Predicting the Quality of Red Wine.ipynb

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  "metadata": {
    "colab": {
      "name": "Predicting the Quality of Red Wine",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EOHFA-GOAT/machine-learning-projects/blob/master/Predicting%20the%20Quality%20of%20Red%20Wine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujhv-P5uslLd",
        "colab_type": "text"
      },
      "source": [
        "##Introduction\n",
        "In this Machine Learning project, I will first do some exploratory data analysis (EDA) on the dataset, and then use multiple models and algorithms to predict the quality of red wine. Finally, the best model will be increased in accuracy. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7C89xr-Kfyv",
        "colab_type": "text"
      },
      "source": [
        "##Uploading the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxGcO-FcKiVO",
        "colab_type": "code",
        "outputId": "45ac9d85-6618-4546-aa78-ea08ea0c2593",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "#Importing all necessary files\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
        "%matplotlib inline\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-bee0629a-7023-47bd-8faa-1e63ac1146ce\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-bee0629a-7023-47bd-8faa-1e63ac1146ce\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving winequality-red.csv to winequality-red (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3sfKeFFMWco",
        "colab_type": "text"
      },
      "source": [
        "## Doing EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIG0Ts6QMeQm",
        "colab_type": "code",
        "outputId": "2c8e338c-e1cc-41c5-eb1f-802e96b61c8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "data = pd.read_csv(\"winequality-red.csv\")\n",
        "data.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>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": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hiMZCTKMx-S",
        "colab_type": "text"
      },
      "source": [
        "I am going to get some info on the columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHP85f0lM43H",
        "colab_type": "code",
        "outputId": "03eb1280-eaeb-4185-b3a5-e9cc78dff281",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        }
      },
      "source": [
        "#info on columns\n",
        "data.info()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1599 entries, 0 to 1598\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   fixed acidity         1599 non-null   float64\n",
            " 1   volatile acidity      1599 non-null   float64\n",
            " 2   citric acid           1599 non-null   float64\n",
            " 3   residual sugar        1599 non-null   float64\n",
            " 4   chlorides             1599 non-null   float64\n",
            " 5   free sulfur dioxide   1599 non-null   float64\n",
            " 6   total sulfur dioxide  1599 non-null   float64\n",
            " 7   density               1599 non-null   float64\n",
            " 8   pH                    1599 non-null   float64\n",
            " 9   sulphates             1599 non-null   float64\n",
            " 10  alcohol               1599 non-null   float64\n",
            " 11  quality               1599 non-null   int64  \n",
            "dtypes: float64(11), int64(1)\n",
            "memory usage: 150.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GuTWDrrGNEyq",
        "colab_type": "text"
      },
      "source": [
        "So, I have 11 float types and 1 int types to keep in mind"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_x6gesw3OiBf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "e51f8412-2d66-4bc0-bd1b-d4634b848439"
      },
      "source": [
        "data['grade'] = 1 # good\n",
        "data.grade[data.quality < 7] = 0 # not good\n",
        "\n",
        "plt.figure(figsize = (8,8))\n",
        "labels = data.grade.value_counts().index\n",
        "plt.pie(data.grade.value_counts(), autopct='%1.1f%%')\n",
        "plt.legend(labels, loc=\"best\")\n",
        "plt.axis('equal')\n",
        "plt.title('Quality Pie Chart')\n",
        "plt.show()\n",
        "print('The good quality wines count for ',round(data.grade.value_counts(normalize=True)[1]*100,1),'%.')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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nCescIimU6cIUkR4q0NB4UNvl2yyNW56zziKSMiuqdSCNMEVqxFIGDD6obdKI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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The good quality wines count for  13.6 %.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UgcKaTPMNYSG",
        "colab_type": "text"
      },
      "source": [
        "I will start to plot now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-gVb1PXNP2p",
        "colab_type": "code",
