Clustering_shared.ipynb

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        "<a href=\"https://colab.research.google.com/github/LilianYou/Sea_Hero_Quest/blob/main/Clustering_shared.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "## Characterizing self-reported navigation ability using demographic information\n",
        "\n",
        "### read data"
      ]
    },
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      },
      "source": [
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "drive.mount('/content/drive')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYS2n6Gyxjo8"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8L80ydzbvbBo"
      },
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/SHQ/Data/2019_cleaned_demographics_3121970.csv')"
      ],
      "execution_count": null,
      "outputs": []
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        "df"
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              "         age  education  gender  hand  home_environment navigating_skills  \\\n",
              "0       68.0          1       0     0                 2               bad   \n",
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      "source": [
        "#df.head()\n",
        "df = df.drop(['navigating_skills'], axis= 1)"
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              "         age  education  gender  hand  home_environment  sleep  travel_time  \\\n",
              "0       68.0          1       0     0                 2    8.0            0   \n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771627</th>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>771628 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db028eac-5fd5-4f7c-8cb3-879f76ab325b')\"\n",
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              "\n",
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              "          const element = document.querySelector('#df-db028eac-5fd5-4f7c-8cb3-879f76ab325b');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7Wxqme88Zc-"
      },
      "source": [
        "## Clustering\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4Ux8UGVPt1K"
      },
      "source": [
        "from sklearn.metrics.cluster import adjusted_rand_score\n",
        "from sklearn.preprocessing import scale,RobustScaler,StandardScaler, MinMaxScaler\n",
        "from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN\n",
        "#import mglearn\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "from matplotlib import cm\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "2ftZWWpIGQmi",
        "outputId": "7181773a-45dd-4527-ad52-1db03c303189"
      },
      "source": [
        "# MinMax scaled\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaler.fit(df)\n",
        "df2 = scaler.transform(df)\n",
        "df2 = pd.DataFrame(df2,index=df.index,columns=['age',\t'gender',\t'hand',\t'nav_num',\t'sleep',\t'education',\t'travel_time',\t'home_environment'])\n",
        "# df2 = pd.DataFrame(df2,index=df.index,columns=['age',\t'gender',\t'hand',\t'navigating_skills',\t'sleep',\t'edu_level',\t'commute_level',\t'home_env'])\n",
        "df2[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  gender  hand  nav_num  sleep  education  travel_time  \\\n",
              "0  0.960784     1.0   0.0      0.0    1.0   0.555556          0.0   \n",
              "1  0.039216     1.0   0.0      0.0    0.5   0.444444          0.5   \n",
              "2  0.333333     1.0   1.0      0.0    0.0   0.222222          0.5   \n",
              "3  0.000000     0.0   1.0      0.0    0.5   0.555556          0.0   \n",
              "4  0.823529     1.0   1.0      0.0    0.5   0.444444          0.0   \n",
              "5  0.196078     1.0   1.0      0.0    0.0   0.444444          1.0   \n",
              "6  0.000000     1.0   0.0      0.0    0.5   0.555556          0.0   \n",
              "7  0.372549     1.0   1.0      0.0    0.0   0.444444          0.0   \n",
              "8  0.392157     1.0   1.0      0.0    1.0   0.333333          0.0   \n",
              "9  0.490196     0.0   1.0      0.0    0.5   0.444444          0.5   \n",
              "\n",
              "   home_environment  \n",
              "0          0.333333  \n",
              "1          0.666667  \n",
              "2          1.000000  \n",
              "3          0.666667  \n",
              "4          0.666667  \n",
              "5          1.000000  \n",
              "6          0.333333  \n",
              "7          0.666667  \n",
              "8          1.000000  \n",
              "9          0.666667  "
            ],
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              "      <th>6</th>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "\n",
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              "          document.querySelector('#df-f1b98ffd-8411-489c-a07e-1a7b9cc4d262 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-f1b98ffd-8411-489c-a07e-1a7b9cc4d262');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8hDZ2q5WRk6"
      },
      "source": [
        "df3 = df2.drop(columns=\"nav_num\", axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SCcIO9m7WeLE",
        "outputId": "1e04426d-8e20-4df3-99b2-f348b9055017"
      },
      "source": [
        "df3.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  gender  hand  sleep  education  travel_time  home_environment\n",
              "0  0.960784     1.0   0.0    1.0   0.555556          0.0          0.333333\n",
              "1  0.039216     1.0   0.0    0.5   0.444444          0.5          0.666667\n",
              "2  0.333333     1.0   1.0    0.0   0.222222          0.5          1.000000\n",
              "3  0.000000     0.0   1.0    0.5   0.555556          0.0          0.666667\n",
              "4  0.823529     1.0   1.0    0.5   0.444444          0.0          0.666667"
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              "      <td>0.222222</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.823529</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df5df246-6343-46a8-94e1-f62e067dfcd9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-df5df246-6343-46a8-94e1-f62e067dfcd9 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-df5df246-6343-46a8-94e1-f62e067dfcd9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "n-KYWSXpX_YY",
        "outputId": "d6ae5940-8bf5-4c5a-ec31-1364e35cb2a8"
      },
      "source": [
        "results = dict()\n",
        "k_cand = [1, 2,3,4,5,6,7] #\n",
        "\n",
        "for k in k_cand:\n",
        "    kmeans = KMeans(n_clusters=k, random_state=0).fit(df3)\n",
        "    score0 = kmeans.inertia_\n",
        "    #score1 = silhouette_score(df2,kmeans.labels_,metric='euclidean')\n",
        "    #score2 = silhouette_score(df2,kmeans.labels_,metric='correlation')\n",
        "    results[k] = {'k':kmeans,'s0':score0} #,'s1':score1,'s2':score2\n",
        "\n",
        "fig,axs = plt.subplots(1,2,sharex=True,figsize=(10,3))\n",
        "axs[0].plot([i for i in results.keys()],[i['s0'] for i in results.values()],'o-',label='Inertia')\n",
        "#axs[1].plot([i for i in results.keys()],[i['s1'] for i in results.values()],'o-',label='Euclidean')\n",
        "#axs[1].plot([i for i in results.keys()],[i['s2'] for i in results.values()],'o-',label='Correlation')\n",
        "for ax in axs:\n",
        "    ax.set_xticks(k_cand)\n",
        "    ax.set_xlabel('Number of Cluster')\n",
        "    ax.set_ylabel('Within Cluster Sum of Square')\n",
        "    ax.set_ylim(0, 1000000)\n",
        "    ax.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3B3tI-njzGW"
      },
      "source": [
