song_popularity.ipynb

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  "metadata": {
    "colab": {
      "name": "song popularity.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMrBabn86MnbKdD2u8WeQZc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Arbarbn/song-popularity-kaggle-competition/blob/main/song_popularity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -q kaggle"
      ],
      "metadata": {
        "id": "UMsCEGvXjW5a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
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              "status": 200,
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          },
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "id": "nd0T4xIljZK_",
        "outputId": "321ec27f-4ef7-4968-c1a4-5c2d585314a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "\n",
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              "     <output id=\"result-bedaef40-625f-406c-afd4-fd75689db00e\">\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> "
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"arbarobbani\",\"key\":\"57c5e10f0dd3953ce6a01de50df52d26\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "Hb2PDNZFj7Sn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "V2NtTf_Zk7o9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle competitions download -c song-popularity-prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FS4Yvus2k_FE",
        "outputId": "376ab7cb-bc8d-4b26-fd5b-85f7e2612e2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "Downloading train.csv.zip to /content\n",
            "  0% 0.00/3.33M [00:00<?, ?B/s]\n",
            "100% 3.33M/3.33M [00:00<00:00, 109MB/s]\n",
            "Downloading test.csv.zip to /content\n",
            "  0% 0.00/849k [00:00<?, ?B/s]\n",
            "100% 849k/849k [00:00<00:00, 122MB/s]\n",
            "Downloading sample_submission.csv to /content\n",
            "  0% 0.00/67.3k [00:00<?, ?B/s]\n",
            "100% 67.3k/67.3k [00:00<00:00, 69.3MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir train\n",
        "! unzip train.csv.zip -d train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xzvs3-owlB3P",
        "outputId": "803b9581-88de-4a15-8a40-927509d9f8d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  train.csv.zip\n",
            "  inflating: train/train.csv         \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir test\n",
        "! unzip test.csv.zip -d test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nG_AWiSflu-p",
        "outputId": "d9ad0557-4926-43eb-cba8-00d4a24097e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  test.csv.zip\n",
            "  inflating: test/test.csv           \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What will do? \n",
        "- Classify song is popular or not (Binary classification)\n",
        "\n",
        "Steps (CRISP-DM) :\n",
        "1. **data understanding** : statistics, null, correlation, distribution\n",
        "2. **data preparation** : feature engineering -> filling NaN value, handle distribution\n",
        "3. **modeling** : XGBOOST \n",
        "4. **evaluation** : if balance, we use classification_report from sklearn if imbalance, we use imbalance_classification_report from imblearn\n",
        "5. **deployment** : prepare submit file and push into kaggle"
      ],
      "metadata": {
        "id": "Ll3OSn8iqHHc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "jDqKegFJl4Dp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/train/train.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "FhJgsFjDmXO5",
        "outputId": "cf982309-7bcf-4785-ce9b-c2b1d20ee628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c9d20678-e2eb-41d4-a66c-efc13e2eb912\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>song_duration_ms</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>audio_mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>audio_valence</th>\n",
              "      <th>song_popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>212990.0</td>\n",
              "      <td>0.642286</td>\n",
              "      <td>0.856520</td>\n",
              "      <td>0.707073</td>\n",
              "      <td>0.002001</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-5.619088</td>\n",
              "      <td>0</td>\n",
              "      <td>0.082570</td>\n",
              "      <td>158.386236</td>\n",
              "      <td>4</td>\n",
              "      <td>0.734642</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.054866</td>\n",
              "      <td>0.733289</td>\n",
              "      <td>0.835545</td>\n",
              "      <td>0.000996</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.436428</td>\n",
              "      <td>-5.236965</td>\n",
              "      <td>1</td>\n",
              "      <td>0.127358</td>\n",
              "      <td>102.752988</td>\n",
              "      <td>3</td>\n",
              "      <td>0.711531</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>193213.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.188387</td>\n",
              "      <td>0.783524</td>\n",
              "      <td>-0.002694</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.170499</td>\n",
              "      <td>-4.951759</td>\n",
              "      <td>0</td>\n",
              "      <td>0.052282</td>\n",
              "      <td>178.685791</td>\n",
              "      <td>3</td>\n",
              "      <td>0.425536</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>249893.0</td>\n",
              "      <td>0.488660</td>\n",
