Airbnb presents listings of properties which includes detailed data and rent price, thereby it can be assumed that there is a reasonable connection between the property details and the rental price asked. This assumption can be interpreted to mathematical and statistical models, that can be learned by learning algorithms.
Machine learning is the scientific study of algorithms that build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. In this project, we will examine the performance of the most well-known machine learnings algorithms on the Airbnb data, in order to complete the project’s goal which is to show success in the prediction of the prices, while the other details are used as features.
In this project we were first introduced to the world of machine learning. Therefore, the first step was learning the theory of the algorithms and the working methods. Later, we first learned to work with Python programing language which is best suited for machine learning implementations.
The process of testing the algorithms involved considering correct pre-process of the data, problem fitted estimation parameters and features manipulations for best performance. Performance assessments were made for both regression and classification problems, and details of the assessment parameters (success rate, training time and inference time) are presented in the report.