ml/2024_06_25_kmeans_clustering.ipynb

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        "# KMeans Clustering\n",
        "\n",
        "K-means clustering is an example of a model that is not trained. So, there is no training set, which is\n",
        "called an unsupervised model, meaning without labels. K-means clustering is a type of classification\n",
        "problem. The goal is to put data into groups by something they have in common, meaning put them in\n",
        "clusters. Example use cases are:\n",
        "\n",
        "• Recommending videos on TikTok or YouTube\n",
        "• High credit risk borrowers\n",
        "• popular social media influencers\n",
        "• finding spambots\n",
        "\n",
        "\n",
        "![](https://raw.githubusercontent.com/werowe/HypatiaAcademy/master/images/kmeans-clustering.png)\n",
        "\n",
        "\n",
        "\n",
        "$$\\huge \\sqrt{(a - b)^2}$$\n",
        "\n",
        "\n",
        "\n",
        "![](https://raw.githubusercontent.com/werowe/HypatiaAcademy/master/images/distance-between-points.png)\n",
        "\n",
        "\n",
        "$$\\huge a = (x1,y1)$$\n",
        "$$\\huge b = (x2, y2)$$\n",
        "\n",
        "Pythagorean theorem\n",
        "\n",
        "$$\\huge a^2 + b^2 = c^2$$\n",
        "\n",
        "\n",
        "$$\\huge height = y2 - y1$$\n",
        "\n",
        "$$\\huge base = x2 -  x1 $$\n",
        "\n",
        "length of distance ab:\n",
        "\n",
        "$$\\huge \\sqrt{(y2 - y1)^2 + (x2 - x1)^2}$$"
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        "\n",
        "\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Do we need Y?**\n",
        "\n",
        "The y values generated by make_blobs represent the true labels of the clusters and are typically used for evaluating the performance of clustering algorithms. However, the KMeans algorithm itself does not require the y values to calculate the clusters. KMeans is an unsupervised learning algorithm, meaning it only needs the input data X to find the clusters.\n",
        "\n",
        "The y values are useful if you want to assess the quality of the clustering using metrics such as the Adjusted Rand Index (ARI) or Normalized Mutual Information (NMI). These metrics compare the clusters found by the algorithm to the true clusters."
      ],
      "metadata": {
        "id": "0w_1s_KeyS0V"
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    {
      "cell_type": "code",
      "source": [
        "!ls sample_data/"
      ],
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          "text": [
            "anscombe.json\t\t     california_housing_train.csv  mnist_train_small.csv\n",
            "california_housing_test.csv  mnist_test.csv\t\t   README.md\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "df=pd.read_csv('sample_data/california_housing_test.csv')\n",
        "\n",
        "\n",
        "df"
      ],
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      "execution_count": 106,
      "outputs": [
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              "      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0       -122.05     37.37                27.0       3885.0           661.0   \n",
              "1       -118.30     34.26                43.0       1510.0           310.0   \n",
              "2       -117.81     33.78                27.0       3589.0           507.0   \n",
              "3       -118.36     33.82                28.0         67.0            15.0   \n",
              "4       -119.67     36.33                19.0       1241.0           244.0   \n",
              "...         ...       ...                 ...          ...             ...   \n",
              "2995    -119.86     34.42                23.0       1450.0           642.0   \n",
              "2996    -118.14     34.06                27.0       5257.0          1082.0   \n",
              "2997    -119.70     36.30                10.0        956.0           201.0   \n",
              "2998    -117.12     34.10                40.0         96.0            14.0   \n",
              "2999    -119.63     34.42                42.0       1765.0           263.0   \n",
              "\n",
              "      population  households  median_income  median_house_value  \n",
              "0         1537.0       606.0         6.6085            344700.0  \n",
              "1          809.0       277.0         3.5990            176500.0  \n",
              "2         1484.0       495.0         5.7934            270500.0  \n",
              "3           49.0        11.0         6.1359            330000.0  \n",
              "4          850.0       237.0         2.9375             81700.0  \n",
              "...          ...         ...            ...                 ...  \n",
              "2995      1258.0       607.0         1.1790            225000.0  \n",
              "2996      3496.0      1036.0         3.3906            237200.0  \n",
              "2997       693.0       220.0         2.2895             62000.0  \n",
              "2998        46.0        14.0         3.2708            162500.0  \n",
              "2999       753.0       260.0         8.5608            500001.0  \n",
              "\n",
              "[3000 rows x 9 columns]"
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              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
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              "      <th>0</th>\n",
              "      <td>-122.05</td>\n",
              "      <td>37.37</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3885.0</td>\n",
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              "      <td>6.6085</td>\n",
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              "      <th>1</th>\n",
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              "      <td>34.26</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>809.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>3.5990</td>\n",
              "      <td>176500.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>-117.81</td>\n",
              "      <td>33.78</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3589.0</td>\n",
