Regarding my background as a student of OAMK Business Information Technology I have learnt several programming languages and their uses. However, I have not been taught machine learning nor have I been familiar with what it can achieve. Being both a technology and business student I think it is important to teach OAMK students machine learning since it will teach them about modern technologies as well help them in the business side by aiding decision making. This thesis will present how simple it is to implement machine learning as well to demonstrate its effectiveness by using real life examples. It is to motivate the reader that they can use machine learning as well with little to no experience and to highlight the benefits gained from the experience. Examples of machine learning models will be coded and run in python language since it has many resources as well as support for machine learning. The models covered in the thesis are linear regression, K Nearest Neighbors, and decision tree classifier. The list of concepts will include overfitting and underfitting, supervised and unsupervised machine learning, and classification and regression. The main focus of this thesis will be supervised learning. After these models have been coded, examples of them used in real life will be discussed.

The resulting scores from the models created have been relatively high therefore it can be understood that machine learning is easy to implement and beneficial to research upon. The models use can also be extended to uses case found in real life such as selecting the best tenant or estimating the price of a used car. However, it must be reiterated that the models as well as the process of machine learning in this thesis is simple and will not reflect real life situations. But a lot can be learnt for those who are unfamiliar to machine learning and possibly motivate readers to use machine learning in their whether for personal use or in work life.