ium_444354/.ipynb_checkpoints/lab2-checkpoint.ipynb

!pip install opendatasets

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import opendatasets as od
od.download('https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009')

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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