ProgettoIDS_Pedemonte_Giacomo_S4861715_Da_presentare.ipynb

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    {
      "cell_type": "markdown",
      "id": "6a6aff0a",
      "metadata": {
        "id": "6a6aff0a"
      },
      "source": [
        "# ***Progetto Introduzione alla Data Science***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bf9fb287",
      "metadata": {
        "id": "bf9fb287"
      },
      "source": [
        "\n",
        "***Pedemonte Giacomo S4861715***\n",
        "\n",
        "Progetto di Introduzione alla Data Science riguardante l'analisi del dataset contenente le 50 canzoni migliori del 2019."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ac179ea",
      "metadata": {
        "id": "9ac179ea"
      },
      "source": [
        "### **SCOPO DELL'ANALISI**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a66ca4a0",
      "metadata": {
        "id": "a66ca4a0"
      },
      "source": [
        "In questa analisi quindi avremo in mente uno scopo: risponderci alla seguente domanda:\n",
        "- ***quali sono le caratteristiche che rendono una canzone di successo?***\n",
        "\n",
        "Per poter risponderci a questa domanda il progetto si suddivide in questi 4 passi:\n",
        "- ***Descrizione Dataset***;\n",
        "- ***Esplorazione dei Dati***;\n",
        "- ***Visualizzazione e Analisi dei Dati***;\n",
        "- ***MACHINE LEARNING***;"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ee330ee9",
      "metadata": {
        "id": "ee330ee9"
      },
      "source": [
        "***Importazione librerie per ambiente di lavoro:***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "532fec45",
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        "id": "532fec45"
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      "outputs": [],
      "source": [
        "#librerie di visualizzazione\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "import plotly.express as px #importazione libreria comoda per la visualizzazione di box plot\n",
        "import plotly.graph_objects as go #libreria per grafici 3d\n",
        "\n",
        "\n",
        "#librerie per il machine learning\n",
        "from sklearn import preprocessing \n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn import preprocessing\n",
        "import random"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9d7177c0",
      "metadata": {
        "id": "9d7177c0"
      },
      "source": [
        "## ***DESCRIZIONE DATASET***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e14ccc5",
      "metadata": {
        "id": "4e14ccc5"
      },
      "source": [
        "Importazione e visualizzazione della \"testa\" del database contenente la playlist spotify: top50 del 2019."
      ]
    },
    {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Position                     Track.Name    Artist.Name           Genre  \\\n",
              "0         1                       Senorita   Shawn Mendes    canadian pop   \n",
              "1         2                          China       Anuel AA  reggaeton flow   \n",
              "2         3  boyfriend (with Social House)  Ariana Grande       dance pop   \n",
              "\n",
              "   Beats.Per.Minute  Energy  Danceability  Loudness..dB..  Liveness  Valence.  \\\n",
              "0               117      55            76              -6         8        75   \n",
              "1               105      81            79              -4         8        61   \n",
              "2               190      80            40              -4        16        70   \n",
              "\n",
              "   Length.  Acousticness..  Speechiness.  Popularity  \n",
              "0      191               4             3          79  \n",
              "1      302               8             9          92  \n",
              "2      186              12            46          85  "
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              "      <td>70</td>\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "top50 = pd.read_csv('top50.csv')\n",
        "top50.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "4d575a07",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4d575a07",
        "outputId": "f8d577a7-0c76-4040-a547-725e4811bf77"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "top50.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e196d851",
      "metadata": {
        "id": "e196d851"
      },
      "source": [
        "### **DESCRIZIONE DELLE FEATURES**\n",
        "\n",
        "Abbiamo quindi ***le migliori 50 canzoni del 2019 su spotify e 14 attributi legati ad esse***:\n",
        "\n",
        "- **Position** = identificativo della canzone che rappresenta la posizione nella top50 playlist\n",
        "\n",
        "- **Track.Name** = nome della traccia\n",
        "\n",
        "- **Artist.name** = nome dell'artista\n",
        "\n",
        "- **Genre** = genere della traccia\n",
        "\n",
        "- **Beats.Per.Minute** = il \"tempo\" della canzone. \n",
        "\n",
        "- **Energy** = l'energia di una canzone - maggiore è il valore più la canzone ne ha\n",
        "\n",
        "- **Danceability** = maggiore è il valore, più è facile da ballare la canzone\n",
        "\n",
        "- **Loudness..dB** = maggiore è il valore, maggiore è la \"rumorosità\" della canzone\n",
        "\n",
        "- **Liveness** = più è alto più è come se fosse una registrazione dal vivo..\n",
        "\n",
        "- **Valence** = piu è alto il valore, maggiore è il mood positivo della canzone.\n",
        "\n",
        "- **Length** = lunghezza della canzone/durata.\n",
        "\n",
        "- **Acousticness** = maggiore è il valore, più la canzone è \"acustica\"\n",
        "\n",
        "- **Speechness** = maggiore è il valore, maggiore sarà il numero delle parole utilizzate all'interno della canzone.\n",
        "\n",
        "- **Popularity** = più è alto il valore, più la canzone sarà popolare."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***PULIZIA DEI DATI***"
      ],
      "metadata": {
        "id": "NjQenG2wSWQ1"
      },
      "id": "NjQenG2wSWQ1"
    },
    {
      "cell_type": "markdown",
      "id": "32acaea9",
      "metadata": {
        "id": "32acaea9"
      },
      "source": [
        "Prima di tutto, dalla head di prima notiamo che certi attributi vanno \"puliti\" da caratteri di punteggiatura in eccesso:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "34f936f3",
      "metadata": {
        "id": "34f936f3"
      },
      "outputs": [],
      "source": [
        "top50.rename(columns={\"Track.Name\":\"Track\",\"Artist.Name\":\"Artist\",\"Beats.Per.Minute\":\"BPM\",\"Loudness..dB..\": \"Loudness\", \"Valence.\": \"Valence\", \"Length.\":\"Length\", \"Acousticness..\":\"Acousticness\",\"Speechiness.\":\"Speechiness\"}, inplace = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a6ce34d",
      "metadata": {
        "id": "8a6ce34d"
      },
      "source": [
        "## ***ESPLORAZIONE DEI DATI***"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***SUDDIVISIONE FEATURES QUALITATIVE E QUANTITATIVE***"
      ],
      "metadata": {
        "id": "81Y6fOEpSDmq"
      },
      "id": "81Y6fOEpSDmq"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "fad8b7a5",
      "metadata": {
        "id": "fad8b7a5"
      },
      "outputs": [],
      "source": [
        "quantitative_top50 = top50[['Position','BPM','Energy','Danceability','Loudness','Liveness','Valence','Length','Acousticness','Speechiness','Popularity']]\n",
        "qualitative_top50 = top50[['Track','Artist','Genre']]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1dcf2eb8",
      "metadata": {
        "id": "1dcf2eb8"
      },
      "source": [
        "Visualizziamo con il metodo describe() di pandas alcuni dati statistici come percentile, media e std dei valori numerici del dataframe."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "7d3e452c",
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          "height": 300
        },
        "id": "7d3e452c",
        "outputId": "857acad7-55f3-4ce9-a39f-69ec65b0bfca"
      },
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Position         BPM     Energy  Danceability   Loudness   Liveness  \\\n",
              "count  50.00000   50.000000  50.000000      50.00000  50.000000  50.000000   \n",
              "mean   25.50000  120.060000  64.060000      71.38000  -5.660000  14.660000   \n",
              "std    14.57738   30.898392  14.231913      11.92988   2.056448  11.118306   \n",
              "min     1.00000   85.000000  32.000000      29.00000 -11.000000   5.000000   \n",
              "25%    13.25000   96.000000  55.250000      67.00000  -6.750000   8.000000   \n",
              "50%    25.50000  104.500000  66.500000      73.50000  -6.000000  11.000000   \n",
              "75%    37.75000  137.500000  74.750000      79.75000  -4.000000  15.750000   \n",
              "max    50.00000  190.000000  88.000000      90.00000  -2.000000  58.000000   \n",
              "\n",
              "         Valence      Length  Acousticness  Speechiness  Popularity  \n",
              "count  50.000000   50.000000     50.000000    50.000000   50.000000  \n",
              "mean   54.600000  200.960000     22.160000    12.480000   87.500000  \n",
              "std    22.336024   39.143879     18.995553    11.161596    4.491489  \n",
              "min    10.000000  115.000000      1.000000     3.000000   70.000000  \n",
              "25%    38.250000  176.750000      8.250000     5.000000   86.000000  \n",
              "50%    55.500000  198.000000     15.000000     7.000000   88.000000  \n",
              "75%    69.500000  217.500000     33.750000    15.000000   90.750000  \n",
              "max    95.000000  309.000000     75.000000    46.000000   95.000000  "
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              "      <th>count</th>\n",
              "      <td>50.00000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.00000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>25.50000</td>\n",
              "      <td>120.060000</td>\n",
              "      <td>64.060000</td>\n",
              "      <td>71.38000</td>\n",
              "      <td>-5.660000</td>\n",
              "      <td>14.660000</td>\n",
              "      <td>54.600000</td>\n",
              "      <td>200.960000</td>\n",
              "      <td>22.160000</td>\n",
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              "      <th>std</th>\n",
              "      <td>14.57738</td>\n",
              "      <td>30.898392</td>\n",
              "      <td>14.231913</td>\n",
              "      <td>11.92988</td>\n",
              "      <td>2.056448</td>\n",
              "      <td>11.118306</td>\n",
              "      <td>22.336024</td>\n",
              "      <td>39.143879</td>\n",
              "      <td>18.995553</td>\n",
              "      <td>11.161596</td>\n",
              "      <td>4.491489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>29.00000</td>\n",
              "      <td>-11.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.25000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>55.250000</td>\n",
              "      <td>67.00000</td>\n",
              "      <td>-6.750000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>38.250000</td>\n",
              "      <td>176.750000</td>\n",
              "      <td>8.250000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>86.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>25.50000</td>\n",
              "      <td>104.500000</td>\n",
              "      <td>66.500000</td>\n",
              "      <td>73.50000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>55.500000</td>\n",
              "      <td>198.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>88.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>37.75000</td>\n",
              "      <td>137.500000</td>\n",
              "      <td>74.750000</td>\n",
              "      <td>79.75000</td>\n",
              "      <td>-4.000000</td>\n",
              "      <td>15.750000</td>\n",
              "      <td>69.500000</td>\n",
              "      <td>217.500000</td>\n",
              "      <td>33.750000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>90.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.00000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>90.00000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>309.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>95.000000</td>\n",
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          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "quantitative_top50.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c8f36da1",
      "metadata": {
        "id": "c8f36da1"
      },
      "source": [
        "Per le caratteristiche qualitative la describe() ci restituisce una tabella contenente i valori unici, i dati con frequenza più alta."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "c6fe38a3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "c6fe38a3",
        "outputId": "1a570087-ae2d-4192-9e23-a920c1d96473"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Track      Artist      Genre\n",
              "count         50          50         50\n",
              "unique        50          38         21\n",
              "top     Senorita  Ed Sheeran  dance pop\n",
              "freq           1           4          8"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Track</th>\n",
              "      <th>Artist</th>\n",
              "      <th>Genre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>50</td>\n",
              "      <td>38</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Senorita</td>\n",
              "      <td>Ed Sheeran</td>\n",
              "      <td>dance pop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\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-138df98e-1ee5-43da-8995-7c043634d6be');\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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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "qualitative_top50.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bc86d5c9",
      "metadata": {
        "id": "bc86d5c9"
      },
      "source": [
        "Notiamo da queste describe che non ci sono dati mancanti nel nostro dataset dato che tutti gli attributi hanno count di 50."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2177c37e",
      "metadata": {
        "id": "2177c37e"
      },
      "source": [
        "## ***VISUALIZZAZIONE  E ANALISI DEI DATI***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cb7b8c5f",
      "metadata": {
        "id": "cb7b8c5f"
      },
      "source": [
        "Visualizziamo alcune delle features, per estrapolare interessanti informazioni. Per esempio:\n",
        "\n",
        "- quale è la distribuzione della popolarità tra le canzoni presenti in classifica?\n",
        "- qual è la popolarità in base al sesso dell'artista?\n",
        "- qual è il genere più popolare?\n",
        "- qual è l'artista più popolare?\n",
        "- Che diversità ci sono tra l'analizzare il genere ed il macrogenere?\n",
        "- Come si può visualizzare la \"distribuzione\" del genere e del macro-genere in classifica? box plot\n",
        "\n",
        "Tutto questo per continuare a provare a risponderci alla domanda iniziale: riusciamo a capire cosa rende una canzone popolare?\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_artista = top50['Artist'].value_counts()\n",
        "\n",
        "font1 = {'family':'serif','color':'blue','size':20}\n",
        "font2 = {'family':'serif','color':'darkred','size':15}\n",
        "\n",
        "plt.title(\"Canzoni per Artista\", fontdict = font1)\n",
        "plt.xlabel(\"Artista\", fontdict = font2)\n",
        "plt.ylabel(\"Numero Canzoni\", fontdict = font2)\n",
        "\n",
        "num_artista.plot(kind = 'bar',figsize = (15,5))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "RN2lklMYfcwr",
        "outputId": "5c5ad521-d4da-4bd8-c821-a62631127bdd"
      },
      "id": "RN2lklMYfcwr",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ed Sheeran domina la classifica e presenta 4 canzoni all'interno di essa: ma sarà anche l'artista più popolare?"