              "      <td>0.585234</td>\n",
              "      <td>0.552685</td>\n",
              "      <td>0.000608</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.094805</td>\n",
              "      <td>-7.893694</td>\n",
              "      <td>0</td>\n",
              "      <td>0.035618</td>\n",
              "      <td>128.715630</td>\n",
              "      <td>3</td>\n",
              "      <td>0.453597</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>165969.0</td>\n",
              "      <td>0.493017</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.740982</td>\n",
              "      <td>0.002033</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.094891</td>\n",
              "      <td>-2.684095</td>\n",
              "      <td>0</td>\n",
              "      <td>0.050746</td>\n",
              "      <td>121.928157</td>\n",
              "      <td>4</td>\n",
              "      <td>0.741311</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9d20678-e2eb-41d4-a66c-efc13e2eb912')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c9d20678-e2eb-41d4-a66c-efc13e2eb912 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-c9d20678-e2eb-41d4-a66c-efc13e2eb912');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   id  song_duration_ms  ...  audio_valence  song_popularity\n",
              "0   0          212990.0  ...       0.734642                0\n",
              "1   1               NaN  ...       0.711531                1\n",
              "2   2          193213.0  ...       0.425536                0\n",
              "3   3          249893.0  ...       0.453597                0\n",
              "4   4          165969.0  ...       0.741311                0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data understanding"
      ],
      "metadata": {
        "id": "rUUiUeU_0UIa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "WLuk2n_Yp381",
        "outputId": "6b2150d9-45fc-4f39-8359-b158f8236a85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b58e1926-292c-48a6-81c9-4676ce38ef09\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>id</th>\n",
              "      <th>song_duration_ms</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>audio_mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>audio_valence</th>\n",
              "      <th>song_popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>40000.00000</td>\n",
              "      <td>35899.000000</td>\n",
              "      <td>36008.000000</td>\n",
              "      <td>35974.000000</td>\n",
              "      <td>36025.000000</td>\n",
              "      <td>36015.000000</td>\n",
              "      <td>35935.000000</td>\n",
              "      <td>35914.000000</td>\n",
              "      <td>36043.000000</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>40000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19999.50000</td>\n",
              "      <td>193165.847572</td>\n",
              "      <td>0.276404</td>\n",
              "      <td>0.570951</td>\n",
              "      <td>0.683932</td>\n",
              "      <td>0.036527</td>\n",
              "      <td>5.042605</td>\n",
              "      <td>0.198514</td>\n",
              "      <td>-7.407596</td>\n",
              "      <td>0.321150</td>\n",
              "      <td>0.094107</td>\n",
              "      <td>116.562815</td>\n",
              "      <td>3.394375</td>\n",
              "      <td>0.580645</td>\n",
              "      <td>0.364400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11547.14972</td>\n",
              "      <td>45822.127679</td>\n",
              "      <td>0.297928</td>\n",
              "      <td>0.190010</td>\n",
              "      <td>0.212662</td>\n",
              "      <td>0.150024</td>\n",
              "      <td>3.372728</td>\n",
              "      <td>0.151670</td>\n",
              "      <td>3.877198</td>\n",
              "      <td>0.466924</td>\n",
              "      <td>0.083591</td>\n",
              "      <td>26.167911</td>\n",
              "      <td>0.524405</td>\n",
              "      <td>0.237351</td>\n",
              "      <td>0.481268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>25658.000000</td>\n",
              "      <td>-0.013551</td>\n",
              "      <td>0.043961</td>\n",
              "      <td>-0.001682</td>\n",
              "      <td>-0.004398</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.027843</td>\n",
              "      <td>-32.117911</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015065</td>\n",
              "      <td>62.055779</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.013398</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9999.75000</td>\n",
              "      <td>166254.500000</td>\n",
              "      <td>0.039618</td>\n",
              "      <td>0.424760</td>\n",
              "      <td>0.539276</td>\n",
              "      <td>0.000941</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.111796</td>\n",
              "      <td>-9.578139</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.038500</td>\n",
              "      <td>96.995309</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.398669</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>19999.50000</td>\n",
              "      <td>186660.000000</td>\n",
              "      <td>0.140532</td>\n",
              "      <td>0.608234</td>\n",
              "      <td>0.704453</td>\n",
              "      <td>0.001974</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.135945</td>\n",
