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belushkin/Keras last version.ipynb

Created February 25, 2019 15:29
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Keras last version.ipynb
{
"nbformat_minor": 1,
"cells": [
{
"execution_count": 21,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 21,
"metadata": {},
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>fixed acidity</th>\n <th>volatile acidity</th>\n <th>citric acid</th>\n <th>residual sugar</th>\n <th>chlorides</th>\n <th>free sulfur dioxide</th>\n <th>total sulfur dioxide</th>\n <th>density</th>\n <th>pH</th>\n <th>sulphates</th>\n <th>alcohol</th>\n <th>quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>7.0</td>\n <td>0.27</td>\n <td>0.36</td>\n <td>20.7</td>\n <td>0.045</td>\n <td>45.0</td>\n <td>170.0</td>\n <td>1.0010</td>\n <td>3.00</td>\n <td>0.45</td>\n <td>8.8</td>\n <td>6</td>\n </tr>\n <tr>\n <th>1</th>\n <td>6.3</td>\n <td>0.30</td>\n <td>0.34</td>\n <td>1.6</td>\n <td>0.049</td>\n <td>14.0</td>\n <td>132.0</td>\n <td>0.9940</td>\n <td>3.30</td>\n <td>0.49</td>\n <td>9.5</td>\n <td>6</td>\n </tr>\n <tr>\n <th>2</th>\n <td>8.1</td>\n <td>0.28</td>\n <td>0.40</td>\n <td>6.9</td>\n <td>0.050</td>\n <td>30.0</td>\n <td>97.0</td>\n <td>0.9951</td>\n <td>3.26</td>\n <td>0.44</td>\n <td>10.1</td>\n <td>6</td>\n </tr>\n <tr>\n <th>3</th>\n <td>7.2</td>\n <td>0.23</td>\n <td>0.32</td>\n <td>8.5</td>\n <td>0.058</td>\n <td>47.0</td>\n <td>186.0</td>\n <td>0.9956</td>\n <td>3.19</td>\n <td>0.40</td>\n <td>9.9</td>\n <td>6</td>\n </tr>\n <tr>\n <th>4</th>\n <td>7.2</td>\n <td>0.23</td>\n <td>0.32</td>\n <td>8.5</td>\n <td>0.058</td>\n <td>47.0</td>\n <td>186.0</td>\n <td>0.9956</td>\n <td>3.19</td>\n <td>0.40</td>\n <td>9.9</td>\n <td>6</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n0 7.0 0.27 0.36 20.7 0.045 \n1 6.3 0.30 0.34 1.6 0.049 \n2 8.1 0.28 0.40 6.9 0.050 \n3 7.2 0.23 0.32 8.5 0.058 \n4 7.2 0.23 0.32 8.5 0.058 \n\n free sulfur dioxide total sulfur dioxide density pH sulphates \\\n0 45.0 170.0 1.0010 3.00 0.45 \n1 14.0 132.0 0.9940 3.30 0.49 \n2 30.0 97.0 0.9951 3.26 0.44 \n3 47.0 186.0 0.9956 3.19 0.40 \n4 47.0 186.0 0.9956 3.19 0.40 \n\n alcohol quality \n0 8.8 6 \n1 9.5 6 \n2 10.1 6 \n3 9.9 6 \n4 9.9 6 "
},
"output_type": "execute_result"
}
],
"source": "# The code was removed by Watson Studio for sharing."
