10_dentmax_hierarchical_clustering.ipynb

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        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/alesaccoia/IULM_DDM2324_Notebooks/blob/main/10_dentmax_hierarchical_clustering.ipynb)"
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        "# Capitolo 5: Dentmax, Exploratory Data Analysis"
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              "  Observations / Segmentation Variables  Time Spent at DentMax Booth  \\\n",
              "0                          Respondent 1                           12   \n",
              "1                          Respondent 2                           13   \n",
              "2                          Respondent 3                           12   \n",
              "3                          Respondent 4                           11   \n",
              "4                          Respondent 5                           12   \n",
              "\n",
              "   Time Spent at DentMed/OxyMax Booth  Total Time spent at show  \\\n",
              "0                                  22                       150   \n",
              "1                                  22                       150   \n",
              "2                                  21                       150   \n",
              "3                                  21                       150   \n",
              "4                                  21                       150   \n",
              "\n",
              "   % Buying from DentMax in the past  Historical Sales Index  Sales Frequency  \\\n",
              "0                                 32                      21                3   \n",
              "1                                 32                      20                3   \n",
              "2                                 32                      22                3   \n",
              "3                                 33                      21                3   \n",
              "4                                 32                      21                3   \n",
              "\n",
              "   Length of Relationship  Number of Referrals Offered  \\\n",
              "0                       4                            1   \n",
              "1                       4                            1   \n",
              "2                       4                            1   \n",
              "3                       4                            1   \n",
              "4                       4                            1   \n",
              "\n",
              "   Importance of Image Quality  Importance of Diagnostic Flexibility  \\\n",
              "0                            5                                     5   \n",
              "1                            5                                     5   \n",
              "2                            5                                     5   \n",
              "3                            5                                     5   \n",
              "4                            5                                     5   \n",
              "\n",
              "   Importance of Software Integration  Importance of Technical Assistance  \\\n",
              "0                                   5                                   6   \n",
              "1                                   5                                   7   \n",
              "2                                   5                                   6   \n",
              "3                                   5                                   6   \n",
              "4                                   5                                   7   \n",
              "\n",
              "   Importance of Price  Trust in TV ads  Trust in Radio Ads  \\\n",
              "0                    7                5                   5   \n",
              "1                    7                5                   5   \n",
              "2                    7                5                   5   \n",
              "3                    7                4                   5   \n",
              "4                    7                5                   4   \n",
              "\n",
              "   Trust in Internet Ads  Trust in Dental Magazine Ads  Trust in Peers  \n",
              "0                      4                             5               6  \n",
              "1                      4                             5               5  \n",
              "2                      4                             6               6  \n",
              "3                      4                             5               5  \n",