      ],
      "metadata": {
        "id": "dJjNuD0zfh5n"
      },
      "id": "dJjNuD0zfh5n"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "99d77c57",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "99d77c57",
        "outputId": "94de28ce-929e-4c77-924d-525452d3acdf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "popolarità_artista = top50.groupby('Artist')['Popularity'].mean().sort_values(ascending = True)\n",
        "\n",
        "plt.title(\"Popolarità Media Canzoni dell'Artista\", fontdict = font1)\n",
        "plt.xlabel(\"Artista\", fontdict = font2)\n",
        "plt.ylabel(\"Popolarità Media Canzoni\", fontdict = font2)\n",
        "\n",
        "popolarità_artista.plot(kind = 'bar',figsize = (15,5))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cad3001a",
      "metadata": {
        "id": "cad3001a"
      },
      "source": [
        "Notiamo come Ed Sheeran anche se è il cantante che compare di più nella playlist non è quello che ha la media della popolarità delle canzoni più alta, questo primato se lo aggiudica bad bunny con una popolarità media molto alta(circa 92).\n",
        "\n",
        "Inoltre Shawn Mendes, che è l'artista che presenta la canzone posizionata al primo posto in classifica è l'artista che ha la popolarità media delle sue canzoni più bassa di tutti."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***DISTRIBUZIONE DELLA POPOLARITA'***"
      ],
      "metadata": {
        "id": "YPj7qWVtm-Mo"
      },
      "id": "YPj7qWVtm-Mo"
    },
    {
      "cell_type": "markdown",
      "id": "2c95b7ca",
      "metadata": {
        "id": "2c95b7ca"
      },
      "source": [
        "Partiamo visualizzando ed analizzando quindi la distribuzione della popolarità nella nostra classifica:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "f9e7c306",
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "f9e7c306",
        "outputId": "0b9d7295-5e40-434c-a2d8-f54f8998cc0f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Popularity = top50['Popularity']\n",
        "Popularity = Popularity.value_counts(bins=5, normalize = True, sort = False)\n",
        "\n",
        "plt.title(\"Distribuzione della Popolarità\", fontdict = font1)\n",
        "plt.xlabel(\"Popolarità\", fontdict = font2)\n",
        "plt.ylabel(\"Percentuale di canzoni\", fontdict = font2)\n",
        "\n",
        "Popularity.plot(kind = 'bar',figsize = (15,5))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "34f79821",
      "metadata": {
        "id": "34f79821"
      },
      "source": [
        "Possiamo quindi notare da questo istogramma sopra riportato che il 50% delle canzoni ha una popolarità compresa tra 85 e 90, ma solo il 25% delle canzoni ha una popolarità superiore al 90% . D'altro canto, solo 1 canzone ha come valore di popolarità un valore minore di 75.\n",
        "\n",
        "Quindi la maggioranza delle canzoni presenti nel nostro dataset ha popolarità alta."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e01a114e",
      "metadata": {
        "id": "e01a114e"
      },
      "source": [
        "Verifichiamo come si distribuisce la **POPOLARITA'** in base al *genere*:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "0772b895",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "0772b895",
        "outputId": "2d1976e6-2a45-4460-c16c-2bc6460a9bad"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                            \n",
              "var gd = document.getElementById('71c39b75-7482-4cc0-b457-2b1d69737a36');\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = px.box(top50, x=\"Genre\", y=\"Popularity\", color=\"Genre\", title=\"DISTRIBUZIONE POPOLARITA' in base al GENERE\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f2bef687",
      "metadata": {
        "id": "f2bef687"
      },
      "source": [
        "Vediamo come sia il canadian pop il genere ad avere una la distribuzione di popolarità minore mentre prevalentemente tutti gli altri generi hanno una distribuzione nettamente superiore.\n",
        "\n",
        "Rap, electropop, e raggaeton risultano avere la mediana di popolarità più alta rispetto agli altri generi."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7959c3ac",
      "metadata": {
        "id": "7959c3ac"
      },
      "source": [
        "Possiamo rieffettuare questo tipo di visualizzazione per vedere la distribuzione del **POSIZIONAMENTO** in base al genere:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "f91ed8d3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "f91ed8d3",
        "outputId": "4855ce42-a325-414c-88ef-78473c9d5333"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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              "                            \n",
              "var gd = document.getElementById('5cf1474e-b6f2-4738-88ea-f8247ea19b31');\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = px.box(top50, x=\"Genre\", y=\"Position\", color=\"Genre\",title=\"DISTRIBUZIONE POSIZIONE in base al GENERE\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "da82e94e",
      "metadata": {
        "id": "da82e94e"
      },
      "source": [
        "Notiamo subito come, nonostante la poca popolarità del genere riscontrata nel box plot precedente, il canadian pop contenga la canzone prima in classifica e ha un valore di mediana di posizionamento molto alto.\n",
        "\n",
        "Possiamo inoltre vedere che il pop è il genere con la varianza maggiore di posizionamenti delle canzoni che si distribuiscono pressochè su tutta la classificia. \n",
        "\n",
        "Questo in un modo simile alle canzoni appartenenti al genere dance pop anche se la mediana del pop risulta essere più bassa e quindi significa posizionamenti generalmente più bassi del pop rispetto al dance pop."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***AGGIUNTA MACRO GENERE***"
      ],
      "metadata": {
        "id": "ugOEmaxWTZ8r"
      },
      "id": "ugOEmaxWTZ8r"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dae6d082",
        "outputId": "b609582a-343c-49c6-dc7e-fe7166cad21a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['canadian pop', 'reggaeton flow', 'dance pop', 'pop', 'dfw rap',\n",
              "       'trap music', 'country rap', 'electropop', 'reggaeton',\n",
              "       'panamanian pop', 'canadian hip hop', 'latin', 'escape room',\n",
              "       'pop house', 'australian pop', 'edm', 'atl hip hop', 'big room',\n",
              "       'boy band', 'r&b en espanol', 'brostep'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "top50.Genre.unique()"
      ],
      "id": "dae6d082"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "79a92295"
      },
      "outputs": [],
      "source": [
        "top50['MacroGenere'] = 'Other'\n",
        "top50.loc[(top50['Genre'] == 'pop') | (top50['Genre'] == 'canadian pop') | (top50['Genre'] == 'panamanian pop') | (top50['Genre'] == 'australian pop') | (top50['Genre'] == 'pop house') | (top50['Genre'] == 'dance pop') | (top50['Genre'] == 'electropop'), 'MacroGenere'] = 'Pop'\n",
        "top50.loc[(top50['Genre'] == 'dfw rap') | (top50['Genre'] == 'trap music') |(top50['Genre'] == 'country rap'), 'MacroGenere'] = 'Rap'\n",
        "top50.loc[(top50['Genre'] == 'reggaeton flow') | (top50['Genre'] == 'reggaeton') | (top50['Genre'] == 'latin') | (top50['Genre'] == 'r&b en espanol'), 'MacroGenere'] = 'Reggaeton'"
      ],
      "id": "79a92295"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "4ecb7e5e",
        "outputId": "e7a77d3e-414d-4226-d375-ae4dfec60a36"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "num_macro_genere = top50['MacroGenere'].value_counts()\n",
        "\n",
        "font1 = {'family':'serif','color':'blue','size':20}\n",
        "font2 = {'family':'serif','color':'darkred','size':15}\n",
        "\n",
        "plt.title(\"Canzoni per Macrogenere\", fontdict = font1)\n",
        "plt.xlabel(\"MacroGenere\", fontdict = font2)\n",
        "plt.ylabel(\"Numero Canzoni\", fontdict = font2)\n",
        "\n",
        "num_macro_genere.plot(kind = 'bar',figsize = (15,5))\n",
        "plt.show()"
      ],
      "id": "4ecb7e5e"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1197a59f"
      },
      "source": [
        "Con una semplice describe come precedentemente utilizzata per gli altri metodi qualitativi, questa volta, dopo il raggruppamento effettuato, si nota subito come il macro genere più frequente(che quindi contiene più canzoni) è quello pop con ben 23 canzoni presenti in classifica."
