predicting_wine_quality_OLS.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "sGq3CFMx6M-t"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "3XdwV4nLuQz8"
      },
      "outputs": [],
      "source": [
        "df_wine_quality = pd.read_csv('winequality-red.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "RVXJVvOk-6CF",
        "outputId": "577d1f61-0c0c-4219-8410-1d4a568dcfb9"
      },
      "outputs": [
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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>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",
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            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wine_quality.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a74nf3Z9-8ri",
        "outputId": "a3b16a61-cb2d-49cf-93c0-b5df4741e3cd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([5, 6, 7, 4, 8, 3])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wine_quality['quality'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5EvOYq1__RpB",
        "outputId": "7bbba50c-d416-4548-b0f6-5f0167913f13"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wine_quality.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sq43-MZKCU7Q",
        "outputId": "19c546e5-bfa2-4d9d-9bb5-e59e95d4f80b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1599"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df_wine_quality)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-IH4EVbeoKl9",
        "outputId": "d9d452f7-d73e-4dd9-a1b0-36157d5ba5f5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  density  pH    sulphates  alcohol  quality\n",
              "7.5            0.510             0.02         1.7             0.084      13.0                 31.0                  0.99538  3.36  0.54       10.5     6          4\n",
              "6.7            0.460             0.24         1.7             0.077      18.0                 34.0                  0.99480  3.39  0.60       10.6     6          4\n",
              "7.2            0.360             0.46         2.1             0.074      24.0                 44.0                  0.99534  3.40  0.85       11.0     7          4\n",
              "               0.695             0.13         2.0             0.076      12.0                 20.0                  0.99546  3.29  0.54       10.1     5          4\n",
              "8.3            0.650             0.10         2.9             0.089      17.0                 40.0                  0.99803  3.29  0.55       9.5      5          3\n",
              "                                                                                                                                                                 ..\n",
              "7.5            0.430             0.30         2.2             0.062      6.0                  12.0                  0.99495  3.44  0.72       11.5     7          1\n",
              "               0.420             0.32         2.7             0.067      7.0                  25.0                  0.99628  3.24  0.44       10.4     5          1\n",
              "                                 0.31         1.6             0.080      15.0                 42.0                  0.99780  3.31  0.64       9.0      5          1\n",
              "               0.410             0.15         3.7             0.104      29.0                 94.0                  0.99786  3.14  0.58       9.1      5          1\n",
              "15.9           0.360             0.65         7.5             0.096      22.0                 71.0                  0.99760  2.98  0.84       14.9     5          1\n",
              "Length: 1359, dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wine_quality.value_counts() # checking for druplicate rows in dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8jYte0ToZG4"
      },
      "source": [
        "There are duplicate rows inside this dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "FRGx53LpCfUL"
      },
      "outputs": [],
      "source": [
        "\n",
        "df_wine_quality.drop_duplicates(inplace= True) #dropping duplicate rows on training dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "6153J9ksChjr",
        "outputId": "f04aa936-8081-425a-c9c3-8c5026ba733e"
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      "outputs": [
        {
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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>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
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              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.700</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>0.99780</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>0.880</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>0.99680</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>0.760</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>0.99700</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>0.280</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>0.99800</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>5</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.660</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.075</td>\n",
              "      <td>13.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99780</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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1593</th>\n",
