utf-8''wine-quality.ipynb

{
  "cells": [
    {
      "metadata": {
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "markdown",
      "source": "\n# Datasets\nFor this task, I'll use the wine quality dataset from the UCI repository."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Saving Data Files"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "!curl https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality.names -O\n!curl https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv -O\n!curl https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv -O",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n100  3305  100  3305    0     0  15022      0 --:--:-- --:--:-- --:--:-- 15022\n  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n100 84199  100 84199    0     0   247k      0 --:--:-- --:--:-- --:--:--  253k\n  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n100  258k  100  258k    0     0   744k      0 --:--:-- --:--:-- --:--:--  742k\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "!ls",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "README.md\t    winequality.names\t winequality-white.csv\r\nwine-quality.ipynb  winequality-red.csv\r\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Import Data"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df1 = pd.read_csv('winequality-white.csv', sep=';')\ndf1.head()",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "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  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Show the number of rows and columns of df1 (white wine)..."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df1.shape",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "(4898, 12)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df2 = pd.read_csv('winequality-red.csv', sep=';')\ndf2.head()",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "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  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Show the number of rows and columns of df2 (red wine)..."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df2.shape",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "(1599, 12)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Combine white and red wine dataframes"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = df1.append(df2)\ndf.head()",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "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  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.shape",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "(6497, 12)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Create column 'type'"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df2 = df2.assign(type='red')\ndf1 = df1.assign(type='white')\ndf = df1.append(df2)\ndf.head()",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "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      <th>type</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      <td>white</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      <td>white</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      <td>white</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      <td>white</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      <td>white</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   type  \n0      8.8        6  white  \n1      9.5        6  white  \n2     10.1        6  white  \n3      9.9        6  white  \n4      9.9        6  white  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Clean Data"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.isnull().values.any()",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "False"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "//Luckily, there is no null value in this dataset (also noted in the official description)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Explore Data"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Aggregation by type"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.groupby(['type']).agg(['mean'])",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\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    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n      <th>mean</th>\n    </tr>\n    <tr>\n      <th>type</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>red</th>\n      <td>8.319637</td>\n      <td>0.527821</td>\n      <td>0.270976</td>\n      <td>2.538806</td>\n      <td>0.087467</td>\n      <td>15.874922</td>\n      <td>46.467792</td>\n      <td>0.996747</td>\n      <td>3.311113</td>\n      <td>0.658149</td>\n      <td>10.422983</td>\n      <td>5.636023</td>\n    </tr>\n    <tr>\n      <th>white</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  </tbody>\n</table>\n</div>",
            "text/plain": "      fixed acidity volatile acidity citric acid residual sugar chlorides  \\\n               mean             mean        mean           mean      mean   \ntype                                                                        \nred        8.319637         0.527821    0.270976       2.538806  0.087467   \nwhite      6.854788         0.278241    0.334192       6.391415  0.045772   \n\n      free sulfur dioxide total sulfur dioxide   density        pH sulphates  \\\n                     mean                 mean      mean      mean      mean   \ntype                                                                           \nred             15.874922            46.467792  0.996747  3.311113  0.658149   \nwhite           35.308085           138.360657  0.994027  3.188267  0.489847   \n\n         alcohol   quality  \n            mean      mean  \ntype                        \nred    10.422983  5.636023  \nwhite  10.514267  5.877909  