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     \"column\": \"Historical Sales Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 8,\n        \"max\": 65,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          63,\n          57,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Length of Relationship\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 4,\n        \"max\": 9,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9,\n          8,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Referrals Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 12,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          3,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Image Quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6,\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Diagnostic Flexibility\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7,\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Software Integration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6,\n          7,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Technical Assistance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 7,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6,\n          7,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6,\n          5,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in TV ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in Radio Ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in Internet Ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6,\n          7,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in Dental Magazine Ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6,\n          4,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in Peers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 7,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6,\n          5,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from scipy.stats import pearsonr\n",
        "!wget \"https://github.com/LeoLin72/IULM_DDM2324_Notebooks/raw/main/data/Capitolo5e6-DentMax.xlsx\"\n",
        "# Read the data\n",
        "df = pd.read_excel(\"Capitolo5e6-DentMax.xlsx\")\n",
        "\n",
        "# Display the first few rows of the dataframe\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gK5Hm0E4EscI",
        "outputId": "18227acf-85ed-4d44-eeb5-721e7f5b68e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2300 entries, 0 to 2299\n",
            "Data columns (total 19 columns):\n",
            " #   Column                                 Non-Null Count  Dtype \n",
            "---  ------                                 --------------  ----- \n",
            " 0   Observations / Segmentation Variables  2300 non-null   object\n",
            " 1   Time Spent at DentMax Booth            2300 non-null   int64 \n",
            " 2   Time Spent at DentMed/OxyMax Booth     2300 non-null   int64 \n",
            " 3   Total Time spent at show               2300 non-null   int64 \n",
            " 4   % Buying from DentMax in the past      2300 non-null   int64 \n",
            " 5   Historical Sales Index                 2300 non-null   int64 \n",
            " 6   Sales Frequency                        2300 non-null   int64 \n",
            " 7   Length of Relationship                 2300 non-null   int64 \n",
            " 8   Number of Referrals Offered            2300 non-null   int64 \n",
            " 9   Importance of Image Quality            2300 non-null   int64 \n",
            " 10  Importance of Diagnostic Flexibility   2300 non-null   int64 \n",
            " 11  Importance of Software Integration     2300 non-null   int64 \n",
            " 12  Importance of Technical Assistance     2300 non-null   int64 \n",
            " 13  Importance of Price                    2300 non-null   int64 \n",
            " 14  Trust in TV ads                        2300 non-null   int64 \n",
            " 15  Trust in Radio Ads                     2300 non-null   int64 \n",
            " 16  Trust in Internet Ads                  2300 non-null   int64 \n",
            " 17  Trust in Dental Magazine Ads           2300 non-null   int64 \n",
            " 18  Trust in Peers                         2300 non-null   int64 \n",
            "dtypes: int64(18), object(1)\n",
            "memory usage: 341.5+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "0eIA1bwsEscJ",
        "outputId": "d491d94e-493d-48b2-8597-67dc9fef9c45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Time Spent at DentMax Booth  Time Spent at DentMed/OxyMax Booth  \\\n",
              "count                  2300.000000                         2300.000000   \n",
              "mean                     20.237826                           32.433913   \n",
              "std                      13.996379                           24.603166   \n",
              "min                       5.000000                           10.000000   \n",
              "25%                      12.000000                           21.000000   \n",
              "50%                      12.000000                           21.000000   \n",
              "75%                      36.000000                           26.000000   \n",
              "max                      46.000000                           79.000000   \n",
              "\n",
              "       Total Time spent at show  % Buying from DentMax in the past  \\\n",
              "count               2300.000000                        2300.000000   \n",
              "mean                 138.923478                          38.606957   \n",
              "std                   38.743412                          25.769014   \n",
              "min                   59.000000                           5.000000   \n",
              "25%                  130.000000                          32.000000   \n",
              "50%                  150.000000                          32.000000   \n",
              "75%                  151.000000                          45.000000   \n",
              "max                  182.000000                         101.000000   \n",
              "\n",
              "       Historical Sales Index  Sales Frequency  Length of Relationship  \\\n",
