Multi_Class_Classifier_Music_Mental_Health.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn import preprocessing\n",
        "import xgboost as xgb"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing"
      ],
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      "cell_type": "code",
      "source": [
        "data = pd.read_csv('https://bitbucket.org/silverxlibra/music-mental-health/downloads/mxmh_survey_results.csv')"
      ],
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        "id": "WgCDc1B-06J4"
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      "execution_count": 5,
      "outputs": []
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            "text/plain": [
              "(736, 33)"
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          "execution_count": 6
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        "data.head(5)"
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              "            Timestamp   Age Primary streaming service  Hours per day  \\\n",
              "0  8/27/2022 19:29:02  18.0                   Spotify            3.0   \n",
              "1  8/27/2022 19:57:31  63.0                   Pandora            1.5   \n",
              "2  8/27/2022 21:28:18  18.0                   Spotify            4.0   \n",
              "3  8/27/2022 21:40:40  61.0             YouTube Music            2.5   \n",
              "4  8/27/2022 21:54:47  18.0                   Spotify            4.0   \n",
              "\n",
              "  While working Instrumentalist Composer         Fav genre Exploratory  \\\n",
              "0           Yes             Yes      Yes             Latin         Yes   \n",
              "1           Yes              No       No              Rock         Yes   \n",
              "2            No              No       No  Video game music          No   \n",
              "3           Yes              No      Yes              Jazz         Yes   \n",
              "4           Yes              No       No               R&B         Yes   \n",
              "\n",
              "  Foreign languages  ...  Frequency [R&B]  Frequency [Rap] Frequency [Rock]  \\\n",
              "0               Yes  ...        Sometimes  Very frequently            Never   \n",
              "1                No  ...        Sometimes           Rarely  Very frequently   \n",
              "2               Yes  ...            Never           Rarely           Rarely   \n",
              "3               Yes  ...        Sometimes            Never            Never   \n",
              "4                No  ...  Very frequently  Very frequently            Never   \n",
              "\n",
              "  Frequency [Video game music] Anxiety Depression Insomnia  OCD Music effects  \\\n",
              "0                    Sometimes     3.0        0.0      1.0  0.0           NaN   \n",
              "1                       Rarely     7.0        2.0      2.0  1.0           NaN   \n",
              "2              Very frequently     7.0        7.0     10.0  2.0     No effect   \n",
              "3                        Never     9.0        7.0      3.0  3.0       Improve   \n",
              "4                       Rarely     7.0        2.0      5.0  9.0       Improve   \n",
              "\n",
              "     Permissions  \n",
              "0  I understand.  \n",
              "1  I understand.  \n",
              "2  I understand.  \n",
              "3  I understand.  \n",
              "4  I understand.  \n",
              "\n",
              "[5 rows x 33 columns]"
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              "      <td>8/27/2022 19:57:31</td>\n",
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              "      <th>2</th>\n",
              "      <td>8/27/2022 21:28:18</td>\n",
              "      <td>18.0</td>\n",
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              "Index(['Timestamp', 'Age', 'Primary streaming service', 'Hours per day',\n",
              "       'While working', 'Instrumentalist', 'Composer', 'Fav genre',\n",
              "       'Exploratory', 'Foreign languages', 'BPM', 'Frequency [Classical]',\n",
              "       'Frequency [Country]', 'Frequency [EDM]', 'Frequency [Folk]',\n",
              "       'Frequency [Gospel]', 'Frequency [Hip hop]', 'Frequency [Jazz]',\n",
              "       'Frequency [K pop]', 'Frequency [Latin]', 'Frequency [Lofi]',\n",
              "       'Frequency [Metal]', 'Frequency [Pop]', 'Frequency [R&B]',\n",
              "       'Frequency [Rap]', 'Frequency [Rock]', 'Frequency [Video game music]',\n",
              "       'Anxiety', 'Depression', 'Insomnia', 'OCD', 'Music effects',\n",
              "       'Permissions'],\n",
              "      dtype='object')"
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      "source": [
        "data[\"Music effects\"].unique() #labels"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDZKE3vd51CE",
        "outputId": "35e7760f-4370-41fb-b1f2-31584b5db783"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'No effect', 'Improve', 'Worsen'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWfJ_fYT27Gx",