      ],
      "id": "1197a59f"
    },
    {
      "cell_type": "markdown",
      "id": "9cda5338",
      "metadata": {
        "id": "9cda5338"
      },
      "source": [
        "Ed ora visualizziamo le **distribuzioni** precedentemente discusse per il genere in base al **MACRO genere**:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "dceb94c5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "dceb94c5",
        "outputId": "608a2d8b-600a-42b7-e490-69ecf67d426d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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          "metadata": {}
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      ],
      "source": [
        "fig = px.box(top50, x=\"MacroGenere\", y=\"Popularity\", color=\"MacroGenere\", title=\"DISTRIBUZIONE POPOLARITA' in base al MACROGENERE\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f559eb0c",
      "metadata": {
        "id": "f559eb0c"
      },
      "source": [
        "Vediamo come sia il pop il macro genere ad avere la distribuzione più \"corposa\": da notare come contenga sia il valore più basso di popolarità(70) che la canzone con il valore più alto(95).\n",
        "\n",
        "Mentre il rap è il macrogenere che ha la distribuzione della popolarità delle canzoni più alta."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "76690be4",
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          "height": 542
        },
        "id": "76690be4",
        "outputId": "4ef0f51a-27e8-4efa-fd6d-a19e082f4fa4"
      },
      "outputs": [
        {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = px.box(top50, x=\"MacroGenere\", y=\"Position\", color=\"MacroGenere\", title=\"DISTRIBUZIONE POSIZIONAMENTO in base al MACROGENERE\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d7af907f",
      "metadata": {
        "id": "d7af907f"
      },
      "source": [
        "Il Macro genere rap spicca anche in questo box plot distinguendosi come il macro genere con varianza minore(anche dovuto dal numero minore di canzoni presenti rispetto agli altri macro generi) e posizionamento mediano maggiore.\n",
        "\n",
        "Quindi il macro genere rap è risultato essere il più popolare e il posizionato meglio."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ae237f63",
      "metadata": {
        "id": "ae237f63"
      },
      "source": [
        "### ***CORRELAZIONE ALLA POPOLARITA***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9921c3d",
      "metadata": {
        "id": "c9921c3d"
      },
      "source": [
        "Ora ci chiediamo, c'e' qualche correlazione tra popolazione e qualcuna delle altre features riportate dal dataset? \n",
        "Ad esempio, la lunghezza della canzone, influenza la popolarità delle canzoni? \n",
        "\n",
        "Verifichiamolo, calcolando l'**indice di correlazione di Pearson** tra le varie features a disposizione e la popolarità."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cbdfa854",
      "metadata": {
        "id": "cbdfa854"
      },
      "source": [
        "Per provare a vedere quindi se c'è una correlazione tra alcune della nostra features e la popolarità visualizziamo la correlazione tra le features con una heatmap e quindi un grafico che segna con colori più caldi le correlazioni più forti tra loro."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "d8bcdac8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "d8bcdac8",
        "outputId": "2b4a56ab-9cd2-4628-9e06-8cd12fea6070"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6b68d48c90>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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As0OvugOBFWb2PfA28FtJTUPHhd+GsHLxZjrnnMsQNWpWSI0HAElPA92AXEkLiHrI1QIws4eAN4DfA/OAVcC5YVuepFuAyeFQN5tZSR0hUuKFkXPOZYgKan4DwMzOKGW7AZcUs20YMKzCMkMVKowk5QOziPL0GXCOma3ajP23B+4zs1MldQC2N7M3wrYTgHZmNnALZN1tRcyMJx+9i5lTx1G7Tl0u6HsjLXdvu0m8F/71AOPfe52VK3/m4WfGbrRt0ofv8PIzj4Bg55Z7cOGVt1ZW9l2Gq8iaUVVTZQoj4Fcz6wAg6UngQuDuVHc2s++AU8NqB6AzUTUTMxtF1P7pXLnMnDqeRd9/wx0Pvsj8zz9h5EMDueGfIzaJ16HLYRz1+9P468V/2Cj8h+++4bV/j+D6gY/SoGFjfvqx3K0bbitSkTWjqqaqdmD4AGglKVvSy2EoigmS9gGQ1FXSjLBMl9RIUktJn0iqDdwMnB62ny6ph6TBYd+WkkaHY74raecQPiIMfTFe0peSTi02d26rNX3S+xzS7Tgk0apNe1at/Jkf85ZuEq9Vm/Y0yc7dJPz9/7zMkb//Iw0aNgagcZPsLZ5nV33UqKmUl0xTlWpGAEiqSTQMxVvATcB0MztJ0hHASKJaz1XAJWY2TlJDYHXh/ma2VtINQGczuzQcs0csifuBx83scUnnAfcBJ4VtLYBDgbZENakXttyZuky0PG8J2bnNi9ab5mzL8rzFSQueZH747hsAbr3mfAoKCjipe0/26XjwFsmrq36yalfV+kP5VaUzqydpBjAF+AZ4jKhgeALAzEYDOZIaA+OAuyX1BZqY2frNSOcg4Knw/omQRqGXzazAzGYDzTfZE5DUS9IUSVNefm74ZiTrHBQU5LPo+2+55taHuejKWxkxZAArf/k53dlyGUI1lPKSaapSzajonlEhKfkFNbOBkl4n6nY4TtIxxGpH5bAmnnwxaRc91ezDAW0d/vvGc7z/n5cB2LV1O/KWLiratnzZYppmb5vysZrmbMvue+xFzZo1adZ8B5pvvzOLvv+G3VrvVeH5dtWPsqpS/aFiVfUz+wD4E4CkbsBSM/tJ0u5mNsvM7iDq657YnelnoFExxxwPdA/v/xTScK5YR/3+NG655yluuecpOh7QjXFjXsfMmDd3FvUaNEy5iQ6g4wFdmfPJNAB+/ulHFn33Dds2L/ewXm4rUSNLKS+ZpqoXRv2BTpJmAgOBwnkz+oXOCjOBdcCbCfu9B7Qr7MCQsK0PcG7Y9yzgsi2We1ft7NvpEJo134G/XHgyI4YM4Ozefy3a9vd+Zxa9f3bEfVx+/nGsXbOay88/jpeejoYIa7/fQTRstA3XXXoad/ztQk7rcRkNGzep9PNwmak6N9Mpeq7JlYU30znnUlURo3ZP6XpQyt85nd//KKNKpKp0z8g551wJVKOqN2aVnRdGzjmXITKx+S1VXhg551yGyMSOCanywsg55zKE14ycc86lnd8zckntvXJ8urPgnMsYx5b7CFm1vDByzjmXZt5M55xzLu28mc4551zaVeeaUfUtZp1zrpqpyOGAJB0raa6keZKuSbJ9UGzeuM8l/Rjblh/bViETl3rNyDnnMkSNmlkVchxJWcAQ4GhgATBZ0qgwfQ4AZnZ5LH4fYL/YITaZZaG8ql1hJCkfmEU0BUQ+cKmZjZfUEvgMmAvUBsYCFwM7A18BA8zsb+EYucD3wMOFE/Q5l2j8x59x5xMvRpPkdTuQHiccvdH2f73xHq+89xFZWTVo2rghN/Q8kxbNfGZXV3YV+NDr/sA8M/sSQNIzwInA7GLinwHcWFGJJ1Mdm+l+NbMOZrYvcC1we2zb/FCa7wO0Y8MMr18Bx8Xi/RH4tDIy6zJTfkEBd4x4nvv+0pvn/3Etb380jS8X/LBRnLa77MgTt17FMwOv4cj9O3Df0xXSmuG2YhXYTLcD8G1sfUEI2zRNaRdgV2B0LLhumGR0gqSTku23uapjYRTXGFieGBhmhh0PtApBq4DPJHUO66cDz1VKDl1G+nT+/9ipeTN23DaXWjVr8tsDO/L+1Fkbxem8V2vq1qkNwN6tWrIo78dkh3IuZapRI/UlNit1WHqVMdnuwAtmlh8L28XMOgNnAvdI2r2851btmunYMH15XaAFcERiBEn1gSOBG2LBzwDdJS0iat77Dth+y2fXZaLFeStonrNhHqJts5vwyfz/FRv/lTETOHjfPSsja64a25zedPFZqZNYCOwUW98xhCXTHbgk4dgLw+uXksYQ3U+an3LmkqiONaPCZrq2RI88j9SG+ct3DwXVOOB1M4tPyvcW0c287sCzxR08/mtj+ItvbKFTcNXJGx9O5rMvv+Hs449Md1ZchqvAZrrJQGtJu0qqTfS9t0k7sqS2QFPgo1hYU0l1wvtc4BCKv9eUsupYMypiZh+Fi9UsBM0vrgeIma2VNBW4kuh+0gnFxCv6tfHzlLd8cr2t1LbZ27Bo2YZmt8V5P7Jt0202iTfxk7kMe+Udhv6tD7VrVet/N1cJKqo3nZmtl3Qp8DaQBQwzs08l3QxMMbPCgqk78IxtPAvrnsDDkgqIKjQD473wyqpa/3eEUj0LWAbUT2GXu4D3zSxvQ2XKuU21221nvv1hCQsXL2Pb7G34z4Rp3HrJ2RvFmfP1Am577Fnu/+uFZG/TKE05ddVJRY7AYGZvAG8khN2QsN4/yX7jgfYVlpGgOhZGhfeMIOrefY6Z5adSuJjZp3gvOpeCmllZXN3jFPrc8SD5BQWc0PVAdt+xBQ+98AZ77roTXTu1576nXuHX1Wu45t4RADTPbcqgK3umN+Mus1XjH8nauPblNoc30znnUtWo87HlLkkWXnZ6yt85O9z7bEaVXNWxZuScc9WSD5TqnHMu7arzQKleGDnnXIaoqN50VZEXRs45lyG8ZuSS+rrJfqVHcs45KqgvtN8zcs45l27V+flHL4yccy5DeG8655xzaSfvwOCccy7dvAODc865tJO8mc4551y6ec2o8knKB+JTZz5jZgPTlR/nAMyMYQ/fx/QpE6hdpw6XXn4tu7Vqs0m8+V/MZcig21i7di37dT6Q83r3RRJfzf+CoUPuYt3atdTIyqLnxZfTuk27NJyJy0TVuQNDVT6zwknyCpdyF0SSqmzh6zLD9CkT+P67Bdz/yFNc2Odqhg65O2m8Rx64iwv7/oX7H3mK779bwPSpEwF4YviD/PHMHtw5eBjd/3weTwx/qDKz7zJcBU6uV+VU5cIoKUlfS7pJ0jRJs8KcRUhqIGmYpEmSpks6MYT3kDRK0mjgXUn1JT0nabaklyRNlNRZ0nmS7oml01PSoDSdpquiJk/4kG5HHIMk9mi7F6tW/sLyvKUbxVmet5RVq1axR9u9kES3I45h8kcfANFzIr+uWgnAqpUryc7OrfRzcJlLWVkpL5mmKtcU4vMSAdxuZoXTgS81s46SLgauAi4ArgdGm9l5kpoAkyT9N8TvCOwTJs27ClhuZu0k7Q0UpvEccL2kq81sHXAu0HsLn6PLMMuWLSWn2bZF69m5zVi2bClNY4XKsmVLyclptkkcgHN79uHWG65i5GMPYGYMuPOBysu8y3zeTJcWic10z8a2vRhepwItw/vfAteEAmwMUBfYOWx7x8zywvtDgWcAzOwTYGZ4/wswGjg+1LZqmVn8nhUAknpJmiJpygvPPFFBp+q2Fm+/8Qo9el7Kw4//mx49L+WBe+5Id5ZcBpGU8pJpqnLNqCRrwms+G85BwClmNjceUdIBwMoUj/socB0wBxieLIKZDQWGAsyat8gn19sKvPnai7z71msA7L5HW5YtWVy0LW/pEnJyNm5qy8nJZdmyJUnjvP/uW5zXuy8ABx16OA/e+48tnX1XnXjNKCO8DfRR+EkgqbhRTMcBp4U47YiNX2hmE4GdgDOBp7dobl3G+N3xf+DOwcO4c/Aw9j/wMMaMfhsz4/M5n1K/QYONmugAmmbnUr9+fT6f8ylmxpjRb9PlwEPDthw+nRW1DM/6eBottt+x0s/HZa6K7MAg6VhJcyXNk3RNku09JC2RNCMsF8S2nSPpi7CcUxHnVpVrRon3jN4ys00uWMwtwD3ATEVPhn0FHJ8k3gPA45JmE9WAPgVWxLY/B3Qws+Xlyr2rljp2OZBpUz7i0gvOoE6dOlx8+bVF26669DzuHDwMgAsuvoIhg25n7Zo17Nf5APbrfCAAF/b9C8Mfvo/8gnxq1apN7z5Xp+U8XIaqoIdeJWUBQ4CjgQXAZEmjzGx2QtRnzezShH2zgRuBzoABU8O+5frOlNnW1dIUPoRaZrZa0u7Af4E2ZrY2bH8NGGRm75Z2LG+mc86lqn2r5uW+kbPy4etT/s5p0HtAselJOgjob2bHhPVrAczs9licHkDnJIXRGUA3M+sd1h8GxphZuVqTqlMzXarqAx9K+hh4CbjYzNZKaiLpc6KOE6UWRM45V+lqKOUl3tkqLL1iR9oB+Da2viCEJTpF0kxJL0jaaTP33SxVuZluizCzn4mql4nhPwJ7VH6OnHMuNZszAkO8s1UZvQo8bWZrJPUGHgeOKMfxSrQ11oyccy4zSakvJVtI1Fmr0I4hrIiZLTOzwp7LjwKdUt23LLwwcs65TFGjRupLySYDrSXtKqk20B0YFY8gqUVs9QTgs/D+beC3kppKakr0jOfb5T21ra6ZriIJ77/gnKs8FTXMj5mtl3QpUSGSBQwzs08l3QxMMbNRQF9JJwDrgTygR9g3T9ItRAUawM2xQQXKbKvrTVeRPpn3g18851xK9m61Xbl70/36r9tS/s6p9+frMmoYBq8ZOedcpsjA0bhT5YWRc85lCJ/p1TnnXPp5zcg551zaec0oEpsKvBZRD4uRREPnFGyBvKWap5bAa2a2d5JtjwJ3m9lsSV8TDW2xVNJ4Mzs47HuwmT1ViVl2GaZwqvFpUyZSu04d+lx+Lbu12vT56PlfzGXwoNtZu3YtHTsfUDTV+F0D+/PdguiB9ZUrf6FBg4bcNfgxfv5pBf+87QbmfzGXbkcdS8+L+lX2qblMk4GT5qVqc2tGv5pZBwBJ2wJPAY2JBs2rcszsgmLCDw5vWxKN0O2FkSvWtCkT+f67BQx+5Em+mDuboUPuZuCgTacLH/rA3VzU92pat2nHgBv/wvSpE+nY+UCuvKZ/UZwRjw6hfv0GANSqXZszzjqfb/73Fd/876vKOh2XyapxzajMZ2Zmi4FewKWKtJT0QZgOfJqkgwEkdZM0JoxtNEfSk7FpHrpIGi/p4zBdeCNJWZL+KWlyGBOpcDC+hpLe1Ybpxk+MZadmOO5nIZ36YZ8xkjYZ+kfSL+HtQOCwMDz65ZLGSuoQi/ehpH3Leo1c9TB5wod0jU01vnLlLyzPW7ZRnOV5yzaaarzrEccw6aMPN4pjZoz/4D0O7XoUAHXr1mPPvfahVq3alXYuLsNtxth0maZcxayZfUn0wNS2wGLgaDPrCJwO3BeLuh/QD2gH7AYcEp76fRa4zMz2BY4CfgXOB1aYWRegC9BT0q7AauDkcPzDgbsKCzWgDfCAme0J/ARcnOIpXAN8EGaSHQQ8RniwS9IeQF0z+3gzL4urZvKWLSU3NtV4Tm6zjSbPA1i2bMlGU43n5DYjL0w1Xmj2pzNp0iSb7XfwOYxcGalG6kuGqcgc1wIekTQLeJ6o4Ck0ycwWhHtLM4iax9oA35vZZAAz+8nM1hMNLXF2mMtoIpADtCaayfU2STOJpn3YAWgejv+tmY0L7/9FNLV4WTxPNO14LeA8YEQZj+PcJj58/78c2vXIdGfDZbKKG5uuyilXbzpJuxFN/b2Y6L7RImBfokJudSzqmtj7+FThSQ8L9DGzjcY6CnNrNAM6mdm60CGhbtic+FRymUZGMLNVkt4BTiSaDbZTYpwwDHsvgBtu+Qd/7H5WWZJyVdybr73Ef8NU4632aMPS2FTjy5ZuXAsCyMnZuLa0bOkSsmPTkefnr2fi+A/4573lGUTZbfWq8bTjZS6MJDUDHgIGm5lJ2gZYYGYFYRra0rp9zAVaSOpiZpMlNSJqpnsbuEjS6FDo7EE0Iuw2wOIQdjiwS+xYO0s6yMw+IuqQ8CGp+RlolBD2KNHQ6R8km7kwPuh/O7kAACAASURBVCy7DwdUff3u+JP53fEnAzB10ke8+dqLHNr1SL6YOztMNZ6zUfym2TlFU423btOO90e/ze/+75Si7TOnT2WHHXcmJ3dbnCuzGt6brlDhVOCFXbufAO4O2x4A/i3pbOAtYGVJBwoT2p0O3C+pHlFBdBRRYdASmBbuCS0BTgKeBF4NzYBTiKYMLzQXuETSMGA28GCK5zMTyA8T7Y0ws0FmNlXST8DwFI/hqrloqvEJXHLBmdSpU4dLLr+maNuVl57PXYMfA6DnxZczeNDAoqnGO3Y+oCjeh2NHJ22iu/Dc0/l11UrWr1/PpI8+5IZb72SnnVtu8XNyGaoa14x8oNQEkrYHxgBtS3t+ymtGzrlUVcRAqavffizl75y6x5yfUTeOqm8xWwahVjcRuD6dD/I651xS1bg3nQ8HFGNmI4lGlXDOuaonA3vJpcoLI+ecyxDmwwE555xLuwxsfkuVF0bOOZcpvDByydTU+nRnwTm3FbEKvGck6VjgXqJnQh81s4EJ268ALiB6jGcJcJ6Z/S9sK5zBAeAbMzuhvPnxwsg55zJFBdWMJGUBQ4CjgQXAZEmjzGx2LNp0oml3Vkm6CPgH0bijEJvBoaJU3zqfc85VNxU3Nt3+wDwz+9LM1gLPEA2DVsTM3jOzVWF1ArBFR/j1wsg55zKEZWWlvEjqJWlKbOkVO9QOwLex9QUhrDjnA2/G1uuGY06QdFJFnJs30znnXKbYjGa6+Dia5UpS+jPQGegaC97FzBaGwbJHS5plZvPLk44XRs45lyGs4nrTLQR2iq3vGMI2Iuko4Hqgq5kVzb5gZgvD65eSxhDNWVeuwqjSmulis6tW5DGTzuTqXEUyM4Y+NJje559F34svYP68z5PGm/fF5/S96AJ6n38WQx8aTOK4jy+/+Bwn/v5IflqxAoCJH42j78UX0O/SXlzR9yJmfzor2WGd26Di7hlNBlpL2jVMdNodGLVxUtoPeBg4IczsXRjeVFKd8D4XOIRogOpy8ZqRc6WYOmUS3y9cwEOPjuTzuZ/x4OB7ufOeIZvEe2jIPVxy2RXs0WZPbr7hWqZNmUSnLtHI3UuWLGb6tKk0i80Yu0+Hjux/4MFI4uuv5vOP22/hgaEjKuu0XAaqqJqRma2XdCnRlD1ZwDAz+1TSzcAUMxsF/BNoCDwfJtUu7MK9J/CwpAKiCs3AhF54ZZLWDgySOoQbYDMlvSSpaQgvqvFIyg0T6SGpnqRnJH0m6SWgXuxYv0gaIOnjcMzmIbyZpH9LmhyWQ0J4V0kzwjJdUiNJLSSNDWGfSDqssq+Jq3omTRjH4Uf+Fkm0aduOlSt/IS9v2UZx8vKWsWrVKtq0bYckDj/yt0ycMK5o+2NDH6DHeb1Q7BdrvXr1itZXr1690TbnkqqRlfpSCjN7w8z2MLPdzWxACLshFESY2VFm1tzMOoTlhBA+3szam9m+4fWxCjm1ijhIOYwE/mpm+xA9QHVjKfEvAlaZ2Z4hbnwm1gbABDPbFxgL9Azh9wKDzKwLcArRfEkAVwGXhL7yhxHNp3Qm8HYI25doinS3lVu2dCm5zTbM7Jqb24xlS5duEicnd0OcnNzcojgTPxpHTk4uu+62+ybH/mj8h1zcqwe33Hg9ffpdtYXOwFUXJqW8ZJq0FUZhZtgmZvZ+CHoc+E0pu/0G+BeAmc0kmhyv0FrgtfB+KtEEfRBN2Dc4TAo4CmgsqSEwDrhbUt+Qj/VE7ajnSuoPtDezn5Pku6i75HPPPLk5p+y2QmtWr+b5Z5/izLN6JN1+0MGH8sDQEVz395t58okRlZo3l4F8ColKt54NBWXdFPdZZxvuGOez4dxqAAea2eqE+AMlvQ78Hhgn6RgzGyvpN8BxwAhJd4dpJYrEu0vOmb/AJ9erpl5/9WXeefsNAFq1bsPSJUuKti1duoSc3NyN4kc1oQ1xoppSLt9//x2LF/1Av0t6Fe17ed8LuXPQEJpmZxfF36v9Piwa9D0/rVhB42222ZKn5jKYkXk1nlSlrTAysxWSlks6zMw+AM4CCmtJXxM1wU0CTo3tNpaoKW20pL2BfVJI6j9AH6KbcUjqYGYzJO1uZrOAWZK6AG0l/QosMLNHQm+Rjvj8Rlul4/7vJI77v+hZvimTJvD6qy9zWNfD+XzuZzRo0IDs7JyN4mdn51C/fn3mzpnNHm325L13/8NxJ5xMy113Y+TT/y6K17PHmdx174M03mYbvv9uIdu12B5JzJ/3OevWraVR48aVep4us1Rg1+4qpzILo/qSFsTW7wbOAR6SVB/4Ejg3bLsTeC48Mfx6bJ8HgeGSPgM+I2qOK01fYIikmUTnOxa4EOgn6XCgAPiU6Oni7sDVktYBvwBnl+lMXbXSqcsBTJk8kQvPP4s6derS5/Kri7b1u7QX9wyOnivsffFl3DfoH6xds4aOnfenU+f9Szzu+HFjee/dd6hZsya1a9fm6mv+7p0YXMmqcWGkxGchXOq8mc45l6q2u+9Y7l8aeTM/SPk7J3ufwzLql01VvWfknHMuUTWuOXth5JxzGcLvGTnnnEs7703nnHMu7bxm5JxzLv38npFLpunaRenOgnMuY5R/otQClT7mXKbywsg55zKEN9M555xLO+/A4JxzLu28ZuSccy7tMnFqiFR5YeSccxnCOzBsAZJ+MbOGCWEXEk2e5yNluypj4rSPue/RkRQUFHDc0Yfz51NO2Gj72nXrGHDPg3w+/ysaN2pI/6v60qJ5M9atW8+dDz7KnHlfUaOG6Hv+2ezXvh0AV900kGXLfyQ/P5992rXl8l7nkpVVfZtgXMWozveMqtRfv5k95AWRq0ry8wsY9PBw/nnDXxh5/z9594PxfP3tgo3ivP7OGBo1bMDTDw3itBN+x0Mjnwbg1XdGA/D4fXdwd/9rGTL8XxQUFABw09V9GX7PQB6/7x/8uOInxoyfULkn5jKSqUbKS2kkHStprqR5kq5Jsr2OpGfD9omSWsa2XRvC50o6piLOrUoVRpL6S7pKUltJk2LhLSXNCu87SXpf0lRJb0tqEcLHSLpD0iRJn0s6LIRnSfqnpMmSZkrqHcJbSBoraYakTyQdFuKOCOuzJF2ejuvgqo7PvpjHDi2as/12zalVqyZHHnoQH07ceOaSDydN4djDDwOg68EHMG3mJ5gZX3+7kI7t9wKgaZNtaNigAXPmfQlAg/r1AcjPz2f9+vXV+mFGV3EMpbyURFIWMAT4HdAOOENSu4Ro5wPLzawVMAi4I+zbjmi6nb2AY4EHwvHKpUoVRoXMbA5QW9KuIeh04FlJtYD7gVPNrBMwDBgQ27Wmme0P9ANuDGHnAyvMrAvQBegZjnsm8LaZdQD2BWYAHYAdzGxvM2sPDN+iJ+qqvKV5y9k2d8NEes1yslmSl1dsnJpZWTSoX58VP/9Mq5Y7M27yVNbn5/PdosV8Pv8rFi/dsO+V/W/nhHMupH69enQ76IDKOSGX0SqwZrQ/MM/MvjSztcAzwIkJcU4EHg/vXwCOVDTh1onAM2a2xsy+AuaF45VLlSyMgueICiHC67NAG2Bv4B1JM4C/sfFjzS+G16lAy/D+t8DZIf5EIAdoDUwGzpXUH2hvZj8TTfC3m6T7JR0L/JSYKUm9JE2RNOWJ515M3Oxckd8f1Y1mOTn0uvJv3P/YE+zVtjVZNTb8Yr2r/7W8NPwB1q5bx7RZn6Yxpy5TbE7NKP5dFZZesUPtAHwbW18QwkgWx8zWAyuIvj9T2XezVeXedM8Cz0t6ETAz+0JSe+BTMzuomH3WhNd8NpybgD5m9nZiZEm/AY4DRki628xGStoXOIZoNtjTgPPi+5jZUGAowKLPpvrketVcbnZTFi9dVrS+ZFkezbKzk8bZNjeH9fn5rFy1im0aNUISfc4/qyjeRX+9kZ12aLHRvnVq1+bQAzrx4aQpdOnQfsuejMt4BZtRf4h/V2WCKlszMrP5RIXK34kKJoC5QDNJBwFIqiVpr1IO9TZwUWjiQ9IekhpI2gVYZGaPAI8CHSXlAjXM7N9Eta6OFX5iLqO0bb07C77/ge8WLWbduvW8++FHHLJ/p43iHLJ/J9567wMA3h8/kY7t90ISq9es4dfVqwGYPGMWWVlZtNxpR1b9upqlecsBWJ+fz0dTZrDzDttX7om5jGTUSHkpxUJgp9j6jiEsaRxJNYFtgGUp7rvZ0lkzqi8p3i3p7iRxngX+CewKYGZrJZ0K3CdpG6L83wOU1MbxKFGT3bTQ3rkEOAnoBlwtaR3wC3A2UVVzuFTU4Hpt2U7NVRc1s7Lo17MHV900kIL8An5/VDd23XlHHnvqedq02o1D9+/EcUd1Y8A9D3DGhZfTqFED+l/ZB4DlP/7EVTcNRDVEs+ym/K3fRQCsXrOG6267i7Xr1mFm7Ld3O0489qh0nqbLEBXYtXsy0DrcP19I1CHhzIQ4o4BzgI+AU4HRZmaSRgFPSbob2J7otsckyklm3tJUVt5M55xLVfM9O5W7JJk7/9uUv3Pa7L5TielJ+j3Rj/ksYJiZDZB0MzDFzEZJqgs8AewH5AHdzezLsO/1RLcw1gP9zOzNMp1QPD9eGJWdF0bOuVRVRGE0Z/6ClL9z2u6+Y0Y9L1CVOzA455yLqc4jMHhh5JxzGaLAqmyfs3Lzwsg55zKE14xcUuN+3i/dWXDOZYg/VMAxvDByzjmXdmZeGDnnnEuzAq8ZOeecSzfvwOCccy7t/J6Rc865tPN7Rs4559LOa0ZbmKT3gIHxaR4k9QPamNlFSeKPAa4ysymVl0vnwMx49YnbmPvxWGrXqcupvW5jh5abDhw/7B89+fnHJRQUrKdlm86ceM7fqVEjizee/idzpr9HVs1aZG+7E6f2vI16DRqn4UxcJqrONaOqcjfsaaJRY+O6h3Dnqoy5H49l2aL/cdWdb3HyeTfx8vCbk8Y7s88gLrvtZfrd/iorf8pj1sS3AGi198FcdvsoLrvtFXK3a8mYVzNmuhlXBRRsxpJpqkph9AJwnKTaAJJaEg1NfkaYofBTSTcl21HSbyV9JGmapOclNQzhX0u6KYTPktQ2hDeUNDyEzZR0SknHcS7us2mj2e/QE5HEzq06sHrVT/z04+JN4tWtF/35FOSvJ3/9OqLZS2CP9oeQlRU1SOzcal9W5C2qvMy7jFdgNVJeMk2VyLGZ5RHNh/G7ENSdaNrx682sM7AP0FXSPvH9wmR4fwOOMrOOwBTgiliUpSH8QeCqEPZ3YIWZtTezfYDRKRzHOQBWLF9Ek+ztita3yd6On/I2LYwAhv3jAm695FDq1GvA3vsfs8n2Ke+/SJt9D9tieXXVj5lSXjJNlSiMgnhTXWET3WmSpgHTgb2Adgn7HBjCxkmaQTQR1C6x7S+G16lEE+wBHAUMKYxgZstTOE6R+Lzy/3nJm1hc8c77y6Ncd/9Y1q9by/xPJ2y07b1XHqJGVhYdDv6/NOXOZSJDKS+Zpkp0YAheAQZJ6gjUJ5rM6Sqgi5ktlzQCqJuwj4B3zOyMYo65JrzmU/K5lnacIvF55V+cVODzGW0FPnrnSSaPeQGAHXfbmx/zfijatiLvBxpnb1vsvrVq16FdpyOYPW00rdsfAsDUsS/x2YwxXHDN8KLmO+dSUZ2/capMzcjMfgHeA4YR1YoaAyuBFZKas6EJL24CcIikVgCSGkjao5Sk3gEuKVyR1LSMx3FbiYOO/hN9B7xE3wEv0a7TkUz/8BXMjG/mzaBu/UY0brJxYbRm9cqi+0j5+euZM+N9mm2/GwBzZ37A2Ncf4+zLH6B2nXqVfi4us3nNqPI8DbxENL3tHEnTgTnAt8C4xMhmtkRSD+BpSXVC8N+Az0tI41ZgiKRPiGpMN5nZi2U4jtsKtdm3K3NnjOXOq46hVu26nNrztqJt911/Mn0HvMTaNb8y8u5LyF+/FisoYLd2B3DAEacDMOrxW8lfv5Zhd5wPwE6t9uXkc/un41RcBsrEe0Gp8mnHy8Gb6ZxzqfrD/jXKXZK8O2t1yt85R7avW+b0JGUDzxLda/8aOC3cX4/H6UDUOawx0Q/7AWb2bNg2AugKrAjRe5jZjJLSrDLNdM4550pWib3prgHeNbPWwLthPdEq4Gwz2ws4FrhHUpPY9qvNrENYSiyIwAsj55zLGGapL+V0IvB4eP84cNKmebHPzeyL8P47YDHQrKwJemHknHMZYnM6MMQfQwlLr81IqrmZfR/e/wA0LymypP2B2sD8WPCAMLDAoNi9+GJVtQ4MzjnnirE5d6njj6EkI+m/wHZJNl2fcByTVGzKkloATwDnmFnhSETXEhVitUMe/gokHzsr8MKoHFo1XZruLDjnMkbxz6OlqqCg4nrTmdlRxW2TtEhSCzP7PhQ2SYcZkdQYeJ1otJyiJ7tjtao1koazYQScYnkznXPOZYgClPJSTqOIRqIhvL6SGCGMJfoSMNLMXkjY1iK8iuh+0yelJeiFkXPOZYhK7MAwEDha0hdEQ6gNBJDUWdKjIc5pwG+AHpJmhKVD2PakpFnALCCX6PnOEvlzRuUw84vFfvGccynZp/W25a+uTMlP+TvnhM5ZGfWErN8zcs65DFGdH7P3wsg55zJEdW7IqtKFkaRfzGyLTXIXpjYfamarKiM9l5nMjOFD72XalAnUqVOHS/pdx26t2mwSb/68uQwZdBtr166hY+cDObfXZUjiqy+/4JEhd7J27VqysrK44KIraN2mHStX/sL9d97C0iWLyC/I54STu3P40cel4QxdpsivxmPTbe0dGPoRTVfhXLGmT5nA998t4P6hT9P70r/wyAN3JY33yJC7uLDPX7h/6NN8/90CZkydCMC/hj/IH884lzvvH87pfzqffw1/EIC3X3+RHXduyZ2DR9D/9vt4/LEhrFu3rtLOy2WeSuzAUOkyrjCStLuktyRNlfRBbDrxEZLukzRe0peSTg3hNSQ9IGmOpHckvSHpVEl9iaY2f0/Se7HjD5D0saQJYeoKt5WbPPFDuh5xLJLYo+1erFz5C8vzNn7GbHneUn79dSV7tN0LSXQ94lgmTfgAiCbLWrVqJYTXpjm5IVz8+usqzIzVv/5Kw0aNycrKqtRzc5nFC6OqZSjQx8w6ET1I9UBsWwvgUOB4QldE4A9EI8+2A84CDgIws/uA74DDzezwELcBMMHM9gXGAj236Jm4jJC3bAk5uRseWMzJaUbesqUJcZaSk9MsIc4SAHr06ssTwx/gwh6nMPKxIfzpnN4AHHv8KSz89n/0Ovskrry0B+f26kuNGpn4L+kqS4Ep5SXTVOl7RokkNQQOBp6PzZAZH/Po5TAcxexYreZQ4PkQ/kO8FpTEWuC18H4qcHSFZd5ttf7zxsv0uKAPBx7SjfEfjObBewdyw4B7mDFtIi13a8WNt93LD98v5Ja/X8Gee+1L/foN0p1lV0VlYo0nVZn2M6wG8GNsWPIOZrZnbPua2Puy/DRYZxsevEo6VXl88MEXnhlZhiRcJnjrtRe5qs+5XNXnXJo2zWHZ0g2joSxbtoTs0NRWKDsnl2WhJrQhTlRTGvPuWxxwcFcADjr0cOZ9/hkA7/33DQ44qCuSaLH9jmzbvAULv/3flj41l8HyC1JfMk1GFUZm9hPwlaQ/QjTUhKR9S9ltHHBKuHfUHOgW2/Yz0Ggz8zDUzDqbWedTu5+9Obu6DHLs8X/gzvuHc+f9w+ly0GG8P/otzIzP53xK/foNaZq9cWHUNDuXevUa8PmcTzEz3h/9Fl0OOBSA7OxcZs+KpnP55OOpbLf9jgDkNmvOrI+nAvDj8jy+W/ANzbfbvhLP0mWaSpzPqNJV9Wa6+pIWxNbvBv4EPCjpb0At4Bng4xKO8W/gSGA20fTl09gw++BQ4C1J38XuGzm3kY6dD2L6lAn06dmd2nXqckm/a4u2XdUn6iUH0PPiK4q6dnfodCD7dT4QgN59/sLwofdSkJ9Prdq16d3nLwCc2r0HQ+65jSsuOQfM+PO5F9J4myabZsC5oDo3020VwwFJamhmv0jKASYBh5jZD+U9rg8H5JxLVUUMBzRiDCl/5/ToVv7RUitTVa8ZVZTXwnS4tYFbKqIgcs65ylad6w5bRWFkZt3SnQfnnCsvL4ycc86lXSb2kkuVF0bOOZchCrwwcs45l27eTOeSqlNjTemRnHOugnhh5JxzLu2q8+R6GTUCg3PObc3MLOWlPCRlh1kOvgivTYuJly9pRlhGxcJ3lTRR0jxJz0qqXVqaXhg551yGyM9PfSmna4B3zaw18G5YT+bX2DihJ8TC7wAGmVkrYDlwfmkJemHknHMZohLnMzoReDy8fxw4KdUdFU2pcATwwubs74WRc85liAJLfYnPMBCWXpuRVHMz+z68/wEobqLRuuHYEyQVFjg5RLMrrA/rC4AdSkswpQ4MIZGXgD3NbE4q+5SVpJbAwWb2VFjvDJxtZn23ZLrOFcfMeOThIUyZPIk6derQ74q/sHur1pvEm/fF59x79z9Ys3YtnbvsT8/elyCJf40czsQJ46lRowbbbNOEy664mpycXGbNnMGAm2+g+XYtADjo4EPpfuZZlX16LoNsTo3HzIYSDQadlKT/Atsl2XR9wnFMUnEp72JmCyXtBoyWNIsNA1FvllR7050BfBhebyxLQpuhJXAm8BSAmU0BpmzhNJ0r1tQpk/hu4UIefvRx5s79jAcH38ud9wzeJN6DQ+7lksuuoE2bPbnphuuYNmUynbrszx9OPY0/n30uAK++8hLPPvUvLu7TD4B2e7XnhpsGVOr5uMxlm9WdruRxUs3sqGL3lBZJamFm30tqASxOFs/MFobXLyWNAfYjmimhiaSaoXa0I7CwtNyW2kwXZlc9lOgGVPcQliXpTkmfSJopqU8IP1LSdEmzJA2TVCeEfy0pN7zvHDKNpK6xnhjTJTUimi78sBB2uaRukl4rzIuk4eH4MyWdEsJ/kTRA0sehutg8hDeT9G9Jk8NySHHpSmohaWwI+0TSYaVdG7d1mDhhPIcfeTSSaNu2HStX/kJe3rKN4uTlLWPVqlW0bdsOSRx+5NFMmDAOYKOZW1ev/rVs0z46R6VOrjcKOCe8Pwd4JTGCpKax7/hc4BBgdpig9D3g1JL2T5TKPaMTgbfM7HNgmaROQC+iGkwHM9sHeFJSXWAEcLqZtSeqdV1UyrGvAi4xsw7AYcCvRL02Pgi9MwYlxP87sMLM2od0R4fwBsAEM9sXGAv0DOH3EvXo6AKcAjxaQrpnAm+HsH2BGSlcG7cVWLZ0Kc2aNStaz8ltxrKlSzeJk5u7YcK93IQ4Tzw+jPPOPoP3x4zmT2f1KAqfO2c2fS/pRf+/X8s3//t6i52Dqx4KCizlpZwGAkdL+gI4KqwXViYKv0f3BKZI+pio8BloZrPDtr8CV0iaR3QP6bHSEkylMDqDaAI7wusZIXMPF96gMrM8oA3wVSi0IOpB8ZtSjj0OuFtSX6BJ7IZXcY4ChhSumNny8HYt8Fp4P5WooCyMP1jSDKKSvnGo6SVLdzJwrqT+QHsz+zlZBuI3BZ995slSsutc5KxzzmPYyKfp2u0IXn81+pG4e6vWPDriKe4bMpTjTziJAbds6RZwl+kqqzedmS0zsyPNrLWZHRW+4zGzKWZ2QXg/PlQM9g2vj8X2/9LM9jezVmb2RzMrdbiaEgsjSdlEXfQelfQ1cDVwWhnObX0srbqxDA8ELgDqAeMktS3DsQHW2YanvPLZcC+sBnBgrB/8Dmb2S7J0zWwsUeG5EBghKemc4vFpx0/v/qcyZtdVda+/+gqXXdqbyy7tTXZ2NkuWLCnatmzpEnJyN552PCc3l6WxmtDSJHEAuh1+JOPHfQBEzXf16tUDoHOXA8hfv56fVpTp3q/bSlRi1+5KV1rN6FTgCTPbxcxamtlOwFdE03z3llQTigqtuUBLSa3CvmcB74f3XwOdwvtTCg8uaXczm2VmdxDVTNoCPwONisnPO8Alsf2TPhUc8x+gTyx+h+LSlbQLsMjMHiFqzutYyrFdNXbc/53IvYMf5t7BD3PAQYfw3rvvYGbMmTOb+g0akJ2ds1H87Owc6tevz5w5szEz3nv3HQ448GAAvlu4oCjexAnj2XHHnQBYnpdX9KT853PnUGAFNGrcuJLO0GWiArOUl0xTWm+6M4iepI37N1Fb4TfATEnrgEfMbLCkc4HnQyE1GXgo7HMT8JikW4AxsWP1k3Q4UAB8CrwZ3ueHdsgRwPRY/FuBIZI+IaoB3QS8WEL++4b4M8O5jgUuLCbd7sDV4Xx+AZLWjNzWp3OXA5g6eRK9zz+bOnXq0Pfyq4u2XXZpb+4d/DAAF17cl3sH/ZO1a9bQsfP+dOq8PwCPD3+UhQsXIIltt23OxZdGPenGjRvLm6+/SlZWFrVr1+bqv/6N6HlB55KzajyFhMo7htHWbO78b/3iOedS0mb3ncr9S+PGketS/s656exaGfXLxkftds65DFGdKw9eGDnnXIaozlNIeGHknHMZYvNGYMgsXhg551yGqMatdF4YOedcpqiAkRWqLC+MymHHxVPTnQXnXKbYfadyH6Ig3wsj55xzaZaJD7Omygsj55zLEN612znnXNr5PSPnnHNpV40rRl4YOedcpsivgFnzqqrNKowkXU80CV0+0SCjvc1s4pbIWCzNr4HOZrY0IfwEoF2YDsK5SjFu5lzufGoU+QXGyb/pwrnHH77R9n+9NZaXxk4mq0YNmjZqwI3n/5HtczcMLv/Lr6s59bq76NZxL64566TKzr7LcP7QKyDpIOB4oKOZrQnTzNbeYjkrhZmNIpowz7lKkV9QwB1PvPz/7d17lJVV/cfx9yeuXuI2Kgh0ISUUdBxJSJcmkGQZFNbPa7l+kulEN6OWpslaBpi/XxYFGcaKFDFsKVguSOEnJJe0RJQfgwOSKEswkEq5SCKIzJxvf+x94GGYmXMOM3MO58z3tdazzjn72c+z9zmwznf2fp6zeL2cSQAADHVJREFUv/zq5uvp3q0z10yYypCz+/ORXt0P1On3oV48+MNzOaZDex5ZspxfzFnAXd84mPdq2qOLGNjvI4XovisBpRyMssn0mnYysC2dsc/MtpnZVkmbJP1E0hpJz6XzGUk6UdIfJD0ft/Nj+XGSZsS6VZJGxfI2kiZJWiupWtK3E21/W9Kq2MZpsf5oSVPj85mS7pb0jKRXJaVzryPp5th+taQJiT7Ml/RCbO/KWP5jSeti3UlH/Km6krT21c307l5G75PKaNe2LZ/++Fksq1p3SJ1Bp5/CMR3C32hnnvJB3thxMFneuk1b2L7rbc4d0Dev/XalI2XZb8Uml2C0CPiApJcl/UrSkMS+XWZ2JjAVmBLLfgFMNrNBhIR66bzp44AlZjYYGAb8VNJxQCUhXXiFmZUDyZze28xsIDANuKmB/p0MXEAYvaXztV8M9AUGAxXAxyRdCHwG2BrT5Z4BPCGpDPgCMCC2/6McPhvXCry5cxc9unU58Pqkrp15Y2fDmVnnPvU855f3AyCVSjH5ofl896oRLd5PV7osZVlvTSGpm6Q/SXolPh6WyFTSMEmrE9u7ki6N+2ZK2pjYV5GpzayDkZntJmRrrQTeBGZLGh13P5R4PC8+Hw5MlbSaMJ3WSdLxwMXArbF8GSEN+Qdj/V+bWU1sb0ei+XQCvf8nBKz6zDWzlJmtA9LzJhfHrQpYRcgk2xdYA3xK0l2SPmFmu4BdwLuEJIBfBPbU14ikSkkrJa2cMXdRQx+Xa+XmP7OKdRu38N+XhL/Z5ix5lvPP6kf3RDBzLldmlvXWRLcCi82sL7A4vq7bl6VmVmFmFcAnCd+ZyS/Fm9P7zWx1pgZzuoHBzGoJAWSZpDXAteldyWrx8X3AuWb2bvIcCqks/8vM1tcpb6zpffGxtpE+70s8V+Lxf83s13UrSxoIfBb4kaTFZjZR0mDgIkK69W8RPuBDmNl0YDrAO8vnFuFg2B2pE7t25p873jrw+o2duzipa+fD6q148RXue2wJ9/5gDO3bhf+uaza8RtXLG3lk8bPs3beP/TW1HNuhAzdecUne+u+KXx7vphsFDI3PHyB879/SSP3LgP8zs3r/iM9G1iMjSf0kJSe7K4DX4vMrE4/L4/NFwIHrPolh2kLCNSDF8rNj+Z+Ar8WU5UjqlsP7aMhC4Lo4IkNSL0knSeoJ7DGzB4GfAgNjnc5mtgD4LnBWM7TvSsiAPr3Z/K/tvP7mDvbX1LBwxQsMOfv0Q+q89Nrr3DnzUaZ8ZzTdOh1/oPzOMVez4Oe3Mf9ntzL2yhGMOH+gByKXs3xN0wHdzewf8fk/OTjb1JCrODhDlnZnvP4+WVKHTA3mMjI6HvilpC5ADbCBMGU3EugqqZowOrk61r8RuCeWtwWeAsYAdxCuK1VLeh+wMZ7jXuCjsXw/8BvCNagjZmaLJJ0OLI+xbzdwDXAq4VpVCtgPfB14PzBPUkfCiOp7TWnblZ62bdpwyzWj+Oak+0ilUnz+E4M4pVcPpj26iP59ejPk7P5Mmb2APfve4/v3PAhAj7IuTBk7urAddyUjlyAjqZLwHZ02Pc7spPc/CfSo59Bxh7RpZpIabFjSycCZhD/+035ACGLtCTNJtwATG+1vU+cWG/odUGvg03TOuWwdd96ljV6LyMbo8f/K+jtn5vjuR9yepPXAUDP7Rww2y8ysXwN1v0O48auygf1DgZvMbGRjbeZyN51zzrkCyuM03R85eE/AtcC8RupeTZ0puhjA0vcIXAqszdRgk5cDMrMPN/UczjnnMsvjqt0/BuZI+irh3oArACSdA4wxs+vj6w8DHwD+XOf430k6kXDJYzXhEk2jfG0655wrErU1+bmbzsy2E+4srlu+Erg+8XoT0KueeofdiZyJByPnnCsSns/IOedcwVnKV+129Vh2YWO/AXPOuYNG7G/6Ku2eXM8551zB+TSdc865gkvl6QaGQvBg5JxzRSJlHoycc84VWCkn1/Ng5JxzRaKUg1GLLgckqTYmVlor6RFJxzbz+ZfFXwTncsxEScPj87HN3SfXepX/5n8Y/vozXFj1WKG74kpUHvMZ5V1Lr023NyZWOgN4jyyWhGhJktqY2e1m9mQsGgt4MHLNYssDj/LcyOszV3TuCKVSqay3YpPPhVKfBk6N6WznxjwXz0oqB5A0XtIsSctjqtsbYvlQSY+nTyJpaiLDLInyaTED64uSJiTKN8WMrquAy2M63Msk3Qj0BJZKWirpOklTEsfdIGlyi30aruTs+MtK9u9oOA25c02Vqq3Neis2eQlGMWHeJYR03xOAKjMrB24DfpuoWk7IrnoecHtMgpetcWZ2TjzHkHSQi7ab2UAzezhdYGZ3A1uBYWY2DJgDfE5Su1jlK8CMXN6nc861pDyu2p13LR2MjpG0GlgJ/B24D7gAmAVgZkuAMkmdYv15ZrY35kZaCgzOoa0r4uinChgA9E/sm53pYDPbDSwBRko6DWhnZmvq1pNUGUdgK59IvXXYeZxzrqWUcjBq6bvp9ppZRbIgZlxtSN1P0AhZZZNBs2PdgyT1AW4CBpnZTkkz69R7J8v+3ksYrb0E3F9vB0OmxOkA89v1K75/cedc0Srl3xkVIrne08CX4UAGwG1m9u+4b5SkjpLKgKHA84RcGv0ldYgpzw9b1hzoRAg4uyR1J0wJZuNtQrpxAMxsBSE3x5c4PJ+7c84VlI+Mmtd4YIakamAPB7MJAlQTpudOAO4ws60AkuYQMgVuJEzDHcLMXpBURRjRbAb+mmVfpgNPSNoarxtBuHZUYWY7c31jrnWrmPUzyoYMpv0JXfnkxj/zysRfsvn+3xe6W66ElPKq3Tpa7keXNB7YbWaTCtyPx4HJZrY4U12fpnPOZWvE/vWNXqPIxkVXPZf1d87ihwc3ub18KsQ03VFJUhdJLxOuc2UMRM45l2+plGW9FZujZjkgMxtf4PbfAj5ayD4451xjSnmazkdGzjlXJPJ1A4Oky+MCAqnGllyT9BlJ6yVtkHRroryPpBWxfLak9pna9GDknHNFwiyV9dZEa4EvAk81VEFSG+Aewt3L/YGrJaV/33kX4dr7qcBO4KuZGvRg5JxzRSJVU5v11hRm9jczW5+h2mBgg5m9ambvAQ8Tfp4jwko66VtJHwAy5lw/aq4ZFaPmuDvGlR5JlfHH0c41q788NiTr7xxJlUBlomh6M/+/7EX4KU3aFuDjQBnwlpnVJMp7ZTqZByPnml8lcZUO5woluVpMfSQ9CfSoZ9c4M5vXYh1rgAcj55xrhcxseBNP8TphxZq03rFsO9BFUts4OkqXN8qvGTnnnDsSzwN9451z7YGrgD9aWElhKXBZrHctkHGk5cHIuebnU3SuqEn6gqQthHQ+8yUtjOU9JS0AiKOebwELgb8Bc8zsxXiKW4DvSdpAuIZ0X8Y2j5blgJxzzrVePjJyzjlXcB6MnHPOFZwHI9fqSKqVtFrSWkmPSDq2mc+/rLElVBo4ZqKk4fH52Obuk3NHOw9GrjXaa2YVZnYG8B4wppCdkdTGzG43sydj0VjAg5FrVTwYudbuaeBUSd0kzZVULelZSeUQ8mxJmiVpuaRXJN0Qy4fG3FfE11Mlja57cknTJK2Mi05OSJRvknSXpFXA5ZJmSrpM0o1AT2CppKWSrpM0JXHcDZImt9in4VyBeDByrZaktoRFHtcAE4AqMysHbgN+m6haTlhr6zzgdkk9c2hmnJmdE88xJB3kou1mNtDMHk4XmNndwFZgWMw+PAf4nKR2scpXgBm5vE/nioEHI9caHSNpNbAS+DvhNxAXALMAzGwJUCapU6w/z8z2mtk2wo/5BufQ1hVx9FMFDCCsbpw2O9PBZrYbWAKMlHQa0M7M1uTQvnNFwZcDcq3RXjOrSBaEhYYbVPfHeAbUcOgfcx3rHiSpD3ATMMjMdkqaWafeO1n2917CaO0l4P4sj3GuqPjIyLngaeDLEK4HAdvM7N9x3yhJHSWVAUMJy6C8BvSX1EFSF+Cies7ZiRBwdknqTpgSzMbbwPvTL8xsBWENsC8BD+X4vpwrCj4yci4YD8yQVA3sIaynlVZNmJ47AbjDzLYCSJpDSEK2kTANdwgze0FSFWFEsxn4a5Z9mQ48IWlrvG4E4dpRhZntzPWNOVcMfDkg5xohaTyw28wmFbgfjxMyZy4uZD+cayk+TefcUUxSF0kvE65zeSByJctHRs455wrOR0bOOecKzoORc865gvNg5JxzruA8GDnnnCs4D0bOOecK7j9j5aQw7FFYMQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import seaborn as sns #importazione libreria per la heatmap sopra descritta e sotto visualizzata\n",
        "plt.title(\"Correlazione alla Popolarità\\n\", fontdict = font1)\n",
        "sns.heatmap(top50.corr(method='pearson')[['Popularity']], vmin = -1, vmax = +1, annot = True, cmap = 'coolwarm')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5d5d4afe",
      "metadata": {
        "id": "5d5d4afe"
      },
      "source": [
        "Non si è riportata la correlazione di tutta la tabella per risaltare appunto solo la correlazione tra la popolarità e le altre features, in modo appunto da capire direttamente quali sono le più correlate ad essa.\n",
        "\n",
        "Consideriamo quindi BPM e Speechiness come features correlate positivamente anche se con un bassissimo indice di correlazione.\n",
        "\n",
        "Mentre prendiamo la Valenza della canzone come unica features correlata negativamente.\n",
        "\n",
        "Quindi le features più correlate alla popolarità sono: *BPM, SPEECHINESS E VALENCE*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "d4f7297a",