              "count             2300.000000      2300.000000             2300.000000   \n",
              "mean                32.860435         3.298261                6.420435   \n",
              "std                 21.341852         1.440632                2.332929   \n",
              "min                  8.000000         0.000000                4.000000   \n",
              "25%                 21.000000         3.000000                4.000000   \n",
              "50%                 21.000000         3.000000                8.000000   \n",
              "75%                 58.000000         4.000000                9.000000   \n",
              "max                 65.000000         6.000000                9.000000   \n",
              "\n",
              "       Number of Referrals Offered  Importance of Image Quality  \\\n",
              "count                  2300.000000                  2300.000000   \n",
              "mean                      2.142609                     5.257826   \n",
              "std                       3.728569                     0.849765   \n",
              "min                       0.000000                     3.000000   \n",
              "25%                       0.000000                     5.000000   \n",
              "50%                       1.000000                     5.000000   \n",
              "75%                       2.000000                     6.000000   \n",
              "max                      12.000000                     7.000000   \n",
              "\n",
              "       Importance of Diagnostic Flexibility  \\\n",
              "count                           2300.000000   \n",
              "mean                               5.179565   \n",
              "std                                0.853017   \n",
              "min                                3.000000   \n",
              "25%                                5.000000   \n",
              "50%                                5.000000   \n",
              "75%                                5.000000   \n",
              "max                                7.000000   \n",
              "\n",
              "       Importance of Software Integration  Importance of Technical Assistance  \\\n",
              "count                         2300.000000                         2300.000000   \n",
              "mean                             5.000870                            6.134348   \n",
              "std                              0.798541                            0.495954   \n",
              "min                              4.000000                            5.000000   \n",
              "25%                              4.000000                            6.000000   \n",
              "50%                              5.000000                            6.000000   \n",
              "75%                              5.000000                            6.000000   \n",
              "max                              7.000000                            7.000000   \n",
              "\n",
              "       Importance of Price  Trust in TV ads  Trust in Radio Ads  \\\n",
              "count          2300.000000      2300.000000         2300.000000   \n",
              "mean              6.408696         4.533913            4.364348   \n",
              "std               0.904733         0.506742            0.560664   \n",
              "min               4.000000         3.000000            3.000000   \n",
              "25%               6.000000         4.000000            4.000000   \n",
              "50%               7.000000         5.000000            4.000000   \n",
              "75%               7.000000         5.000000            5.000000   \n",
              "max               7.000000         5.000000            5.000000   \n",
              "\n",
              "       Trust in Internet Ads  Trust in Dental Magazine Ads  Trust in Peers  \n",
              "count            2300.000000                   2300.000000     2300.000000  \n",
              "mean                4.236522                      5.352609        5.940870  \n",
              "std                 1.020310                      0.832421        0.695464  \n",
              "min                 2.000000                      4.000000        5.000000  \n",
              "25%                 4.000000                      5.000000        5.000000  \n",
              "50%                 4.000000                      5.000000        6.000000  \n",
              "75%                 4.000000                      6.000000        6.000000  \n",
              "max                 7.000000                      7.000000        7.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-54a04e68-0fc4-4cbd-9569-51137d0649aa\" 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>Time Spent at DentMax Booth</th>\n",
              "      <th>Time Spent at DentMed/OxyMax Booth</th>\n",
              "      <th>Total Time spent at show</th>\n",
              "      <th>% Buying from DentMax in the past</th>\n",
              "      <th>Historical Sales Index</th>\n",
              "      <th>Sales Frequency</th>\n",
              "      <th>Length of Relationship</th>\n",
              "      <th>Number of Referrals Offered</th>\n",
              "      <th>Importance of Image Quality</th>\n",
              "      <th>Importance of Diagnostic Flexibility</th>\n",