              "      <td>6.8</td>\n",
              "      <td>0.620</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.068</td>\n",
              "      <td>28.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.99651</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.82</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1594</th>\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>1595</th>\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>0.76</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\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>1598</th>\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>1359 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0               7.4             0.700         0.00             1.9      0.076   \n",
              "1               7.8             0.880         0.00             2.6      0.098   \n",
              "2               7.8             0.760         0.04             2.3      0.092   \n",
              "3              11.2             0.280         0.56             1.9      0.075   \n",
              "5               7.4             0.660         0.00             1.8      0.075   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "1593            6.8             0.620         0.08             1.9      0.068   \n",
              "1594            6.2             0.600         0.08             2.0      0.090   \n",
              "1595            5.9             0.550         0.10             2.2      0.062   \n",
              "1597            5.9             0.645         0.12             2.0      0.075   \n",
              "1598            6.0             0.310         0.47             3.6      0.067   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
              "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
              "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
              "5                    13.0                  40.0  0.99780  3.51       0.56   \n",
              "...                   ...                   ...      ...   ...        ...   \n",
              "1593                 28.0                  38.0  0.99651  3.42       0.82   \n",
              "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
              "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
              "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
              "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
              "\n",
              "      alcohol  quality  \n",
              "0         9.4        5  \n",
              "1         9.8        5  \n",
              "2         9.8        5  \n",
              "3         9.8        6  \n",
              "5         9.4        5  \n",
              "...       ...      ...  \n",
              "1593      9.5        6  \n",
              "1594     10.5        5  \n",
              "1595     11.2        6  \n",
              "1597     10.2        5  \n",
              "1598     11.0        6  \n",
              "\n",
              "[1359 rows x 12 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wine_quality"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ayQjZYM_o8TO"
      },
      "outputs": [],
      "source": [
        "corr_matrix = df_wine_quality.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "HAkVPj8ppBni",
        "outputId": "3ba94bd7-6e61-48f8-d95d-2cbd813e3d48"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#print(\"Correlation matrix \\n \", corr_matrix)\n",
        "fig = plt.figure(figsize = (10,10))\n",
        "ax = sns.heatmap(data = corr_matrix, annot = True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ztEtZ0z5MSWH"
      },
      "outputs": [],
      "source": [
        "dwq_norm = df_wine_quality[[\"volatile acidity\",\"citric acid\",\"sulphates\", \"alcohol\", \"quality\"]].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LG6iLTd9QMV6",
        "outputId": "97c01cf1-a35b-47bc-e692-22f13941da42"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_wine_quality['quality'].min()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mR2-nw9XOTzR",
        "outputId": "ebb56b75-6d72-43e4-fa53-198885ab934e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1359, 5)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(dwq_norm).shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLGBJHqeOza3",
        "outputId": "65b101db-e5c1-4d96-ef00-4090a288423d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dwq_norm.iloc[:,1].max()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "JIxd3GfCHZZk"
      },
      "outputs": [],
      "source": [
        "for col in range(dwq_norm.shape[1]): # normalizing the dataset\n",
        "  for row in range(dwq_norm.shape[0]):\n",
        "    dwq_norm.iloc[row,col] = (dwq_norm.iloc[row,col]-dwq_norm.iloc[:,col].min())/(dwq_norm.iloc[:,col].max()-dwq_norm.iloc[:,col].min())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "kMla_HEGL6oz",
        "outputId": "49ddfb54-0358-4ff0-ab2c-18b7a08a6229"
      },
      "outputs": [
        {
          "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>volatile acidity</th>\n",
              "      <th>citric acid</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>0.397260</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.137725</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.520548</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.291190</td>\n",
              "      <td>0.654147</td>\n",