},
{
"execution_count": 26,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 4898 entries, 0 to 4897\nData columns (total 12 columns):\nfixed acidity 4898 non-null float64\nvolatile acidity 4898 non-null float64\ncitric acid 4898 non-null float64\nresidual sugar 4898 non-null float64\nchlorides 4898 non-null float64\nfree sulfur dioxide 4898 non-null float64\ntotal sulfur dioxide 4898 non-null float64\ndensity 4898 non-null float64\npH 4898 non-null float64\nsulphates 4898 non-null float64\nalcohol 4898 non-null float64\nquality 4898 non-null int64\ndtypes: float64(11), int64(1)\nmemory usage: 459.3 KB\nNone\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1599 entries, 0 to 1598\nData columns (total 12 columns):\nfixed acidity 1599 non-null float64\nvolatile acidity 1599 non-null float64\ncitric acid 1599 non-null float64\nresidual sugar 1599 non-null float64\nchlorides 1599 non-null float64\nfree sulfur dioxide 1599 non-null float64\ntotal sulfur dioxide 1599 non-null float64\ndensity 1599 non-null float64\npH 1599 non-null float64\nsulphates 1599 non-null float64\nalcohol 1599 non-null float64\nquality 1599 non-null int64\ndtypes: float64(11), int64(1)\nmemory usage: 150.0 KB\nNone\n"
}
],
"source": "# Print info on white wine\nprint(white.info())\n\n# Print info on red wine\nprint(red.info())"
},
{
"execution_count": 27,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 27,
"metadata": {},
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>fixed acidity</th>\n <th>volatile acidity</th>\n <th>citric acid</th>\n <th>residual sugar</th>\n <th>chlorides</th>\n <th>free sulfur dioxide</th>\n <th>total sulfur dioxide</th>\n <th>density</th>\n <th>pH</th>\n <th>sulphates</th>\n <th>alcohol</th>\n <th>quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>7.4</td>\n <td>0.70</td>\n <td>0.00</td>\n <td>1.9</td>\n <td>0.076</td>\n <td>11.0</td>\n <td>34.0</td>\n <td>0.9978</td>\n <td>3.51</td>\n <td>0.56</td>\n <td>9.4</td>\n <td>5</td>\n </tr>\n <tr>\n <th>1</th>\n <td>7.8</td>\n <td>0.88</td>\n <td>0.00</td>\n <td>2.6</td>\n <td>0.098</td>\n <td>25.0</td>\n <td>67.0</td>\n <td>0.9968</td>\n <td>3.20</td>\n <td>0.68</td>\n <td>9.8</td>\n <td>5</td>\n </tr>\n <tr>\n <th>2</th>\n <td>7.8</td>\n <td>0.76</td>\n <td>0.04</td>\n <td>2.3</td>\n <td>0.092</td>\n <td>15.0</td>\n <td>54.0</td>\n <td>0.9970</td>\n <td>3.26</td>\n <td>0.65</td>\n <td>9.8</td>\n <td>5</td>\n </tr>\n <tr>\n <th>3</th>\n <td>11.2</td>\n <td>0.28</td>\n <td>0.56</td>\n <td>1.9</td>\n <td>0.075</td>\n <td>17.0</td>\n <td>60.0</td>\n <td>0.9980</td>\n <td>3.16</td>\n <td>0.58</td>\n <td>9.8</td>\n <td>6</td>\n </tr>\n <tr>\n <th>4</th>\n <td>7.4</td>\n <td>0.70</td>\n <td>0.00</td>\n <td>1.9</td>\n <td>0.076</td>\n <td>11.0</td>\n <td>34.0</td>\n <td>0.9978</td>\n <td>3.51</td>\n <td>0.56</td>\n <td>9.4</td>\n <td>5</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n0 7.4 0.70 0.00 1.9 0.076 \n1 7.8 0.88 0.00 2.6 0.098 \n2 7.8 0.76 0.04 2.3 0.092 \n3 11.2 0.28 0.56 1.9 0.075 \n4 7.4 0.70 0.00 1.9 0.076 \n\n free sulfur dioxide total sulfur dioxide density pH sulphates \\\n0 11.0 34.0 0.9978 3.51 0.56 \n1 25.0 67.0 0.9968 3.20 0.68 \n2 15.0 54.0 0.9970 3.26 0.65 \n3 17.0 60.0 0.9980 3.16 0.58 \n4 11.0 34.0 0.9978 3.51 0.56 \n\n alcohol quality \n0 9.4 5 \n1 9.8 5 \n2 9.8 5 \n3 9.8 6 \n4 9.4 5 "
},
"output_type": "execute_result"
}
],
"source": "# First rows of `red` \nred.head()"
},
{
"execution_count": 28,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 28,
"metadata": {},
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>fixed acidity</th>\n <th>volatile acidity</th>\n <th>citric acid</th>\n <th>residual sugar</th>\n <th>chlorides</th>\n <th>free sulfur dioxide</th>\n <th>total sulfur dioxide</th>\n <th>density</th>\n <th>pH</th>\n <th>sulphates</th>\n <th>alcohol</th>\n <th>quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>4893</th>\n <td>6.2</td>\n <td>0.21</td>\n <td>0.29</td>\n <td>1.6</td>\n <td>0.039</td>\n <td>24.0</td>\n <td>92.0</td>\n <td>0.99114</td>\n <td>3.27</td>\n <td>0.50</td>\n <td>11.2</td>\n <td>6</td>\n </tr>\n <tr>\n <th>4894</th>\n <td>6.6</td>\n <td>0.32</td>\n <td>0.36</td>\n <td>8.0</td>\n <td>0.047</td>\n <td>57.0</td>\n <td>168.0</td>\n <td>0.99490</td>\n <td>3.15</td>\n <td>0.46</td>\n <td>9.6</td>\n <td>5</td>\n </tr>\n <tr>\n <th>4895</th>\n <td>6.5</td>\n <td>0.24</td>\n <td>0.19</td>\n <td>1.2</td>\n <td>0.041</td>\n <td>30.0</td>\n <td>111.0</td>\n <td>0.99254</td>\n <td>2.99</td>\n <td>0.46</td>\n <td>9.4</td>\n <td>6</td>\n </tr>\n <tr>\n <th>4896</th>\n <td>5.5</td>\n <td>0.29</td>\n <td>0.30</td>\n <td>1.1</td>\n <td>0.022</td>\n <td>20.0</td>\n <td>110.0</td>\n <td>0.98869</td>\n <td>3.34</td>\n <td>0.38</td>\n <td>12.8</td>\n <td>7</td>\n </tr>\n <tr>\n <th>4897</th>\n <td>6.0</td>\n <td>0.21</td>\n <td>0.38</td>\n <td>0.8</td>\n <td>0.020</td>\n <td>22.0</td>\n <td>98.0</td>\n <td>0.98941</td>\n <td>3.26</td>\n <td>0.32</td>\n <td>11.8</td>\n <td>6</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n4893 6.2 0.21 0.29 1.6 0.039 \n4894 6.6 0.32 0.36 8.0 0.047 \n4895 6.5 0.24 0.19 1.2 0.041 \n4896 5.5 0.29 0.30 1.1 0.022 \n4897 6.0 0.21 0.38 0.8 0.020 \n\n free sulfur dioxide total sulfur dioxide density pH sulphates \\\n4893 24.0 92.0 0.99114 3.27 0.50 \n4894 57.0 168.0 0.99490 3.15 0.46 \n4895 30.0 111.0 0.99254 2.99 0.46 \n4896 20.0 110.0 0.98869 3.34 0.38 \n4897 22.0 98.0 0.98941 3.26 0.32 \n\n alcohol quality \n4893 11.2 6 \n4894 9.6 5 \n4895 9.4 6 \n4896 12.8 7 \n4897 11.8 6 "
},
"output_type": "execute_result"
}
],
"source": "# Last rows of `white`\nwhite.tail()"
},
{
"execution_count": 29,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 29,
"metadata": {},
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>fixed acidity</th>\n <th>volatile acidity</th>\n <th>citric acid</th>\n <th>residual sugar</th>\n <th>chlorides</th>\n <th>free sulfur dioxide</th>\n <th>total sulfur dioxide</th>\n <th>density</th>\n <th>pH</th>\n <th>sulphates</th>\n <th>alcohol</th>\n <th>quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>441</th>\n <td>11.9</td>\n <td>0.40</td>\n <td>0.65</td>\n <td>2.15</td>\n <td>0.068</td>\n <td>7.0</td>\n <td>27.0</td>\n <td>0.99880</td>\n <td>3.06</td>\n <td>0.68</td>\n <td>11.3</td>\n <td>6</td>\n </tr>\n <tr>\n <th>785</th>\n <td>9.9</td>\n <td>0.35</td>\n <td>0.41</td>\n <td>2.30</td>\n <td>0.083</td>\n <td>11.0</td>\n <td>61.0</td>\n <td>0.99820</td>\n <td>3.21</td>\n <td>0.50</td>\n <td>9.5</td>\n <td>5</td>\n </tr>\n <tr>\n <th>387</th>\n <td>8.3</td>\n <td>0.66</td>\n <td>0.15</td>\n <td>1.90</td>\n <td>0.079</td>\n <td>17.0</td>\n <td>42.0</td>\n <td>0.99720</td>\n <td>3.31</td>\n <td>0.54</td>\n <td>9.6</td>\n <td>6</td>\n </tr>\n <tr>\n <th>191</th>\n <td>6.4</td>\n <td>0.37</td>\n <td>0.25</td>\n <td>1.90</td>\n <td>0.074</td>\n <td>21.0</td>\n <td>49.0</td>\n <td>0.99740</td>\n <td>3.57</td>\n <td>0.62</td>\n <td>9.8</td>\n <td>6</td>\n </tr>\n <tr>\n <th>795</th>\n <td>10.8</td>\n <td>0.89</td>\n <td>0.30</td>\n <td>2.60</td>\n <td>0.132</td>\n <td>7.0</td>\n <td>60.0</td>\n <td>0.99786</td>\n <td>2.99</td>\n <td>1.18</td>\n <td>10.2</td>\n <td>5</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n441 11.9 0.40 0.65 2.15 0.068 \n785 9.9 0.35 0.41 2.30 0.083 \n387 8.3 0.66 0.15 1.90 0.079 \n191 6.4 0.37 0.25 1.90 0.074 \n795 10.8 0.89 0.30 2.60 0.132 \n\n free sulfur dioxide total sulfur dioxide density pH sulphates \\\n441 7.0 27.0 0.99880 3.06 0.68 \n785 11.0 61.0 0.99820 3.21 0.50 \n387 17.0 42.0 0.99720 3.31 0.54 \n191 21.0 49.0 0.99740 3.57 0.62 \n795 7.0 60.0 0.99786 2.99 1.18 \n\n alcohol quality \n441 11.3 6 \n785 9.5 5 \n387 9.6 6 \n191 9.8 6 \n795 10.2 5 "
},
"output_type": "execute_result"
}
],
"source": "# Take a sample of 5 rows of `red`\nred.sample(5)"
},
{
"execution_count": 30,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 30,
"metadata": {},
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>fixed acidity</th>\n <th>volatile acidity</th>\n <th>citric acid</th>\n <th>residual sugar</th>\n <th>chlorides</th>\n <th>free sulfur dioxide</th>\n <th>total sulfur dioxide</th>\n <th>density</th>\n <th>pH</th>\n <th>sulphates</th>\n <th>alcohol</th>\n <th>quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n <td>4898.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>6.854788</td>\n <td>0.278241</td>\n <td>0.334192</td>\n <td>6.391415</td>\n <td>0.045772</td>\n <td>35.308085</td>\n <td>138.360657</td>\n <td>0.994027</td>\n <td>3.188267</td>\n <td>0.489847</td>\n <td>10.514267</td>\n <td>5.877909</td>\n </tr>\n <tr>\n <th>std</th>\n <td>0.843868</td>\n <td>0.100795</td>\n <td>0.121020</td>\n <td>5.072058</td>\n <td>0.021848</td>\n <td>17.007137</td>\n <td>42.498065</td>\n <td>0.002991</td>\n <td>0.151001</td>\n <td>0.114126</td>\n <td>1.230621</td>\n <td>0.885639</td>\n </tr>\n <tr>\n <th>min</th>\n <td>3.800000</td>\n <td>0.080000</td>\n <td>0.000000</td>\n <td>0.600000</td>\n <td>0.009000</td>\n <td>2.000000</td>\n <td>9.000000</td>\n <td>0.987110</td>\n <td>2.720000</td>\n <td>0.220000</td>\n <td>8.000000</td>\n <td>3.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>6.300000</td>\n <td>0.210000</td>\n <td>0.270000</td>\n <td>1.700000</td>\n <td>0.036000</td>\n <td>23.000000</td>\n <td>108.000000</td>\n <td>0.991723</td>\n <td>3.090000</td>\n <td>0.410000</td>\n <td>9.500000</td>\n <td>5.