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              "      <td>1484.0</td>\n",
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              "      <td>5.7934</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>33.82</td>\n",
              "      <td>28.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.1359</td>\n",
              "      <td>330000.0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>36.33</td>\n",
              "      <td>19.0</td>\n",
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              "      <td>850.0</td>\n",
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              "      <td>1450.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1.1790</td>\n",
              "      <td>225000.0</td>\n",
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              "      <th>2996</th>\n",
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              "      <td>34.06</td>\n",
              "      <td>27.0</td>\n",
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              "      <td>1082.0</td>\n",
              "      <td>3496.0</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>3.3906</td>\n",
              "      <td>237200.0</td>\n",
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              "      <th>2997</th>\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "        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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "        document.querySelector('#id_ddd932b4-51b0-4ff6-bc42-a87a173b639a button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3000,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9949362939550175,\n        \"min\": -124.18,\n        \"max\": -114.49,\n        \"num_unique_values\": 607,\n        \"samples\": [\n          -121.15,\n          -121.46,\n          -121.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.129669523343834,\n        \"min\": 32.56,\n        \"max\": 41.92,\n        \"num_unique_values\": 587,\n        \"samples\": [\n          40.17,\n          33.69,\n          39.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.555395554955753,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          14.0,\n          49.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2155.593331625581,\n        \"min\": 6.0,\n        \"max\": 30450.0,\n        \"num_unique_values\": 2215,\n        \"samples\": [\n          1961.0,\n          1807.0,\n          680.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 415.6543681363229,\n        \"min\": 2.0,\n        \"max\": 5419.0,\n        \"num_unique_values\": 1055,\n        \"samples\": [\n          532.0,\n          764.0,\n          2162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1030.5430124122424,\n        \"min\": 5.0,\n        \"max\": 11935.0,\n        \"num_unique_values\": 1802,\n        \"samples\": [\n          947.0,\n          1140.0,\n          2019.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 365.42270980552627,\n        \"min\": 2.0,\n        \"max\": 4930.0,\n        \"num_unique_values\": 1026,\n        \"samples\": [\n          646.0,\n          629.0,\n          504.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.854511729691479,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 2578,\n        \"samples\": [\n          1.725,\n          0.7403,\n          2.6964\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 113119.68746964628,\n        \"min\": 22500.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 1784,\n        \"samples\": [\n          71900.0,\n          63000.0,\n          115800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import seaborn as sns\n",
        "\n",
        "data=df.loc[:,['longitude', 'latitude', 'median_house_value']]\n",
        "\n",
        "sns.scatterplot(data, x = 'longitude', y = 'latitude', hue = 'median_house_value')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "1VZXj-Nm67i3",
        "outputId": "8e7b7e8c-42bd-427c-b7c1-f66280bf2b0b"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='longitude', ylabel='latitude'>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
        "\n",
        "origdf=df.copy()\n",
        "\n",
        "\n",
        "df.drop('longitude', axis=1,inplace=True)\n",
        "df.drop('latitude', axis=1,inplace=True)\n",
        "\n",
        "X=df.to_numpy()\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "\n",
        "model = KMeans(n_clusters=7, random_state=42)\n",
        "model.fit(X)\n",
        "\n",
        "print(model.labels_)\n",
        "print(model.cluster_centers_)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2obrhzj-jKz",
        "outputId": "a9413518-bda9-40fd-c4fc-6490695c0f89"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5 6 5 ... 4 6 2]\n",
            "[[0.21795495 0.30730858 0.33588931 0.37598179 0.32688399 0.25448102\n",
            "  0.36620582]\n",
            " [0.59108076 0.06697222 0.08310639 0.11218663 0.08655349 0.19102709\n",
            "  0.2897191 ]\n",
            " [0.60895141 0.10443451 0.09858738 0.10296913 0.10100314 0.45767645\n",
            "  0.93420994]\n",
            " [0.83160004 0.06598312 0.07770096 0.08588775 0.08131346 0.24773011\n",
            "  0.57554074]\n",
            " [0.28356676 0.08340596 0.09874557 0.11233505 0.09625142 0.18105641\n",
            "  0.21797777]\n",
            " [0.3601047  0.10193085 0.10377556 0.12413061 0.10807098 0.32610751\n",
            "  0.54865022]\n",
            " [0.82635427 0.05010596 0.06657517 0.08987224 0.0686715  0.12802186\n",
            "  0.19741462]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "-6LV4sbBF0zZ",
        "outputId": "9a6397b5-7bc6-4f49-eba0-df8c49f7efa3"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      housing_median_age  total_rooms  total_bedrooms  population  households  \\\n",
              "0                   27.0       3885.0           661.0      1537.0       606.0   \n",
              "1                   43.0       1510.0           310.0       809.0       277.0   \n",
              "2                   27.0       3589.0           507.0      1484.0       495.0   \n",
