Predictive analytics is a branch of the advanced analytics which is utilized to make predictions about anonymous upcoming events. Predictive analytics utilizes numerous techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze present data to build expectations about future.
It utilizes several of data mining, predictive modeling and analytical techniques to take together the management, information technology, and modeling business process to create predictions about future. The patterns found in historical and transnational data can be used to recognize risks and opportunities for future. Predictive analytics models capture relationships between many factors to assess risk with a particular set of conditions to hand over a score or weight age. By effectively applying predictive analytics the businesses can successfully interpret big data to their advantage.
The data mining and text analytics along with statistics enable the business users to make predictive intelligence by recognizing patterns and relationships in both the structured and unstructured data. The data which can be utilized readily for analysis are structured data, examples of age, gender, and marital status, and income, sales. Unstructured data are textual data in call center notes, social media content, or further type of open text which require being extracting from the text, along with the sentiment, and then using in the model building process.
Predictive analytics enables associations to end up proactive, forward-looking, anticipating outcomes and practices in view of the data and not on a hunch or assumptions. Prescriptive analytics goes additional and propose actions to benefit from the prediction and also provide choice options to benefit from the predictions and its implications.
Predictive Analytics Process
Here is the step by step Predictive Analytics Process:
Step 1 – Define the Project
Define the project outcomes, deliverables, scoping of the effort, business objectives, identify the data sets which are going to be used.
Step 2 – Data Collection
Data mining for predictive analytics prepares data from multiple sources for analysis. This provides a complete view of the customer relations.
Step 3 – Data Analysis
Data Analysis is a process of inspecting, cleaning, transforming, and modeling data with the idea of discovering useful information, arriving at conclusions.
Step 4 – Statistical Analysis
Statistical Analysis allows validating the assumptions, hypotheses and testing them with using standard statistical models.
Step 5 – Predictive Modeling
Predictive Modeling provides the ability to automatically generate accurate predictive models about future. There are also options to choose the best solution for multi-model evaluation.
Step 6 – Predictive Model Deployment
Predictive Model Deployment provides the option to deploy the analytical results into the daily decision-making process to get results, reports, and output by automating the decisions based on the modeling.
Step 7 – Model Managing
Models are managed and monitored to review the model performance to ensure that it is providing the results expected.
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