Defining Predictive Modeling in Machine Learning

Neelam Tyagi
Feb 3, 2020 · 4 min read
Defining Predictive modeling in ML

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

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

2. Non-parametric Model

Benefits and Challenges of Predictive Modeling

Benefits:

  1. Churn analysis and planning for manpower,
  2. Influenced external factors forecasting,
  3. Opponent identification, and
  4. Equipment preservation and conservation.

Challenges:

  1. Large and comprehensive data handling,
  2. Data management and cleansing, and
  3. Model adaptability to new business problems.

Process of Predictive Modeling

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.

Highlighting the workflow of data analysis and transformation in predictive modeling using heterogeneous datasets.
Highlighting the workflow of data analysis and transformation in predictive modeling using heterogeneous datasets.
The workflow of Data analysis and transformation

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.

Conclusion

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.

Analytics Steps

Analytics Steps, the epic platform is providing analytical…

Analytics Steps

Analytics Steps, the epic platform is providing analytical rich content and focuses on solitary growth exclusively with the vision to empower users full of information about Analytics. Visit www.analyticssteps.com

Neelam Tyagi

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The Single-minded determination to win is crucial- Dr. Daisaku Ikeda | LinkedIn: http://linkedin.com/in/neelam-tyagi-32011410b

Analytics Steps

Analytics Steps, the epic platform is providing analytical rich content and focuses on solitary growth exclusively with the vision to empower users full of information about Analytics. Visit www.analyticssteps.com