K_means_income.ipynb

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        "<a href=\"https://colab.research.google.com/github/Mathurshab2210/Ml_with_Mathur/blob/main/K_means_income.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "outputs": [],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/income.csv')"
      ],
      "metadata": {
        "id": "c4EswwCJ7aDk"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "df.head(5)"
      ],
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      "execution_count": 3,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      Name  Age  Income($)\n",
              "0      Rob   27      70000\n",
              "1  Michael   29      90000\n",
              "2    Mohan   29      61000\n",
              "3   Ismail   28      60000\n",
              "4     Kory   42     150000"
            ],
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income($)</th>\n",
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              "      <td>Rob</td>\n",
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              "      <td>70000</td>\n",
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              "      <th>1</th>\n",
              "      <td>Michael</td>\n",
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              "      <td>90000</td>\n",
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              "      <td>Mohan</td>\n",
              "      <td>29</td>\n",
              "      <td>61000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ismail</td>\n",
              "      <td>28</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kory</td>\n",
              "      <td>42</td>\n",
              "      <td>150000</td>\n",
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      "cell_type": "code",
      "source": [
        "plt.scatter(df['Age'],df['Income($)'])"
      ],
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        {
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            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fce3849de50>"
            ]
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "km=KMeans(n_clusters=3)"
      ],
      "metadata": {
        "id": "HDXPfUfH79Bz"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=km.fit_predict(df[['Age','Income($)']])\n"
      ],
      "metadata": {
        "id": "wQNudm3U8GVm"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8qtEfN8p8Wvo",
        "outputId": "d7fbde1e-1e7c-47a3-d4ff-8ef8bc99588a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2],\n",
              "      dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ppq-8UEC8dUY",
        "outputId": "e4f40084-2391-46e1-c40b-7283469cd3ea"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fce38321150>]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cluster']=y_pred"
      ],
      "metadata": {
        "id": "UyuNlcKG8hO2"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4yhuXiQB8nUS",
        "outputId": "736b00dc-b8ef-48a8-ee10-40841621e416"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Age  Income($)  cludter  cluster\n",
              "0      Rob   27      70000        0        0\n",
              "1  Michael   29      90000        0        0\n",
              "2    Mohan   29      61000        2        2\n",
              "3   Ismail   28      60000        2        2\n",
              "4     Kory   42     150000        1        1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47e38f82-a01c-4c8e-850c-6f76c00732ab\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income($)</th>\n",
              "      <th>cludter</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rob</td>\n",
              "      <td>27</td>\n",
              "      <td>70000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Michael</td>\n",
              "      <td>29</td>\n",
              "      <td>90000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mohan</td>\n",
