In the result, we can see the r square is really low, and it says the MTA turnstile has weak relation to the weather condition. After removing 2 of weather conditions(snow, cloudy), the r square is still remaining 0.013, it tells us that the snow and cloudy don't have an impact on the prediction. On the other hand, I am also wondering if there is any relation between weekday and the turnstile, so I add week dummy variables and run the regression. Apparently, the r square increase to 0.032, and it tells me that there is a pattern of the turnstile based on the time. To improve the work, I think I have to add more feature to improve the prediction of MTA turnstile.

Reference:

NYC's Hottest Subway Stations, Mapped : http://www.wnyc.org/story/hottest-subway-stations-map-nyc/
Impacts of weather on public transport ridership: Results from mining data from different sources

Deliverable:

A real-time prediction of the number of people takes MTA based on the weather condition.
Agency: MTA