hack _the_cause.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S3N2CYN6STcX"
      },
      "outputs": [],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"creulty (2).csv\")\n",
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VCpuOyg0mnCo",
        "outputId": "31494054-3593-4e8c-a46a-627309c2eeb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Districts  Severity  No of Animals\n",
              "0   Trichur       4.9              1\n",
              "1   Kakkoor       4.1              2\n",
              "2    Kollam       2.8              1\n",
              "3   Wayanad       4.9              1\n",
              "4  Thrissur       3.8              1"
            ],
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              "      <th>Districts</th>\n",
              "      <th>Severity</th>\n",
              "      <th>No of Animals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Trichur</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kakkoor</td>\n",
              "      <td>4.1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Kollam</td>\n",
              "      <td>2.8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Wayanad</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thrissur</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1</td>\n",
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              "                                                     [key], {});\n",
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      "cell_type": "code",
      "source": [
        "plt.scatter(df.Severity,df['No of Animals'])\n",
        "plt.xlabel('Severity')\n",
        "plt.ylabel('No of Animals')"
      ],
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          "height": 299
        },
        "outputId": "05bd0555-e2b4-4bdd-951f-0715484d11ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'No of Animals')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
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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)\n",
        "y_predicted = km.fit_predict(df[['Severity','No of Animals']])\n",
        "y_predicted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2KZLjRGthXB",
        "outputId": "04cecfa7-4e48-475d-85af-c54edaa2539d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,\n",
              "       0, 0, 2, 0, 2, 2, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cluster']=y_predicted\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5YBAJ1cfvx1J",
        "outputId": "8e80790a-559f-415f-e3f8-2dd313fb02d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Districts  Severity  No of Animals  cluster\n",
              "0   Trichur       4.9              1        0\n",
              "1   Kakkoor       4.1              2        0\n",
              "2    Kollam       2.8              1        0\n",
              "3   Wayanad       4.9              1        0\n",
              "4  Thrissur       3.8              1        0"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Districts</th>\n",
              "      <th>Severity</th>\n",
              "      <th>No of Animals</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Trichur</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kakkoor</td>\n",
              "      <td>4.1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Kollam</td>\n",
              "      <td>2.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Wayanad</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thrissur</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-d4021574-1c45-4de1-a75c-21b735292439 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-d4021574-1c45-4de1-a75c-21b735292439');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "km.cluster_centers_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwW59O6Ov2aQ",
        "outputId": "0a9e82bb-94ad-4afa-b02b-0bfda9a0a0d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  3.88      ,   2.44285714],\n",
              "       [  4.1       , 117.        ],\n",
              "       [  4.54      ,  30.3       ]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "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.Severity,df1['No of Animals'],color='green')\n",
        "plt.scatter(df2.Severity,df2['No of Animals'],color='red')\n",
        "plt.scatter(df3.Severity,df3['No of Animals'],color='black')\n",
        "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
        "plt.xlabel('Severity')\n",
        "plt.ylabel('No of Animals')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "k8HwUr6Mv4zo",
        "outputId": "2ac762a2-3203-4aad-fa40-13957bacb0b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa09988c890>"
            ]
          },
          "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": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "scaler.fit(df[['No of Animals']])\n",
        "df['No of Animals'] = scaler.transform(df[['No of Animals']])\n",
        "\n",
        "scaler.fit(df[['Severity']])\n",
        "df['Severity'] = scaler.transform(df[['Severity']])"
      ],
      "metadata": {
        "id": "sLqqIJ6HwLp6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "f3ssR_YRwVT_",
        "outputId": "d088ea55-0591-4a27-e9e1-d391331050b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Districts  Severity  No of Animals  cluster\n",
