agglomerativeClustering.ipynb

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      "source": [
        "#@title Importing necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
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      "execution_count": null,
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      "source": [
        "#@title Reading the dataset\n",
        "\n",
        "df = pd.read_csv(\"tracks_features.csv\")"
      ]
    },
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            "text/plain": [
              "                       id                   name                      album  \\\n",
              "0  7lmeHLHBe4nmXzuXc0HDjk                Testify  The Battle Of Los Angeles   \n",
              "1  1wsRitfRRtWyEapl0q22o8        Guerrilla Radio  The Battle Of Los Angeles   \n",
              "2  1hR0fIFK2qRG3f3RF70pb7       Calm Like a Bomb  The Battle Of Los Angeles   \n",
              "3  2lbASgTSoDO7MTuLAXlTW0              Mic Check  The Battle Of Los Angeles   \n",
              "4  1MQTmpYOZ6fcMQc56Hdo7T  Sleep Now In the Fire  The Battle Of Los Angeles   \n",
              "\n",
              "                 album_id                       artists  \\\n",
              "0  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "1  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "2  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "3  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "4  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "\n",
              "                   artist_ids  track_number  disc_number explicit  \\\n",
              "0  ['2d0hyoQ5ynDBnkvAbJKORj']           1.0          1.0    False   \n",
              "1  ['2d0hyoQ5ynDBnkvAbJKORj']           2.0          1.0     True   \n",
              "2  ['2d0hyoQ5ynDBnkvAbJKORj']           3.0          1.0    False   \n",
              "3  ['2d0hyoQ5ynDBnkvAbJKORj']           4.0          1.0     True   \n",
              "4  ['2d0hyoQ5ynDBnkvAbJKORj']           5.0          1.0    False   \n",
              "\n",
              "   danceability  ...  speechiness  acousticness  instrumentalness  liveness  \\\n",
              "0         0.470  ...       0.0727       0.02610          0.000011    0.3560   \n",
              "1         0.599  ...       0.1880       0.01290          0.000071    0.1550   \n",
              "2         0.315  ...       0.4830       0.02340          0.000002    0.1220   \n",
              "3         0.440  ...       0.2370       0.16300          0.000004    0.1210   \n",
              "4         0.426  ...       0.0701       0.00162          0.105000    0.0789   \n",
              "\n",
              "   valence    tempo  duration_ms  time_signature    year  release_date  \n",
              "0    0.503  117.906     210133.0             4.0  1999.0    1999-11-02  \n",
              "1    0.489  103.680     206200.0             4.0  1999.0    1999-11-02  \n",
              "2    0.370  149.749     298893.0             4.0  1999.0    1999-11-02  \n",
              "3    0.574   96.752     213640.0             4.0  1999.0    1999-11-02  \n",
              "4    0.539  127.059     205600.0             4.0  1999.0    1999-11-02  \n",
              "\n",
              "[5 rows x 24 columns]"
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          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": [
        "df.head(5)"
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              "      <th>artist_ids</th>\n",
              "      <th>track_number</th>\n",
              "      <th>disc_number</th>\n",
              "      <th>explicit</th>\n",
              "      <th>danceability</th>\n",
              "      <th>...</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>year</th>\n",
              "      <th>release_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7lmeHLHBe4nmXzuXc0HDjk</td>\n",
              "      <td>Testify</td>\n",
              "      <td>The Battle Of Los Angeles</td>\n",
              "      <td>2eia0myWFgoHuttJytCxgX</td>\n",
              "      <td>['Rage Against The Machine']</td>\n",
              "      <td>['2d0hyoQ5ynDBnkvAbJKORj']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.470</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0727</td>\n",
              "      <td>0.026100</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>0.3560</td>\n",
              "      <td>0.503</td>\n",
              "      <td>117.906</td>\n",
              "      <td>210133.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>1999-11-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1wsRitfRRtWyEapl0q22o8</td>\n",
              "      <td>Guerrilla Radio</td>\n",
              "      <td>The Battle Of Los Angeles</td>\n",
              "      <td>2eia0myWFgoHuttJytCxgX</td>\n",
              "      <td>['Rage Against The Machine']</td>\n",
              "      <td>['2d0hyoQ5ynDBnkvAbJKORj']</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>True</td>\n",
              "      <td>0.599</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1880</td>\n",
              "      <td>0.012900</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.1550</td>\n",
              "      <td>0.489</td>\n",
              "      <td>103.680</td>\n",
              "      <td>206200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>1999-11-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1hR0fIFK2qRG3f3RF70pb7</td>\n",
              "      <td>Calm Like a Bomb</td>\n",
              "      <td>The Battle Of Los Angeles</td>\n",
              "      <td>2eia0myWFgoHuttJytCxgX</td>\n",
              "      <td>['Rage Against The Machine']</td>\n",
              "      <td>['2d0hyoQ5ynDBnkvAbJKORj']</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.315</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4830</td>\n",
              "      <td>0.023400</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.1220</td>\n",
              "      <td>0.370</td>\n",
              "      <td>149.749</td>\n",
              "      <td>298893.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>1999-11-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2lbASgTSoDO7MTuLAXlTW0</td>\n",
              "      <td>Mic Check</td>\n",
              "      <td>The Battle Of Los Angeles</td>\n",
              "      <td>2eia0myWFgoHuttJytCxgX</td>\n",
              "      <td>['Rage Against The Machine']</td>\n",
              "      <td>['2d0hyoQ5ynDBnkvAbJKORj']</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>True</td>\n",
              "      <td>0.440</td>\n",
              "      <td>...</td>\n",
              "      <td>0.2370</td>\n",
              "      <td>0.163000</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>0.574</td>\n",