        "outputId": "4e5f68be-80dd-4863-a7b1-90b87bc99bc0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleaning"
      ],
      "metadata": {
        "id": "LCJ5bBx9xdYn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop NULL or replace?\n",
        "*   Looks like dropping nulls will suffice. Compared to the amount of data entries, dropping shouldn't be a concern here [except BPM]\n",
        "\n",
        "Dropping columns?\n",
        "\n",
        "*   BPM is not a column we are intending to use, so we can drop. (it also has the most nulls)\n",
        "* Perhaps drop Timestamp as well? Not sure how necessary it is for us.\n",
        "\n"
      ],
      "metadata": {
        "id": "XyUD62wE75HX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#See nulls across the dataset\n",
        "data.isna().sum()"
      ],
      "metadata": {
        "id": "YzIgo4mzyNDp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b306596-a6d0-4de7-8c20-ffd0b9d7556e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp                         0\n",
              "Age                               1\n",
              "Primary streaming service         1\n",
              "Hours per day                     0\n",
              "While working                     3\n",
              "Instrumentalist                   4\n",
              "Composer                          1\n",
              "Fav genre                         0\n",
              "Exploratory                       0\n",
              "Foreign languages                 4\n",
              "BPM                             107\n",
              "Frequency [Classical]             0\n",
              "Frequency [Country]               0\n",
              "Frequency [EDM]                   0\n",
              "Frequency [Folk]                  0\n",
              "Frequency [Gospel]                0\n",
              "Frequency [Hip hop]               0\n",
              "Frequency [Jazz]                  0\n",
              "Frequency [K pop]                 0\n",
              "Frequency [Latin]                 0\n",
              "Frequency [Lofi]                  0\n",
              "Frequency [Metal]                 0\n",
              "Frequency [Pop]                   0\n",
              "Frequency [R&B]                   0\n",
              "Frequency [Rap]                   0\n",
              "Frequency [Rock]                  0\n",
              "Frequency [Video game music]      0\n",
              "Anxiety                           0\n",
              "Depression                        0\n",
              "Insomnia                          0\n",
              "OCD                               0\n",
              "Music effects                     8\n",
              "Permissions                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop nulls\n",
        "clean_data = data.dropna()"
      ],
      "metadata": {
        "id": "KKAR-_SDIkK2"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(clean_data)"
      ],
      "metadata": {
        "id": "-PD-Cu7ZNMcX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42590c06-0af4-41e2-890a-92280e22a428"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "616"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#verify nulls are gone\n",
        "clean_data.isna().sum()"
      ],
      "metadata": {
        "id": "VN_6cnAtIuFg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94258444-e187-461b-de9d-dbb9724d30d5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp                       0\n",
              "Age                             0\n",
              "Primary streaming service       0\n",
              "Hours per day                   0\n",
              "While working                   0\n",
              "Instrumentalist                 0\n",
              "Composer                        0\n",
              "Fav genre                       0\n",
              "Exploratory                     0\n",
              "Foreign languages               0\n",
              "BPM                             0\n",
              "Frequency [Classical]           0\n",
              "Frequency [Country]             0\n",
              "Frequency [EDM]                 0\n",
              "Frequency [Folk]                0\n",
              "Frequency [Gospel]              0\n",
              "Frequency [Hip hop]             0\n",
              "Frequency [Jazz]                0\n",
              "Frequency [K pop]               0\n",
              "Frequency [Latin]               0\n",
              "Frequency [Lofi]                0\n",
              "Frequency [Metal]               0\n",
              "Frequency [Pop]                 0\n",
              "Frequency [R&B]                 0\n",
              "Frequency [Rap]                 0\n",
              "Frequency [Rock]                0\n",
              "Frequency [Video game music]    0\n",
              "Anxiety                         0\n",
              "Depression                      0\n",
              "Insomnia                        0\n",
              "OCD                             0\n",
              "Music effects                   0\n",
