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An overview of feature engineering for regression and machine learning algorithms





In [1]:



    


import pandas as pd
%matplotlib inline

A simple example to illustrate the intuition behind dummy variables





In [2]:



    


df = pd.DataFrame({'key':['b','b','a','c','a','b'],'data1':range(6)})



    











In [3]:



    


df



    


















    
Out[3]:










  
    

      
      data1
      key
    

  
  
    

      0
      0
      b
    

    

      1
      1
      b
    

    

      2
      2
      a
    

    

      3
      3
      c
    

    

      4
      4
      a
    

    

      5
      5
      b
    

  




















In [4]:



    


pd.get_dummies(df['key'],prefix='key')



    


















    
Out[4]:










  
    

      
      key_a
      key_b
      key_c
    

  
  
    

      0
      0.0
      1.0
      0.0
    

    

      1
      0.0
      1.0
      0.0
    

    

      2
      1.0
      0.0
      0.0
    

    

      3
      0.0
      0.0
      1.0
    

    

      4
      1.0
      0.0
      0.0
    

    

      5
      0.0
      1.0
      0.0

Now we have a matrix of values based on the presence of absence of the attribute value in our dataset

Another example with the flight statistics

Now let's look at another example using our flight data





In [5]:



    


df = pd.read_csv('data/ontime_reports_may_2015_ny.csv')



    











In [6]:



    


#count number of NaNs in column
df['ARR_DELAY'].isnull().sum()



    


















    
Out[6]:








945

















In [7]:



    


#calculate the percentage this represents of the total number of instances
df['ARR_DELAY'].isnull().sum()/df['ARR_DELAY'].sum()



    


















    
Out[7]:








0.0086069493146318145

We could explore whether the NaNs are actually zero delays, but we'll just filter them out for now, especially since they represent such a small number of instances





In [8]:



    


#filter ARR_DELAY NaNs
df = df[pd.notnull(df['ARR_DELAY'])]

We can discretize the continuous DEP_DELAY value by giving it a value of 0 if it's delayed and a 1 if it's not. We record this value into a separate column. (We could also code -1 for early, 0 for ontime, and 1 for late)





In [9]:



    


#code whether delay or not delayed
df['IS_DELAYED'] = df['ARR_DELAY'].apply(lambda x: 1 if x>0 else 0 )



    











In [10]:



    


#Let's check that our column was created properly
df[['ARR_DELAY','IS_DELAYED']]



    


















    
Out[10]:










  
    

      
      ARR_DELAY
      IS_DELAYED
    

  
  
    

      0
      -17.0
      0
    

    

      1
      -14.0
      0
    

    

      2
      -11.0
      0
    

    

      3
      4.0
      1
    

    

      4
      -18.0
      0
    

    

      5
      -6.0
      0
    

    

      6
      -11.0
      0
    

    

      7
      -15.0
      0
    

    

      8
      5.0
      1
    

    

      9
      31.0
      1
    

    

      10
      -11.0
      0
    

    

      11
      -10.0
      0
    

    

      12
      -10.0
      0
    

    

      13
      157.0
      1
    

    

      14
      -8.0
      0
    

    

      15
      5.0
      1
    

    

      16
      7.0
      1
    

    

      17
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      0
    

    

      18
      -24.0
      0
    

    

      19
      -8.0
      0
    

    

      20
      -29.0
      0
    

    

      21
      89.0
      1
    

    

      22
      -16.0
      0
    

    

      23
      -13.0
      0
    

    

      24
      -11.0
      0
    

    

      25
      30.0
      1
    

    

      26
      8.0
      1
    

    

      27
      -8.0
      0
    

    

      28
      -20.0
      0
    

    

      29
      -10.0
      0
    

    

      ...
      ...
      ...
    

    

      45610
      243.0
      1
    

    

      45613
      -10.0
      0
    

    

      45614
      -1.0
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      45615
      -10.0
      0
    

    

      45616
      14.0
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      45617
      -2.0
      0
    

    

      45618
      -13.0
      0
    

    

      45619
      -7.0
      0
    

    

      45620
      7.0
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      45621
      -10.0
      0
    

    

      45622
      17.0
      1
    

    

      45623
      -7.0
      0
    

    

      45624
      0.0
      0
    

    

      45625
      52.0
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      45626
      24.0
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      45627
      -11.0
      0
    

    

      45628
      -13.0
      0
    

    

      45629
      -4.0
      0
    

    

      45633
      -5.0
      0
    

    

      45634
      138.0
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      45635
      1.0
      1
    

    

      45636
      -8.0
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      45637
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      45639
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      45642
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      45643
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      45644
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      45645
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      45646
      -14.0
      0
    

    

      45647
      -2.0
      0
    

  



44703 rows × 2 columns



















In [11]:



    


pd.get_dummies(df['ORIGIN'],prefix='origin') #We'd want to drop one of these before we actually used this in our algorithm



    


















    
Out[11]:










  
    