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.groupby(['type']).agg(['min'])",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\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    <tr>\n      <th></th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n      <th>min</th>\n    </tr>\n    <tr>\n      <th>type</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>red</th>\n      <td>4.6</td>\n      <td>0.12</td>\n      <td>0.0</td>\n      <td>0.9</td>\n      <td>0.012</td>\n      <td>1.0</td>\n      <td>6.0</td>\n      <td>0.99007</td>\n      <td>2.74</td>\n      <td>0.33</td>\n      <td>8.4</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>white</th>\n      <td>3.8</td>\n      <td>0.08</td>\n      <td>0.0</td>\n      <td>0.6</td>\n      <td>0.009</td>\n      <td>2.0</td>\n      <td>9.0</td>\n      <td>0.98711</td>\n      <td>2.72</td>\n      <td>0.22</td>\n      <td>8.0</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      fixed acidity volatile acidity citric acid residual sugar chlorides  \\\n                min              min         min            min       min   \ntype                                                                        \nred             4.6             0.12         0.0            0.9     0.012   \nwhite           3.8             0.08         0.0            0.6     0.009   \n\n      free sulfur dioxide total sulfur dioxide  density    pH sulphates  \\\n                      min                  min      min   min       min   \ntype                                                                      \nred                   1.0                  6.0  0.99007  2.74      0.33   \nwhite                 2.0                  9.0  0.98711  2.72      0.22   \n\n      alcohol quality  \n          min     min  \ntype                   \nred       8.4       3  \nwhite     8.0       3  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.groupby(['type']).agg(['max'])",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\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    <tr>\n      <th></th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>type</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>red</th>\n      <td>15.9</td>\n      <td>1.58</td>\n      <td>1.00</td>\n      <td>15.5</td>\n      <td>0.611</td>\n      <td>72.0</td>\n      <td>289.0</td>\n      <td>1.00369</td>\n      <td>4.01</td>\n      <td>2.00</td>\n      <td>14.9</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>white</th>\n      <td>14.2</td>\n      <td>1.10</td>\n      <td>1.66</td>\n      <td>65.8</td>\n      <td>0.346</td>\n      <td>289.0</td>\n      <td>440.0</td>\n      <td>1.03898</td>\n      <td>3.82</td>\n      <td>1.08</td>\n      <td>14.2</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      fixed acidity volatile acidity citric acid residual sugar chlorides  \\\n                max              max         max            max       max   \ntype                                                                        \nred            15.9             1.58        1.00           15.5     0.611   \nwhite          14.2             1.10        1.66           65.8     0.346   \n\n      free sulfur dioxide total sulfur dioxide  density    pH sulphates  \\\n                      max                  max      max   max       max   \ntype                                                                      \nred                  72.0                289.0  1.00369  4.01      2.00   \nwhite               289.0                440.0  1.03898  3.82      1.08   \n\n      alcohol quality  \n          max     max  \ntype                   \nred      14.9       8  \nwhite    14.2       9  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.groupby(['type']).std()",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "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    <tr>\n      <th>type</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>red</th>\n      <td>1.741096</td>\n      <td>0.179060</td>\n      <td>0.194801</td>\n      <td>1.409928</td>\n      <td>0.047065</td>\n      <td>10.460157</td>\n      <td>32.895324</td>\n      <td>0.001887</td>\n      <td>0.154386</td>\n      <td>0.169507</td>\n      <td>1.065668</td>\n      <td>0.807569</td>\n    </tr>\n    <tr>\n      <th>white</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  </tbody>\n</table>\n</div>",
            "text/plain": "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\ntype                                                                  \nred         1.741096          0.179060     0.194801        1.409928   \nwhite       0.843868          0.100795     0.121020        5.072058   \n\n       chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\ntype                                                                    \nred     0.047065            10.460157             32.895324  0.001887   \nwhite   0.021848            17.007137             42.498065  0.002991   \n\n             pH  sulphates   alcohol   quality  \ntype                                            \nred    0.154386   0.169507  1.065668  0.807569  \nwhite  0.151001   0.114126  1.230621  0.885639  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df0 = pd.get_dummies(df, columns=['type'])\ndf0.head()",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "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      <th>type_red</th>\n      <th>type_white</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      <td>0</td>\n      <td>1</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      <td>0</td>\n      <td>1</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      <td>0</td>\n      <td>1</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      <td>0</td>\n      <td>1</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      <td>0</td>\n      <td>1</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  