              "Permissions                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop unneccsary columns\n",
        "clean_data = clean_data.drop(columns=['Timestamp', 'Composer','BPM','Permissions'])"
      ],
      "metadata": {
        "id": "t4Tdyc5tp4QR"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#verify columns are dropped\n",
        "clean_data.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "Iai_Qw2Eqdee",
        "outputId": "de983a5d-0b8c-4488-f9c9-f5160b19b999"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Age Primary streaming service  Hours per day While working  \\\n",
              "2  18.0                   Spotify            4.0            No   \n",
              "3  61.0             YouTube Music            2.5           Yes   \n",
              "\n",
              "  Instrumentalist         Fav genre Exploratory Foreign languages  \\\n",
              "2              No  Video game music          No               Yes   \n",
              "3              No              Jazz         Yes               Yes   \n",
              "\n",
              "  Frequency [Classical] Frequency [Country]  ... Frequency [Pop]  \\\n",
              "2                 Never               Never  ...          Rarely   \n",
              "3             Sometimes               Never  ...       Sometimes   \n",
              "\n",
              "  Frequency [R&B] Frequency [Rap] Frequency [Rock]  \\\n",
              "2           Never          Rarely           Rarely   \n",
              "3       Sometimes           Never            Never   \n",
              "\n",
              "  Frequency [Video game music] Anxiety Depression Insomnia  OCD Music effects  \n",
              "2              Very frequently     7.0        7.0     10.0  2.0     No effect  \n",
              "3                        Never     9.0        7.0      3.0  3.0       Improve  \n",
              "\n",
              "[2 rows x 29 columns]"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "clean_data"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode our categorical variables into numeric values\n",
        "\n",
        "encoded_data = pd.DataFrame()\n",
        "\n",
        "encoder = preprocessing.LabelEncoder()\n",
        "\n",
        "for column in clean_data.select_dtypes(include='object'):\n",
        "    encoded_data[column] = encoder.fit_transform(clean_data[column])\n",
        "\n",
        "encoded_data.head()\n"
      ],
      "metadata": {
        "id": "xev2yIB76psa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "112db9b4-b74f-4fac-ff51-58fcfb123731"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Primary streaming service  While working  Instrumentalist  Fav genre  \\\n",
              "0                          4              0                0         15   \n",
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              "   Exploratory  Foreign languages  Frequency [Classical]  Frequency [Country]  \\\n",
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              "   Frequency [EDM]  Frequency [Folk]  ...  Frequency [K pop]  \\\n",
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              "   Frequency [R&B]  Frequency [Rap]  Frequency [Rock]  \\\n",
              "0                0                1                 1   \n",
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              "   Frequency [Video game music]  Music effects  \n",
              "0                             3              1  \n",
              "1                             0              0  \n",
              "2                             1              0  \n",
              "3                             0              0  \n",
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            "text/plain": [
              "Primary streaming service        5\n",
              "While working                    1\n",
              "Instrumentalist                  1\n",
              "Fav genre                       15\n",
              "Exploratory                      1\n",
              "Foreign languages                1\n",
              "Frequency [Classical]            3\n",
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              "Frequency [EDM]                  3\n",
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              "Frequency [Gospel]               3\n",
              "Frequency [Hip hop]              3\n",
              "Frequency [Jazz]                 3\n",
              "Frequency [K pop]                3\n",
              "Frequency [Latin]                3\n",
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              "Frequency [R&B]                  3\n",
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              "Frequency [Video game music]     3\n",
              "Music effects                    2\n",
              "dtype: int64"
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          "height": 429
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      "execution_count": 19,
      "outputs": [
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          "data": {
            "text/plain": [