              "      <td>96.752</td>\n",
              "      <td>213640.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>1999-11-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1MQTmpYOZ6fcMQc56Hdo7T</td>\n",
              "      <td>Sleep Now In the Fire</td>\n",
              "      <td>The Battle Of Los Angeles</td>\n",
              "      <td>2eia0myWFgoHuttJytCxgX</td>\n",
              "      <td>['Rage Against The Machine']</td>\n",
              "      <td>['2d0hyoQ5ynDBnkvAbJKORj']</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.426</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0701</td>\n",
              "      <td>0.001620</td>\n",
              "      <td>0.105000</td>\n",
              "      <td>0.0789</td>\n",
              "      <td>0.539</td>\n",
              "      <td>127.059</td>\n",
              "      <td>205600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>1999-11-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1053314</th>\n",
              "      <td>02sWMvITk9AUPeh47etHYf</td>\n",
              "      <td>Anyway Anytime</td>\n",
              "      <td>Titties -n- Beer</td>\n",
              "      <td>1q0uMoANW2JVTUm4fHXBFE</td>\n",
              "      <td>['REVENGE']</td>\n",
              "      <td>['1KfNZD8DNPG6B2HevRbz23']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.236</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0724</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000286</td>\n",
              "      <td>0.3450</td>\n",
              "      <td>0.352</td>\n",
              "      <td>139.243</td>\n",
              "      <td>184000.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>2006-09-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1053315</th>\n",
              "      <td>1MQeKfHrPNPN4ngq1vMbdt</td>\n",
              "      <td>Crush On You</td>\n",
              "      <td>Titties -n- Beer</td>\n",
              "      <td>1q0uMoANW2JVTUm4fHXBFE</td>\n",
              "      <td>['REVENGE']</td>\n",
              "      <td>['1KfNZD8DNPG6B2HevRbz23']</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.377</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0679</td>\n",
              "      <td>0.001940</td>\n",
              "      <td>0.015400</td>\n",
              "      <td>0.0969</td>\n",
              "      <td>0.783</td>\n",
              "      <td>134.191</td>\n",
              "      <td>168667.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>2006-09-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1053316</th>\n",
              "      <td>4XvYOLTyVsNSC5kFJquDrS</td>\n",
              "      <td>Hot Child In The City</td>\n",
              "      <td>Titties -n- Beer</td>\n",
              "      <td>1q0uMoANW2JVTUm4fHXBFE</td>\n",
              "      <td>['REVENGE']</td>\n",
              "      <td>['1KfNZD8DNPG6B2HevRbz23']</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.529</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0565</td>\n",
              "      <td>0.008980</td>\n",
              "      <td>0.000274</td>\n",
              "      <td>0.0785</td>\n",
              "      <td>0.692</td>\n",
              "      <td>117.797</td>\n",
              "      <td>184413.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>2006-09-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1053317</th>\n",
              "      <td>5ydDSj4DzbuU4d16WcB6kM</td>\n",
              "      <td>Surrender</td>\n",
              "      <td>Titties -n- Beer</td>\n",
              "      <td>1q0uMoANW2JVTUm4fHXBFE</td>\n",
              "      <td>['REVENGE']</td>\n",
              "      <td>['1KfNZD8DNPG6B2HevRbz23']</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.259</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0673</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.001550</td>\n",
              "      <td>0.1150</td>\n",
              "      <td>0.270</td>\n",
              "      <td>142.382</td>\n",
              "      <td>253133.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>2006-09-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1053318</th>\n",
              "      <td>74Pycx88Oh04JGsn945kdO</td>\n",
              "      <td>Rock S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1053319 rows × 24 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-cb2ab70c-4841-4e62-a1c3-57b92af3de0d')\"\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "        document.querySelector('#df-cb2ab70c-4841-4e62-a1c3-57b92af3de0d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
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              "          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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              "        element.appendChild(docLink);\n",
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              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    0% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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",
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              "    (() => {\n",
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              "        document.querySelector('#df-8fd401b0-5b4d-4c85-b040-56a65161000a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                             id                   name  \\\n",
              "0        7lmeHLHBe4nmXzuXc0HDjk                Testify   \n",
              "1        1wsRitfRRtWyEapl0q22o8        Guerrilla Radio   \n",
              "2        1hR0fIFK2qRG3f3RF70pb7       Calm Like a Bomb   \n",
              "3        2lbASgTSoDO7MTuLAXlTW0              Mic Check   \n",
              "4        1MQTmpYOZ6fcMQc56Hdo7T  Sleep Now In the Fire   \n",
              "...                         ...                    ...   \n",
              "1053314  02sWMvITk9AUPeh47etHYf         Anyway Anytime   \n",
              "1053315  1MQeKfHrPNPN4ngq1vMbdt           Crush On You   \n",
              "1053316  4XvYOLTyVsNSC5kFJquDrS  Hot Child In The City   \n",
              "1053317  5ydDSj4DzbuU4d16WcB6kM              Surrender   \n",
              "1053318  74Pycx88Oh04JGsn945kdO                 Rock S   \n",
              "\n",
              "                             album                album_id  \\\n",
              "0        The Battle Of Los Angeles  2eia0myWFgoHuttJytCxgX   \n",
              "1        The Battle Of Los Angeles  2eia0myWFgoHuttJytCxgX   \n",
              "2        The Battle Of Los Angeles  2eia0myWFgoHuttJytCxgX   \n",
              "3        The Battle Of Los Angeles  2eia0myWFgoHuttJytCxgX   \n",
              "4        The Battle Of Los Angeles  2eia0myWFgoHuttJytCxgX   \n",