              "      <td>-6.345413</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.055881</td>\n",
              "      <td>113.795959</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.598827</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>29999.25000</td>\n",
              "      <td>215116.000000</td>\n",
              "      <td>0.482499</td>\n",
              "      <td>0.718464</td>\n",
              "      <td>0.870503</td>\n",
              "      <td>0.003225</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.212842</td>\n",
              "      <td>-4.620711</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.118842</td>\n",
              "      <td>128.517383</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.759635</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>39999.00000</td>\n",
              "      <td>491671.000000</td>\n",
              "      <td>1.065284</td>\n",
              "      <td>0.957131</td>\n",
              "      <td>1.039741</td>\n",
              "      <td>1.075415</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.065298</td>\n",
              "      <td>-0.877346</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.560748</td>\n",
              "      <td>219.163578</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.022558</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b58e1926-292c-48a6-81c9-4676ce38ef09');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                id  song_duration_ms  ...  audio_valence  song_popularity\n",
              "count  40000.00000      35899.000000  ...   40000.000000     40000.000000\n",
              "mean   19999.50000     193165.847572  ...       0.580645         0.364400\n",
              "std    11547.14972      45822.127679  ...       0.237351         0.481268\n",
              "min        0.00000      25658.000000  ...       0.013398         0.000000\n",
              "25%     9999.75000     166254.500000  ...       0.398669         0.000000\n",
              "50%    19999.50000     186660.000000  ...       0.598827         0.000000\n",
              "75%    29999.25000     215116.000000  ...       0.759635         1.000000\n",
              "max    39999.00000     491671.000000  ...       1.022558         1.000000\n",
              "\n",
              "[8 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xvsg81CGp82i",
        "outputId": "273720fb-dc85-49e7-e124-bf808c7f20b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                     0\n",
              "song_duration_ms    4101\n",
              "acousticness        3992\n",
              "danceability        4026\n",
              "energy              3975\n",
              "instrumentalness    3985\n",
              "key                 4065\n",
              "liveness            4086\n",
              "loudness            3957\n",
              "audio_mode             0\n",
              "speechiness            0\n",
              "tempo                  0\n",
              "time_signature         0\n",
              "audio_valence          0\n",
              "song_popularity        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['song_popularity'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XuP0jRGqqEJb",
        "outputId": "534c2220-1e0e-45a2-fc84-9db3658a75d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "popular = df[df.song_popularity==1].shape[0]\n",
        "unpopular = df[df.song_popularity==0].shape[0]\n",
        "print(\"Number of popular song in training:\", str(round(popular*100/df.shape[0], 2))+\"%\")\n",
        "print(\"Number of unpopular song in training:\", str(round(unpopular*100/df.shape[0], 2))+\"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72XHOEZ1uf5j",
        "outputId": "5da77e7b-f81b-4b29-cdfb-a2df8154c1d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of popular song in training: 36.44%\n",
            "Number of unpopular song in training: 63.56%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This shows that data is imbalance with number of unpopular more than popular song."
      ],
      "metadata": {
        "id": "EJ4QZGRuvjYC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution Class -> Imbalance 64 : 36"
      ],
      "metadata": {
        "id": "r7F4UYDF9Lul"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax = plt.subplots(figsize=(12,8))\n",
        "sns.countplot(x='song_popularity', data=df, ax=ax)\n",
        "ax.set_title('Distribution Class \\n 0 : unpopular | 1 : popular')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "X6OQAwyV7bix",
        "outputId": "929b8b33-e474-4850-e2d6-ddcb87017c05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution Class \\n 0 : unpopular | 1 : popular')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation"
      ],
      "metadata": {
        "id": "5r5m_RWT9T7d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "_, ax = plt.subplots(figsize=(12,12))\n",
        "sns.heatmap(df.drop('id', axis=1).corr(), annot=True, ax = ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "GmUmYRppqdkk",
        "outputId": "8428bba8-819e-4ed2-8d96-55508a16d428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe19b1f9910>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From heatmap, we can conclude feature energy and loudness has positive high correlation and negative high correlation between acousticness and loudness and between acousticness and energy .in this heatmap, there are no correlation feature with target class. But, if we look at higher correlation we get energy, audio_valence, loudness 😢😢😢😢😢"