              "    Age Primary streaming service  Hours per day While working  \\\n",
              "2  18.0                   Spotify            4.0            No   \n",
              "3  61.0             YouTube Music            2.5           Yes   \n",
              "4  18.0                   Spotify            4.0           Yes   \n",
              "5  18.0                   Spotify            5.0           Yes   \n",
              "6  18.0             YouTube Music            3.0           Yes   \n",
              "\n",
              "  Instrumentalist         Fav genre Exploratory Foreign languages  \\\n",
              "2              No  Video game music          No               Yes   \n",
              "3              No              Jazz         Yes               Yes   \n",
              "4              No               R&B         Yes                No   \n",
              "5             Yes              Jazz         Yes               Yes   \n",
              "6             Yes  Video game music         Yes               Yes   \n",
              "\n",
              "  Frequency [Classical] Frequency [Country]  ...  Frequency [Pop]  \\\n",
              "2                 Never               Never  ...           Rarely   \n",
              "3             Sometimes               Never  ...        Sometimes   \n",
              "4                 Never               Never  ...        Sometimes   \n",
              "5                Rarely           Sometimes  ...  Very frequently   \n",
              "6             Sometimes               Never  ...           Rarely   \n",
              "\n",
              "   Frequency [R&B]  Frequency [Rap] Frequency [Rock]  \\\n",
              "2            Never           Rarely           Rarely   \n",
              "3        Sometimes            Never            Never   \n",
              "4  Very frequently  Very frequently            Never   \n",
              "5  Very frequently  Very frequently  Very frequently   \n",
              "6           Rarely            Never            Never   \n",
              "\n",
              "  Frequency [Video game music] Anxiety Depression Insomnia  OCD Music effects  \n",
              "2              Very frequently     7.0        7.0     10.0  2.0     No effect  \n",
              "3                        Never     9.0        7.0      3.0  3.0       Improve  \n",
              "4                       Rarely     7.0        2.0      5.0  9.0       Improve  \n",
              "5                        Never     8.0        8.0      7.0  7.0       Improve  \n",
              "6                    Sometimes     4.0        8.0      6.0  0.0       Improve  \n",
              "\n",
              "[5 rows x 29 columns]"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Primary streaming service</th>\n",
              "      <th>Hours per day</th>\n",
              "      <th>While working</th>\n",
              "      <th>Instrumentalist</th>\n",
              "      <th>Fav genre</th>\n",
              "      <th>Exploratory</th>\n",
              "      <th>Foreign languages</th>\n",
              "      <th>Frequency [Classical]</th>\n",
              "      <th>Frequency [Country]</th>\n",
              "      <th>...</th>\n",
              "      <th>Frequency [Pop]</th>\n",
              "      <th>Frequency [R&amp;B]</th>\n",
              "      <th>Frequency [Rap]</th>\n",
              "      <th>Frequency [Rock]</th>\n",
              "      <th>Frequency [Video game music]</th>\n",
              "      <th>Anxiety</th>\n",
              "      <th>Depression</th>\n",
              "      <th>Insomnia</th>\n",
              "      <th>OCD</th>\n",
              "      <th>Music effects</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.0</td>\n",
              "      <td>Spotify</td>\n",
              "      <td>4.0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Video game music</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Never</td>\n",
              "      <td>Never</td>\n",
              "      <td>...</td>\n",
              "      <td>Rarely</td>\n",
              "      <td>Never</td>\n",
              "      <td>Rarely</td>\n",
              "      <td>Rarely</td>\n",
              "      <td>Very frequently</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>No effect</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>61.0</td>\n",
              "      <td>YouTube Music</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Jazz</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Never</td>\n",
              "      <td>...</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Never</td>\n",
              "      <td>Never</td>\n",
              "      <td>Never</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Improve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18.0</td>\n",
              "      <td>Spotify</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>R&amp;B</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Never</td>\n",
              "      <td>Never</td>\n",
              "      <td>...</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Very frequently</td>\n",
