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!pip install opendatasets


Requirement already satisfied: opendatasets in c:\users\riraa\anaconda3\lib\site-packages (0.1.20)
Requirement already satisfied: tqdm in c:\users\riraa\anaconda3\lib\site-packages (from opendatasets) (4.59.0)
Requirement already satisfied: click in c:\users\riraa\anaconda3\lib\site-packages (from opendatasets) (7.1.2)
Requirement already satisfied: kaggle in c:\users\riraa\appdata\roaming\python\python38\site-packages (from opendatasets) (1.5.12)
Requirement already satisfied: requests in c:\users\riraa\anaconda3\lib\site-packages (from kaggle->opendatasets) (2.25.1)
Requirement already satisfied: six>=1.10 in c:\users\riraa\anaconda3\lib\site-packages (from kaggle->opendatasets) (1.15.0)
Requirement already satisfied: certifi in c:\users\riraa\anaconda3\lib\site-packages (from kaggle->opendatasets) (2020.12.5)
Requirement already satisfied: urllib3 in c:\users\riraa\anaconda3\lib\site-packages (from kaggle->opendatasets) (1.26.4)
Requirement already satisfied: python-slugify in c:\users\riraa\appdata\roaming\python\python38\site-packages (from kaggle->opendatasets) (6.1.1)
Requirement already satisfied: python-dateutil in c:\users\riraa\anaconda3\lib\site-packages (from kaggle->opendatasets) (2.8.1)
Requirement already satisfied: text-unidecode>=1.3 in c:\users\riraa\appdata\roaming\python\python38\site-packages (from python-slugify->kaggle->opendatasets) (1.3)
Requirement already satisfied: idna<3,>=2.5 in c:\users\riraa\anaconda3\lib\site-packages (from requests->kaggle->opendatasets) (2.10)
Requirement already satisfied: chardet<5,>=3.0.2 in c:\users\riraa\anaconda3\lib\site-packages (from requests->kaggle->opendatasets) (4.0.0)

import opendatasets as od
od.download('https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009')
100%|█████████████████████████████████████████████████████████████████████████████| 25.6k/25.6k [00:00<00:00, 1.68MB/s]
Downloading red-wine-quality-cortez-et-al-2009.zip to .\red-wine-quality-cortez-et-al-2009


import pandas as pd
wine=pd.read_csv('./red-wine-quality-cortez-et-al-2009/winequality-red.csv')
wine




  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
      quality
    

  
  
    

      0
      7.4
      0.700
      0.00
      1.9
      0.076
      11.0
      34.0
      0.99780
      3.51
      0.56
      9.4
      5
    

    

      1
      7.8
      0.880
      0.00
      2.6
      0.098
      25.0
      67.0
      0.99680
      3.20
      0.68
      9.8
      5
    

    

      2
      7.8
      0.760
      0.04
      2.3
      0.092
      15.0
      54.0
      0.99700
      3.26
      0.65
      9.8
      5
    

    

      3
      11.2
      0.280
      0.56
      1.9
      0.075
      17.0
      60.0
      0.99800
      3.16
      0.58
      9.8
      6
    

    

      4
      7.4
      0.700
      0.00
      1.9
      0.076
      11.0
      34.0
      0.99780
      3.51
      0.56
      9.4
      5
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      1594
      6.2
      0.600
      0.08
      2.0
      0.090
      32.0
      44.0
      0.99490
      3.45
      0.58
      10.5
      5
    

    

      1595
      5.9
      0.550
      0.10
      2.2
      0.062
      39.0
      51.0
      0.99512
      3.52
      0.76
      11.2
      6
    

    

      1596
      6.3
      0.510
      0.13
      2.3
      0.076
      29.0
      40.0
      0.99574
      3.42
      0.75
      11.0
      6
    

    

      1597
      5.9
      0.645
      0.12
      2.0
      0.075
      32.0
      44.0
      0.99547
      3.57
      0.71
      10.2
      5
    

    

      1598
      6.0
      0.310
      0.47
      3.6
      0.067
      18.0
      42.0
      0.99549
      3.39
      0.66
      11.0
      6
    

  



1599 rows × 12 columns


from sklearn.model_selection import train_test_split
wine_train, wine_test = train_test_split(wine, test_size=50, random_state=1,stratify=wine["quality"])
wine_train["quality"].value_counts().sort_index(ascending=False) 
8     17
7    193
6    618
5    660
4     51
3     10
Name: quality, dtype: int64
Wielkość zbioru i podzbiorów


Dla całego zbioru


wine.head()




  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
      quality
    

  
  
    

      0
      7.4
      0.70
      0.00
      1.9
      0.076
      11.0
      34.0
      0.9978
      3.51
      0.56
      9.4
      5
    

    

      1
      7.8
      0.88
      0.00
      2.6
      0.098
      25.0
      67.0
      0.9968
      3.20
      0.68
      9.8
      5
    

    

      2
      7.8
      0.76
      0.04
      2.3
      0.092
      15.0
      54.0
      0.9970
      3.26
      0.65
      9.8
      5
    