type_red  type_white  \n0      8.8        6         0           1  \n1      9.5        6         0           1  \n2     10.1        6         0           1  \n3      9.9        6         0           1  \n4      9.9        6         0           1  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df1 = pd.get_dummies(df1, columns=['type'])\ndf2 = pd.get_dummies(df2, columns=['type'])\ndf1.head()",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "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      <th>type_white</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      <td>1</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      <td>1</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      <td>1</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      <td>1</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      <td>1</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  type_white  \n0      8.8        6           1  \n1      9.5        6           1  \n2     10.1        6           1  \n3      9.9        6           1  \n4      9.9        6           1  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Visualize Data"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Correlation Matrix"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nimport seaborn as sns\nplt.figure(figsize=(9, 9))\nsns.heatmap(df0.corr(),linewidths=0.6,annot=True,cmap='ocean_r',cbar=True)",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 48,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ac9eae400>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 648x648 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Look at the above figure, it is the correlation matrix for different attributes of all types wines.\nWe can see the relation (positive or negative) between different attributions.\nFor example,\nThere is a significant positive relation between 'total sulfur dioxide' and 'free sulfur dioxide' (0.72);\nThe amount of relation between wine type and other attributes has no difference, only positive and negative different;\nAlcohol can be a significant influence of quality (0.44), while other attributes may not have significant relation with quality."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Histogram plot"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\ndf1.hist(column='fixed acidity', color='red') # red wine\ndf2.hist(column='fixed acidity', color='grey') # white wine\ndf1.hist(column='alcohol', color='red') # red wine\ndf2.hist(column='alcohol', color='grey') # white wine\ndf1.hist(column='quality', color='red') # red wine\ndf2.hist(column='quality', color='grey') # white wine",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f0acc316208>]],\n      dtype=object)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Above is the example histogram for 3 attributes of white and red wine.\nWe can see the distribution of the data."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Barplot for quality vs alcohol\nsns.barplot(x='quality', y = 'alcohol', hue = 'type' , data = df)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_501/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f0acbfbb978>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Countplot for Wine quality for both types\nsns.countplot(x = df['quality'], data=df, hue='type')",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f0acbb9b080>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Scatter plot"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.pairplot(df,hue='type',x_vars = \n            ['alcohol','free sulfur dioxide', 'total sulfur dioxide','residual sugar'],\n            y_vars = ['fixed acidity','volatile acidity','pH','quality'])",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": "<seaborn.axisgrid.PairGrid at 0x7f0acbb155c0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 783.375x720 with 16 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Split Data"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.model_selection import train_test_split\ntrain_x, test_x, train_y, test_y = train_test_split(df0.drop('quality', axis=1), df0['quality'], test_size=0.2, random_state=42)",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "//use 0.2 test ratio, also pick a random number 42 as its internal state"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_x.shape",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "(5197, 13)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_x.shape",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "(1300, 13)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_y.shape",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "(5197,)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_y.shape",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "(1300,)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Training the Model"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## RandomForest (model, model0)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.ensemble import RandomForestClassifier\n\nmodel = RandomForestClassifier(random_state=13)\nmodel.fit(train_x, train_y)",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_501/lib/python3.6/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n            max_depth=None, max_features='auto', max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n            oob_score=False, random_state=13, verbose=0, warm_start=False)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predicted = model.predict(test_x)\nmodel.score(test_x, test_y)",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/plain": "0.6630769230769231"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "markdown",