              "      <td>Very frequently</td>\n",
              "      <td>Never</td>\n",
              "      <td>Rarely</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Improve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>18.0</td>\n",
              "      <td>Spotify</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Jazz</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Rarely</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>...</td>\n",
              "      <td>Very frequently</td>\n",
              "      <td>Very frequently</td>\n",
              "      <td>Very frequently</td>\n",
              "      <td>Very frequently</td>\n",
              "      <td>Never</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Improve</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>18.0</td>\n",
              "      <td>YouTube Music</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Video game music</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Never</td>\n",
              "      <td>...</td>\n",
              "      <td>Rarely</td>\n",
              "      <td>Rarely</td>\n",
              "      <td>Never</td>\n",
              "      <td>Never</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Improve</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d886e601-feea-4c28-ad0f-b5846ec5de8a')\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d886e601-feea-4c28-ad0f-b5846ec5de8a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d886e601-feea-4c28-ad0f-b5846ec5de8a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e6fb3fde-c16a-48f5-8035-43ba4d75b718\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e6fb3fde-c16a-48f5-8035-43ba4d75b718')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "    }\n",
              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e6fb3fde-c16a-48f5-8035-43ba4d75b718 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "clean_data"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_data['Frequency [Pop]'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JYKdw4aVKeXZ",
        "outputId": "f64b5954-2566-47c9-ff95-7d2939ca8e6f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Rarely', 'Sometimes', 'Very frequently', 'Never'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_data['Frequency [Pop]'].unique()"
      ],
      "metadata": {
        "id": "ZVlKpy_cK152",
        "outputId": "7c5e2526-ef06-491b-f188-3dd814dfbacf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Validate both datasets are consistent post encoder. \\nClean dataset: {len(clean_data)} \\nEncoded Dataset: {len(encoded_data)}')"
      ],
      "metadata": {
        "id": "A7lCakM2MUak",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f9a5d74-a3f2-4bba-d147-6629ded3aa78"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validate both datasets are consistent post encoder. \n",
            "Clean dataset: 616 \n",
            "Encoded Dataset: 616\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#looks at max value of each column (i.e. 0-15) to make sure all categorical variables have been accounted for to their numerical counterpart\n",
        "encoded_data.min(axis=0)"
      ],
      "metadata": {
        "id": "WOCWyMtKKk7Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d97c7f11-1410-4969-da9a-3b1dc1282197"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Primary streaming service       0\n",
              "While working                   0\n",
              "Instrumentalist                 0\n",
              "Fav genre                       0\n",
              "Exploratory                     0\n",
              "Foreign languages               0\n",
              "Frequency [Classical]           0\n",
              "Frequency [Country]             0\n",
              "Frequency [EDM]                 0\n",
              "Frequency [Folk]                0\n",
              "Frequency [Gospel]              0\n",
              "Frequency [Hip hop]             0\n",
              "Frequency [Jazz]                0\n",
              "Frequency [K pop]               0\n",
              "Frequency [Latin]               0\n",
              "Frequency [Lofi]                0\n",
              "Frequency [Metal]               0\n",
              "Frequency [Pop]                 0\n",
              "Frequency [R&B]                 0\n",
              "Frequency [Rap]                 0\n",
              "Frequency [Rock]                0\n",
              "Frequency [Video game music]    0\n",
              "Music effects                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#one hot encoding for music genre columns\n",
        "data = pd.get_dummies(data, columns = ['Frequency [Classical]', 'Frequency [Country]', 'Frequency [EDM]',\n",
        "                                       'Frequency [Folk]', 'Frequency [Gospel]', 'Frequency [Hip hop]',\n",
        "                                       'Frequency [Jazz]', 'Frequency [K pop]', 'Frequency [Latin]',\n",
        "                                       'Frequency [Lofi]', 'Frequency [Metal]', 'Frequency [Pop]',\n",
