Machine Learning & Predictive Modeling


What is Predictive Modeling?

Predictive modeling is a process of developing statistical models and using the models to predict future events and discover relationships among factors based on historical data. Predictive modeling has been widely used in retail and banking in customer loyalty management and credit card fraud detections. In recent years, predictive models have been successfully applied to individual movie selection. 

Many types of statistical models can be used for predictions. However, commonly used models are regression and logistic regression models. Regression models are used to predict continuous numbers such as individual customer future spending, number of online purchases, and product sales. Logistic regression models are for predicting probabilities of future purchases, coupon redemptions, or credit and loan defaults at individual level. For Artificial Intelligent BI apps, predictive models can be used to do predictions. 

If you are interested in adding predictive modeling in your business analyses and BI tools, we can help to get  the ball rolling. We use Python package to develop models and use models to predict futures and uncover tangible relationships, which can automatically process in such more intelligent applications. 

What is Machine Learning?

Machine learning is a process for computers to automatically train models from historical data and use the models to perform tasks automatically, such as to predict future events, detect abnormalities, or recognize patterns.  As new data is fed, the modeling algorithms automatically train statistical models in order to fit new data. The process keeps going so that the models are updated to produce updated outcomes. 

There are 3 types of machine learning, they are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is for solving problems with target values which are the directions of predictions. Regression and classification analyses fall into supervised learning. Unsupervised learning has no targets so computers search for the best fit groups from input entries. Clustering analysis and Bayesian analysis can be unsupervised learning. Reinforcement learning has no clear targets but has indicators for computers to learn and achieve the best results, such as neural network.  

In general, machine learning has six components:

  • Statistical Methodologies, such as classification, linear models, Bayesian probability, and neural network, are foundations of machine learning. 
  • Modern Computers and Information Technologies facilitate data following from input to output, display analytical results, and interact with users. 
  • Statistical Analysis Software, such as Python, SAS, and R, handles lengthy complicated statistical computations which is impossible to be performed by people. 
  • Efficient Computing Programs with Sophisticated Algorithms carry data step-by-step toward solving problems and finding solutions.
  • Data Availability ensures continuous data feeding to produce updated results from the machine. 
  • Human Intelligences and Instructions make machines to do the right jobs.

With increasing computer speed and storage and decreasing in computation cost, machine learning processes are becoming popular in commercial use, academics, and many other fields. It is clear that machine learning will continue growing and getting more mature and more uses in next decades.  

Please contact us if you are interested in adding machine learning processes in your business data analytics or advance BI tools. 

Algorithms for Machine Learning

Modeling Learning For Business