              "0   Trichur  0.973684       0.007463        0\n",
              "1   Kakkoor  0.763158       0.014925        0\n",
              "2    Kollam  0.421053       0.007463        0\n",
              "3   Wayanad  0.973684       0.007463        0\n",
              "4  Thrissur  0.684211       0.007463        0"
            ],
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              "      <th>Districts</th>\n",
              "      <th>Severity</th>\n",
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              "      <th>cluster</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Trichur</td>\n",
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              "      <td>Thrissur</td>\n",
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              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          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": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df.Severity,df['No of Animals'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "jBSIADpOwYvo",
        "outputId": "da93055a-f55e-4b2c-ff7b-2e0920a95b32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa09974be10>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "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": [
        "\n",
        "km = KMeans(n_clusters=3)\n",
        "y_predicted = km.fit_predict(df[['Severity','No of Animals']])\n",
        "y_predicted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsUWZlWqwhXf",
        "outputId": "a2797848-0b92-463e-9b8f-830430a777f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 1, 1, 0, 1, 2, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 2, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 2], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cluster']=y_predicted\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CXqlM6tpwp6_",
        "outputId": "eae2acce-4482-4c69-da0d-8dcfe84c264d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Districts  Severity  No of Animals  cluster\n",
              "0   Trichur  0.973684       0.007463        1\n",
              "1   Kakkoor  0.763158       0.014925        1\n",
              "2    Kollam  0.421053       0.007463        0\n",
              "3   Wayanad  0.973684       0.007463        1\n",
              "4  Thrissur  0.684211       0.007463        1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-614fa122-7b14-4db7-a08c-08210d4f67b5\">\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>Districts</th>\n",
              "      <th>Severity</th>\n",
              "      <th>No of Animals</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Trichur</td>\n",
              "      <td>0.973684</td>\n",
              "      <td>0.007463</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kakkoor</td>\n",
              "      <td>0.763158</td>\n",
              "      <td>0.014925</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Kollam</td>\n",
              "      <td>0.421053</td>\n",
              "      <td>0.007463</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Wayanad</td>\n",
              "      <td>0.973684</td>\n",
              "      <td>0.007463</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thrissur</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.007463</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-614fa122-7b14-4db7-a08c-08210d4f67b5')\"\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-614fa122-7b14-4db7-a08c-08210d4f67b5 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-614fa122-7b14-4db7-a08c-08210d4f67b5');\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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l1=[]\n",
        "l2=[]\n",
        "l3=[]\n",
        "for ind in df.index:\n",
        "  if df['cluster'][ind]==0:\n",
        "    l1.append(df['Districts'][ind])\n",
        "  if df['cluster'][ind]==1:\n",
        "    l2.append(df['Districts'][ind])\n",
        "  if df['cluster'][ind]==2:\n",
        "    l3.append(df['Districts'][ind])\n",
        "l1=list(set(l1))\n",
        "l2=list(set(l2))\n",
        "l3=list(set(l3))\n",
        "l=[len(l1),len(l2),len(l3)]\n",
        "m=max(l)\n",
        "l1.extend([''] * (m-len(l1)))\n",
        "l2.extend([''] * (m-len(l2)))\n",
        "l3.extend([''] * (m-len(l3)))"
      ],
      "metadata": {
        "id": "xfNzk5S9iU8b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ld = pd.DataFrame({'1':l1,'2':l2,'3':l3})\n",
        "ld.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4oSYTAXypD-G",
        "outputId": "e66c13e0-0d43-4552-9fe7-f6895888c1a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           1                                    2           3\n",
              "0   Thrissur                          Perumbavoor  Trivandrum\n",
              "1  Guruvayur  Pathanamthitta and Kollam districts    Thrissur\n",
              "2                                   Kunnamangalam      Kollam\n",
              "3                                       Kozhikode            \n",
              "4                                        Palakkad            "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-053f471b-61e0-4bfc-b6a4-5529f2ef6f0b\">\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>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Thrissur</td>\n",
