Project_7_Wine_Quality_Prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/justSammy1604/Python-Learning-Stuff/blob/main/Project_7_Wine_Quality_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBHsHWmumNKn"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mXdf5RGkzlk"
      },
      "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\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bURpf8bkm7-M"
      },
      "source": [
        "Data Collection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWKcl9eNm6F_"
      },
      "source": [
        "# loading the dataset to a Pandas DataFrame\n",
        "wine_dataset = pd.read_csv('/content/winequality-red.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        },
        "id": "g_My291znM6F",
        "outputId": "4aeed5bd-32b6-4f55-f221-4b58075290a6"
      },
      "source": [
        "# number of rows & columns in the dataset\n",
        "wine_dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
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          "height": 215
        },
        "id": "gwMuYfkznSrt",
        "outputId": "1f8ddc2a-f497-43ad-e3d9-f4069dc5e3fd"
      },
      "source": [
        "# first 5 rows of the dataset\n",
        "wine_dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
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        }
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        "id": "sm4Tve1gncni",
        "outputId": "6ce110f2-2f70-4904-8d2e-2f3a327176fe"
      },
      "source": [
        "# checking for missing values\n",
        "wine_dataset.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-MFdFXsoAto"
      },
      "source": [
        "Data Analysis and Visulaization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "Y7o-Nl_EnyIE",
        "outputId": "4e4a0e7a-d1f6-406f-dfc2-e346c3b52b04"
      },
      "source": [
        "# statistical measures of the dataset\n",
        "wine_dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.319637</td>\n",
              "      <td>0.527821</td>\n",
              "      <td>0.270976</td>\n",
              "      <td>2.538806</td>\n",
              "      <td>0.087467</td>\n",
              "      <td>15.874922</td>\n",
              "      <td>46.467792</td>\n",
              "      <td>0.996747</td>\n",
              "      <td>3.311113</td>\n",
              "      <td>0.658149</td>\n",
              "      <td>10.422983</td>\n",
              "      <td>5.636023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.741096</td>\n",
              "      <td>0.179060</td>\n",
              "      <td>0.194801</td>\n",
              "      <td>1.409928</td>\n",
              "      <td>0.047065</td>\n",
              "      <td>10.460157</td>\n",
              "      <td>32.895324</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>0.169507</td>\n",
              "      <td>1.065668</td>\n",
              "      <td>0.807569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996750</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.997835</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fixed acidity  volatile acidity  ...      alcohol      quality\n",
              "count    1599.000000       1599.000000  ...  1599.000000  1599.000000\n",
              "mean        8.319637          0.527821  ...    10.422983     5.636023\n",
              "std         1.741096          0.179060  ...     1.065668     0.807569\n",
              "min         4.600000          0.120000  ...     8.400000     3.000000\n",
              "25%         7.100000          0.390000  ...     9.500000     5.000000\n",
              "50%         7.900000          0.520000  ...    10.200000     6.000000\n",
              "75%         9.200000          0.640000  ...    11.100000     6.000000\n",
              "max        15.900000          1.580000  ...    14.900000     8.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
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          "height": 401
        },
        "id": "B6mep7GEoLNp",
        "outputId": "72d7351e-0d02-48d5-f8a6-2a6072b9a4b2"
      },
      "source": [
        "# number of values for each quality\n",
        "sns.catplot(x='quality', data = wine_dataset, kind = 'count')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fb106addb50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "COy4yhieo0v7",
        "outputId": "e59aeba2-d52c-4422-c642-82b6a4e0f80e"
      },
      "source": [
        "# volatile acidity vs Quality\n",
        "plot = plt.figure(figsize=(5,5))\n",
        "sns.barplot(x='quality', y = 'volatile acidity', data = wine_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb10649bbd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "C3KRFO91phMV",
        "outputId": "4a5f8751-7211-4bdd-e153-e9e8803e8cc1"
      },
      "source": [
        "# citric acid vs Quality\n",
        "plot = plt.figure(figsize=(5,5))\n",
        "sns.barplot(x='quality', y = 'citric acid', data = wine_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb106506a10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jguNai1nqUhZ"
      },
      "source": [
        "Correlation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xPj4k63cqcOU"
      },
      "source": [
        "1. Positive Correlation\n",
        "2. Negative Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uEI6JRkqAvA"
