{"id":266241,"date":"2024-10-04T09:30:12","date_gmt":"2024-10-04T09:30:12","guid":{"rendered":"https:\/\/imarticus.org\/blog\/?p=266241"},"modified":"2024-10-04T09:30:12","modified_gmt":"2024-10-04T09:30:12","slug":"what-is-predictive-analytics","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/what-is-predictive-analytics\/","title":{"rendered":"What Is Predictive Analytics? A Comprehensive  Guide to Understanding the Basics"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">In the era of data renaissance and artificial intelligence, predictive analytics is a specialised vertical of data science utilised for extracting future outcomes fairly accurately. Predictive analytics uses historical data, big data mining systems, statistical modelling and machine learning processes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Organisations use predictive analytics to understand the business risk to face the upcoming challenges more smartly. Predictive analytics can foretell future sales revenue, cash flow and the profit margin.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Besides, predictive analytics also highlights key information regarding project overruns, risks associated with supply chain management, logistics production\/execution etc. It also helps to provide a guideline for navigating new business geography.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Types of Predictive Analytics<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Broadly, there are ten predictive analytics techniques. These are as follows \u2013<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Classification model\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This elementary predictive analytics tool classifies data based on closed-ended queries, whose response may be obtained through\u2019 responses like yes or no.\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Forecast model\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This model is also another common model that utilises historical data. Response received to queries in this system is numerical and useful in forecasting sales or revenue estimates.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Clustering model\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This model groups data based on the same or similar features. The collective data from different groups is then utilised to find out the overall outcome of the cluster.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Hard clustering is a process in which data is grouped based on the characteristics which completely match the cluster. However, another type of clustering, namely soft clustering, is also applied based on probability theory. In this case, probability or weightage is added to each data to tag its similarity percentage.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Outliers model\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This model locates if there is any individual unusual data within a pool of given data. This outlying information may have been generated due to some abnormal or abrupt change in the controlling parameters of business or a case of some potential fraud in financial transactions.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Time series model\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This is a predictive analytics tool where historical data over a specific time range is utilised to predict future trends over the same time series i.e. the same months.\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Decision tree algorithm\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This predictive analytics model uses graphs plotted based on data obtained from different sources. The purpose of this tool is to identify the different future outcomes based on the different decisions the management undertakes. This compensates for incomplete and missing data and makes it easy for interdepartmental reviews and presentations.\u00a0\u00a0\u00a0\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Neural network model\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This model simulates neurons or the human brain through several complex algorithms and provides outcomes from different patterns or cluster data.\u00a0\u00a0\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">General linear model\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">It is a statistical tool that can compare two dependent variables over a regression analysis.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Gradient boosted model\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">In this model, flaws of several decision trees are corrected and ranked. The outcome is a product of several ranked or boosted decision trees.\u00a0\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Prophet model\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This model may be used along with time series and forecast models to achieve a specific or desired outcome in future.\u00a0\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Predictive Analytics Examples<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">In today\u2019s world, predictive analytics is a subject that finds application across industries. Below are a few real-world predictive analytics examples for a better understanding of what is predictive analytics.\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Insurance sector\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Nowadays, health and all general forms of insurance offerings are guided by predictive analytics. Historical data concerning the percentage of premature claims for customers with similar portfolios are studied.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This tool not only makes the offer more competitive but also helps craft out a better terms package for the client while keeping the profit margin untouched for the insurance company.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Automotive industry\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The neural network model of predictive analytics finds its application in self-driven cars. The car sensors assess and mitigate all safety concerns and challenges a moving vehicle should encounter. Furthermore, historical data can help car dealers or service providers prepare a maintenance schedule for specific car models.\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Financial services\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">One of the best examples of predictive analytics is its ability to run financial institutions profitably by locating fraudulent activities, identifying potential customers, eliminating loan defaulters and scrutinising other dynamic market scenarios.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Besides the above functions, credit scoring is a major function of financial institutions, and this function is driven by predictive analytics. CIBIL scores for individuals and organisations determine their trustworthiness in securing loans.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Healthcare\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">In all modern countries, predictive analytics has become a stable cornerstone for the healthcare industry. Historical records of patient data regarding medicine and surgical techniques with the outcomes have become the backbone of future healthcare systems, ailment-wise. These records have also helped create smooth readmission of patients and immediate diagnosis in each case.\u00a0\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">\u00a0Marketing and retail sector\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Nowadays digital marketing has taken over the age-old traditional marketing practices. Search engines recommend desired products to customers and provide their specifications, prices and past reviews.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Digital marketing techniques target customers based on their recent searches. The retail sector has now become extremely competitive with data-oriented<\/span><\/p>\n<p><span style=\"font-weight: 400;\">tailor-made and client-centred products and services.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The target audience may be reached quickly, thereby increasing the sales footprint. Predictive analytics tools also scrutinise client behaviours, purchase power and patterns to improve customer relationships and return on investments.