              "3                   28.0         67.0            15.0        49.0        11.0   \n",
              "4                   19.0       1241.0           244.0       850.0       237.0   \n",
              "...                  ...          ...             ...         ...         ...   \n",
              "2995                23.0       1450.0           642.0      1258.0       607.0   \n",
              "2996                27.0       5257.0          1082.0      3496.0      1036.0   \n",
              "2997                10.0        956.0           201.0       693.0       220.0   \n",
              "2998                40.0         96.0            14.0        46.0        14.0   \n",
              "2999                42.0       1765.0           263.0       753.0       260.0   \n",
              "\n",
              "      median_income  median_house_value  \n",
              "0            6.6085            344700.0  \n",
              "1            3.5990            176500.0  \n",
              "2            5.7934            270500.0  \n",
              "3            6.1359            330000.0  \n",
              "4            2.9375             81700.0  \n",
              "...             ...                 ...  \n",
              "2995         1.1790            225000.0  \n",
              "2996         3.3906            237200.0  \n",
              "2997         2.2895             62000.0  \n",
              "2998         3.2708            162500.0  \n",
              "2999         8.5608            500001.0  \n",
              "\n",
              "[3000 rows x 7 columns]"
            ],
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              "      <th></th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
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              "      <th>median_house_value</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.0</td>\n",
              "      <td>3885.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>1537.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>6.6085</td>\n",
              "      <td>344700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43.0</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>809.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>3.5990</td>\n",
              "      <td>176500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27.0</td>\n",
              "      <td>3589.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>495.0</td>\n",
              "      <td>5.7934</td>\n",
              "      <td>270500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.1359</td>\n",
              "      <td>330000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19.0</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>2.9375</td>\n",
              "      <td>81700.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>23.0</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1.1790</td>\n",
              "      <td>225000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>27.0</td>\n",
              "      <td>5257.0</td>\n",
              "      <td>1082.0</td>\n",
              "      <td>3496.0</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>3.3906</td>\n",
              "      <td>237200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>10.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>693.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>2.2895</td>\n",
              "      <td>62000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>40.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.2708</td>\n",
              "      <td>162500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>42.0</td>\n",
              "      <td>1765.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>753.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.5608</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 7 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3000,\n  \"fields\": [\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.555395554955753,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          14.0,\n          49.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2155.593331625581,\n        \"min\": 6.0,\n        \"max\": 30450.0,\n        \"num_unique_values\": 2215,\n        \"samples\": [\n          1961.0,\n          1807.0,\n          680.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 415.6543681363229,\n        \"min\": 2.0,\n        \"max\": 5419.0,\n        \"num_unique_values\": 1055,\n        \"samples\": [\n          532.0,\n          764.0,\n          2162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1030.5430124122424,\n        \"min\": 5.0,\n        \"max\": 11935.0,\n        \"num_unique_values\": 1802,\n        \"samples\": [\n          947.0,\n          1140.0,\n          2019.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 365.42270980552627,\n        \"min\": 2.0,\n        \"max\": 4930.0,\n        \"num_unique_values\": 1026,\n        \"samples\": [\n          646.0,\n          629.0,\n          504.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.854511729691479,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 2578,\n        \"samples\": [\n          1.725,\n          0.7403,\n          2.6964\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 113119.68746964628,\n        \"min\": 22500.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 1784,\n        \"samples\": [\n          71900.0,\n          63000.0,\n          115800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Elbow Method**\n",
        "\n",
        "there is a another way to do this which is to just let the algorithm pick the number of clusters itself.\n",
        "\n",
        "often that is the most logical thing to do.  If we pick 7 that's just a guess.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "NaUGU0XsBVKE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **Elbow method** is a popular technique to determine the optimal number of clusters for K-means clustering. It involves plotting the within-cluster sum of squares (WCSS) against the number of clusters and identifying the \"elbow point,\" where the rate of decrease sharply shifts. This point is considered indicative of the optimal number of clusters."
