Predictive Data Mining Models

Predictive Analytics & Data Mining: Model Development
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Register Now. Time and Location. Who should attend: Data Scientists Analytics Architect. Workshop Agenda. Overview Develop predictive models and reports by leveraging native algorithms or by importing models from third-party data mining vendors like R and PMML, in hands-on exercises using MicroStrategy Developer.

Clustering Cluster analysis. Instructor Bio Mr. Predictive analytics is used in actuarial science , [4] marketing , [5] financial services , [6] insurance , telecommunications , [7] retail , [8] travel , [9] mobility , [10] healthcare , [11] child protection , [12] [13] pharmaceuticals , [14] capacity planning , [15] social networking [16] and other fields.

One of the best-known applications is credit scoring , [1] which is used throughout financial services. Scoring models process a customer's credit history , loan application , customer data, etc. Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns.

Module 2: Decision Trees

Authors: Olson, David L., Wu, Desheng. This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock. This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles.

The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling , machine learning , AI , deep learning algorithms and data mining. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i. This distinguishes it from forecasting. For example, "Predictive analytics—Technology that learns from experience data to predict the future behavior of individuals in order to drive better decisions. Generally, the term predictive analytics is used to mean predictive modeling , "scoring" data with predictive models, and forecasting.

www.cantinesanpancrazio.it/components/ramehabuw/858-come-verificare.php However, people are increasingly using the term to refer to related analytical disciplines, such as descriptive modeling and decision modeling or optimization. These disciplines also involve rigorous data analysis, and are widely used in business for segmentation and decision making, but have different purposes and the statistical techniques underlying them vary. Predictive modelling uses predictive models to analyze the relationship between the specific performance of a unit in a sample and one or more known attributes or features of the unit.

The objective of the model is to assess the likelihood that a similar unit in a different sample will exhibit the specific performance.

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This category encompasses models in many areas, such as marketing, where they seek out subtle data patterns to answer questions about customer performance, or fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision. With advancements in computing speed, individual agent modeling systems have become capable of simulating human behaviour or reactions to given stimuli or scenarios.

The available sample units with known attributes and known performances is referred to as the "training sample". The units in other samples, with known attributes but unknown performances, are referred to as "out of [training] sample" units. The out of sample units do not necessarily bear a chronological relation to the training sample units.

For example, the training sample may consist of literary attributes of writings by Victorian authors, with known attribution, and the out-of sample unit may be newly found writing with unknown authorship; a predictive model may aid in attributing a work to a known author. Another example is given by analysis of blood splatter in simulated crime scenes in which the out of sample unit is the actual blood splatter pattern from a crime scene. The out of sample unit may be from the same time as the training units, from a previous time, or from a future time.

Descriptive models quantify relationships in data in a way that is often used to classify customers or prospects into groups. Unlike predictive models that focus on predicting a single customer behavior such as credit risk , descriptive models identify many different relationships between customers or products.

Descriptive models do not rank-order customers by their likelihood of taking a particular action the way predictive models do. Instead, descriptive models can be used, for example, to categorize customers by their product preferences and life stage. Descriptive modeling tools can be utilized to develop further models that can simulate large number of individualized agents and make predictions.

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Decision models describe the relationship between all the elements of a decision—the known data including results of predictive models , the decision, and the forecast results of the decision—in order to predict the results of decisions involving many variables.

These models can be used in optimization, maximizing certain outcomes while minimizing others. Decision models are generally used to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance.

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Although predictive analytics can be put to use in many applications, we outline a few examples where predictive analytics has shown positive impact in recent years. Analytical customer relationship management CRM is a frequent commercial application of predictive analysis. Methods of predictive analysis are applied to customer data to pursue CRM objectives, which involve constructing a holistic view of the customer no matter where their information resides in the company or the department involved.

CRM uses predictive analysis in applications for marketing campaigns, sales, and customer services to name a few. These tools are required in order for a company to posture and focus their efforts effectively across the breadth of their customer base. They must analyze and understand the products in demand or have the potential for high demand, predict customers' buying habits in order to promote relevant products at multiple touch points , and proactively identify and mitigate issues that have the potential to lose customers or reduce their ability to gain new ones.

Analytical customer relationship management can be applied throughout the customers' lifecycle acquisition , relationship growth , retention , and win-back. Several of the application areas described below direct marketing, cross-sell, customer retention are part of customer relationship management. Over the last 5 years, some child welfare agencies have started using predictive analytics to flag high risk cases. Experts use predictive analysis in health care primarily to determine which patients are at risk of developing certain conditions, like diabetes, asthma, heart disease, and other lifetime illnesses.

Differences between Data Mining and Predictive Analytics

Additionally, sophisticated clinical decision support systems incorporate predictive analytics to support medical decision making at the point of care. A working definition has been proposed by Jerome A. Osheroff and colleagues: [24] Clinical decision support CDS provides clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care.

It encompasses a variety of tools and interventions such as computerized alerts and reminders, clinical guidelines, order sets, patient data reports and dashboards, documentation templates, diagnostic support, and clinical workflow tools. A study of neurodegenerative disorders provides a powerful example of a CDS platform to diagnose, track, predict and monitor the progression of Parkinson's disease. They employed classical model-based and machine learning model-free methods to discriminate between different patient and control groups.