      ],
      "metadata": {
        "id": "-UEUrPkL-Hy1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution per feature to target class"
      ],
      "metadata": {
        "id": "CghI1L4A-kIP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import gridspec \n",
        "\n",
        "v_features = df.drop(['song_popularity', 'id'], axis=1).columns\n",
        "plt.figure(figsize=(12, 13*4))\n",
        "gs = gridspec.GridSpec(13,1)\n",
        "for i, cn in enumerate(df[v_features]):\n",
        "  ax = plt.subplot(gs[i])\n",
        "  sns.histplot(df[cn][df.song_popularity==0], color='blue', bins=35)\n",
        "  sns.histplot(df[cn][df.song_popularity==1], color='red', bins=35)\n",
        "  ax.set_xlabel('')\n",
        "  ax.set_title('Histogram of '+str(cn))\n",
        "  ax.legend(['unpopular', 'popular'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7WfTVtkq915U",
        "outputId": "2970ec43-2eff-4aeb-951b-6c9846111145"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x3744 with 13 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df.song_popularity==1].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "SrkQb4jg4wGT",
        "outputId": "362145ed-bb2f-4eec-ea7a-0618f65ddf48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-73ca8a2e-7414-4b63-ba3f-ba373fdca7a5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>id</th>\n",
              "      <th>song_duration_ms</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>audio_mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>audio_valence</th>\n",
              "      <th>song_popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>14576.000000</td>\n",
              "      <td>13093.000000</td>\n",
              "      <td>13149.000000</td>\n",
              "      <td>13050.000000</td>\n",
              "      <td>13173.000000</td>\n",
              "      <td>13156.000000</td>\n",
              "      <td>13118.000000</td>\n",
              "      <td>13075.000000</td>\n",
              "      <td>13137.000000</td>\n",
              "      <td>14576.000000</td>\n",
              "      <td>14576.000000</td>\n",
              "      <td>14576.000000</td>\n",
              "      <td>14576.000000</td>\n",
              "      <td>14576.000000</td>\n",
              "      <td>14576.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19952.620472</td>\n",
              "      <td>192926.389521</td>\n",
              "      <td>0.286342</td>\n",
              "      <td>0.575376</td>\n",
              "      <td>0.668628</td>\n",
              "      <td>0.034646</td>\n",
              "      <td>5.121817</td>\n",
              "      <td>0.201835</td>\n",
              "      <td>-7.615769</td>\n",
              "      <td>0.320252</td>\n",
              "      <td>0.096781</td>\n",
              "      <td>116.155018</td>\n",
              "      <td>3.384193</td>\n",
              "      <td>0.566373</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11574.629212</td>\n",
              "      <td>45622.433532</td>\n",
              "      <td>0.308891</td>\n",
              "      <td>0.190052</td>\n",
              "      <td>0.214797</td>\n",
              "      <td>0.145207</td>\n",
              "      <td>3.392557</td>\n",
              "      <td>0.155449</td>\n",
              "      <td>3.996839</td>\n",
              "      <td>0.466589</td>\n",
              "      <td>0.085458</td>\n",
              "      <td>26.651180</td>\n",
              "      <td>0.519303</td>\n",
              "      <td>0.236396</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>33994.000000</td>\n",
              "      <td>-0.012245</td>\n",
              "      <td>0.049807</td>\n",
              "      <td>0.002322</td>\n",
              "      <td>-0.003854</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.034597</td>\n",
              "      <td>-30.170399</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015153</td>\n",
              "      <td>62.889002</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.013398</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9897.500000</td>\n",
              "      <td>167210.000000</td>\n",
              "      <td>0.040459</td>\n",
              "      <td>0.427033</td>\n",
              "      <td>0.529351</td>\n",
              "      <td>0.000867</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.113356</td>\n",
              "      <td>-9.897729</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.039289</td>\n",
              "      <td>96.085274</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.384517</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>19901.500000</td>\n",
              "      <td>187350.000000</td>\n",
              "      <td>0.139611</td>\n",
              "      <td>0.611323</td>\n",
              "      <td>0.684349</td>\n",
              "      <td>0.001902</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.136270</td>\n",
              "      <td>-6.555986</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.057684</td>\n",
              "      <td>112.222666</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.581656</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>29999.250000</td>\n",
              "      <td>213717.000000</td>\n",
              "      <td>0.505333</td>\n",
              "      <td>0.725616</td>\n",
              "      <td>0.857799</td>\n",
              "      <td>0.003231</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.211746</td>\n",