              "      <td>0.605263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.438356</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.275080</td>\n",
              "      <td>0.654147</td>\n",
              "      <td>0.605263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.109589</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.237492</td>\n",
              "      <td>0.654147</td>\n",
              "      <td>0.736842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.374325</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.226752</td>\n",
              "      <td>0.627021</td>\n",
              "      <td>0.605263</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>1593</th>\n",
              "      <td>0.606912</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.792856</td>\n",
              "      <td>0.846100</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>0.586223</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.516663</td>\n",
              "      <td>0.936630</td>\n",
              "      <td>0.823256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>0.534501</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.723808</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>0.632773</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.666268</td>\n",
              "      <td>0.926241</td>\n",
              "      <td>0.823256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>0.286234</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.608728</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1359 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      volatile acidity  citric acid  sulphates   alcohol   quality\n",
              "0             0.397260         0.00   0.137725  0.153846  0.400000\n",
              "1             0.520548         0.00   0.291190  0.654147  0.605263\n",
              "2             0.438356         0.04   0.275080  0.654147  0.605263\n",
              "3             0.109589         0.56   0.237492  0.654147  0.736842\n",
              "5             0.374325         0.00   0.226752  0.627021  0.605263\n",
              "...                ...          ...        ...       ...       ...\n",
              "1593          0.606912         0.08   0.792856  0.846100  1.000000\n",
              "1594          0.586223         0.08   0.516663  0.936630  0.823256\n",
              "1595          0.534501         0.10   0.723808  1.000000  1.000000\n",
              "1597          0.632773         0.12   0.666268  0.926241  0.823256\n",
              "1598          0.286234         0.47   0.608728  1.000000  1.000000\n",
              "\n",
              "[1359 rows x 5 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dwq_norm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iHFxI5E51DXV"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pILjTwtW1JeC"
      },
      "source": [
        "## We will select only four features that have high correlation with our target variable quality - volatile acidity (-0.39) , citric acid (0.23 ), sulphates (0.25)and  alcohol (0.48)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "Kft6CVf4MAcC"
      },
      "outputs": [],
      "source": [
        "features = [\"volatile acidity\",\"citric acid\",\"sulphates\", \"alcohol\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "liKiIGs2TdME"
      },
      "outputs": [],
      "source": [
        "x_df = dwq_norm[features] #taking X dataframe of features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "zm7wvxZsUtWQ"
      },
      "outputs": [],
      "source": [
        "y_df = dwq_norm['quality'] # taking Y dataframe of target variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "DaWgFpF8UvQA"
      },
      "outputs": [],
      "source": [
        "x_train = np.zeros((int(x_df.shape[0]*0.8),x_df.shape[1]+1)) #making x matrix from first 80% of X dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "yYqlm0TjW4Rd"
      },
      "outputs": [],
      "source": [
        "for i in x_train:\n",
        "  i[0]=1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VPnb2SznYwvM",
        "outputId": "e23feaaf-cb02-4009-d95f-7bc77d197924"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       ...,\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0.]])"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DeQMxH7XfHk8",
        "outputId": "355cea3e-1a8e-41c8-df8c-86fcdd1e1dd7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1087, 5)"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "hvtEmC51W6n2"
      },
      "outputs": [],
      "source": [
        "for i in range(x_train.shape[0]):\n",
        "  for j in range(1,x_train.shape[1]):\n",
        "    x_train[i][j]=x_df.iloc[i][j-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HXZflVa7hIGf",
        "outputId": "435124e9-b3da-4f0f-be40-16ac884838ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[1.        , 0.39726027, 0.        , 0.13772455, 0.15384615],\n",
              "       [1.        , 0.52054795, 0.        , 0.29118971, 0.6541471 ],\n",
              "       [1.        , 0.43835616, 0.04      , 0.27508039, 0.6541471 ],\n",
              "       ...,\n",
              "       [1.        , 0.27587932, 0.        , 0.33275488, 0.8215103 ],\n",
              "       [1.        , 0.35346368, 0.2       , 0.24934924, 0.68020595],\n",
              "       [1.        , 0.35346368, 0.13      , 0.33275488, 0.8215103 ]])"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D0c5J9TcXrw1"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "3teLak4tcrwA"
