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Machine Learning

Machine learning is a computational technique in which different algorithms can be used to generate models from data in real-time. These models are then used to produce consumable results from fresh data. As more data is fed into the system with time, the model automatically evolves based on its new learnings. Machine Learning requires large quantities of data to work well, and provide more accurate results. The growth of data, generated from sources like IoT and web scraping has helped boost Machine Learning.

While you can theoretically run any algorithm on any data-set, for obtaining the best results, the type and format of the data need to be evaluated first. Machine learning allows the processing of data in real-time, and most models consume a continuous stream of data and grow on their own.

The term  Machine Learning can be used for both supervised as well as unsupervised learning. In supervised learning – the data set is tagged and the model is first to run on the tagged data to learn from it. Then it is run on untagged data, to produce predictions. In the case of unsupervised learning, the entire data is untagged, and the algorithms usually use different data points to find patterns, similarities, and differences in the dataset. You can understand the difference through a use case of each:

a). In Supervised Learning, you can train your machine on labeled images of cats and dogs, and once it is trained, you can input unlabelled images of cats and dogs and test the machine’s predictive capabilities.

b). In Un-Supervised Learning, you would give multiple unlabeled images of both cats and dogs and test whether the machine can separate them.


Machine Learning
Figure: Predictive Analysis vs Machine Learning

Predictive Analysis

The use of Predictive Analysis has been around long before the beginning of AI or even the growth of modern computational machines. It involves the compression of large quantities of data that will be more human-readable. In its simplest form, it can be calculating averages, counts, or medians. It is usually used to find an answer to a specific question such as:

a). Which are the best-selling categories on eCommerce websites in winter?

b). What are the keywords that can be included in an article to make sure that it reaches a large audience?

While it does involve the study of both historical and current data, the stress is mainly on a large historical dataset, and it cannot be used on a continuous stream of data. Any Predictive Analysis combines three major components:

a). Data: The quality, quantity, and breadth of the data will define the success of a Predictive Analysis. In case the data falls short on any of these 3 fronts, we are likely to see a biased result.

b). Assumptions: Even before a study is conducted, there are certain assumptions made about the data at hand. For instance, if you are calculating the cumulative sales of the last 10 years to find out the possible growth in the current year, you are assuming that the growth metrics will follow the same pattern.

c). Statistical Techniques: Statistical learning techniques like regression and decision trees form the core computation required to consume the data at hand, and hence understanding these techniques is a must before handling the data.

Which is Better?

Both Machine Learning and Predictive Analysis are computational techniques and both are run on machines today. It would be difficult to state which one is better since both address different problem statements. We can however discuss some of the pros and cons of each.

Machine Learning is a more advanced science and it can be used on almost any type of data, be it satellite imagery or a dataset of student details. The amount of data that you feed to a Machine Learning model and its cleanliness together determine how well your model will perform in real life.

Predictive Analytics is better suited for problem statements where you already have a question and a brief understanding of where the data can lead you. It is usually guided by historical data. However, in cases where such data is not available, or if the historical trends are not likely to match with current data, due to certain deviations, it can prove to be unusable.

Machine Learning Predictive Analysis
Uses algorithm Models, which are created by using training data Uses a set of predefined rules which can be updated
Can adapt itself automatically and learn from fresh data Usually needs to be tweaked to handle edge cases and changes
You can use one of the pre-existing algorithms that best suits the data at hand You need to write the code for your specific use-case
It can run without historical data, as  it can run on a live stream of data as well Historical data is required before creating a set of rules
Data-Driven Solution Use-Case based Solution
Machine Learning models can take longer to be ready Predictive Analysis models can be ready for testing much faster

Table: Predictive Analysis vs Machine Learning

Since Predictive Analysis involves the study of historical data, inferences, or models can be generated quickly and applied to current data. On the other hand, Machine Learning models usually need to train on a data stream over a prolonged period to be able to handle edge cases and improve their accuracy.

The downside is the Predictive Analytics model’s inability to adapt to variations in data streams. Deviations in data can render a Predictive Analytics model unusable and the Data Team would need to head back to the table to make some manual changes.  Machine Learning models, when training on a diverse and continuous data flow, can easily adapt to changes or deviations that are present in the data.

The Overlap

Improving predictions and real-time adaptation is built into the design of Machine Learning models. On the other hand, Predictive Analytics works on a static data-set, and any change in the data set requires recalibration of different parameters. The major difference lies in the fact that human intervention is relied upon to interpret the results and associations in the case of Predictive Analytics.

However, in certain cases, Predictive Analysis can piggyback on Machine Learning to generate more accurate results – and in such a scenario it can become a subset of Machine Learning. If you already have a specific problem statement but are unsure about the direction, Machine Learning can produce usable insights. In a different process, Machine Learning can also be used to process the raw data and produce a more consumable data-set that can then be used for Predictive Analysis.

However, in certain cases, Predictive Analysis can piggyback on Machine Learning to generate more accurate results – and in such a scenario it can become a subset of Machine Learning. If you already have a specific problem statement but are unsure about the direction, Machine Learning can produce usable insights. In a different process, Machine Learning can also be used to process the raw data and produce a more consumable data-set that can then be used for Predictive Analysis.

What are the Use Cases?

A). Marketing Campaigns

Marketing campaigns have gone digital in a bid to increase conversion rates and reduce expenditures. Targeted marketing campaigns usually use Predictive Analysis to consume data from the past as well as data from campaigns conducted by other companies. Companies also use user demographic data such as location, age, gender, marital status, date of birth, to showcase products to the right customer at the right time.

Previous search history and buying patterns are also used to decide which products to show customers. In this way, no two customers are shown the same products on the homepage.

B). Warehouse Management

Large eCommerce companies use information related to search history and previous buying patterns to decide which items to keep in which warehouse, especially when they have multiple warehouses spread across cities or countries. These optimizations not only reduce the cost to the company but also ensure product delivery timelines are shorter for customers.

Machine Learning has seen rapid growth and algorithms like neural networks have seen major uses like cancer cell detection and sales forecasting. Some of the major use cases of Machine Learning today include:

a). Image Recognition

b). Speech Recognition

c). Traffic Prediction

d). Autonomous Bots

e). Spam and Malware filtering

f). Virtual Assistants

g). Fraud Detection

h). Medical Diagnosis

Where can you Get the Data?

Usually, the same Data Science team of a tech company works on both Machine Learning and Predictive Analysis and applies either of them based on the problem statement at hand. No matter which process is applied, nothing can be achieved without the right data.

Web Scraping is one of the most popular ways to gather data today and DaaS providers like PromptCloud help their customers to get the right data for different types of objectives. Our team makes gathering data a two-step process – you give us the requirements and we give you the data. No matter which algorithm you use, and which technology you leverage our clean data feed can empower your company to stay ahead of the curve.

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