              "...                            ...                     ...   \n",
              "1053314           Titties -n- Beer  1q0uMoANW2JVTUm4fHXBFE   \n",
              "1053315           Titties -n- Beer  1q0uMoANW2JVTUm4fHXBFE   \n",
              "1053316           Titties -n- Beer  1q0uMoANW2JVTUm4fHXBFE   \n",
              "1053317           Titties -n- Beer  1q0uMoANW2JVTUm4fHXBFE   \n",
              "1053318                        NaN                     NaN   \n",
              "\n",
              "                              artists                  artist_ids  \\\n",
              "0        ['Rage Against The Machine']  ['2d0hyoQ5ynDBnkvAbJKORj']   \n",
              "1        ['Rage Against The Machine']  ['2d0hyoQ5ynDBnkvAbJKORj']   \n",
              "2        ['Rage Against The Machine']  ['2d0hyoQ5ynDBnkvAbJKORj']   \n",
              "3        ['Rage Against The Machine']  ['2d0hyoQ5ynDBnkvAbJKORj']   \n",
              "4        ['Rage Against The Machine']  ['2d0hyoQ5ynDBnkvAbJKORj']   \n",
              "...                               ...                         ...   \n",
              "1053314                   ['REVENGE']  ['1KfNZD8DNPG6B2HevRbz23']   \n",
              "1053315                   ['REVENGE']  ['1KfNZD8DNPG6B2HevRbz23']   \n",
              "1053316                   ['REVENGE']  ['1KfNZD8DNPG6B2HevRbz23']   \n",
              "1053317                   ['REVENGE']  ['1KfNZD8DNPG6B2HevRbz23']   \n",
              "1053318                           NaN                         NaN   \n",
              "\n",
              "         track_number  disc_number explicit  danceability  ...  speechiness  \\\n",
              "0                 1.0          1.0    False         0.470  ...       0.0727   \n",
              "1                 2.0          1.0     True         0.599  ...       0.1880   \n",
              "2                 3.0          1.0    False         0.315  ...       0.4830   \n",
              "3                 4.0          1.0     True         0.440  ...       0.2370   \n",
              "4                 5.0          1.0    False         0.426  ...       0.0701   \n",
              "...               ...          ...      ...           ...  ...          ...   \n",
              "1053314           1.0          1.0    False         0.236  ...       0.0724   \n",
              "1053315           2.0          1.0    False         0.377  ...       0.0679   \n",
              "1053316           3.0          1.0    False         0.529  ...       0.0565   \n",
              "1053317           4.0          1.0    False         0.259  ...       0.0673   \n",
              "1053318           NaN          NaN      NaN           NaN  ...          NaN   \n",
              "\n",
              "         acousticness  instrumentalness  liveness  valence    tempo  \\\n",
              "0            0.026100          0.000011    0.3560    0.503  117.906   \n",
              "1            0.012900          0.000071    0.1550    0.489  103.680   \n",
              "2            0.023400          0.000002    0.1220    0.370  149.749   \n",
              "3            0.163000          0.000004    0.1210    0.574   96.752   \n",
              "4            0.001620          0.105000    0.0789    0.539  127.059   \n",
              "...               ...               ...       ...      ...      ...   \n",
              "1053314      0.000004          0.000286    0.3450    0.352  139.243   \n",
              "1053315      0.001940          0.015400    0.0969    0.783  134.191   \n",
              "1053316      0.008980          0.000274    0.0785    0.692  117.797   \n",
              "1053317      0.000009          0.001550    0.1150    0.270  142.382   \n",
              "1053318           NaN               NaN       NaN      NaN      NaN   \n",
              "\n",
              "         duration_ms  time_signature    year  release_date  \n",
              "0           210133.0             4.0  1999.0    1999-11-02  \n",
              "1           206200.0             4.0  1999.0    1999-11-02  \n",
              "2           298893.0             4.0  1999.0    1999-11-02  \n",
              "3           213640.0             4.0  1999.0    1999-11-02  \n",
              "4           205600.0             4.0  1999.0    1999-11-02  \n",
              "...              ...             ...     ...           ...  \n",
              "1053314     184000.0             4.0  2006.0    2006-09-20  \n",
              "1053315     168667.0             4.0  2006.0    2006-09-20  \n",
              "1053316     184413.0             4.0  2006.0    2006-09-20  \n",
              "1053317     253133.0             4.0  2006.0    2006-09-20  \n",
              "1053318          NaN             NaN     NaN           NaN  \n",
              "\n",
              "[1053319 rows x 24 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZt3CMl9dMSv",
        "outputId": "e43904a8-c662-481c-9f5d-ce954e893b2b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1065094, 24)"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Shve6-89dzow",
        "outputId": "23bb1069-06e7-4a7d-bb7e-df85926ca744"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id                  0\n",
              "name                0\n",
              "album               0\n",
              "album_id            0\n",
              "artists             0\n",
              "artist_ids          0\n",
              "track_number        0\n",
              "disc_number         0\n",
              "explicit            0\n",
              "danceability        0\n",
              "energy              0\n",
              "key                 0\n",
              "loudness            0\n",
              "mode                0\n",
              "speechiness         0\n",
              "acousticness        0\n",
              "instrumentalness    1\n",
              "liveness            1\n",
              "valence             1\n",
              "tempo               1\n",
              "duration_ms         1\n",
              "time_signature      1\n",
              "year                1\n",
              "release_date        1\n",
              "dtype: int64"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P5y177vgqiaQ",
        "outputId": "4e4cf575-12f6-41d6-a4bf-28d9c1608ef9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-1-3781b53595ce>:6: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  data_filled = data.fillna(data.mean())\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('tracks_features.csv', nrows = 30000)\n",
        "\n",
        "# Replace NaN values with mean values in each column\n",
        "data_filled = data.fillna(data.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7OcJRobwd2cS",
