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Definition[ edit ] Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns.
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.
Predictive Analytics Process Predictive analytics process[ edit ] Define project: Define the project outcomes, deliverable, scope of the effort, business objectives, identify the data sets that are going to be used.
Data mining for predictive analytics prepares data from multiple sources for analysis. This provides a complete view of customer interactions. Data Analysis is the process of inspecting, cleaning and modelling data with the objective of discovering useful information, arriving at conclusion Statistics: Statistical Analysis enables to validate the assumptions, hypothesis and test them using standard statistical models.
Predictive modelling provides the ability to automatically create accurate predictive models about future. There are also options to choose the best solution with multi-modal evaluation. Predictive model deployment provides the option to deploy the analytical results into everyday decision making process to get results, reports and output by automating the decisions based on the modelling.
Models are managed and monitored to review the model performance to ensure that it is providing the results expected. Types[ edit ] Generally, the term predictive analytics is used to mean predictive modeling"scoring" data with predictive models, and forecasting.
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 models[ edit ] Predictive models are models of the relation 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[ edit ] 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 riskdescriptive models identify many different relationships between customers or products.
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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. Decision model Decision models describe the relationship between all the elements of a decision—the known data including results of predictive modelsthe 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. Applications[ edit ] 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 [ edit ] Analytical customer relationship management CRM is a frequent commercial application of predictive analysis.JOIN US ON OUR JOURNEY Our environment is facing so many pressures, from plastic pollution to climate change, from habitat destruction to the unsustainable use of .
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Add citations directly into your paper, Check for unintentional plagiarism and check for writing mistakes. The annual report is a major report, especially when shareholders have a stake in the business. The report is final and removes all excuses from quarterly performance dips.
Accountability is the theme of any annual report, because businesses seek profitability and growth. Writing enables students to process, organize, formulate, and extend their thinking about what they have been learning.
In addition, teachers can also assign writing to help students evaluate what they know and understand about a topic. These writing-to-learn strategies help foster students' abilities to make predictions, build connections, raise questions, discover new ideas, and promote.
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