Hypeparametro_gridsearchcv_modelo_clasificacion_spotify.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Naren8520/Python/blob/main/Hypeparametro_gridsearchcv_modelo_clasificacion_spotify.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Cancelación de Reserva**\n",
        "from IPython.display import Image\n",
        "Image(\"/content/drive/MyDrive/Curso de ML 3 version/Sesión N° 03/imagen/Spotify-Hyperparametro.png\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "78iPlrqCjMQw",
        "outputId": "616baec4-8fd5-44c2-ffea-d499d0deebe1",
        "cellView": "form"
      },
      "id": "78iPlrqCjMQw",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dfad924c",
      "metadata": {
        "papermill": {
          "duration": 0.058437,
          "end_time": "2021-09-08T16:52:19.970545",
          "exception": false,
          "start_time": "2021-09-08T16:52:19.912108",
          "status": "completed"
        },
        "tags": [],
        "id": "dfad924c"
      },
      "source": [
        "# Spotify - Clasificación de popularidad\n",
        "### Datos de todos los tiempos de la década de 2000"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "05e79a70",
      "metadata": {
        "_kg_hide-input": false,
        "execution": {
          "iopub.execute_input": "2021-09-07T14:23:40.853471Z",
          "iopub.status.busy": "2021-09-07T14:23:40.851467Z",
          "iopub.status.idle": "2021-09-07T14:23:40.865498Z",
          "shell.execute_reply": "2021-09-07T14:23:40.863253Z",
          "shell.execute_reply.started": "2021-09-07T14:23:40.853404Z"
        },
        "papermill": {
          "duration": 0.060019,
          "end_time": "2021-09-08T16:52:20.091126",
          "exception": false,
          "start_time": "2021-09-08T16:52:20.031107",
          "status": "completed"
        },
        "tags": [],
        "id": "05e79a70"
      },
      "source": [
        "TABLA DE CONTENIDOS\n",
        "\n",
        "0. INTRODUCCIÓN Y OBJETIVO DEL PROYECTO\n",
        "1. IMPORTACIÓN DE BIBLIOTECAS\n",
        "2. DESCRIPCIÓN Y LIMPIEZA DE DATOS\n",
        "3. ANÁLISIS EXPLORATORIO Y VISUALIZACIONES\n",
        "4. DATOS DEL MODELO\n",
        "5. CONCLUSIONES FINALES"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fcfa34dc",
      "metadata": {
        "papermill": {
          "duration": 0.058248,
          "end_time": "2021-09-08T16:52:20.210970",
          "exception": false,
          "start_time": "2021-09-08T16:52:20.152722",
          "status": "completed"
        },
        "tags": [],
        "id": "fcfa34dc"
      },
      "source": [
        "### 0 INTRODUCCIÓN\n",
        "\n",
        "\n",
        "#### Contexto\n",
        "\n",
        "- Este conjunto de datos contiene estadísticas de audio de aproximadamente las 2000 mejores pistas entre 1956 y 2019 en Spotify. Los datos contienen 15 columnas, cada una de las cuales describe la pista y sus cualidades.\n",
        "\n",
        "#### Agradecimientos\n",
        "\n",
        "Estos datos se extraen de la lista de reproducción de Spotify - Top 2000s en PlaylistMachinery(@plamere) usando Selenium con Python. Más específicamente, se extrajo de http://sortyourmusic.playlistmachinery.com/.\n",
        "\n",
        "#### Información de los Datos\n",
        "\n",
        "1. Index: ID\n",
        "2. Title: Name of the Track\n",
        "3. Artist: Name of the Artist\n",
        "4. Top Genre: Genre of the track\n",
        "5. Year: Release Year of the track\n",
        "6. Beats per Minute(BPM): The tempo of the song\n",
        "7. Energy: The energy of a song - the higher the value, the more energtic. song\n",
        "8. Danceability: The higher the value, the easier it is to dance to this song.\n",
        "9. Loudness: The higher the value, the louder the song.\n",
        "10. Valence: The higher the value, the more positive mood for the song.\n",
        "11. Length: The duration of the song.\n",
        "12. Acoustic: The higher the value the more acoustic the song is.\n",
        "13. Speechiness: The higher the value the more spoken words the song contains\n",
        "15. Popularity: The higher the value the more popular the song is."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dcb2aed4",
      "metadata": {
        "papermill": {
          "duration": 0.058396,
          "end_time": "2021-09-08T16:52:20.327907",
          "exception": false,
          "start_time": "2021-09-08T16:52:20.269511",
          "status": "completed"
        },
        "tags": [],
        "id": "dcb2aed4"
      },
      "source": [
        "# OBJETIVO E INTERESES DEL PROYECTO:\n",
        "\n",
        "1. El objetivo es construir modelos de clasificación\n",
        "- Buscará clasificar el nivel de popularidad de una canción en función de las métricas de características dadas como se mencionó anteriormente.\n",
        "\n",
        "En el camino veremos otros intereses como:\n",
        "\n",
        "2. ¿Géneros y artistas más populares de todos los tiempos desde 1950 hasta 2000?\n",
        "3. ¿Hay una tendencia en los géneros preferidos en el pasado vs ahora?\n",
        "4. ¿Qué otras variables tienen un impacto en la métrica de popularidad?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GymzC0HJkBe5",
        "outputId": "1ad17264-f1a8-4eaf-f713-7a59d6918309"
      },
      "id": "GymzC0HJkBe5",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6a9d4db3",
      "metadata": {
        "papermill": {
          "duration": 0.05819,
          "end_time": "2021-09-08T16:52:20.444072",
          "exception": false,
          "start_time": "2021-09-08T16:52:20.385882",
          "status": "completed"
        },
        "tags": [],
        "id": "6a9d4db3"
      },
      "source": [
        "# 1 Importando librerias"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "241dbfda",
      "metadata": {
        "papermill": {
          "duration": 3.192038,
          "end_time": "2021-09-08T16:52:23.694247",
          "exception": false,
          "start_time": "2021-09-08T16:52:20.502209",
          "status": "completed"
        },
        "tags": [],
        "id": "241dbfda"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy import stats, special\n",
        "\n",
        "import seaborn as sns\n",
        "from seaborn import pairplot, heatmap\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from plotly.offline import plot, iplot, init_notebook_mode\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
        "\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV,cross_val_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ec897b40",
      "metadata": {
        "papermill": {
          "duration": 0.058896,
          "end_time": "2021-09-08T16:52:23.812726",
          "exception": false,
          "start_time": "2021-09-08T16:52:23.753830",
          "status": "completed"
        },
        "tags": [],
        "id": "ec897b40"
      },
      "source": [
        "# 2 Descripción de los Datos y Limpiezaa\n",
        "\n",
        "#### 2.1 DATOS DESCRIPCIÓN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "e8dfada7",
      "metadata": {
        "papermill": {
          "duration": 0.122351,
          "end_time": "2021-09-08T16:52:23.996190",
          "exception": false,
          "start_time": "2021-09-08T16:52:23.873839",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "e8dfada7",
        "outputId": "7713e5d7-cdcb-444d-d208-5e79a6d69f0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Index                   Title             Artist            Top Genre  \\\n",
              "0      1                 Sunrise        Norah Jones      adult standards   \n",
              "1      2             Black Night        Deep Purple           album rock   \n",
              "2      3          Clint Eastwood           Gorillaz  alternative hip hop   \n",
              "3      4           The Pretender       Foo Fighters    alternative metal   \n",
              "4      5  Waitin' On A Sunny Day  Bruce Springsteen         classic rock   \n",
              "\n",
              "   Year  Beats Per Minute (BPM)  Energy  Danceability  Loudness (dB)  \\\n",
              "0  2004                     157      30            53            -14   \n",
              "1  2000                     135      79            50            -11   \n",
              "2  2001                     168      69            66             -9   \n",
              "3  2007                     173      96            43             -4   \n",
              "4  2002                     106      82            58             -5   \n",
              "\n",
              "   Liveness  Valence Length (Duration)  Acousticness  Speechiness  Popularity  \n",
              "0        11       68               201            94            3          71  \n",
              "1        17       81               207            17            7          39  \n",
              "2         7       52               341             2           17          69  \n",
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      ],
      "source": [
        "# Loading datadataset & View\n",
        "\n",
        "spotify_df = pd.read_csv(\"/content/drive/MyDrive/Curso de ML 3 version/Sesión N° 03/Spotify-2000.csv\")\n",
        "spotify_df.head()"
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    {
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      "id": "AIr6jswmqwvp",
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              "      Index                                              Title         Artist  \\\n",
              "1018   1019  Tom Traubert's Blues (Four Sheets To The Wind ...      Tom Waits   \n",
              "1040   1041                               She's Always a Woman     Billy Joel   \n",
              "1901   1902    Lucy In The Sky With Diamonds - Remastered 2009    The Beatles   \n",
              "567     568                                 Une belle histoire  Michel Fugain   \n",
              "971     972                                          Landslide  Fleetwood Mac   \n",
              "...     ...                                                ...            ...   \n",
              "918     919                                  Always On My Mind  Elvis Presley   \n",
              "1115   1116                                           The Rose   Bette Midler   \n",
              "522     523                                         Wake Me Up         Avicii   \n",
              "1722   1723                                       Karma Police      Radiohead   \n",
              "22       23                           Als De Morgen Is Gekomen       Jan Smit   \n",
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              "             Top Genre  Year  Beats Per Minute (BPM)  Energy  Danceability  \\\n",
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              "567            chanson  2014                      83      58            27   \n",
              "971         album rock  1975                     159      16            41   \n",
              "...                ...   ...                     ...     ...           ...   \n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1994 entries, 0 to 1993\n",
            "Data columns (total 15 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   Index                   1994 non-null   int64 \n",
            " 1   Title                   1994 non-null   object\n",
            " 2   Artist                  1994 non-null   object\n",
            " 3   Top Genre               1994 non-null   object\n",
            " 4   Year                    1994 non-null   int64 \n",
            " 5   Beats Per Minute (BPM)  1994 non-null   int64 \n",
            " 6   Energy                  1994 non-null   int64 \n",
            " 7   Danceability            1994 non-null   int64 \n",
            " 8   Loudness (dB)           1994 non-null   int64 \n",
            " 9   Liveness                1994 non-null   int64 \n",
            " 10  Valence                 1994 non-null   int64 \n",
            " 11  Length (Duration)       1994 non-null   object\n",
            " 12  Acousticness            1994 non-null   int64 \n",
            " 13  Speechiness             1994 non-null   int64 \n",
            " 14  Popularity              1994 non-null   int64 \n",
            "dtypes: int64(11), object(4)\n",
            "memory usage: 233.8+ KB\n"
          ]
        }
      ],
      "source": [
        "# Descripción general de la información del conjunto de datos y los tipos de datos\n",
        "spotify_df.info() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "7feb456b",
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          "data": {
            "text/plain": [
              "             Index         Year  Beats Per Minute (BPM)       Energy  \\\n",
              "count  1994.000000  1994.000000             1994.000000  1994.000000   \n",
              "mean    997.500000  1992.992979              120.215647    59.679539   \n",
              "std     575.762538    16.116048               28.028096    22.154322   \n",
              "min       1.000000  1956.000000               37.000000     3.000000   \n",
              "25%     499.250000  1979.000000               99.000000    42.000000   \n",
              "50%     997.500000  1993.000000              119.000000    61.000000   \n",
              "75%    1495.750000  2007.000000              136.000000    78.000000   \n",
              "max    1994.000000  2019.000000              206.000000   100.000000   \n",
              "\n",
              "       Danceability  Loudness (dB)     Liveness      Valence  Acousticness  \\\n",
              "count   1994.000000    1994.000000  1994.000000  1994.000000   1994.000000   \n",
              "mean      53.238215      -9.008526    19.012036    49.408726     28.858074   \n",
              "std       15.351507       3.647876    16.727378    24.858212     29.011986   \n",
              "min       10.000000     -27.000000     2.000000     3.000000      0.000000   \n",
              "25%       43.000000     -11.000000     9.000000    29.000000      3.000000   \n",
              "50%       53.000000      -8.000000    12.000000    47.000000     18.000000   \n",
              "75%       64.000000      -6.000000    23.000000    69.750000     50.000000   \n",
              "max       96.000000      -2.000000    99.000000    99.000000     99.000000   \n",
              "\n",
              "       Speechiness  Popularity  \n",
              "count  1994.000000  1994.00000  \n",
              "mean      4.994985    59.52658  \n",
              "std       4.401566    14.35160  \n",
              "min       2.000000    11.00000  \n",
              "25%       3.000000    49.25000  \n",
              "50%       4.000000    62.00000  \n",
              "75%       5.000000    71.00000  \n",
              "max      55.000000   100.00000  "
            ],