              "      <td>-4.679423</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.123746</td>\n",
              "      <td>128.871533</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.741884</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>39997.000000</td>\n",
              "      <td>470577.000000</td>\n",
              "      <td>1.062516</td>\n",
              "      <td>0.954945</td>\n",
              "      <td>1.033747</td>\n",
              "      <td>1.064847</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.028600</td>\n",
              "      <td>-1.218480</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.533257</td>\n",
              "      <td>215.861368</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.013484</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73ca8a2e-7414-4b63-ba3f-ba373fdca7a5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-73ca8a2e-7414-4b63-ba3f-ba373fdca7a5 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-73ca8a2e-7414-4b63-ba3f-ba373fdca7a5');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 id  song_duration_ms  ...  audio_valence  song_popularity\n",
              "count  14576.000000      13093.000000  ...   14576.000000          14576.0\n",
              "mean   19952.620472     192926.389521  ...       0.566373              1.0\n",
              "std    11574.629212      45622.433532  ...       0.236396              0.0\n",
              "min        1.000000      33994.000000  ...       0.013398              1.0\n",
              "25%     9897.500000     167210.000000  ...       0.384517              1.0\n",
              "50%    19901.500000     187350.000000  ...       0.581656              1.0\n",
              "75%    29999.250000     213717.000000  ...       0.741884              1.0\n",
              "max    39997.000000     470577.000000  ...       1.013484              1.0\n",
              "\n",
              "[8 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df.song_popularity==0].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "Os_67eiu45GU",
        "outputId": "27652379-6b71-406e-f530-e8d6194ce45d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7ecc2d8e-c577-46a1-b7d7-8b46105997dc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>id</th>\n",
              "      <th>song_duration_ms</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>audio_mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>audio_valence</th>\n",
              "      <th>song_popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25424.000000</td>\n",
              "      <td>22806.000000</td>\n",
              "      <td>22859.000000</td>\n",
              "      <td>22924.000000</td>\n",
              "      <td>22852.000000</td>\n",
              "      <td>22859.000000</td>\n",
              "      <td>22817.000000</td>\n",
              "      <td>22839.000000</td>\n",
              "      <td>22906.000000</td>\n",
              "      <td>25424.000000</td>\n",
              "      <td>25424.000000</td>\n",
              "      <td>25424.000000</td>\n",
              "      <td>25424.000000</td>\n",
              "      <td>25424.000000</td>\n",
              "      <td>25424.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>20026.376809</td>\n",
              "      <td>193303.321231</td>\n",
              "      <td>0.270688</td>\n",
              "      <td>0.568432</td>\n",
              "      <td>0.692754</td>\n",
              "      <td>0.037609</td>\n",
              "      <td>4.997064</td>\n",
              "      <td>0.196612</td>\n",
              "      <td>-7.288205</td>\n",
              "      <td>0.321665</td>\n",
              "      <td>0.092573</td>\n",
              "      <td>116.796612</td>\n",
              "      <td>3.400212</td>\n",
              "      <td>0.588827</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11531.507655</td>\n",
              "      <td>45936.814889</td>\n",
              "      <td>0.291289</td>\n",
              "      <td>0.189945</td>\n",
              "      <td>0.210922</td>\n",
              "      <td>0.152720</td>\n",
              "      <td>3.360504</td>\n",
              "      <td>0.149434</td>\n",
              "      <td>3.801833</td>\n",
              "      <td>0.467124</td>\n",
              "      <td>0.082464</td>\n",
              "      <td>25.884407</td>\n",
              "      <td>0.527229</td>\n",
              "      <td>0.237515</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>25658.000000</td>\n",
              "      <td>-0.013551</td>\n",
              "      <td>0.043961</td>\n",
              "      <td>-0.001682</td>\n",
              "      <td>-0.004398</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.027843</td>\n",
              "      <td>-32.117911</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015065</td>\n",
              "      <td>62.055779</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.027657</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10070.750000</td>\n",
              "      <td>165677.000000</td>\n",
              "      <td>0.039132</td>\n",
              "      <td>0.423877</td>\n",
              "      <td>0.545368</td>\n",
              "      <td>0.000989</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.110872</td>\n",
              "      <td>-9.387710</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.038097</td>\n",
              "      <td>97.493855</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.408112</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20065.500000</td>\n",
              "      <td>186261.500000</td>\n",
