Bike Sharing Demand analysis

We must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period.

Data

1. datetime - hourly date + timestamp
2. season - 1 = spring, 2 = summer, 3 = fall, 4 = winter
3. holiday - whether the day is considered a holiday
4. workingday - whether the day is neither a weekend nor holiday
5. weather - 1: Clear, Few clouds, Partly cloudy, Partly cloudy 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
6. temp - temperature in Celsius
7. atemp - "feels like" temperature in Celsius
8. humidity - relative humidity
9. windspeed - wind speed
10. casual - number of non-registered user rentals initiated
11. registered - number of registered user rentals initiated
12. count - number of total rentals

Key inferences

1. Use all information stored in date field. Feeds must include:

   • hour
   • month
   • day_of_weed (1-7)
   • year

2. It is a good idea to use log transform (np.log1p, np.expm1) for 'count' column. The data is skewed to the left so log transformation makes it more 'normal' thus improves overall score from 0.5 to 0.42 (the most valuable improvement)

Model

1. tuned LGB (0.4)
2. tuned XGB (0.41)