        "outputId": "324fb6bc-dd14-4294-980b-27220bed0813",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "#plotting fixed acidity over quality.\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'fixed acidity', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d7b0f5f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7plwLAGAVTPlpwUryxiRXd/e/m2odAIBVMuWWq9OTnJXk6VV11eLr2ROuBwAwu8lOxdDd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\ngcQVAMBA4goAYCBxBQAwkLgCABjo/wIYr3dAj+OZSgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQ7PFt2tObmh",
        "colab_type": "text"
      },
      "source": [
        "Fixed acidity does not give any indications to classify the quality (Pretty much even throughout)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TONDTUK1O0Ko",
        "colab_type": "code",
        "outputId": "8cd6165f-6e77-4e96-d14b-93b8dd9a1b9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "#plotting volatile acidity over quality\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'volatile acidity', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6b3e59e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RAAADKGEAAAMoYQAAA0xawqrqvKp6X1XtqapLVznvO6uqq2rblHkAANaLyUpYVW1KclmS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\nQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcw6H9BHPEbE",
        "colab_type": "text"
      },
      "source": [
        "Clearly a downward trend in this plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6r3H272TPXwC",
        "colab_type": "code",
        "outputId": "b3971e16-2e40-4066-9da0-5e7bcb249378",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        }
      },
      "source": [
        "#plotting the composition of citric acid over quality\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'citric acid', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6ff400f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RN57d6T8PloQ",
        "colab_type": "text"
      },
      "source": [
        "Clearly an upward trend in this plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-WIFB8XPrlZ",
        "colab_type": "code",
        "outputId": "95c6b3ca-c116-4cdb-d6e3-a38a9c6cefd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "#plotting residual sugar over quality\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'residual sugar', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6af6cc50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POWRdBdtQCKf",
        "colab_type": "text"
      },
      "source": [
        "No important specifications in this plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8agUhIOQR3s",
        "colab_type": "code",
        "outputId": "4da3c55b-69e8-4b9d-e9bd-f2a8b6bd0d9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "#plotting the composition of chlorides over quality\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'chlorides', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d97d26128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r1HL2L1V1y36+9hbg5CQndc/lPBvYNGPOJuDcbnk98KmqKuAO4CUASQ4HfhT40n6+vyRJ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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9MBm85lPQbYk",
        "colab_type": "text"
      },
      "source": [
        "A bit of a downward trend in this plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OR-Fmq-Qfui",
        "colab_type": "code",
        "outputId": "d7df994e-c09a-4c42-8670-c5536dbebc11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "#plotting free sulfur dioxide (a measure of the amount of sulfur dioxide that is not present in wine) over quality\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'free sulfur dioxide', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6add32b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cSZIkNcRyJkmS1BDLmSRJUkMsZ5IkSQ35/4Py7QDL9cDSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uk_x-jL_Rdoi",
        "colab_type": "text"
      },
      "source": [
        "No obvious trend in this plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8bqdv2RRgyl",
        "colab_type": "code",
        "outputId": "82fc0804-d6ae-4aa1-9a07-2f9c6364e2ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        }
      },
      "source": [
        "#plotting total sulfur dioxide over quality\n",
        "fig = plt.figure(figsize=(10,6) )\n",
        "sns.barplot(x = 'quality', y = 'total sulfur dioxide', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6acc8da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AKF4AAIMoXgAAg+xzjldVXZnb3iroHw8l6e7+hslSAQAsofUm1z9uWAoAgC1gn8Wru68e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\nEMULAGAQxQsAYBDFCwBgkP8Pb6riQLKhlY4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGE9lJYgSMM2",
        "colab_type": "text"
      },
      "source": [
        "Ehhhh.... Nothing to notice"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bdj4hT32UelI",
        "colab_type": "code",
