Uber_NY_Unsupervised_ML.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Unsupervised Machine Learning with Uber"
      ],
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      "cell_type": "code",
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      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors\n",
        "import seaborn as sns"
      ]
    },
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      "cell_type": "code",
      "source": [
        "# set grey layout with white grid for all plots\n",
        "plt.rcParams['figure.figsize'] = (10, 5)\n",
        "plt.style.use('ggplot')\n",
        "ggplot_styles = {'axes.facecolor': '8d8d8d',} \n",
        "plt.rcParams.update(ggplot_styles)"
      ],
      "metadata": {
        "id": "cVnGnr7H4LSO"
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "url = 'https://github.com/fivethirtyeight/uber-tlc-foil-response/blob/master/uber-trip-data/uber-raw-data-jun14.csv?raw=true'\n",
        "\n",
        "data = pd.read_csv(url)"
      ],
      "metadata": {
        "id": "ItGhAqwaDkQH"
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      "execution_count": null,
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      "source": [
        "Check dataset"
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      "source": [
        "data.head(10)"
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      "cell_type": "markdown",
      "source": [
        "Sample dataset to 10% to make modeling easier"
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        "data.info()"
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        "outputId": "fe132ebe-8c49-4891-df60-2e436ea9872c"
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      "execution_count": null,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 66384 entries, 348280 to 4012\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date/Time  66384 non-null  object \n",
            " 1   Lat        66384 non-null  float64\n",
            " 2   Lon        66384 non-null  float64\n",
            " 3   Base       66384 non-null  object \n",
            "dtypes: float64(2), object(2)\n",
            "memory usage: 2.5+ MB\n"
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    {
      "cell_type": "markdown",
      "source": [
        "## Data cleaning & EDA"
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              "Date/Time    0\n",
              "Lat          0\n",
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          "metadata": {},
          "execution_count": 7
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        "num_data = data.select_dtypes(exclude=['object'])\n",
        "num_data"
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              "            + ' 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",
              "  "
            ],
            "text/plain": [
              "            Lat      Lon\n",
              "348280  40.6936 -73.9560\n",
              "156470  40.7355 -74.0037\n",
              "532688  40.7070 -74.0137\n",
              "90217   40.7051 -74.0100\n",
              "103242  40.7601 -73.9717\n",
              "...         ...      ...\n",
              "258226  40.7230 -74.0032\n",
              "470420  40.7510 -73.9869\n",
              "575523  40.7602 -73.9788\n",
              "161597  40.6984 -73.9086\n",
              "4012    40.7578 -73.9666\n",
              "\n",
              "[66384 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check data distribution in both columns"
      ],
      "metadata": {
        "id": "CYD8eXtGesXx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=num_data, color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "TPHT22K9E7kK",
        "outputId": "08e47311-5716-4a0e-8173-40b208814a19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(y='Lat', data=data, color='black')"
      ],
      "metadata": {
        "id": "2m6e3E5OFUqo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "6fd2c275-874e-4711-ac8a-7f85f102d451"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f78dc904dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(y='Lon', data=data, color='black')"
      ],
      "metadata": {
        "id": "L85S6tv0Gcu6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "3b89b338-4122-44f1-85d1-09175b5b5531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f78dc880150>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatter plot of clean data "
      ],
      "metadata": {
        "id": "GPT9Drq2gXex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data=data, x='Lat', y='Lon', color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "fHXZ21RngV11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "65d8718e-b5ca-47bf-cf66-e39f41274f6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are clustering tendencies in the dataset, let's build clustering models to identify these clusters."
      ],
      "metadata": {
        "id": "meaueqrmhDXe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data prep for modeling"
      ],
      "metadata": {
        "id": "XcdGkZTFRDzx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# convert datetime column to datetime format\n",
        "data['Date/Time'] = pd.to_datetime(data['Date/Time'])"
      ],
      "metadata": {
        "id": "rBUuiyd1TIxP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# add hour and weekday columns to my dataframe\n",
        "data['Hour'] = data['Date/Time'].dt.hour\n",
        "data['Weekday'] = data['Date/Time'].dt.day_name()"
      ],
      "metadata": {
        "id": "zs5dveEqPjzm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check datapoints distribution by weekdays\n",
        "sns.countplot(x='Weekday', data=data, color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "fKUwrraaQqTV",
        "outputId": "582c0746-9e19-4dc8-d7cb-192b46fcd0d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check datapoints distribution by hour\n",
        "sns.histplot(data=data, x='Hour', color='black', bins=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "YBlFptLOWepA",
        "outputId": "eeed5e3f-f309-4ab5-e833-10b70a0d6b90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modeling"
      ],
      "metadata": {
        "id": "MY3EFBY2gLjl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Assign modeling variables to X"
      ],
      "metadata": {
        "id": "HVGJuth5hYQR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=data[['Lat','Lon']]"
      ],
      "metadata": {
        "id": "dKxTpjHVLKom"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Clustering with KMeans"
      ],
      "metadata": {
        "id": "bYSH0_tmQahi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creation of loop to find the right cluster\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "dict_kmeans={}\n",
        "for i in range (2,11):\n",
        "    kmeans = KMeans(n_clusters=i)\n",
        "    kmeans.fit(X)\n",
        "    key=\"kmeans\"+str(i)\n",
        "    dict_kmeans[key] = kmeans.labels_"
      ],
      "metadata": {
        "id": "KC3RUC7cLxdc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dict_kmeans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4c1tP_hgSiMq",
        "outputId": "384562ef-7017-4b16-a6c0-b264476234bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kmeans10': array([5, 0, 0, ..., 4, 5, 4], dtype=int32),\n",
              " 'kmeans2': array([0, 0, 0, ..., 0, 0, 0], dtype=int32),\n",
              " 'kmeans3': array([1, 1, 1, ..., 0, 0, 0], dtype=int32),\n",
              " 'kmeans4': array([0, 0, 0, ..., 1, 0, 1], dtype=int32),\n",
              " 'kmeans5': array([1, 1, 1, ..., 0, 1, 0], dtype=int32),\n",
              " 'kmeans6': array([5, 2, 2, ..., 0, 5, 0], dtype=int32),\n",
              " 'kmeans7': array([6, 2, 2, ..., 0, 6, 0], dtype=int32),\n",
              " 'kmeans8': array([2, 0, 0, ..., 4, 2, 4], dtype=int32),\n",
              " 'kmeans9': array([8, 1, 1, ..., 3, 8, 3], dtype=int32)}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.cluster import KElbowVisualizer\n",
        "\n",
        "kmeans = KMeans()\n",
        "\n",
        "visualizer = KElbowVisualizer(estimator = kmeans, k = (2,11), metric='silhouette')\n",
        "visualizer.fit(X)\n",
        "visualizer.poof()"
      ],
      "metadata": {
        "id": "sYMB3THHMDvl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "d1c1f883-d9bf-46e5-9690-ad5c2e3b83cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb09e9977d0>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Elbow indicates an optimal number of clusters of 2 with a silhouette score of 0.721."