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Machines and industry automation\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Predictive analytics also finds its application in this sector. Machines are prone to breakdowns that result in production downtime and sometimes employee safety risks. Historical data on these machines help in preventive maintenance thereby minimising machine failures improving employee safety factors and boosting workforce morale.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Energy and utilities\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Oil and gas services manage a serious business. Their management must make informed decisions regarding resource allocation and optimum utilisation. Similarly, based on the actual demand based on weather conditions and available supply, these companies must determine the optimum prices for the energy charges.\u00a0\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Manufacturing and supply chain management\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Product manufacturing is directly linked to the demand and supply ecosystem. Predictive analytics take inputs from historical data to predict accurate market demand over a specific time.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Demand depends on factors like market trends, weather, consumer behaviour interests, etc. Past data on manufacturing help the organisation eliminate erroneous or age-old processes, thus speeding up production.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Supply chain and logistics historical data help to speed up and improve the product delivery process to the client, thereby increasing client satisfaction.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Stock trading markets\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Predictive analytics is a very crucial tool when it comes to stock trading. Investing in IPOs and stocks is based on historical data.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Human resources\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The human resource team in an organisation often uses predictive analytics to determine highly productive processes. They also use predictive analytics to analyse the skill requirements in human resources for future business activities.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Besides the above examples, predictive analytics has its footprint virtually everywhere. Even mere typing on the mobile or computer system is supported by a predictive text. Predictive analytics have gained immense importance today and have spiralled as a lucrative career opportunity.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Students are encouraged to pursue a holistic <\/span><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\"><span style=\"font-weight: 400;\">data science course<\/span><\/a><span style=\"font-weight: 400;\"> from a good institution. Read about <\/span><a href=\"https:\/\/imarticus.org\/blog\/the-career-outlook-for-data-scientists-with-a-postgraduate-degree-in-data-analytics\/\"><span style=\"font-weight: 400;\">data Scientists<\/span><\/a><span style=\"font-weight: 400;\"> and the possible career opportunities to learn more.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Benefits of Predictive Modelling<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Today an organisation invests a lot of money in predictive analytics programs to gain the below-mentioned benefits &#8211;<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Data security\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Every organisation must be concerned with security first. Automation in collaboration with predictive analytics takes care of the security issues by flagging unusual and suspicious behaviours in network systems.\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Reduction of risk\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Nowadays, companies consider risk as an opportunity. Thus, mitigation of risk is important and not aversion. Predictive analytics, with the input of historical data, has the capability of risk reduction.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Operational efficiency\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Efficient work processes result in shorter production cycles and hence, better profitability.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">\n<h3><span style=\"font-weight: 400;\">Improved decision making\u00a0<\/span><\/h3>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Last but not least, nobody can deny that an organisation succeed or fails only based on the key decisions made. Nowadays, all key business calls like expansion, merger auction etc. are made based on the inputs from predictive analytics.\u00a0\u00a0\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Conclusion<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Predictive analytics is the future and goal of artificial intelligence. It combines with machine learning to deliver the desired results. The objective of predictive analytics is to forecast future events. The process eliminates past operational errors and suggests a more pragmatic solution in several business sectors.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Imarticus Learning\u2019s <\/span><span style=\"font-weight: 400;\">Postgraduate Program In Data Science and Analytics<\/span><span style=\"font-weight: 400;\"> can help prospective candidates get lucrative opportunities in this domain. The duration of this data science and <\/span><a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\"><span style=\"font-weight: 400;\">data analytics course<\/span><\/a><span style=\"font-weight: 400;\"> is 6 months.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">FAQs<\/span><b><\/b><\/h2>\n<ul>\n<li aria-level=\"1\"><b>What is the predictive model in data mining?<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The purpose of applying a predictive model in data mining is to extrapolate the missing data with the help of other available data in the group. The process involves the imposition of statistical models and machine learning algorithms to determine the pattern and relationship of missing data with those available in the system.\u00a0<\/span><b><\/b><\/p>\n<ul>\n<li aria-level=\"1\"><b>How is data collected for predictive analytics?<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Data may be available over various platforms like industry databases, social media platforms and the historical data of the firm planning to conduct the predictive analytics process.\u00a0\u00a0<\/span><b><\/b><\/p>\n<ul>\n<li aria-level=\"1\"><b>How accurate is the predictive analytics process?\u00a0<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Subjective expert opinion is an outcome of experience and may vary from one individual to another based on the extent of exposure received. However, predictive analytics is data-driven and forecasts accurate outcomes, provided that no large-scale disruptive events or exceptions come in between.<\/span><b><\/b><\/p>\n<ul>\n<li aria-level=\"1\"><b>Is predictive analytics a part of AI (Artificial Intelligence)?\u00a0<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Predictive analytics is a core attribute of artificial intelligence.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In the era of data renaissance and artificial intelligence, predictive analytics is a specialised vertical of data science utilised for extracting future outcomes fairly accurately. Predictive analytics uses historical data, big data mining systems, statistical modelling and machine learning processes. Organisations use predictive analytics to understand the business risk to face the upcoming challenges more [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":266242,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[23,1807],"tags":[650],"class_list":["post-266241","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","category-management","tag-predictive-analytics"],"acf":[],"aioseo_notices":[],"modified_by":"Imarticus Learning","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/266241","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/comments?post=266241"}],"version-history":[{"count":1,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/266241\/revisions"}],"predecessor-version":[{"id":266243,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/266241\/revisions\/266243"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media\/266242"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=266241"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=266241"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=266241"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}