        "# Higher value of CH index means the clusters are dense and well separated, although there is no “acceptable” cut-off value. \n",
        "# We need to choose that solution which gives a peak or at least an abrupt elbow on the line plot of CH indices. On the other hand, \n",
        "# if the line is smooth (horizontal or ascending or descending) then there is no such reason to prefer one solution over others. \n",
        "\n",
        "# The Davies–Bouldin index (DBI) (introduced by David L. Davies and Donald W. Bouldin in 1979), a metric for evaluating clustering algorithms, \n",
        "# is an internal evaluation scheme, where the validation of how well the clustering has been done is made using quantities and features inherent to the dataset.\n",
        "# Lower the DB index value, better is the clustering. It also has a drawback. A good value reported by this method does not imply the best information retrieval."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "yF3fibRPjzNb",
        "outputId": "461e5539-445d-4cac-9a54-54a1db34c4c1"
      },
      "source": [
        "results = dict()\n",
        "k_cand = [ 2,3,4,5,6,7] #\n",
        "\n",
        "for k in k_cand:\n",
        "    kmeans = KMeans(n_clusters=k, random_state=0).fit(df3)\n",
        "    labels = kmeans.labels_\n",
        "    score0 = metrics.calinski_harabasz_score(df3, labels)\n",
        "    #score1 = silhouette_score(df2,kmeans.labels_,metric='euclidean')\n",
        "    #score2 = silhouette_score(df2,kmeans.labels_,metric='correlation')\n",
        "    results[k] = {'k':kmeans,'s0':score0} #,'s1':score1,'s2':score2\n",
        "\n",
        "fig,axs = plt.subplots(1,2,sharex=True,figsize=(10,3))\n",
        "axs[0].plot([i for i in results.keys()],[i['s0'] for i in results.values()],'o-',label='Inertia')\n",
        "#axs[1].plot([i for i in results.keys()],[i['s1'] for i in results.values()],'o-',label='Euclidean')\n",
        "#axs[1].plot([i for i in results.keys()],[i['s2'] for i in results.values()],'o-',label='Correlation')\n",
        "for ax in axs:\n",
        "    ax.set_xticks(k_cand)\n",
        "    ax.set_xlabel('Number of Cluster')\n",
        "    ax.set_ylabel('Calinski Harabasz Score')\n",
        "    ax.legend()    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "QoWNWdPwjzWQ",
        "outputId": "3f34d92d-d159-4a0e-fb89-e484cef51f67"
      },
      "source": [
        "results = dict()\n",
        "k_cand = [ 2,3,4,5,6,7] #\n",
        "\n",
        "for k in k_cand:\n",
        "    kmeans = KMeans(n_clusters=k, random_state=0).fit(df3)\n",
        "    labels = kmeans.labels_\n",
        "    score0 = metrics.davies_bouldin_score(df3, labels)\n",
        "    #score1 = silhouette_score(df2,kmeans.labels_,metric='euclidean')\n",
        "    #score2 = silhouette_score(df2,kmeans.labels_,metric='correlation')\n",
        "    results[k] = {'k':kmeans,'s0':score0} #,'s1':score1,'s2':score2\n",
        "\n",
        "fig,axs = plt.subplots(1,2,sharex=True,figsize=(10,3))\n",
        "axs[0].plot([i for i in results.keys()],[i['s0'] for i in results.values()],'o-',label='Inertia')\n",
        "#axs[1].plot([i for i in results.keys()],[i['s1'] for i in results.values()],'o-',label='Euclidean')\n",
        "#axs[1].plot([i for i in results.keys()],[i['s2'] for i in results.values()],'o-',label='Correlation')\n",
        "for ax in axs:\n",
        "    ax.set_xticks(k_cand)\n",
        "    ax.set_xlabel('Number of Cluster')\n",
        "    ax.set_ylabel('Davies Bouldin Score')\n",
        "    ax.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "THNQpno0jLwD",
        "outputId": "aafa1178-3256-4c5c-ceec-5b2c801a135e"
      },
      "source": [
        "results = dict()\n",
        "k_cand = [ 2,3,4,5, 6] \n",
        "sil = [0.2907570615986244, 0.30833652748415463, 0.35319903869020247, 0.3005087485722123, 0.28350636635398946]\n",
        "\n",
        "for k in k_cand:\n",
        "    kmeans = KMeans(n_clusters=k, random_state=0).fit(df3)\n",
        "    labels = kmeans.labels_\n",
        "    score0 = sil[k-2]  #metrics.davies_bouldin_score(df3, labels)\n",
        "    #score1 = silhouette_score(df2,kmeans.labels_,metric='euclidean')\n",
        "    #score2 = silhouette_score(df2,kmeans.labels_,metric='correlation')\n",
        "    results[k] = {'k':kmeans,'s0':score0} #,'s1':score1,'s2':score2\n",
        "\n",
        "fig,axs = plt.subplots(1,2,sharex=True,figsize=(10,3))\n",
        "axs[0].plot([i for i in results.keys()],[i['s0'] for i in results.values()],'o-',label='Inertia')\n",
        "#axs[1].plot([i for i in results.keys()],[i['s1'] for i in results.values()],'o-',label='Euclidean')\n",
        "#axs[1].plot([i for i in results.keys()],[i['s2'] for i in results.values()],'o-',label='Correlation')\n",
        "for ax in axs:\n",
        "    ax.set_xticks(k_cand)\n",
        "    ax.set_xlabel('Number of Cluster')\n",
        "    ax.set_ylabel('Average Silhouette Score')\n",
        "    ax.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x216 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzFnowJMAwbD"
      },
      "source": [
        "# siloutte. Basically, in this visualization as depicted above, each color represents a cluster, \n",
        "# its thickness denotes the cluster size. The red dotted line represents the average silhouette coefficient. \n",
        "# For a good cluster, silhouette coefficient will be close to 1 and similar in size compared to other clusters.\n",
        "\n",
        "# The value of the Silhouette score varies from -1 to 1. If the score is 1, the cluster is dense and well-separated \n",
        "# than other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary \n",
        "# of the neighboring clusters. A negative score [-1, 0] indicates that the samples might have got assigned to the wrong clusters.\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "NbqujZggTdPr",
        "outputId": "159b8d9d-6480-4169-e68a-11a8173df863"
      },
      "source": [
        "range_n_clusters = [2]\n",
        "\n",
        "for n_clusters in range_n_clusters:\n",
        "    # Create a subplot with 1 row and 2 columns\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "    fig.set_size_inches(18, 7)\n",
        "\n",
        "    # The 1st subplot is the silhouette plot\n",
        "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
        "    # lie within [-0.1, 1]\n",
        "    ax1.set_xlim([-0.1, 1])\n",
        "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
        "    # plots of individual clusters, to demarcate them clearly.\n",
        "    ax1.set_ylim([0, len(df3) + (n_clusters + 1) * 10])\n",
        "\n",
        "    # Initialize the clusterer with n_clusters value and a random generator\n",
        "    # seed of 10 for reproducibility.\n",
        "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
        "    cluster_labels = clusterer.fit_predict(df3)\n",
        "\n",
        "    # The silhouette_score gives the average value for all the samples.\n",
        "    # This gives a perspective into the density and separation of the formed\n",
        "    # clusters\n",
        "    silhouette_avg = silhouette_score(df3, cluster_labels)\n",
        "    print(\"For n_clusters =\", n_clusters,\n",
        "          \"The average silhouette_score is :\", silhouette_avg)\n",
        "\n",
        "    # Compute the silhouette scores for each sample\n",
        "    sample_silhouette_values = silhouette_samples(df3, cluster_labels)\n",
        "\n",
        "    y_lower = 10\n",
        "    for i in range(n_clusters):\n",
        "        # Aggregate the silhouette scores for samples belonging to\n",
        "        # cluster i, and sort them\n",
        "        ith_cluster_silhouette_values = \\\n",