      
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      origin_BQN
      ...
      origin_SJU
      origin_SLC
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      origin_STL
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      origin_TPA
      origin_XNA
    

  
  
    

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      ...
      0.0
      0.0
      0.0
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      45626
      0.0
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      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
    

    

      45627
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
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      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
    

    

      45628
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
    

    

      45629
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
    

    

      45633
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
    

    

      45634
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
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      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
    

    

      45635
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
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      0.0
      0.0
      0.0
    

    

      45636
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
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      0.0
      ...
      0.0
      0.0
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      0.0
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      45637
      0.0
      0.0
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      0.0
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      ...
      0.0
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      45639
      0.0
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      45642
      0.0
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      ...
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      45643
      0.0
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      45644
      0.0
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      45645
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      ...
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      1.0
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      45646
      0.0
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      0.0
      0.0
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      45647
      0.0
      0.0
      0.0
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      0.0
    

  



44703 rows × 92 columns

Multicollinearity





In [12]:



    


df = pd.read_csv('data/heights_weights_genders.csv')



    











In [13]:



    


pd.get_dummies(df['Gender'],prefix='gender').corr()



    


















    
Out[13]:










  
    

      
      gender_Female
      gender_Male
    

  
  
    

      gender_Female
      1.0
      -1.0
    

    

      gender_Male
      -1.0
      1.0

That's the dummy variable trap

We can also normalize variables across a range





In [14]:



    


from sklearn import preprocessing

x = df[['Height','Weight']].values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df_normalized = pd.DataFrame(x_scaled)



    











In [16]:



    


df['Height'].describe()



    


















    
Out[16]:








count    10000.000000
mean        66.367560
std          3.847528
min         54.263133
25%         63.505620
50%         66.318070
75%         69.174262
max         78.998742
Name: Height, dtype: float64

















In [15]:



    


df_normalized



    


















    
Out[15]:










  
    

      
      0
      1
    

  
  
    

      0
      0.791728
      0.863139
    

    

      1
      0.586958
      0.475476
    

    

      2
      0.802364
      0.721131
    

    

      3
      0.706182
      0.756699
    

    

      4
      0.631424
      0.689999
    

    

      5
      0.525149
      0.426286
    

    

      6
      0.587087
      0.580778
    

    

      7
      0.569437
      0.503050
    

    

      8
      0.515686
      0.541817
    

    

      9
      0.371665
      0.446684
    

    

      10
      0.684529
      0.593819
    

    

      11
      0.702537
      0.726004
    

    

      12
      0.424618
      0.498941
    

    

      13
      0.607219
      0.607659
    

    

      14
      0.605629
      0.592987
    

    

      15
      0.541021
      0.523586
    

    

      16
      0.733970
      0.639723
    

    

      17
      0.392600
      0.526979
    

    

      18
      0.621651
      0.590794
    

    

      19
      0.552761
      0.573466
    

    

      20
      0.551914
      0.532983
    

    

      21
      0.613541
      0.648018
    

    

      22
      0.480522
      0.411484
    

    

      23
      0.846668
      0.799172
    

    

      24
      0.551030
      0.473996
    

    

      25
      0.561171
      0.621775
    

    

      26
      0.599399
      0.583250
    

    

      27
      0.749434
      0.692330
    

    

      28
      0.531950
      0.538331
    

    

      29
      0.575417
      0.436664
    

    

      ...
      ...
      ...
    

    

      9970
      0.459079
      0.422819
    

    

      9971
      0.419521
      0.441416
    

    

      9972
      0.258356
      0.284399
    

    

      9973
      0.261724
      0.273888
    

    

      9974
      0.449991
      0.381499
    

    

      9975
      0.484677
      0.288957
    

    

      9976
      0.291595
      0.276132
    

    

      9977
      0.413643
      0.412600
    

    

      9978
      0.125836
      0.241084
    

    

      9979
      0.315047
      0.294393
    

    

      9980
      0.251020
      0.224405
    

    

      9981
      0.249848
      0.345167
    

    

      9982
      0.630887
      0.551864
    

    

      9983
      0.467649
      0.331862
    

    

      9984
      0.193401
      0.228980
    

    

      9985
      0.557008
      0.515438
    

    

      9986
      0.367469
      0.376085
    

    

      9987
      0.458736
      0.421207
    

    

      9988
      0.213279
      0.275439
    

    

      9989
      0.270539
      0.150941
    

    

      9990
      0.360467
      0.372966
    

    

      9991
      0.338522
      0.185852
    

    

      9992
      0.315929
      0.360425
    

    

      9993
      0.233158
      0.160687
    

    

      9994
      0.195472
      0.223243
    

    

      9995
      0.481473
      0.351101
    

    

      9996
      0.517635
      0.517161
    

    

      9997
      0.388301
      0.310660
    

    

      9998
      0.597160
      0.482988
    

    

      9999
      0.310529
      0.238439
    

  



10000 rows × 2 columns

this helps keep the attributes within a standard range





In [ ]:

Content source: ledeprogram/algorithms

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