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              "      <th>Index</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>1994.00000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>997.500000</td>\n",
              "      <td>1992.992979</td>\n",
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              "      <td>4.401566</td>\n",
              "      <td>14.35160</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1956.000000</td>\n",
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              "      <td>3.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>-27.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>11.00000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>499.250000</td>\n",
              "      <td>1979.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>-11.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>49.25000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>997.500000</td>\n",
              "      <td>1993.000000</td>\n",
              "      <td>119.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>62.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1495.750000</td>\n",
              "      <td>2007.000000</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>69.750000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>71.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1994.000000</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>206.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>100.00000</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Descripción general de los datos numéricos del conjunto de datos\n",
        "\n",
        "spotify_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "116154d9",
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        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "df6127a1",
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          "duration": 0.071297,
          "end_time": "2021-09-08T16:52:24.575095",
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        },
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "149"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Número de géneros que han aparecido en el top 2000 de todos los tiempos.\n",
        "\n",
        "len(spotify_df[\"Top Genre\"].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "f7c452ec",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "album rock           413\n",
              "adult standards      123\n",
              "dutch pop             88\n",
              "alternative rock      86\n",
              "dance pop             83\n",
              "                    ... \n",
              "streektaal             1\n",
              "indie anthem-folk      1\n",
              "la pop                 1\n",
              "ccm                    1\n",
              "bebop                  1\n",
              "Name: Top Genre, Length: 149, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Número de veces que cada género aparece en el top 2000 de todos los tiempos.\n",
        "\n",
        "spotify_df[\"Top Genre\"].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "df020bf5",
      "metadata": {
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          "duration": 0.062957,
          "end_time": "2021-09-08T16:52:24.836489",
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      "source": [
        "### Resumen del conjunto de datos sin procesar:\n",
        "\n",
        "#### El conjunto de datos contiene:\n",
        "- 1994 entradas\n",
        "- 1994 entradas no nulas\n",
        "- 15 columnas variables totales\n",
        "- 149 entradas de género\n",
        "\n",
        "#### Tipos de datos:\n",
        "- 4 columnas categóricas\n",
        "- 11 columnas numéricas\n",
        "\n",
        "#### Datos numéricos:\n",
        "- El conjunto de datos es entre los años 1994 y 2019. Solo alrededor de 63 años de las canciones más populares clasificadas por spotify.\n",
        "- La popularidad mínima de una canción es 11 y la máxima es 100.\n",
        "\n",
        "### Análisis inicial y progresión:\n",
        "\n",
        "1. Está claro que la música rock parece ser el género favorito de todos los tiempos con más características. Pero dicho esto, los datos están inclinados hacia antes de la década de 2000 y el gusto musical tiende a cambiar a lo largo de los años, por lo que esto puede investigarse más a fondo en la EDA.\n"
      ]
    },
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      "cell_type": "markdown",
      "id": "2e81649e",
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      "source": [
        "### 2.2 Limpieza de datos\n",
        "\n",
        "#### Acción:\n",
        "\n",
        "1. Convierta tipos de datos de columna.\n",
        "2. Elimina las columnas innecesarias.\n",
        "3. Ajuste los títulos de las columnas.\n",
        "4. Consolidar la columna de género ya que hay muchas variaciones de un solo género, p. 'pop holandés' y 'dance pop' o 'álbum rock' y rock alternativo. haremos que estas columnas simplemente 'pop' o simplemente 'rock' para proporcionar una representación resumida más precisa de esos géneros."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "7199458e",
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          "status": "completed"
        },
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        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7199458e",
        "outputId": "b3208a22-8129-49a5-d545-dfa13509fd83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length (Duration) ahora es un --> int64 data type\n"
          ]
        }
      ],
      "source": [
        "# Conversión de longitud (duración) a un tipo de datos entero\n",
        "\n",
        "spotify_df.replace(',','', regex=True, inplace=True)\n",
        "spotify_df['Length (Duration)']= spotify_df['Length (Duration)'].apply(pd.to_numeric,errors='coerce')\n",
        "print('Length (Duration) ahora es un -->',spotify_df['Length (Duration)'].dtype, 'data type')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "0ed29a4d",
      "metadata": {
        "papermill": {
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          "end_time": "2021-09-08T16:52:25.324830",
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          "start_time": "2021-09-08T16:52:25.251147",
          "status": "completed"
        },
        "tags": [],
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      },
      "outputs": [],
      "source": [
        "# Eliminando the Index column.\n",
        "spotify_df.drop(columns = ['Index'], inplace = True)\n",
        "\n",
        "# Conversión de todos los títulos de las columnas a minúsculas.\n",
        "spotify_df.columns = map(str.lower, spotify_df.columns)\n",
        "\n",
        "# Conversión de nombres de columna para que no tengan espacio entre ellos, si lo tienen, reemplace el espacio con un guión bajo \"_\"\n",
        "spotify_df.rename(columns = {'top genre' : 'genre', 'beats per minute (bpm)':'beats_per_minute','loudness (db)': 'loudness','length (duration)': 'duration'}, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "efd77ad5",
      "metadata": {
        "papermill": {
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          "end_time": "2021-09-08T16:52:25.471119",
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          "start_time": "2021-09-08T16:52:25.389136",
          "status": "completed"
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1994 entries, 0 to 1993\n",
            "Data columns (total 14 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   title             1994 non-null   object\n",
            " 1   artist            1994 non-null   object\n",
            " 2   genre             1994 non-null   object\n",
            " 3   year              1994 non-null   int64 \n",
            " 4   beats_per_minute  1994 non-null   int64 \n",
            " 5   energy            1994 non-null   int64 \n",
            " 6   danceability      1994 non-null   int64 \n",
            " 7   loudness          1994 non-null   int64 \n",
            " 8   liveness          1994 non-null   int64 \n",
            " 9   valence           1994 non-null   int64 \n",
            " 10  duration          1994 non-null   int64 \n",
            " 11  acousticness      1994 non-null   int64 \n",
            " 12  speechiness       1994 non-null   int64 \n",
            " 13  popularity        1994 non-null   int64 \n",
            "dtypes: int64(11), object(3)\n",
            "memory usage: 218.2+ KB\n"
          ]
        }
      ],
      "source": [
        "spotify_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e8ce67d6",
      "metadata": {
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          "status": "completed"
        },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "e8ce67d6",
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "            title       artist                genre  year  beats_per_minute  \\\n",
              "0         Sunrise  Norah Jones      adult standards  2004               157   \n",
              "1     Black Night  Deep Purple           album rock  2000               135   \n",
              "2  Clint Eastwood     Gorillaz  alternative hip hop  2001               168   \n",
              "\n",
              "   energy  danceability  loudness  liveness  valence  duration  acousticness  \\\n",
              "0      30            53       -14        11       68       201            94   \n",
              "1      79            50       -11        17       81       207            17   \n",
              "2      69            66        -9         7       52       341             2   \n",
              "\n",
              "   speechiness  popularity  \n",
              "0            3          71  \n",
              "1            7          39  \n",
              "2           17          69  "
            ],
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "spotify_df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10c5b9f1",
      "metadata": {
        "papermill": {
          "duration": 0.064776,
          "end_time": "2021-09-08T16:52:25.750772",
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          "status": "completed"
        },
        "tags": [],
        "id": "10c5b9f1"
      },
      "source": [
        "### Columna de consolidación de género"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "7b65d4a5",
      "metadata": {
        "papermill": {
          "duration": 0.16069,
          "end_time": "2021-09-08T16:52:25.981023",
          "exception": false,
          "start_time": "2021-09-08T16:52:25.820333",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7b65d4a5",
        "outputId": "e77080e0-ff4c-4fc3-b4bc-a57dd0ccfd9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nuevo Total de Géneros de 146 a --> 68\n"
          ]
        }
      ],
      "source": [
        "# función para dividir la columna de género\n",
        "    \n",
        "def genre_splitter(genre):\n",
        "    result = genre.copy()\n",
        "    result = result.str.split(\" \",1)\n",
        "    for i in range(len(result)):\n",
        "        if (len(result[i]) > 1):\n",
        "            result[i] = [result[i][1]]\n",
        "    return result.str.join('')\n",
        "\n",
        "# bucle hasta que el género no se puede dividir más\n",
        "\n",
        "new_genre = spotify_df['genre'].copy()\n",
        "while(max((new_genre.str.split(\" \", 1)).str.len()) > 1):\n",
        "    new_genre = genre_splitter(new_genre)\n",
        "    \n",
        "print('Nuevo Total de Géneros de 146 a -->', len(new_genre.unique()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "a2565bcd",
      "metadata": {
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        },
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rock           856\n",
              "pop            333\n",
              "standards      123\n",
              "metal           93\n",
              "indie           77\n",
              "              ... \n",
              "laboratorio      1\n",
              "holler           1\n",
              "hardcore         1\n",
              "trance           1\n",
              "bebop            1\n",
              "Name: genre, Length: 68, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "new_genre.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b97dfa9b",
      "metadata": {
        "papermill": {
          "duration": 0.064196,
          "end_time": "2021-09-08T16:52:26.272284",
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          "start_time": "2021-09-08T16:52:26.208088",
          "status": "completed"
        },
        "tags": [],
        "id": "b97dfa9b"
      },
      "source": [
        "### Análisis:\n",
        "- Arriba se muestra una columna de género consolidada en géneros individuales/más generalistas en rock, pop, etc.\n",
        "- También se espera un aumento en los valores debido a la consolidación de los géneros."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "1ba21018",
      "metadata": {
        "papermill": {
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          "end_time": "2021-09-08T16:52:26.418850",
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          "start_time": "2021-09-08T16:52:26.340219",
          "status": "completed"
        },
        "tags": [],
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        },
        "id": "1ba21018",
        "outputId": "c51131be-4998-462f-a866-9798b79b58f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       standards\n",
              "1            rock\n",
              "2             hop\n",
              "3           metal\n",
              "4            rock\n",
              "          ...    \n",
              "1989    standards\n",
              "1990    standards\n",
              "1991         rock\n",
              "1992        bebop\n",
              "1993    standards\n",
              "Name: genre, Length: 1994, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# ingresando nuevos valores de columna de new_genre a gender en dataframe.\n",
        "\n",
        "spotify_df['genre'] = new_genre\n",
        "spotify_df['genre']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f87fda0",
      "metadata": {
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          "duration": 0.066987,
          "end_time": "2021-09-08T16:52:26.551089",
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          "start_time": "2021-09-08T16:52:26.484102",
          "status": "completed"
        },
        "tags": [],
        "id": "1f87fda0"
      },
      "source": [
        "# 3 ANÁLISIS EXPLORATORIO Y VISUALIZACIONES\n",
        "\n",
        "\n",
        "- En esta sección investigaremos los datos. Echando un vistazo particular a nuestras características objetivo: \"Popularidad\" y \"Género\" y sus variables correlacionadas dentro del conjunto de datos."