        "            sample_silhouette_values[cluster_labels == i]\n",
        "\n",
        "        ith_cluster_silhouette_values.sort()\n",
        "\n",
        "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
        "        y_upper = y_lower + size_cluster_i\n",
        "\n",
        "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
        "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
        "                          0, ith_cluster_silhouette_values,\n",
        "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
        "\n",
        "        # Label the silhouette plots with their cluster numbers at the middle\n",
        "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
        "\n",
        "        # Compute the new y_lower for next plot\n",
        "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
        "\n",
        "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
        "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
        "    ax1.set_ylabel(\"Cluster label\")\n",
        "\n",
        "    # The vertical line for average silhouette score of all the values\n",
        "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
        "\n",
        "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
        "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
        "\n",
        "    # 2nd Plot showing the actual clusters formed\n",
        "    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
        "    ax2.scatter(df3[\"gender\"], df3[\"home_environment\"], marker='.', s=30, lw=0, alpha=0.7,\n",
        "                c=colors, edgecolor='k')\n",
        "\n",
        "    # Labeling the clusters\n",
        "    centers = clusterer.cluster_centers_\n",
        "    # Draw white circles at cluster centers\n",
        "    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
        "                c=\"white\", alpha=1, s=200, edgecolor='k')\n",
        "\n",
        "    for i, c in enumerate(centers):\n",
        "        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n",
        "                    s=50, edgecolor='k')\n",
        "\n",
        "    ax2.set_title(\"The visualization of the clustered data.\")\n",
        "    ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
        "    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
        "\n",
        "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
        "                  \"with n_clusters = %d\" % n_clusters),\n",
        "                 fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# average score = 0.2907570615986244"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 2 The average silhouette_score is : 0.2907570615986244\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZwD40xAATeWt",
        "outputId": "70a442ad-d9ae-4f42-f159-ed36459027a0"
      },
      "source": [
        "range_n_clusters = [3]\n",
        "\n",
        "for n_clusters in range_n_clusters:\n",
        "    # Create a subplot with 1 row and 2 columns\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "    fig.set_size_inches(18, 7)\n",
        "\n",
        "    # The 1st subplot is the silhouette plot\n",
        "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
        "    # lie within [-0.1, 1]\n",
        "    ax1.set_xlim([-0.1, 1])\n",
        "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
        "    # plots of individual clusters, to demarcate them clearly.\n",
        "    ax1.set_ylim([0, len(df3) + (n_clusters + 1) * 10])\n",
        "\n",
        "    # Initialize the clusterer with n_clusters value and a random generator\n",
        "    # seed of 10 for reproducibility.\n",
        "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
        "    cluster_labels = clusterer.fit_predict(df3)\n",
        "\n",
        "    # The silhouette_score gives the average value for all the samples.\n",
        "    # This gives a perspective into the density and separation of the formed\n",
        "    # clusters\n",
        "    silhouette_avg = silhouette_score(df3, cluster_labels)\n",
        "    print(\"For n_clusters =\", n_clusters,\n",
        "          \"The average silhouette_score is :\", silhouette_avg)\n",
        "\n",
        "    # Compute the silhouette scores for each sample\n",
        "    sample_silhouette_values = silhouette_samples(df3, cluster_labels)\n",
        "\n",
        "    y_lower = 10\n",
        "    for i in range(n_clusters):\n",
        "        # Aggregate the silhouette scores for samples belonging to\n",
        "        # cluster i, and sort them\n",
        "        ith_cluster_silhouette_values = \\\n",
        "            sample_silhouette_values[cluster_labels == i]\n",
        "\n",
        "        ith_cluster_silhouette_values.sort()\n",
        "\n",
        "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
        "        y_upper = y_lower + size_cluster_i\n",
        "\n",
        "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
        "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
        "                          0, ith_cluster_silhouette_values,\n",
        "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
        "\n",
        "        # Label the silhouette plots with their cluster numbers at the middle\n",
        "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
        "\n",
        "        # Compute the new y_lower for next plot\n",
        "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
        "\n",
        "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
        "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
        "    ax1.set_ylabel(\"Cluster label\")\n",
        "\n",
        "    # The vertical line for average silhouette score of all the values\n",
        "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
        "\n",
        "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
        "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
        "\n",
        "    # 2nd Plot showing the actual clusters formed\n",
        "    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
        "    ax2.scatter(df3[\"gender\"], df3[\"home_environment\"], marker='.', s=30, lw=0, alpha=0.7,\n",
        "                c=colors, edgecolor='k')\n",
        "\n",
        "    # Labeling the clusters\n",
        "    centers = clusterer.cluster_centers_\n",
        "    # Draw white circles at cluster centers\n",
        "    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
        "                c=\"white\", alpha=1, s=200, edgecolor='k')\n",
        "\n",
        "    for i, c in enumerate(centers):\n",
        "        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n",
        "                    s=50, edgecolor='k')\n",
        "\n",
        "    ax2.set_title(\"The visualization of the clustered data.\")\n",
        "    ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
        "    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
        "\n",
        "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
        "                  \"with n_clusters = %d\" % n_clusters),\n",
        "                 fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# For n_clusters = 3 The average silhouette_score is : 0.30833652748415463\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "For n_clusters = 3 The average silhouette_score is : 0.30833652748415463\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "ydTL933l5UKM",
        "outputId": "abb59f8f-ae05-4878-cfad-2768cd1c581e"
      },
      "source": [
        "range_n_clusters = [4]\n",
        "\n",
        "for n_clusters in range_n_clusters:\n",