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>6.800000</td>\n <td>0.260000</td>\n <td>0.320000</td>\n <td>5.200000</td>\n <td>0.043000</td>\n <td>34.000000</td>\n <td>134.000000</td>\n <td>0.993740</td>\n <td>3.180000</td>\n <td>0.470000</td>\n <td>10.400000</td>\n <td>6.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>7.300000</td>\n <td>0.320000</td>\n <td>0.390000</td>\n <td>9.900000</td>\n <td>0.050000</td>\n <td>46.000000</td>\n <td>167.000000</td>\n <td>0.996100</td>\n <td>3.280000</td>\n <td>0.550000</td>\n <td>11.400000</td>\n <td>6.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>14.200000</td>\n <td>1.100000</td>\n <td>1.660000</td>\n <td>65.800000</td>\n <td>0.346000</td>\n <td>289.000000</td>\n <td>440.000000</td>\n <td>1.038980</td>\n <td>3.820000</td>\n <td>1.080000</td>\n <td>14.200000</td>\n <td>9.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " fixed acidity volatile acidity citric acid residual sugar \\\ncount 4898.000000 4898.000000 4898.000000 4898.000000 \nmean 6.854788 0.278241 0.334192 6.391415 \nstd 0.843868 0.100795 0.121020 5.072058 \nmin 3.800000 0.080000 0.000000 0.600000 \n25% 6.300000 0.210000 0.270000 1.700000 \n50% 6.800000 0.260000 0.320000 5.200000 \n75% 7.300000 0.320000 0.390000 9.900000 \nmax 14.200000 1.100000 1.660000 65.800000 \n\n chlorides free sulfur dioxide total sulfur dioxide density \\\ncount 4898.000000 4898.000000 4898.000000 4898.000000 \nmean 0.045772 35.308085 138.360657 0.994027 \nstd 0.021848 17.007137 42.498065 0.002991 \nmin 0.009000 2.000000 9.000000 0.987110 \n25% 0.036000 23.000000 108.000000 0.991723 \n50% 0.043000 34.000000 134.000000 0.993740 \n75% 0.050000 46.000000 167.000000 0.996100 \nmax 0.346000 289.000000 440.000000 1.038980 \n\n pH sulphates alcohol quality \ncount 4898.000000 4898.000000 4898.000000 4898.000000 \nmean 3.188267 0.489847 10.514267 5.877909 \nstd 0.151001 0.114126 1.230621 0.885639 \nmin 2.720000 0.220000 8.000000 3.000000 \n25% 3.090000 0.410000 9.500000 5.000000 \n50% 3.180000 0.470000 10.400000 6.000000 \n75% 3.280000 0.550000 11.400000 6.000000 \nmax 3.820000 1.080000 14.200000 9.000000 "
},
"output_type": "execute_result"
}
],
"source": "# Describe `white`\nwhite.describe()\n"
},
{
"execution_count": 31,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 31,
"metadata": {},
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>fixed acidity</th>\n <th>volatile acidity</th>\n <th>citric acid</th>\n <th>residual sugar</th>\n <th>chlorides</th>\n <th>free sulfur dioxide</th>\n <th>total sulfur dioxide</th>\n <th>density</th>\n <th>pH</th>\n <th>sulphates</th>\n <th>alcohol</th>\n <th>quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>5</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>6</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>7</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>8</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>9</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>10</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>11</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>12</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>13</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>14</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>15</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>16</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>17</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>18</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>19</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>20</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>21</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>22</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>23</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>24</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>25</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>26</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>27</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>28</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>29</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1569</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1570</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1571</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1572</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1573</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1574</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1575</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1576</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1577</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1578</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