        "outputId": "33c2d2d9-82aa-4910-d99c-48bda87f3b66"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id                  0\n",
              "name                0\n",
              "album               0\n",
              "album_id            0\n",
              "artists             0\n",
              "artist_ids          0\n",
              "track_number        0\n",
              "disc_number         0\n",
              "explicit            0\n",
              "danceability        0\n",
              "energy              0\n",
              "key                 0\n",
              "loudness            0\n",
              "mode                0\n",
              "speechiness         0\n",
              "acousticness        0\n",
              "instrumentalness    0\n",
              "liveness            0\n",
              "valence             0\n",
              "tempo               0\n",
              "duration_ms         0\n",
              "time_signature      0\n",
              "year                0\n",
              "release_date        0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8yiQIYNeMnj",
        "outputId": "af73f444-1b64-4135-c451-b2f070529330"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "id                   object\n",
            "name                 object\n",
            "album                object\n",
            "album_id             object\n",
            "artists              object\n",
            "artist_ids           object\n",
            "track_number          int64\n",
            "disc_number           int64\n",
            "explicit               bool\n",
            "danceability        float64\n",
            "energy              float64\n",
            "key                   int64\n",
            "loudness            float64\n",
            "mode                  int64\n",
            "speechiness         float64\n",
            "acousticness        float64\n",
            "instrumentalness    float64\n",
            "liveness            float64\n",
            "valence             float64\n",
            "tempo               float64\n",
            "duration_ms           int64\n",
            "time_signature      float64\n",
            "year                  int64\n",
            "release_date         object\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(data.dtypes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PBXH2ubNfwgB"
      },
      "outputs": [],
      "source": [
        "#@title Dropping all categorical values\n",
        "\n",
        "df = df.drop(['id', 'name', 'album', 'album_id', 'artists', 'artist_ids', 'release_date'], axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5RRqfaMsgKdf",
        "outputId": "b9f34943-ce13-45ee-9ae1-c168449ac19e"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-f6ca615e-c9b8-4da7-a281-b2b19048593b\" 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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              "\n",
              "    .dataframe tbody tr th {\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>track_number</th>\n",
              "      <th>disc_number</th>\n",
              "      <th>explicit</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.599</td>\n",
              "      <td>0.957</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0.1550</td>\n",
              "      <td>0.489</td>\n",
              "      <td>103.680</td>\n",
              "      <td>206200</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
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              "      <td>7</td>\n",
              "      <td>-5.424</td>\n",
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              "      <td>149.749</td>\n",
              "      <td>298893</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.967</td>\n",
              "      <td>11</td>\n",
              "      <td>-5.830</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2370</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>0.574</td>\n",
              "      <td>96.752</td>\n",
              "      <td>213640</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>0.426</td>\n",
              "      <td>0.929</td>\n",
              "      <td>2</td>\n",
              "      <td>-6.729</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0701</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1204020</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
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              "      <td>5</td>\n",
              "      <td>-6.970</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.3370</td>\n",
              "      <td>0.415</td>\n",
              "      <td>159.586</td>\n",
              "      <td>276213</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1204021</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
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              "      <td>0.701</td>\n",
              "      <td>11</td>\n",
              "      <td>-6.602</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0883</td>\n",
              "      <td>0.10400</td>\n",
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              "      <td>0.781</td>\n",
              "      <td>121.980</td>\n",
              "      <td>363179</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1204022</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>0.785</td>\n",
              "      <td>0.796</td>\n",
              "      <td>9</td>\n",
              "      <td>-5.960</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0564</td>\n",
              "      <td>0.03040</td>\n",
              "      <td>0.918000</td>\n",
              "      <td>0.0664</td>\n",
              "      <td>0.467</td>\n",
              "      <td>121.996</td>\n",
              "      <td>385335</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1204023</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>0.665</td>\n",
              "      <td>0.856</td>\n",
              "      <td>6</td>\n",
              "      <td>-6.788</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0409</td>\n",
              "      <td>0.00007</td>\n",
              "      <td>0.776000</td>\n",
              "      <td>0.1170</td>\n",
              "      <td>0.227</td>\n",
              "      <td>124.986</td>\n",
              "      <td>324455</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1204024</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>0.736</td>\n",
              "      <td>0.708</td>\n",
              "      <td>2</td>\n",
              "      <td>-9.279</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0539</td>\n",
              "      <td>0.01680</td>\n",
              "      <td>0.296000</td>\n",
              "      <td>0.2790</td>\n",
