The dataset consists of 26,000 people counts (about every 10 minutes) over the last year. In addition, I gathered extra info including weather and semester-specific information that might affect how crowded it is. The label is the number of people, which I'd like to predict given some subset of the features.

Label:

• Number of people

Features:

• date (string; datetime of data)

• timestamp (int; number of seconds since beginning of day)

• day_of_week (int; 0 [monday] - 6 [sunday])

• is_weekend (int; 0 or 1) [boolean, if 1, it's either saturday or sunday, otherwise 0]

• is_holiday (int; 0 or 1) [boolean, if 1 it's a federal holiday, 0 otherwise]

• temperature (float; degrees fahrenheit)

• is_start_of_semester (int; 0 or 1) [boolean, if 1 it's the beginning of a school semester, 0 otherwise]

• month (int; 1 [jan] - 12 [dec])

• hour (int; 0 - 23)