1579</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1580</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1581</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1582</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1583</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1584</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1585</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1586</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1587</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1588</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1589</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1590</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1591</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1592</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1593</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1594</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1595</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1596</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1597</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1598</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n<p>1599 rows \u00d7 12 columns</p>\n</div>",
"text/plain": " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n0 False False False False False \n1 False False False False False \n2 False False False False False \n3 False False False False False \n4 False False False False False \n5 False False False False False \n6 False False False False False \n7 False False False False False \n8 False False False False False \n9 False False False False False \n10 False False False False False \n11 False False False False False \n12 False False False False False \n13 False False False False False \n14 False False False False False \n15 False False False False False \n16 False False False False False \n17 False False False False False \n18 False False False False False \n19 False False False False False \n20 False False False False False \n21 False False False False False \n22 False False False False False \n23 False False False False False \n24 False False False False False \n25 False False False False False \n26 False False False False False \n27 False False False False False \n28 False False False False False \n29 False False False False False \n... ... ... ... ... ... \n1569 False False False False False \n1570 False False False False False \n1571 False False False False False \n1572 False False False False False \n1573 False False False False False \n1574 False False False False False \n1575 False False False False False \n1576 False False False False False \n1577 False False False False False \n1578 False False False False False \n1579 False False False False False \n1580 False False False False False \n1581 False False False False False \n1582 False False False False False \n1583 False False False False False \n1584 False False False False False \n1585 False False False False False \n1586 False False False False False \n1587 False False False False False \n1588 False False False False False \n1589 False False False False False \n1590 False False False False False \n1591 False False False False False \n1592 False False False False False \n1593 False False False False False \n1594 False False False False False \n1595 False False False False False \n1596 False False False False False \n1597 False False False False False \n1598 False False False False False \n\n free sulfur dioxide total sulfur dioxide density pH sulphates \\\n0 False False False False False \n1 False False False False False \n2 False False False False False \n3 False False False False False \n4 False False False False False \n5 False False False False False \n6 False False False False False \n7 False False False False False \n8 False False False False False \n9 False False False False False \n10 False False False False False \n11 False False False False False \n12 False False False False False \n13 False False False False False \n14 False False False False False \n15 False False False False False \n16 False False False False False \n17 False False False False False \n18 False False False False False \n19 False False False False False \n20 False False False False False \n21 False False False False False \n22 False False False False False \n23 False False False False False \n24 False False False False False \n25 False False False False False \n26 False False False False False \n27 False False False False False \n28 False False False False False \n29 False False False False False \n... ... ... ... ... ... \n1569 False False False False False \n1570 False False False False False \n1571 