              "      <td>29</td>\n",
              "      <td>61000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ismail</td>\n",
              "      <td>28</td>\n",
              "      <td>60000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kory</td>\n",
              "      <td>42</td>\n",
              "      <td>150000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47e38f82-a01c-4c8e-850c-6f76c00732ab')\"\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-47e38f82-a01c-4c8e-850c-6f76c00732ab 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-47e38f82-a01c-4c8e-850c-6f76c00732ab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df[df.cluster==0]\n",
        "df2=df[df.cluster==1]\n",
        "df3=df[df.cluster==2]\n",
        "plt.scatter(df1.Age,df1['Income($)'],color='green')\n",
        "plt.scatter(df2.Age,df2['Income($)'],color='red')\n",
        "plt.scatter(df3.Age,df3['Income($)'],color='black')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "fCosfUZd8vh6",
        "outputId": "9383b5bb-dd25-459d-f46c-2566ff3aa84d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fce35554990>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using sacler to normelize the data and get it in a homogenous range for this part we are using MinMax scalar\n"
      ],
      "metadata": {
        "id": "D7Fgkx0I_DAn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler=MinMaxScaler()"
      ],
      "metadata": {
        "id": "w2lh6Hzl9ej7"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " df['Income($)']=scaler.fit_transform(df[['Income($)']])"
      ],
      "metadata": {
        "id": "PWPo5Npx-XHQ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "m7-xItSX-obk",
        "outputId": "7f22b335-0b3f-48a4-fe2e-abebdea69a4c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Name  Age  Income($)  cludter  cluster\n",
              "0        Rob   27   0.213675        0        0\n",
              "1    Michael   29   0.384615        0        0\n",
              "2      Mohan   29   0.136752        2        2\n",
              "3     Ismail   28   0.128205        2        2\n",
              "4       Kory   42   0.897436        1        1\n",
              "5     Gautam   39   0.940171        1        1\n",
              "6      David   41   0.982906        1        1\n",
              "7     Andrea   38   1.000000        1        1\n",
              "8       Brad   36   0.948718        1        1\n",
              "9   Angelina   35   0.726496        1        1\n",
              "10    Donald   37   0.786325        1        1\n",
              "11       Tom   26   0.000000        2        2\n",
              "12    Arnold   27   0.025641        2        2\n",
              "13     Jared   28   0.051282        2        2\n",
              "14     Stark   29   0.038462        2        2\n",
              "15    Ranbir   32   0.068376        2        2\n",
              "16    Dipika   40   0.170940        2        2\n",
              "17  Priyanka   41   0.153846        2        2\n",
              "18      Nick   43   0.162393        2        2\n",
              "19      Alia   39   0.299145        0        0\n",
              "20       Sid   41   0.316239        0        0\n",
              "21     Abdul   39   0.111111        2        2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13b2e7bf-157d-4955-9d8f-1944c8c02b9b\">\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>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income($)</th>\n",
              "      <th>cludter</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rob</td>\n",
              "      <td>27</td>\n",
              "      <td>0.213675</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Michael</td>\n",
              "      <td>29</td>\n",
              "      <td>0.384615</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mohan</td>\n",
              "      <td>29</td>\n",