              "      <td>Perumbavoor</td>\n",
              "      <td>Trivandrum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Guruvayur</td>\n",
              "      <td>Pathanamthitta and Kollam districts</td>\n",
              "      <td>Thrissur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>Kunnamangalam</td>\n",
              "      <td>Kollam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>Kozhikode</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>Palakkad</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-053f471b-61e0-4bfc-b6a4-5529f2ef6f0b')\"\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",
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              "  </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-053f471b-61e0-4bfc-b6a4-5529f2ef6f0b 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-053f471b-61e0-4bfc-b6a4-5529f2ef6f0b');\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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "ld.to_csv('output.csv') \n",
        "files.download('output.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "YIC2kDouruYk",
        "outputId": "21f8f2e5-b1f7-445d-ebe1-0d126891913f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_432c0348-ee78-4cc8-8522-cee4ab2fa75f\", \"output.csv\", 612)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "km.cluster_centers_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtTNY7R7wr2J",
        "outputId": "5c62ce3c-13a5-4e56-c434-1050c3535770"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.76315789, 0.63619403],\n",
              "       [0.84968778, 0.04211991],\n",
              "       [0.34210526, 0.01335428]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df1 = df[df.cluster==0]\n",
        "df2 = df[df.cluster==1]\n",
        "df3 = df[df.cluster==2]\n",
        "plt.scatter(df1.Severity,df1['No of Animals'],color='red')\n",
        "plt.scatter(df2.Severity,df2['No of Animals'],color='orange')\n",
        "plt.scatter(df3.Severity,df3['No of Animals'],color='green')\n",
        "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
        "plt.xlabel('Severity')\n",
        "plt.ylabel('No of Animals')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "D5cPGN2xwvTI",
        "outputId": "f292615b-e88c-4adf-a412-db034795f3cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd4d07f4f90>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "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": [],
      "metadata": {
        "id": "Jw1FYamJbSjE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "from google.colab import files\n",
        "\n",
        "def fun():\n",
        "    df = pd.read_csv(\"/content/creulty (2).csv\")\n",
        "    #df.head()\n",
        "    km = KMeans(n_clusters=3)\n",
        "    y_predicted = km.fit_predict(df[['Severity','No of Animals']])\n",
        "    df['cluster']=y_predicted\n",
        "    scaler = MinMaxScaler()\n",
        "\n",
        "    scaler.fit(df[['No of Animals']])\n",
        "    df['No of Animals'] = scaler.transform(df[['No of Animals']])\n",
        "\n",
        "    scaler.fit(df[['Severity']])\n",
        "    df['Severity'] = scaler.transform(df[['Severity']])\n",
        "    km = KMeans(n_clusters=3)\n",
        "    y_predicted = km.fit_predict(df[['Severity','No of Animals']])\n",
        "    df['cluster']=y_predicted\n",
        "    df1 = df[df.cluster==0]\n",
        "    df2 = df[df.cluster==1]\n",
        "    df3 = df[df.cluster==2]\n",
        "    plt.scatter(df1.Severity,df1['No of Animals'],color='red')\n",
        "    plt.scatter(df2.Severity,df2['No of Animals'],color='orange')\n",
        "    plt.scatter(df3.Severity,df3['No of Animals'],color='green')\n",
        "    plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
        "    plt.xlabel('Severity')\n",
        "    plt.ylabel('No of Animals')\n",
        "    plt.legend()\n",
        "    plt.savefig('/content/FigureName.png')\n",
        "    return '/content/FigureName.png'\n",
        "    l1=[]\n",
        "    l2=[]\n",
        "    l3=[]\n",
        "    for ind in df.index:\n",
        "    if df['cluster'][ind]==0:\n",
        "        l1.append(df['Districts'][ind])\n",
        "    if df['cluster'][ind]==1:\n",
        "        l2.append(df['Districts'][ind])\n",
        "    if df['cluster'][ind]==2:\n",
        "        l3.append(df['Districts'][ind])\n",
        "    l1=list(set(l1))\n",
        "    l2=list(set(l2))\n",
        "    l3=list(set(l3))\n",
        "    l=[len(l1),len(l2),len(l3)]\n",
        "    m=max(l)\n",
        "    l1.extend([''] * (m-len(l1)))\n",
        "    l2.extend([''] * (m-len(l2)))\n",
        "    l3.extend([''] * (m-len(l3)))\n",
        "    ld = pd.DataFrame({'1':l1,'2':l2,'3':l3})\n",
        "    ld.head()\n",
        "    ld.to_csv('output.csv') \n",
        "    files.download('output.csv')\n",
        "    \n",
        "    \n",
        "r= fun()\n",
        "r"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "s72lPAHMbSpA",
        "outputId": "403ea9fa-2894-40eb-f42a-b922a3ded673"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/FigureName.png'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}