    

      3
      11.2
      0.28
      0.56
      1.9
      0.075
      17.0
      60.0
      0.9980
      3.16
      0.58
      9.8
      6
    

    

      4
      7.4
      0.70
      0.00
      1.9
      0.076
      11.0
      34.0
      0.9978
      3.51
      0.56
      9.4
      5
    

  



wine.describe()




  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
      quality
    

  
  
    

      count
      1599.000000
      1599.000000
      1599.000000
      1599.000000
      1599.000000
      1599.000000
      1599.000000
      1599.000000
      1599.000000
      1599.000000
      1599.000000
      1599.000000
    

    

      mean
      8.319637
      0.527821
      0.270976
      2.538806
      0.087467
      15.874922
      46.467792
      0.996747
      3.311113
      0.658149
      10.422983
      5.636023
    

    

      std
      1.741096
      0.179060
      0.194801
      1.409928
      0.047065
      10.460157
      32.895324
      0.001887
      0.154386
      0.169507
      1.065668
      0.807569
    

    

      min
      4.600000
      0.120000
      0.000000
      0.900000
      0.012000
      1.000000
      6.000000
      0.990070
      2.740000
      0.330000
      8.400000
      3.000000
    

    

      25%
      7.100000
      0.390000
      0.090000
      1.900000
      0.070000
      7.000000
      22.000000
      0.995600
      3.210000
      0.550000
      9.500000
      5.000000
    

    

      50%
      7.900000
      0.520000
      0.260000
      2.200000
      0.079000
      14.000000
      38.000000
      0.996750
      3.310000
      0.620000
      10.200000
      6.000000
    

    

      75%
      9.200000
      0.640000
      0.420000
      2.600000
      0.090000
      21.000000
      62.000000
      0.997835
      3.400000
      0.730000
      11.100000
      6.000000
    

    

      max
      15.900000
      1.580000
      1.000000
      15.500000
      0.611000
      72.000000
      289.000000
      1.003690
      4.010000
      2.000000
      14.900000
      8.000000
    

  



wine["quality"].value_counts().sort_index(ascending=False)
8     18
7    199
6    638
5    681
4     53
3     10
Name: quality, dtype: int64
wine["quality"].value_counts().sort_index(ascending=False).plot(kind="bar")
<AxesSubplot:>
Dla podzbioru _train


wine_train.head()




  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
      quality
    

  
  
    

      1453
      7.6
      0.49
      0.33
      1.9
      0.074
      27.0
      85.0
      0.99706
      3.41
      0.58
      9.0
      5
    

    

      1295
      6.6
      0.63
      0.00
      4.3
      0.093
      51.0
      77.5
      0.99558
      3.20
      0.45
      9.5
      5
    

    

      778
      8.3
      0.43
      0.30
      3.4
      0.079
      7.0
      34.0
      0.99788
      3.36
      0.61
      10.5
      5
    

    

      692
      8.6
      0.49
      0.51
      2.0
      0.422
      16.0
      62.0
      0.99790
      3.03
      1.17
      9.0
      5
    

    

      166
      6.8
      0.64
      0.10
      2.1
      0.085
      18.0
      101.0
      0.99560
      3.34
      0.52
      10.2
      5
    

  



wine_train.describe()




  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
      quality
    

  
  
    

      count
      1549.000000
      1549.000000
      1549.000000
      1549.000000
      1549.000000
      1549.000000
      1549.000000
      1549.000000
      1549.000000
      1549.000000
      1549.000000
      1549.000000
    

    

      mean
      8.327566
      0.528128
      0.271252
      2.529987
      0.086944
      15.832150
      46.415107
      0.996746
      3.310484
      0.656727
      10.419141
      5.635249
    

    

      std
      1.744692
      0.180152
      0.194249
      1.380202
      0.043732
      10.450522
      32.884454
      0.001877
      0.154269
      0.166558
      1.067245
      0.807313
    

    

      min
      4.600000
      0.120000
      0.000000
      0.900000
      0.012000
      1.000000
      6.000000
      0.990070
      2.860000
      0.330000
      8.400000
      3.000000
    

    

      25%
      7.100000
      0.390000
      0.090000
      1.900000
      0.070000
      7.000000
      22.000000
      0.995600
      3.210000
      0.550000
      9.500000
      5.000000
    

    

      50%
      7.900000
      0.520000
      0.260000
      2.200000
      0.079000
      13.000000
      38.000000
      0.996750
      3.310000
      0.620000
      10.100000
      6.000000
    

    

      75%
      9.200000
      0.640000
      0.430000
      2.600000
      0.090000
      21.000000
      62.000000
      0.997860
      3.400000
      0.730000
      11.100000
      6.000000
    

    

      max
      15.900000
      1.580000
      0.790000
      15.500000
      0.467000
      72.000000
      289.000000
      1.003690
      4.010000
      1.980000
      14.900000
      8.000000
    

