better, but costlier, machine learning algorithm.
The key principle here is that the sampling should be wiped out in such a fashion that the sample generated should have approximately equivalent properties because the original dataset, meaning that the sample is representative. This involves choosing the right sample size and sampling strategy.
Simple sampling dictates that there's an equal probability of choosing any particular entity. Two main variations also include:
- Sampling without Replacement: As each item is chosen, it's far away from the set of all the objects that form the entire dataset.
- Sampling with Replacement: Items aren't far away from the entire dataset after getting selected. This suggests they will get selected preferably.
4. Dimensionality Reduction
Most world datasets have an outsized number of features. For instance, considering a picture processing problem, we'd need to affect thousands of features, also called dimensions because the name suggests, dimensionality reduction aims to scale back the number of features - but not just by selecting a sample of features from the feature-set, which are some things else — Feature Subset Selection or just Feature Selection.
Conceptually, dimension refers to the number of geometric planes the dataset lies in, which might be high such a lot in order that it can't be visualized with pen and paper. The more the number of such planes, the more is that the complexity of the data set.