              "      <th>Importance of Software Integration</th>\n",
              "      <th>Importance of Technical Assistance</th>\n",
              "      <th>Importance of Price</th>\n",
              "      <th>Trust in TV ads</th>\n",
              "      <th>Trust in Radio Ads</th>\n",
              "      <th>Trust in Internet Ads</th>\n",
              "      <th>Trust in Dental Magazine Ads</th>\n",
              "      <th>Trust in Peers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "      <td>2300.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>20.237826</td>\n",
              "      <td>32.433913</td>\n",
              "      <td>138.923478</td>\n",
              "      <td>38.606957</td>\n",
              "      <td>32.860435</td>\n",
              "      <td>3.298261</td>\n",
              "      <td>6.420435</td>\n",
              "      <td>2.142609</td>\n",
              "      <td>5.257826</td>\n",
              "      <td>5.179565</td>\n",
              "      <td>5.000870</td>\n",
              "      <td>6.134348</td>\n",
              "      <td>6.408696</td>\n",
              "      <td>4.533913</td>\n",
              "      <td>4.364348</td>\n",
              "      <td>4.236522</td>\n",
              "      <td>5.352609</td>\n",
              "      <td>5.940870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.996379</td>\n",
              "      <td>24.603166</td>\n",
              "      <td>38.743412</td>\n",
              "      <td>25.769014</td>\n",
              "      <td>21.341852</td>\n",
              "      <td>1.440632</td>\n",
              "      <td>2.332929</td>\n",
              "      <td>3.728569</td>\n",
              "      <td>0.849765</td>\n",
              "      <td>0.853017</td>\n",
              "      <td>0.798541</td>\n",
              "      <td>0.495954</td>\n",
              "      <td>0.904733</td>\n",
              "      <td>0.506742</td>\n",
              "      <td>0.560664</td>\n",
              "      <td>1.020310</td>\n",
              "      <td>0.832421</td>\n",
              "      <td>0.695464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>151.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>46.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>182.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54a04e68-0fc4-4cbd-9569-51137d0649aa')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-54a04e68-0fc4-4cbd-9569-51137d0649aa 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-54a04e68-0fc4-4cbd-9569-51137d0649aa');\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",
              "\n",
              "\n",
              "<div id=\"df-2c5f4fd8-b8bb-4290-8d46-ec52224b98f2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2c5f4fd8-b8bb-4290-8d46-ec52224b98f2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2c5f4fd8-b8bb-4290-8d46-ec52224b98f2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Time Spent at DentMax Booth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 805.9541088305448,\n        \"min\": 5.0,\n        \"max\": 2300.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2300.0,\n          20.23782608695652,\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Spent at DentMed/OxyMax Booth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 802.6303605938581,\n        \"min\": 10.0,\n        \"max\": 2300.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2300.0,\n          32.43391304347826,\n          26.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Time spent at show\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 771.7842587753747,\n        \"min\": 38.74341225442203,\n        \"max\": 2300.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          138.92347826086956,\n          150.0,\n          2300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"% Buying from DentMax in the past\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 799.5361145963639,\n        \"min\": 5.0,\n        \"max\": 2300.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2300.0,\n          38.60695652173913,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Historical Sales Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 801.9375071939799,\n        \"min\": 8.0,\n        \"max\": 2300.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2300.0,\n          32.86043478260869,\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 812.1272180191819,\n        \"min\": 0.0,\n        \"max\": 2300.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2300.0,\n          3.2982608695652176,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Length of Relationship\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.017262378846,\n        \"min\": 2.3329289411886647,\n        \"max\": 2300.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2300.0,\n          6.420434782608695,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Referrals Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 812.1279029193008,\n        \"min\": 0.0,\n        \"max\": 2300.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2300.0,\n          2.1426086956521737,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Image Quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.5533289221082,\n        \"min\": 0.8497649882198854,\n        \"max\": 2300.