Similar approaches may be used for predictive diagnosis and disease progression forecasting in many neurodegenerative disorders like Alzheimer's , Huntington's , amyotrophic lateral sclerosis , and for other clinical and biomedical applications where Big Data is available. Many portfolios have a set of delinquent customers who do not make their payments on time.

The financial institution has to undertake collection activities on these customers to recover the amounts due. A lot of collection resources are wasted on customers who are difficult or impossible to recover. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective collection agencies, contact strategies, legal actions and other strategies to each customer, thus significantly increasing recovery at the same time reducing collection costs.

Often corporate organizations collect and maintain abundant data e. For an organization that offers multiple products, predictive analytics can help analyze customers' spending, usage and other behavior, leading to efficient cross sales , or selling additional products to current customers. With the number of competing services available, businesses need to focus efforts on maintaining continuous customer satisfaction , rewarding consumer loyalty and minimizing customer attrition.

In addition, small increases in customer retention have been shown to increase profits disproportionately. At this stage, the chance of changing the customer's decision is almost zero. Proper application of predictive analytics can lead to a more proactive retention strategy. By a frequent examination of a customer's past service usage, service performance, spending and other behavior patterns, predictive models can determine the likelihood of a customer terminating service sometime soon.

Silent attrition, the behavior of a customer to slowly but steadily reduce usage, is another problem that many companies face. Predictive analytics can also predict this behavior, so that the company can take proper actions to increase customer activity. When marketing consumer products and services, there is the challenge of keeping up with competing products and consumer behavior.

Apart from identifying prospects, predictive analytics can also help to identify the most effective combination of product versions, marketing material, communication channels and timing that should be used to target a given consumer. The goal of predictive analytics is typically to lower the cost per order or cost per action. Fraud is a big problem for many businesses and can be of various types: inaccurate credit applications, fraudulent transactions both offline and online , identity thefts and false insurance claims.

Some examples of likely victims are credit card issuers , insurance companies, [27] retail merchants, manufacturers, business-to-business suppliers and even services providers. A predictive model can help weed out the "bads" and reduce a business's exposure to fraud. Predictive modeling can also be used to identify high-risk fraud candidates in business or the public sector.

Mark Nigrini developed a risk-scoring method to identify audit targets. He describes the use of this approach to detect fraud in the franchisee sales reports of an international fast-food chain. Each location is scored using 10 predictors. The 10 scores are then weighted to give one final overall risk score for each location.

The same scoring approach was also used to identify high-risk check kiting accounts, potentially fraudulent travel agents, and questionable vendors.

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A reasonably complex model was used to identify fraudulent monthly reports submitted by divisional controllers. Recent [ when? This type of solution utilizes heuristics in order to study normal web user behavior and detect anomalies indicating fraud attempts. The predicting of the outcome of juridical decisions can be done by AI programs. These programs can be used as assistive tools for professions in this industry.

Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy. For example, a retailer might be interested in predicting store-level demand for inventory management purposes.

Predictive Data Analytics in UNDER 5 Minutes

Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year. These types of problems can be addressed by predictive analytics using time series techniques see below. They can also be addressed via machine learning approaches which transform the original time series into a feature vector space, where the learning algorithm finds patterns that have predictive power. When employing risk management techniques, the results are always to predict and benefit from a future scenario.

The capital asset pricing model CAP-M "predicts" the best portfolio to maximize return. Probabilistic risk assessment PRA when combined with mini- Delphi techniques and statistical approaches yields accurate forecasts. These are examples of approaches that can extend from project to market, and from near to long term. Underwriting see below and other business approaches identify risk management as a predictive method.

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Many businesses have to account for risk exposure due to their different services and determine the cost needed to cover the risk. For example, auto insurance providers need to accurately determine the amount of premium to charge to cover each automobile and driver. A financial company needs to assess a borrower's potential and ability to pay before granting a loan. For a health insurance provider, predictive analytics can analyze a few years of past medical claims data, as well as lab, pharmacy and other records where available, to predict how expensive an enrollee is likely to be in the future.

Modeling Phase — Select and apply appropriate modeling techniques and calibrate model settings to optimize results.

Difference Between Descriptive and Predictive Data Mining

Evaluation Phase — Models must be evaluated for quality and effectiveness before we deploy. Also, determine whether the model, in fact, achieves the objectives set for it in phase 1. Deployment Phase — Make use of models in production. Define Business Goal — What business goal to be achieved and how data fits. For example, business goal is more effective offers to new customers and data needed is segmentation of customers with specific attributes.

Collect Additional Data — Additional data needed might be user profile data from online system or data from third-party tools to better understand data. This helps to find a reason behind the pattern. Sometimes Marketing surveys are conducted to collect data c. Draft Predictive Model — Model created with newly collected data and business knowledge. Deeply understand customer segments across different dimensions b.

Get performance pattern specific to KPIs Eg. Is subscription increasing with active users count? Identify Fraudulent activity attempts and prevent it. System performance patterns Eg -Page loading time across different devices — any pattern? Vision — Helps to see what is invisible to others.

Predictive analytics can go through a lot of past customer data, associate it with other pieces of data, and assemble all the pieces in the right order. Decision — A well made predictive analytics model provides analytical results free of emotion and bias. It provides consistent and unbiased insights to support decisions.