      },
      "source": [
        "correlation = wine_dataset.corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "id": "6-wxjoy8qbBG",
        "outputId": "5dc66dc3-a6c1-4dc8-8512-2545db16c85e"
      },
      "source": [
        "# constructing a heatmap to understand the correlation between the columns\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(correlation, cbar=True, square=True, fmt = '.1f', annot = True, annot_kws={'size':8}, cmap = 'Blues')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0fd356950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8AqGg1zr69_"
      },
      "source": [
        "Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqi_YdKGrG1P"
      },
      "source": [
        "# separate the data and Label\n",
        "X = wine_dataset.drop('quality',axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x510k7kjsMfZ",
        "outputId": "8ebdd146-992e-401a-b9aa-a849d31139df"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      fixed acidity  volatile acidity  citric acid  ...    pH  sulphates  alcohol\n",
            "0               7.4             0.700         0.00  ...  3.51       0.56      9.4\n",
            "1               7.8             0.880         0.00  ...  3.20       0.68      9.8\n",
            "2               7.8             0.760         0.04  ...  3.26       0.65      9.8\n",
            "3              11.2             0.280         0.56  ...  3.16       0.58      9.8\n",
            "4               7.4             0.700         0.00  ...  3.51       0.56      9.4\n",
            "...             ...               ...          ...  ...   ...        ...      ...\n",
            "1594            6.2             0.600         0.08  ...  3.45       0.58     10.5\n",
            "1595            5.9             0.550         0.10  ...  3.52       0.76     11.2\n",
            "1596            6.3             0.510         0.13  ...  3.42       0.75     11.0\n",
            "1597            5.9             0.645         0.12  ...  3.57       0.71     10.2\n",
            "1598            6.0             0.310         0.47  ...  3.39       0.66     11.0\n",
            "\n",
            "[1599 rows x 11 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TNLpHsJ5sUnx"
      },
      "source": [
        "Label Binarizaton"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZO0UMUbRsNmt"
      },
      "source": [
        "Y = wine_dataset['quality'].apply(lambda y_value: 1 if y_value>=7 else 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Wp_Uzbps9nk",
        "outputId": "4e0fdda4-de8d-4239-df34-c051eed0162a"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0       0\n",
            "1       0\n",
            "2       0\n",
            "3       0\n",
            "4       0\n",
            "       ..\n",
            "1594    0\n",
            "1595    0\n",
            "1596    0\n",
            "1597    0\n",
            "1598    0\n",
            "Name: quality, Length: 1599, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0T7pk6WtDSR"
      },
      "source": [
        "Train & Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KXd_4XvCs-sT"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIlkPlx1ttpR",
        "outputId": "a9625ba8-7974-4724-d3f7-25fb71f126ab"
      },
      "source": [
        "print(Y.shape, Y_train.shape, Y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1599,) (1279,) (320,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5MZuPZht60I"
      },
      "source": [
        "Model Training:\n",
        "\n",
        "Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLYtbQDht0Uq"
      },
      "source": [
        "model = RandomForestClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "doympXrQu__E",
        "outputId": "cc55f7f7-36a0-4a8d-aa09-81343494ff2b"
      },
      "source": [
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1lChonUvS1d"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-I7bqyLvVJ_"
      },
      "source": [
        "Accuracy Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NsuQMf9cvKmK"
      },
      "source": [
        "# accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkyRHd1Dv5gU",
        "outputId": "e710b573-cf50-46d2-e863-00b3a1f862c7"
      },
      "source": [
        "print('Accuracy : ', test_data_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy :  0.925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbptIZOLwMwj"
      },
      "source": [
        "Building a Predictive System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_RRHB94v91w",
        "outputId": "9bb5032a-8711-4f3a-a9cc-2df74c5ff210"
      },
      "source": [
        "input_data = (7.5,0.5,0.36,6.1,0.071,17.0,102.0,0.9978,3.35,0.8,10.5)\n",
        "\n",
        "# changing the input data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the data as we are predicting the label for only one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "prediction = model.predict(input_data_reshaped)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0]==1):\n",
        "  print('Good Quality Wine')\n",
        "else:\n",
        "  print('Bad Quality Wine')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\n",
            "Bad Quality Wine\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}