Bike Sharing Demand

Libraries

• numpy
• pandas
• matplotlib
• seaborn
• datetime
• calendar
• sklearn
  • RandomForestRegressor
  • preprocessing

Columns

• 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

Process

1. 匯入資料和套件
2. 處理時間格式
3. 異常值分析
4. 觀察參數分布情形
5. 將風速為0的資料進行補遺
6. 相關性分析
7. 透過取log使單車租借數量(count)分布接近Normal Distribution
8. 特徵選取
9. 使用Random forest進行預測
10. 訓練模型和預測結果
11. 提交結果

Model Performance

Public Score: 0.42968