      "source": "Notice the predict accuracy is not high (0.66), parameters tuning is required."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.model_selection import RandomizedSearchCV \ncriterion = ['gini', 'entropy']\nclass_weight = ['balanced', None]  \n# Number of trees in random forest\nn_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n# Number of features to consider at every split\nmax_features = ['auto', 'sqrt']\n# Maximum number of levels in tree\nmax_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\nmax_depth.append(None)\n# Minimum number of samples required to split a node\nmin_samples_split = [2, 5, 10]\n# Minimum number of samples required at each leaf node\nmin_samples_leaf = [1, 2, 4]\n# Method of selecting samples for training each tree\nbootstrap = [True, False]\n# Create the random grid\nrandom_grid = {'n_estimators': n_estimators,\n               'max_features': max_features,\n               'max_depth': max_depth,\n               'min_samples_split': min_samples_split,\n               'min_samples_leaf': min_samples_leaf,\n               'bootstrap': bootstrap,\n              'criterion': criterion,\n              'class_weight': class_weight}\nprint(random_grid)",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": "{'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'class_weight': ['balanced', None]}\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Just skip next cell...(commented)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def evaluate(model, test_features, test_labels):\n    predictions = model.predict(test_features)\n    errors = abs(predictions-test_labels)\n    mape = 100 * np.mean(errors/test_labels)\n    accuracy = 100 - mape\n    print('Model Performance')\n    print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))\n    print('Accuracy = {:0.2f}%.'.format(accuracy))\n    return accuracy\n\n## Use the random grid to search for best hyperparameters\n## First create the base model to tune\n#rf = RandomForestClassifier()\n## Random search of parameters, using 5 fold cross validation,\n## search across 100 different combinations, and use all available cores\n#rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 5, verbose=2, random_state=42, n_jobs = -1)\n## Fit the random search model\n#rf_random.fit(train_x, train_y)\n#params = rf_random.best_params_\n\n#base_accuracy = evaluate(params, test_x, test_y)\n#print(base_accuracy)",
      "execution_count": 31,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Unforturnately, the above step leads to \"memory leak\", thus I run it on local python, and the best parameteres are:\n{'n_estimators': 600, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 30, 'criterion': 'entropy', 'class_weight': 'balanced', 'bootstrap': True}"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "params = {'n_estimators': 600, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', \n          'max_depth': 30, 'bootstrap': True, 'criterion':'entropy', 'class_weight':'balanced'}",
      "execution_count": 32,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model0 = RandomForestClassifier(bootstrap=True, class_weight='balanced', criterion='entropy',\n            max_depth=30, max_features='sqrt', max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, n_estimators=600, n_jobs=None,\n            oob_score=False, random_state=13, verbose=0, warm_start=False)\nmodel0.fit(train_x, train_y)\npredicted = model0.predict(test_x)\nmodel0.score(test_x, test_y)",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": "0.7046153846153846"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Just a little bit better..."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Bayes (model 1)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.naive_bayes import MultinomialNB as mnb\nmodel1 = mnb()\nmodel1.fit(train_x, train_y)",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 34,
          "data": {
            "text/plain": "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predicted = model1.predict(test_x)\nmodel1.score(test_x, test_y)",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "text/plain": "0.3553846153846154"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## SVM (model 2)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn import svm\nfrom sklearn.model_selection import GridSearchCV as gs",
      "execution_count": 36,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def svc_param_selection(X, y, nfolds):\n    Cs = [1000]\n    gammas = [0.001, 0.01, 0.1, 1]\n    degrees = [0]\n    param_grid = {'C':Cs,'gamma': gammas,'degree':degrees}\n    grid_search = gs(svm.SVC(kernel='sigmoid'), param_grid, cv=nfolds, verbose=2)\n    grid_search.fit(X, y)\n    return grid_search.best_params_",
      "execution_count": 37,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "parameters = svc_param_selection(train_x, train_y, 3) #return the best parameter of SVM",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n[CV] C=1000, degree=0, gamma=0.001 ...................................\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "[CV] .................... C=1000, degree=0, gamma=0.001, total=   2.1s\n[CV] C=1000, degree=0, gamma=0.001 ...................................\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.3s remaining:    0.0s\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "[CV] .................... C=1000, degree=0, gamma=0.001, total=   2.4s\n[CV] C=1000, degree=0, gamma=0.001 ...................................\n[CV] .................... C=1000, degree=0, gamma=0.001, total=   2.4s\n[CV] C=1000, degree=0, gamma=0.01 ....................................\n[CV] ..................... C=1000, degree=0, gamma=0.01, total=   1.7s\n[CV] C=1000, degree=0, gamma=0.01 ....................................\n[CV] ..................... C=1000, degree=0, gamma=0.01, total=   1.7s\n[CV] C=1000, degree=0, gamma=0.01 ....................................