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2300.0,\n          5.257826086956522,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Diagnostic Flexibility\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.6074388119183,\n        \"min\": 0.8530169484864465,\n        \"max\": 2300.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2300.0,\n          5.179565217391304,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Software Integration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.6190529520595,\n        \"min\": 0.7985410415699253,\n        \"max\": 2300.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2300.0,\n          5.000869565217391,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Technical Assistance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.3251708605457,\n        \"min\": 0.49595428102229905,\n        \"max\": 2300.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2300.0,\n          6.134347826086956,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance of Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.2404240074933,\n        \"min\": 0.9047326360045903,\n        \"max\": 2300.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2300.0,\n          6.408695652173913,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in TV ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.8084781849693,\n        \"min\": 0.5067421822171824,\n        \"max\": 2300.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2300.0,\n          4.533913043478261,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in Radio Ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.8646497797627,\n        \"min\": 0.5606637625225768,\n        \"max\": 2300.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2300.0,\n          4.364347826086957,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in Internet Ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.8485197759574,\n        \"min\": 1.020309672885304,\n        \"max\": 2300.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2300.0,\n          4.236521739130435,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in Dental Magazine Ads\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.4987277018648,\n        \"min\": 0.8324211080851871,\n        \"max\": 2300.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2300.0,\n          5.352608695652174,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trust in Peers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 811.3751239339176,\n        \"min\": 0.6954641650739039,\n        \"max\": 2300.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2300.0,\n          5.940869565217391,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSvOsK3lEscJ"
      },
      "source": [
        "Standardizzazione delle variabili"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99JCcJpEEscJ",
        "outputId": "93f41581-b47d-4199-d50f-0b797686d051"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.58869639, -0.42418045,  0.2859565 , ..., -0.23186409,\n",
              "        -0.42368625,  0.08504147],\n",
              "       [-0.5172338 , -0.42418045,  0.2859565 , ..., -0.23186409,\n",
              "        -0.42368625, -1.35315983],\n",
              "       [-0.58869639, -0.46483447,  0.2859565 , ..., -0.23186409,\n",
              "         0.77789004,  0.08504147],\n",
              "       ...,\n",
              "       [ 1.05494318,  1.85244436,  1.06045007, ...,  1.72875138,\n",
              "         0.77789004,  1.52324276],\n",
              "       [-0.58869639, -0.46483447,  0.2859565 , ..., -0.23186409,\n",
              "        -0.42368625,  0.08504147],\n",
              "       [-0.58869639, -0.46483447,  0.2859565 , ..., -0.23186409,\n",
              "        -0.42368625, -1.35315983]])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "df_scaled = scaler.fit_transform(df.iloc[:, 1:])\n",
        "df_scaled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "AjkEBl-WEscK"
      },
      "outputs": [],
      "source": [
        "# Clustering gerarchico\n",
        "Z = linkage(df_scaled, method='ward', metric='euclidean')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "2558KLDGEscK",
        "outputId": "2d067193-44d4-48f9-8055-385459e841b2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualizza il dendrogramma\n",
        "plt.figure(figsize=(12, 6))\n",
        "dendrogram(Z, p=10, truncate_mode='level')\n",
        "plt.xlabel('Nodi')\n",
        "plt.ylabel('Distanza')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKF-MkTOEscM"
      },
      "source": [
        "# Decisione dell'altezza di \"taglio\"\n",
        "\n",
        "Dopo aver esaminato il dendrogramma, é possibile valre quali e quanti gruppi tenere. Anche se é difficile trovare l'altezza giusta dal grafico precedente, é possibile comunque chiedere al programma quanti cluster rimangono dopo aver \"tagliato\" il dendrogramma a una certa altezza. Proviamo con 75, per ottenere 4 cluster"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NZe8G6lSEscM"
      },
      "outputs": [],
      "source": [
        "threshold = 75"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xh-K_puQEscM",
        "outputId": "6dccba8b-ee11-40d6-ceec-687ad49e0b8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Effettua il clustering in base all'altezza di taglio scelta\n",
        "clusters = fcluster(Z, t=threshold, criterion='distance')\n",
        "np.unique(clusters)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gshAklrEscM"
      },
      "source": [
