| Predictive Modeling in Data Science is more like the answer to the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of Data Science and it is mainly divided into predictive and preventive modeling. Predictive modeling is a process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models. Predictive Modeling is also referred to as Predictive Analytics. Top 5 Predictive Models: - Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer yes or no types.
- Clustering Model: This model groups data points into separate groups, based on similar behavior.
- Forecast Model: One of the most widely used predictive analytics models, deals in metric value prediction, this model can be applied wherever historical numerical data is available.
- Outliers Model: The Outliers model as the name suggests is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
- Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.
To find out which predictive model is best for your analysis, you need to do your homework:- Start by finding what questions you are looking to answer
- What you are expecting to do with that information.
- What data do you need to make that decision?
- How can you gather that data?
- The quality of the data you will collect.
- Errors that might creep in during the data collection process.
Watch global experts from our Data Science channel on how they have used Predictive Modeling in their projects here: AntWak videos on Predictive Modeling in Data Science.
- Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.
- By extrapolating and sharing these insights, data scientists help organizations to solve vexing problems. Combining computer science, modeling, statistics, analytics, and math skills—along with sound business sense—data scientists uncover the answers to major questions that help organizations make objective decisions.
- Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.
In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business. - Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.
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