False False False False False \n1572 False False False False False \n1573 False False False False False \n1574 False False False False False \n1575 False False False False False \n1576 False False False False False \n1577 False False False False False \n1578 False False False False False \n1579 False False False False False \n1580 False False False False False \n1581 False False False False False \n1582 False False False False False \n1583 False False False False False \n1584 False False False False False \n1585 False False False False False \n1586 False False False False False \n1587 False False False False False \n1588 False False False False False \n1589 False False False False False \n1590 False False False False False \n1591 False False False False False \n1592 False False False False False \n1593 False False False False False \n1594 False False False False False \n1595 False False False False False \n1596 False False False False False \n1597 False False False False False \n1598 False False False False False \n\n alcohol quality \n0 False False \n1 False False \n2 False False \n3 False False \n4 False False \n5 False False \n6 False False \n7 False False \n8 False False \n9 False False \n10 False False \n11 False False \n12 False False \n13 False False \n14 False False \n15 False False \n16 False False \n17 False False \n18 False False \n19 False False \n20 False False \n21 False False \n22 False False \n23 False False \n24 False False \n25 False False \n26 False False \n27 False False \n28 False False \n29 False False \n... ... ... \n1569 False False \n1570 False False \n1571 False False \n1572 False False \n1573 False False \n1574 False False \n1575 False False \n1576 False False \n1577 False False \n1578 False False \n1579 False False \n1580 False False \n1581 False False \n1582 False False \n1583 False False \n1584 False False \n1585 False False \n1586 False False \n1587 False False \n1588 False False \n1589 False False \n1590 False False \n1591 False False \n1592 False False \n1593 False False \n1594 False False \n1595 False False \n1596 False False \n1597 False False \n1598 False False \n\n[1599 rows x 12 columns]"
},
"output_type": "execute_result"
}
],
"source": "# Double check for null values in `red`\npd.isnull(red)"
},
{
"execution_count": 34,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f57bd5bfc50>"
},
"metadata": {}
}
],
"source": "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(1, 2)\n\nax[0].hist(red.alcohol, 10, facecolor='red', alpha=0.5, label=\"Red wine\")\nax[1].hist(white.alcohol, 10, facecolor='white', ec=\"black\", lw=0.5, alpha=0.5, label=\"White wine\")\n\nfig.subplots_adjust(left=0, right=1, bottom=0, top=0.5, hspace=0.05, wspace=1)\nax[0].set_ylim([0, 1000])\nax[0].set_xlabel(\"Alcohol in % Vol\")\nax[0].set_ylabel(\"Frequency\")\nax[1].set_xlabel(\"Alcohol in % Vol\")\nax[1].set_ylabel(\"Frequency\")\nfig.suptitle(\"Distribution of Alcohol in % Vol\")\n\nplt.show()"
},
{
"execution_count": 35,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "(array([ 0, 7, 673, 452, 305, 133, 21, 8]), array([ 7, 8, 9, 10, 11, 12, 13, 14, 15]))\n(array([ 0, 317, 1606, 1256, 906, 675, 131, 7]), array([ 7, 8, 9, 10, 11, 12, 13, 14, 15]))\n"
}
],
"source": "import numpy as np\nprint(np.histogram(red.alcohol, bins=[7,8,9,10,11,12,13,14,15]))\nprint(np.histogram(white.alcohol, bins=[7,8,9,10,11,12,13,14,15]))"
},
{
"execution_count": 36,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f57bd5562b0>"
},
"metadata": {}
}
],
"source": "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(1, 2, figsize=(8, 4))\n\nax[0].scatter(red['quality'], red[\"sulphates\"], color=\"red\")\nax[1].scatter(white['quality'], white['sulphates'], color=\"white\", edgecolors=\"black\", lw=0.5)\n\nax[0].set_title(\"Red Wine\")\nax[1].set_title(\"White Wine\")\nax[0].set_xlabel(\"Quality\")\nax[1].set_xlabel(\"Quality\")\nax[0].set_ylabel(\"Sulphates\")\nax[1].set_ylabel(\"Sulphates\")\nax[0].set_xlim([0,10])\nax[1].set_xlim([0,10])\nax[0].set_ylim([0,2.5])\nax[1].set_ylim([0,2.5])\nfig.subplots_adjust(wspace=0.5)\nfig.suptitle(\"Wine Quality by Amount of Sulphates\")\n\nplt.show()"
},
{
"execution_count": 37,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f57bd4ac240>"
},
"metadata": {}
}
],
