Wine_Quality_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Wine Quality Prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPli4gQzlwEYZcW6UY2RPTl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nandikonda-vishal21/Machine-learning-projects/blob/main/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": "0uo0kMsGKH2s"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gl_hr_ZCI2g_"
      },
      "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": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uK776eTQKq3w"
      },
      "source": [
        "Data Collection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J9DJNz58KsAF"
      },
      "source": [
        "# loading the dataset to a Pandas DataFrame\n",
        "wine_dataset = pd.read_csv('/content/archive (4).zip')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzFeAlSvK9My",
        "outputId": "e3d430c2-3459-49e8-e353-3add67a60a7a"
      },
      "source": [
        "# number of rows & columns in the dataset\n",
        "wine_dataset.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "tCLeFgWiLCjf",
        "outputId": "1c8c7170-011f-4201-8adf-acead7ca426f"
      },
      "source": [
        "# first 5 rows of the dataset\n",
        "wine_dataset.head()"
      ],
      "execution_count": 4,
      "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>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  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]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYL2jICJLSKM",
        "outputId": "72483d7d-cafd-46ff-ffca-ce9c57b59acd"
      },
      "source": [
        "# checking for missing values\n",
        "wine_dataset.isnull().sum()"
      ],
      "execution_count": 5,
      "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": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_bhWdBY2LXKu"
      },
      "source": [
        "Data Analysis and Visulaization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "0mdylAv-LYhe",
        "outputId": "cab4c5f0-f075-4f48-d09f-7613bd648d3f"
      },
      "source": [
        "# statistical measures of the dataset\n",
        "wine_dataset.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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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": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "Ne14WHSqLhBA",
        "outputId": "138810d4-4e33-4c1b-c096-3e89a7d7e471"
      },
      "source": [
        "# number of values for each quality\n",
        "sns.catplot(x='quality', data = wine_dataset, kind = 'count')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fae7c43ccd0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "PgwbAJSTLmz9",
        "outputId": "61bd7c91-dc31-4a67-8f16-80ec4f910189"
      },
      "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": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae7c43cb90>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "mFTnfx5TLsos",
        "outputId": "056bf7f0-bb9d-42eb-8f83-7a3cf5a58b45"
      },
      "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": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae6e897f10>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5rhkzKNrMDng"
      },
      "source": [
        "Correlation\n",
        "\n",
        "Positive Correlation\n",
        "Negative Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ziLoO0siMFeB"
      },
      "source": [
        "correlation = wine_dataset.corr()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "ptIZD_1DMPjM",
        "outputId": "792736e3-3746-4b58-84a6-4cfd3ac5de1c"
      },
      "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": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae8ff851d0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-jKKKx0MW-5"
      },
      "source": [
        "Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhAzeliVMYAu"
      },
      "source": [
        "# separate the data and Label\n",
        "X = wine_dataset.drop('quality',axis=1)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dRRjbmqMhJJ",
        "outputId": "dded65eb-54c9-4509-9c29-1b6835bc442e"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9LDFbDVMm7i"
      },
      "source": [
        "Label Binarizaton"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "df5ajNw8MoJz"
      },
      "source": [
        "Y = wine_dataset['quality'].apply(lambda y_value: 1 if y_value>=7 else 0)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "apHhqtKfMxQX",
        "outputId": "b32a23b4-ba9f-4605-9f35-abbff1b52457"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQunMtMPM6zx"
      },
      "source": [
        "Train & Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LEyX6vGM8NK"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zw5cgEMNF81",
        "outputId": "ed959c58-2962-43b7-d2f6-1f7644bb1fc1"
      },
      "source": [
        "print(Y.shape, Y_train.shape, Y_test.shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1599,) (1279,) (320,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hePb08pNKSI"
      },
      "source": [
        "Model Training:\n",
        "\n",
        "Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pp-tQc5SNLfx"
      },
      "source": [
        "model = RandomForestClassifier()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aOoZjcNlNYIC",
        "outputId": "7ae3c268-2147-44bd-ac5d-e7b049d46afb"
      },
      "source": [
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": 19,
      "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": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o4Zp3rTxNhQ6"
      },
      "source": [
        "Model Evaluation\n",
        "\n",
        "Accuracy Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jHzF55K7NlMf"
      },
      "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": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Y8UU03lOI68",
        "outputId": "2be396ab-265c-43a0-c5fa-3abc8d8c5102"
      },
      "source": [
        "print('Accuracy : ', test_data_accuracy)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.93125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDZwZRj4ONUM"
      },
      "source": [
        "Building a Predictive System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_XWHKkMOObp",
        "outputId": "debaa65b-f560-4561-c252-98b9db53e7a8"
      },
      "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": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "Bad Quality Wine\n"
          ]
        }
      ]
    }
  ]
}