              "      <td>0.136752</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ismail</td>\n",
              "      <td>28</td>\n",
              "      <td>0.128205</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kory</td>\n",
              "      <td>42</td>\n",
              "      <td>0.897436</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gautam</td>\n",
              "      <td>39</td>\n",
              "      <td>0.940171</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>David</td>\n",
              "      <td>41</td>\n",
              "      <td>0.982906</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Andrea</td>\n",
              "      <td>38</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Brad</td>\n",
              "      <td>36</td>\n",
              "      <td>0.948718</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Angelina</td>\n",
              "      <td>35</td>\n",
              "      <td>0.726496</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Donald</td>\n",
              "      <td>37</td>\n",
              "      <td>0.786325</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Tom</td>\n",
              "      <td>26</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Arnold</td>\n",
              "      <td>27</td>\n",
              "      <td>0.025641</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Jared</td>\n",
              "      <td>28</td>\n",
              "      <td>0.051282</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Stark</td>\n",
              "      <td>29</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Ranbir</td>\n",
              "      <td>32</td>\n",
              "      <td>0.068376</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Dipika</td>\n",
              "      <td>40</td>\n",
              "      <td>0.170940</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Priyanka</td>\n",
              "      <td>41</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Nick</td>\n",
              "      <td>43</td>\n",
              "      <td>0.162393</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Alia</td>\n",
              "      <td>39</td>\n",
              "      <td>0.299145</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Sid</td>\n",
              "      <td>41</td>\n",
              "      <td>0.316239</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Abdul</td>\n",
              "      <td>39</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13b2e7bf-157d-4955-9d8f-1944c8c02b9b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age']=scaler.fit_transform(df[['Age']])"
      ],
      "metadata": {
        "id": "Np0gCALk-2cG"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "c0BfYyol-_lR",
        "outputId": "69a5a1c9-f500-479f-a1b4-30d67f88db91"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Name       Age  Income($)  cludter  cluster\n",
              "0        Rob  0.058824   0.213675        0        0\n",
              "1    Michael  0.176471   0.384615        0        0\n",
              "2      Mohan  0.176471   0.136752        2        2\n",
              "3     Ismail  0.117647   0.128205        2        2\n",
              "4       Kory  0.941176   0.897436        1        1\n",
              "5     Gautam  0.764706   0.940171        1        1\n",
              "6      David  0.882353   0.982906        1        1\n",
              "7     Andrea  0.705882   1.000000        1        1\n",
              "8       Brad  0.588235   0.948718        1        1\n",
              "9   Angelina  0.529412   0.726496        1        1\n",
              "10    Donald  0.647059   0.786325        1        1\n",
              "11       Tom  0.000000   0.000000        2        2\n",
              "12    Arnold  0.058824   0.025641        2        2\n",
              "13     Jared  0.117647   0.051282        2        2\n",
              "14     Stark  0.176471   0.038462        2        2\n",
              "15    Ranbir  0.352941   0.068376        2        2\n",
              "16    Dipika  0.823529   0.170940        2        2\n",
              "17  Priyanka  0.882353   0.153846        2        2\n",
              "18      Nick  1.000000   0.162393        2        2\n",
              "19      Alia  0.764706   0.299145        0        0\n",
              "20       Sid  0.882353   0.316239        0        0\n",
              "21     Abdul  0.764706   0.111111        2        2"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income($)</th>\n",
              "      <th>cludter</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rob</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.213675</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Michael</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.384615</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mohan</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.136752</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ismail</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.128205</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kory</td>\n",
              "      <td>0.941176</td>\n",
              "      <td>0.897436</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>5</th>\n",
              "      <td>Gautam</td>\n",
              "      <td>0.764706</td>\n",
              "      <td>0.940171</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>David</td>\n",
              "      <td>0.882353</td>\n",
              "      <td>0.982906</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Andrea</td>\n",
              "      <td>0.705882</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Brad</td>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.948718</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Angelina</td>\n",
              "      <td>0.529412</td>\n",
              "      <td>0.726496</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Donald</td>\n",
              "      <td>0.647059</td>\n",
              "      <td>0.786325</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Tom</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Arnold</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.025641</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Jared</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.051282</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Stark</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Ranbir</td>\n",
              "      <td>0.352941</td>\n",
              "      <td>0.068376</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Dipika</td>\n",
              "      <td>0.823529</td>\n",
              "      <td>0.170940</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Priyanka</td>\n",
              "      <td>0.882353</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Nick</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.162393</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Alia</td>\n",
              "      <td>0.764706</td>\n",
              "      <td>0.299145</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Sid</td>\n",
              "      <td>0.882353</td>\n",
              "      <td>0.316239</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Abdul</td>\n",
              "      <td>0.764706</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>2</td>\n",
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              "\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=km.fit_predict(df[['Age','Income($)']])"
      ],
      "metadata": {
        "id": "ZRZEkI1P_Al5"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['cluster']=y_pred"
      ],
      "metadata": {
        "id": "JPbICHbY_jAd"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()\n",
        "df.drop('cludter',axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "iEXcuGLb_nft",
        "outputId": "3e9c2149-8512-49c7-8031-4f409740b598"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Name       Age  Income($)  cluster\n",