      ],
      "metadata": {
        "id": "hpxbTFRZmRMM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting KMeans with 2 clusters\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "kmeans = KMeans(n_clusters=2)\n",
        "kmeans.fit(X)\n",
        "y_kmeans = kmeans.predict(X) #y-pred\n",
        "\n",
        "data['kmeans_cluster'] = kmeans.labels_\n",
        "k_centers = kmeans.cluster_centers_\n",
        "\n",
        "# plot clusters on dataset plot\n",
        "fig, ax = plt.subplots()\n",
        "sns.scatterplot(data=data, x='Lat', y='Lon', hue=y_kmeans, palette=['black', 'white'], ax=ax)\n",
        "#sns.scatterplot(x=k_centers[:, 0], y=k_centers[:, 1], palette='blue', size=200, ax=ax)\n",
        "plt.scatter(k_centers[:, 0], k_centers[:, 1], c='blue', s=200, alpha=0.5)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "nzNCfovnLKeB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "048a37e0-f004-43fe-cadb-43d3a4311c62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Clustering with DBScan"
      ],
      "metadata": {
        "id": "xaDEtt4Cfu_O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# \n"
      ],
      "metadata": {
        "id": "tpKILsMPD_Nf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eps=[0.02, 0.15, 0.18, 0.1]\n",
        "min_samples=[2, 4, 4, 2]\n",
        "k=0\n",
        "for i,j in zip(eps, min_samples):\n",
        "    k=k+1\n",
        "    print(i)\n",
        "    print(j)\n",
        "    dbs = DBSCAN(eps=i, min_samples=j)\n",
        "    dbs.fit(X)\n",
        "    key2=\"db\"+str(k)\n",
        "    dict_kmeans[key2] = dbs.labels_"
      ],
      "metadata": {
        "id": "Fxs-PIZLRPTw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Clustering WITH SpectralClustering"
      ],
      "metadata": {
        "id": "6TtpkCT_d7JI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find the optimal numbers of clusters with SpectralClustering\n",
        "from sklearn.cluster import SpectralClustering\n",
        "\n",
        "dict_sc={}\n",
        "for i in range (2,11):\n",
        "    SC = SpectralClustering(n_clusters=2, assign_labels='discretize',random_state=0)\n",
        "    SC.fit(X)\n",
        "    key=\"SC\"+str(i)\n",
        "    dict_sc[key] = SC.labels_\n",
        "\n",
        "dict_sc"
      ],
      "metadata": {
        "id": "Tg0rLmcYdHMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  Clustering WITH GAUSSIAN MIXTURE MODEL"
      ],
      "metadata": {
        "id": "Pt_M6wbUf0IR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find the optimal numbers of clusters with GAUSSIAN\n",
        "from sklearn.mixture import GaussianMixture\n",
        "\n",
        "\n",
        "dict_gmm={}\n",
        "for i in range (2,11):\n",
        "    gmm = GaussianMixture(n_components=i)\n",
        "    gmm.fit(X)\n",
        "    key=\"gmm\"+str(i)\n",
        "    dict_gmm[key] = gmm.predict(X)\n",
        "\n",
        "dict_gmm\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWV5eUxBOaf3",
        "outputId": "037d0236-79fb-46fc-e735-d4f34eda1811"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'gmm10': array([8, 3, 3, ..., 0, 4, 0]),\n",
              " 'gmm2': array([1, 0, 0, ..., 0, 1, 0]),\n",
              " 'gmm3': array([1, 0, 0, ..., 0, 1, 0]),\n",
              " 'gmm4': array([2, 0, 0, ..., 0, 3, 1]),\n",
              " 'gmm5': array([4, 0, 3, ..., 0, 4, 0]),\n",
              " 'gmm6': array([0, 4, 4, ..., 1, 0, 1]),\n",
              " 'gmm7': array([5, 2, 2, ..., 0, 4, 0]),\n",
              " 'gmm8': array([2, 3, 3, ..., 0, 2, 0]),\n",
              " 'gmm9': array([8, 0, 0, ..., 2, 5, 2])}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting Gaussian with 2 clusters\n",
        "from sklearn.mixture import GaussianMixture\n",
        "\n",
        "gmm = GaussianMixture(n_components=2)\n",
        "gmm.fit(X)\n",
        "y_gmm = gmm.predict(X)\n",
        "\n",
        "data['gmm_cluster'] = gmm.predict(X)\n",
        "\n",
        "# plot clusters on dataset plot\n",
        "fig, ax = plt.subplots()\n",
        "sns.scatterplot(data=data, x='Lat', y='Lon', hue=y_gmm, palette=['black', 'white'], ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "xaZQ9oZnZJAd",
        "outputId": "ac6b050b-7fde-468a-b87e-1fa729ce64b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clustering with AgglomerativeClustering"