      "metadata": {
        "id": "d4f7297a"
      },
      "outputs": [],
      "source": [
        "features_correlate_alla_popolarità = ['BPM','Speechiness','Valence']; #unite per comodità"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd8bdf9f",
      "metadata": {
        "id": "dd8bdf9f"
      },
      "source": [
        "### ***PRECISAZIONE TEST STATISTICI***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "86c5b632",
      "metadata": {
        "id": "86c5b632"
      },
      "source": [
        "Non è possibile eseguire i test statistici visti a lezione date le seguenti motivazioni:\n",
        "\n",
        "   - ***T-TEST***:\n",
        "        - non è possibile rispettare entrambe le condizioni necessarie per eseguire il t-test ovvero:\n",
        "            - campione di almeno 30 elementi;\n",
        "            - popolazione di almeno 10 volte superiore il numero di elementi presenti dentro il campione;\n",
        "   - ***CHI-QUADRATO***:\n",
        "        - non è possibile rispettare entrambe le condizioni necessarie per eseguire il test del chi-quadrato ovvero:\n",
        "            - almeno 5 conteggi;\n",
        "            - popolazione di almeno 10 volte superiore il numero di elementi presenti dentro il campione;\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "646b205c",
      "metadata": {
        "id": "646b205c"
      },
      "source": [
        "# **MACHINE LEARNING**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "70153453",
      "metadata": {
        "id": "70153453"
      },
      "source": [
        "Il machine learning fornisce predizioni: dobbiamo solo vedere quale usare in base a cosa vogliamo ottenere:\n",
        "- Apprendimento Non supervisionato\n",
        "    - Dato un insieme di dati (solo dati/input, niente output), lo scopo del Machine Learning non supervisionato è identificare la presenza di stutture all’interno dei dati stessi.\n",
        "    - Idea di implementazione : \n",
        "        - predirre le canzoni più e meno popolari della classifica.\n",
        "- Apprendimento Supervisionato :\n",
        "    - Dato un insieme di coppe input-output, lo scopo del Machine Learning supervisionato è stimare una funzione capace di fittare i dati a disposizione e generalizzare a dati futuri.\n",
        "    - Idea di implementazione : \n",
        "        - predirre la popolarità della canzone a seconda delle features che la caratterizzano. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8c224964",
      "metadata": {
        "id": "8c224964"
      },
      "source": [
        "### ***APPRENDIMENTO NON SUPERVISIONATO***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "245739d5",
      "metadata": {
        "id": "245739d5"
      },
      "source": [
        "In questo caso invece andiamo a rendere il problema della predizione della popolarità un problema di classificazione andando ad individuare e distinguere dalle canzoni con una popolarità alta da quelle con una popolarità bassa:"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5d0de3c1",
      "metadata": {
        "id": "5d0de3c1"
      },
      "source": [
        "Questo problema quindi possiamo \"risolverlo\" utilizzando la tecinca del clustering: ovvero una tecnica per individuare strutture (gruppi di dati \"coerenti\" rispetto ad una qualche misura) all’interno dei dati:\n",
        "\n",
        "Due elementi importanti:\n",
        "- Cluster: è un gruppo di dati che si «comporta in modo analogo»\n",
        "- Centroide: è il «centro» del cluster (ad esempio il punto medio, o centroide)\n",
        "\n",
        "Utilizziamo l'algoritmo del Kmeans per effettuare il fitting e la predizione dei dati.\n",
        "\n",
        "Definiamo la funzione della silhouette per definirci con esattezza il numero di cluster da passare in input al KMeans per ottenere un risultato migliore(crea i centroidi in modo randomico, quindi non tutte le esecuzioni porteranno allo stesso risultato).\n",
        "\n",
        "Questa funzione ci ritorna quindi un *Silhouette coefficent* = sc il quale viene calcolato effettuando questa operazione:\n",
        "\n",
        "- (b - a)/max(a,b) = sc \n",
        "\n",
        "dove,\n",
        "- b è la distanza media EXTRA_CLUSTER(più i cluster sono lontani mediamente più otterremo un buon risultato e quindi un coefficente di silhouette alto),\n",
        "- a è la distanza media INTRA_CLUSTER(più i dati all'interno dei cluster sono vicini mediamente più otterremo un buon risultato e quindi un coefficente di silhouette alto)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "a5af42ef",
      "metadata": {
        "id": "a5af42ef"
      },
      "outputs": [],
      "source": [
        "def silhouette(x):\n",
        "    silhouette_avg = []\n",
        "    for i in range(2, 10):\n",
        "        kmeans = KMeans(n_clusters = i, max_iter = 10, random_state = 0)\n",
        "        cluster_labels = kmeans.fit_predict(x)\n",
        "        silhouette_avg.append(silhouette_score(x, cluster_labels))\n",
        "\n",
        "    plt.plot(range(2, 10), silhouette_avg,'-o')\n",
        "    plt.title('The silhouette method\\n',  fontdict = font1)\n",
        "    plt.xlabel('Number of clusters', fontdict = font2)\n",
        "    plt.ylabel('silhouette coefficient',  fontdict = font2)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a57b56f4",
      "metadata": {
        "id": "a57b56f4"
      },
      "source": [
        "Prendiamo quindi il dataset considerando solo le features che dall'analisi precedente sono risultate le più correlate alla popolarità e standardizziamo il set ottenuto:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "acaa4b13",
      "metadata": {
        "id": "acaa4b13"
      },
      "outputs": [],
      "source": [
        "X = top50[features_correlate_alla_popolarità]\n",
        "X = np.asarray(X)\n",
        "#standardizzo il dataset ristretto alle sole features correlare alla popolarità\n",
        "scaler = preprocessing.StandardScaler().fit(X)\n",
        "X = scaler.transform(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c43a8872",
      "metadata": {
        "id": "c43a8872"
      },
      "source": [
        "Ora andiamo ad eseguire la funzione di Silhouette sopra descritta per visualizzare qual'è il miglior numero di cluster da passare come argomento all'algoritmo di k-means(il numeor di cluster associato al silhouette coefficent più alto):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "4a471688",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "4a471688",
        "outputId": "687d938c-6ebf-4853-e150-f6d32dab6efb"
      },
      "outputs": [
        {
          "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": [
        "silhouette(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b585c1f9",
      "metadata": {
        "id": "b585c1f9"
      },
      "source": [
        "Quindi il numero di cluster da usare è 2 con un coefficente di silhouette corrispondente di 0.46.\n",
        "\n",
        "Andiamo quindi ad eseguire l'algoritmo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "d1b0d092",
      "metadata": {
        "id": "d1b0d092"
      },
      "outputs": [],
      "source": [
        "kmeans=KMeans(n_clusters=2, random_state=0).fit(X)\n",
        "y=kmeans.labels_"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "da1a249f",
      "metadata": {
        "id": "da1a249f"
      },
      "source": [
        "Come andiamo quindi a visualizzare il risultato? Prima di tutto assegnamo questa y ottenuta, ovvero le etichette dei cluster ottenuti ed associamole al nostro dataset aggiungendo appunto la colonna cluster:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "704e6359",
      "metadata": {
        "id": "704e6359"
      },
      "outputs": [],
      "source": [
        "top50['cluster'] = y"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a20b2484",
      "metadata": {
        "id": "a20b2484"
      },
      "source": [
        "Ora con un diagramma a dispersione su 3 dimensioni, ovvero le 3 features usate per ottenere questi 2 gruppi di dati distinti, visualizziamo appunto l'effetto di questa clusterizzazione e cerchiamo di capire appunto se siamo riusciti ad ottenere due \"gruppi di dati\" distinti:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "0fce7963",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0fce7963",
        "outputId": "00ab0d1a-8ea6-485f-8150-024d96696e97"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<body>\n",