        "75 pare un'altezza corretta e rileviamo 4 cluster (la variabile contiene gli ID ordinati, uno per osservazione)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3lDCgOKAEscM",
        "outputId": "9fc6c9bc-1e41-4fe0-f732-d762f4cd3368"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 4, 4, ..., 2, 4, 4], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "clusters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-lwcDAOEscM"
      },
      "source": [
        "Aggiungiamo la colonna con l'ID del cluster al cluster originale, e non a quello normalizzato.\n",
        "In tal modo abbiamo operato su dati normalizzati, ma interpreteremo dati sulle loro scale reali"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "415KjXqYEscN"
      },
      "outputs": [],
      "source": [
        "# Aggiungi la colonna dei cluster al DataFrame originale (non a quello normalizzato, in tal modo )\n",
        "df['Cluster'] = clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "HTKfDDoJEscN",
        "outputId": "21fdf9ad-2676-4f05-aaf0-4bde3904b99b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualizza le differenze tra i cluster mediante boxplot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(x='Cluster', y='Historical Sales Index', data=df)\n",
        "plt.xlabel('Cluster')\n",
        "plt.ylabel('Valutazione')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8_ZC9_7EscN"
      },
      "source": [
        "## Proporzione per cluster"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BkZ0odwqEscN",
        "outputId": "bb07b270-24a3-4481-89aa-f861bc81d572"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster\n",
            "4    0.49\n",
            "2    0.22\n",
            "3    0.17\n",
            "1    0.12\n",
            "Name: count, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "cluster_counts = df['Cluster'].value_counts()\n",
        "proportions = cluster_counts / df.shape[0]\n",
        "\n",
        "print(proportions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbwMj0eQEscN",
        "outputId": "2f1e71d0-55f8-45cd-df11-d20e48afbdc9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Cluster  Observations / Segmentation Variables  \\\n",
            "0        1                                    NaN   \n",
            "1        2                                    NaN   \n",
            "2        3                                    NaN   \n",
            "3        4                                    NaN   \n",
            "\n",
            "   Time Spent at DentMax Booth  Time Spent at DentMed/OxyMax Booth  \\\n",
            "0                    45.003623                           25.971014   \n",
            "1                    35.994071                           78.027668   \n",
            "2                     6.038363                           10.979540   \n",
            "3                    12.024845                           20.989352   \n",
            "\n",
            "   Total Time spent at show  % Buying from DentMax in the past  \\\n",
            "0                130.003623                          99.974638   \n",
            "1                180.023715                          45.057312   \n",
            "2                 60.015345                           5.971867   \n",
            "3                150.031056                          32.004437   \n",
            "\n",
            "   Historical Sales Index  Sales Frequency  Length of Relationship  \\\n",
            "0               58.018116         5.989130                8.007246   \n",
            "1               64.001976         4.266798                8.982213   \n",
            "2                9.015345         0.987212                8.751918   \n",
            "3               20.990240         3.006211                4.072760   \n",
            "\n",
            "   Number of Referrals Offered  Importance of Image Quality  \\\n",
            "0                    12.000000                     6.739130   \n",
            "1                     2.073123                     6.005929   \n",
            "2                     0.000000                     3.984655   \n",
            "3                     0.503106                     5.000887   \n",
            "\n",
            "   Importance of Diagnostic Flexibility  Importance of Software Integration  \\\n",
            "0                              6.971014                            4.210145   \n",
            "1                              5.484190                            6.225296   \n",
            "2                              3.989770                            4.012788   \n",
            "3                              5.016859                            4.987578   \n",
            "\n",
            "   Importance of Technical Assistance  Importance of Price  Trust in TV ads  \\\n",
            "0                            5.510870             4.300725         5.000000   \n",
            "1                            5.972332             5.998024         4.235178   \n",
            "2                            6.982097             7.000000         3.976982   \n",
            "3                            6.065661             6.904170         4.747116   \n",
            "\n",
            "   Trust in Radio Ads  Trust in Internet Ads  Trust in Dental Magazine Ads  \\\n",
            "0            4.014493               3.764493                      6.518116   \n",
            "1            4.758893               6.005929                      5.986166   \n",