      ]
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      "source": [
        "### 3.1 ¿Géneros y artistas más populares de 1950 a 2000?"
      ]
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              "           popularity\n",
              "genre                \n",
              "rock            52025\n",
              "pop             19390\n",
              "standards        7697\n",
              "metal            6141\n",
              "soul             3032\n",
              "indie            2912\n",
              "wave             2571\n",
              "invasion         2451\n",
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        "# Crea una función top_10, que toma un solo parámetro para una columna.\n",
        "# Agrupe los datos por la entrada de columna deseada, sume los valores de las columnas restantes, ordene los valores de suma por la columna 'Popularidad'\n",
        "# de mayor a menor, imprime las 10 filas superiores.\n",
        "\n",
        "def top_10(column):\n",
        "    top_10_songs = spotify_df.groupby([column]).sum().sort_values('popularity', ascending=False).head(10)\n",
        "    return(top_10_songs[['popularity']])  # Only show 'popularity' column.\n",
        "\n",
        "top_10('genre')"
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              "                    popularity\n",
              "artist                        \n",
              "The Beatles               2451\n",
              "Queen                     2085\n",
              "Coldplay                  1888\n",
              "Michael Jackson           1511\n",
              "The Rolling Stones        1496\n",
              "U2                        1476\n",
              "Bruce Springsteen         1378\n",
              "David Bowie               1311\n",
              "ABBA                      1186\n",
              "Fleetwood Mac             1083"
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              "                                                     [key], {});\n",
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      ],
      "source": [
        "# Usa la misma función para los Artistas\n",
        "top_10('artist')"
      ]
    },
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      "source": [
        "#### 3.1 Conclusión:\n",
        "\n",
        "- Lo anterior muestra la acumulación de los géneros y artistas más populares de todos los tiempos. También podemos ver que hay una cantidad significativa de entradas de géneros de música Rock en comparación con el resto de los géneros. Por lo tanto, siempre hubo una gran posibilidad de que este sea el más popular en general. Aunque Michael Jackson, sería un caso atípico aquí debido a su popularidad.\n",
        "\n",
        "- Pero en términos de popularidad 'pura' como se ve a continuación, valores de 0 a 100, la historia es un poco diferente. Podemos ver que la música pop y sus muchas variaciones tienen una popularidad mayoritaria en los años más recientes. Esto puede indicar un cambio en la popularidad a lo largo de los años, así como tendencias de escucha musical debido a los avances tecnológicos."
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              "           genre  year  popularity\n",
              "793          pop  2019         100\n",
              "787          pop  2019          98\n",
              "786   electropop  2019          95\n",
              "1641         pop  1994          95\n",
              "688         rock  2017          88\n",
              "726          pop  2018          88\n",
              "683          pop  2017          87\n",
              "687          pop  2017          87\n",
              "745          pop  2018          87\n",
              "529       mellow  2013          86"
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              "      <th>726</th>\n",
              "      <td>pop</td>\n",
              "      <td>2018</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>pop</td>\n",
              "      <td>2017</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>687</th>\n",
              "      <td>pop</td>\n",
              "      <td>2017</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>745</th>\n",
              "      <td>pop</td>\n",
              "      <td>2018</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>mellow</td>\n",
              "      <td>2013</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d27c2db5-ba97-462d-8950-578becde52c9')\"\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d27c2db5-ba97-462d-8950-578becde52c9 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-d27c2db5-ba97-462d-8950-578becde52c9');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "pure_popularity = spotify_df.sort_values('popularity', ascending=False).head(10)\n",
        "pure_popularity[['genre', 'year', 'popularity']]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8f7356e3",
      "metadata": {
        "papermill": {
          "duration": 0.067384,
          "end_time": "2021-09-08T16:52:27.569926",
          "exception": false,
          "start_time": "2021-09-08T16:52:27.502542",
          "status": "completed"
        },
        "tags": [],
        "id": "8f7356e3"
      },
      "source": [
        "## 3.2 ¿Hay una tendencia/cambio en los géneros preferidos antes de la década de 2000 frente a los actuales a lo largo de los años?\n",
        "\n",
        "#### - Para abordar esto, **dividiré el conjunto de datos en cuartos (n/4) y realizaré un seguimiento del cambio de popularidad** en el género, así como el recuento de entradas de género a lo largo de los años."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "74bbd259",
      "metadata": {
        "papermill": {
          "duration": 0.104534,
          "end_time": "2021-09-08T16:52:27.741256",
          "exception": false,
          "start_time": "2021-09-08T16:52:27.636722",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74bbd259",
        "outputId": "db36eb67-8758-4eb7-a0d2-d341237f8dd8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    520\n",
              "3    500\n",
              "4    496\n",
              "2    478\n",
              "Name: year, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Dividir los datos de la columna 'Año' en 4 cubos igualmente dispersos en orden ascendente desde 1956 hasta 2019:\n",
        "\n",
        "spotify_df['year'] = pd.qcut(spotify_df['year'], q=4, labels=[1, 2, 3, 4]) \n",
        "spotify_df['year'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "94fe6260",
      "metadata": {
        "papermill": {
          "duration": 0.273146,
          "end_time": "2021-09-08T16:52:28.081741",
          "exception": false,
          "start_time": "2021-09-08T16:52:27.808595",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "94fe6260",
        "outputId": "2927932e-1723-47fb-fc7e-3399fb8ade34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Year Count'), Text(0.5, 0, 'Year Block (YB) divided into 4/4')]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "x=['YB-1', 'YB-2', 'YB-3', 'YB-4']\n",
        "y=spotify_df['year'].value_counts()\n",
        "\n",
        "_ = sns.barplot(x=x, y=y, palette=\"BrBG\")\n",
        "_.set(xlabel='Year Block (YB) divided into 4/4', ylabel='Year Count')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "931e49bf",
      "metadata": {
        "papermill": {
          "duration": 0.079001,
          "end_time": "2021-09-08T16:52:28.234346",
          "exception": false,
          "start_time": "2021-09-08T16:52:28.155345",
          "status": "completed"
        },
        "tags": [],
        "id": "931e49bf"
      },
      "outputs": [],
      "source": [
        "# Función para crear conjuntos de bloques de año (1 - 4):\n",
        "\n",
        "def year_block(year_no):\n",
        "    block = spotify_df.loc[spotify_df['year'] == year_no]\n",
        "    return block[['genre', 'year', 'popularity']].sort_values('popularity', ascending=False)\n",
        "\n",
        "# Función para crear recuentos de valores de los 5 géneros principales para el gráfico circular visual:\n",
        "\n",
        "def genre_count(year_block):\n",
        "    return year_block['genre'].value_counts().head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c1fbb5e4",
      "metadata": {
        "papermill": {
          "duration": 0.074023,
          "end_time": "2021-09-08T16:52:28.379181",
          "exception": false,
          "start_time": "2021-09-08T16:52:28.305158",
          "status": "completed"
        },
        "tags": [],
        "id": "c1fbb5e4"
      },
      "source": [
        "## Visuales de gráfico circular de bloques de año que muestran tendencia/cambio en géneros entre (1956 - 2019)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "45939e2e",
      "metadata": {
        "papermill": {
          "duration": 0.279259,
          "end_time": "2021-09-08T16:52:28.727542",
          "exception": false,
          "start_time": "2021-09-08T16:52:28.448283",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "45939e2e",
        "outputId": "64eb7492-df7e-4df4-be5d-a37abbdda230"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Creación del bloque de año 1 y contador de género para gráfico circular visual:\n",
        "year_block_1 = year_block(1) \n",
        "genre_count(year_block_1)\n",
        "   \n",
        "# Gráfico circular para el bloque de año 1:\n",
        "values = genre_count(year_block_1).values\n",
        "names = genre_count(year_block_1).index\n",
        "colors = ['gold', 'mediumturquoise', 'darkorange', 'lightgreen', 'AntiqueWhite']\n",
        "\n",
        "fig = go.Figure(data=[go.Pie(labels=names, values=values, pull=[0.1, 0, 0, 0, 0])])\n",
        "fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20,\n",
        "                  marker=dict(colors=colors, line=dict(color='#000000', width=1.5)))\n",
        "fig.update_layout(title_text='Año  1')\n",
        "fig.show()\n",
        "   \n",
        "    \n",
        "# Creación del bloque de año 2 y contador de género para gráfico circular visual:\n",
        "year_block_2 = year_block(2) \n",
        "genre_count(year_block_2)\n",
        "   \n",
        "# Gráfico circular para el bloque de año 2:\n",
        "values = genre_count(year_block_2).values\n",
        "names = genre_count(year_block_2).index\n",
        "colors = ['gold', 'mediumturquoise', 'darkorange', 'lightgreen', 'AntiqueWhite']\n",
        "\n",
        "fig = go.Figure(data=[go.Pie(labels=names, values=values, pull=[0.1, 0, 0, 0, 0])])\n",
        "fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20,\n",
        "                  marker=dict(colors=colors, line=dict(color='#000000', width=1.5)))\n",
        "fig.update_layout(title_text='Año 2')\n",
        "fig.show()\n",
        "\n",
        "\n",
        "# Creation of Year block 3 & Genre counter for Pie Chart Viaual:\n",
        "year_block_3 = year_block(3) \n",
        "genre_count(year_block_3)\n",
        "   \n",
        "# Gráfico circular para el bloque de año 3:\n",
        "values = genre_count(year_block_3).values\n",
        "names = genre_count(year_block_3).index\n",
        "colors = ['gold', 'mediumturquoise', 'darkorange', 'lightgreen', 'AntiqueWhite']\n",
        "\n",
        "fig = go.Figure(data=[go.Pie(labels=names, values=values, name=\"Year Block 3\", pull=[0.1, 0, 0, 0, 0])])\n",
        "fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20,\n",
        "                  marker=dict(colors=colors, line=dict(color='#000000', width=1.5)))\n",
        "fig.update_layout(title_text='Año 3')\n",
        "fig.show()\n",
        "\n",
        "\n",
        "# Creación del bloque de año 4 y contador de género para gráfico circular visual:\n",
        "year_block_4 = year_block(4) \n",
        "genre_count(year_block_4)\n",
        "   \n",
        "# Gráfico circular para el bloque de año 4:\n",
        "values = genre_count(year_block_4).values\n",
        "names = genre_count(year_block_4).index\n",
        "colors = ['mediumturquoise','gold', 'darkorange', 'lightgreen', 'AntiqueWhite']\n",
        "\n",
        "fig = go.Figure(data=[go.Pie(labels=names, values=values, pull=[0.1, 0, 0, 0, 0])])\n",
        "fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20,\n",
        "                  marker=dict(colors=colors, line=dict(color='#000000', width=1.5)))\n",
        "fig.update_layout(title_text='Año 4')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "554afaf8",
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          "duration": 0.093501,
          "end_time": "2021-09-08T16:52:28.916404",
          "exception": false,
          "start_time": "2021-09-08T16:52:28.822903",
          "status": "completed"
        },
        "tags": [],
        "id": "554afaf8"
      },
      "source": [
        "#### 3.2 Conclusión: análisis de gráfico circular:\n",
        "- Según lo anterior, podemos ver claramente un cambio en la preferencia de género, desde el Rock inclinándose hacia la música pop al final del cuarto 4/4. Pero, el nivel de popularidad de la música pop tiene una tasa mucho más alta que la de la música rock en su apogeo, como veremos a continuación."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f6f3bf53",
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          "duration": 0.150549,
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          "start_time": "2021-09-08T16:52:29.011661",
          "status": "completed"
        },
        "tags": [],
        "id": "f6f3bf53"
      },
      "source": [
        "## Recuento de popularidad del género a lo largo de los años"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "50aa60a1",
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          "status": "completed"
        },
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          "height": 677
