What are the benefits?
Significant increases in computing power have made it possible for machine learning models to learn from vast amounts of data comprising millions of entries and hundreds of variables. These advances have made it possible to unearth hidden or complex relationships between variables, an achievement that no previous methodology could have achieved.
The identification of these relationships paves the way for the generation of models that are able to predict outcomes, estimate the probabilities of various events, give a score to a particular entry, recognize patterns, group entries into homogeneous clusters, or assess sentiments.
Examples of business applications
Nearly any organization can greatly benefit from the adoption of machine learning, which provides tools that are adequate to substantiate most business decisions.
Real-life examples of applications of machine learning include:
How to get started?
- Robot financial advisers
- Medical condition assessment
- Loan application scoring
- Power pricing
- Delay predictions
- Social media sentiment analysis
- Fraud detection
- Customer engagement
- Marketing campaigns
- Insurance premiums
- Financial viability
- Business forecasts
The development of a machine learning model starts by formulating the right question (e.g., "How should I price my products to maximize profits?"), which is usually different from the stated objective (e.g "How can I increase my profits?"). Failing this, and the question will be too broad, and/or the amount of data too large, to arrive at any meaningful and reliable conclusion.
Formulating the right question is often an iterative process, at each step of which available data is further explored to identify or confirm promising avenues for further investigation.
To answer this question, relevant data will need to be made available in a quantity that is sufficient to ensure the statistical significance of the conclusions that will be reached. Such data can come from different sources and be kept in various formats.
Once the right question has been formulated and the data has been made available, we will restructure and "clean" the data to account for missing or erroneous entries. We will then process the data through statistical methods to identify irrelevant variables.
The next phase will be to build several machine learning algorithms, or set of algorithms, and identify which has the lowest margin of error. Once built and trained, the model will be subjected to new data to verify or improve its efficacy.