teghwin_portfolio | insurance companies

teghwin_portfolio is a Jupyter Notebook library. teghwin_portfolio has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

insurance companies are constantly looking for ways to accurately assess the risk of insuring different vehicles. this is important, as it allows them to set appropriate premiums and ensure that they can cover potential losses. this project presents a statistical analysis that uses machine learning to predict the risk rating of automobiles based on their characteristics. more specifically, it will investigate the affect of a car’s specifications on the assigned insurance risk rating (symbol) of a vehicle which is a significant factor when calculating insurance premiums. this analysis will not only assist insurance companies but also help consumers become more aware about the key drivers of their insurance rates. tools used: r studio, random forests, boosted forests, pca, clustering, gg plot. public bike sharing services often face an unbalanced distribution of bikes among the stations across the city. as users tend to follow specific destination flows when going to work, school, or other daily activities (mainly moving from residential areas to downtown in the morning, and vice-versa in the afternoon), many stations with a high demand for bikes end up being full, which stops other users from being able to dock their bicycles at their destination.). the final objective was to develop a model which suggests the optimal bike relocating strategy around the city, and consequently allowing more customers to utilize the