        },
        "id": "50aa60a1",
        "outputId": "15e6fdc9-6df9-42f2-80ff-6d7d987d4c5e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           genre year  popularity\n",
              "1113        rock    1          83\n",
              "943         rock    1          82\n",
              "999         rock    1          82\n",
              "884         rock    1          82\n",
              "1947    invasion    1          82\n",
              "1237        rock    2          84\n",
              "1204        rock    2          84\n",
              "1157        rock    2          83\n",
              "1452        punk    2          83\n",
              "1315        rock    2          83\n",
              "1641         pop    3          95\n",
              "398      electro    3          84\n",
              "30          wave    3          84\n",
              "100        metal    3          83\n",
              "239         wave    3          82\n",
              "793          pop    4         100\n",
              "787          pop    4          98\n",
              "786   electropop    4          95\n",
              "726          pop    4          88\n",
              "688         rock    4          88"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>genre</th>\n",
              "      <th>year</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1113</th>\n",
              "      <td>rock</td>\n",
              "      <td>1</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>943</th>\n",
              "      <td>rock</td>\n",
              "      <td>1</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>rock</td>\n",
              "      <td>1</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>884</th>\n",
              "      <td>rock</td>\n",
              "      <td>1</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1947</th>\n",
              "      <td>invasion</td>\n",
              "      <td>1</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1237</th>\n",
              "      <td>rock</td>\n",
              "      <td>2</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1204</th>\n",
              "      <td>rock</td>\n",
              "      <td>2</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1157</th>\n",
              "      <td>rock</td>\n",
              "      <td>2</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1452</th>\n",
              "      <td>punk</td>\n",
              "      <td>2</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1315</th>\n",
              "      <td>rock</td>\n",
              "      <td>2</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1641</th>\n",
              "      <td>pop</td>\n",
              "      <td>3</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>electro</td>\n",
              "      <td>3</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>wave</td>\n",
              "      <td>3</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>metal</td>\n",
              "      <td>3</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>wave</td>\n",
              "      <td>3</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>793</th>\n",
              "      <td>pop</td>\n",
              "      <td>4</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>787</th>\n",
              "      <td>pop</td>\n",
              "      <td>4</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>786</th>\n",
              "      <td>electropop</td>\n",
              "      <td>4</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>pop</td>\n",
              "      <td>4</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>688</th>\n",
              "      <td>rock</td>\n",
              "      <td>4</td>\n",
              "      <td>88</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e9e946cd-c341-4b65-a7fa-3456ce748e3c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Uniéndose a las cabezas de year_block para el conteo de género superior:\n",
        "\n",
        "frames = [year_block_1.head(), year_block_2.head(), year_block_3.head(), year_block_4.head()]\n",
        "top_genre_df = pd.concat(frames)\n",
        "top_genre_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7445ecec",
      "metadata": {
        "papermill": {
          "duration": 0.097428,
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          "start_time": "2021-09-08T16:52:29.487256",
          "status": "completed"
        },
        "tags": [],
        "id": "7445ecec"
      },
      "source": [
        "## 3.3 ¿Qué otras características tienen un impacto en la popularidad de una canción?\n",
        "\n",
        "- Buscaremos explorar variables numéricas como las funciones de audio y buscaremos la correlación entre cada función para ayudar a definir las funciones utilizables para construir un modelo preciso. Primero, eliminemos las columnas 'título' y 'año' ya que todos los valores son únicos y no ayudarán con nuestro modelo de clasificación."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "e66859f1",
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          "status": "completed"
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          "height": 206
        },
        "id": "e66859f1",
        "outputId": "f08ce2f4-ab51-49b4-b702-7aa4ceca13fd"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              artist      genre  beats_per_minute  energy  danceability  \\\n",
              "0        Norah Jones  standards               157      30            53   \n",
              "1        Deep Purple       rock               135      79            50   \n",
              "2           Gorillaz        hop               168      69            66   \n",
              "3       Foo Fighters      metal               173      96            43   \n",
              "4  Bruce Springsteen       rock               106      82            58   \n",
              "\n",
              "   loudness  liveness  valence  duration  acousticness  speechiness  \\\n",
              "0       -14        11       68       201            94            3   \n",
              "1       -11        17       81       207            17            7   \n",
              "2        -9         7       52       341             2           17   \n",
              "3        -4         3       37       269             0            4   \n",
              "4        -5        10       87       256             1            3   \n",
              "\n",
              "   popularity  \n",
              "0          71  \n",
              "1          39  \n",
              "2          69  \n",
              "3          76  \n",
              "4          59  "
            ],
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              "\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "spotify_df.drop(columns = ['title','year'], inplace = True)\n",
        "spotify_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1369a022",
      "metadata": {
        "papermill": {
          "duration": 0.10194,
          "end_time": "2021-09-08T16:52:30.006561",
          "exception": false,
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          "status": "completed"
        },
        "tags": [],
        "id": "1369a022"
      },
      "source": [
        "#### Nota:\n",
        "- Los géneros musicales que tienen valores únicos harían que nuestro modelo fuera ineficiente, ya que no tiene suficientes datos con los que trabajar, por lo que se eliminarán estos valores y las filas correspondientes en el marco de datos original. Por lo tanto:\n",
        "\n",
        "#### - Se eliminarán los géneros con un valor de recuento inferior a 20x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "a5c18bd4",
      "metadata": {
        "papermill": {
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          "status": "completed"
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        },
        "id": "a5c18bd4",
        "outputId": "7c862edc-c5a2-4627-f7fa-4e12318b0c4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Género Valores que se eliminarán del conjunto de datos = 56\n"
          ]
        }
      ],
      "source": [
        "unique = spotify_df['genre'].unique()\n",
        "to_remove = [] \n",
        "\n",
        "# los géneros que tienen una sola instancia solo se colocarán dentro de la matriz to_remove\n",
        "for genre in unique:\n",
        "    if spotify_df['genre'].value_counts()[genre] < 20: \n",
        "        to_remove += [genre]\n",
        "\n",
        "print('Género Valores que se eliminarán del conjunto de datos =', len(to_remove))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "652106cd",
      "metadata": {
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          "status": "completed"
        },
        "tags": [],
        "id": "652106cd"
      },
      "source": [
        "#### - Ahora para reemplazar nuestra columna de género original con la versión actualizada"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "1dfd2b45",
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          "status": "completed"
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          "height": 238
        },
        "id": "1dfd2b45",
        "outputId": "4d89e381-3db3-4add-f6f2-2f4dbbf8e4da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      artist      genre  beats_per_minute  energy  \\\n",
              "genre                                                               \n",
              "standards        Norah Jones  standards               157      30   \n",
              "rock             Deep Purple       rock               135      79   \n",
              "hop                 Gorillaz        hop               168      69   \n",
              "metal           Foo Fighters      metal               173      96   \n",
              "rock       Bruce Springsteen       rock               106      82   \n",
              "\n",
              "           danceability  loudness  liveness  valence  duration  acousticness  \\\n",
              "genre                                                                          \n",
              "standards            53       -14        11       68       201            94   \n",
              "rock                 50       -11        17       81       207            17   \n",
              "hop                  66        -9         7       52       341             2   \n",
              "metal                43        -4         3       37       269             0   \n",
              "rock                 58        -5        10       87       256             1   \n",
              "\n",
              "           speechiness  popularity  \n",
              "genre                               \n",
              "standards            3          71  \n",
              "rock                 7          39  \n",
              "hop                 17          69  \n",
              "metal                4          76  \n",
              "rock                 3          59  "
            ],
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              "      <th>genre</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>standards</th>\n",
              "      <td>Norah Jones</td>\n",
              "      <td>standards</td>\n",
              "      <td>157</td>\n",
              "      <td>30</td>\n",
              "      <td>53</td>\n",
              "      <td>-14</td>\n",
              "      <td>11</td>\n",
              "      <td>68</td>\n",
              "      <td>201</td>\n",
              "      <td>94</td>\n",
              "      <td>3</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rock</th>\n",
              "      <td>Deep Purple</td>\n",
              "      <td>rock</td>\n",
              "      <td>135</td>\n",
              "      <td>79</td>\n",
              "      <td>50</td>\n",
              "      <td>-11</td>\n",
              "      <td>17</td>\n",
              "      <td>81</td>\n",
              "      <td>207</td>\n",
              "      <td>17</td>\n",
              "      <td>7</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hop</th>\n",
              "      <td>Gorillaz</td>\n",
              "      <td>hop</td>\n",
              "      <td>168</td>\n",
              "      <td>69</td>\n",
              "      <td>66</td>\n",
              "      <td>-9</td>\n",
              "      <td>7</td>\n",
              "      <td>52</td>\n",
              "      <td>341</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>metal</th>\n",
              "      <td>Foo Fighters</td>\n",
              "      <td>metal</td>\n",
              "      <td>173</td>\n",
              "      <td>96</td>\n",
              "      <td>43</td>\n",
              "      <td>-4</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>269</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rock</th>\n",
              "      <td>Bruce Springsteen</td>\n",
              "      <td>rock</td>\n",
              "      <td>106</td>\n",
              "      <td>82</td>\n",
              "      <td>58</td>\n",
              "      <td>-5</td>\n",
              "      <td>10</td>\n",
              "      <td>87</td>\n",
              "      <td>256</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>59</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",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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",
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              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1228625f-1677-40ae-b353-cd88cbc7829f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "spotify_df.set_index([\"genre\"],drop = False, inplace = True)\n",
        "for name in to_remove:\n",
        "    type(name)\n",
