wine (1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "wine.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "QF_IAeI5e4A9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "660cc606-41cd-4bbe-d6eb-9fc3970774f5"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00wgRXWsgOmD",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "d310223f-cd81-4ead-c80b-8ae6fe403463"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()#upload wine.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-04986442-1521-4864-88c1-015069c18591\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-04986442-1521-4864-88c1-015069c18591\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving wine.csv to wine.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EIpeWGofEzJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import io\n",
        "wine = pd.read_csv(io.BytesIO(uploaded['wine.csv']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9Cg0k61gtSQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "6232095d-ec11-431a-f20a-f3101a4a71a5"
      },
      "source": [
        "wine"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>type</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>white</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.270</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>1.00100</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>white</td>\n",
              "      <td>6.3</td>\n",
              "      <td>0.300</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>0.99400</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>white</td>\n",
              "      <td>8.1</td>\n",
              "      <td>0.280</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>0.99510</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>white</td>\n",
              "      <td>7.2</td>\n",
              "      <td>0.230</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>0.99560</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>white</td>\n",
              "      <td>7.2</td>\n",
              "      <td>0.230</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>0.99560</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>...</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>red</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6493</th>\n",
              "      <td>red</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.062</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6494</th>\n",
              "      <td>red</td>\n",
              "      <td>6.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.076</td>\n",
              "      <td>29.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99574</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.75</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6495</th>\n",
              "      <td>red</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.075</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99547</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6496</th>\n",
              "      <td>red</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.067</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.99549</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6497 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       type  fixed acidity  volatile acidity  ...  sulphates  alcohol  quality\n",
              "0     white            7.0             0.270  ...       0.45      8.8        6\n",
              "1     white            6.3             0.300  ...       0.49      9.5        6\n",
              "2     white            8.1             0.280  ...       0.44     10.1        6\n",
              "3     white            7.2             0.230  ...       0.40      9.9        6\n",
              "4     white            7.2             0.230  ...       0.40      9.9        6\n",
              "...     ...            ...               ...  ...        ...      ...      ...\n",
              "6492    red            6.2             0.600  ...       0.58     10.5        5\n",
              "6493    red            5.9             0.550  ...        NaN     11.2        6\n",
              "6494    red            6.3             0.510  ...       0.75     11.0        6\n",
              "6495    red            5.9             0.645  ...       0.71     10.2        5\n",
              "6496    red            6.0             0.310  ...       0.66     11.0        6\n",
              "\n",
              "[6497 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_TAbAYt6IaB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PX49pCIxg_Cs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "a2aada9a-6392-4285-dfd3-24abbd7e26a2"
      },
      "source": [
        "\n",
        "sns.relplot(x = 'fixed acidity', y = 'volatile acidity',data = wine,hue='type');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 423.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wG3HtTu2s4yF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "3cd5a1c5-36a7-4020-e533-a90a2c9d6700"
      },
      "source": [
        "wine['type'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "white    4898\n",
              "red      1599\n",
              "Name: type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4G7BHlOI6MOI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "ddbadf82-04fb-43cf-9923-547ec36b66ff"
      },
      "source": [
        "# Looking at number of missing values using 'isnull()' function\n",
        "print( 'Columns and number of missing values')\n",
        "for column in wine.columns:\n",
        "    print(column +\":\" + str(wine[column].isnull().sum()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Columns and number of missing values\n",
            "type:0\n",
            "fixed acidity:10\n",
            "volatile acidity:8\n",
            "citric acid:3\n",
            "residual sugar:2\n",
            "chlorides:2\n",
            "free sulfur dioxide:0\n",
            "total sulfur dioxide:0\n",
            "density:0\n",
            "pH:9\n",
            "sulphates:4\n",
            "alcohol:0\n",
            "quality:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SCrZAcvE7yWA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ec0e86fa-4cd8-4b1f-a55c-bb9cea510185"
      },
      "source": [
        "wine['residual sugar'].median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJnolwj_88NB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine['residual sugar']=wine['residual sugar'].fillna(wine['residual sugar'].median())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjpxXJQd9NRF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "2d9e8880-501f-4565-cb00-c6fbbf311af0"
      },
      "source": [
        "wine.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type                     0\n",
              "fixed acidity           10\n",
              "volatile acidity         8\n",
              "citric acid              3\n",
              "residual sugar           0\n",
              "chlorides                2\n",
              "free sulfur dioxide      0\n",
              "total sulfur dioxide     0\n",
              "density                  0\n",
              "pH                       9\n",
              "sulphates                4\n",
              "alcohol                  0\n",
              "quality                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhwWBg2f9Yxg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine['fixed acidity'].median()\n",
