The amount of data consumed is increasing exponentially, today, a large volume of big data is accumulated over organizations, this might be related to business associates, consumers, application allies, internal and external executives, visitors, etc. Data is churned and characterized to identify and analyze trends.
On the other hand, Data Analytics refers to the process involving various tools and technique for qualitative and quantitative research that utilizes this accumulated data and produce some outcomes which are used to improve performance, yield, risk reduction, enhance business productivity.
Introduction to Predictive Modeling
Data analysis have variation from company to company depending upon the needs, so various data model has been designed to meet the requirements. Predictive modeling is the subpart of data analytics that uses data mining and probability to predict results.
Each model is built up by the number of predictors that are highly favorable to determine future decisions. Once the data is received for a specific predictor, an analytical model is formulated.
A model can apply a simple linear equation or a complex neural structure outlined by concerned software, also if in case, additional data is available then the analytical model is revised.
Moreover, Predictive Modeling employs different regression algorithms and analytics or statistics to estimate the probability of an event using detection theory and largely employed in the field of Machine Learning(ML), and Artificial Intelligence(AI).
In simple words, predictive modeling is usually practiced statistical technique to foretell future outcomes, these are solutions in terms of data mining technology to analyze past and recent data and produce a model to identify future behavior from data.
There are basically two types of predictive modeling;
1. Parametric Model
Assumptions are the crucial part of any data model, it not only makes the model easy also improves predictions, so the algorithms that consider assumptions and make the function simple are known as parametric ML algorithms, and a learning model that compiles data with different parameters of a predetermined size, independent to number of training variables, is termed as parametric model.
2. Non-parametric Model
ML algorithms that enable to make strong assumptions in terms of the mapping function are called non-parametric Ml algorithms and without worth assumptions, ML algorithms are available to pick up any functional form training data. Non-parametric models are a good fit for the huge amount of data with no previous knowledge.
Benefits and Challenges of Predictive Modeling
In core aspects of benefits, predictive modeling reduces the cost necessary for businesses to foretell business outcomes, economic and environmental factors, market circumstances, etc, but it doesn’t mean benefits appear aimlessly, even predictive modeling shows the number of challenges also, here are few benefits and challenges given below;
- Forecast cost and demand in business,
- Churn analysis and planning for manpower,
- Influenced external factors forecasting,
- Opponent identification, and
- Equipment preservation and conservation.
- Data privacy and security,
- Large and comprehensive data handling,
- Data management and cleansing, and
- Model adaptability to new business problems.
Process of Predictive Modeling
It incorporates algorithms execution on running data for prediction, this process is iterative in nature as it trains the model to get the best-suited information for business purposes such as various applications in business analytics. In order to dive in the process of predictive modeling, find below the description;
1. Data collection and purification: Data is accumulated from all the sources to extract the required information by cleaning data with some operations that eliminate loud data to get accurate estimations. Various sources are included Transaction and customer assistance data, survey and economic data, demographic and geographical data, machine and web-generated data, etc.
2. Data transformation: Data need to be transformed through accurate processing to get normalized data. The values are scaled in a provided range of normalized data, extraneous elements get removed by correlation analysis to conclude the final decision.
3. Formulation of the predictive model: Any predictive model often employs regression techniques to design a predictive model by using the classification algorithm. During this process, test data is recognized, classification decisions get implemented on test data to determine the performance of the model.
4. Inferences or conclusion: At last, inferences are drawn from the model, for this, cluster analysis is performed.
The core ides behind the formulation of Predictive Modeling is, data that is being generated on a daily basis or the historical data that may contain the most relevant information for the present business scenarios in order to get maximum profit with suitable models and accurate predictions. The predictive modeling process involves the fundamental task to drag out needful information from structured or unstructured data.
With all this data, different tools are necessary components to extract inference and patterns, such as machine learning techniques are needed to identify trends in data and design model that estimates future conclusions. A variety of ML algorithms are available for predictive modeling, linear and nonlinear regression, neural networks, SVM, decision trees, and many more included. Hopefully, this blog can give the basic touch to predictive modeling and its type and process along with benefits and challenges.