        "    spotify_df.drop(index = str(name), inplace = True)\n",
        "    \n",
        "spotify_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "c2f71670",
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          "end_time": "2021-09-08T16:52:31.028401",
          "exception": false,
          "start_time": "2021-09-08T16:52:30.909656",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "c2f71670",
        "outputId": "671bc485-3c58-4e3e-ef28-70fc407cb9d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              artist      genre  beats_per_minute  energy  danceability  \\\n",
              "0        Norah Jones  standards               157      30            53   \n",
              "1        Deep Purple       rock               135      79            50   \n",
              "2           Gorillaz        hop               168      69            66   \n",
              "3       Foo Fighters      metal               173      96            43   \n",
              "4  Bruce Springsteen       rock               106      82            58   \n",
              "\n",
              "   loudness  liveness  valence  duration  acousticness  speechiness  \\\n",
              "0       -14        11       68       201            94            3   \n",
              "1       -11        17       81       207            17            7   \n",
              "2        -9         7       52       341             2           17   \n",
              "3        -4         3       37       269             0            4   \n",
              "4        -5        10       87       256             1            3   \n",
              "\n",
              "   popularity  \n",
              "0          71  \n",
              "1          39  \n",
              "2          69  \n",
              "3          76  \n",
              "4          59  "
            ],
            "text/html": [
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              "      <td>Gorillaz</td>\n",
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              "      <th>3</th>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
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              "      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",
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              "      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",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fcbb5446-83fe-4cfd-b229-8d67b1bacf7e');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "spotify_df = spotify_df.reset_index(drop=True)\n",
        "spotify_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c0912b07",
      "metadata": {
        "papermill": {
          "duration": 0.098314,
          "end_time": "2021-09-08T16:52:31.226131",
          "exception": false,
          "start_time": "2021-09-08T16:52:31.127817",
          "status": "completed"
        },
        "tags": [],
        "id": "c0912b07"
      },
      "source": [
        "#### - Como puede ver, se han eliminado los géneros con aquellos que tienen una instancia inferior a 20."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "96686e5a",
      "metadata": {
        "papermill": {
          "duration": 0.936313,
          "end_time": "2021-09-08T16:52:32.259873",
          "exception": false,
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          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "96686e5a",
        "outputId": "2df770d9-69a5-43ee-d374-2da7fd0ef383"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "sns.heatmap(spotify_df.corr(),annot=True,cmap='summer')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "61fc1239",
      "metadata": {
        "papermill": {
          "duration": 0.10111,
          "end_time": "2021-09-08T16:52:32.669724",
          "exception": false,
          "start_time": "2021-09-08T16:52:32.568614",
          "status": "completed"
        },
        "tags": [],
        "id": "61fc1239"
      },
      "source": [
        "### 3.3 Conclusión:\n",
        "\n",
        "#### Característica objetivo: 'Popularidad'\n",
        "Del mapa de calor anterior, aunque no muy extenso, podemos ver que las características más fuertes que se correlacionan con la popularidad son:\n",
        "- sonoridad (17%)\n",
        "- bailabilidad (13%)\n",
        "- energía (12%)\n",
        "- valencia (10%)\n",
        "\n",
        "#### Relaciones de variables:\n",
        "Podemos ver la relación más fuerte entre las variables excl. popularidad son:\n",
        "- sonoridad y energía (74%)\n",
        "- valencia y bailabilidad (53%)\n",
        "- valencia y energía (43%)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1055d1c0",
      "metadata": {
        "papermill": {
          "duration": 0.103411,
          "end_time": "2021-09-08T16:52:32.874316",
          "exception": false,
          "start_time": "2021-09-08T16:52:32.770905",
          "status": "completed"
        },
        "tags": [],
        "id": "1055d1c0"
      },
      "source": [
        "# 4 DATOS DE MODELADO"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9e43717a",
      "metadata": {
        "papermill": {
          "duration": 0.105101,
          "end_time": "2021-09-08T16:52:33.080158",
          "exception": false,
          "start_time": "2021-09-08T16:52:32.975057",
          "status": "completed"
        },
        "tags": [],
        "id": "9e43717a"
      },
      "source": [
        "##  Modelo (Clasificación de popularidad)\n",
        "\n",
        "**Modelo predictor de popularidad:** buscará clasificar el nivel de popularidad de una canción en función de las métricas de características dadas, como se mencionó anteriormente.\n",
        "\n",
        "**Pasos:**\n",
        "1. Necesitamos una codificación en caliente (obtener variables ficticias) para la función de género, artista y popularidad para una mayor precisión.\n",
        "2. Dividir y escalar datos\n",
        "3. Datos del modelo y de entrenamiento"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5086fb50",
      "metadata": {
        "papermill": {
          "duration": 0.102736,
          "end_time": "2021-09-08T16:52:33.287615",
          "exception": false,
          "start_time": "2021-09-08T16:52:33.184879",
          "status": "completed"
        },
        "tags": [],
        "id": "5086fb50"
      },
      "source": [
        "### Paso 1: Una codificación One hot-encode de género y artista:\n",
        "#### - Crea variables ficticias para la columna de género."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "97e2d948",
      "metadata": {
        "papermill": {
          "duration": 0.14904,
          "end_time": "2021-09-08T16:52:33.537654",
          "exception": false,
          "start_time": "2021-09-08T16:52:33.388614",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "97e2d948",
        "outputId": "28c536fe-293b-4871-bdfc-39bbe46656b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   beats_per_minute  energy  danceability  loudness  liveness  valence  \\\n",
              "0               157      30            53       -14        11       68   \n",
              "1               135      79            50       -11        17       81   \n",
              "2               168      69            66        -9         7       52   \n",
              "3               173      96            43        -4         3       37   \n",
              "4               106      82            58        -5        10       87   \n",
              "\n",
              "   duration  acousticness  speechiness  popularity  ...  \\\n",
              "0       201            94            3          71  ...   \n",
              "1       207            17            7          39  ...   \n",
              "2       341             2           17          69  ...   \n",
              "3       269             0            4          76  ...   \n",
              "4       256             1            3          59  ...   \n",
              "\n",
              "   artist_Within Temptation  artist_Wolfmother  artist_Xavier Rudd  \\\n",
              "0                         0                  0                   0   \n",
              "1                         0                  0                   0   \n",
              "2                         0                  0                   0   \n",
              "3                         0                  0                   0   \n",
              "4                         0                  0                   0   \n",
              "\n",
              "   artist_Yann Tiersen  artist_Yes  artist_Youp van 't Hek  \\\n",
              "0                    0           0                       0   \n",
              "1                    0           0                       0   \n",
              "2                    0           0                       0   \n",
              "3                    0           0                       0   \n",
              "4                    0           0                       0   \n",
              "\n",
              "   artist_Yusuf / Cat Stevens  artist_ZZ Top  artist_Zucchero  artist_a-ha  \n",
              "0                           0              0                0            0  \n",
              "1                           0              0                0            0  \n",
              "2                           0              0                0            0  \n",
              "3                           0              0                0            0  \n",
              "4                           0              0                0            0  \n",
              "\n",
              "[5 rows x 604 columns]"
            ],
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>168</td>\n",
              "      <td>69</td>\n",
              "      <td>66</td>\n",
              "      <td>-9</td>\n",
              "      <td>7</td>\n",
              "      <td>52</td>\n",
              "      <td>341</td>\n",
              "      <td>2</td>\n",
              "      <td>17</td>\n",
              "      <td>69</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>173</td>\n",
              "      <td>96</td>\n",
              "      <td>43</td>\n",
              "      <td>-4</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>269</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>76</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>106</td>\n",
              "      <td>82</td>\n",
              "      <td>58</td>\n",
              "      <td>-5</td>\n",
              "      <td>10</td>\n",
              "      <td>87</td>\n",
              "      <td>256</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>59</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 604 columns</p>\n",
              "</div>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Función para crear variables ficticias\n",
        "\n",
        "def dummies(df, column, prefix):\n",
        "    df = df.copy()\n",
        "    dummies = pd.get_dummies(df[column], prefix=prefix)\n",
        "    df = pd.concat([df, dummies], axis=1)\n",
        "    df = df.drop(column, axis=1)\n",
        "    return df\n",
        "\n",
        "spotify_df = dummies(spotify_df, 'genre', 'genre')\n",
        "spotify_df = dummies(spotify_df, 'artist', 'artist')\n",
        "spotify_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6f37bfcc",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-09-07T23:34:04.060955Z",
          "iopub.status.busy": "2021-09-07T23:34:04.060509Z",
          "iopub.status.idle": "2021-09-07T23:34:04.068567Z",
          "shell.execute_reply": "2021-09-07T23:34:04.067063Z",
          "shell.execute_reply.started": "2021-09-07T23:34:04.060919Z"
        },
        "papermill": {
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          "status": "completed"
        },
        "tags": [],
        "id": "6f37bfcc"
      },
      "source": [
        "#### - Clasifique la función de destino: 'popularidad' en contenedores 2x, para ayudar a clasificar mejor nuestros datos para nuestro modelo predictivo.\n",
        "\n",
        "- 1 = 'más popular'\n",
        "- 0 = 'menos popular'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "f57adf52",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  popularity\n",
              "0          1\n",
              "1          0\n",
              "2          1\n",
              "3          1\n",
              "4          0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc7ac55e-b83f-4e0d-b585-8d4f4c43c319\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>popularity</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "\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-fc7ac55e-b83f-4e0d-b585-8d4f4c43c319 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-fc7ac55e-b83f-4e0d-b585-8d4f4c43c319');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "spotify_df['popularity'] = pd.qcut(spotify_df['popularity'], q=2, labels=[0, 1]) \n",
        "spotify_df[['popularity']].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "245ab5dc",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.514\n",
              "1    0.486\n",
              "Name: popularity, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "target_v = spotify_df['popularity'].value_counts(normalize=True).round(3)\n",
        "target_v"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "f1a9fcf5",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ab0ac8c50>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.barplot(x=target_v.index, y=target_v, palette=\"BrBG\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b26088b8",
      "metadata": {
        "papermill": {
          "duration": 0.103785,
          "end_time": "2021-09-08T16:52:34.742576",
          "exception": false,
          "start_time": "2021-09-08T16:52:34.638791",
          "status": "completed"
