This project looks at two different datasets: One that has information about NYC rides, and one that has information
about Bay Area bike sharing rides. The data contains information about ride durations for rider and surrounding
information on the rider and weather, etc. This project is a data analysis project that hopes to see how these
surrounding factors can influence the duration of the drives.
One thing I like about this project is how they have multiple datasets. For a project as narrow as this, multiple datasets
are important. I also like the idea of assessing ride duration by comparing it to google maps expected times. Lastly, I like how the datasets constitute 4 consecutive years, going from 2013-2017.
In terms of areas of improvement, one thing I am worried about is that you guys don't have enough features to accurately predict ride length. Maybe bringing in some additional datasets would be helpful. Additionally, in terms of both your datasets, I am unsure how, considering the datasets come from different years, you can combine the features together. Additionally, I think you guys would need to perform a lot of feature transformations considering the limited number of features.
This project looks at two different datasets: One that has information about NYC rides, and one that has information about Bay Area bike sharing rides. The data contains information about ride durations for rider and surrounding information on the rider and weather, etc. This project is a data analysis project that hopes to see how these surrounding factors can influence the duration of the drives.
One thing I like about this project is how they have multiple datasets. For a project as narrow as this, multiple datasets are important. I also like the idea of assessing ride duration by comparing it to google maps expected times. Lastly, I like how the datasets constitute 4 consecutive years, going from 2013-2017.
In terms of areas of improvement, one thing I am worried about is that you guys don't have enough features to accurately predict ride length. Maybe bringing in some additional datasets would be helpful. Additionally, in terms of both your datasets, I am unsure how, considering the datasets come from different years, you can combine the features together. Additionally, I think you guys would need to perform a lot of feature transformations considering the limited number of features.