              "      <td>0.140956</td>\n",
              "      <td>0.606252</td>\n",
              "      <td>0.718353</td>\n",
              "      <td>0.002020</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.135706</td>\n",
              "      <td>-6.232257</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.054780</td>\n",
              "      <td>114.334715</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.609070</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>29997.250000</td>\n",
              "      <td>215940.500000</td>\n",
              "      <td>0.468349</td>\n",
              "      <td>0.714748</td>\n",
              "      <td>0.876354</td>\n",
              "      <td>0.003222</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.213425</td>\n",
              "      <td>-4.586064</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.116147</td>\n",
              "      <td>128.295829</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.770013</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>39999.000000</td>\n",
              "      <td>491671.000000</td>\n",
              "      <td>1.065284</td>\n",
              "      <td>0.957131</td>\n",
              "      <td>1.039741</td>\n",
              "      <td>1.075415</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.065298</td>\n",
              "      <td>-0.877346</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.560748</td>\n",
              "      <td>219.163578</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.022558</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ecc2d8e-c577-46a1-b7d7-8b46105997dc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
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              "          document.querySelector('#df-7ecc2d8e-c577-46a1-b7d7-8b46105997dc button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-7ecc2d8e-c577-46a1-b7d7-8b46105997dc');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 id  song_duration_ms  ...  audio_valence  song_popularity\n",
              "count  25424.000000      22806.000000  ...   25424.000000          25424.0\n",
              "mean   20026.376809     193303.321231  ...       0.588827              0.0\n",
              "std    11531.507655      45936.814889  ...       0.237515              0.0\n",
              "min        0.000000      25658.000000  ...       0.027657              0.0\n",
              "25%    10070.750000     165677.000000  ...       0.408112              0.0\n",
              "50%    20065.500000     186261.500000  ...       0.609070              0.0\n",
              "75%    29997.250000     215940.500000  ...       0.770013              0.0\n",
              "max    39999.000000     491671.000000  ...       1.022558              0.0\n",
              "\n",
              "[8 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From this histogram, we cant distinguish between popular and unpopular song. We can see with same range but with different count because of the imbalance. So, we must to data preparation. "
      ],
      "metadata": {
        "id": "oBXojJln0BXL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "we will look at test data to synchronize with data train"
      ],
      "metadata": {
        "id": "-4_E6mC8-6n8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTlLbaC0q-QW",
        "outputId": "f9908c67-83f7-4f34-ad39-9326eeeb5534"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df.drop(['song_popularity', 'id'], axis=1)\n",
        "y_train = df['song_popularity']"
      ],
      "metadata": {
        "id": "e-Xmj6V8EJUy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DS1OQNNqx_G",
        "outputId": "05682dd0-7097-487b-8249-95fa138f80cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdomJCaRrEFv",
        "outputId": "a1a72903-b9c9-429b-d012-e040e0728d2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000,)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "\n",
        "imputer = IterativeImputer(missing_values=np.nan, initial_strategy=\"median\", max_iter=70)\n",
        "for y in X_train.columns:\n",
        "    X_train[y] = imputer.fit_transform(X_train[[y]]).ravel()\n",
        "          \n",
        "X_train['song_popularity'] = df.song_popularity"
      ],
      "metadata": {
        "id": "quNxstrJXHCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = X_train.copy()"
      ],
      "metadata": {
        "id": "QiAwu-Kmc2Lm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBwykBU7rZd5",
        "outputId": "4d77db69-3b46-4a63-d3ec-c73386d40f68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlqatWPNrdDV",
        "outputId": "710868be-d35c-4557-b6e1-9f12e0572b71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "song_duration_ms    0\n",
              "acousticness        0\n",
              "danceability        0\n",
              "energy              0\n",
              "instrumentalness    0\n",
              "key                 0\n",
              "liveness            0\n",
              "loudness            0\n",
              "audio_mode          0\n",
              "speechiness         0\n",
              "tempo               0\n",
              "time_signature      0\n",
              "audio_valence       0\n",
              "song_popularity     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('/content/test/test.csv')\n",
        "\n",
        "test = df_test.drop('id', axis=1)\n",
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Ugk_7BMN-vUH",