        },
        "tags": [],
        "id": "b26088b8"
      },
      "source": [
        "### Análisis\n",
        "\n",
        "- Como vemos arriba, tenemos una muy buena división para nuestra variable objetivo. Esto minimizará en gran medida el desequilibrio de clases y permitirá que nuestro modelo sea más preciso."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "14e658d6",
      "metadata": {
        "papermill": {
          "duration": 0.111399,
          "end_time": "2021-09-08T16:52:34.959775",
          "exception": false,
          "start_time": "2021-09-08T16:52:34.848376",
          "status": "completed"
        },
        "tags": [],
        "id": "14e658d6"
      },
      "source": [
        "### Step 2: Splitting & Scaling Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "f8626a60",
      "metadata": {
        "papermill": {
          "duration": 0.11763,
          "end_time": "2021-09-08T16:52:35.185819",
          "exception": false,
          "start_time": "2021-09-08T16:52:35.068189",
          "status": "completed"
        },
        "tags": [],
        "id": "f8626a60"
      },
      "outputs": [],
      "source": [
        "# Elección de variables independientes y dependientes:\n",
        "\n",
        "y = spotify_df.loc[:,'popularity'] #dependant \n",
        "X = spotify_df[['loudness', 'danceability', 'energy', 'valence']] #independant \n",
        "\n",
        "# Split data into training and test sets: 80% training, 20% test split.\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "aad7e41d",
      "metadata": {
        "papermill": {
          "duration": 0.115814,
          "end_time": "2021-09-08T16:52:35.404157",
          "exception": false,
          "start_time": "2021-09-08T16:52:35.288343",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aad7e41d",
        "outputId": "3758b26a-b80b-4ca7-cec6-8946b86d5f41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamaño de la Data:\n",
            "-\n",
            "X_train: (1382, 4) y_train: (1382,)\n",
            "X_test: (346, 4) y_test: (346,)\n"
          ]
        }
      ],
      "source": [
        "print('Tamaño de la Data:')\n",
        "print('-')\n",
        "print('X_train:', X_train.shape, 'y_train:', y_train.shape)\n",
        "print('X_test:', X_test.shape,'y_test:', y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "01d8e54f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-09-08T16:52:35.628824Z",
          "iopub.status.busy": "2021-09-08T16:52:35.628158Z",
          "iopub.status.idle": "2021-09-08T16:52:35.634922Z",
          "shell.execute_reply": "2021-09-08T16:52:35.634170Z"
        },
        "papermill": {
          "duration": 0.123653,
          "end_time": "2021-09-08T16:52:35.635081",
          "exception": false,
          "start_time": "2021-09-08T16:52:35.511428",
          "status": "completed"
        },
        "tags": [],
        "id": "01d8e54f"
      },
      "outputs": [],
      "source": [
        "# Scale the independant variable for relatively normal distribution of the data. \n",
        "\n",
        "X_train = StandardScaler().fit_transform(X_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "62a642fa",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-09-07T23:12:20.469536Z",
          "iopub.status.busy": "2021-09-07T23:12:20.468925Z",
          "iopub.status.idle": "2021-09-07T23:12:20.475209Z",
          "shell.execute_reply": "2021-09-07T23:12:20.474207Z",
          "shell.execute_reply.started": "2021-09-07T23:12:20.469491Z"
        },
        "papermill": {
          "duration": 0.106575,
          "end_time": "2021-09-08T16:52:35.846514",
          "exception": false,
          "start_time": "2021-09-08T16:52:35.739939",
          "status": "completed"
        },
        "tags": [],
        "id": "62a642fa"
      },
      "source": [
        "## Modeling & Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b00ccb23-2c71-4897-b8bd-2bca63289597",
      "metadata": {
        "id": "b00ccb23-2c71-4897-b8bd-2bca63289597"
      },
      "source": [
        "<center><h1 style=\"font-size:230%; font-family:cursive; background:lime; color:black; border-radius:10px 10px; padding:10px;\"> Decision Tree</h1></center>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "cd99e9f5-f62f-496c-82fd-d58ec36dce96",
      "metadata": {
        "id": "cd99e9f5-f62f-496c-82fd-d58ec36dce96"
      },
      "outputs": [],
      "source": [
        "cross_valid_scores = {}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "9f8afccc-52ce-4f21-a83f-0a0d817a6408",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f8afccc-52ce-4f21-a83f-0a0d817a6408",
        "outputId": "5cd55493-163a-4ff8-d7c4-f56e2e197536"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9992764109985528\n",
            "CPU times: user 8.13 ms, sys: 0 ns, total: 8.13 ms\n",
            "Wall time: 9.19 ms\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "parameters = {\n",
        "    \"max_depth\": [3, 5, 7, 9, 11, 13],}\n",
        "\n",
        "# El modelo sin el GridSearchCV\n",
        "model_desicion_tree = DecisionTreeClassifier(\n",
        "    random_state=123,\n",
        "    class_weight='balanced',)\n",
        "\n",
        "model_desicion_tree.fit(X_train, y_train)\n",
        "print(model_desicion_tree.score(X_train, y_train))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Aplicar Hyperparametro GridSearchCV\n",
        "model_desicion_tree = GridSearchCV(\n",
        "    model_desicion_tree, \n",
        "    parameters, \n",
        "    cv=5,\n",
        "    scoring='accuracy',)\n",
        "\n",
        "model_desicion_tree.fit(X_train, y_train)\n",
        "\n",
        "print('-----')\n",
        "print(f'Mejor Paramétro {model_desicion_tree.best_params_}')\n",
        "print(\n",
        "    f'Mean cross-validated accuracy score del best_estimator: ' + \\\n",
        "    f'{model_desicion_tree.best_score_:.3f}'\n",
        ")\n",
        "cross_valid_scores['desicion_tree'] = model_desicion_tree.best_score_\n",
        "print('-----')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tXNq3tc_mbMx",
        "outputId": "7e97d357-77f9-41c9-bff8-59805b50e9c5"
      },
      "id": "tXNq3tc_mbMx",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----\n",
            "Mejor Paramétro {'max_depth': 9}\n",
            "Mean cross-validated accuracy score del best_estimator: 0.561\n",
            "-----\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d426fb14-dbaa-4499-b429-f86f6820daa2",
      "metadata": {
        "id": "d426fb14-dbaa-4499-b429-f86f6820daa2"
      },
      "source": [
        "<center><h1 style=\"font-size:230%; font-family:cursive; background:lime; color:black; border-radius:10px 10px; padding:10px;\">Random Forest</h1></center>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "150f7ac0-31d5-4753-8ce7-9ec96af72034",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "150f7ac0-31d5-4753-8ce7-9ec96af72034",
        "outputId": "15cae6eb-702d-4fa8-f61c-a7bee4a037dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----\n",
            "Best parameters {'max_depth': 5, 'n_estimators': 15}\n",
            "Mean cross-validated accuracy score of the best_estimator: 0.577\n",
            "-----\n",
            "CPU times: user 22.7 s, sys: 128 ms, total: 22.9 s\n",
            "Wall time: 22.8 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "SEED=123\n",
        "parameters = {\n",
        "    \"n_estimators\": [5, 10, 15, 100, 200], \n",
        "    \"max_depth\": [3, 5, 7, 9, 11, 13],\n",
        "}\n",
        "\n",
        "model_random_forest = RandomForestClassifier(\n",
        "    random_state=SEED,\n",
        "    class_weight='balanced',)\n",
        "\n",
        "model_random_forest = GridSearchCV(\n",
        "    model_random_forest, \n",
        "    parameters, \n",
        "    cv=5,\n",
        "    scoring='accuracy',)\n",
        "\n",
        "model_random_forest.fit(X_train, y_train)\n",
        "\n",
        "print('-----')\n",
        "print(f'Best parameters {model_random_forest.best_params_}')\n",
        "print(\n",
        "    f'Mean cross-validated accuracy score of the best_estimator: '+ \\\n",
        "    f'{model_random_forest.best_score_:.3f}'  )\n",
        "cross_valid_scores['random_forest'] = model_random_forest.best_score_\n",
        "print('-----')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cd571cbb-377b-4d02-8e80-307024f3111a",
      "metadata": {
        "id": "cd571cbb-377b-4d02-8e80-307024f3111a"
      },
      "source": [
        "<center><h1 style=\"font-size:230%; font-family:cursive; background:lime; color:black; border-radius:10px 10px; padding:10px;\">AdaBoost</h1></center>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "064a4404-4a34-47b0-a1fa-8a410efe397f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "064a4404-4a34-47b0-a1fa-8a410efe397f",
        "outputId": "978dda79-538e-47e4-c7ba-3e2529cd7cc2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----\n",
            "Best parameters {'learning_rate': 1.0, 'n_estimators': 25}\n",
            "Mean cross-validated accuracy score of the best_estimator: 0.557\n",
            "-----\n",
            "CPU times: user 13 s, sys: 65.3 ms, total: 13.1 s\n",
            "Wall time: 13.1 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "parameters = {\n",
        "    \"n_estimators\": [5, 10, 15, 20, 25, 50, 75, 100], \n",
        "    \"learning_rate\": [0.001, 0.01, 0.1, 1.],\n",
        "}\n",
        "\n",
        "model_adaboost = AdaBoostClassifier(\n",
        "    random_state=SEED,)\n",
        "\n",
        "model_adaboost = GridSearchCV(\n",
        "    model_adaboost, \n",
        "    parameters, \n",
        "    cv=5,\n",
        "    scoring='accuracy',)\n",
        "\n",
        "model_adaboost.fit(X_train, y_train)\n",
        "\n",
        "print('-----')\n",
        "print(f'Best parameters {model_adaboost.best_params_}')\n",
        "print(\n",
        "    f'Mean cross-validated accuracy score of the best_estimator: '+ \\\n",
        "    f'{model_adaboost.best_score_:.3f}')\n",
        "cross_valid_scores['ada_boost'] = model_adaboost.best_score_\n",
        "print('-----')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "646a0797-512c-4abe-8e7b-6bff3ac4b215",
      "metadata": {
        "id": "646a0797-512c-4abe-8e7b-6bff3ac4b215"
      },
      "source": [
        "<center><h1 style=\"font-size:230%; font-family:cursive; background:lime; color:black; border-radius:10px 10px; padding:10px;\">XGBoost</h1></center>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "0c7b4f94-0e47-410c-a27b-d5688b194f1c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0c7b4f94-0e47-410c-a27b-d5688b194f1c",
        "outputId": "433a08c2-3510-4aec-d796-09e6766166f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----\n",
            "Best parameters {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 15}\n",
            "Mean cross-validated accuracy score of the best_estimator: 0.576\n",
            "-----\n",
            "CPU times: user 14.8 s, sys: 223 ms, total: 15.1 s\n",
            "Wall time: 15 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "parameters = {\n",
        "    'max_depth': [3, 5, 7, 9], \n",
        "    'n_estimators': [5, 10, 15, 20, 25, 50, 100],\n",
        "    'learning_rate': [0.01, 0.05, 0.1] }\n",
        "\n",
        "model_xgb = xgb.XGBClassifier(\n",
        "    random_state=SEED,)\n",
        "\n",
        "model_xgb = GridSearchCV(\n",
        "    model_xgb, \n",
        "    parameters, \n",
        "    cv=5,\n",
        "    scoring='accuracy',)\n",
        "\n",
        "model_xgb.fit(X_train, y_train)\n",
        "\n",
        "print('-----')\n",
        "print(f'Best parameters {model_xgb.best_params_}')\n",
        "print(\n",
        "    f'Mean cross-validated accuracy score of the best_estimator: ' + \n",
        "    f'{model_xgb.best_score_:.3f}'\n",
        ")\n",
        "cross_valid_scores['xgboost'] = model_xgb.best_score_\n",
        "print('-----')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a9acd33-2fdf-45fe-b6cb-1ca58f91d791",
      "metadata": {
        "id": "1a9acd33-2fdf-45fe-b6cb-1ca58f91d791"
      },
      "source": [
        "<center><h1 style=\"font-size:230%; font-family:cursive; background:lime; color:black; border-radius:10px 10px; padding:10px;\">Regresión Logística</h1></center>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "97a68f62-4276-429f-8599-265748c3cd35",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97a68f62-4276-429f-8599-265748c3cd35",
        "outputId": "798bf0d9-1e7d-4132-f2ac-a9d10d9937bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----\n",
            "Best parameters {'C': 0.1, 'penalty': 'l1'}\n",
            "Mean cross-validated accuracy score of the best_estimator: 0.557\n",
            "-----\n",
            "CPU times: user 135 ms, sys: 0 ns, total: 135 ms\n",
            "Wall time: 135 ms\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "parameters = {\n",
        "    \"C\": [0.001, 0.01, 0.1, 1.],\n",
        "    \"penalty\": [\"l1\", \"l2\"]\n",
        "}\n",
        "\n",
        "model_logistic_regression = LogisticRegression(\n",
        "    random_state=SEED,\n",
        "    class_weight=\"balanced\", solver=\"liblinear\",)\n",
        "\n",
        "model_logistic_regression = GridSearchCV(\n",
        "    model_logistic_regression, \n",
        "    parameters, \n",
        "    cv=5,\n",
        "    scoring='accuracy',)\n",
        "\n",
        "model_logistic_regression.fit(X_train, y_train)\n",
        "\n",
        "print('-----')\n",
        "print(f'Best parameters {model_logistic_regression.best_params_}')\n",
        "print(\n",
        "    f'Mean cross-validated accuracy score of the best_estimator: ' + \n",
        "    f'{model_logistic_regression.best_score_:.3f}')\n",