        "wine['fixed acidity']=wine['fixed acidity'].fillna(wine['fixed acidity'].median())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWILNOf59rmB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "03872141-9b2b-4191-ea20-fbebffbde6d1"
      },
      "source": [
        "wine.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type                    0\n",
              "fixed acidity           0\n",
              "volatile acidity        8\n",
              "citric acid             3\n",
              "residual sugar          0\n",
              "chlorides               2\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      9\n",
              "sulphates               4\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHagHLCR97VD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ec054544-1faa-43d7-f03c-ffa077d4194d"
      },
      "source": [
        "wine['volatile acidity'].median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MFtkwc_-Njv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine['volatile acidity']=wine['volatile acidity'].fillna(wine['volatile acidity'].median())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2bRLANW-Vzv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "8b8f0628-1f8c-4143-8ae2-ffa7be238695"
      },
      "source": [
        "wine.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type                    0\n",
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             3\n",
              "residual sugar          0\n",
              "chlorides               2\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      9\n",
              "sulphates               4\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZZIPfp8-awE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine['pH'].median()\n",
        "wine['pH']=wine['pH'].fillna(wine['pH'].median())\n",
        "wine['citric acid'].median()\n",
        "wine['citric acid']=wine['citric acid'].fillna(wine['citric acid'].median())\n",
        "wine['chlorides']=wine['chlorides'].median()\n",
        "wine['chlorides']=wine['chlorides'].fillna(wine['chlorides'].median())\n",
        "wine['sulphates'].median()\n",
        "wine['sulphates']=wine['sulphates'].fillna(wine['sulphates'].median())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U01V-tC7_LyT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "bab5b151-0914-49c9-c311-94adca02f5f9"
      },
      "source": [
        "wine.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type                    0\n",
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             0\n",
              "residual sugar          0\n",
              "chlorides               0\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBxt4DpyfZCw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine=wine.drop(columns=['density','quality'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJYq6K7wfg9D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine=wine.drop_duplicates().copy()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUIpAh-bfjL_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "7394c07a-c480-41cf-bfcb-90dc475809f6"
      },
      "source": [
        "wine"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\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>type</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>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>white</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.270</td>\n",
              "      <td>0.36</td>\n",
              "      <td>20.7</td>\n",
              "      <td>0.047</td>\n",
              "      <td>45.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>8.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>white</td>\n",
              "      <td>6.3</td>\n",
              "      <td>0.300</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.047</td>\n",
              "      <td>14.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.49</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>white</td>\n",
              "      <td>8.1</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.40</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.047</td>\n",
              "      <td>30.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>white</td>\n",
              "      <td>7.2</td>\n",
              "      <td>0.230</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.047</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>white</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0.320</td>\n",
              "      <td>0.16</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>30.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>3.18</td>\n",
              "      <td>0.47</td>\n",
              "      <td>9.6</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6491</th>\n",
              "      <td>red</td>\n",
              "      <td>6.8</td>\n",
              "      <td>0.620</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.047</td>\n",
              "      <td>28.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.82</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>red</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6493</th>\n",
              "      <td>red</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.047</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.51</td>\n",
              "      <td>11.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6495</th>\n",
              "      <td>red</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6496</th>\n",
              "      <td>red</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.047</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5328 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       type  fixed acidity  volatile acidity  ...    pH  sulphates  alcohol\n",
              "0     white            7.0             0.270  ...  3.00       0.45      8.8\n",
              "1     white            6.3             0.300  ...  3.30       0.49      9.5\n",
              "2     white            8.1             0.280  ...  3.26       0.44     10.1\n",
              "3     white            7.2             0.230  ...  3.19       0.40      9.9\n",
              "6     white            6.2             0.320  ...  3.18       0.47      9.6\n",
              "...     ...            ...               ...  ...   ...        ...      ...\n",
              "6491    red            6.8             0.620  ...  3.42       0.82      9.5\n",
              "6492    red            6.2             0.600  ...  3.45       0.58     10.5\n",
              "6493    red            5.9             0.550  ...  3.52       0.51     11.2\n",
              "6495    red            5.9             0.645  ...  3.57       0.71     10.2\n",
              "6496    red            6.0             0.310  ...  3.39       0.66     11.0\n",
              "\n",
              "[5328 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_57YqqPfxxX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "da532e9e-1646-4ad0-f338-06fa9a3d4d07"
      },
      "source": [
        "wine.isnull().sum()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type                    0\n",
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             0\n",
              "residual sugar          0\n",
              "chlorides               0\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQQInHI9fzrs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "00c7463c-7559-40b2-d8b1-75f84a39bef1"