        "outputId": "65a76d8b-fd8e-434b-c8f8-8d6e23557645",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'density', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6af51a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7t27atGmSYQEAVtOUEXZtkmOr6piqOiTJWUm2L96gqo5P8t+zEGD3TDgLAMCaMlmEdffD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\nxXMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zj4sdlAHUlNS",
        "colab_type": "text"
      },
      "source": [
        "Yeah.... nothing to worry about here!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nvfd1IxUUqdL",
        "colab_type": "code",
        "outputId": "51ae3572-6c25-455f-e22a-332374594975",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "#plotting pH over quality\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'pH', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6acd1080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GEGEAAAOIMACAAUQYAMAAIgwAYAARBgAwgAgDABhAhAEADCDCAAAGEGEAAAOIMACAAUQY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\nAGAAEQYAMIAIAwAYQIQBAAwgwgAABvj/FsA1yP/hxNMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MOwtygAPU34s",
        "colab_type": "text"
      },
      "source": [
        "As with the previous plot, nothing to worry about here!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRqJttcmSXKp",
        "colab_type": "code",
        "outputId": "10855684-16f8-4de5-9cc6-5d938bfb0057",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "#plotting sulfate levels over quality\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'sulphates', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6acd1cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JmhcGPR35oL7NU4Bx4KxOEkmSJM0Dg56O3MHUmrAAdwE3Ac/vKJMkSdLIG/SK+X8KPKqq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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lw3BeUpHSWfL",
        "colab_type": "text"
      },
      "source": [
        "An upwards trend!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPJJApEwS7ll",
        "colab_type": "code",
        "outputId": "bc7e7650-94e0-42bd-b59f-99daa6af7522",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        }
      },
      "source": [
        "#plotting alcohol levels over quality\n",
        "fig = plt.figure(figsize = (10,6))\n",
        "sns.barplot(x = 'quality', y = 'alcohol', data = data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6af58d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1kOyPriu7+93395ru3tndy929vLS0NHZAAIA5mOJbjZXkjUlu6e7/NHp9AICpTLHF64Ik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\nABhEeAEADPL/ABMvaownJDcyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GxepO1dT8U4",
        "colab_type": "text"
      },
      "source": [
        "An upward trend in this plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJ3rjxHLUF_I",
        "colab_type": "text"
      },
      "source": [
        "## Quick Data Analysis Summary\n",
        "It turns out that 6 out of the 11 columns that were plotted against quality showed no trends.\n",
        "\n",
        " Out of the remaing 5, alcohol, sulfate levels, and composition of citric acid showed an upward trend. In more applicable terms, the greater the values of these substances, the better the quality of the wine.\n",
        "\n",
        "However, thankfully, the composition of chlorides and volatile acidity showed a downward trend. Thus, in more applicable terms, when there is less chlorides and volatile acidity involved, the quality of the wine is better."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vSeOuSujdSc2"
      },
      "source": [
        "##Spliting the Dataset into Training and Testing Sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FaDu4toZPgxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "import xgboost\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "df_train_features = data.drop(['quality','grade'], axis =1)\n",
        "n = 11\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_train_features, data['grade'], test_size=0.1, random_state=7)\n",
        "\n",
        "x_train_mat = x_train.values.reshape((len(x_train), n))\n",
        "x_test_mat = x_test.values.reshape((len(x_test), n))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPEKUumtSAiG",
        "colab_type": "text"
      },
      "source": [
        "##Beginning Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g185BuLQSNs2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8204c8eb-ecac-4029-f34b-bf52e8ca551f"
      },
      "source": [
        "print('Start Predicting...')\n",
        "\n",
        "decision_tree = DecisionTreeClassifier()\n",
        "decision_tree.fit(x_train_mat,y_train)\n",
        "tree_pred = decision_tree.predict(x_test_mat)\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(x_train_mat,y_train)\n",
        "rf_pred = rf.predict(x_test_mat)\n",
        "\n",
        "KN = KNeighborsClassifier()\n",