        "cross_valid_scores['logistic_regression'] = model_logistic_regression.best_score_\n",
        "print('-----')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9f279150-e944-4f03-bbbe-05d3d6ffdc50",
      "metadata": {
        "id": "9f279150-e944-4f03-bbbe-05d3d6ffdc50"
      },
      "source": [
        "<center><h1 style=\"font-size:230%; font-family:cursive; background:lime; color:black; border-radius:10px 10px; padding:10px;\"> SVC</h1></center>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(frac=0.6)"
      ],
      "metadata": {
        "id": "AQjljgK-qq-P"
      },
      "id": "AQjljgK-qq-P",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dfb1c922-2a3c-4cba-8ad9-c75adf21011f",
      "metadata": {
        "id": "dfb1c922-2a3c-4cba-8ad9-c75adf21011f"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "parameters = {\n",
        "    \"C\": [0.001, 0.01, 0.1, 1.],\n",
        "    \"kernel\": [\"linear\", \"poly\", \"rbf\", \"sigmoid\"],\n",
        "    \"gamma\": [\"scale\", \"auto\"],\n",
        "}\n",
        "\n",
        "model_svc = SVC(\n",
        "    random_state=SEED,\n",
        "    class_weight=\"balanced\", probability=True,)\n",
        "\n",
        "model_svc = GridSearchCV(\n",
        "    model_svc, \n",
        "    parameters, \n",
        "    cv=5, scoring='accuracy',)\n",
        "\n",
        "model_svc.fit(X_train, y_train)\n",
        "\n",
        "print('-----')\n",
        "print(f'Best parameters {model_svc.best_params_}')\n",
        "print(\n",
        "    f'Mean cross-validated accuracy score of the best_estimator: ' + \n",
        "    f'{model_svc.best_score_:.3f}'\n",
        ")\n",
        "cross_valid_scores['svc'] = model_svc.best_score_\n",
        "print('-----')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ddb3b76e-f11e-4db6-9b0e-d6163d861275",
      "metadata": {
        "id": "ddb3b76e-f11e-4db6-9b0e-d6163d861275"
      },
      "source": [
        "<center><h1 style=\"font-size:230%; font-family:cursive; background:lime; color:black; border-radius:10px 10px; padding:10px;\">K-Nearest Neighbors</h1></center>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "b5613d26-6902-4c16-b65c-da2133055e5f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "b5613d26-6902-4c16-b65c-da2133055e5f",
        "outputId": "f1c97434-5c8e-40f6-ca1a-b9aecdf31fd8"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-886700b98eb4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"params = {\\n    'n_neighbors': [3,5,7,9,11,13],\\n    'weights': ['uniform', 'distance'],\\n    'p': [1,2]\\n}\\n\\nmodel_k_neighbors = KNeighborsClassifier()\\n\\nmodel_k_neighbors = GridSearchCV(\\n    model_k_neighbors, \\n    params, \\n    cv=5,\\n    scoring='accuracy',)\\n\\nmodel_k_neighbors.fit(X_train, y_train)\\n\\nprint('-----')\\nprint(f'Best parameters {model_k_neighbors.best_params_}')\\nprint(\\n    f'Mean cross-validated accuracy score of the best_estimator: ' + \\n    f'{model_k_neighbors.best_score_:.3f}'\\n)\\ncross_valid_scores['k_neighbors'] = model_k_neighbors.best_score_\\nprint('-----')\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2115\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2116\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2117\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2118\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<decorator-gen-53>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1191\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1192\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1193\u001b[0;31m             \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1194\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1195\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'KNeighborsClassifier' is not defined"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "params = {\n",
        "    'n_neighbors': [3,5,7,9,11,13],\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'p': [1,2]\n",
        "}\n",
        "\n",
        "model_k_neighbors = KNeighborsClassifier()\n",
        "\n",
        "model_k_neighbors = GridSearchCV(\n",
        "    model_k_neighbors, \n",
        "    params, \n",
        "    cv=5,\n",
        "    scoring='accuracy',)\n",
        "\n",
        "model_k_neighbors.fit(X_train, y_train)\n",
        "\n",
        "print('-----')\n",
        "print(f'Best parameters {model_k_neighbors.best_params_}')\n",
        "print(\n",
        "    f'Mean cross-validated accuracy score of the best_estimator: ' + \n",
        "    f'{model_k_neighbors.best_score_:.3f}'\n",
        ")\n",
        "cross_valid_scores['k_neighbors'] = model_k_neighbors.best_score_\n",
        "print('-----')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b9d77595-7c14-44a5-abaf-24ec8208ac3c",
      "metadata": {
        "id": "b9d77595-7c14-44a5-abaf-24ec8208ac3c"
      },
      "source": [
        "<center><h1 style=\"font-size:230%; font-family:cursive; background:lime; color:black; border-radius:10px 10px; padding:10px;\">Comparación de Resultados de GridSearchCV</h1></center>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "a3b3fa94-70a2-4c57-9310-8f20b4346a7f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a3b3fa94-70a2-4c57-9310-8f20b4346a7f",
        "outputId": "67c4b365-9089-4e2f-9ab8-aba5ce3cdbf1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     cross_valid_score\n",
              "desicion_tree                 0.560762\n",
              "random_forest                 0.577471\n",
              "ada_boost                     0.557178\n",
              "xgboost                       0.576022\n",
              "logistic_regression           0.557207"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-891bc28c-eb4c-4108-92ab-4d396f892f6e\">\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>cross_valid_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>desicion_tree</th>\n",
              "      <td>0.560762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>random_forest</th>\n",
              "      <td>0.577471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada_boost</th>\n",
              "      <td>0.557178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xgboost</th>\n",
              "      <td>0.576022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>logistic_regression</th>\n",
              "      <td>0.557207</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "models=pd.DataFrame(cross_valid_scores, index=['cross_valid_score']).T\n",
        "models"
      ]
    },
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      "id": "fc0e5c7a",
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      },
      "source": [
        "## Salidas de Accuracy del modelo de entrenamiento:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "4b6b716b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-09-08T16:52:36.280156Z",
          "iopub.status.busy": "2021-09-08T16:52:36.279378Z",
          "iopub.status.idle": "2021-09-08T16:52:36.458759Z",
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          "status": "completed"
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        "tags": [],
        "colab": {
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        },
        "id": "4b6b716b",
        "outputId": "5c65044a-407e-4244-c1d0-f0f15c020b1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Model Accuracy Outputs:\n",
            "-\n",
            "Logistic Regression: 56.8 %\n",
            "Decision Tree: 99.93 %\n",
            "Naive Bayes: 55.64 %\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import model_selection, metrics, linear_model, tree \n",
        "# Creando Modelo\n",
        "\n",
        "logr_model = linear_model.LogisticRegression(solver='liblinear')\n",
        "dtree_model = tree.DecisionTreeClassifier()\n",
        "nb_model = GaussianNB()\n",
        "\n",
        "# Entrenando los modelos\n",
        "\n",
        "logr_model.fit(X_train, y_train)\n",
        "dtree_model.fit(X_train, y_train)\n",
        "nb_model.fit(X_train, y_train)\n",
        "\n",
        "# Accuracy de con los Datos de entrenamiento\n",
        "\n",
        "logr_train_acc = logr_model.score(X_train, y_train)\n",
        "dtree_train_acc = dtree_model.score(X_train, y_train)\n",
        "nb_train_acc = nb_model.score(X_train, y_train)\n",
        "\n",
        "print('Training Model Accuracy Outputs:')\n",
        "print('-')\n",
        "print('Logistic Regression:', round(logr_train_acc*100,2),'%')\n",
        "print('Decision Tree:', round(dtree_train_acc*100,2),'%')\n",
        "print('Naive Bayes:', round(nb_train_acc*100,2),'%')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "9ccf9947",
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          "iopub.status.idle": "2021-09-08T16:52:38.101378Z",
          "shell.execute_reply": "2021-09-08T16:52:38.102126Z"
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        "outputId": "9efd928b-eeff-42aa-8410-70772279f7e2"
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      "source": [
        "### Análisis:\n",
        "- Como se ve arriba, parece que el modelo de árbol de decisiones tiene una tasa de precisión realmente alta en comparación con los datos de entrenamiento. Pero esto parece un poco demasiado alto y sospecho que se ajusta demasiado. Validaré esto a través de la validación cruzada k-fold."
      ]
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      },
      "source": [
        "## Validating models with k-fold cross validation:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "6ed6a0eb",
      "metadata": {
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      "outputs": [
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          "text": [
            "k-fold Cross Validation Accuracy Outputs:\n",
            "-\n",
            "Logistic Regression: 56.67 %\n",
            "Decision Tree: 54.35 %\n",
            "Naive Bayes: 55.65 %\n"
          ]
        }
      ],
      "source": [
        "# Validating models with k-fold cross validation method\n",
        "\n",
        "kf = model_selection.KFold(n_splits=10, shuffle=True, random_state=24)\n",
        "\n",
        "accuracy_logr = cross_val_score(logr_model, X_train, y_train, scoring=\"accuracy\", cv=kf)\n",
        "accuracy_dtree = cross_val_score(dtree_model, X_train, y_train, scoring=\"accuracy\", cv=kf)\n",
        "accuracy_nb = cross_val_score(nb_model, X_train, y_train, scoring=\"accuracy\", cv=kf)\n",
        "\n",
        "accuracy_logr = accuracy_logr.mean()\n",
        "accuracy_dtree = accuracy_dtree.mean()\n",
        "accuracy_nb = accuracy_nb.mean()\n",
        "\n",
        "print('k-fold Cross Validation Accuracy Outputs:')\n",
        "print('-')\n",
        "print(\"Logistic Regression:\", round(accuracy_logr*100,2),\"%\")\n",
        "print(\"Decision Tree:\", round(accuracy_dtree*100,2),\"%\")\n",
        "print(\"Naive Bayes:\", round(accuracy_nb*100,2),\"%\")"
      ]
    },
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      ],
      "source": [
        "fig = px.bar(x=['Logistic Regression', 'Decision Tree model', 'Naive Bayes'], \n",
        "             y=[accuracy_logr, accuracy_dtree, accuracy_nb], \n",
        "            color=['Logistic Regression', 'Decision Tree model', 'Naive Bayes'],\n",
        "             labels={'x': 'Model', 'y': 'Accuracy'},\n",
        "            title='Validating models with k-fold cross validation')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d9a726d3",
      "metadata": {
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          "duration": 0.119171,
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          "status": "completed"
        },
        "tags": [],
        "id": "d9a726d3"
      },
      "source": [
        "### Análisis:\n",
        "- Como se vio anteriormente, esto parece un poco más preciso, siendo la mejor Accuracy el modelo de regresión logística. Parecería que con un conjunto de datos más grande, el Árbol de decisiones funciona bien, pero cuando hay datos mínimos, no tanto."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "970edc8f",
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      },
      "source": [
        "## Test Data Output from trained models:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a98edc65",
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        "execution": {
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test data - Model Accuracy Outputs:\n",
            "-\n",
            "Logistic Regression: 53.47 %\n",
            "Decision Tree: 53.47 %\n",
            "Naive Bayes: 53.18 %\n"
          ]
        }
      ],
      "source": [
        "# using the test data for our trained models:\n",
        "\n",
        "print('Test data - Model Accuracy Outputs:')\n",
        "print('-')\n",
        "print('Logistic Regression:', round(logr_model.score(X_test, y_test)*100,2),'%')\n",
        "print('Decision Tree:', round(dtree_model.score(X_test, y_test)*100,2),'%')\n",
        "print('Naive Bayes:', round(nb_model.score(X_test, y_test)*100,2),'%')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3e2d89d0",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-09-08T00:47:51.03704Z",
          "iopub.status.busy": "2021-09-08T00:47:51.036438Z",
          "iopub.status.idle": "2021-09-08T00:47:51.044628Z",
          "shell.execute_reply": "2021-09-08T00:47:51.043359Z",
          "shell.execute_reply.started": "2021-09-08T00:47:51.037Z"
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      "source": [
        "### Análisis y próximos pasos:\n",
        "- Con los datos de prueba, tenemos una coincidencia idéntica con Regresión logística y Árbol de decisiones.\n",
        "- Los índices de Accuracy son muy bajos.\n",
        "- Buscaré incluir ahora todas las variables para ayudar a mejorar la precisión de los modelos."
