_Assignment 16.ipynb

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      "source": [
        "import pandas as pd\n",
        "zoo = pd.read_csv('/content/Zoo.csv')\n",
        "zoo"
      ],
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    animal name  hair  feathers  eggs  milk  airborne  aquatic  predator  \\\n",
              "0      aardvark     1         0     0     1         0        0         1   \n",
              "1      antelope     1         0     0     1         0        0         0   \n",
              "2          bass     0         0     1     0         0        1         1   \n",
              "3          bear     1         0     0     1         0        0         1   \n",
              "4          boar     1         0     0     1         0        0         1   \n",
              "..          ...   ...       ...   ...   ...       ...      ...       ...   \n",
              "96      wallaby     1         0     0     1         0        0         0   \n",
              "97         wasp     1         0     1     0         1        0         0   \n",
              "98         wolf     1         0     0     1         0        0         1   \n",
              "99         worm     0         0     1     0         0        0         0   \n",
              "100        wren     0         1     1     0         1        0         0   \n",
              "\n",
              "     toothed  backbone  breathes  venomous  fins  legs  tail  domestic  \\\n",
              "0          1         1         1         0     0     4     0         0   \n",
              "1          1         1         1         0     0     4     1         0   \n",
              "2          1         1         0         0     1     0     1         0   \n",
              "3          1         1         1         0     0     4     0         0   \n",
              "4          1         1         1         0     0     4     1         0   \n",
              "..       ...       ...       ...       ...   ...   ...   ...       ...   \n",
              "96         1         1         1         0     0     2     1         0   \n",
              "97         0         0         1         1     0     6     0         0   \n",
              "98         1         1         1         0     0     4     1         0   \n",
              "99         0         0         1         0     0     0     0         0   \n",
              "100        0         1         1         0     0     2     1         0   \n",
              "\n",
              "     catsize  type  \n",
              "0          1     1  \n",
              "1          1     1  \n",
              "2          0     4  \n",
              "3          1     1  \n",
              "4          1     1  \n",
              "..       ...   ...  \n",
              "96         1     1  \n",
              "97         0     6  \n",
              "98         1     1  \n",
              "99         0     7  \n",
              "100        0     2  \n",
              "\n",
              "[101 rows x 18 columns]"
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              "      <td>antelope</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>bass</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "<p>101 rows × 18 columns</p>\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
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              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
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              "      .colab-df-generate:hover {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "zoo",
              "summary": "{\n  \"name\": \"zoo\",\n  \"rows\": 101,\n  \"fields\": [\n    {\n      \"column\": \"animal name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 100,\n        \"samples\": [\n          \"squirrel\",\n          \"opossum\",\n          \"rhea\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hair\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feathers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eggs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"milk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airborne\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"aquatic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toothed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"backbone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"breathes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venomous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"legs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tail\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"domestic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"catsize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HSNCATXvb-kK",