              "      <td>0.204</td>\n",
              "      <td>117.991</td>\n",
              "      <td>304982</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1204025 rows × 17 columns</p>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         track_number  disc_number  explicit  danceability  energy  key  \\\n",
              "0                   1            1     False         0.470   0.978    7   \n",
              "1                   2            1      True         0.599   0.957   11   \n",
              "2                   3            1     False         0.315   0.970    7   \n",
              "3                   4            1      True         0.440   0.967   11   \n",
              "4                   5            1     False         0.426   0.929    2   \n",
              "...               ...          ...       ...           ...     ...  ...   \n",
              "1204020             2            1     False         0.264   0.966    5   \n",
              "1204021             3            1     False         0.796   0.701   11   \n",
              "1204022             4            1     False         0.785   0.796    9   \n",
              "1204023             5            1     False         0.665   0.856    6   \n",
              "1204024             6            1     False         0.736   0.708    2   \n",
              "\n",
              "         loudness  mode  speechiness  acousticness  instrumentalness  \\\n",
              "0          -5.399     1       0.0727       0.02610          0.000011   \n",
              "1          -5.764     1       0.1880       0.01290          0.000071   \n",
              "2          -5.424     1       0.4830       0.02340          0.000002   \n",
              "3          -5.830     0       0.2370       0.16300          0.000004   \n",
              "4          -6.729     1       0.0701       0.00162          0.105000   \n",
              "...           ...   ...          ...           ...               ...   \n",
              "1204020    -6.970     0       0.0672       0.00935          0.002240   \n",
              "1204021    -6.602     0       0.0883       0.10400          0.644000   \n",
              "1204022    -5.960     0       0.0564       0.03040          0.918000   \n",
              "1204023    -6.788     0       0.0409       0.00007          0.776000   \n",
              "1204024    -9.279     0       0.0539       0.01680          0.296000   \n",
              "\n",
              "         liveness  valence    tempo  duration_ms  time_signature  year  \n",
              "0          0.3560    0.503  117.906       210133             4.0  1999  \n",
              "1          0.1550    0.489  103.680       206200             4.0  1999  \n",
              "2          0.1220    0.370  149.749       298893             4.0  1999  \n",
              "3          0.1210    0.574   96.752       213640             4.0  1999  \n",
              "4          0.0789    0.539  127.059       205600             4.0  1999  \n",
              "...           ...      ...      ...          ...             ...   ...  \n",
              "1204020    0.3370    0.415  159.586       276213             4.0  2014  \n",
              "1204021    0.0749    0.781  121.980       363179             4.0  2014  \n",
              "1204022    0.0664    0.467  121.996       385335             4.0  2014  \n",
              "1204023    0.1170    0.227  124.986       324455             4.0  2014  \n",
              "1204024    0.2790    0.204  117.991       304982             4.0  2014  \n",
              "\n",
              "[1204025 rows x 17 columns]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "omFBQeAMgnTn",
        "outputId": "739aa54c-54fe-477b-b936-77351bb94bbf"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "    <div>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>track_number</th>\n",
              "      <th>disc_number</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "      <td>1.204025e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.656352e+00</td>\n",
              "      <td>1.055906e+00</td>\n",
              "      <td>4.930565e-01</td>\n",
              "      <td>5.095363e-01</td>\n",
              "      <td>5.194151e+00</td>\n",
              "      <td>-1.180870e+01</td>\n",
              "      <td>6.714595e-01</td>\n",
              "      <td>8.438219e-02</td>\n",
              "      <td>4.467511e-01</td>\n",
              "      <td>2.828605e-01</td>\n",
              "      <td>2.015994e-01</td>\n",
              "      <td>4.279866e-01</td>\n",
              "      <td>1.176344e+02</td>\n",
              "      <td>2.488399e+05</td>\n",
              "      <td>3.832494e+00</td>\n",
              "      <td>2.007328e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.994977e+00</td>\n",
              "      <td>2.953752e-01</td>\n",
              "      <td>1.896694e-01</td>\n",
              "      <td>2.946839e-01</td>\n",
              "      <td>3.536731e+00</td>\n",
              "      <td>6.982132e+00</td>\n",
              "      <td>4.696827e-01</td>\n",
              "      <td>1.159914e-01</td>\n",
              "      <td>3.852014e-01</td>\n",
              "      <td>3.762844e-01</td>\n",
              "      <td>1.804591e-01</td>\n",
              "      <td>2.704846e-01</td>\n",
              "      <td>3.093705e+01</td>\n",
              "      <td>1.622104e+05</td>\n",
              "      <td>5.611826e-01</td>\n",
              "      <td>1.210117e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-6.000000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.560000e-01</td>\n",
              "      <td>2.520000e-01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>-1.525400e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.510000e-02</td>\n",
              "      <td>3.760000e-02</td>\n",
              "      <td>7.600000e-06</td>\n",
              "      <td>9.680000e-02</td>\n",
              "      <td>1.910000e-01</td>\n",
              "      <td>9.405400e+01</td>\n",
              "      <td>1.740900e+05</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>2.002000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>5.010000e-01</td>\n",
              "      <td>5.240000e-01</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>-9.791000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>4.460000e-02</td>\n",
              "      <td>3.890000e-01</td>\n",
              "      <td>8.080000e-03</td>\n",
              "      <td>1.250000e-01</td>\n",
              "      <td>4.030000e-01</td>\n",
              "      <td>1.167260e+02</td>\n",
              "      <td>2.243390e+05</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>2.009000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.330000e-01</td>\n",