      ]
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          "exception": false,
          "start_time": "2021-09-08T16:52:40.343419",
          "status": "completed"
        },
        "tags": [],
        "id": "2c1f6614"
      },
      "source": [
        "# --> Model (Popularity Classification) Pt.2 \n",
        "\n",
        "- Using all independant variables to try and achieve a greater accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "252c96b4",
      "metadata": {
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        },
        "tags": [],
        "id": "252c96b4"
      },
      "source": [
        "### Splitting & Scaling Data.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d52c0fc5",
      "metadata": {
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      "source": [
        "# Chosing independant and dependant variables:\n",
        "\n",
        "y_1 = spotify_df.loc[:,'popularity'] #dependant/target \n",
        "X_1 = spotify_df.drop('popularity', axis=1)#independant \n",
        "\n",
        "# Split data into training and test sets: 80% training, 20% test split.\n",
        "\n",
        "X_train_1, X_test_1, y_train_1, y_test_1 = train_test_split(X_1, y_1, test_size=0.2, random_state=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "968b1f7a",
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          "end_time": "2021-09-08T16:52:41.238227",
          "exception": false,
          "start_time": "2021-09-08T16:52:41.096132",
          "status": "completed"
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        "tags": [],
        "id": "968b1f7a",
        "outputId": "6a1abf3c-04db-4535-bd5d-067eddf77c3d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of Data.2:\n",
            "-\n",
            "X_train_1: (1382, 603) y_train_1: (1382,)\n",
            "X_test_1: (346, 603) y_test_1: (346,)\n"
          ]
        }
      ],
      "source": [
        "print('Shape of Data.2:')\n",
        "print('-')\n",
        "print('X_train_1:', X_train_1.shape, 'y_train_1:', y_train_1.shape)\n",
        "print('X_test_1:',X_test_1.shape,'y_test_1:', y_test_1.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6e3b8bd9",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-09-08T16:52:41.518492Z",
          "iopub.status.busy": "2021-09-08T16:52:41.517649Z",
          "iopub.status.idle": "2021-09-08T16:52:41.561761Z",
          "shell.execute_reply": "2021-09-08T16:52:41.560481Z"
        },
        "papermill": {
          "duration": 0.179175,
          "end_time": "2021-09-08T16:52:41.561969",
          "exception": false,
          "start_time": "2021-09-08T16:52:41.382794",
          "status": "completed"
        },
        "tags": [],
        "id": "6e3b8bd9"
      },
      "outputs": [],
      "source": [
        "# Scale the independant variable for relatively normal distribution of the data. \n",
        "\n",
        "X_train_1 = StandardScaler().fit_transform(X_train_1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "16cb9e5b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-09-08T00:53:56.583129Z",
          "iopub.status.busy": "2021-09-08T00:53:56.582649Z",
          "iopub.status.idle": "2021-09-08T00:53:56.589463Z",
          "shell.execute_reply": "2021-09-08T00:53:56.588009Z",
          "shell.execute_reply.started": "2021-09-08T00:53:56.583089Z"
        },
        "papermill": {
          "duration": 0.123521,
          "end_time": "2021-09-08T16:52:41.809212",
          "exception": false,
          "start_time": "2021-09-08T16:52:41.685691",
          "status": "completed"
        },
        "tags": [],
        "id": "16cb9e5b"
      },
      "source": [
        "## Modeling & Training.2\n",
        "\n",
        "- Same a previosuly done with the 3x classification models."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0bbdc4d5",
      "metadata": {
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          "duration": 0.122368,
          "end_time": "2021-09-08T16:52:42.056681",
          "exception": false,
          "start_time": "2021-09-08T16:52:41.934313",
          "status": "completed"
        },
        "tags": [],
        "id": "0bbdc4d5"
      },
      "source": [
        "#### Training Model Accuracy Outputs.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0b662021",
      "metadata": {
        "execution": {
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          "status": "completed"
        },
        "tags": [],
        "id": "0b662021",
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Model Accuracy Outputs.2:\n",
            "-\n",
            "Logistic Regression: 85.53 %\n",
            "Decision Tree: 100.0 %\n",
            "Naive Bayes: 77.71 %\n"
          ]
        }
      ],
      "source": [
        "# Creating Models\n",
        "\n",
        "logr_model_1 = linear_model.LogisticRegression(solver='liblinear')\n",
        "dtree_model_1 = tree.DecisionTreeClassifier()\n",
        "nb_model_1 = GaussianNB()\n",
        "\n",
        "# Training the models\n",
        "\n",
        "logr_model_1.fit(X_train_1, y_train_1)\n",
        "dtree_model_1.fit(X_train_1, y_train_1)\n",
        "nb_model_1.fit(X_train_1, y_train_1)\n",
        "\n",
        "# Accuracy of trained models with training data:\n",
        "\n",
        "logr_train_acc_1 = logr_model_1.score(X_train_1, y_train_1)\n",
        "dtree_train_acc_1 = dtree_model_1.score(X_train_1, y_train_1)\n",
        "nb_train_acc_1 = nb_model_1.score(X_train_1, y_train_1)\n",
        "\n",
        "print('Training Model Accuracy Outputs.2:')\n",
        "print('-')\n",
        "print('Logistic Regression:', round(logr_train_acc_1*100,2),'%')\n",
        "print('Decision Tree:', round(dtree_train_acc_1*100,2),'%')\n",
        "print('Naive Bayes:', round(nb_train_acc_1*100,2),'%')"
      ]
    },
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      "execution_count": null,
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      ],
      "source": [
        "fig = px.bar(x=['Logistic Regression', 'Decision Tree model', 'Naive Bayes'], \n",
        "             y=[logr_train_acc_1, dtree_train_acc_1, nb_train_acc_1], \n",
        "            color=['Logistic Regression', 'Decision Tree model', 'Naive Bayes'],\n",
        "             labels={'x': 'Model', 'y': 'Accuracy'},\n",
        "            title='Accuracy of trained models with tusing all independant variables on training data')\n",
        "fig.show()"
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    },
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      "source": [
        "### Analysis:\n",
        "- As seen above it appears that the accuracy has increased quite a bit for all models, again, Decision Tree Model has a really high accuracy rate against the training data. So will validate this through k-fold cross validation."
      ]
    },
    {
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          "status": "completed"
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      "source": [
        "#### Validating models with k-fold cross validation.2"
      ]
    },
    {
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            "k-fold Cross Validation Accuracy Outputs.2:\n",
            "-\n",
            "Logistic Regression: 69.46 %\n",
            "Decision Tree: 61.79 %\n",
            "Naive Bayes: 63.81 %\n"
          ]
        }
      ],
      "source": [
        "# Validating new models with k-fold cross validation method\n",
        "\n",
        "accuracy_logr_1 = cross_val_score(logr_model_1, X_train_1, y_train_1, scoring=\"accuracy\", cv=kf)\n",
        "accuracy_dtree_1 = cross_val_score(dtree_model_1, X_train_1, y_train_1, scoring=\"accuracy\", cv=kf)\n",
        "accuracy_nb_1 = cross_val_score(nb_model_1, X_train_1, y_train_1, scoring=\"accuracy\", cv=kf)\n",
        "\n",
        "accuracy_logr_1 = accuracy_logr_1.mean()\n",
        "accuracy_dtree_1 = accuracy_dtree_1.mean()\n",
        "accuracy_nb_1 = accuracy_nb_1.mean()\n",
        "\n",
        "print('k-fold Cross Validation Accuracy Outputs.2:')\n",
        "print('-')\n",
        "print(\"Logistic Regression:\", round(accuracy_logr_1*100,2),\"%\")\n",
        "print(\"Decision Tree:\", round(accuracy_dtree_1*100,2),\"%\")\n",
        "print(\"Naive Bayes:\", round(accuracy_nb_1*100,2),\"%\")"
      ]
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      "source": [
        "fig = px.bar(x=['Logistic Regression', 'Decision Tree model', 'Naive Bayes'], \n",
        "             y=[accuracy_logr_1, accuracy_dtree_1, accuracy_nb_1], \n",
        "            color=['Logistic Regression', 'Decision Tree model', 'Naive Bayes'],\n",
        "             labels={'x': 'Model', 'y': 'Accuracy'},\n",
        "            title='Validating new models with k-fold cross validation')\n",
        "fig.show()"
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      "source": [
        "### Analysis:\n",
        "- With the increase in data for the feature variables, we have increased validation of the models, with logistic regression coming out on top and decision trees coming in as least favourable."
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      "source": [
        "#### Test Data Output from trained models.2"
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          "text": [
            "Test data - Model Accuracy Outputs.2:\n",
            "-\n",
            "Logistic Regression: 55.49\n",
            "Decision Tree: 50.0\n",
            "Naive Bayes: 62.14\n"
          ]
        }
      ],
      "source": [
        "# using the test data for our trained models:\n",
        "\n",
        "print('Test data - Model Accuracy Outputs.2:')\n",
        "print('-')\n",
        "print('Logistic Regression:', round(logr_model_1.score(X_test_1, y_test_1)*100,2))\n",
        "print('Decision Tree:', round(dtree_model_1.score(X_test_1, y_test_1)*100,2))\n",
        "print('Naive Bayes:', round(nb_model_1.score(X_test_1, y_test_1)*100,2))"
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      "source": [
        "## Summary of Model Accuracy:"
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        "#### Model (Popularity Classification) \n",
        "- With 'loudness', 'danceability', 'energy', 'valence' as independant variables due to highest correlation.\n",
        "\n",
        "Trained Model:\n",
        "- LR Accuracy: 56%\n",
        "- DT Accuracy: 99%\n",
        "- NB Accuracy: 55%\n",
        "\n",
        "K-fold Cross Val:\n",
        "- LR Accuracy: 56%\n",
        "- DT Accuracy: 54%\n",
        "- NB Accuracy: 55%\n",
        "\n",
        "Test Data Result:\n",
        "- LR Accuracy: 53%\n",
        "- DT Accuracy: 53%\n",
        "- NB Accuracy: 53%\n",
        "\n",
        "\n",
        "#### Model (Popularity Classification) pt.2\n",
        "- With all independant data variables included.\n",
        "\n",
        "Trained Model.2:\n",
        "- LR Accuracy: 85%\n",
        "- DT Accuracy: 100%\n",
        "- NB Accuracy: 77%\n",
        "\n",
        "K-fold Cross Val.2:\n",
        "- LR Accuracy: 69%\n",
        "- DT Accuracy: 62%\n",
        "- NB Accuracy: 63%\n",
        "\n",
        "Test Data Result.2:\n",
        "- LR Accuracy: 55%\n",
        "- DT Accuracy: 50%\n",
        "- NB Accuracy: 62%"
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      "source": [
        "# 5 FINAL CONCLUSIONS"
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      "source": [
        "1. The accuracy of the data sets are quite vaired throughout the models used.\n",
        "2. Overall, it seems the models perform better with more data than less. My assumption is this is due to the lack of strong correlation between what makes a song popular. As well as a shift in music trends over the years which makes prediction more challenging. In retrospect I could have split the modeling over the year blocks created. This may produce a more accurate model and relevant model.\n",
        "3. In conclusion, the best performing in general, either the **Linear Regression** should be used with a larger dataset (+1000) with an 85% accuracy . And **Naive Bayes** models should be used with a smaller data set with a 62% accuracy."
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