      },
      "source": [
        "wine_feature=wine.loc[:,['fixed acidity','volatile acidity','residual sugar','pH','alcohol']]\n",
        "wine_feature.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>pH</th>\n",
              "      <th>alcohol</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>20.70</td>\n",
              "      <td>3.00</td>\n",
              "      <td>8.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.60</td>\n",
              "      <td>3.30</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.28</td>\n",
              "      <td>6.90</td>\n",
              "      <td>3.26</td>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>8.50</td>\n",
              "      <td>3.19</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.32</td>\n",
              "      <td>7.00</td>\n",
              "      <td>3.18</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.50</td>\n",
              "      <td>3.22</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.45</td>\n",
              "      <td>2.99</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8.6</td>\n",
              "      <td>0.23</td>\n",
              "      <td>4.20</td>\n",
              "      <td>3.14</td>\n",
              "      <td>9.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>7.9</td>\n",
              "      <td>0.18</td>\n",
              "      <td>1.20</td>\n",
              "      <td>3.18</td>\n",
              "      <td>10.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>6.6</td>\n",
              "      <td>0.16</td>\n",
              "      <td>1.50</td>\n",
              "      <td>3.54</td>\n",
              "      <td>12.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    fixed acidity  volatile acidity  residual sugar    pH  alcohol\n",
              "0             7.0              0.27           20.70  3.00      8.8\n",
              "1             6.3              0.30            1.60  3.30      9.5\n",
              "2             8.1              0.28            6.90  3.26     10.1\n",
              "3             7.2              0.23            8.50  3.19      9.9\n",
              "6             6.2              0.32            7.00  3.18      9.6\n",
              "9             8.1              0.22            1.50  3.22     11.0\n",
              "10            8.1              0.27            1.45  2.99     12.0\n",
              "11            8.6              0.23            4.20  3.14      9.7\n",
              "12            7.9              0.18            1.20  3.18     10.8\n",
              "13            6.6              0.16            1.50  3.54     12.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JB4jMBsGghYS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "af843bb0-90e1-4243-c531-a64fe3fad9e6"
      },
      "source": [
        "wine_label=wine.loc[:,['type']]\n",
        "wine_label.tail(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6486</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6487</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6488</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6489</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6490</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6491</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6493</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6495</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6496</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     type\n",
              "6486  red\n",
              "6487  red\n",
              "6488  red\n",
              "6489  red\n",
              "6490  red\n",
              "6491  red\n",
              "6492  red\n",
              "6493  red\n",
              "6495  red\n",
              "6496  red"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVNzkU0egyyj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train, y_test = train_test_split(wine_feature,wine_label,random_state=0,test_size =0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WKIJmptqg_bc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nM-hbooMhC_e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr = LogisticRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9OywDjQhGyh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "899783fe-9997-4d3f-c01e-3acf44ef052c"
      },
      "source": [
        "lr = lr.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4nN1n_RhKVi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "87c196cc-0f1d-41a1-8a8c-6cfaf858d118"
      },
      "source": [
        "y_pred_lr=lr.predict(X_test)\n",
        "print(\"The predicted values are:\",y_pred_lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The predicted values are: ['red' 'white' 'white' ... 'red' 'white' 'white']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUQiZJ-_hPNE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "807d71b5-61f9-4b53-81ed-9d85b6e39ffd"
      },
      "source": [
        "from sklearn import metrics\n",
        "accuracy_lr  = metrics.accuracy_score(y_test,y_pred_lr)\n",
        "print(\"Accuracy of the Logistic regression:\",accuracy_lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the Logistic regression: 0.9634146341463414\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fG3wu3XBhUXo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "eb27956b-0c96-4f0b-85c1-3f644cafdd2a"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test,y_pred_lr)\n",
        "print ( \"Confusion Matrix of Logistic Regression: \\n\", cm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix of Logistic Regression: \n",
            " [[248  25]\n",
            " [ 14 779]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kC12kjxh3Zg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "1e9b655e-813b-43e7-a6d5-1bfd70373fcb"
      },
      "source": [
        "wine"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>type</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>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>white</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.270</td>\n",
              "      <td>0.36</td>\n",
              "      <td>20.7</td>\n",
              "      <td>0.047</td>\n",
              "      <td>45.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>8.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>white</td>\n",
              "      <td>6.3</td>\n",
              "      <td>0.300</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.047</td>\n",
              "      <td>14.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.49</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>white</td>\n",
              "      <td>8.1</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.40</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.047</td>\n",
              "      <td>30.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>white</td>\n",
              "      <td>7.2</td>\n",
              "      <td>0.230</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.047</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>white</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0.320</td>\n",
              "      <td>0.16</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>30.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>3.18</td>\n",
              "      <td>0.47</td>\n",
              "      <td>9.6</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6491</th>\n",