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Cluster sulle 3 features usate: BPM(asse x), Valence(asse y), Speechinees(asse z)\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "PLOT = go.Figure()\n",
        "\n",
        "for C in list(top50.cluster.unique()):\n",
        "    \n",
        "    PLOT.add_trace(go.Scatter3d(x = top50[top50.cluster == C]['BPM'],\n",
        "                                y = top50[top50.cluster == C]['Speechiness'],\n",
        "                                z = top50[top50.cluster == C]['Valence'],\n",
        "                                mode = 'markers', marker_size = 8, marker_line_width = 1,\n",
        "                                name = 'Cluster ' + str(C)))\n",
        "\n",
        "PLOT.update_layout(width = 900, height = 500, autosize = True, showlegend = True,\n",
        "                   scene = dict(xaxis=dict(title = 'BPM', titlefont_color = 'black'),\n",
        "                                yaxis=dict(title = 'Speechiness', titlefont_color = 'black'),\n",
        "                                zaxis=dict(title = 'Valence', titlefont_color = 'black')),\n",
        "                   font = dict(family = \"Gilroy\", color  = 'black', size = 12),\n",
        "                   title = \"Visualizzazione Cluster sulle 3 features usate: BPM(asse x), Valence(asse y), Speechinees(asse z)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eb66eed1",
      "metadata": {
        "id": "eb66eed1"
      },
      "source": [
        "Effettivamente si riescono proprio a distinguere due gruppi di dati distinti e quindi possiamo ritenerci soddisfatti della clusterizzazione ottenuta.\n",
        "\n",
        "Per avere un ulteriore riscontro di ciò visualizzato sopra andiamo a vedere se siamo riusciti a \"staccare\" la features di popolarità dalle sue correlate e quindi se siamo effettivamente riusciti ad ottenere due gruppi di dati distinti per quanto riguarda la popolarità.\n",
        "\n",
        "Saremo quindi contenti se otteniamo due cluster ben distinti in cui in uno sono contenute le canzoni meno popolari in classifica e nell'altro le canzoni più popolari:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "3e598dbb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3e598dbb",
        "outputId": "f8902755-daee-493c-e733-575d9a7ba6e8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        }
      ],
      "source": [
        "fig = px.box(top50, x=\"cluster\", y=\"Popularity\", color=\"cluster\", title=\"Popolarità in base ai Cluster generati\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "98a9a0f6",
      "metadata": {
        "id": "98a9a0f6"
      },
      "source": [
        "Vediamo anche da questo box plot quindi che siamo riusciti a separare la popolarità dalle features che abbiamo usato per ottenere questa predizione su insiemi di dati comuni: ciò ci da quindi la possibilità di ottenere due cluster:\n",
        "- cluster 0 dove vi sono canzoni meno popolari rispetto all'altro cluster;\n",
        "- cluster 1 che invece contien canzoni prevalentemente più popolari rispetto appunto all'altro cluster."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "46a12185",
      "metadata": {
        "id": "46a12185"
      },
      "source": [
        "### ***APPRENDIMENTO SUPERVISIONATO***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f0a74132",
      "metadata": {
        "id": "f0a74132"
      },
      "source": [
        "Utilizziamo quindi l'apprendimento supervisionato per vedere se riusciamo a predirre la popolarità della canzone a seconda delle features che la caratterizzano.\n",
        "\n",
        "Utilizziamo il calcolo degli errori sulle predizioni confrontandole con un modello nullo per vedere quante caratteristiche dobbiamo utilizzare per riuscire a predirre con maggiore successo appunto questa proprietà"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6a0af27f",
      "metadata": {
        "id": "6a0af27f"
      },
      "source": [
        "Alleniamo quindi un modello di regressione lineare. \n",
        "\n",
        "Ricordate che dobbiamo dividere il dataset a disposizione in due diversi gruppi:\n",
        "\n",
        "- Training Set\n",
        "- Validation Set\n",
        "\n",
        "Eseguiamo 70/30 per training/test.\n",
        "\n",
        "Andiamo quindi a calcolarci questi errori che ci permetteranno di valutare i modelli a regressione di machine learning:\n",
        "\n",
        "- lo scarto medio assoluto (MAE - Mean Absolute Error);\n",
        "- l’errore quadratico medio (MSE - Mean Squared Error);\n",
        "- la radice dell’errore quadratico medio (RMSE - Root of Mean Squared Error)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "d0f72c54",
      "metadata": {
        "id": "d0f72c54"
      },
      "outputs": [],
      "source": [
        "def MSE (y_pred,y_true):\n",
        "    return 1/y_pred.shape[0]*np.sum((y_pred-y_true)**2)\n",
        "def MAE (y_pred,y_true):\n",
        "    return 1/y_pred.shape[0]*np.sum(np.abs(y_pred-y_true))\n",
        "def RMSE (y_pred,y_true):\n",
        "    return np.sqrt(1/y_pred.shape[0]*np.sum((y_pred-y_true)**2))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d201c73c",
      "metadata": {
        "id": "d201c73c"
      },
      "source": [
        "Adesso possiamo usare il modello imparato, per effettuare predizioni sul validation set. E' sufficiente usare la funzione predict sull'oggetto restituito da fit, passando come argomento x_valid"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3377551f",
      "metadata": {
        "id": "3377551f"
      },
      "source": [
        "Verifichiamo se si può effettuare questa predizione con più features, successivamente con le features correlate prese insieme."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "a0eb1396",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a0eb1396",
        "outputId": "425c6562-e847-498e-9956-580da3344792"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['BPM' 'Energy' 'Danceability' 'Loudness' 'Liveness' 'Valence' 'Length'\n",
            " 'Acousticness' 'Speechiness']\n",
            "\n",
            "Con tutte le features a nostra disposizione:\n",
            "\n",
            "MAE: 4.589901697647539\n",
            "MSE: 34.88193531379405\n",
            "RMSE: 5.906093066807706\n",
            "\n",
            "Solo con le features correlate trovate nella analisi precedente:\n",
            "\n",
            "['BPM', 'Speechiness', 'Valence']\n",
            "MAE: 2.877948501776448\n",
            "MSE: 13.61125201396483\n",
            "RMSE: 3.6893430328399703\n"
          ]
        }
      ],
      "source": [
        "#utilizzare tutte le features insieme\n",
        "features = np.asarray(list(quantitative_top50.columns))[1:quantitative_top50.shape[1]-1]\n",
        "print(features)\n",
        "x = top50[features]\n",
        "y = top50['Popularity']\n",
        "regressor = LinearRegression()\n",
        "x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size = 0.3,  random_state = 10)\n",
        "lin_reg = regressor.fit(x_train, y_train)\n",
        "y_pred = lin_reg.predict(x_valid)\n",
        "print('\\nCon tutte le features a nostra disposizione:\\n')\n",
        "print('MAE:', MAE(y_pred,y_valid))\n",
        "print('MSE:', MSE(y_pred,y_valid))\n",
        "print('RMSE:', RMSE(y_pred, y_valid))\n",
        "\n",
        "\n",
        "#features correlate alla popolarità, insieme\n",
        "print('\\nSolo con le features correlate trovate nella analisi precedente:\\n')\n",
        "print(features_correlate_alla_popolarità)\n",
        "x = top50[features_correlate_alla_popolarità]\n",
        "y = top50['Popularity']\n",
        "regressor = LinearRegression()\n",
        "x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size = 0.3,  random_state = 10)\n",
        "lin_reg = regressor.fit(x_train, y_train)\n",
        "y_pred = lin_reg.predict(x_valid)\n",
        "print('MAE:', MAE(y_pred,y_valid))\n",
        "print('MSE:', MSE(y_pred,y_valid))\n",
        "print('RMSE:', RMSE(y_pred, y_valid))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "43ac6a0c",
      "metadata": {
        "id": "43ac6a0c"
      },
      "source": [
        "Possiamo notare subito come l'errore diminuisce con l'uso delle features che prima sono risultate essere le più correlate alla popolarità.\n",
        "\n",
        "Bisogna comunque precisare che data la scarsità di dati a nostra disposizione questi valori sopra raccolti \"sballano\" molto di esecuzione in esecuzione proprio perchè i dati vengono splittati al 70% nel training test e il 30% nel validation test in modo completamente casuale."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "E ora con le features correlate una alla volta"
