The machine learning technology has proved its value in solving a wide range of problems across industries, but this doesn’t mean that a business should hastily rush for implementing ML to its operations without knowing the important factors mentioned in this post.
Businesses and enterprises are using Machine Learning to create predictive algorithms in various operations; from forecasting stock market to predicting when manufacturing equipment will fail and when a specific patient may be at the risk of opioid abuse.
However, businesses still want their teams to get going with ML projects, but it isn’t the only solution for every problem according to machine learning practitioners. They will require putting their focus more on predictive algorithm too.
There are four necessary components required before a business moves into machine learning predictive modeling.
- a chosen algorithm to be worked with
- a large amount of good unsiloed data;
- compute resources, (cloud based preferred); and
- a team with the right set of skills.
So, what is predictive modeling?
There are a number of predictive modeling methods from machine learning, artificial intelligence, and statistics currently in trend. The process of predictive modeling is to create, test, and validate a model that best predicts an outcome’s probability. A model is selected on the basis of testing, validation, and evaluation using the detection theory to presume the possibility of an outcome in a given set of data input. One or more classifiers can be used in models to determine the probability of a set of data suiting to another set. Different models available on a modeling portfolio of predictive analytics software enable to get new information about the data to develop predictive models. Every model comes with a unique set of strengths and weaknesses and fits specific sort of problem. Models are reusable and created by training an algorithm with using the historical data.
So, a predictive model is based on the process of using data mining and probability to predict outcomes. It’s made up of several predictors, which are variables that probably influence or affect future results. When data is collected for related predictors, a statistical model is devised. Depending of requirements, a model can use a simple linier equation or a complex neural network. Afterwards, with the availability of additional data, the statistical analysis of a model is validated or revised.
A machine learning application works only when it is properly addressed for specific business problems and the established rules around it.
Here are the key components a machine learning solution must have:
- Good data
- Compute resources
Some important considerations
- An enterprise, going to implement machine learning solution, should be comfortable with storing their data outside their own servers. A large amount of ML data is stored in the cloud. Storing data in cloud cuts significant infrastructural costs.
- An important point to be noted is that the data aimed for analysis with a predictive algorithm should be unsoiled across proprietary systems. It needs to be integrated and, thus there is the need of a data preparation process.
- Make sure that you are integrating not only data and platforms but also domain experts. Domain experts bring priceless information, knowledge, and skills to the data science team
Commonly used machine learning algorithms
- Linear Regression
- Logistic Regression
- Decision Tree
- Naive Bayes
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting algorithms
Features in predictive modeling
Data Analysis and manipulation: Tools for data analysis, create new data sets, modify club, categorize, mere and filter data sets
- Visualization: The Visualization feature includes interactive graphics and reports
- Statistics: Statistics tools to form and confirm the relationship between variable in the data. Statistics from diverse statistical software can be integrated to some of the solutions.
- Hypotheses testing: Creating models, evolution and selecting the right model.
Of course, it’s critical to choose the right machine learning algorithm and, because there are plenty of them available, it becomes further difficult to come to a decision. But experts machine learning developers and consultants can make the job easier.
Applications of machine learning and predictive algorithms
Predictive analytics is generally applied in the ML application development for security, marketing, operations, and in the detection risks and frauds. Some of the areas where machine learning application development has been applied are as follows:
- Banking and Financial Services=
Machine learning and predictive algorithm help banks and financial service provider to detect and reduce fraud, measure market risks, and spot opportunities and so on.
- Cyber Security
Cyber security is the top concern of almost all enterprises. No surprise ML and predictive analytics can also be used to detect anomalies, frauds, and to understand the behavior of consumers.
Online retailers are also using machine learning and predictive algorithm to have in-depth understanding of consumer behaviors like who buys what, when and where.
Author Bio. :- Sofia Coppol is a digital marketing expert in Rapidsoft Technologies, a leading IOT Development Company which provides Software for Education, Automation, Construction and Finance across the global. She loves to write on latest mobile trends, mobile technologies, startups and enterprises.