      ],
      "metadata": {
        "id": "qbVR_XK3YPrB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find the optimal numbers of clusters for AC\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "\n",
        "agc_dict={}\n",
        "for i in range (2,4):\n",
        "    agc = AgglomerativeClustering(n_clusters=i)\n",
        "    agc.fit(X)\n",
        "    key1=\"agc\"+str(i)\n",
        "    agc_dict[key1] = agc.labels_\n",
        "\n",
        "agc_dict"
      ],
      "metadata": {
        "id": "3VkVgOM6Et1L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "cluster = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='ward')  \n",
        "cluster.fit_predict(data_scaled)"
      ],
      "metadata": {
        "id": "-nG6Rb6dEKPq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#AGGLOMEATIVECLUSTERING\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "\n",
        "agc = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')  #3: so only 3 label\n",
        "agc.fit_predict(data_scaled)\n",
        "y_agglomerative=agc.labels_\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 7))  \n",
        "plt.scatter(data['X1'], data['X2'], c=y_agglomerative,  cmap='viridis') \n",
        "plt.xlabel(\"X1X1\")\n",
        "plt.ylabel(\"X2X2\")"
      ],
      "metadata": {
        "id": "XtCb7XAmLKP8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Comparison"
      ],
      "metadata": {
        "id": "rYklMkF-f5lp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def unsupervised_metrics(X, y_pred):\n",
        "    \"\"\"\n",
        "    Function to evaluate models based on different metrics:\n",
        "    Homogeneity, Completeness, V-measure, adjusted_rand, AMI, \n",
        "    Silhouette Coefficient, Variance Ratio Criterion, Fowlkes Mallows\n",
        "    \n",
        "    \"\"\"\n",
        "    from sklearn import metrics\n",
        "    from sklearn.metrics import silhouette_score, calinski_harabasz_score\n",
        "\n",
        "    score_ss = metrics.silhouette_score(X, y_pred)\n",
        "    score_vrc = metrics.calinski_harabasz_score(X, y_pred)\n",
        "    \n",
        "    print(\"Silhouette Coefficient: %0.3f\" % score_ss)\n",
        "    print('Variance Ratio Criterion: %0.3f' % score_vrc)\n"
      ],
      "metadata": {
        "id": "33Pz0k2jMDg8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for key in dict_gmm:\n",
        "    print('\\n', key)\n",
        "    unsupervised_metrics(X=X, y_pred=dict_gmm[key])"
      ],
      "metadata": {
        "id": "KZD4MhB-YvsD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26ae55a9-e34b-487f-d375-e2b327ea0ba1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " gmm2\n",
            "Silhouette Coefficient: 0.523\n",
            "Variance Ratio Criterion: 11963.945\n",
            "\n",
            " gmm3\n",
            "Silhouette Coefficient: 0.513\n",
            "Variance Ratio Criterion: 13868.857\n",
            "\n",
            " gmm4\n",
            "Silhouette Coefficient: 0.306\n",
            "Variance Ratio Criterion: 8350.740\n",
            "\n",
            " gmm5\n",
            "Silhouette Coefficient: 0.312\n",
            "Variance Ratio Criterion: 9933.340\n",
            "\n",
            " gmm6\n",
            "Silhouette Coefficient: 0.426\n",
            "Variance Ratio Criterion: 11314.289\n",
            "\n",
            " gmm7\n",
            "Silhouette Coefficient: 0.432\n",
            "Variance Ratio Criterion: 9992.610\n",
            "\n",
            " gmm8\n",
            "Silhouette Coefficient: 0.299\n",
            "Variance Ratio Criterion: 11996.665\n",
            "\n",
            " gmm9\n",
            "Silhouette Coefficient: 0.360\n",
            "Variance Ratio Criterion: 7575.404\n",
            "\n",
            " gmm10\n",
            "Silhouette Coefficient: 0.443\n",
            "Variance Ratio Criterion: 22682.709\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cluster analysis"
      ],
      "metadata": {
        "id": "MakPUQhIbSK5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check cluster distribution\n",
        "data['kmeans_cluster'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M009uvsmd4bV",
        "outputId": "80dea316-5c6c-4e63-aef3-fbac72312319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    62026\n",