      ],
      "metadata": {
        "id": "2KoCAlt7_AV3"
      },
      "id": "2KoCAlt7_AV3"
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "b4a579d5",
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          "height": 1000
        },
        "id": "b4a579d5",
        "outputId": "260cbed6-56b5-4bd2-e94c-2de6d134cf96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BPM :\n",
            "intercept: 84.37300588037397\n",
            "coefficient: [0.02492158]\n",
            "MAE: 2.741935990749343\n",
            "MSE: 11.829668657730775\n",
            "RMSE: 3.4394285365058503 \n",
            "\n",
            "Speechiness :\n",
            "intercept: 85.78458475447557\n",
            "coefficient: [0.13350849]\n",
            "MAE: 2.938471748508142\n",
            "MSE: 14.080034646060911\n",
            "RMSE: 3.752337224459032 \n",
            "\n",
            "Valence :\n",
            "intercept: 91.15749512555108\n",
            "coefficient: [-0.06963338]\n",
            "MAE: 2.8796683987368565\n",
            "MSE: 12.327602460926675\n",
            "RMSE: 3.5110685639740327 \n",
            "\n"
          ]
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
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        }
      ],
      "source": [
        "for i in range(0, len(features_correlate_alla_popolarità)):\n",
        "    features = features_correlate_alla_popolarità[i]\n",
        "    x = top50[[features]]\n",
        "    y = top50['Popularity']\n",
        "    X_train, X_valid, y_train, y_valid = train_test_split(x, y, test_size = 0.3, random_state = 10)\n",
        "\n",
        "    regressor = LinearRegression()\n",
        "    lin_reg = regressor.fit(X_train, y_train)\n",
        "    \n",
        "    print(features,':')\n",
        "    print('intercept:', lin_reg.intercept_)\n",
        "    print('coefficient:', lin_reg.coef_)\n",
        "    \n",
        "    f = plt.figure(figsize=(15,5))\n",
        "    ax = f.add_subplot(121)\n",
        "    ax2 = f.add_subplot(122)\n",
        "\n",
        "    ax.scatter(X_train, y_train, alpha = 0.2)\n",
        "    y_hat = X_train*lin_reg.coef_ + lin_reg.intercept_\n",
        "    ax.plot(X_train, y_hat, color='r')\n",
        "    ax.set_ylabel(\"Popolarità\", fontdict = font2)\n",
        "    ax.set_xlabel(features, fontdict = font2)\n",
        "    ax.set_title(\"\\n Training Set \" + features, fontdict = font2)\n",
        "    \n",
        "    \n",
        "    y_hat = regressor.predict(X_valid)\n",
        "    ax2.scatter(X_valid, y_hat)\n",
        "    ax2.scatter(X_valid, y_valid, c = 'r', alpha = 0.2)\n",
        "    ax2.set_ylabel(\"Popolarità\", fontdict = font2)\n",
        "    ax2.set_xlabel(features, fontdict = font2)\n",
        "    ax2.set_title(\"\\n Validation Set \" + features, fontdict = font2)\n",
        "    \n",
        "    f.suptitle('Regressione Lineare ' + features, fontdict = font1, fontsize = 20)\n",
        "    \n",
        "    #calcoliamo le predizioni sul validation set (y_pred)\n",
        "    y_pred = regressor.predict(X_valid)\n",
        "    print('MAE:', MAE(y_pred,y_valid))\n",
        "    print('MSE:', MSE(y_pred,y_valid))\n",
        "    print('RMSE:', RMSE(y_pred,y_valid),'\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0aa6a01a",
      "metadata": {
        "id": "0aa6a01a"
      },
      "source": [
        "Gli errori quindi trovati in precedenza ora devono essere confrontati con il modello nullo per vedere quali predizioni poter \"accettare\":"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "3835aaac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3835aaac",
        "outputId": "652a31af-0830-423d-a29e-42fd2d06d298"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 3.24\n",
            "MSE: 19.77\n",
            "RMSE: 4.446346815083142\n"
          ]
        }
      ],
      "source": [
        "#calcoliamo la popolarità media\n",
        "popolarità_media = top50['Popularity'].mean()\n",
        "\n",
        "# create la nuova x con lo stesso numero di campioni del dataset originale, e con tutti i valori uguali al valore medio del diametro appena calcolato\n",
        "null_model_y = [popolarità_media] * top50.shape[0]\n",
        "\n",
        "# valutiamo il modello nullo usando le funzioni implementate \n",
        "print ('MAE:', metrics.mean_absolute_error(y, null_model_y))\n",
        "print ('MSE:', metrics.mean_squared_error(y, null_model_y))\n",
        "print ('RMSE:', np.sqrt(metrics.mean_squared_error(y, null_model_y)))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f93c3abd",
      "metadata": {
        "id": "f93c3abd"
      },
      "source": [
        "Traiamo quindi le nostre ***conclusioni***:\n",
        "- ***tra le features correlate prendendole una ad una*** : BPM, Speechiness e Valenza, andandole ad esaminare e plottare confrontandole una ad una, restituiscono un errore minore all'errore prodotto nel modello nullo quindi possiamo accettare la predizione della popolarità effettuata su queste features prese una alla volta.\n",
        "- ***con l'utilizzo di tutte le features quantitative a nostra disposizione nel dataset tranne la popolarità*** : si ottiene un errore più alto rispetto all'errore riscontrato nel modello nullo e quindi è da scartare l'utilizzo di queste features per ottenere la predizione della popolarità;\n",
        "- ***usando le sole features correlate alla popolarità insieme*** : Anche in questo caso l'errore ottenuto è minore a quello ottenuto nel modello nullo."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dececf90",
      "metadata": {
        "id": "dececf90"
      },
      "source": [
        "### *CONCLUSIONE*"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fcdba92b",
      "metadata": {
        "id": "fcdba92b"
      },
      "source": [
        "Ricapitolando: nell'elaborazione e visualizzazione iniziale del dataset a nostra disposizione sono emersi dati interessanti come:\n",
        "- l'artista più presente in classifica: Ed Sheeran che però non risulta essere l'artista con le canzoni più popolari mediamente.\n",
        "- l'artista con le canzoni mediamente meno popolari: Shawn Mendes, il quale però e l'artista che presenta la canzone prima in classifica.\n",
        "- il genere ed il macrogenere più presenti all'interno della classifica, ovvero il dance pop e il pop rispettivamente.\n",
        "- la distribuzione della popolarità delle canzoni in generale(divisone in bins), in base al genere, in base al macrogenere, in base al sesso dell'artista dalle quali abbiamo estrapolato le informazioni descritte precedentemente.\n",
        "- quali features effettivamente fossero collegate alla popolarità per andare cosi ad effettuare il machine learning sopra riportato portandoci dietro appunto queste informazioni estratte dall'analisi effettuata"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c38faca",
      "metadata": {
        "id": "5c38faca"
      },
      "source": [
        "Per concludere la nostra analisi dobbiamo ancora vedere se siamo riusciti a rispondere alla domanda dalla quale è iniziato tutto: *quali sono le caratteristiche che rendono una canzone di successo?*\n",
        "\n",
        "Possiamo dire che non siamo riusciti direttamente a capire quali sono gli attributi e quindi le caratteristiche appunto che rendono una canzone più o meno popolare ma siamo riusciti ad ottenere informazioni sulle features più correlate alla popolarità riuscendo cosi ad allenare due modelli di machine learning:\n",
        "- Supervisionato, implementato attraverso l'utilizzo della regressione lineare ottenendo con successo una predizione della popolarità utilizzando le features che dall'elaborazione dei dati sono risultate essere appunto le più correlate come detto in precedenza.\n",
        "- Non supervisionato, realizzato con la clusterizzazione per risolvere il problema di classificazione per ottenere una divisione del nostro dataset in canzoni più popolari e canzoni meno popolari. Anche in questo caso si sono utilizzate solo le features più correlate alla popolarità per ottenere appunto \"gruppi di dati coesi\" visualizzati attraverso un plot in tre dimensioni, corrispondenti alle features utilizzate proprio per la divisione in cluster. Con il box plot si può apprezzare come effettivamente siamo riusciti ad ottenere due gruppi di dati che si differenziano proprio per la popolarità.\n",
        "\n"
      ]
    }
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