As international or domestic students, many students arrive from various locations and countries, making it difficult for them to estimate the rent they must pay. Moreover, each student has unique requirements and options. As a result, their calculations for budgeting and spending could be challenging. Since housing is one of the most important basic requirements, we decided to do an analysis that will be useful for the current students as well as future cohorts in determining the proper rent price that is most suitable for them in order to attend the university. Each student has a unique background and specific preference. This is why we chose this dataset, which included characteristics such as the number of bedrooms, bathrooms, furnished options, laundry, parking options, pets allowed, smoking allowed or not, wheelchair accessibility, or even electric vehicle charging capabilities. We believe that these characteristics could be the factor contributing to the rental price.
- Which residential type is the most suitable to rent based on the individual requirements and amenities? (Logistic Regression)
- According to the students' individual needs and amenities, which price is the most suitable? (Random Forest Regression and XgBoost Regression)
- With respect to price and amenities, which region to expect? In addition, which features play an important role in prediction. (Random Forest Classifier)
- What is the expected number of beds based on the region and amenities? (Random Forest Classifier)