              "      <td>red</td>\n",
              "      <td>6.8</td>\n",
              "      <td>0.620</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.047</td>\n",
              "      <td>28.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.82</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>red</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6493</th>\n",
              "      <td>red</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.047</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.51</td>\n",
              "      <td>11.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6495</th>\n",
              "      <td>red</td>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6496</th>\n",
              "      <td>red</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.047</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5328 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       type  fixed acidity  volatile acidity  ...    pH  sulphates  alcohol\n",
              "0     white            7.0             0.270  ...  3.00       0.45      8.8\n",
              "1     white            6.3             0.300  ...  3.30       0.49      9.5\n",
              "2     white            8.1             0.280  ...  3.26       0.44     10.1\n",
              "3     white            7.2             0.230  ...  3.19       0.40      9.9\n",
              "6     white            6.2             0.320  ...  3.18       0.47      9.6\n",
              "...     ...            ...               ...  ...   ...        ...      ...\n",
              "6491    red            6.8             0.620  ...  3.42       0.82      9.5\n",
              "6492    red            6.2             0.600  ...  3.45       0.58     10.5\n",
              "6493    red            5.9             0.550  ...  3.52       0.51     11.2\n",
              "6495    red            5.9             0.645  ...  3.57       0.71     10.2\n",
              "6496    red            6.0             0.310  ...  3.39       0.66     11.0\n",
              "\n",
              "[5328 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lA76wNTToG2Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "tr= DecisionTreeClassifier(max_depth=3)\n",
        "tr = tr.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zbExJpooRdP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "fb1881d0-84ea-4fc6-affb-d64493a295bf"
      },
      "source": [
        "from sklearn import tree\n",
        "tree.plot_tree(tr);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbotQUDXoWeG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "30d7dda7-068f-4e0f-dd37-90c406b0e644"
      },
      "source": [
        "y_pred2=tr.predict(X_test)\n",
        "print(\"Prediction of test data:\",y_pred2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction of test data: ['red' 'red' 'white' ... 'red' 'white' 'white']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbcObsIuoaVI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "812e4b7d-8f87-4bf2-b3bb-2c9f0f88df34"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "print(metrics.accuracy_score(y_test,y_pred2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9118198874296435\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "903udTsNomCm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "334a1b25-8994-444c-a3a0-dd25de15323e"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn=KNeighborsClassifier(n_neighbors =1)\n",
        "knn= knn.fit(X_train,y_train)\n",
        "y_pred1=knn.predict(X_test)\n",
        "print(\"Prediction of test data:\",y_pred1)\n",
        "X_test.head(10)\n",
        "## accuracy\n",
        "from sklearn import metrics\n",
        "\n",
        "print(metrics.accuracy_score(y_test,y_pred1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction of test data: ['red' 'white' 'white' ... 'red' 'red' 'white']\n",
            "0.9352720450281425\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AkObx9pr-u2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "1873a688-ea23-4df4-e173-348030119165"
      },
      "source": [
        "wine_feature"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>residual sugar</th>\n",
              "      <th>pH</th>\n",
              "      <th>alcohol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.270</td>\n",
              "      <td>20.7</td>\n",
              "      <td>3.00</td>\n",
              "      <td>8.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.300</td>\n",
              "      <td>1.6</td>\n",
              "      <td>3.30</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.280</td>\n",
              "      <td>6.9</td>\n",
              "      <td>3.26</td>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.230</td>\n",
              "      <td>8.5</td>\n",
              "      <td>3.19</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.320</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.18</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6491</th>\n",
              "      <td>6.8</td>\n",
              "      <td>0.620</td>\n",
              "      <td>1.9</td>\n",
              "      <td>3.42</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.45</td>\n",
              "      <td>10.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6493</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>2.2</td>\n",
              "      <td>3.52</td>\n",
              "      <td>11.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6495</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.57</td>\n",
              "      <td>10.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6496</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>3.6</td>\n",
              "      <td>3.39</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5328 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed acidity  volatile acidity  residual sugar    pH  alcohol\n",
              "0               7.0             0.270            20.7  3.00      8.8\n",
              "1               6.3             0.300             1.6  3.30      9.5\n",
              "2               8.1             0.280             6.9  3.26     10.1\n",
              "3               7.2             0.230             8.5  3.19      9.9\n",
              "6               6.2             0.320             7.0  3.18      9.6\n",
              "...             ...               ...             ...   ...      ...\n",
              "6491            6.8             0.620             1.9  3.42      9.5\n",
              "6492            6.2             0.600             2.0  3.45     10.5\n",
              "6493            5.9             0.550             2.2  3.52     11.2\n",
              "6495            5.9             0.645             2.0  3.57     10.2\n",
              "6496            6.0             0.310             3.6  3.39     11.0\n",
              "\n",
              "[5328 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kx6S-xPsh_x2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "02b46e4c-6ebc-4e2f-cfa3-d5c154b09ab9"
      },
      "source": [
        "new_wine=[[3.8,0.311,12.0,3.75,12.5]]\n",
        "new_wine=pd.DataFrame(new_wine)\n",
        "new_pred=lr.predict(new_wine)\n",
        "print(new_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['white']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qoJ2Z9njchXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}