            "2            3.757033               2.984655                      4.000000   \n",
            "3            4.483585               3.992014                      5.251996   \n",
            "\n",
            "   Trust in Peers  \n",
            "0        6.003623  \n",
            "1        6.970356  \n",
            "2        5.063939  \n",
            "3        5.767524  \n"
          ]
        }
      ],
      "source": [
        "# Calculate centroids for each cluster\n",
        "df = df.apply(pd.to_numeric, errors='coerce')\n",
        "\n",
        "cluster_centroids = df.groupby('Cluster').mean().reset_index()\n",
        "print(cluster_centroids)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YANpBz6nEscN"
      },
      "source": [
        "## Trasposizione dei cluster per visualizzazione tabella"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "yNHwaRsxEscN",
        "outputId": "0ce7228a-afdc-4444-f961-68fba4c12460"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cluster                                   1.0     2.0    3.0     4.0\n",
              "Observations / Segmentation Variables     NaN     NaN    NaN     NaN\n",
              "Time Spent at DentMax Booth             45.00   35.99   6.04   12.02\n",
              "Time Spent at DentMed/OxyMax Booth      25.97   78.03  10.98   20.99\n",
              "Total Time spent at show               130.00  180.02  60.02  150.03\n",
              "% Buying from DentMax in the past       99.97   45.06   5.97   32.00\n",
              "Historical Sales Index                  58.02   64.00   9.02   20.99\n",
              "Sales Frequency                          5.99    4.27   0.99    3.01\n",
              "Length of Relationship                   8.01    8.98   8.75    4.07\n",
              "Number of Referrals Offered             12.00    2.07   0.00    0.50\n",
              "Importance of Image Quality              6.74    6.01   3.98    5.00\n",
              "Importance of Diagnostic Flexibility     6.97    5.48   3.99    5.02\n",
              "Importance of Software Integration       4.21    6.23   4.01    4.99\n",
              "Importance of Technical Assistance       5.51    5.97   6.98    6.07\n",
              "Importance of Price                      4.30    6.00   7.00    6.90\n",
              "Trust in TV ads                          5.00    4.24   3.98    4.75\n",
              "Trust in Radio Ads                       4.01    4.76   3.76    4.48\n",
              "Trust in Internet Ads                    3.76    6.01   2.98    3.99\n",
              "Trust in Dental Magazine Ads             6.52    5.99   4.00    5.25\n",
              "Trust in Peers                           6.00    6.97   5.06    5.77"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e409969-70a3-4401-af90-e73800e195d1\" 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",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Cluster</th>\n",
              "      <th>1.0</th>\n",
              "      <th>2.0</th>\n",
              "      <th>3.0</th>\n",
              "      <th>4.0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Observations / Segmentation Variables</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time Spent at DentMax Booth</th>\n",
              "      <td>45.00</td>\n",
              "      <td>35.99</td>\n",
              "      <td>6.04</td>\n",
              "      <td>12.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time Spent at DentMed/OxyMax Booth</th>\n",
              "      <td>25.97</td>\n",
              "      <td>78.03</td>\n",
              "      <td>10.98</td>\n",
              "      <td>20.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Time spent at show</th>\n",
              "      <td>130.00</td>\n",
              "      <td>180.02</td>\n",
              "      <td>60.02</td>\n",
              "      <td>150.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Buying from DentMax in the past</th>\n",
              "      <td>99.97</td>\n",
              "      <td>45.06</td>\n",
              "      <td>5.97</td>\n",
              "      <td>32.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Historical Sales Index</th>\n",
              "      <td>58.02</td>\n",
              "      <td>64.00</td>\n",
              "      <td>9.02</td>\n",
              "      <td>20.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sales Frequency</th>\n",
              "      <td>5.99</td>\n",
              "      <td>4.27</td>\n",
              "      <td>0.99</td>\n",
              "      <td>3.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Length of Relationship</th>\n",
              "      <td>8.01</td>\n",
              "      <td>8.98</td>\n",
              "      <td>8.75</td>\n",
              "      <td>4.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number of Referrals Offered</th>\n",
              "      <td>12.00</td>\n",
              "      <td>2.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Importance of Image Quality</th>\n",
              "      <td>6.74</td>\n",
              "      <td>6.01</td>\n",
              "      <td>3.98</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Importance of Diagnostic Flexibility</th>\n",
              "      <td>6.97</td>\n",
              "      <td>5.48</td>\n",
              "      <td>3.99</td>\n",
              "      <td>5.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Importance of Software Integration</th>\n",
