

In [13]:

    
import pandas as pd
import numpy as np
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.metrics import make_scorer

Read data



In [4]:

    
train = pd.read_csv('train.csv')
train.head()









    Out[4]:






  
    
      
      datetime
      season
      holiday
      workingday
      weather
      temp
      atemp
      humidity
      windspeed
      casual
      registered
      count
    
  
  
    
      0
      2011-01-01 00:00:00
      1
      0
      0
      1
      9.84
      14.395
      81
      0
      3
      13
      16
    
    
      1
      2011-01-01 01:00:00
      1
      0
      0
      1
      9.02
      13.635
      80
      0
      8
      32
      40
    
    
      2
      2011-01-01 02:00:00
      1
      0
      0
      1
      9.02
      13.635
      80
      0
      5
      27
      32
    
    
      3
      2011-01-01 03:00:00
      1
      0
      0
      1
      9.84
      14.395
      75
      0
      3
      10
      13
    
    
      4
      2011-01-01 04:00:00
      1
      0
      0
      1
      9.84
      14.395
      75
      0
      0
      1
      1

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

Build a model



In [7]:

    
model = ExtraTreesRegressor()

train

Prepare two things: X - features. Matrix. y - target variable. Vector.



In [8]:

    
X = train[ ['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed'] ].values
y = train['count'].values

model.fit(X, y)









    Out[8]:





ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,
          max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
          min_samples_split=2, min_weight_fraction_leaf=0.0,
          n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
          verbose=0, warm_start=False)

Evoluation a model

$$ \sqrt{\frac{1}{n} \sum_{i=1}^n (\log(p_i + 1) - \log(a_i+1))^2 }$$

where
n is the number of hours in the test set
pi is your predicted count
ai is the actual count
log(x) is the natural logarithm



In [10]:

    
def rmsle(y_true, y_pred):
    diff = np.log(y_pred + 1) - np.log(y_true + 1)
    mean_error = np.square(diff).mean()
    return np.sqrt(mean_error)

scorer = make_scorer(rmsle, greater_is_better=False)



In [14]:

    
y_pred = model.predict(X)
rmsle(y, y_pred)









    Out[14]:





0.56071612274478655

That's all :)

To be honest this solution is very simple, but contains some big problems... do you see it?



In [ ]:

	datetime	season	weather	temp	atemp	humidity	casual	registered	count
0	2011-01-01 00:00:00	1	1	9.84	14.395	81	3	13	16
1	2011-01-01 01:00:00	1	1	9.02	13.635	80	8	32	40
2	2011-01-01 02:00:00	1	1	9.02	13.635	80	5	27	32
3	2011-01-01 03:00:00	1	1	9.84	14.395	75	3	10	13
4	2011-01-01 04:00:00	1	1	9.84	14.395	75	0	1	1