              "0        Rob  0.058824   0.213675        0\n",
              "1    Michael  0.176471   0.384615        0\n",
              "2      Mohan  0.176471   0.136752        0\n",
              "3     Ismail  0.117647   0.128205        0\n",
              "4       Kory  0.941176   0.897436        2\n",
              "5     Gautam  0.764706   0.940171        2\n",
              "6      David  0.882353   0.982906        2\n",
              "7     Andrea  0.705882   1.000000        2\n",
              "8       Brad  0.588235   0.948718        2\n",
              "9   Angelina  0.529412   0.726496        2\n",
              "10    Donald  0.647059   0.786325        2\n",
              "11       Tom  0.000000   0.000000        0\n",
              "12    Arnold  0.058824   0.025641        0\n",
              "13     Jared  0.117647   0.051282        0\n",
              "14     Stark  0.176471   0.038462        0\n",
              "15    Ranbir  0.352941   0.068376        0\n",
              "16    Dipika  0.823529   0.170940        1\n",
              "17  Priyanka  0.882353   0.153846        1\n",
              "18      Nick  1.000000   0.162393        1\n",
              "19      Alia  0.764706   0.299145        1\n",
              "20       Sid  0.882353   0.316239        1\n",
              "21     Abdul  0.764706   0.111111        1"
            ],
            "text/html": [
              "\n",
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              "      <th>0</th>\n",
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              "      <td>0.384615</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mohan</td>\n",
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              "      <td>0.136752</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ismail</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.128205</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kory</td>\n",
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              "      <td>0.897436</td>\n",
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              "      <th>5</th>\n",
              "      <td>Gautam</td>\n",
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              "      <td>0.940171</td>\n",
              "      <td>2</td>\n",
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              "      <th>6</th>\n",
              "      <td>David</td>\n",
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              "      <td>0.982906</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Andrea</td>\n",
              "      <td>0.705882</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Brad</td>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.948718</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Angelina</td>\n",
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              "      <td>0.726496</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Donald</td>\n",
              "      <td>0.647059</td>\n",
              "      <td>0.786325</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Tom</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Arnold</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.025641</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Jared</td>\n",
              "      <td>0.117647</td>\n",
              "      <td>0.051282</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Stark</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Ranbir</td>\n",
              "      <td>0.352941</td>\n",
              "      <td>0.068376</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Dipika</td>\n",
              "      <td>0.823529</td>\n",
              "      <td>0.170940</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Priyanka</td>\n",
              "      <td>0.882353</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Nick</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.162393</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Alia</td>\n",
