As Artificial Intelligence (AI) continues to progress rapidly, achieving mastery over Machine Learning (ML) is becoming increasingly important for all the players in this field. This is because both AI and ML complement each other.

People are looking for different methods of investment, from currency exchanges to buying companies’ shares and trading futures. All these methods share one major flaw – already heavily applied Machine Learning and prediction models damaged these markets so severely, that it is almost impossible for the small and middle-size investor (who have a couple of thousands of dollars) to make a profit.

Sports forecasting is different – results of sporting events cannot be skewed by any number of subjects (bettors) operating on the market. The match will end exactly the same no matter how many people bets on which team.

And the results can be predicted with extreme accuracy: I already have Machine Learning model which is able to predict Soccer results with over 80% accuracy, and it indeed makes a profit (for leagues I have data about). The same modeling technique can be applied to any sport… All is needed is a constant flow of statistical sports data… Already achieved 80% accuracy on the sports market actually gives a higher profit than any above-mentioned investment method.

Business organizations and companies today are on the lookout for software that can monitor and analyze the company performance and predict future prices of various stocks. And with so much data available on the stock market, it is a hotbed of opportunities for data scientists with an inclination for finance.

• Predictive Analysis: Leveraging various AI techniques for different data processes such as data mining, data exploration, etc. to ‘predict’ the behavior of possible outcomes.
• Regression Analysis: Regressive analysis is a kind of predictive technique based on the interaction between a dependent (target) and independent variable/s (predictor).
• Action Analysis: In this method, all the actions carried out by the two techniques mentioned above are analyzed after which the outcome is fed into the machine learning memory.
• Statistical Modeling: It involves building a mathematical description of a real-world process and elaborating the uncertainties, if any, within that process.