      ],
      "metadata": {
        "id": "e9M77pOLHOEP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming X is your data matrix\n",
        "wcss= []\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
        "    kmeans.fit(X)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(range(1, 11), wcss, marker='o', linestyle='--')\n",
        "plt.title('Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.xticks(range(1, 11))\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "h2pwFtMJGAU-",
        "outputId": "53183929-8917-4b55-9090-583fc6dcea69"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kneed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zwqmq4kkHzgp",
        "outputId": "8850b69b-f61a-45dd-92f1-a98f0dd1d155"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kneed in /usr/local/lib/python3.10/dist-packages (0.8.5)\n",
            "Requirement already satisfied: numpy>=1.14.2 in /usr/local/lib/python3.10/dist-packages (from kneed) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from kneed) (1.11.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from kneed import KneeLocator\n",
        "\n",
        "# Using KneeLocator to find the elbow point\n",
        "kneedle = KneeLocator(range(1, 11), wcss, curve='convex', direction='decreasing')\n",
        "elbow_point = kneedle.elbow\n",
        "\n",
        "print(f\"The optimal number of clusters is: {elbow_point}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqyfe934Hnzu",
        "outputId": "5d3f6c2a-241d-4c9b-b785-449292313150"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The optimal number of clusters is: 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = KMeans(n_clusters=elbow_point, random_state=42)\n",
        "model.fit(X)\n",
        "\n",
        "print(model.labels_)\n",
        "print(model.cluster_centers_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mu9NDLy7GTRS",
        "outputId": "a8aa2445-d4f0-4a8d-bc8e-193a9598b4bc"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 0 2 ... 1 0 2]\n",
            "[[0.73158285 0.05772439 0.07261591 0.09717311 0.07557711 0.17418972\n",
            "  0.28374493]\n",
            " [0.31270668 0.10996013 0.12477329 0.14537357 0.12378195 0.21718899\n",
            "  0.30488194]\n",
            " [0.61504674 0.09549757 0.0964599  0.1032545  0.10013004 0.37351602\n",
            "  0.77812078]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds=model.predict(X)"
      ],
      "metadata": {
        "id": "rCoLg-VhIAcI"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Boxplot Explained](https://www.simplypsychology.org/boxplots.html)"
      ],
      "metadata": {
        "id": "Z4G8Rfid7XBz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "origdf.drop(\"longitude\",axis=1,inplace=True)\n",
        "origdf.drop(\"latitude\",axis=1,inplace=True)\n",
        "\n",
        "origdf['Cluster'] = preds\n",
        "\n",
        "#Step 4: Create a boxplot for each feature, grouped by the cluster\n",
        "num_features = X.shape[1]\n",
        "num_rows = (num_features + 3) // 3  # Calculate the number of rows needed with 4 plots per row\n",
        "fig, axes = plt.subplots(num_rows, 3, figsize=(20, 5 * num_rows))\n",
        "\n",
        "# Flatten axes array for easy iteration\n",
        "axes = axes.flatten()\n",
        "\n",
        "for i, feature in enumerate(origdf.columns[:-1]):  # Exclude the cluster column\n",
        "    sns.boxplot(x='Cluster', y=feature, data=origdf, ax=axes[i], showfliers=False)\n",
        "    axes[i].set_title(f'Boxplot of {feature} by Cluster')\n",
        "    axes[i].set_xlabel('Cluster')\n",
        "    axes[i].set_ylabel(feature)\n",
        "\n",
        "# Hide any unused subplots\n",
        "for j in range(i + 1, len(axes)):\n",
        "    fig.delaxes(axes[j])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 848
        },
        "id": "0553Idn9IifQ",
        "outputId": "a1c14a88-8211-48d2-cb11-d34ad5943199"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 7 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "origdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "m8YQFYqBIrew",
        "outputId": "27ce422a-9a09-494f-8303-f0744117f323"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      housing_median_age  total_rooms  total_bedrooms  population  households  \\\n",
              "0                   27.0       3885.0           661.0      1537.0       606.0   \n",
              "1                   43.0       1510.0           310.0       809.0       277.0   \n",
              "2                   27.0       3589.0           507.0      1484.0       495.0   \n",
              "3                   28.0         67.0            15.0        49.0        11.0   \n",
              "4                   19.0       1241.0           244.0       850.0       237.0   \n",
              "...                  ...          ...             ...         ...         ...   \n",
              "2995                23.0       1450.0           642.0      1258.0       607.0   \n",
              "2996                27.0       5257.0          1082.0      3496.0      1036.0   \n",
              "2997                10.0        956.0           201.0       693.0       220.0   \n",
              "2998                40.0         96.0            14.0        46.0        14.0   \n",
              "2999                42.0       1765.0           263.0       753.0       260.0   \n",
              "\n",
              "      median_income  median_house_value  Cluster  \n",