              "      <td>7.660000e-01</td>\n",
              "      <td>8.000000e+00</td>\n",
              "      <td>-6.717000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>7.230000e-02</td>\n",
              "      <td>8.610000e-01</td>\n",
              "      <td>7.190000e-01</td>\n",
              "      <td>2.450000e-01</td>\n",
              "      <td>6.440000e-01</td>\n",
              "      <td>1.370460e+02</td>\n",
              "      <td>2.858400e+05</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>2.015000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000e+01</td>\n",
              "      <td>1.300000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.100000e+01</td>\n",
              "      <td>7.234000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>9.690000e-01</td>\n",
              "      <td>9.960000e-01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.489340e+02</td>\n",
              "      <td>6.061090e+06</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>2.020000e+03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-019ccf54-4e78-4909-b5af-c3fde09b024c')\"\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "      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",
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              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      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-019ccf54-4e78-4909-b5af-c3fde09b024c 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-019ccf54-4e78-4909-b5af-c3fde09b024c');\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",
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              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      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-00394023-057f-4fde-8133-d130303e1445 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       track_number   disc_number  danceability        energy           key  \\\n",
              "count  1.204025e+06  1.204025e+06  1.204025e+06  1.204025e+06  1.204025e+06   \n",
              "mean   7.656352e+00  1.055906e+00  4.930565e-01  5.095363e-01  5.194151e+00   \n",
              "std    5.994977e+00  2.953752e-01  1.896694e-01  2.946839e-01  3.536731e+00   \n",
              "min    1.000000e+00  1.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
              "25%    3.000000e+00  1.000000e+00  3.560000e-01  2.520000e-01  2.000000e+00   \n",
              "50%    7.000000e+00  1.000000e+00  5.010000e-01  5.240000e-01  5.000000e+00   \n",
              "75%    1.000000e+01  1.000000e+00  6.330000e-01  7.660000e-01  8.000000e+00   \n",
              "max    5.000000e+01  1.300000e+01  1.000000e+00  1.000000e+00  1.100000e+01   \n",
              "\n",
              "           loudness          mode   speechiness  acousticness  \\\n",
              "count  1.204025e+06  1.204025e+06  1.204025e+06  1.204025e+06   \n",
              "mean  -1.180870e+01  6.714595e-01  8.438219e-02  4.467511e-01   \n",
              "std    6.982132e+00  4.696827e-01  1.159914e-01  3.852014e-01   \n",
              "min   -6.000000e+01  0.000000e+00  0.000000e+00  0.000000e+00   \n",
              "25%   -1.525400e+01  0.000000e+00  3.510000e-02  3.760000e-02   \n",
              "50%   -9.791000e+00  1.000000e+00  4.460000e-02  3.890000e-01   \n",
              "75%   -6.717000e+00  1.000000e+00  7.230000e-02  8.610000e-01   \n",
              "max    7.234000e+00  1.000000e+00  9.690000e-01  9.960000e-01   \n",
              "\n",
              "       instrumentalness      liveness       valence         tempo  \\\n",
              "count      1.204025e+06  1.204025e+06  1.204025e+06  1.204025e+06   \n",
              "mean       2.828605e-01  2.015994e-01  4.279866e-01  1.176344e+02   \n",
              "std        3.762844e-01  1.804591e-01  2.704846e-01  3.093705e+01   \n",
              "min        0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
              "25%        7.600000e-06  9.680000e-02  1.910000e-01  9.405400e+01   \n",
              "50%        8.080000e-03  1.250000e-01  4.030000e-01  1.167260e+02   \n",
              "75%        7.190000e-01  2.450000e-01  6.440000e-01  1.370460e+02   \n",
              "max        1.000000e+00  1.000000e+00  1.000000e+00  2.489340e+02   \n",
              "\n",
              "        duration_ms  time_signature          year  \n",
              "count  1.204025e+06    1.204025e+06  1.204025e+06  \n",
              "mean   2.488399e+05    3.832494e+00  2.007328e+03  \n",
              "std    1.622104e+05    5.611826e-01  1.210117e+01  \n",
              "min    1.000000e+03    0.000000e+00  0.000000e+00  \n",
              "25%    1.740900e+05    4.000000e+00  2.002000e+03  \n",
              "50%    2.243390e+05    4.000000e+00  2.009000e+03  \n",
              "75%    2.858400e+05    4.000000e+00  2.015000e+03  \n",
              "max    6.061090e+06    5.000000e+00  2.020000e+03  "
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5D2RPfNNgoTQ",
        "outputId": "2920c109-e224-48f1-ecb3-55ea0a76b311"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "track_number            50\n",
              "disc_number             13\n",
              "explicit                 2\n",
              "danceability          1362\n",
              "energy                3441\n",
              "key                     12\n",
              "loudness             39805\n",
              "mode                     2\n",
              "speechiness           1653\n",
              "acousticness          5398\n",
              "instrumentalness      5402\n",
              "liveness              1799\n",
              "valence               1884\n",
              "tempo               140472\n",
              "duration_ms         210013\n",
              "time_signature           5\n",
              "year                   101\n",
              "dtype: int64"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ho-eXBkfg5I2"
      },
      "outputs": [],
      "source": [
        "#@title scaling the features\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "scaled_features = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KMZmJJ9TheJx"
      },
      "outputs": [],
      "source": [
        "first_row_df = df.iloc[[0]]\n",
        "print(first_row_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nSQqHwAJhnQO"
      },
      "outputs": [],
      "source": [
        "from sklearn.manifold import TSNE\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "tsne = TSNE(n_components=2, perplexity=30, random_state=0)\n",
        "tsne_results = tsne.fit_transform(scaled_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SMDqahbthrxv"
      },
      "outputs": [],
      "source": [