        "outputId": "afcc97da-9630-4b13-8cb1-8b817002d8d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 101 entries, 0 to 100\n",
            "Data columns (total 18 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   animal name  101 non-null    object\n",
            " 1   hair         101 non-null    int64 \n",
            " 2   feathers     101 non-null    int64 \n",
            " 3   eggs         101 non-null    int64 \n",
            " 4   milk         101 non-null    int64 \n",
            " 5   airborne     101 non-null    int64 \n",
            " 6   aquatic      101 non-null    int64 \n",
            " 7   predator     101 non-null    int64 \n",
            " 8   toothed      101 non-null    int64 \n",
            " 9   backbone     101 non-null    int64 \n",
            " 10  breathes     101 non-null    int64 \n",
            " 11  venomous     101 non-null    int64 \n",
            " 12  fins         101 non-null    int64 \n",
            " 13  legs         101 non-null    int64 \n",
            " 14  tail         101 non-null    int64 \n",
            " 15  domestic     101 non-null    int64 \n",
            " 16  catsize      101 non-null    int64 \n",
            " 17  type         101 non-null    int64 \n",
            "dtypes: int64(17), object(1)\n",
            "memory usage: 14.3+ KB\n"
          ]
        }
      ],
      "source": [
        "zoo.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zoo.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eciMchN0dB5O",
        "outputId": "67b8a633-b90f-44b6-f9df-5f546771a88f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "animal name    0\n",
              "hair           0\n",
              "feathers       0\n",
              "eggs           0\n",
              "milk           0\n",
              "airborne       0\n",
              "aquatic        0\n",
              "predator       0\n",
              "toothed        0\n",
              "backbone       0\n",
              "breathes       0\n",
              "venomous       0\n",
              "fins           0\n",
              "legs           0\n",
              "tail           0\n",
              "domestic       0\n",
              "catsize        0\n",
              "type           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "RrxofYkidbZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.boxplot(data = zoo)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "7xVS6TpDdnh4",
        "outputId": "d752f8cb-a07c-4a5d-ab5f-6a5fe2219f95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zoo['feathers'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EonXzXN8evMg",
        "outputId": "2d3ff430-2e89-4abd-a95d-eb6a4fd8e833"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "feathers\n",
              "0    81\n",
              "1    20\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zoo.hist(bins = 15,figsize = (10,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-YImRt9de-Y6",
        "outputId": "7fc1be5d-f288-4dbf-cf0e-6e9174b79e6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'hair'}>,\n",
              "        <Axes: title={'center': 'feathers'}>,\n",
              "        <Axes: title={'center': 'eggs'}>,\n",
              "        <Axes: title={'center': 'milk'}>],\n",
              "       [<Axes: title={'center': 'airborne'}>,\n",
              "        <Axes: title={'center': 'aquatic'}>,\n",
              "        <Axes: title={'center': 'predator'}>,\n",
              "        <Axes: title={'center': 'toothed'}>],\n",
              "       [<Axes: title={'center': 'backbone'}>,\n",