              "1     4358\n",
              "Name: kmeans_cluster, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# assign cluster variables\n",
        "cluster0 = data[data['kmeans_cluster']==0]\n",
        "cluster1 = data[data['kmeans_cluster']==1]"
      ],
      "metadata": {
        "id": "bKF0v2lReDoA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot pickup peaks for cluster1\n",
        "sns.heatmap(pd.crosstab(cluster0.Hour, cluster0.Weekday, normalize='columns'), cmap='coolwarm')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "bfHEEGHIe4d1",
        "outputId": "4fe0eec8-c650-43cf-8736-17ac1794230e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot pickup peaks for cluster1\n",
        "sns.heatmap(pd.crosstab(cluster1.Hour, cluster1.Weekday, normalize='columns'), cmap='coolwarm')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "yB6NpnWKbRad",
        "outputId": "c0d61158-ed5c-4b24-c8ea-8aa906a8c79d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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noVKp8N133yExMRFJSUnQarXIyMgQGYmIiMi6SSTGPcyM8BEcrVaLiooKSKVSVFRUwN3dXXQkIiIi68U5OA9OLpfjb3/7G15//XXY29sjNDQUoaGhIiMRERFZNZ0ZjsYYQ2iBU1JSgszMTKSkpMDJyQkfffQRfv75ZwwcONBgO7ZqICIiMhELmYMjtMA5fvw4PD094erqCgDo378/zp49W6PAYasGIiIiE7GQAkfoWXh4eODcuXO4d+8edDodjh8/jk6dOomMREREZNV0EolRD3MjdASne/fuiIiIwKxZsyCVSuHj46MfqSEiIiIBLGQER/hdVHFxcYiLixMdg4iIiACzvOXbGMILnKYaG3NLdARh3P7IFR1BqBOnfEVHECowWCE6glAd2t4THUGYDm1UoiMIpYNl/MI1XgfTHo63iRMREZGlMcf5NMYQXuB8++232Lt3L3Q6HQYPHozY2FjRkYiIiKyXhczBEXoWV65cwd69e/HBBx9g6dKlyM7Oxs2bN0VGIiIiIgsgtMC5fv06unXrBgcHB0ilUgQGBuLQoUMiIxEREVk1ncTGqIe5EZrI29sbv//+O4qLi3Hv3j0cOXIE+fn5IiMRERFZNzbbfHCdO3fG008/jffffx+Ojo7w8fGBjYXM3iYiImqNWmo0JicnB+vWrYNWq8XgwYMxbNgwg/crKyuxYsUKXLhwAS4uLoiPj4enpycA4PLly/jss89w9+5dSCQSLFq0CPb29vUeT/gk45iYGMTExAAA/vWvf0GhqHkrbPVeVGP/3yST5iMiIrIqLTAao9VqsWbNGsybNw8KhQJz5sxBWFgYOnfurN8mLS0Nzs7OWL58Ofbv34+NGzdi6tSp0Gg0WL58Od544w34+PiguLgYtrYNly/Ch0sKCwsBALdv38Zvv/2GRx99tMY2Q4YMQWJiIhITE00dj4iIyLpIbIx71CM3NxdeXl5o3749bG1tERkZiczMTINtsrKyEBUVBQCIiIjAiRMnoNPpcPToUXTp0gU+Pj4AABcXl0Zd7RE+gpOUlKSvxsaPHw9nZ2fRkYiIiKxWS6yDo1KpDK7QKBQKnDt3rs5tpFIpnJycUFxcjLy8PEgkEiQkJKCoqAiRkZF4+umnGzym8ALn3XffFR2BiIiI7jNyDk716SRA1dWX5ugvqdFo8Pvvv2PRokVwcHDAu+++C19fX/Ts2bPe/YQXOERERGQ+jG2NUV9BI5fLDe6Szs/Ph1wur3UbhUIBjUaDsrIyuLi4QKFQIDAwEK6urgCA3r174+LFi5ZX4Hx3snPDG1moJ4PVoiMINXRg/TPmLd2Fm8KnzAmVd8dBdARhbhWbuBeRmXF20IiOIFSwiY/XEndR+fn5IS8vD0qlEnK5HBkZGZgyZYrBNn379kV6ejr8/f1x8OBBBAcHQyKRIDQ0FLt27cK9e/dga2uL06dPN6rrgUkKnJUrVyI7Oxtubm5ISkoCAJSUlCA5ORm3bt1Cu3btMHXqVMhkMlPEISIiorq0QIEjlUoxbtw4JCQkQKvVIjo6Gt7e3tiyZQv8/PwQFhaGmJgYrFixApMnT4ZMJkN8fDwAQCaTITY2FnPmzIFEIkHv3r3Rp0+fhk9Dp9Ppmv1M/uTUqVNwdHRESkqKvsDZsGEDZDIZhg0bhp07d6KkpAQjR45s8Gt9vKvF45qtJ4MviY4g1IWSTqIjCHXhpnWPYMmcrPf/fVup6ARiWfsIzt/6mvZiS8HRfUbt5x46qJmTPBiTjHkHBQXVGJ3JzMzEoEFVH8agQYNq3C5GREREpmcprRqEzcEpLCyEu7s7AKBt27b69XCIiIhIIDNsu2AMs5hkLJFIILGQD5SIiKg1M8fRGGMIK3Dc3NxQUFAAd3d3FBQU6G//qk31e+u9IheZKiIREZHVMfY2cXMjrMAJCwvDvn37MGzYMOzbtw/9+vWrc9vq99Zb8yRjIiKilsYRnCb4+OOPcerUKRQXF+O1115DXFwchg0bhuTkZKSlpelvEyciIiJqDiYpcO7fy/5n8+fPN8XhiYiIqLEsZE6sWUwyJiIiIvOgM80KMi2u1RU4/xN8TXQEYTzvnBUdQahd13xERxDq+vVS0RGE6t3TSXQEYQZ4nBAdQaiTd3uIjmBVWqKbuAiNKtO0Wi0mT56MysrKls5DREREAlnVQn82NjawsbFBZWUl7OzsmnyQ2npRHThwAP/+979x/fp1fPDBB/Dz82vy1yUiIqLmZSm3iTe65Bo6dCiSk5Nx6tQp3Lx5E3/88Yf+0ZCoqCjMnTvX4DVvb2/MmDEDgYGBTU9NRERELcKqRnAAYO3atQCAY8eO1Xhvy5Yt9e4bFBQEpVJp8Frnzp0be2giIiIyEUuZg9PoAqehIoaIiIhaP0u5RNUq7qKq3qph7P+bJDgNERGR5TLHy03GaHSBM3/+/DobYi5cuLDZAtWmequGM+evtuixiIiIrJnVjeDExMQYPL9z5w5++uknDBgwoNlDERERkRhWN4ITFRVV47WIiAisXLkSw4cPr3ff2npRyWQyrF27FkVFRUhMTISPjw/efvvtJp8AERERNR+rG8GpjVwux+XLlxvcrq5eVOHh4Q9yeCIiImpmVjeCk5aWZvC8oqIChw4dgr+/f7OHqk+Zto1Jj2dOUjWPiY4g1Gi/A6IjCHXcr7foCEIpi3WiIwhzrDRAdASh2tipRUewKlY3gvPLL78YPHdwcECPHj0QGxvb7KGIiIhIDKtbB2fBggVGH6S2Vg1fffUVDh8+DFtbW7Rv3x4TJ06Es7Oz0ccgIiKiB6fTWVmBAwB5eXnYv38/VCoV5HI5HnnkEXTo0KHB/aKiovDEE08gJSVF/1pISAhGjBgBqVSKDRs2YMeOHRg5cmTTz4CIiIjoTxo9kygrKwuzZ8/G9evXIZPJcOPGDcyePRtZWVkN7hsUFASZTGbwWmhoKKRSKQDA398fKpWqidGJiIiouelgY9TD3DR6BGfTpk2YOXMmHnroIf1rJ0+exNq1axEWFvZAIdLS0hAZGflAX4OIiIgenNVNMlapVDU6fwcEBCA/P/+BAmzfvh1SqZQLBhIREZkBSylwGj2m5OPjg927dxu89s0338DHx8fog6enp+Pw4cOYMmVKnW0ggKpeVLNnz8bs2bONPhYRERE1TAeJUQ9z0+gRnPHjx2PJkiX47rvvoFAocPv2bTg4OGDWrFlGHTgnJwdff/01Fi5cCAcHh3q3rd6L6si520Ydj4iIiBpmjsWKMSQ6na7Rq2dpNBqcO3dOfxdV9+7d9ROF61O9VYObmxvi4uKwY8cOqNVq/eTj7t27Y8KECQ1+LWsucC4WyEVHEGqQ00HREYQ6Dmtf6K/+P4QsmYujRnQEoax9ob+oh0y7wO2p3BtG7RfUrWMzJ3kwDY7g1NdF/L6GuonX1qrhz807iYiISDxLGcFpsMD5cyGyZs0ajB8/vsUCERERkThWU+D8uYv4+vXra+0sbiqu0iJhxxbtEZeGG5taNOsepUd4wTeiIwh1ynOI6AjCaHXmt8aIKTnblomOIJhpL1FZTYHTHGpr1bB582ZkZWVBIpHAzc0NEydOhFxu3XNMiIiIRLOUVg0m+bMgKioKc+fONXjtqaeewocffoilS5eiT58+2LZtmymiEBERUT20kBj1MDcNjuCcOHHC4LlWq63xWvXVjWsTFBQEpVJp8JqTk5P+v+/du9fgRGYiIiJqeVZziWrVqlUGz2UymcFrEokEK1asMOrgmzZtws8//wwnJ6cH6lZOREREzcNSLlE1WOBU7wDe3F588UW8+OKL2LFjB/bs2YO4uLgWOxYRERE1zFJGcMxiav6AAQNw6NChOt9nqwYiIiLT0OkkRj3MjUnuoqpNXl4eOnToAADIzMxEx451r4BYvVXD+QsXTJKPiIjIGlnKCI5JCpzqrRpee+01xMXFITs7G3l5eZBIJPDw8GhUmwYiIiKixjBJgcNWDURERK2DOV5uMoawS1RERERkfrSiAzSTVlfgdDqbJjqCOBX3RCcQ6sb270RHEMr5fwaKjiBUgP2voiMIo7VzFB1BKPsz2aIjiNVtlkkPxxEcIiIisjiWMsnYJLeJr1y5Eq+88gqmT59e473du3cjLi4ORUXW20STiIjIXFjKbeLCelEBwO3bt3Hs2DF4eHiYIgYRERE1QAeJUY+G5OTk4M0338TkyZOxc+fOGu9XVlYiOTkZkydPxty5c2u0eLp9+zZGjRqFXbt2Neo8TFLgBAUFQSaT1Xh9/fr1eOmll9iHioiIyExodcY96v2aWi3WrFmDuXPnIjk5Gfv378e1a9cMtklLS4OzszOWL1+O2NhYbNy40eD99evXo3fv3o0+D2ErGWdmZkIul8PHx0dUBCIiIvqTlhjByc3NhZeXF9q3bw9bW1tERkYiMzPTYJusrCxERUUBACIiInDixAnodFWV02+//QZPT0907ty50echpMC5d+8eduzYgeeff75R27NVAxERkWm0xBwclUoFhUKhf65QKKBSqercRiqVwsnJCcXFxSgvL8fXX3+N5557rknnIeQuqj/++ANKpRIzZ84EAOTn52PWrFlYtGgR2rZtW2P76q0ayvd8btKsRERE1kTXwOWmuqSmpiI1NVX/vPrv7gexdetWxMbGwtGxacslCClwunTpgs8//79CZdKkSVi0aBFcXV1FxCEiIqL/0hp5m3h9BY1cLkd+fr7+eX5+PuRyea3bKBQKaDQalJWVwcXFBbm5uTh06BA2btyI0tJSSCQS2Nvb44knnqg3j7BeVGzVQEREZH5a4pZvPz8/5OXlQalUQi6XIyMjA1OmTDHYpm/fvkhPT4e/vz8OHjyI4OBgSCQSvPvuu/pttm7dCkdHxwaLG0BgL6rqUlJSTBGDiIiIGmDsJar6SKVSjBs3DgkJCdBqtYiOjoa3tze2bNkCPz8/hIWFISYmBitWrMDkyZMhk8karB0aItHpWuJUWs6J3JuiIwjTVXVYdASh7Aqs93sPAGnPLBUdQSjPIwdERxAmSJna8EYWzEZ5reGNLJjj36ea9Hg/HK0war/HQ+2bOcmDYasGIiIi0mtoTZvWwiQFzsqVK5GdnQ03NzckJSUBqLqOtnfvXv3E4hdffBF9+vQxRRwiIiKqgzm2XTCGSQqcqKgoPPHEEzXm2sTGxuKpp54yRQQiIiJqhNY1caVuQls1EBEREbUEoXNwvv/+e/z888/w9fXFyy+/zCKIiIhIMGPXwTE3wnpRPf7441i+fDmWLFkCd3d3fPnll6KiEBER0X/pdMY9zI2wEZzqLRkGDx6MxYsX17lt9eWfR77yYPfFExERUd04yfgBFRQUwN3dHUBVl1Bvb+86t62+/LM1r4NDRETU0nibeBPU1qrh5MmTuHTpEiQSCdq1a4cJEyaYIgoRERHVwxwvNxlDWKsG9qIiIiIyPzoLmWTMlYyJiIhIj5eoBOmW+43oCOIU3RGdQKhbvYeKjiBUSM420RGE8jj+hegIwtwMjRUdQShPG2E3/FolXqJqgtpaNQDAd999h++//x42Njbo06cPRo4caYo4REREVAcWOE1QW6uGEydOICsrC0uXLoWdnR0KCwtNEYWIiIjqobWQ28SFtWr44Ycf8PTTT8POzg4A4ObmZoooREREVA8u9PeA8vLy8Pvvv2Pz5s2ws7PDqFGj0K1bN1FxiIiICOZZrBhD2MwtrVaLkpISJCQkYNSoUUhOTobOUj5VIiKiVkqrM+5hboSN4MjlcoSHh0MikaBbt26wsbFBcXExXF1da2xbvVXDO1Ec5SEiImopbNXwgPr164eTJ0/ioYcewo0bN6BWq+Hi4lLrttVbNZTv+dyUMYmIiKyKpVxMEdaqISYmBitXrsT06dNha2uLSZMmQSKxjKqRiIiotTLHy03GENaqAQCmTJliisMTERGRlWl1KxkTERFRy+ElKlEq7olOIMyRntbdcT1U+aPoCELd9OolOoJYtnaiEwijVLcXHUGoCi9H0RGE8jfx8VjgEBERkcXhHJwmqK0XVXJyMm7cuAEAKCsrg5OTE5YuXWqKOERERFQHjuA0QW29qKZOnar/7y+//BJOTk6miEJERET10GpFJ2gewnpR3afT6XDgwMoYQa8AACAASURBVAE88sgjpohCRERE9WAvqmZy+vRpuLm5oUOHDqKjEBERWT1zLFaMIbzA2b9/f4OjNwatGiK9TRGLiIjIKnGScTPQaDT47bffkJiYWO92Bq0adqXUuy0REREZz/jG1+bVjUBogXP8+HF07NgRCoVCZAwiIiL6L16iaoK6elE15vIUERERmY6l3EUltBfVpEmTTHF4IiIiaiSO4AiS2vlV0RGE6YejoiMIZVNyR3QEobxuZIuOIJZT7UtNWINONldERxDqjs5DdASrwknGREREZHE4gtMEtbVquHTpEv75z3+ioqICUqkUr7zyCrp162aKOERERFQHndFDOOZ1F5VJVjKOiorC3LlzDV7bsGEDhg8fjqVLlyIuLg4bNmwwRRQiIiKqh1Zn3MPcCGvVIJFIcPfuXQBVzTbd3d1NEYWIiIisgLA5OKNHj0ZCQgK++uoraLVavP/++6KiEBER0X9Zyhwck4zg1OaHH37A6NGjsWrVKowePRqrV68WFYWIiIj+S6vVGfUwN8IKnH379qF///4AgIcffhi5ubl1bpuamorZs2dj9uzZpopHRERkldhN/AHJ5XKcOnUKwcHBOHHiBLy8vOrctnovqm+y1aaKSEREZHXMsVgxhrBWDa+++irWrVsHrVYLOzs7vPqq9S7gR0REZC60FlLhCG3VsHjxYlMcnoiIiBpJx15UREREZGl0HMERI9LuoOgIwmi0dqIjCFXaKVB0BKEcft4lOoJQ2r6PiI4gTJty6+7DpjiXKjqCWN2mmvRwLdVNPCcnRz81ZfDgwRg2bJjB+5WVlVixYgUuXLgAFxcXxMfHw9PTE8eOHcPGjRuhVqtha2uLUaNG4aGHHmrweMJbNZSXl6Ndu3aYMmUKnJycTBGHiIiI6tASIzharRZr1qzBvHnzoFAoMGfOHISFhaFz5876bdLS0uDs7Izly5dj//792LhxI6ZOnQoXFxfMmjULcrkcV65cQUJCAj799NMGjymsVcOnn36Kl156CUlJSQgPD8euXdb91ykREZE5aIlWDbm5ufDy8kL79u1ha2uLyMhIZGZmGmyTlZWFqKgoAEBERAROnDgBnU6Hv/zlL5DL5QAAb29vVFRUoLKyssHzMMkITlBQEJRKpcFrN27cQGBg1SWHkJAQJCQk4IUXXjBFHCIiIqqDsc02U1NTkZr6f5cTqy/xolKpoFAo9O8pFAqcO3fOYP/q20ilUjg5OaG4uBiurq76bQ4dOgRfX1/Y2TU8ZUPYHBxvb29kZmYiPDwcBw8eRH5+vqgoRERE9F/GXqGqXtC0hKtXr2Ljxo14++23G7W9sJWMX3/9dfzwww+YNWsW7t69C1vbVjffmYiIyOK0RKsGuVxuMJCRn5+vv+xU2zYajQZlZWVwcXHRb//hhx9i0qRJ9S4MXJ2wqqJTp06YN28egKrLVdnZ2XVuW33Y662XnjRJPiIiImvUEpOM/fz8kJeXB6VSCblcjoyMDEyZMsVgm759+yI9PR3+/v44ePAggoODIZFIUFpaisTERIwYMQIBAQGNPqawAqewsBBubm7QarXYvn07HnvssTq3NbiOd/xXU0UkIiKyOi2x0J9UKsW4ceOQkJAArVaL6OhoeHt7Y8uWLfDz80NYWBhiYmKwYsUKTJ48GTKZTL9I8J49e3Dz5k1s27YN27ZtAwDMmzcPbm5u9R5TojPBij7VWzW4ubkhLi4O5eXl+P777wEA4eHhGDFiBCQSSYNfy5oLHI2Nda+DY6PTiI4glLWvgwMrXgenok1b0RGEcjqXJTqCUI5/N+06ODNWlRm134evm9dSL0JbNQwdOtQUhyciIiIrw5m9REREpMdWDYKo//WZ6AjC3Bz3oegIQnVT/iI6glDnvz0kOoJQHW+rREcQ5uB7+0RHEGrwN2+JjmBVGrojqrUwSYFz+/ZtpKSk4M6dO5BIJBgyZAiGDh2KkpISJCcn49atW2jXrh2mTp0KmUxmikhERERUCwsZwDFNgSOVSjFq1Cj4+vri7t27mD17NkJCQpCeno6ePXti2LBh2LlzJ3bu3ImRI0eaIhIRERHVwtiVjM2NSRb6c3d3h6+vLwCgTZs26NSpE1QqFTIzMzFo0CAAwKBBg2r0pSAiIiLT0up0Rj3Mjcnn4CiVSly8eBHdunVDYWEh3N3dAQBt27ZFYWGhqeMQERFRNZYygmPSAqe8vBxJSUkYM2YMnJwM75eXSCSNWgeHiIiIWg4LnCZSq9VISkrCgAED0L9/fwCAm5sbCgoK4O7ujoKCAoOOodVVb9UwjTUQERFRi7GQ+sY0BY5Op8Pq1avRqVMnPPnk//WSCgsLw759+zBs2DDs27cP/fr1q3X/6q0alHNeNkVkIiIiq8QRnCY4c+YMfv75Z3Tp0gUzZ84EALz44osYNmwYkpOTkZaWpr9NnIiIiMThQn9NEBAQgK1bt9b63vz5800RgYiIiBqBC/0RERGRxeEIjiCugwaKjiBMkU2p6AhCXfHqLzqCUJ7BO0VHIEEG73lbdAShir16iI4glKOJj8c5OERERGRxWOA0QV29qA4cOIB///vfuH79Oj744AP4+fmZIg4RERHVwRxXJTaG0F5U3t7emDFjBj77zHo7hBMREZkTjuA0gbu7u74lQ/VeVCEhIaY4PBEREVkZob2oiIiIyLzwLioj1NeLqj7VWzW8E8XCiIiIqKVwHZwmqq0XVWNVb9VQvufzlohHRERE4BycJqmrFxURERGZF16iaoK6elGp1WqsXbsWRUVFSExMhI+PD95+27oXtCIiIhJJp9WKjtAshPeiCg8PN0UEIiIiagTOwSEiIiKLw0tUolTcE51AmKtlXqIjCNVbfUB0BKEq7Vrf/67N6e4LU0RHEMb5arboCEK1+cXK+7AFm7YPHycZN0FdrRq++uorHD58GLa2tmjfvj0mTpwIZ2dnU0QiIiKiWrDAaYK6WjWEhIRgxIgRkEql2LBhA3bs2IGRI0eaIhIRERHVQquzjEnGNqY4iLu7O3x9fQEYtmoIDQ2FVCoFAPj7+0OlUpkiDhEREdVBp9UZ9TA3ZtOqIS0tDZGRkaaOQ0RERNWYY7FiDLNo1bB9+3ZIpVIMGDDAlHGIiIjoT3gXVRPV1aohPT0dhw8fxvz58yGRSGrd16AXVaS3SfISERFZIy0X+mu8ulo15OTk4Ouvv8bChQvh4OBQ5/4Gvah2pbR4XiIiImvFS1RNUFerhnXr1kGtVuO9994DAHTv3h0TJkwwRSQiIiKqhc5C7qIS2qqhT58+pjg8ERERNZKljOCY5DZxIiIiIlNqdWu/l3sHio4gzMmrjqIjCPVw5SnREYRSjZ0nOoJQNjqN6AjC7PeMEx1BqBCvo6IjCCUz8fEsZQRHaKuGzZs3IysrCxKJBG5ubpg4cSLkcrkpIhEREVEtLGUlY6GtGp566im88MILAIBvv/0W27Zt4yRjIiIigTiC0wTu7u5wd3cHYNiqoXPnzvpt7t27V+c6OERERGQaOq6DY5w/t2rYtGkTfv75Zzg5OWHBggWmjkNERETVWMoIjknvoqqtVcOLL76IVatW4dFHH8WePXtMGYeIiIj+RKfTGvUwNyYrcOpq1XDfgAEDcOjQoVr3TU1NxezZszF79uyWjklERGTVtFqdUQ9zI7RVQ15eHjp06AAAyMzMRMeOHWvdv3qrhjtH0lo+MBERkZXiHJwmqKtVQ1paGvLy8iCRSODh4cE7qIiIiARrqTk4OTk5WLduHbRaLQYPHoxhw4YZvF9ZWYkVK1bgwoULcHFxQXx8PDw9PQEAO3bsQFpaGmxsbDB27Fj06tWrweOxVQMRERHptcR8Gq1WizVr1mDevHlQKBSYM2cOwsLCDO6mTktLg7OzM5YvX479+/dj48aNmDp1Kq5du4aMjAx89NFHKCgowHvvvYdly5bBxqb+WTZs1UBERER6Oq3OqEd9cnNz4eXlhfbt28PW1haRkZHIzMw02CYrKwtRUVEAgIiICJw4cQI6nQ6ZmZmIjIyEnZ0dPD094eXlhdzc3AbPgwUOERER6em0WqMe9VGpVFAoFPrnCoUCKpWqzm2kUimcnJxQXFxcY1+5XF5j39q0ul5UbXvHCD1+amqqfsKzqb3RW8hhDYg8f+ANQcetIvbcAV9hR64i+vxFE3n+PkKOakjs97/mnbemZG3/9n/dPcio/VJTU5Gamqp/Xv0GIRE4gtNE1b951siaz9+azx3g+fP8rff8rfncm2LIkCFITEzUP6oXN3K5HPn5+frn+fn5NXpPVt9Go9GgrKwMLi4uNfZVqVSN6lvJAoeIiIhalJ+fH/Ly8qBUKqFWq5GRkYGwsDCDbfr27Yv09HQAwMGDBxEcHAyJRIKwsDBkZGSgsrISSqUSeXl5+m4I9Wl1l6iIiIiodZFKpRg3bhwSEhKg1WoRHR0Nb29vbNmyBX5+fggLC0NMTAxWrFiByZMnQyaTIT4+HgDg7e2Nhx9+GNOmTYONjQ3Gjx/f4B1UAAucJrOm67C1sebzt+ZzB3j+PH/rPX9rPvfm1KdPnxrLwzz//PP6/7a3t8e0adNq3ffZZ5/Fs88+26TjSXQ6nfmtr0xERET0ADgHh4iIiCyO1V6iev7559GlSxf985kzZ+qXhL5v0aJFmDJlCpydnQ1e37p1KxwdHfHUU0+ZJOuDiouLw6OPPoopU6YAqJqdPmHCBHTv3r1ZGpia6+exfft2/Prrr7CxsYFEItGfc23S09MREhLSqJn59Zk0aRIWLVoEV1fXB/o6za0pn0VjmeP3vbi4GO+++y4A4M6dO7CxsYGrqytu3boFd3d3JCcnt+jx09PTcf78eYwfP75Fj9OQuj4HoOrnmq1t8/zoP3nyJHbv3m3yRshffPEF2rVrh9jYWABAQkICFAoFXnvtNQDAl19+CblcbtD7sDZKpRKLFy9GUlJSi2UdNWoUvvrqqxb7+lQ3qy1w7O3tsXTp0lrf0+l00Ol0mDNnjolTtQwHBwdcvXoVFRUVsLe3x7Fjxx74F7m5O3v2LA4fPozFixfDzs4ORUVFUKvVdW6fnp4Ob2/vJn0uGo0GUqm0OeK2qKZ+Fq2Zi4uL/v/r6gXY/V9kxmot3+v76vocLEVAQAAyMjIQGxsLrVaLoqIilJWV6d8/c+YMRo8eLTAhmQOrLXD+TKlUIiEhAd27d8eFCxcwZ84cvPPOO/q/xrdv3459+/bB1dUVCoUCvr5Vy66lpqZi7969UKvVaN++PSZPngytVosZM2Zg2bJlsLW1RVlZGWbOnKl/LkLv3r2RnZ2NiIgI7N+/H4888gh+//13AEBJSQlWrlwJpVIJBwcHTJgwAV27dsXWrVtx+/ZtKJVK3L59G0OHDsXQoUMBwOw/j4KCAri4uMDOzg4A9H+9btu2DYcPH0ZFRQX8/f0xYcIEHDp0COfPn8cnn3wCe3t7JCQkYOrUqfrv/fnz5/HVV1/hnXfewdatW/HHH39AqVRCoVBg/PjxWLZsGVQqFfz9/VF9StuSJUuQn5+PyspKDB06FEOGDEFaWhquXLmCMWPG6D+va9eu6Z+b8rOoPtr053Nsrd/3+mi1WqxevRpnz56FXC7HW2+9BXt7e7zzzjsYNWoU/Pz8UFRUhDlz5iAlJQXp6ek4dOgQysvLodVqER8fj48//hhlZWXQarV45ZVXEBgYiJ9++gk7d+6Ek5MTunbtqv+cs7KysH37dqjVari4uGDy5MlwdXVFfHw83n//fbi6ukKr1eLNN99EQkJCi4/6paSkoG/fvoiIiABgOLKwa9cuHDhwAJWVlQgPD0dcXBzKy8uRnJwMlUoFrVaLv//974iMjEROTg6++OILODg4oEePHvqvn5ubi3Xr1qGyshL29vaYOHEiOnbsiAULFmDs2LHw8fEBAPzjH//A+PHj9c+N4e/vj/Xr1wMArl27Bm9vb9y5cwclJSVwcHDA9evXIZFIsGDBApSXl8PV1RUTJ06Eu7s7Lly4gFWrVgEAQkJC9F8zPT0dWVlZuHfvHv744w+Eh4dj5MiRAICjR49i69at+n/