\n[CV] ..................... C=1000, degree=0, gamma=0.01, total=   1.8s\n[CV] C=1000, degree=0, gamma=0.1 .....................................\n[CV] ...................... C=1000, degree=0, gamma=0.1, total=   1.6s\n[CV] C=1000, degree=0, gamma=0.1 .....................................\n[CV] ...................... C=1000, degree=0, gamma=0.1, total=   1.4s\n[CV] C=1000, degree=0, gamma=0.1 .....................................\n[CV] ...................... C=1000, degree=0, gamma=0.1, total=   1.3s\n[CV] C=1000, degree=0, gamma=1 .......................................\n[CV] ........................ C=1000, degree=0, gamma=1, total=   1.3s\n[CV] C=1000, degree=0, gamma=1 .......................................\n[CV] ........................ C=1000, degree=0, gamma=1, total=   1.5s\n[CV] C=1000, degree=0, gamma=1 .......................................\n[CV] ........................ C=1000, degree=0, gamma=1, total=   1.2s\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:   32.3s finished\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "for k, v in parameters.items():\n    model2=svm.SVC().set_params(C=parameters['C'], gamma=parameters['gamma'])\n    # print(clf)\n    break",
      "execution_count": 39,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model2.fit(train_x, train_y)\npredicted = model2.predict(test_x)\nmodel2.score(test_x, test_y)",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": "0.5907692307692308"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Predict wine type"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "markdown",
      "source": "// Use all attributes except type to predict the type of wine..."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.model_selection import train_test_split\ntrain_x1, test_x1, train_y1, test_y1 = train_test_split(df0.drop(['type_white','type_red'], axis=1), df0['type_red'], test_size=0.2, random_state=42)\nmodel0.fit(train_x1,train_y1)\npredicted = model0.predict(test_x1)\nmodel0.score(test_x1, test_y1)",
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 57,
          "data": {
            "text/plain": "0.9961538461538462"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "markdown",
      "source": "## Function for predict quality/type"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def predict_quality(f_acid, v_acid, c_acid, r_sugar, chlo, free_sulfur, total_sulfur, density, pH, sulphates, \n                   alcohol, type1, type2):\n    input = [{'fixed acidity': f_acid,\n              'volatile acidity': v_acid,\n              'citric acid': c_acid,\n              'residual sugar': r_sugar,\n              'chlorides': chlo,\n              'free sulfur dioxide': free_sulfur,\n              'total sulfur dioxide': total_sulfur,\n              'density': density,\n              'pH': pH,\n              'sulphates': sulphates,\n              'alcohol': alcohol,\n              'type_white': type1,\n              'type_red': type2}]\n    return model0.predict(pd.DataFrame(input))[0]",
      "execution_count": 50,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predict_quality(8.5, 0.32, 0.41, 8.8, 0.052, 38.0, 122.0, 0.9875, 3.3, 0.48, \n                12.0, 0, 1)\n# Predict quality with random variables...",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 51,
          "data": {
            "text/plain": "6"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predict_quality(8.5, 0.32, 0.41, 8.8, 0.052, 38.0, 122.0, 0.9875, 3.3, 0.48, \n                12.0, 1, 0)\n# Predict quality with random variables...",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 52,
          "data": {
            "text/plain": "6"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "// As the output above shown, our best model (model0: the RandomForest one) predicts the quality of the above white/red wine is 6."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def predict_type(f_acid, v_acid, c_acid, r_sugar, chlo, free_sulfur, total_sulfur, density, pH, sulphates, \n                   alcohol, quality):\n    input = [{'fixed acidity': f_acid,\n              'volatile acidity': v_acid,\n              'citric acid': c_acid,\n              'residual sugar': r_sugar,\n              'chlorides': chlo,\n              'free sulfur dioxide': free_sulfur,\n              'total sulfur dioxide': total_sulfur,\n              'density': density,\n              'pH': pH,\n              'sulphates': sulphates,\n              'alcohol': alcohol,\n              'quality': quality}]\n    return model0.predict(pd.DataFrame(input))[0]",
      "execution_count": 54,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predict_type(8.5, 0.32, 0.41, 8.8, 0.052, 38.0, 122.0, 0.9875, 3.3, 0.48, \n                12.0, 7)\n# Predict quality with random variables...",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 58,
          "data": {
            "text/plain": "1"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "// As the output above shown, our best model (model0: the RandomForest one) predicts the type of the above wine is red (type_red = 1)."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Future..."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "//Improvements could be done...\n- Multicollinearity issue (not considered in this predict function, e.g. may not need all variables to predict?)\n- Accuracy is not high.. (0.7) //I'm confused if an attribute of dataset can be well predicted by other atrributes.. (well-conditioned problem?)\n- Not tuning other model (e.g. Bayes, kNN?)"
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python36",
      "display_name": "Python 3.6",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6",
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}