        "KN.fit(x_train_mat,y_train)\n",
        "KN_pred = KN.predict(x_test_mat)\n",
        "\n",
        "Gaussian = GaussianNB()\n",
        "Gaussian.fit(x_train_mat,y_train)\n",
        "Gaussian_pred = Gaussian.predict(x_test_mat)\n",
        "\n",
        "svc = SVC()\n",
        "svc.fit(x_train_mat,y_train)\n",
        "svc_pred = svc.predict(x_test_mat)\n",
        "\n",
        "xgb = xgboost.XGBClassifier()\n",
        "xgb.fit(x_train_mat,y_train)\n",
        "xgb_pred = xgb.predict(x_test_mat)\n",
        "\n",
        "print('...Complete')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Start Predicting...\n",
            "...Complete\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WIpY4yabSZBa",
        "colab_type": "text"
      },
      "source": [
        "##Obtaining Accuracy Scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGUNi_fRSmbB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "e9c5d135-e67c-4461-9772-37af2e3be112"
      },
      "source": [
        "print('Decision Tree:', accuracy_score(y_test, tree_pred)*100,'%')\n",
        "print('Random Forest:', accuracy_score(y_test, rf_pred)*100,'%')\n",
        "print('KNeighbors:',accuracy_score(y_test, KN_pred)*100,'%')\n",
        "print('GaussianNB:',accuracy_score(y_test, Gaussian_pred)*100,'%')\n",
        "print('SVC:',accuracy_score(y_test, svc_pred)*100,'%')\n",
        "print('XGB:',accuracy_score(y_test, xgb_pred)*100,'%')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Decision Tree: 89.375 %\n",
            "Random Forest: 92.5 %\n",
            "KNeighbors: 87.5 %\n",
            "GaussianNB: 85.0 %\n",
            "SVC: 88.75 %\n",
            "XGB: 88.75 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGz32AirSqo0",
        "colab_type": "text"
      },
      "source": [
        "Random Forest is the best prediction model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUhKS8w3TMMf",
        "colab_type": "text"
      },
      "source": [
        "##Tuning the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AatgSMwVTS-9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "99f4fe67-3159-4bcb-d9a4-3879f9bf1ed0"
      },
      "source": [
        "k = [10,20,30,40,50]\n",
        "for i in k:\n",
        "    rf_tune = RandomForestClassifier(n_estimators=50, random_state=i)\n",
        "    rf_tune.fit(x_train_mat,y_train)\n",
        "    y_pred = rf_tune.predict(x_test_mat)\n",
        "    print(accuracy_score(y_test, y_pred)*100,'%')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "93.125 %\n",
            "92.5 %\n",
            "91.875 %\n",
            "90.625 %\n",
            "90.0 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kiede0koUxRM",
        "colab_type": "text"
      },
      "source": [
        "After tuning, the machine got an accuracy score of 93.125% with the Random Forest model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwOugYAYTuhT",
        "colab_type": "text"
      },
      "source": [
        "##Adding all Train Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeAM95wqTtA1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "612fc877-8750-4e2d-9637-8ce0ed5e0874"
      },
      "source": [
        "x_train_check = df_train_features.values.reshape((len(df_train_features), n))\n",
        "x_test_check = data['grade'].values.reshape((len(data['grade']), 1))\n",
        "\n",
        "k = [10,20,30,40,50]\n",
        "for i in k:\n",
        "    rf_tune = RandomForestClassifier(n_estimators=50, random_state=i)\n",
        "    rf_tune.fit(x_train_mat,y_train)\n",
        "    yy_pred = rf_tune.predict(x_train_check)\n",
        "    print(accuracy_score(x_test_check, yy_pred)*100,'%')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99.24953095684803 %\n",
            "99.24953095684803 %\n",
            "99.1869918699187 %\n",
            "98.99937460913071 %\n",
            "98.99937460913071 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfyJFVWKUSgm",
        "colab_type": "text"
      },
      "source": [
        "Woww! Accuracy Score of a little over 99%!!!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZU_jLUOB_H2",
        "colab_type": "text"
      },
      "source": [
        "## Conclusion\n",
        "After spliting the dataset into testing and training sets, many models were applied to allow the machine to learn. It turned out that the Random Forest algorithm was the best model. Thus, this was tuned for better accuracy. Later, all of the training data were added to the model to greatly impact the accuracy score. The machine ultimately recieved a score of 99.2495% in accuracy. In other words, one can surely rely on this model to predict the quality of wine!\n",
        "\n",
        "I hope that you enjoyed this project!\n",
        "\n",
        "Emini Offutt, High School Junior\n",
        "\n",
        "09 April 2020 \n",
        "\n",
        "(Happy B-Day Dr. Thompson!)"
      ]
    }
  ]
}