"source": "import matplotlib.pyplot as plt\nimport numpy as np\n\nnp.random.seed(570)\n\nredlabels = np.unique(red['quality'])\nwhitelabels = np.unique(white['quality'])\n\nimport matplotlib.pyplot as plt\nfig, ax = plt.subplots(1, 2, figsize=(8, 4))\nredcolors = np.random.rand(6,4)\nwhitecolors = np.append(redcolors, np.random.rand(1,4), axis=0)\n\nfor i in range(len(redcolors)):\n redy = red['alcohol'][red.quality == redlabels[i]]\n redx = red['volatile acidity'][red.quality == redlabels[i]]\n ax[0].scatter(redx, redy, c=redcolors[i])\nfor i in range(len(whitecolors)):\n whitey = white['alcohol'][white.quality == whitelabels[i]]\n whitex = white['volatile acidity'][white.quality == whitelabels[i]]\n ax[1].scatter(whitex, whitey, c=whitecolors[i])\n \nax[0].set_title(\"Red Wine\")\nax[1].set_title(\"White Wine\")\nax[0].set_xlim([0,1.7])\nax[1].set_xlim([0,1.7])\nax[0].set_ylim([5,15.5])\nax[1].set_ylim([5,15.5])\nax[0].set_xlabel(\"Volatile Acidity\")\nax[0].set_ylabel(\"Alcohol\")\nax[1].set_xlabel(\"Volatile Acidity\")\nax[1].set_ylabel(\"Alcohol\") \n#ax[0].legend(redlabels, loc='best', bbox_to_anchor=(1.3, 1))\nax[1].legend(whitelabels, loc='best', bbox_to_anchor=(1.3, 1))\n#fig.suptitle(\"Alcohol - Volatile Acidity\")\nfig.subplots_adjust(top=0.85, wspace=0.7)\n\nplt.show()"
},
{
"execution_count": 38,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "# Append `white` to `red`\nwines = red.append(white, ignore_index=True)"
},
{
"execution_count": 39,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 39,
"metadata": {},
"data": {
"text/plain": "(6497, 12)"
},
"output_type": "execute_result"
}
],
"source": "wines.shape"
},
{
"execution_count": 41,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 41,
"metadata": {},
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f57bc553828>"
},
"output_type": "execute_result"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f57bc5b8c50>"
},
"metadata": {}
}
],
"source": "import seaborn as sns\ncorr = wines.corr()\nsns.heatmap(corr, \n xticklabels=corr.columns.values,\n yticklabels=corr.columns.values,\n cmap=\"YlGnBu\")"
},
{
"execution_count": 43,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "# Isolate target labels\ny = wines.quality\n\n# Isolate data\nX = wines.drop('quality', axis=1) "
},
{
"execution_count": 44,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": "# Import `StandardScaler` from `sklearn.preprocessing`\nfrom sklearn.preprocessing import StandardScaler\nimport numpy as np\n\n# Scale the data with `StandardScaler`\nX = StandardScaler().fit_transform(X)"
},
{
"execution_count": 45,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": "Using TensorFlow backend.\n"
},
{
"output_type": "stream",
"name": "stdout",
"text": "Epoch 1/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 2/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 3/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 4/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 5/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 6/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 7/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 8/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 9/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 10/10\n5195/5195 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 1/10\n5197/5197 [==============================] - 0s - loss: 1.2763 - mean_absolute_error: 0.8008 \nEpoch 2/10\n5197/5197 [==============================] - 0s - loss: 0.6533 - mean_absolute_error: 0.6268 \nEpoch 3/10\n5197/5197 [==============================] - 0s - loss: 0.5847 - mean_absolute_error: 0.5956 \nEpoch 4/10\n5197/5197 [==============================] - 0s - loss: 0.5395 - mean_absolute_error: 0.5681 \nEpoch 5/10\n5197/5197 [==============================] - 0s - loss: 0.5229 - mean_absolute_error: 0.5656 \nEpoch 6/10\n5197/5197 [==============================] - 0s - loss: 0.5151 - mean_absolute_error: 0.5624 \nEpoch 7/10\n5197/5197 [==============================] - 0s - loss: 0.5066 - mean_absolute_error: 0.5526 \nEpoch 8/10\n5197/5197 [==============================] - 0s - loss: 0.4903 - mean_absolute_error: 0.5460 \nEpoch 9/10\n5197/5197 [==============================] - 0s - loss: 0.4809 - mean_absolute_error: 0.5406 \nEpoch 10/10\n5197/5197 [==============================] - 0s - loss: 0.4734 - mean_absolute_error: 0.5363 \nEpoch 1/10\n5197/5197 [==============================] - 0s - loss: 1.2114 - mean_absolute_error: 0.7928 \nEpoch 2/10\n5197/5197 [==============================] - 0s - loss: 0.6343 - mean_absolute_error: 0.6211 \nEpoch 3/10\n5197/5197 [==============================] - 0s - loss: 0.5698 - mean_absolute_error: 0.5855 \nEpoch 4/10\n5197/5197 [==============================] - 0s - loss: 0.5135 - mean_absolute_error: 0.5628 \nEpoch 5/10\n5197/5197 [==============================] - 0s - loss: 0.5212 - mean_absolute_error: 0.5666 \nEpoch 6/10\n5197/5197 [==============================] - 0s - loss: 0.5049 - mean_absolute_error: 0.5534 \nEpoch 7/10\n5197/5197 [==============================] - 0s - loss: 0.4939 - mean_absolute_error: 0.5480 \nEpoch 8/10\n5197/5197 [==============================] - 0s - loss: 0.4916 - mean_absolute_error: 0.5500 \nEpoch 9/10\n5197/5197 [==============================] - 0s - loss: 0.4900 - mean_absolute_error: 0.5472 \nEpoch 10/10\n5197/5197 [==============================] - 0s - loss: 0.4885 - mean_absolute_error: 0.5482 \nEpoch 1/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 2/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 3/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 4/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 5/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 6/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 7/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 8/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 9/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 10/10\n5199/5199 [==============================] - 0s - loss: nan - mean_absolute_error: nan \nEpoch 1/10\n5200/5200 [==============================] - 0s - loss: 1.6669 - mean_absolute_error: 0.7919 \nEpoch 2/10\n5200/5200 [==============================] - 0s - loss: 0.5404 - mean_absolute_error: 0.5697 \nEpoch 3/10\n5200/5200 [==============================] - 0s - loss: 0.5198 - mean_absolute_error: 0.5576 \nEpoch 4/10\n5200/5200 [==============================] - 0s - loss: 0.5077 - mean_absolute_error: 0.5532 \nEpoch 5/10\n5200/5200 [==============================] - 0s - loss: 0.4896 - mean_absolute_error: 0.5421 \nEpoch 6/10\n5200/5200 [==============================] - 0s - loss: 0.4882 - mean_absolute_error: 0.5402 \nEpoch 7/10\n5200/5200 [==============================] - 0s - loss: 0.4824 - mean_absolute_error: 0.5398 \nEpoch 8/10\n5200/5200 [==============================] - 0s - loss: 0.4792 - mean_absolute_error: 0.5379 \nEpoch 9/10\n5200/5200 [==============================] - 0s - loss: 0.4731 - mean_absolute_error: 0.5367 \nEpoch 10/10\n5200/5200 [==============================] - 0s - loss: 0.4678 - mean_absolute_error: 0.5324 \n"
}
],
"source": "import numpy as np\nfrom sklearn.model_selection import StratifiedKFold\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import SGD, RMSprop\n\nseed = 7\nnp.random.seed(seed)\n\nkfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)\nfor train, test in kfold.split(X, y):\n model = Sequential()\n model.add(Dense(128, input_dim=11, activation='relu'))\n model.add(Dense(1))\n \n# rmsprop = RMSprop(lr=0.001)\n sgd=SGD(lr=0.1)\n model.compile(optimizer=sgd, loss='mse', metrics=['mae'])\n\n# model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n model.fit(X[train], y[train], epochs=10, verbose=1)"
},
{
"execution_count": 46,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0.49074778186\n0.553630012177\n"
}
],
"source": "mse_value, mae_value = model.evaluate(X[test], y[test], verbose=0)\n\nprint(mse_value)\nprint(mae_value)"
},
{
"execution_count": 95,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"execution_count": 95,
"metadata": {},
"data": {
"text/plain": "0.35426163952067002"
},
"output_type": "execute_result"
}
],
"source": "from sklearn.metrics import r2_score\ny_pred = model.predict(X[test])\nr2_score(y[test], y_pred)"
},
{
"execution_count": 48,
"cell_type": "code",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\ndense_9 (Dense) (None, 128) 1536 \n_________________________________________________________________\ndense_10 (Dense) (None, 1) 129 \n=================================================================\nTotal params: 1,665\nTrainable params: 1,665\nNon-trainable params: 0\n_________________________________________________________________\n"
}
],
"source": "# Model output shape\nmodel.summary()"
},
{
"execution_count": null,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": ""
},
{
"execution_count": null,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": ""
},
{
"execution_count": null,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": ""
},
{
"execution_count": null,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": ""
},
{
"execution_count": null,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": ""
},
{
"execution_count": null,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": ""
},
{
"execution_count": null,
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": ""
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.5 with Spark 2.1",
"name": "python3-spark21",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"version": "3.5.4",
"name": "python",
"file_extension": ".py",
"pygments_lexer": "ipython3",
"codemirror_mode": {
"version": 3,
"name": "ipython"
}
}
},
"nbformat": 4
}
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