  



wine_train["quality"].value_counts().sort_index(ascending=False) #indexy oznaczają jakość wina
8     17
7    193
6    618
5    660
4     51
3     10
Name: quality, dtype: int64
wine_train["quality"].value_counts().sort_index(ascending=False).plot(kind="bar")
<AxesSubplot:>
Dla podzbioru _test


wine_test.head()




  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
      quality
    

  
  
    

      856
      9.3
      0.36
      0.39
      1.5
      0.080
      41.0
      55.0
      0.99652
      3.47
      0.73
      10.9
      6
    

    

      1142
      6.9
      0.45
      0.11
      2.4
      0.043
      6.0
      12.0
      0.99354
      3.30
      0.65
      11.4
      6
    

    

      538
      12.9
      0.35
      0.49
      5.8
      0.066
      5.0
      35.0
      1.00140
      3.20
      0.66
      12.0
      7
    

    

      1324
      6.7
      0.46
      0.24
      1.7
      0.077
      18.0
      34.0
      0.99480
      3.39
      0.60
      10.6
      6
    

    

      288
      8.7
      0.52
      0.09
      2.5
      0.091
      20.0
      49.0
      0.99760
      3.34
      0.86
      10.6
      7
    

  



wine_test.describe()




  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
      quality
    

  
  
    

      count
      50.000000
      50.000000
      50.000000
      50.000000
      50.00000
      50.000000
      50.000000
      50.000000
      50.000000
      50.000000
      50.000000
      50.000000
    

    

      mean
      8.074000
      0.518300
      0.262400
      2.812000
      0.10364
      17.200000
      48.100000
      0.996779
      3.330600
      0.702200
      10.542000
      5.660000
    

    

      std
      1.622899
      0.142197
      0.213155
      2.137769
      0.10746
      10.777906
      33.525653
      0.002199
      0.158338
      0.242035
      1.018621
      0.823383
    

    

      min
      5.600000
      0.310000
      0.000000
      1.500000
      0.03800
      3.000000
      8.000000
      0.992920
      2.740000
      0.370000
      9.000000
      4.000000
    

    

      25%
      6.900000
      0.402500
      0.095000
      1.900000
      0.07325
      10.000000
      25.250000
      0.995445
      3.260000
      0.590000
      9.725000
      5.000000
    

    

      50%
      7.650000
      0.500000
      0.245000
      2.200000
      0.08000
      15.000000
      36.500000
      0.996560
      3.320000
      0.655000
      10.350000
      6.000000
    

    

      75%
      9.150000
      0.625000
      0.400000
      2.675000
      0.08625
      23.750000
      62.000000
      0.997600
      3.400000
      0.770000
      11.175000
      6.000000
    

    

      max
      12.900000
      0.980000
      1.000000
      15.400000
      0.61100
      55.000000
      143.000000
      1.003690
      3.710000
      2.000000
      12.800000
      8.000000
    

  



wine_test["quality"].value_counts().sort_index(ascending=False) #indexy oznaczają jakość wina
8     1
7     6
6    20
5    21
4     2
Name: quality, dtype: int64
wine_test["quality"].value_counts().sort_index(ascending=False).plot(kind="bar")
<AxesSubplot:>
Normalizacja


Podział z wyróżnieniem data/target


x_train,x_test,y_train,y_test = train_test_split(wine.iloc[:,:-1],wine.iloc[:,-1], test_size=0.2, random_state=1,stratify=wine["quality"])
y_train.value_counts().sum()
1279
y_test.value_counts().sum()
320
Normalizacja


from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler()
norm_fit = norm.fit(x_train)
norm_x_train = norm_fit.transform(x_train)
norm_x_test = norm_fit.transform(x_test)
norm_x_train[:5]
array([[0.31858407, 0.15702479, 0.50632911, 0.0890411 , 0.1010989 ,
        0.07042254, 0.01413428, 0.38839941, 0.39130435, 0.21212121,
        0.43076923],
       [0.26548673, 0.14049587, 0.62025316, 0.12328767, 0.17582418,
        0.33802817, 0.19081272, 0.51615272, 0.39130435, 0.16969697,
        0.26153846],
       [0.23893805, 0.17355372, 0.59493671, 0.08219178, 0.14285714,
        0.05633803, 0.01766784, 0.42070485, 0.40869565, 0.12121212,
        0.29230769],
       [0.19469027, 0.31404959, 0.13924051, 0.04109589, 0.13846154,
        0.21126761, 0.15194346, 0.39500734, 0.43478261, 0.27878788,
        0.16923077],
       [0.27433628, 0.65702479, 0.15189873, 0.0890411 , 0.28791209,
        0.08450704, 0.06007067, 0.46475771, 0.42608696, 0.19393939,
        0.27692308]])
Nie ma żadnych null'i do wypełnienia


wine.isnull().sum()
fixed acidity           0
volatile acidity        0
citric acid             0
residual sugar          0
chlorides               0
free sulfur dioxide     0
total sulfur dioxide    0
density                 0
pH                      0
sulphates               0
alcohol                 0
quality                 0
dtype: int64