        "df2 = pd.DataFrame(tsne_results, columns=[\"X\", \"Y\"])\n",
        "df2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mYVSJjlGCWTw"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from google.colab import files\n",
        "\n",
        "# Convert DataFrame to CSV\n",
        "filename = \"tsne12.csv\"\n",
        "df2.to_csv(filename, index=False)\n",
        "\n",
        "# Download the CSV to your local machine\n",
        "files.download(filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TeOcCX1roq7d"
      },
      "outputs": [],
      "source": [
        "# pip install gap-stat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "id": "2qeUlos_tfnS",
        "outputId": "a2014333-423f-421f-dabe-7dab76d37cf9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cluster Distribution:\n",
            "0     2005\n",
            "1     2240\n",
            "2     1904\n",
            "3     2439\n",
            "4      729\n",
            "5     1411\n",
            "6     2028\n",
            "7     1844\n",
            "8     1690\n",
            "9     1338\n",
            "10    1326\n",
            "11    1316\n",
            "12    1175\n",
            "13    1210\n",
            "14    2082\n",
            "15     832\n",
            "16    1003\n",
            "17    1225\n",
            "18     921\n",
            "19    1282\n",
            "Name: Cluster, dtype: int64\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title Agglomerative Clutering\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the data from tsne1.2.csv\n",
        "data = pd.read_csv('tsne1.2.csv', nrows = 30000)\n",
        "\n",
        "# Perform agglomerative clustering\n",
        "num_clusters = 20\n",
        "agg_cluster = AgglomerativeClustering(n_clusters=num_clusters, affinity='euclidean', linkage='ward')\n",
        "cluster_labels = agg_cluster.fit_predict(data)\n",
        "\n",
        "# Add cluster labels to the original data\n",
        "data_with_clusters = data.copy()\n",
        "data_with_clusters['Cluster'] = cluster_labels\n",
        "\n",
        "# Display the cluster distribution\n",
        "cluster_distribution = data_with_clusters['Cluster'].value_counts().sort_index()\n",
        "print(\"Cluster Distribution:\")\n",
        "print(cluster_distribution)\n",
        "\n",
        "# Visualization (scatter plot)\n",
        "plt.scatter(data.iloc[:, 0], data.iloc[:, 1], c=cluster_labels, alpha=0.7)\n",
        "plt.title('Agglomerative Clustering (20 Clusters)')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Feature 2')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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            "text/plain": [
              "   track_number  disc_number  explicit  danceability  energy  key  loudness  \\\n",
              "0             1            1     False         0.470   0.978    7    -5.399   \n",
              "1             2            1      True         0.599   0.957   11    -5.764   \n",
              "2             3            1     False         0.315   0.970    7    -5.424   \n",
              "3             4            1      True         0.440   0.967   11    -5.830   \n",
              "4             5            1     False         0.426   0.929    2    -6.729   \n",
              "\n",
              "   mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
              "0     1       0.0727       0.02610          0.000011    0.3560    0.503   \n",
              "1     1       0.1880       0.01290          0.000071    0.1550    0.489   \n",
              "2     1       0.4830       0.02340          0.000002    0.1220    0.370   \n",
              "3     0       0.2370       0.16300          0.000004    0.1210    0.574   \n",
              "4     1       0.0701       0.00162          0.105000    0.0789    0.539   \n",
              "\n",
              "     tempo  duration_ms  time_signature  year  \n",
              "0  117.906       210133             4.0  1999  \n",
              "1  103.680       206200             4.0  1999  \n",
              "2  149.749       298893             4.0  1999  \n",
              "3   96.752       213640             4.0  1999  \n",
              "4  127.059       205600             4.0  1999  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pandas as pd\n",
        "\n",
        "#Scaling the data\n",
        "df5 = pd.read_csv('tracks_features.csv', nrows = 30000)\n",
        "df6 = df5.copy()\n",
        "\n",
        "df5= df5.drop(['name', 'album', 'artists', 'artist_ids', 'id', 'album_id', 'release_date'], axis=1)\n",
        "\n",
        "df5.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uriE6LpIwlEB",
        "outputId": "d37f2628-3ccd-41e4-f650-fc822de678d4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Normalize all columns of the dataframe\n",
        "scaled_features = pd.DataFrame(scaler.fit_transform(df5), columns=df5.columns)\n",
        "scaled_features\n",
        "\n",
        "agg_cluster = AgglomerativeClustering(n_clusters=20, affinity='euclidean', linkage='ward')\n",
        "df6['AGG_Labels'] = agg_cluster.fit_predict(scaled_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "XVLlbmytxNSJ",
        "outputId": "5bfe3da6-be83-4f51-be8a-1198f29fc30c"
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              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.599</td>\n",
              "      <td>...</td>\n",
              "      <td>0.01290</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.1550</td>\n",
              "      <td>0.489</td>\n",
              "      <td>103.680</td>\n",
              "      <td>206200</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999</td>\n",
              "      <td>1999-11-02</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1hR0fIFK2qRG3f3RF70pb7</td>\n",
              "      <td>Calm Like a Bomb</td>\n",
              "      <td>The Battle Of Los Angeles</td>\n",
              "      <td>2eia0myWFgoHuttJytCxgX</td>\n",
              "      <td>['Rage Against The Machine']</td>\n",
              "      <td>['2d0hyoQ5ynDBnkvAbJKORj']</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>0.315</td>\n",
              "      <td>...</td>\n",
              "      <td>0.02340</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.1220</td>\n",
              "      <td>0.370</td>\n",
              "      <td>149.749</td>\n",
              "      <td>298893</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999</td>\n",
              "      <td>1999-11-02</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2lbASgTSoDO7MTuLAXlTW0</td>\n",
              "      <td>Mic Check</td>\n",
              "      <td>The Battle Of Los Angeles</td>\n",
              "      <td>2eia0myWFgoHuttJytCxgX</td>\n",