              "      <td>0.764706</td>\n",
              "      <td>0.299145</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Sid</td>\n",
              "      <td>0.882353</td>\n",
              "      <td>0.316239</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Abdul</td>\n",
              "      <td>0.764706</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>1</td>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df[df.cluster==0]\n",
        "df2=df[df.cluster==1]\n",
        "df3=df[df.cluster==2]\n",
        "plt.scatter(df1.Age,df1['Income($)'],color='green')\n",
        "plt.scatter(df2.Age,df2['Income($)'],color='red')\n",
        "plt.scatter(df3.Age,df3['Income($)'],color='black')\n",
        "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='Aqua',marker='*')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "3A-AZyD-_qWZ",
        "outputId": "16e49738-7b2b-45d1-e024-9489beb0abba"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fce3546ab10>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "km.cluster_centers_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AYQ2sN1R_tCn",
        "outputId": "b272ce2d-5e46-4c07-c2b8-6c9633ccb2c8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.1372549 , 0.11633428],\n",
              "       [0.85294118, 0.2022792 ],\n",
              "       [0.72268908, 0.8974359 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Elbow method"
      ],
      "metadata": {
        "id": "HbDJTuB4CCx9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sse=[]\n",
        "for k in range(1,10):\n",
        "  km=KMeans(n_clusters=k)\n",
        "  km.fit(df[['Age','Income($)']])\n",
        "  sse.append(km.inertia_)##gives sse\n"
      ],
      "metadata": {
        "id": "dGhRMY_rBG8U"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m76ifv7rCf07",
        "outputId": "d0f51b45-b651-47a2-aefb-bcec4262990a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5.434011511988178,\n",
              " 2.091136388699078,\n",
              " 0.4750783498553096,\n",
              " 0.3491047094419566,\n",
              " 0.26640301246684156,\n",
              " 0.21055478995472496,\n",
              " 0.1685851223602976,\n",
              " 0.13265419827245162,\n",
              " 0.11073569527418643]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(sse)\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('SSE')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "TFN1KJSLCsZq",
        "outputId": "7303f1d1-03cc-416e-f96a-450821856914"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'SSE')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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7ykGcOTdgOg4RuRAL3hARwZMPLEMqk8ETL+41HYeIXIgFb9A1M8J4pHExfrevFa/sPWM6DhG5DAvesM+tnI/6WWV44sW9OD/Asz8R0dRhwRvm93rw1IPL0dY9iL975aDpOETkIix4B7hhbgU+e1sdfrrjfex8v8N0HCJyCRa8Q3xlzRLMLi/Coxv2IJHiKf6I6Oqx4B0iHPThW5+8Dodae/APrx8xHYeIXIAF7yCrlkZx7/Wz8H+2HMaReI/pOESU51jwDvPEn8UQ8nvw2IY9nMaAiK4KC95hakpDeOyeeuw41oFf7jxpOg4R5TEWvAN9qmEubp5fhSd/24J496DpOESUp1jwDuTxCP7mgeUYSGbwzZf2m45DRHmKBe9QC2tK8MVVC/HPuz/A1gNtpuMQUR5iwTvY5+9agEU1JfjrF/aidzBlOg4R5RkWvIMFfB58+8HlON3Vj+9uOmQ6DhHlGRa8wzXUVeEzt8zDP/3+GHaf7DIdh4jyCAs+D3x17VJUlwTxVxv2IJnmNAZENDEs+DxQFvLjm/dfh5YPz+PZ7cdMxyGiPMGCzxMfu24mmmJR/O/Nh3Civc90HCLKAyz4PCEi+Ob918Hn8eDxF/ZAldMYENHYWPB5ZFZ5Eb66dgneeO8sXnjntOk4RORwLPg88+8+cg1umleBb73Ugo7ehOk4RORgLPg84/UInnrwepzvT+J//pbTGBDR5bHg89CSmaX4/F0LsGHXaWx/76zpOETkULYVvIg8KyJtIrLXrm0Usi+uWoj51WE89vwe9CfSpuMQkQPZ+Q7+RwDW2vj6BS3k9+LJB5bhREcfvr/lPdNxiMiBbCt4Vd0GoMOu1yfgtgXV+FRDLdZvO4r9H5w3HYeIHMb4GLyIrBORZhFpjsfjpuPkncfuqUdFkR+PbngXaZ7ij4hyGC94VV2vqg2q2hCJREzHyTsVxQF8/c9i2H3qHH7y5nHTcYjIQYwXPF29T9wwG3cvieDvfncQp7v6TcchIodgwbuAiOBb9y+DKvC1F/ZyGgMiAmDvYZLPAXgTwBIROSUin7NrWwTMrSrGl9csxpYDbXh5zxnTcYjIAew8iuYhVZ2lqn5VrVXVZ+zaFmV99rY6LJ9Tjide3IdzfUnTcYjIMA7RuIjPmz3FX2dfAk+90mI6DhEZxoJ3mWVzyvG5lfPx3FsnseNou+k4RGQQC96FHmlchNrKIjz6/B4MpjiNAVGhYsG7UHHAhycfWI6j8V78YOsR03GIyBAWvEvdtTiCT944Gz987TDea+02HYeIDGDBu9hf3xdDOOjDoxv2IMNpDIgKDgvexapLgnj8nno0v9+J594+YToOEU0zFrzL/ds/qcVtC2bgqZcPoPX8gOk4RDSNWPAuJyL4mweWI5HO4H+8uM90HCKaRiz4AlBXHcZ/W70I/7L3DDbu4zQGRIWCBV8g1t15LZbOLMXXf7MP3QOcxoCoELDgC4TfmsagtXsA3/ndQdNxiGga+EwHoOlz07xK/Idb6/DjN49jeW0Fls8px5zKIpQE+c+AyI34P7vAfOVjS7DlQBu+8svdF+6rKPajtrIIcyqKUFtZbF0XYU5l9nZ5kd9gYiK6Uiz4AlMS9OFfHr4DB1u7caqzH6c7+3G6qw+nOvtxNN6LbYfOoj85cv6a0qDPKvucnUDO7apwACJi6E9ERJfDgi9A4aAPK+ZVYsW8ykseU1V09iVxqrMPpzv7szuBrn6c6szuBHYc7UD3YGrEc4r83svuAGorilBdEoTHwx0A0XRjwdMIIoKqcABV4QCur60YdZ1z/aPvAE539WP3yS50XnSykYDPMzzskzP8M6eiGLWVRYiWheDlDoBoyrHgadLKi/woLyrHdbPLR328dzA1XPrWTuBUV/a6paUNZ3sGR6zv8whmVYQwpyJb9lXhAGaEA6i0rqvCQVSF/agKB1FR5OenAaIJYsHTlAsHfVgcLcXiaOmojw8k09YOoN/aAfRduP3HE13o6E2g56JhoCEeASqKAxc+ZVQVB1BVYl2HA5hREkDlRcshv9fOPy6RY7HgadqF/F4siJRgQaTksusMptLo7E2ivXfwwnVHbwKdvQm09ybQYV2OxHvw9vEEOvsSuNyEmeGAd8ROIPcTwdAnhaqcS1nIxy+NyRVY8ORIQZ8XM8u9mFkemtD6mYziXH8S7b3Zsm/vyV539A4vt/cmEO8ZxKHWHrT3DmIgmRn1tXweyRkeyu4AKor8KAn5UBr0oSToQ0nIj5KgD6WhodvWYyEfivxe7iDIEVjw5Aoeq5Qrw4EJP6cvkbrwSaDd+nTQkXMZuq/lg/M4159E92AKidToO4URWQRW+fsvlH9JMOdi3c7dOQzf9o9Yn18+09VgwVPBKg74UBzwobayeMLPSaQy6B1MoWcwhe6B7HXPYHJ4eeCix6zrLuvIo6H7ehMTO1duccB7ySeEbPn7URryoTjgRZHfi6JA9jJ8O/tJojjgRcjvHbFe0OfhJ4wCwYInmoSAz4OAb3KfFEaTzih6E1bZD6bQnbMz6BnIvZ28ZIfR3tOH7oEUugeS6E+mkUxP7mxdHsGIncLwDsGDYmvHMHT/iB1Ezk5i+P5LdyQhv5efPByCBU9kgNcjKAv5URa6+mkgkukM+pNp9Ceyl75Eevh2Mo2+RAoDyYvuT6TRl0xj4KL1492D1voZ9CVS6E+mL/tdxVgCPg9CPg9Cfq918SDoy16H/N4RyyG/ByHf8HohvxdBv/eS54f8Xmu9oXWG7/N7hZ9KRsGCJ8pzfq8Hfq9nSnYWo8lkFAMpa0dwYacxtJxCfyJz6U4kmcZgMoOBZNq6ZDCQyi53D6QQTw5iMHXp43qFpw72CIZ3Br7hnUTQ5xmxcwj6Pdbfl8Dv9cDn8cDvE/g92ft9XkHAuh6xnteDgFes9T3wewR+nwc+j1z4+/d7h5cveb7HzA6IBU9EY/J45ML3FXZSVSTSGQwkMxhMpnN2AMM7h4HcnUYqu96I+1O562QwaD2vvSeBgWQaiXQGqbRa1xkk04pkOoNkOnPZw2ynit/aQVy6E/EgUhLELz5/65RvkwVPRI4gIgj6ssM3MDCDaTqTLftURpFMZZDMZHcAKWsHMLwzsNbL2Tkk04pUJoNEynr+iPUySIzyOheen1GEA/b8GM/WgheRtQD+HoAXwNOq+pSd2yMiulJej8DrsYo2aDbLVLHtjE4i4gXwAwAfBxAD8JCIxOzaHhERjWTnKftuBnBYVY+qagLAzwHcb+P2iIgoh50FPwfAyZzbp6z7RhCRdSLSLCLN8XjcxjhERIXF+Em3VXW9qjaoakMkEjEdh4jINews+NMA5ubcrrXuIyKiaWBnwb8NYJGIzBeRAIBPA3jRxu0REVEO2w6TVNWUiHwRwO+QPUzyWVXdZ9f2iIhoJFuPg1fVlwG8bOc2iIhodKJXOvmDDUQkDuD9K3x6NYCzUxhnqjDX5DDX5DDX5Lgx1zWqOuoRKo4q+KshIs2q2mA6x8WYa3KYa3KYa3IKLZfxwySJiMgeLHgiIpdyU8GvNx3gMphrcphrcphrcgoql2vG4ImIaCQ3vYMnIqIcLHgiIpfK+4IXkbUiclBEDovIX5nOM0REnhWRNhHZazrLEBGZKyJbRWS/iOwTkYdNZxoiIiEReUtEdlvZvmE60xAR8YrIH0XkJdNZconIcRHZIyLviEiz6TxDRKRCRH4lIgdEpEVEpv5cdJPPtMT6exq6nBeRR0znAgAR+e/Wv/m9IvKciISm7LXzeQzeOqnIIQBNyE5H/DaAh1R1v9FgAETkTgA9AH6iqstM5wEAEZkFYJaq7hKRUgA7AXzSIX9fAiCsqj0i4gewHcDDqvoHw9EgIl8C0ACgTFXvM51niIgcB9Cgqo764Y6I/BjAG6r6tDUPVbGqdpnONcTqjdMAPqKqV/rDyqnKMgfZf+sxVe0XkV8AeFlVfzQVr5/v7+Ade1IRVd0GoMN0jlyq+qGq7rKWuwG0YJQ5+k3QrB7rpt+6GH/3ISK1AO4F8LTpLPlARMoB3AngGQBQ1YSTyt2yGsAR0+WewwegSER8AIoBfDBVL5zvBT+hk4rQpUSkDsBNAHaYTTLMGgp5B0AbgE2q6oRs3wPwVQAZ00FGoQA2ishOEVlnOoxlPoA4gH+yhrWeFpGw6VAX+TSA50yHAABVPQ3gOwBOAPgQwDlV3ThVr5/vBU9XQERKAPwawCOqet50niGqmlbVG5E9d8DNImJ0aEtE7gPQpqo7TeYYw0pVXYHseY+/YA0LmuYDsALAD1X1JgC9AJz03VgAwCcA/NJ0FgAQkUpkRx3mA5gNICwin5mq18/3gudJRSbJGt/+NYCfqeoG03lGY32k3wpgreEotwP4hDXW/XMAq0Tkp2YjDbPe/UFV2wA8j+yQpWmnAJzK+fT1K2QL3yk+DmCXqraaDmJpBHBMVeOqmgSwAcBtU/Xi+V7wPKnIJFhfZD4DoEVVv2s6Ty4RiYhIhbVchOwX5wdMZlLVR1W1VlXrkP23tUVVp+zd1dUQkbD1RTmsIZA1AIwfsaWqZwCcFJEl1l2rARj/Ej/HQ3DI8IzlBIBbRKTY+v+5GtnvxqaErfPB283JJxURkecA3A2gWkROAXhCVZ8xmwq3A/gLAHussW4AeMyat9+0WQB+bB3h4AHwC1V11GGJDhMF8Hy2E+AD8H9V9RWzkS74SwA/s950HQXwHw3nAXBhR9gE4L+YzjJEVXeIyK8A7AKQAvBHTOG0BXl9mCQREV1evg/REBHRZbDgiYhcigVPRORSLHgiIpdiwRMRuRQLnmgMItKTs3yPiBwSkWtMZiKaqLw+Dp5ouojIagDfB/AxB01SRTQmFjzROKw5Xv4RwD2qesR0HqKJ4g+diMYgIkkA3QDuVtV3TechmgyOwRONLQngXwF8znQQosliwRONLQPgU8hOX/yY6TBEk8ExeKJxqGqfiNwL4A0RaXXApHFEE8KCJ5oAVe0QkbUAtolIXFU5LTU5Hr9kJSJyKY7BExG5FAueiMilWPBERC7FgicicikWPBGRS7HgiYhcigVPRORS/x8/ngxHV2DdVgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-dGTARqBCyVP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}