fEydOhKOjIzZu3IisrCxIpVKEhITg5ZdfhlKpxLJly1BeXo5+/frpv355eTmWLFmC0tJSqNVqvPDCC+jXrx+2bNkCmUymH43atGkT3Nzc9P/PkfGstsCpqKjQdzb39PTE6NGjcfPmTUyaNAn+/v4G2164cAH79+/HkiVLoNFoMGvWLP0P9v79++tn2G/evBlpaWn4n//5HwQHByM7Oxvh4eHIyMhA//79hRU3APDII49g27Zt6NOnDy5fvozo6Gh9gbN161b85S9/wVtvvYUTJ05gxYoV+r/+bty4gQULFuDu3buIj4/H448/jitXrpj95xEaGopt27bhzTffRM+ePREZGYmgoCA88cQTGD58OABg+fLlOHz4MCIiIrBnzx79L7mGXLt2De+99x7s7e2xdu1aBAQEYPjw4cjOzkZaWpp+u4kTJ0Imk6GiogJz5sxB//79ERkZiR07dmDkyJGwtbVFeno6JkyY0GKfA1D3Z1Gf1vp9r09eXh7efPNNvPbaa/joo49w8OBBDBw4sN59Ll68iA8//BAymQy7d+9GaGgonn32WWi1Wty7dw8FBQXYunUrFi9eDCcnJyxcuFD/izsgIAAJCQmQSCTYu3cvdu3ahZdffhkDBgzAL7/8gtjYWBw/fhxdu3YVeknz6NGjyMvLwwcffACdToclS5bg1KlTKCoqgru7u34ku6ysDBUVFfj0008xf/58eHl5GVzy69ixI959911IpVIcO3YM//rXvzBjxgxER0cjPT0dY8aMwY0bN1BZWflAxQ1QtSCcjY0Nbt++jTNnzsDf3x8qlQpnz56Fk5MTOnXqhPXr1+Ott96Cq6srMjIysGnTJkycOBErV67EuHHjEBQUVOPS0aVLl7BkyRLY2toiPj4eTzzxBOzt7bF9+3b84x//gKOjI3bu3IlvvvkGf/3rX/Hbb7/h448/hkQiQWlpKQBg3bp1ePzxxzFo0CDs2bNH/7Xt7OwwY8YMODk5oaioCG+//TbCwsIQHR2NpKQk/WhURkYGPvjggwf6fKiK1RY4f75EpVQq4eHhUaO4AYDTp08jPDwcDg4OAGCwONHVq1exefNmlJaWory8HKGhoQCAmJgY7Nq1C+Hh4fjpp5/w6quvtvAZ1a9r1664desW9u/fj969DXs+/P7775g+fToA4KGHHkJJSYl+uLdPnz6ws7ODnZ0d3NzcUFhY2Co+D0dHRyxevBinT5/GyZMnkZycjJdeegmOjo7YtWsX7t27h5KSEnh7e9dYbKohYWFhsLe3B1D1b2PGjBkAqj6r6vO1vv32W30zudu3byMvLw/+/v76X/qdOnWCRqMxmAvWEur6LOrTWr/v9fH09NT/YvX19cWtW7ca3CckJAQymQxA1UJlq1atglqtRnh4OHx8fHD8+HEEBwfrC5SHH34YeXl5AKpWW/34449RUFAAtVqtn+MXHR2NpUuXIjY2Fj/99BOio6Nb4Gwb7+jRozh27BjeeustAFUjDTdv3kRAQAC++uorbNiwAX379kVgYCAuXboET09PdOjQAQAwcOBA/Sq/ZWVlSElJwc2bNwFUXdYDqj6T//3f/8XIkSPx008/6ZspPqgePXrgzJkzOHPmDJ588kmDAkcul+PYsWN47733AFSN3rm7u6O0tBSlpaX6An/gwIHIycnRf82HHnoITk5OAIDOnTvj9u3bKC0txbVr1/CPf/wDAKBWq+Hv7w8nJyfY29tj1apV6Nu3L/r27Qug6vLY/Z+nAwcOxMaNGwFUTX3YtGkTTp8+DYlEApVKhcLCQnh6ekImk+HixYsoLCyEj48PXFxcmuUzsnZWW+DUxtHRscn7pKSkYObMmfDx8UF6ejpOnjwJoOqvtzVr1uDkyZPQarUt/kusMcLCwvSXIYqLixu1T/W/tm1sbPQ/tOpiTp+HjY0NgoODERwcjC5duuDHH3/ElStXsGjRInh4eGDr1q2oqKioc9/7l5sqKysN3rv/C74+J0+exPHjx/H+++/DwcEB77zzjv7rDB48GDt27EDHjh2b7Yd9Q/78WaSnp9d7jq35+16X+5eOgKpzuv+9l0qljfpeBwUFYeHChcjOzkZKSgqefPJJtGnTps7jrV27Fk8++STCwsJw8uRJ/Pvf/wYAeHh4wM3NDSdOnEBubq5+8n9Lk0ql0P63IaJWqzWYhzVs2DA89thjNfZZvHgxsrOzsXnzZvTs2bPePwa2bNmC4OBgzJw5E0qlEgsXLgRQ9RmGhIQgKysLBw4cQGJiYrOcz/0C5+rVq+jSpQs8PDzwzTffoE2bNggODoZKpUJCQoLBPvdHWery538jGo0GOp0OPXv21C86V90HH3yA48eP4+DBg9izZw8WLFgAAJBIJDW2/fXXX1FUVITExETY2tpi0qRJ+n+DgwcPRnp6Ou7cuSO84LUkvE28EQIDA5GZmYmKigrcvXsXhw8f1r9XXl4Od3d3qNVq/PLLLwb7DRw4EJ988onZ/IONjo7G8OHDa/ySCQgI0Gc/efIkXFxc9H/F1KY1fB43btzQ/yUNVA09d+zYEUDVHJTy8nIcOnRI/76joyPu3r2rf+7p6YkLFy4AqFoyvC6BgYH49ddfAQBHjhzR/wAtKyuDs7Ozfj7AuXPn9Pt0794d+fn5+rlQLa22z6Jdu3aNPsf7WsP33Rjt2rVr1Odw69YttG3bFkOGDMHgwYNx8eJFdO/eHadOnUJxcTHUarXB/mVlZfpJ6/v27TP4WjExMVi+fDkiIiIatSJrc6h+nllZWfqiNTQ0FD/99BPKy8sBQD+yoFKpYG9vj4EDB+Kpp57ChQsX0LFjRyiVSv0ozf1/+4Dh+d5fbv++wYMHY926dfDz89OPiD2oHj16IDs7GzKZDDY2NpDJZCgtLcXZs2fxyCOPoKioCGfPngVQNepy9epVODs7w9nZWX95/s//Vmvj7++PM2fO6M+5vLwcN27cQHl5OcrKytCnTx+MGTMGly9f1ufav38/gJqfj5ubG2xtbXHixAmDEcTw8HDk5OTg/Pnz6NWrV7N8PsQRnEbx9fVFZGQkZs6cCVdXV4N5Gs8//zzmzp0LV1dXdO/e3eCX5IABA7B582aT/BJrDIVCUevEtbi4OKxcuRIzZsyAg4MDJk2aVO/XaQ2fR3l5OdauXYvS0lJIpVJ4eXlhwoQJcHZ2xvTp09G2bVuD3FFRUfjnP/+pn2Q8fPhwrF69Glu2bKl3vspzzz2HZcuWYdq0afD394eHhwcAoFevXvjxxx8xdepUdOjQocYt2Q8//DAuXbrUbD/s61PXZ3H9+vVGneN9reH7boy//e1vSE5ORmpqao1VVqu7f0u0VCqFo6Mj3njjDbi7u+O5557DvHnz4OTkZDC35LnnnsNHH30EZ2dnPPTQQ1Aqlfr3wsLCsGrVKpMWfYMHD8bSpUsxc+ZMhIaG6kenQkNDcf36dbz99tsAqor9yZMn4+bNm9iwYQMkEglsbW3xyiuvwN7eHq+++ioSExPh4OCAgIAAfWH09NNPIyUlBdu3b6/xOfr6+qJNmzbNer5dunRBcXExHn30UYPXysvL4ebmhunTp2PdunUoKyuDRqPB0KFD4e3tjYkTJ+onGd+/lFofV1dXTJo0CcuWLdOP8L3wwgto06YNlixZgsrKSuh0Orz88ssAgLFjx2LZsmX4+uuvDSYZP/roo1i8eDGmT58OPz8/dOrUSf+era0tgoOD4ezsbLKC1xpwJeMWdPDgQWRmZmLy5Mmio5gFfh7/JzExEbGxsejZs6foKC2O3/eazp8/j/Xr1+vXqrF0KpUKCxcuRHJyMn+B10Kr1WLWrFmYNm2afn4TPTiO4LSQtWvX4siRIxazls6D4udRpbS0FHPnzkXXrl2torjh972mnTt34ocffjDZ3BvR9u3bh82bN+Pll19mcVOLa9euITExEeHh4SxumhlHcIiIiMjisJwmIiIii8MCh4iIiCwOCxwiIiKyOCxwiKhBcXFx+nVAGvLOO+9g7969LZyIiKh+LHCIWrEdO3bU6FszZcqUWl+7v/gYEZE1YIFD1IoFBgbizJkz+iX4CwoKoNFocPHiRYPXbt68icDAQJFRiYhMiuvgELVi3bp1g0ajwaVLl+Dr64vTp08jODgYf/zxh8Fr7du3h6OjI1atWoUjR45AIpEgOjoacXFx+rVJ0tLSsHv3bty5cwfdunXDhAkT0K5duxrH/P3337Fs2TK88cYbCA4OxrFjx7B27VoUFBRg4MCBqL7yxM2bN/Hpp5/i8uXLkEgkCA0Nxfjx4+Hs7Ixdu3bh7Nmz+malQNW6ORKJBGPHjm35D4+ILBpHcIhaMVtbW30/JKCqu3lAQAACAgIMXgsMDERKSgqkUik++eQTLFmyBEePHtXPlcnMzMSOHTswffp0fP755wgICMCyZctqHC8nJwfLli3D9OnTERwcjKKiInz44Yd44YUXsGbNGrRv3x5nzpwx2OeZZ57Bp59+iuTkZOTn5+ubTg4YMABHjx7V9+/SaDTIyMjAoEGDWuzzIiLrwQKHqJULDAzE6dOnAVSNrgQGBtZ4LSgoCEeOHMGYMWPg6OgINzc3xMbGIiMjAwDw448/4plnnkHnzp0hlUrxzDPP4NKlSwYNAQ8cOIDPPvsMc+bMQbdu3QBUNRj19vZGREQEbG1tERsbi7Zt2+r38fLyQkhICOzs7ODq6orY2Fh94eXu7o7AwEAcOHAAQFXx5OLiAl9f35b/0IjI4vESFVErFxQUhO+//x4lJSUoKipChw4d4ObmhpSUFJSUlODKlSvo1KkTNBoNJkyYoN9Pp9NBoVAAqOqUvW7dOnz55ZcG76tUKv1lqm+//RYDBw406EZfUFCg/xoAIJFIDJ7fuXMHX3zxBU6fPo3y8nJoqEKW4gAAAkxJREFUtVqDBqODBg3CDz/8gCFDhuCXX37BwIEDm/8DIiKrxAKHqJXz9/dHWVkZUlNT0aNHDwCAk5MT3N3dkZqaCrlcDg8PD9ja2mLNmjWQSqU1voaHhweeffZZDBgwoM7jTJ06FatXrzboSt+2bVvk5+frt9HpdAbPN23aBABISkqCTCbDb7/9hrVr1+rf79evHz7//HNcuXIFhw8fxsiRIx/swyAi+i9eoiJq5ezt7eHn54f//Oc/CAgI0L8eEBCA//znPwgMDIS7uztCQ0Px5ZdfoqysDFqtFjdv3tRfLnrsscewc+dOXL16FQBQVlamv3R0n1wux/z58/Htt9/ihx9+AAD06dMHV69exaFDh6DRaPDdd9/hzp07+n3u3r0LR0dHODk5QaVSYffu3TWy9+/fH5988gm6desGDw+PFvmMiMj6sMAhsgBBQUEoLCysUeAUFhbqbw9/4403oFarMW3aNIwdOxYfffQRCgoKAADh4eF4+umn8fHHH2P06NGYPn06jhw5UuM4Hh4emD9/Pnbu3Im9e/fC1dUV06ZNw7/+9S+MGzcOeXl5+lEkAHjuuedw8eJFjB49GosWLUJ4eHiNrxkVFYUrV67w8hQRNSt2EycioW7fvo34+Hh89tlncHJyEh2HiCwER3CISBitVotvvvkGkZGRLG6IqFmxwCEiIcrLyzF69GgcO3YMcXFxouMQkYXhJSoiIiKyOBzBISIiIovDAoeIiIgsDgscIiIisjgscIiIiMjisMAhIiIii8MCh4iIiCzO/wcl27va66vRCgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Vizualise data on a map"
      ],
      "metadata": {
        "id": "frQHMo3P1Rqn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k_centers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_S-HQxbnIjO",
        "outputId": "352acbab-80f9-4c06-9696-a6b05e96947b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 40.74108429, -73.98561056],\n",
              "       [ 40.72494758, -73.81506047]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k_centers_df = pd.DataFrame(data=k_centers, columns=['Lat', 'Lon'])\n",
        "k_centers_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "YiK6atTqoNca",
        "outputId": "93951eba-12f1-483d-fb49-7d22735a35fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-efe261ac-6b3f-41e4-a101-8115228f4e6c\">\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>Lat</th>\n",
              "      <th>Lon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40.741084</td>\n",
              "      <td>-73.985611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>40.724948</td>\n",
              "      <td>-73.815060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efe261ac-6b3f-41e4-a101-8115228f4e6c')\"\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-efe261ac-6b3f-41e4-a101-8115228f4e6c 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-efe261ac-6b3f-41e4-a101-8115228f4e6c');\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",
              "  "
            ],
            "text/plain": [
              "         Lat        Lon\n",
              "0  40.741084 -73.985611\n",
              "1  40.724948 -73.815060"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import folium\n",
        "\n",
        "uber_map = folium.Map(tiles='Stamen Toner', zoom_start=13, prefer_canvas=True)\n",
        "\n",
        "def plotDot(point):\n",
        "    '''input: series that contains a numeric named latitude and a numeric named longitude\n",
        "    this function creates a CircleMarker and adds it to uber_map'''\n",
        "    folium.CircleMarker(location=[point.Lat, point.Lon],\n",
        "                        fill_color='blue',\n",
        "                        radius=20,\n",
        "                        weight=5).add_to(uber_map)\n",
        "\n",
        "#use df.apply(,axis=1) to \"iterate\" through every row in your dataframe\n",
        "k_centers_df.apply(plotDot, axis = 1)\n",
        "\n",
        "#Set the zoom to the maximum possible\n",
        "uber_map.fit_bounds(uber_map.get_bounds())\n",
        "\n",
        "uber_map"
      ],
      "metadata": {
        "id": "wNX4_XMz1RAr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "outputId": "6ac82405-5efe-46e2-9189-4d7d7f8b12a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ],
            "text/plain": [
              "<folium.folium.Map at 0x7f78cc51d8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# export cluster map\n",
        "uber_map.save('uber_map.html')"
      ],
      "metadata": {
        "id": "ZOAEdMFkqswo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}