      },
      "outputs": [],
      "source": [
        "y_train = np.asarray(list(y_df)[0:int(x_df.shape[0]*0.8)]) # making a list of y values from Y dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMzwyF5oiT5s",
        "outputId": "3718e7d7-99c3-4dfe-d727-df5fffcfd4bd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1087,)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "rWSqnz46ihtR"
      },
      "outputs": [],
      "source": [
        "w = np.matmul((np.matmul(np.linalg.inv(np.matmul(x_train.T,x_train)),x_train.T)),y_train) # OLS equation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6cxviyAzxfKA",
        "outputId": "2cc3609c-fecc-4124-d10d-0690ce84ab47"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 0.35321107, -0.24115142,  0.00772533,  0.1053253 ,  0.51454218])"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "w"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Zv8D6fe3lAbz"
      },
      "outputs": [],
      "source": [
        "x_test = np.zeros((x_df.shape[0]-int(x_df.shape[0]*0.8),x_df.shape[1]+1)) # making x matrix from last 20% of X dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "MdPlpqt5oHEa"
      },
      "outputs": [],
      "source": [
        "for i in x_test:\n",
        "  i[0]=1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "yMAPydXfoUYH"
      },
      "outputs": [],
      "source": [
        "for i in range(x_test.shape[0]):\n",
        "  for j in range(1,x_test.shape[1]):\n",
        "    x_test[i][j]=x_df.iloc[i+int(x_df.shape[0]*0.8)][j-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "gThtSZuvozwM"
      },
      "outputs": [],
      "source": [
        "y_test = np.asarray(list(y_df)[int(x_df.shape[0]*0.8):int(x_df.shape[0])]) # y values from last 20% of Y dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kumexlaJsQVH",
        "outputId": "e72412b1-62f5-4470-81d2-a18f9e89ec34"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(272,)"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "0cxTl_FetokA"
      },
      "outputs": [],
      "source": [
        "rmse_train = np.sqrt((np.sum((y_train-np.dot(x_train,w))**2))/x_train.shape[0]) #rmse of x matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MTXRcEXa0331",
        "outputId": "85e89b69-adb9-4d24-fcb1-28bd2b9f68ee"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.08760503750504202"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rmse_train "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "MslhkcIb7n1Q"
      },
      "outputs": [],
      "source": [
        "loss_train = np.sum((y_train-np.dot(x_train,w))**2) #loss on training dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tn4XfelV7y3s",
        "outputId": "922cb203-7ea2-4359-e3c0-9885d98c4fa5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "8.342336502134422"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "loss_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "tRJ5h8Qk70-z"
      },
      "outputs": [],
      "source": [
        "loss_test = np.sum((y_test-np.dot(x_test,w))**2) #loss on testing dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-A7NIHyY790D",
        "outputId": "86b75fba-b1ad-4497-9f50-6513e1d51a31"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3.0064697095032824"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "loss_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "UxR-2SC_0932"
      },
      "outputs": [],
      "source": [
        "rmse_test = np.sqrt((np.sum((y_test-np.dot(x_test,w))**2))/x_test.shape[0]) #rmse of test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jH_BWNEB1Ph0",
        "outputId": "13e84362-f3ba-4d91-bea6-61637730b837"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.10513418788105573"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rmse_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "z8irewqY1Qvg"
      },
      "outputs": [],
      "source": [
        "y_test_pred = np.dot(x_test,w)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "Hm6lSgF03Lz3",
        "outputId": "60b4a9c8-6216-474d-be79-c35845544297"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Test vs Prediction')"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(8,8)) #plotting test data vs test prediction\n",
        "plt.scatter(y_test,y_test_pred)\n",
        "plt.ylim((0,1))\n",
        "plt.xlim((0,1))\n",
        "plt.xlabel(\"Test Label\")\n",
        "plt.ylabel(\"Prediction label\")\n",
        "plt.title(\"Test vs Prediction\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "qwYVxxnQ3Y48"
      },
      "outputs": [],
      "source": [
        "y_train_pred = np.dot(x_train,w)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "joOAYvTj3wH1",
        "outputId": "bcddd704-c80c-4b1f-f6bf-243e3f441a40"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Train vs Prediction')"
            ]
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(8,8))\n",
        "plt.scatter(y_train,y_train_pred)\n",
        "plt.ylim((0,1))\n",
        "plt.xlim((0,1))\n",
        "plt.xlabel(\"Train Label\")\n",
        "plt.ylabel(\"Prediction label\")\n",
        "plt.title(\"Train vs Prediction\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "CSE574-assignment2-part2.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    },
    "kernelspec": {
      "display_name": "Python 3.8.9 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}