              "      <td>4.21</td>\n",
              "      <td>6.23</td>\n",
              "      <td>4.01</td>\n",
              "      <td>4.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Importance of Technical Assistance</th>\n",
              "      <td>5.51</td>\n",
              "      <td>5.97</td>\n",
              "      <td>6.98</td>\n",
              "      <td>6.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Importance of Price</th>\n",
              "      <td>4.30</td>\n",
              "      <td>6.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>6.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trust in TV ads</th>\n",
              "      <td>5.00</td>\n",
              "      <td>4.24</td>\n",
              "      <td>3.98</td>\n",
              "      <td>4.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trust in Radio Ads</th>\n",
              "      <td>4.01</td>\n",
              "      <td>4.76</td>\n",
              "      <td>3.76</td>\n",
              "      <td>4.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trust in Internet Ads</th>\n",
              "      <td>3.76</td>\n",
              "      <td>6.01</td>\n",
              "      <td>2.98</td>\n",
              "      <td>3.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trust in Dental Magazine Ads</th>\n",
              "      <td>6.52</td>\n",
              "      <td>5.99</td>\n",
              "      <td>4.00</td>\n",
              "      <td>5.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trust in Peers</th>\n",
              "      <td>6.00</td>\n",
              "      <td>6.97</td>\n",
              "      <td>5.06</td>\n",
              "      <td>5.77</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e409969-70a3-4401-af90-e73800e195d1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0e409969-70a3-4401-af90-e73800e195d1 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-0e409969-70a3-4401-af90-e73800e195d1');\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",
              "\n",
              "\n",
              "<div id=\"df-fe96e0a8-7fd2-4149-90c9-9024c3bbbccc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fe96e0a8-7fd2-4149-90c9-9024c3bbbccc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fe96e0a8-7fd2-4149-90c9-9024c3bbbccc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cluster_centroids_transposed",
              "summary": "{\n  \"name\": \"cluster_centroids_transposed\",\n  \"rows\": 19,\n  \"fields\": [\n    {\n      \"column\": 1.0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.655058643835325,\n        \"min\": 3.76,\n        \"max\": 130.0,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          45.0,\n          25.97,\n          6.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2.0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.56769918290058,\n        \"min\": 2.07,\n        \"max\": 180.02,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          35.99,\n          78.03,\n          4.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3.0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.22022703818571,\n        \"min\": 0.0,\n        \"max\": 60.02,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6.04,\n          10.98,\n          0.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4.0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.30101546258807,\n        \"min\": 0.5,\n        \"max\": 150.03,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          12.02,\n          20.99,\n          4.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "\n",
        "# Calculate centroids for each cluster\n",
        "cluster_centroids = cluster_centroids.round(2)\n",
        "\n",
        "# Transpose the matrix\n",
        "cluster_centroids_transposed = cluster_centroids.transpose()\n",
        "\n",
        "# Correct the column headers after transposition\n",
        "cluster_centroids_transposed.columns = cluster_centroids_transposed.iloc[0]\n",
        "cluster_centroids_transposed = cluster_centroids_transposed.drop('Cluster')\n",
        "cluster_centroids_transposed\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "WiXLZf9sEscN"
      },
      "outputs": [],
      "source": [
        "cluster_centroids_transposed.to_csv('cluster_centroids_transposed.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3ggpIwBEscN"
      },
      "source": [
        "## Visualizzazione con PCA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "QeiHFIg5EscN",
        "outputId": "4a450d3d-674e-4bc6-bfe4-be0850a437fb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Esegui PCA\n",
        "pca = PCA(n_components=2)\n",
        "df_pca = pca.fit_transform(df_scaled)\n",
        "\n",
        "# Set the style of the plot\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Create a scatter plot of the PCA results with colors based on cluster ID\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(x=df_pca[:, 0], y=df_pca[:, 1], hue=df['Cluster'], palette='tab10', s=80)\n",
        "plt.xlabel('Principal Component 1')\n",
        "plt.ylabel('Principal Component 2')\n",
        "plt.legend(title='Cluster', loc='best', labels=['Cluster ' + str(i) for i in range(1, len(np.unique(clusters)) + 1)])\n",
        "plt.grid(False)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.17"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}