Traffic Jam: Predicting People's Movement into Nairobi
$12,000 USD
6 September 2018–13 January 2019 23:59
Uber and Mobiticket team up to predict demand for public transportation into Nairobi
Exploratory Data Analysis
published 29 Oct 2018, 11:50

Hi all,

I did some EDA on the data as I considered some features I wanted to use to build a model. It's all very rough and I look forward to hearing your thoughts on what might be improved.

https://github.com/princelySid/zindi_traffic/blob/master/EDA.ipynb

It is impressive.

One question. Why didn't you use the to_datetime() function while extrating 'hour_booked'?

I didn't think to:D It shouldn't make a difference though, or does it?

I'm not sure myself but running indexing the hour part in the converted travel_time field should give you the hour.

Something like this:

df["travel_time"] = pd.to_datetime(df["travel_time"],infer_datetime_format=True)

df["hour_booked"] = df["travel_time"].dt.hour