What is Predictive Analysis? Why is it Important?
Updated on Nov 30, 2022 | 8 min read | 5.6k views
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Updated on Nov 30, 2022 | 8 min read | 5.6k views
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The applicability of Data Analytics has transformed how businesses conduct operations and increased profitability across sectors. Analyzing stored data helps better understand how a business will perform and positively affects the decision-making capabilities of any company. Different data analytics fields aim to increase efficiency by eliminating unwanted human labor, which can otherwise be channeled towards novel research-oriented work.
You can study data analytics under four detailed branches:
This article focuses mainly on predictive analytics. We will cover the working of predictive analytics and explore its applications in different industries.
Predictive analytics is a branch of data analytics that predicts future outcomes of events based on past data and information. The results are calculated by using a broad spectrum of modern-day technologies that include various mathematical processes, statistical modeling, machine learning, data mining, big data, and a lot more.
Using predictive analytics, highly accurate predictions are made through multiple cycles of trial and error. The technique is used by businesses to get deep insight into future events to improve decision-making and facilitate maximized sales.
Gaining detailed knowledge of predictive analytics is only possible with a solid descriptive, diagnostic, and prescriptive research background.
Predictive analytics works on the blueprint of leveraging historical data for uncovering real-time insights. It relies on the repetition of several steps in a cyclic order to increase the accuracy and viability of every predictive model.
Here are the steps involved in predictive analytics:
Understanding the demand before providing a solution for its supply is essential. Therefore, the first step involves gathering relative knowledge and information to chalk out a course of action. Next, you need to collect sufficient data for proper training of the predictive model and identification of predictive patterns.
You must analyze the data required to train the model. This means eliminating all unwanted information or noise and ensuring sufficient information for the flawless functioning of the model.
This is the most crucial step. Here, you need to prepare the product according to the results of your research. The modeling is carried out using predictive analytic techniques like machine learning, big data, data mining, statistical analysis, etc. At the end of the training, the model will learn from the historical data and identify trends accordingly.
By working with business analysts and executing trial runs, you can understand whether the model makes sense and delivers according to the needs of the business. This step is a must because complicated algorithms can lead to false predictions, negatively affecting the business.
You can evaluate the accuracy by retraining the model with data sets. This is a continuous process that will progressively increase the model’s efficiency based on the feedback received.
After a while, when the model reaches a specific efficiency level, it can be deployed for practical use in real-world situations to solve real-time problems.
Predictive analytics models form the base of data analytics. In addition, template and prototype models make it easier for users to convert current and past data into mathematically proven predictions that provide future insights. The different types of models used in predictive analytics include:
Prediction and forecasting of data may sound similar, but there is a minute difference between the two. Data forecasting can be projected as a subset of predictive modeling. Prediction is more inclusive of statistical theories, whereas forecasting considers probabilities and time series analysis. To explain in a sentence, we can say that: “all predictions are not forecasts, but all forecasts are predictions.”
You might be wondering if machine learning and predictive analytics follow the same procedure to develop near-human precision models. Although the original idea behind these technologies is similar, a significant difference exists between them.
Machine learning is aimed at the complete independent working of a system and the elimination of any reliance on human interaction. It seeks to establish an autonomously operating ecosystem without the need for human intervention.
However, predictive analytics is designed to be operated and modified with human experts in the loop, according to the needs of a company. Without human input, predictive analytics is a stagnant technology and cannot prosper.
Big data has played a revolutionizing role in providing a structure and shape to predictive analytics. Analyzing gigantic volumes of data to leverage strategic decisions wouldn’t have been possible without the introduction of big data.
Predictive analytics has made its way into various industries across multiple disciplines. From marketing and insurance companies to restaurant chains, every sector has accepted this emerging technology with open hands.
Some sectors where predictive analytics has facilities major development are:
Predictive analytics is an emerging field that is creating widespread demand for itself. In fact, data analytics as a whole will be shaping industries in the future. Not only is it revolutionizing businesses and companies, but it has also played an integral role in generating mass employment.
With the potential of an exponential boom imminent, data analytics and its related fields of study like Machine Learning and Artificial Intelligence will be impacting human lives marginally in the next five to ten years.
As it turns out, now’s an excellent time to kick-start your journey in Data Science & Machine Learning. upGrad’s Advanced Certificate Programme in Data Science 8-month program from IIIT-B covers relevant world-class technologies and concepts like Statistics, Python Programming, Predictive Analytics using Python, Basic & Advanced SQL, Visualization using Python, EDA, Basic & Advanced Machine Learning Algorithms.
The course is taught by renowned data science experts who rely on relevant industry projects and a cutting-edge curriculum to help students develop the necessary skills to succeed in the field. The course also includes 360° career support, industry mentorship & peer to peer networking for sharper outcomes.
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