        "    # Create a subplot with 1 row and 2 columns\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "    fig.set_size_inches(18, 7)\n",
        "\n",
        "    # The 1st subplot is the silhouette plot\n",
        "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
        "    # lie within [-0.1, 1]\n",
        "    ax1.set_xlim([-0.1, 1])\n",
        "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
        "    # plots of individual clusters, to demarcate them clearly.\n",
        "    ax1.set_ylim([0, len(df3) + (n_clusters + 1) * 10])\n",
        "\n",
        "    # Initialize the clusterer with n_clusters value and a random generator\n",
        "    # seed of 10 for reproducibility.\n",
        "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
        "    cluster_labels = clusterer.fit_predict(df3)\n",
        "\n",
        "    # The silhouette_score gives the average value for all the samples.\n",
        "    # This gives a perspective into the density and separation of the formed\n",
        "    # clusters\n",
        "    silhouette_avg = silhouette_score(df3, cluster_labels)\n",
        "    print(\"For n_clusters =\", n_clusters,\n",
        "          \"The average silhouette_score is :\", silhouette_avg)\n",
        "\n",
        "    # Compute the silhouette scores for each sample\n",
        "    sample_silhouette_values = silhouette_samples(df3, cluster_labels)\n",
        "\n",
        "    y_lower = 10\n",
        "    for i in range(n_clusters):\n",
        "        # Aggregate the silhouette scores for samples belonging to\n",
        "        # cluster i, and sort them\n",
        "        ith_cluster_silhouette_values = \\\n",
        "            sample_silhouette_values[cluster_labels == i]\n",
        "\n",
        "        ith_cluster_silhouette_values.sort()\n",
        "\n",
        "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
        "        y_upper = y_lower + size_cluster_i\n",
        "\n",
        "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
        "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
        "                          0, ith_cluster_silhouette_values,\n",
        "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
        "\n",
        "        # Label the silhouette plots with their cluster numbers at the middle\n",
        "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
        "\n",
        "        # Compute the new y_lower for next plot\n",
        "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
        "\n",
        "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
        "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
        "    ax1.set_ylabel(\"Cluster label\")\n",
        "\n",
        "    # The vertical line for average silhouette score of all the values\n",
        "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
        "\n",
        "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
        "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
        "\n",
        "    # 2nd Plot showing the actual clusters formed\n",
        "    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
        "    ax2.scatter(df3[\"gender\"], df3[\"home_environment\"], marker='.', s=30, lw=0, alpha=0.7,\n",
        "                c=colors, edgecolor='k')\n",
        "\n",
        "    # Labeling the clusters\n",
        "    centers = clusterer.cluster_centers_\n",
        "    # Draw white circles at cluster centers\n",
        "    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
        "                c=\"white\", alpha=1, s=200, edgecolor='k')\n",
        "\n",
        "    for i, c in enumerate(centers):\n",
        "        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n",
        "                    s=50, edgecolor='k')\n",
        "\n",
        "    ax2.set_title(\"The visualization of the clustered data.\")\n",
        "    ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
        "    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
        "\n",
        "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
        "                  \"with n_clusters = %d\" % n_clusters),\n",
        "                 fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# For n_clusters = 4 The average silhouette_score is : 0.35319903869020247\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 4 The average silhouette_score is : 0.35319903869020247\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "Sm79phDYpw5O",
        "outputId": "d0c5dd6b-2dfc-43bc-e35a-8f1d9ace95d2"
      },
      "source": [
        "range_n_clusters = [5]\n",
        "\n",
        "for n_clusters in range_n_clusters:\n",
        "    # Create a subplot with 1 row and 2 columns\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "    fig.set_size_inches(18, 7)\n",
        "\n",
        "    # The 1st subplot is the silhouette plot\n",
        "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
        "    # lie within [-0.1, 1]\n",
        "    ax1.set_xlim([-0.1, 1])\n",
        "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
        "    # plots of individual clusters, to demarcate them clearly.\n",
        "    ax1.set_ylim([0, len(df3) + (n_clusters + 1) * 10])\n",
        "\n",
        "    # Initialize the clusterer with n_clusters value and a random generator\n",
        "    # seed of 10 for reproducibility.\n",
        "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
        "    cluster_labels = clusterer.fit_predict(df3)\n",
        "\n",
        "    # The silhouette_score gives the average value for all the samples.\n",
        "    # This gives a perspective into the density and separation of the formed\n",
        "    # clusters\n",
        "    silhouette_avg = silhouette_score(df3, cluster_labels)\n",
        "    print(\"For n_clusters =\", n_clusters,\n",
        "          \"The average silhouette_score is :\", silhouette_avg)\n",
        "\n",
        "    # Compute the silhouette scores for each sample\n",
        "    sample_silhouette_values = silhouette_samples(df3, cluster_labels)\n",
        "\n",
        "    y_lower = 10\n",
        "    for i in range(n_clusters):\n",
        "        # Aggregate the silhouette scores for samples belonging to\n",
        "        # cluster i, and sort them\n",
        "        ith_cluster_silhouette_values = \\\n",
        "            sample_silhouette_values[cluster_labels == i]\n",
        "\n",
        "        ith_cluster_silhouette_values.sort()\n",
        "\n",
        "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
        "        y_upper = y_lower + size_cluster_i\n",
        "\n",
        "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
        "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
        "                          0, ith_cluster_silhouette_values,\n",
        "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
        "\n",
        "        # Label the silhouette plots with their cluster numbers at the middle\n",
        "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
        "\n",
        "        # Compute the new y_lower for next plot\n",
        "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
        "\n",
        "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
        "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
        "    ax1.set_ylabel(\"Cluster label\")\n",
        "\n",
        "    # The vertical line for average silhouette score of all the values\n",
        "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
        "\n",
        "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
        "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
        "\n",
        "    # 2nd Plot showing the actual clusters formed\n",