              "0            6.6085            344700.0        2  \n",
              "1            3.5990            176500.0        0  \n",
              "2            5.7934            270500.0        2  \n",
              "3            6.1359            330000.0        2  \n",
              "4            2.9375             81700.0        1  \n",
              "...             ...                 ...      ...  \n",
              "2995         1.1790            225000.0        1  \n",
              "2996         3.3906            237200.0        1  \n",
              "2997         2.2895             62000.0        1  \n",
              "2998         3.2708            162500.0        0  \n",
              "2999         8.5608            500001.0        2  \n",
              "\n",
              "[3000 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a81f3183-442e-4cbf-9b0c-d5d7731443ab\" 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>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.0</td>\n",
              "      <td>3885.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>1537.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>6.6085</td>\n",
              "      <td>344700.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43.0</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>809.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>3.5990</td>\n",
              "      <td>176500.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27.0</td>\n",
              "      <td>3589.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>495.0</td>\n",
              "      <td>5.7934</td>\n",
              "      <td>270500.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.1359</td>\n",
              "      <td>330000.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19.0</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>2.9375</td>\n",
              "      <td>81700.0</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>23.0</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1.1790</td>\n",
              "      <td>225000.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>27.0</td>\n",
              "      <td>5257.0</td>\n",
              "      <td>1082.0</td>\n",
              "      <td>3496.0</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>3.3906</td>\n",
              "      <td>237200.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>10.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>693.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>2.2895</td>\n",
              "      <td>62000.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>40.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.2708</td>\n",
              "      <td>162500.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>42.0</td>\n",
              "      <td>1765.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>753.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.5608</td>\n",
              "      <td>500001.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 8 columns</p>\n",
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              "        document.querySelector('#df-a81f3183-442e-4cbf-9b0c-d5d7731443ab button.colab-df-convert');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        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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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-282847d5-d4e6-491b-90ac-164301f45656 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f5486d61-64f6-4f82-9f3f-4cff9cbcda85\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('origdf')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f5486d61-64f6-4f82-9f3f-4cff9cbcda85 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('origdf');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "origdf",
              "summary": "{\n  \"name\": \"origdf\",\n  \"rows\": 3000,\n  \"fields\": [\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.555395554955753,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          14.0,\n          49.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2155.593331625581,\n        \"min\": 6.0,\n        \"max\": 30450.0,\n        \"num_unique_values\": 2215,\n        \"samples\": [\n          1961.0,\n          1807.0,\n          680.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 415.6543681363229,\n        \"min\": 2.0,\n        \"max\": 5419.0,\n        \"num_unique_values\": 1055,\n        \"samples\": [\n          532.0,\n          764.0,\n          2162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1030.5430124122424,\n        \"min\": 5.0,\n        \"max\": 11935.0,\n        \"num_unique_values\": 1802,\n        \"samples\": [\n          947.0,\n          1140.0,\n          2019.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 365.42270980552627,\n        \"min\": 2.0,\n        \"max\": 4930.0,\n        \"num_unique_values\": 1026,\n        \"samples\": [\n          646.0,\n          629.0,\n          504.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.854511729691479,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 2578,\n        \"samples\": [\n          1.725,\n          0.7403,\n          2.6964\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 113119.68746964628,\n        \"min\": 22500.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 1784,\n        \"samples\": [\n          71900.0,\n          63000.0,\n          115800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    }
  ]
}