        "outputId": "5a71f442-65f1-4401-8bfc-83a85c39dacd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>song_duration_ms</th>\n",
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              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
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              "      <td>4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.624687</td>\n",
              "      <td>0.710794</td>\n",
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              "      <td>88.703121</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      <script>\n",
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              "          document.querySelector('#df-f65e4ab4-9c8c-44d1-9014-c22e8222a83d 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-f65e4ab4-9c8c-44d1-9014-c22e8222a83d');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   song_duration_ms  acousticness  ...  time_signature  audio_valence\n",
              "0          308523.0      0.019845  ...               4       0.399620\n",
              "1          200011.0      0.070119  ...               3       0.499424\n",
              "2          279758.0      0.810637  ...               3       0.564951\n",
              "3          249197.0           NaN  ...               4       0.906485\n",
              "4               NaN      0.765568  ...               3       0.935571\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for y in test.columns:\n",
        "  test[y] = imputer.fit_transform(test[[y]]).ravel()"
      ],
      "metadata": {
        "id": "57x2nshaiWFo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkbwtgJKFZSU",
        "outputId": "3a002c5d-8c43-4b79-fe79-f0d161448bff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "song_duration_ms    0\n",
              "acousticness        0\n",
              "danceability        0\n",
              "energy              0\n",
              "instrumentalness    0\n",
              "key                 0\n",
              "liveness            0\n",
              "loudness            0\n",
              "audio_mode          0\n",
              "speechiness         0\n",
              "tempo               0\n",
              "time_signature      0\n",
              "audio_valence       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "X = train.drop(['song_popularity'], axis=1)\n",
        "y = train['song_popularity']\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size =0.2, random_state = 662)"
      ],
      "metadata": {
        "id": "fY3NGdTuGHrO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "PCA"
      ],
      "metadata": {
        "id": "A4DEXtWiAhCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler =StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "\n",
        "X_train_scaler = scaler.transform(X_train)\n",
        "X_val_scaler = scaler.transform(X_val)"
      ],
      "metadata": {
        "id": "SSpjX5UTr4BF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# we want the explained variance to be between 95%.\n",
        "pca = PCA(n_components=2)\n",
        "pca.fit(X_train_scaler)\n",
        "pca.n_components_"
      ],
      "metadata": {
        "id": "ajQqfnvntIUw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pca = pca.transform(X_train_scaler)\n",
        "X_val_pca = pca.transform(X_val_scaler)"
      ],
      "metadata": {
        "id": "3BuujxzOtoOL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODELING"
      ],
      "metadata": {
        "id": "97A_LTZeJg0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from imblearn.metrics import classification_report_imbalanced"
      ],
      "metadata": {
        "id": "_5mrdSDTuulO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "wjWYZyqsgRTT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(random_state=1772, class_weight='balanced', max_features= 'auto', n_estimators= 300)\n",
        "model.fit(X_train_pca, y_train)\n",
        "\n",
        "pred = model.predict(X_val_pca)\n",
        "print(classification_report_imbalanced(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3DOKUrEJdK1",
        "outputId": "1b7e47ca-0315-433c-80ee-5404c085782e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   pre       rec       spe        f1       geo       iba       sup\n",
            "\n",
            "          0       0.63      0.85      0.15      0.73      0.36      0.14      5052\n",
            "          1       0.38      0.15      0.85      0.22      0.36      0.12      2948\n",
            "\n",
            "avg / total       0.54      0.60      0.41      0.54      0.36      0.13      8000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import make_scorer\n",
        "\n",
        "params = { 'max_features':['auto', 'log2', 9], \n",
        "               'n_estimators': [100, 200, 300]}\n",
        "roc_auc = make_scorer(roc_auc_score, greater_is_better=True,\n",
        "                             needs_threshold=True)\n",
        "rf_gd=GridSearchCV(RandomForestClassifier(random_state=66), param_grid = params, verbose=True, \n",
        "                cv=3, scoring = roc_auc)\n",
        "rf_gd.fit(X_train_pca, y_train)\n",
        "print(rf_gd.best_score_)\n",
        "print(rf_gd.best_estimator_)\n",
        "print(rf_gd.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YGv58B37thDF",
        "outputId": "b217cbb2-7cfc-4394-8435-0b25d6b974d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n",
            "0.5385003465290965\n",
            "RandomForestClassifier(n_estimators=300, random_state=66)\n",