        "    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
        "    ax2.scatter(df3[\"gender\"], df3[\"home_environment\"], marker='.', s=30, lw=0, alpha=0.7,\n",
        "                c=colors, edgecolor='k')\n",
        "\n",
        "    # Labeling the clusters\n",
        "    centers = clusterer.cluster_centers_\n",
        "    # Draw white circles at cluster centers\n",
        "    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
        "                c=\"white\", alpha=1, s=200, edgecolor='k')\n",
        "\n",
        "    for i, c in enumerate(centers):\n",
        "        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n",
        "                    s=50, edgecolor='k')\n",
        "\n",
        "    ax2.set_title(\"The visualization of the clustered data.\")\n",
        "    ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
        "    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
        "\n",
        "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
        "                  \"with n_clusters = %d\" % n_clusters),\n",
        "                 fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# For n_clusters = 5 The average silhouette_score is : 0.3005087485722123\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 5 The average silhouette_score is : 0.3005087485722123\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdMfpqX_a7G-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "9904f429-532b-4d36-8fe7-fb17eab0092d"
      },
      "source": [
        "range_n_clusters = [6]\n",
        "\n",
        "for n_clusters in range_n_clusters:\n",
        "    # Create a subplot with 1 row and 2 columns\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "    fig.set_size_inches(18, 7)\n",
        "\n",
        "    # The 1st subplot is the silhouette plot\n",
        "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
        "    # lie within [-0.1, 1]\n",
        "    ax1.set_xlim([-0.1, 1])\n",
        "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
        "    # plots of individual clusters, to demarcate them clearly.\n",
        "    ax1.set_ylim([0, len(df3) + (n_clusters + 1) * 10])\n",
        "\n",
        "    # Initialize the clusterer with n_clusters value and a random generator\n",
        "    # seed of 10 for reproducibility.\n",
        "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
        "    cluster_labels = clusterer.fit_predict(df3)\n",
        "\n",
        "    # The silhouette_score gives the average value for all the samples.\n",
        "    # This gives a perspective into the density and separation of the formed\n",
        "    # clusters\n",
        "    silhouette_avg = silhouette_score(df3, cluster_labels)\n",
        "    print(\"For n_clusters =\", n_clusters,\n",
        "          \"The average silhouette_score is :\", silhouette_avg)\n",
        "\n",
        "    # Compute the silhouette scores for each sample\n",
        "    sample_silhouette_values = silhouette_samples(df3, cluster_labels)\n",
        "\n",
        "    y_lower = 10\n",
        "    for i in range(n_clusters):\n",
        "        # Aggregate the silhouette scores for samples belonging to\n",
        "        # cluster i, and sort them\n",
        "        ith_cluster_silhouette_values = \\\n",
        "            sample_silhouette_values[cluster_labels == i]\n",
        "\n",
        "        ith_cluster_silhouette_values.sort()\n",
        "\n",
        "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
        "        y_upper = y_lower + size_cluster_i\n",
        "\n",
        "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
        "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
        "                          0, ith_cluster_silhouette_values,\n",
        "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
        "\n",
        "        # Label the silhouette plots with their cluster numbers at the middle\n",
        "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
        "\n",
        "        # Compute the new y_lower for next plot\n",
        "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
        "\n",
        "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
        "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
        "    ax1.set_ylabel(\"Cluster label\")\n",
        "\n",
        "    # The vertical line for average silhouette score of all the values\n",
        "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
        "\n",
        "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
        "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
        "\n",
        "    # 2nd Plot showing the actual clusters formed\n",
        "    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
        "    ax2.scatter(df3[\"gender\"], df3[\"home_environment\"], marker='.', s=30, lw=0, alpha=0.7,\n",
        "                c=colors, edgecolor='k')\n",
        "\n",
        "    # Labeling the clusters\n",
        "    centers = clusterer.cluster_centers_\n",
        "    # Draw white circles at cluster centers\n",
        "    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
        "                c=\"white\", alpha=1, s=200, edgecolor='k')\n",
        "\n",
        "    for i, c in enumerate(centers):\n",
        "        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n",
        "                    s=50, edgecolor='k')\n",
        "\n",
        "    ax2.set_title(\"The visualization of the clustered data.\")\n",
        "    ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
        "    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
        "\n",
        "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
        "                  \"with n_clusters = %d\" % n_clusters),\n",
        "                 fontsize=14, fontweight='bold')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 6 The average silhouette_score is : 0.28350636635398946\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "2FywF9TH5kLS",
        "outputId": "77498a94-8504-40bf-93bd-3157bbec6542"
      },
      "source": [
        "intertia = []\n",
        "K = range(1,15)\n",
        "for k in K:\n",
        "    km = KMeans(n_clusters=k)\n",
        "    km = km.fit(df3)\n",
        "    intertia.append(km.inertia_)\n",
        "plt.plot(K, intertia, marker= \"x\")\n",
        "plt.xlabel('k')\n",
        "plt.xticks(np.arange(15))\n",
        "plt.ylabel('Intertia')\n",
        "plt.title('Elbow Method')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzO4RNBH70CC"
      },
      "source": [
        "the point before a roughly linear decrease in Inertia — which in this case is k = 2."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhhQor_Aru55"
      },
      "source": [
        "df2 = df3.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nl4sanl88MH5",
        "outputId": "284a522c-fc37-4a05-9369-320d8e3c902d"
      },
      "source": [
        "km = KMeans(n_clusters= 4, n_init = 2, init = \"random\", random_state = 42)\n",
        "%time km.fit(df2)\n",
        "y_kmeans = km.predict(df2)\n",
        "y_kmeans"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.64 s, sys: 12.8 ms, total: 1.65 s\n",
            "Wall time: 966 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 2, ..., 3, 0, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "u6ufCqbV8Pcv",
        "outputId": "865e10f7-2e85-4ff5-e88f-cf787b1a8ec8"
      },
      "source": [
        "df2['cluster'] = y_kmeans\n",
        "df2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  gender  hand  sleep  education  travel_time  home_environment  \\\n",
              "0  0.960784     1.0   0.0    1.0   0.555556          0.0          0.333333   \n",