        "                                       'Frequency [R&B]', 'Frequency [Rock]', 'Frequency [Video game music]'])"
      ],
      "metadata": {
        "id": "gsD0HC4nv1QX"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Docs For Recomender System Reference:\n",
        "* YT Video: https://www.youtube.com/watch?v=G4MBc40rQ2k\n",
        "* Geeksforgeeks(high level w/ implementation): https://www.geeksforgeeks.org/recommendation-system-in-python/\n",
        "* Movie rec system: https://thecleverprogrammer.com/2020/05/20/data-science-project-movie-recommendation-system/\n",
        "* high lvl implementation: https://365datascience.com/tutorials/how-to-build-recommendation-system-in-python/\n"
      ],
      "metadata": {
        "id": "6W9AzKAhPmbe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multi-Class Classifier"
      ],
      "metadata": {
        "id": "1pqAZw246vv_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*The following recommender system combines the previous two. It uses the predictions from each models as features for a new model that predicts the favorite genre. This new model considers both the age and the mental health conditions.*"
      ],
      "metadata": {
        "id": "o2W6G-Fn5RBG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_data[\"Music effects\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Un4jwSyqD_OB",
        "outputId": "0843285b-9d74-47e0-c946-5f9f5bdb6cfa"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_data[\"Music effects\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRLwIQdxEBBL",
        "outputId": "d5557dfb-8b3c-41fe-b813-467d4e5355e4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No effect', 'Improve', 'Worsen'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Use SelectKBest with the ANOVA F-value scoring function to select the top K features that are most relevant to predicting the target variable.\n",
        "2. Train a Decision Tree Classifier using the selected features and evaluate its performance."
      ],
      "metadata": {
        "id": "YcSDF8ArIYZ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#combine models into new model\n",
        "X = encoded_data.drop('Music effects', axis = 1) #all other features\n",
        "y = encoded_data['Music effects'] #target variable\n",
        "\n",
        "#feat selection using SelectKBest w/ ANOVA F-value\n",
        "selector = SelectKBest(score_func = f_classif, k = 3) #select top 3 features\n",
        "X_selected = selector.fit_transform(X, y)\n",
        "\n",
        "#get feat scores\n",
        "feat_scores = selector.scores_\n",
        "#get feat names\n",
        "feat_names = X.columns\n",
        "#bar plot to visualize feat scores\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(feat_names, feat_scores)\n",
        "plt.xlabel('Feature Score')\n",
        "plt.ylabel('Feature Name')\n",
        "plt.title('Feature Scores')\n",
        "plt.show()\n",
        "\n",
        "#get indices of selected feats\n",
        "selected_indices = selector.get_support(indices=True)\n",
        "#get names of selected feats\n",
        "selected_feats = X.columns[selected_indices]\n",
        "print(\"Selected Features:\", selected_feats)"
      ],
      "metadata": {
        "id": "wjHEnmZx5Qwk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "ebf0bcd4-3068-4392-fdc1-6b0d2059bdef"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected Features: Index(['While working', 'Fav genre', 'Exploratory'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following is a random classifier without pulling out the best features for X."
      ],
      "metadata": {
        "id": "10UgXjWIaoxE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#split selected feats, target variable into train/test sets\n",
        "X_train_basic, X_test_basic, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "#initialize decision tree classifier\n",
        "clf_basic = DecisionTreeClassifier()\n",
        "\n",
        "#fit classifier to training data\n",
        "clf_basic.fit(X_train_basic, y_train)\n",
        "\n",
        "#predict labels of the test set\n",
        "y_pred = clf_basic.predict(X_test_basic)\n",
        "\n",
        "#evaluate classifier\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", acc)\n",
        "\n",
        "# Print classification report\n",
        "print(classification_report(y_test, y_pred, zero_division = 'warn'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TyRcBtwamuj",
        "outputId": "17ff4d17-d0b6-48b0-a884-ae8840916f5e"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6693548387096774\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.79      0.79        95\n",
            "           1       0.31      0.30      0.30        27\n",
            "           2       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.67       124\n",
            "   macro avg       0.37      0.36      0.37       124\n",
            "weighted avg       0.68      0.67      0.67       124\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following is a random classifier using the best features we identified through feature engineering. We can see that the overall accuracy has improved by 10%."