            "{'max_features': 'auto', 'n_estimators': 300}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "pred = rf_gd.predict(X_val_pca)\n",
        "print(\"AUC score:\", roc_auc_score(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEYOt6iRrswm",
        "outputId": "abdc0666-7ad1-431b-8652-944a499552b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC score: 0.5039639311539903\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import make_scorer\n",
        "\n",
        "params = {'algorithm': ['auto'], 'weights': ['uniform', 'distance'], 'leaf_size': list(range(1,10,2)), \n",
        "               'n_neighbors': [100, 500]}\n",
        "roc_auc = make_scorer(roc_auc_score, greater_is_better=True,\n",
        "                             needs_threshold=True)\n",
        "knn_gd=GridSearchCV(knn, param_grid = params, verbose=True, \n",
        "                cv=3, scoring = roc_auc)\n",
        "knn_gd.fit(X_train_pca, y_train)\n",
        "print(knn_gd.best_score_)\n",
        "print(knn_gd.best_estimator_)\n",
        "print(knn_gd.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XumrHbzVgSrD",
        "outputId": "56698c61-cf7d-43c8-b66c-2fa1987a0307"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n",
            "0.5536707433991849\n",
            "KNeighborsClassifier(leaf_size=1, n_neighbors=500, weights='distance')\n",
            "{'algorithm': 'auto', 'leaf_size': 1, 'n_neighbors': 500, 'weights': 'distance'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(leaf_size=1, n_neighbors=300, weights='distance')\n",
        "knn.fit(X_train_pca,y_train)\n",
        "\n",
        "knn_pred = knn.predict(X_val_pca)\n",
        "print(classification_report_imbalanced(y_val, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTMsnW5ajPzi",
        "outputId": "863d4eff-a443-44f7-9140-a1c6dfa8c83b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   pre       rec       spe        f1       geo       iba       sup\n",
            "\n",
            "          0       0.64      0.85      0.16      0.73      0.37      0.15      5052\n",
            "          1       0.39      0.16      0.85      0.23      0.37      0.13      2948\n",
            "\n",
            "avg / total       0.54      0.60      0.42      0.54      0.37      0.14      8000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "print(\"AUC score:\", roc_auc_score(y_val, knn_gd.predict(X_val_pca)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GlZMupRVsLXO",
        "outputId": "e8a1bb6b-94c4-4a00-e091-ac7aa140c96f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC score: 0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Deployment"
      ],
      "metadata": {
        "id": "3cb0bDBT_PvZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_scaler = scaler.transform(X)\n",
        "X_pca = pca.transform(X_scaler)"
      ],
      "metadata": {
        "id": "Lt5NTUTyjwt4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(leaf_size=1, n_neighbors=300, weights='distance')\n",
        "knn.fit(X_pca, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rIYYltiAksF4",
        "outputId": "2798b9c3-a294-45d1-cd38-2ca49f5ae618"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(leaf_size=1, n_neighbors=300, weights='distance')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(random_state=1772, n_estimators= 200, class_weight='balanced')\n",
        "model.fit(X_pca, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20b79a89-7a33-46f5-8a5a-c2c22bce3d90",
        "id": "sVxP3_-K_OZl"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(class_weight='balanced', n_estimators=200,\n",
              "                       random_state=1772)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_scaler = scaler.transform(test)\n",
        "test_pca = pca.transform(test_scaler)\n",
        "test_pred1 = knn.predict(test_pca)"
      ],
      "metadata": {
        "id": "MPRhitE88jCu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_pred2 = model.predict(test_pca)"
      ],
      "metadata": {
        "id": "KBhSpRImLun8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submit = pd.DataFrame()\n",
        "submit['id'] = df_test['id']\n",
        "submit['song_popularity']=test_pred1\n",
        "submit.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QwsKvsdbLs7r",
        "outputId": "0eda919c-8398-44b8-a89c-7f12a11ab21a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-6a4c2dff-9af3-433b-b124-55bbf98d7be7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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            "text/plain": [
              "   id  song_popularity\n",
              "0   0                0\n",
              "1   1                0\n",
              "2   2                0\n",
              "3   3                0\n",
              "4   4                0"
            ]
          },
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      ]
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    {
      "cell_type": "code",
      "source": [
        "submit['song_popularity'].value_counts()"
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              "0    9973\n",
              "1      27\n",
              "Name: song_popularity, dtype: int64"
            ]
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        }
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      "source": [
        "submit.to_csv(\"submission.csv\", index = False)"
      ],
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        "id": "6VjwIDv--B2w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_VgbgSjAeJ5y"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}