              "1  0.039216     1.0   0.0    0.5   0.444444          0.5          0.666667   \n",
              "2  0.333333     1.0   1.0    0.0   0.222222          0.5          1.000000   \n",
              "3  0.000000     0.0   1.0    0.5   0.555556          0.0          0.666667   \n",
              "4  0.823529     1.0   1.0    0.5   0.444444          0.0          0.666667   \n",
              "\n",
              "   cluster  \n",
              "0        1  \n",
              "1        1  \n",
              "2        2  \n",
              "3        3  \n",
              "4        2  "
            ],
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7f0aab79-bf0b-4295-bc10-f1ea34f946e3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nTOjlvaXfTMn",
        "outputId": "f42ed63f-54aa-4202-d069-12991ee03737"
      },
      "source": [
        "df['cluster'] = y_kmeans\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age  education  gender  hand  home_environment  sleep  travel_time  \\\n",
              "0       68.0          1       0     0                 2    8.0            0   \n",
              "1       21.0          1       0     0                 1    7.0            1   \n",
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              "...      ...        ...     ...   ...               ...    ...          ...   \n",
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              "771626  48.0          1       1     0                 1    6.0            2   \n",
              "771627  47.0          1       1     1                 2    7.0            2   \n",
              "\n",
              "        nav_num  cluster  \n",
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              "4             2        2  \n",
              "...         ...      ...  \n",
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              "                                                     [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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "p31RGSoY-ZiQ",
        "outputId": "01ba44aa-3899-46f8-c376-b47e80b3dfa0"
      },
      "source": [
        "## change cluster\n",
        "cluster_map = {0: \"group1\", 1: \"group2\", 2: \"group3\", 3:\"group4\"}\n",
        "\n",
        "df['cluster'] = df['cluster'].replace(cluster_map)\n",
        "\n",
        "df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age  education  gender  hand  home_environment  sleep  travel_time  \\\n",
              "0       68.0          1       0     0                 2    8.0            0   \n",
              "1       21.0          1       0     0                 1    7.0            1   \n",
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              "771627  47.0          1       1     1                 2    7.0            2   \n",
              "\n",
              "        nav_num cluster  \n",
              "0             1  group2  \n",
              "1             2  group2  \n",
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              "4             2  group3  \n",
              "...         ...     ...  \n",
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              "771625        3  group4  \n",
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              "771627        3  group1  \n",
              "\n",
              "[771628 rows x 9 columns]"
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              "<p>771628 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5983167e-9c9d-46e4-9140-26d38050a8be')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5983167e-9c9d-46e4-9140-26d38050a8be 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-5983167e-9c9d-46e4-9140-26d38050a8be');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "iKXPJo749SnD",
        "outputId": "70203446-ab7e-48dc-f214-b44855743a9a"
      },
      "source": [
        "df.cluster.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0f9dd4d190>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEjikuEc9gis"
      },
      "source": [
        "#df.to_csv('/content/drive/My Drive/SHQ/Data/2019_cleaned_demographics_with_cluster_3121970.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0SJZhVbq-Rn-"
      },
      "source": [
        "# Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bi2aO-70-WSf"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "import seaborn as sns\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BO0mg6QYbaLZ",
        "outputId": "484577f4-6e91-4b6e-884c-c597cc8058e3"
      },
      "source": [
        "df2_1 = df[df['cluster']  == \"group1\"]\n",
        "\n",
        "\n",
        "#df2_1 = df2_1.drop([\"cluster\", \"navigating_skills\"], axis=1)\n",
        "df2_1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age  education  gender  hand  home_environment  sleep  travel_time  \\\n",
              "5       29.0          1       1     0                 0    7.0            2   \n",
              "11      19.0          1       1     1                 2    7.0            2   \n",
              "12      20.0          1       1     0                 2    6.0            2   \n",
              "15      29.0          1       1     0                 1    7.0            2   \n",
              "17      27.0          1       1     0                 0    6.0            2   \n",
              "...      ...        ...     ...   ...               ...    ...          ...   \n",
              "771613  32.0          1       1     0                 1    6.0            2   \n",
              "771619  19.0          1       1     0                 2    9.0            2   \n",
              "771623  50.0          0       1     1                 0    6.0            2   \n",
              "771626  48.0          1       1     0                 1    6.0            2   \n",
              "771627  47.0          1       1     1                 2    7.0            2   \n",
              "\n",
              "        nav_num cluster  \n",
              "5             3  group1  \n",
              "11            2  group1  \n",
              "12            2  group1  \n",
              "15            3  group1  \n",
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              "...         ...     ...  \n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91d4dfd2-e0f4-4fec-b2e5-899a0e093abf')\"\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91d4dfd2-e0f4-4fec-b2e5-899a0e093abf 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-91d4dfd2-e0f4-4fec-b2e5-899a0e093abf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "rS5odIGcpy4l",
        "outputId": "ad163f2d-82e6-4568-f4de-78ed8bffc47e"
      },
      "source": [
        "df2_1['nav_num'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd05ddafd50>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "IpLW3rGrZQKS",
        "outputId": "ff85dcbe-7ef3-482f-f7f5-0b06c302c446"
      },
      "source": [
        "df2_2 = df[df['cluster']  == \"group2\"]\n",
        "\n",
        "#df2_2 = df2_2.drop([\"cluster\", \"navigating_skills\"], axis=1)\n",
        "df2_2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age  education  gender  hand  home_environment  sleep  travel_time  \\\n",
              "0       68.0          1       0     0                 2    8.0            0   \n",
              "1       21.0          1       0     0                 1    7.0            1   \n",
              "6       19.0          1       0     0                 1    8.0            0   \n",
              "19      24.0          1       0     0                 2    8.0            2   \n",
              "20      50.0          1       0     0                 0    6.0            1   \n",
              "...      ...        ...     ...   ...               ...    ...          ...   \n",
