https://www.kaggle.com/c/bike-sharing-demand/overview

• data/ - the datasets
• submission/ - output of the solution
• Data Visualization.ipynb - data visualization
• GBDT_RF.py - solution based on Gradient Boost and Random Forest, rank 24/3251 (0.36535) within 60 lines of code
• GBDT_RF.ipynb - detailed solution with some explanations

• datetime - hourly date + timestamp
• season - 1 = spring, 2 = summer, 3 = fall, 4 = winter
• holiday - whether the day is considered a holiday
• workingday - whether the day is neither a weekend nor holiday
• 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
• temp - temperature in Celsius
• atemp - "feels like" temperature in Celsius
• humidity - relative humidity
• windspeed - wind speed
• casual - number of non-registered user rentals initiated
• registered - number of registered user rentals initiated
• count - number of total rentals

• Inspired by https://github.com/logicalguess/kaggle-bike-sharing-demand