Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. It uses a number of data mining, predictive modeling and analytical techniques to bring together the management, information technology, and modeling business process to make predictions about future.
The patterns found in historical and transactional data can be used to identify risks and opportunities for future. Predictive analytics models capture relationships among many factors to assess risk with a particular set of conditions to assign a score, or weightage. By successfully applying predictive analytics the businesses can effectively interpret big data for their benefit.
The data mining allows the business users to create predictive intelligence by uncovering patterns and relationships in both the structured and unstructured data. The data which can be used readily for analysis are structured data, examples like age, gender, marital status, income, sales. Unstructured data are textual data in call center notes, social media content, or other type of open text which need to be extracted from the text, along with the sentiment, and then used in the model building process. Predictive analytics allows organizations to become proactive, forward looking, anticipating outcomes and behaviors based upon the data and not on a hunch or assumptions. Prescriptive analytics, goes further and suggest actions to benefit from the prediction and also provide decision options to benefit from the predictions and its implications.
1.Define Project: Define the project outcomes, deliverable, scoping of the effort, business objectives, identify the data sets which are going to be used.
2.Data Collection:Data Mining for predictive analytics</a> prepares data from multiple sources for analysis. This provides a complete view of the customer interactions.
3. Data Analysis: Data Analysis is the process of inspecting, cleaning, transforming, and modeling data with the objective of discovering useful information, arriving at conclusions.
4.Statistics: Statistical Analysis enables to validate the assumptions, hypotheses and test them with using standard statistical models.
5.Modeling: Predictive Modeling</a> provides the ability to automatically create accurate predictive models about future. There are also options to choose the best solution with multi model evaluation.
6.Deployment: Predictive Model Deployment</a> provides the option to deploy the analytical results in to the every day decision making process to get results, reports and output by automating the decisions based on the modeling.
7.Model Monitoring: Models are managed and monitored to review the model performance to ensure that it is providing the results expected.