              "771598  21.0          1       0     1                 2    7.0            0   \n",
              "771602  20.0          1       0     0                 2    8.0            2   \n",
              "771604  56.0          1       0     0                 0    7.0            0   \n",
              "771608  52.0          1       0     0                 1    7.0            2   \n",
              "771615  43.0          1       0     0                 1    7.0            0   \n",
              "\n",
              "        nav_num cluster  \n",
              "0             1  group2  \n",
              "1             2  group2  \n",
              "6             1  group2  \n",
              "19            2  group2  \n",
              "20            1  group2  \n",
              "...         ...     ...  \n",
              "771598        2  group2  \n",
              "771602        2  group2  \n",
              "771604        3  group2  \n",
              "771608        2  group2  \n",
              "771615        3  group2  \n",
              "\n",
              "[264956 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec43f932-d7ae-4838-a262-b1ca3b0cdef6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>gender</th>\n",
              "      <th>hand</th>\n",
              "      <th>home_environment</th>\n",
              "      <th>sleep</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>nav_num</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>68.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>19.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>24.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771598</th>\n",
              "      <td>21.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771602</th>\n",
              "      <td>20.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771604</th>\n",
              "      <td>56.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771608</th>\n",
              "      <td>52.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771615</th>\n",
              "      <td>43.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>group2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>264956 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec43f932-d7ae-4838-a262-b1ca3b0cdef6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ec43f932-d7ae-4838-a262-b1ca3b0cdef6 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-ec43f932-d7ae-4838-a262-b1ca3b0cdef6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "iY98jrSluWnO",
        "outputId": "c0b941ab-5074-4c41-c31e-1bb2d2fdc129"
      },
      "source": [
        "df2_2['nav_num'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd05dcd8550>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "njOwk9TikV-a",
        "outputId": "bffc8eb0-fe27-4f54-8637-1e6606d235b3"
      },
      "source": [
        "df2_3 = df[df['cluster']  == \"group3\"]\n",
        "\n",
        "#df2_3 = df2_3.drop([\"cluster\", \"navigating_skills\"], axis=1)\n",
        "df2_3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age  education  gender  hand  home_environment  sleep  travel_time  \\\n",
              "2       36.0          1       1     0                 0    5.0            1   \n",
              "4       61.0          1       1     0                 1    7.0            0   \n",
              "7       38.0          1       1     0                 0    7.0            0   \n",
              "8       39.0          1       1     0                 2    6.0            0   \n",
              "13      38.0          1       1     0                 1    7.0            0   \n",
              "...      ...        ...     ...   ...               ...    ...          ...   \n",
              "771614  60.0          1       1     1                 0    6.0            0   \n",
              "771616  30.0          1       1     0                 1    7.0            0   \n",
              "771620  33.0          1       1     0                 0    8.0            0   \n",
              "771622  23.0          1       1     0                 0    6.0            1   \n",
              "771624  68.0          1       1     0                 2    8.0            1   \n",
              "\n",
              "        nav_num cluster  \n",
              "2             3  group3  \n",
              "4             2  group3  \n",
              "7             2  group3  \n",
              "8             3  group3  \n",
              "13            3  group3  \n",
              "...         ...     ...  \n",
              "771614        3  group3  \n",
              "771616        3  group3  \n",
              "771620        1  group3  \n",
              "771622        2  group3  \n",
              "771624        2  group3  \n",
              "\n",
              "[207728 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5cd908a3-f27a-4a9e-905f-673cab3784ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>gender</th>\n",
              "      <th>hand</th>\n",
              "      <th>home_environment</th>\n",
              "      <th>sleep</th>\n",
              "      <th>travel_time</th>\n",
              "      <th>nav_num</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771614</th>\n",
              "      <td>60.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771616</th>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771620</th>\n",
              "      <td>33.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771622</th>\n",
              "      <td>23.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771624</th>\n",
              "      <td>68.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>group3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>207728 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5cd908a3-f27a-4a9e-905f-673cab3784ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5cd908a3-f27a-4a9e-905f-673cab3784ec 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-5cd908a3-f27a-4a9e-905f-673cab3784ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "rXNXrPrXuY8H",
        "outputId": "ff288c71-1d48-4478-f6a5-9f662471eb96"
      },
      "source": [
        "df2_3['nav_num'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd05dc68cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tGxb15uZ5jz-",
        "outputId": "e768469f-928b-4267-f107-edc14b5f60d0"
      },
      "source": [
        "df2_4 = df[df['cluster']  == \"group4\"]\n",
        "\n",
        "#df2_4 = df2_4.drop([\"cluster\", \"navigating_skills\"], axis=1)\n",
        "df2_4.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  education  gender  hand  home_environment  sleep  travel_time  \\\n",
              "3   19.0          0       1     0                 1    8.0            0   \n",
              "9   44.0          0       1     0                 1    7.0            1   \n",
              "10  47.0          0       1     1                 1    7.0            1   \n",
              "18  23.0          0       0     0                 2    8.0            1   \n",
              "27  20.0          0       0     0                 1   10.0            0   \n",
              "\n",
              "    nav_num cluster  \n",
              "3         2  group4  \n",
              "9         2  group4  \n",
              "10        2  group4  \n",
              "18        3  group4  \n",
              "27        1  group4  "
            ],
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              "        const buttonEl =\n",
              "          document.querySelector('#df-8f547048-a26d-4ab4-84cf-bb97c18fea1b 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-8f547048-a26d-4ab4-84cf-bb97c18fea1b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "TLlj1JpcuffL",