      ],
      "metadata": {
        "id": "EsGe08gsa-Gc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#split selected feats, target variable into train/test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_selected, y, test_size=0.2, random_state=42)\n",
        "\n",
        "#initialize decision tree classifier\n",
        "clf = DecisionTreeClassifier()\n",
        "\n",
        "#fit classifier to training data\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "#predict labels of the test set\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "#evaluate classifier\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", acc)\n",
        "\n",
        "# Print classification report\n",
        "print(classification_report(y_test, y_pred, zero_division = 'warn'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZBiQcnQ3Jck8",
        "outputId": "06df87af-c21c-46f4-cbe5-0e517b9f6815"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7419354838709677\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.96      0.85        95\n",
            "           1       0.20      0.04      0.06        27\n",
            "           2       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.74       124\n",
            "   macro avg       0.32      0.33      0.30       124\n",
            "weighted avg       0.63      0.74      0.67       124\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#show decision tree\n",
        "plot_tree(clf, filled=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ug5vKpX_KkTS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "fe770687-61a2-4db6-f221-9d5addd5b192"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following is an attempt to enhance the accuracy further through XGBoost. We see that the accuracy has rounded out to a whole point.  "
      ],
      "metadata": {
        "id": "f_xRKud4bJfk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#XGBoost\n",
        "xgb = xgb.XGBClassifier()\n",
        "xgb.fit(X_train, y_train)\n",
        "y_pred_xgb = xgb.predict(X_test)\n",
        "\n",
        "# Evaluate XGBoost classifier\n",
        "acc_xgb = accuracy_score(y_test, y_pred_xgb)\n",
        "print(\"XGBoost Accuracy:\", acc_xgb)\n",
        "\n",
        "# Print classification report for XGBoost\n",
        "print(classification_report(y_test, y_pred_xgb, zero_division='warn'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0UUkIgLYpeC",
        "outputId": "5f7d3af3-2ef3-4e14-983d-abc3bac1d4cf"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Accuracy: 0.75\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.96      0.85        95\n",
            "           1       0.33      0.07      0.12        27\n",
            "           2       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.75       124\n",
            "   macro avg       0.37      0.34      0.33       124\n",
            "weighted avg       0.66      0.75      0.68       124\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While visualizing trees may be helpful in random forest so you can see the path taken in making decisions, for XGBoost that is an ensemble model, visualizing individual trees is not necessarily practical or helpful in understanding how the model makes predictions. Instead, its more insightful to analyze the feature importances provided by XGBoost."
      ],
      "metadata": {
        "id": "_-c_aG9fZwKT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#show feature importances\n",
        "importances = xgb.feature_importances_\n",
        "\n",
        "# Sort feature importances in descending order\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# Print the feature ranking\n",
        "print(\"Feature ranking:\")\n",
        "\n",
        "for f in range(X_train.shape[1]):\n",
        "    print(\"%d. feature %d (%s): %f\" % (f + 1, indices[f], feat_names[indices[f]], importances[indices[f]]))\n",
        "\n",
        "# Plot feature importances\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.title(\"Feature importances\")\n",
        "plt.bar(range(X_train.shape[1]), importances[indices], color=\"r\", align=\"center\")\n",
        "plt.xticks(range(X_train.shape[1]), feat_names[indices], rotation=90)\n",
        "plt.xlim([-1, X_train.shape[1]])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "id": "NAm65p3_ZMSe",
        "outputId": "30f9bfd7-6fb3-4ff3-eb20-d8fd6440ca9d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature ranking:\n",
            "1. feature 1 (While working): 0.362927\n",
            "2. feature 0 (Primary streaming service): 0.328007\n",
            "3. feature 2 (Instrumentalist): 0.309066\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DczEU--LbT-P"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}