              "      <td>['Rage Against The Machine']</td>\n",
              "      <td>['2d0hyoQ5ynDBnkvAbJKORj']</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0.440</td>\n",
              "      <td>...</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>0.574</td>\n",
              "      <td>96.752</td>\n",
              "      <td>213640</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999</td>\n",
              "      <td>1999-11-02</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1MQTmpYOZ6fcMQc56Hdo7T</td>\n",
              "      <td>Sleep Now In the Fire</td>\n",
              "      <td>The Battle Of Los Angeles</td>\n",
              "      <td>2eia0myWFgoHuttJytCxgX</td>\n",
              "      <td>['Rage Against The Machine']</td>\n",
              "      <td>['2d0hyoQ5ynDBnkvAbJKORj']</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>0.426</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00162</td>\n",
              "      <td>0.105000</td>\n",
              "      <td>0.0789</td>\n",
              "      <td>0.539</td>\n",
              "      <td>127.059</td>\n",
              "      <td>205600</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1999</td>\n",
              "      <td>1999-11-02</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 25 columns</p>\n",
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            ],
            "text/plain": [
              "                       id                   name                      album  \\\n",
              "0  7lmeHLHBe4nmXzuXc0HDjk                Testify  The Battle Of Los Angeles   \n",
              "1  1wsRitfRRtWyEapl0q22o8        Guerrilla Radio  The Battle Of Los Angeles   \n",
              "2  1hR0fIFK2qRG3f3RF70pb7       Calm Like a Bomb  The Battle Of Los Angeles   \n",
              "3  2lbASgTSoDO7MTuLAXlTW0              Mic Check  The Battle Of Los Angeles   \n",
              "4  1MQTmpYOZ6fcMQc56Hdo7T  Sleep Now In the Fire  The Battle Of Los Angeles   \n",
              "\n",
              "                 album_id                       artists  \\\n",
              "0  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "1  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "2  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "3  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "4  2eia0myWFgoHuttJytCxgX  ['Rage Against The Machine']   \n",
              "\n",
              "                   artist_ids  track_number  disc_number  explicit  \\\n",
              "0  ['2d0hyoQ5ynDBnkvAbJKORj']             1            1     False   \n",
              "1  ['2d0hyoQ5ynDBnkvAbJKORj']             2            1      True   \n",
              "2  ['2d0hyoQ5ynDBnkvAbJKORj']             3            1     False   \n",
              "3  ['2d0hyoQ5ynDBnkvAbJKORj']             4            1      True   \n",
              "4  ['2d0hyoQ5ynDBnkvAbJKORj']             5            1     False   \n",
              "\n",
              "   danceability  ...  acousticness  instrumentalness  liveness  valence  \\\n",
              "0         0.470  ...       0.02610          0.000011    0.3560    0.503   \n",
              "1         0.599  ...       0.01290          0.000071    0.1550    0.489   \n",
              "2         0.315  ...       0.02340          0.000002    0.1220    0.370   \n",
              "3         0.440  ...       0.16300          0.000004    0.1210    0.574   \n",
              "4         0.426  ...       0.00162          0.105000    0.0789    0.539   \n",
              "\n",
              "     tempo  duration_ms  time_signature  year  release_date  AGG_Labels  \n",
              "0  117.906       210133             4.0  1999    1999-11-02           2  \n",
              "1  103.680       206200             4.0  1999    1999-11-02          10  \n",
              "2  149.749       298893             4.0  1999    1999-11-02           2  \n",
              "3   96.752       213640             4.0  1999    1999-11-02          11  \n",
              "4  127.059       205600             4.0  1999    1999-11-02           0  \n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df6.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PA3cSzwMx0mq",
        "outputId": "39e2263b-deef-4576-af3d-f4877db8f868"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Testify', 'Calm Like a Bomb', 'Born As Ghosts', 'Voice of the Voiceless', 'New Millennium Homes', 'War Within a Breath', 'Settle for Nothing', 'Wake Up', 'Freedom', 'Getaway Car']\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "def recommend(data, given_song_features, N=10):\n",
        "    # Same Cluster Recommendation:\n",
        "    same_cluster_songs = data[data['AGG_Labels'] == given_song_features['AGG_Labels']]\n",
        "\n",
        "    # If there are enough songs in the same cluster, just return the top N from them\n",
        "    if len(same_cluster_songs) >= N:\n",
        "        return same_cluster_songs['name'].head(N).tolist()\n",
        "\n",
        "    # Calculate cosine similarity\n",
        "    features = data.drop(columns=['name', 'album', 'artists', 'AGG_Labels'])  # Drop non-numeric columns\n",
        "    given_song_features = given_song_features.drop(['name', 'album', 'artists', 'AGG_Labels'])\n",
        "    cosine_similarities = cosine_similarity([given_song_features], features)[0]\n",
        "\n",
        "    # Get indices of songs with high cosine similarity\n",
        "    similar_songs_indices = np.argsort(cosine_similarities)[::-1][:N]\n",
        "\n",
        "    # Get the recommended songs\n",
        "    recommended_songs = data.loc[similar_songs_indices, 'name'].tolist()\n",
        "\n",
        "    return recommended_songs\n",
        "\n",
        "# Example usage\n",
        "given_song_features = {\n",
        "    'name': 'Testify',\n",
        "    'album': 'The Battle Of Los Angeles',\n",
        "    'artists': ['Rage Against The Machine'],\n",
        "    'track_number': 1,\n",
        "    'danceability': 0.47,\n",
        "    'energy': 0.978,\n",
        "    'key': 7,\n",
        "    'loudness': -5.399,\n",
        "    'speechiness': 0.0727,\n",
        "    'acousticness': 0.0261,\n",
        "    'instrumentalness': 0.000011,\n",
        "    'liveness': 0.356,\n",
        "    'valence': 0.503,\n",
        "    'tempo': 117.906,\n",
        "    'duration_ms': 210133.0,\n",
        "    'year': 1999.0,\n",
        "    'kmeans_labels': 0,\n",
        "    'GMM_labels': 1,\n",
        "    'AGG_Labels': 2  # Replace with the actual Agglomerative Clustering label\n",
        "}\n",
        "\n",
        "recommended_songs = recommend(df6, given_song_features, N=10)\n",
        "print(recommended_songs)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}