        "outputId": "c035d7d5-e0e9-4ddb-ab3b-e46686ee7822"
      },
      "source": [
        "df2_4['nav_num'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd05db91fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df2_1) + len(df2_2) + len(df2_3) + len(df2_4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MyiLUFyYGia",
        "outputId": "fd9312d8-8009-43a0-9d34-0e389f9afd88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "771628"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6y0tqO_27r7"
      },
      "source": [
        "import matplotlib.patches as mpatches\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#labels = ['male \\n long \\n commuter', 'female \\n tertiary', 'male \\n tertiary \\n short \\n commuter', 'secondary \\n education']\n",
        "means = [df2_1['nav_num'].mean(), df2_2['nav_num'].mean(), df2_3['nav_num'].mean(), df2_4['nav_num'].mean()]\n",
        "stds = [df2_1['nav_num'].std()/len(df2_1), df2_2['nav_num'].std()/len(df2_2), df2_3['nav_num'].std()/len(df2_3), df2_3['nav_num'].std()/len(df2_4)]\n",
        "title = 'group_by_mean'\n",
        "#men_std = [2, 3, 4, 1]\n",
        "#women_std = [3, 5, 2, 3]\n",
        "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
        "\n",
        "# fig, ax = plt.subplots()\n",
        "plt.figure(figsize=(7, 7))\n",
        "\n",
        "ax = plt.gca()\n",
        "\n",
        "# plt.figure(figsize=(10, 10))\n",
        "\n",
        "# ax.set_xlabel(None)\n",
        "ax.set_xticklabels(['male \\n long commuter', 'female \\n tertiary education', 'male \\n tertiary education \\n short commuter', 'secondary education'], rotation = 45)\n",
        "\n",
        "#ax.set_xticklabels(['male \\n long \\n commuter', 'female \\n tertiary', 'male \\n tertiary \\n short \\n commuter', 'secondary \\n education'], rotation = 0)\n",
        "ax.xaxis.set_tick_params(labelsize=20)\n",
        "ax.yaxis.set_tick_params(labelsize=20)\n",
        "ax.set_ylabel('Average Self-Reported Navigation Ability', fontsize=20)\n",
        "ax.set_ylim(0, 3.1)\n",
        "# ax.set_ylabel(None)\n",
        "ax.set_yticklabels(['very bad', '', 'bad', '', 'good', '', 'very good'], rotation = 0)\n",
        "\n",
        "ax.bar(labels, means, width,  yerr=stds\n",
        "      )\n",
        "\n",
        "# ax.set_ylabel('Mean (Standard Deviation)')\n",
        "# ax.set_title('Self-Reported Navigation Skills by Group')\n",
        "ax.legend()\n",
        "\n",
        "plt.savefig(title + '.pdf', bbox_inches=\"tight\")\n",
        "plt.savefig(title + '.png', bbox_inches=\"tight\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "akFqk34EdOH0",
        "outputId": "58ac3420-ebae-4850-a112-af23e2c75d37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.ticker as mtick\n",
        "\n",
        "title = 'group_by_percent'\n",
        "\n",
        "sns.set(color_codes=True)\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "\n",
        "x,y = 'cluster', 'nav_num'\n",
        "#color = sns.color_palette(\"flare\", n_colors=4)\n",
        "color = [sns.color_palette(\"Paired\", n_colors=4)[1], sns.color_palette(\"Paired\", n_colors=4)[0], sns.color_palette(\"Paired\", n_colors=4)[2], sns.color_palette(\"Paired\", n_colors=4)[3]]\n",
        "\n",
        "(df\n",
        ".groupby(x)[y]\n",
        ".value_counts(normalize=True)\n",
        ".mul(100)\n",
        ".rename('percent')\n",
        ".reset_index()\n",
        ".pipe((sns.catplot,'data'), x=x,y='percent',hue= y, kind='bar', palette=color, legend = None))\n",
        "\n",
        "ax = plt.gca()\n",
        "\n",
        "\n",
        "ax.set_xlabel(None)\n",
        "ax.set_xticklabels(['male \\n long commuter', 'female \\n tertiary education', 'male \\n tertiary education \\n short commuter', 'secondary education'], rotation = 45)\n",
        "ax.xaxis.set_tick_params(labelsize=15)\n",
        "ax.yaxis.set_tick_params(labelsize=20)\n",
        "ax.set_ylabel('Percentile', fontsize=22)\n",
        "\n",
        "ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=100))\n",
        "ax.set_yticks(np.arange(0,101,20))\n",
        "#\n",
        "# add legend\n",
        "top_bar = mpatches.Patch(color= sns.color_palette(\"Paired\", n_colors=4)[3], label='very good')\n",
        "second_bar = mpatches.Patch(color= sns.color_palette(\"Paired\", n_colors=4)[2], label='good')\n",
        "third_bar = mpatches.Patch(color= sns.color_palette(\"Paired\", n_colors=4)[0], label='bad')\n",
        "bottom_bar = mpatches.Patch(color=sns.color_palette(\"Paired\", n_colors=4)[1], label='very bad')\n",
        "plt.legend(handles=[top_bar, second_bar, third_bar, bottom_bar],loc='upper right', prop={'size': 18}, bbox_to_anchor=(1.6, 0.7)) # set location bbox_to_anchor=(4, 0.5)\n",
        "\n",
        "plt.savefig(title + '.pdf', bbox_inches=\"tight\")\n",
        "plt.savefig(title + '.png', bbox_inches=\"tight\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "vMET8pbFF-Mu",
        "outputId": "60d46b46-e2a2-4017-cc23-3729d2f82784"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Qualitatively, 1) for males, the proportion of report very good at navigation skills decreased with the education level; \n",
        "## for all the other levels of navigation skills (including good, bad, very bad), the proportion of report increased with the education level;\n",
        "## 2) for females, the proportion of report very good at navigation skills increased with the education level; \n",
        "## for all the other levels of navigation skills (including good, bad, very bad), the proportion of report decreased with the education level.\n",
        "\n"
      ],
      "metadata": {
        "id": "f7jmM8WvLEpT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "5E2kv4adcTZN",
        "outputId": "ff9e0bd1-3011-4413-f7a8-9ed0b04977dc"
      },
      "source": [
        "plt.rcParams['font.size'] = '17.5'\n",
        "\n",
        "\n",
        "hist = df2_1.hist(column = [\"age\", \"gender\",  \"education\", \"sleep\", \"travel_time\", \"home_environment\", \"hand\"], bins=20, layout = (1, 7), figsize=(40,5))\n",
        "#plt.xticks(fontsize=13)\n",
        "\n",
        "#df.hist(column=[\"Test_1\", \"Test_2\"]) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x360 with 7 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "jheqswuEcTpR",
        "outputId": "ad492a3f-6d2d-407a-ebd1-09ff2c470f0f"
      },
      "source": [
        "#@title\n",
        "hist = df2_2.hist(column = [\"age\", \"gender\", \"education\", \"sleep\", \"travel_time\", \"home_environment\", \"hand\"], bins=20, layout = (1, 7), figsize=(40,5))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x360 with 7 Axes>"
            ],
            "image/png": 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uQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "Er47MGIucT4I",
        "outputId": "a8a8e0f0-d0f4-4c1c-86ef-f796271c2290"
      },
      "source": [
        "#@title\n",
        "hist = df2_3.hist(column = [\"age\", \"gender\", \"education\", \"sleep\", \"travel_time\", \"home_environment\", \"hand\"], bins=20, layout = (1, 7), figsize=(40,5))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x360 with 7 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "hvvTdAGa59tc",
        "outputId": "24ebf549-84e8-423c-f419-4492adfef41d"
      },
      "source": [
        "#@title\n",
        "hist = df2_4.hist(column = [\"age\", \"gender\", \"education\", \"sleep\", \"travel_time\", \"home_environment\", \"hand\"], bins=20, layout = (1, 7), figsize=(40,5))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x360 with 7 Axes>"
            ],
            "image/png": 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}