              "        <Axes: title={'center': 'breathes'}>,\n",
              "        <Axes: title={'center': 'venomous'}>,\n",
              "        <Axes: title={'center': 'fins'}>],\n",
              "       [<Axes: title={'center': 'legs'}>,\n",
              "        <Axes: title={'center': 'tail'}>,\n",
              "        <Axes: title={'center': 'domestic'}>,\n",
              "        <Axes: title={'center': 'catsize'}>],\n",
              "       [<Axes: title={'center': 'type'}>, <Axes: >, <Axes: >, <Axes: >]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 20 Axes>"
            ],
            "image/png": 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JJ9GxY0ckJSUZTDMbM2YMRARvvfUWJkyYgFatWuGzzz7Ds88+a1Bv9OjRWL58OWbOnInr16+jVq1aePbZZ/Hyyy9Xeh8QVRXdunXDRx99hKSkJDzxxBNYsWIFjxcWUEl5rpIiIiIilzN79myMHz8ep06dQs2aNUusp9PpEBwcjP79+5uc7klEVBmYEhIREZGRe68JuHnzJhYuXIiGDRsaJDk3b940ulbuvffew6VLl9ClS5fKaCoRkUmcukZERERG+vfvj9q1a6N169a4evUqli1bhsOHD2P58uUG9Xbu3Inx48dj4MCBCAoKwp49e7BkyRI0b94cAwcOtFPriYiY6BAREZEJiYmJ+O9//4vly5ejuLgY0dHRWLlyJQYPHmxQr06dOoiMjMR//vMfXLp0CdWrV8eTTz6JGTNmQKPR2Kn1RES8RoeIiIiIiJwQr9EhIiIiIiKnY3Gic/r0aTz++OMICgqCl5cXWrRogR9++EF5XUTwyiuvIDw8HF5eXoiPj8fRo0et2mgiIiIiIiJzLLpG5/Lly+jUqRO6du2KL7/8EsHBwTh69CiqVaum1HnjjTfwn//8B++++y7q1q2LyZMnIzExEQcPHjRYT78kOp0OZ86cgZ+fn8GNxohsTURw7do1REREVLk16hk3ZA9VOWYAxg3ZR1WOG8YM2UOFYkYsMHHiRHnggQdKfF2n00lYWJi8+eabStmVK1dEq9XKBx98UKbP+OOPPwQAH3zY7fHHH39YEhYOgXHDhz0ftoiZU6dOyWOPPSbVq1cXT09Pad68uezatUt5XafTyeTJkyUsLEw8PT2le/fu8ssvv1j0GYwbPuz54LGGDz4se5QnZiw6o/PZZ58hMTERAwcOxPbt21GzZk0888wzSElJAQCcOHECubm5iI+PV94TEBCAmJgY5OTk4NFHHzXaZmFhIQoLC5Xn8v9rI5w4cQJ+fn5G9YuKirB161Z07doVarXakuY7PfaNeaX1z7Vr11C3bl2T+52j07f5jz/+gL+/v8FrRUVF2LRpExISErhfmMD+Mc9c/+Tn5yMyMtLqMVMZswcAxk1FsH/Ms0fcVAbGTPmxf8yzVcxYlOj8+uuvmD9/PtLS0vDSSy9h165dePbZZ6HRaJCcnIzc3FwAQGhoqMH7QkNDldfulZGRgfT0dKPynJwceHt7m3yPt7c3vvvuO0ua7jLYN+aZ65+CggIAqJKn4/Vt9vf3N3nw8fb2hr+/P/+4msD+Ma8s/WPtmHn99dcRGRmJrKwspaxu3brKv0UEs2fPxssvv4xHHnkEwJ0bVIaGhmLt2rUmB9XMtZtxYzn2j3n2iBvgznXUEydOxJdffomCggI0aNAAWVlZaNeuHYA7sTNlyhQsXrwYV65cQadOnTB//nw0bNiwTNtnzJQf+8c8W8WMRYmOTqdDu3bt8NprrwEA2rRpgwMHDmDBggVITk62+MMBYNKkSUhLS1Oe67O2hIQEoyAC7nREdnY2evTowR3lHuwb80rrn/z8fDu0iojuZYvZA4DxDAJ9zBcVFaGoqMigrv75veV0B/vHPHP9Y6s+q6wzoURViUWJTnh4OKKjow3KmjZtitWrVwMAwsLCAAB5eXkIDw9X6uTl5aF169Ymt6nVaqHVao3K1Wq12R/rpb3uytg35pXUP+wzIsdgi9kDQMkzCDZt2lTiDILs7OwKfBPnx/4xz1T/6GcPWFtlnQklqkosSnQ6deqEI0eOGJT98ssviIqKAnAnoMLCwrBlyxYlscnPz8d3332Hv//979ZpMREROTVbzB4ALJtBwDPk5rF/zDPXP7aaPVAZ11HzLGj5sX/Ms9VZUIsSnfHjx6Njx4547bXXMGjQIHz//fdYtGgRFi1aBODO3Llx48bh1VdfRcOGDZXTohEREejXr1+5G2lK86kbUVhc8ly932b0sernEVV1pcUMwLghx2CL2QNA+WYQ8Ay5ea7YP3VeXFdqHa274I0OpvvHVv1VmddR8yxo+bF/zLP2WVCLEp327dvjk08+waRJkzBt2jTUrVsXs2fPxmOPPabUeeGFF3Djxg2MGjUKV65cwQMPPIANGzZw7icREZUJZw8QWc7e11Hrz2JN/sENhTrzg2oHpiaWqz1VGc+Cmmers6AWJToA8NBDD+Ghhx4q8XWVSoVp06Zh2rRp5W4UERG5LkeaPUBUVTjKddSFOlWpswdc+Ye+K54FtYS1z4JWrVvyEhGR09PPHvjggw/QvHlzTJ8+3eTsgbFjx2LUqFFo3749rl+/ztkD5NIsOROqpz8TGhsbW6ltJaosFp/RISIisjXOHiCyDM+EEhnjGR0iG6tTpw5UKpXRIzU1FQDQpUsXo9eefvppO7eaiIiqEp4JJTLGMzpENrZr1y4UFxcrzw8cOIAePXpg4MCBSllKSorByHRJq9kQERGVhGdCiQwx0SGyseDgYIPnM2bMQP369REXF6eUeXt7KxeKEhEREVHFMdEhqkS3bt3CsmXLkJaWBpXqr1Vpli9fjmXLliEsLAx9+/bF5MmTzZ7VKc9N3LRuUmr7XPFGZryJm3m2uokbERGRrTHRIapEa9euxZUrVzBs2DClbOjQoYiKikJERAT279+PiRMn4siRI1izZk2J2ynPTdymt9OV2r7169eX/iWcFG/iZp61b+JGRERka0x0iCrRkiVL0KtXL0RERChlo0aNUv7dokULhIeHo3v37jh+/Djq169vcju8iZv18CZu5tnqJm5ERES2xkSHqJKcPHkSmzdvNnumBgBiYmIAAMeOHSsx0eFN3KyPN3Ezz9o3cSMiIrI1Li9NVEmysrIQEhKCPn36mK23b98+ADC4czURERERWYZndIgqgU6nQ1ZWFpKTk+Hh8VfYHT9+HCtWrEDv3r0RFBSE/fv3Y/z48ejcuTNatmxpxxYTERERVW1MdIgqwebNm/H7779jxIgRBuUajQabN2/G7NmzcePGDURGRiIpKQkvv/yynVpKRERE5ByY6BBVgoSEBIgYL+8cGRmJ7du326FFRERERM6N1+gQEREREZHTYaJDREREREROh4kOERERERE5HSY6RERERETkdJjoEBERERGR02GiQ0REREREToeJDhEREREROR0mOkRERERE5HSY6BARERERkdNhokNERERERE6HiQ4RERERETkdJjpEREREROR0mOgQEREREZHTYaJDREREREROh4kOERERERE5HQ97N4DIWuq8uM7s61p3wRsdKqkxRERERGRXFTqjM2PGDKhUKowbN04pu3nzJlJTUxEUFARfX18kJSUhLy+vou0kIiIiIiIqs3InOrt27cLChQvRsmVLg/Lx48fj888/x6pVq7B9+3acOXMG/fv3r3BDiYiIiIiIyqpcic7169fx2GOPYfHixahWrZpSfvXqVSxZsgQzZ85Et27d0LZtW2RlZWHHjh3YuXOn1RpNRESuw56zB5pP3Yg6L64r8UFERI6rXIlOamoq+vTpg/j4eIPy3bt3o6ioyKC8SZMmqF27NnJycirWUiIicjmcPUBUPry8gKgcixGsXLkSe/bswa5du4xey83NhUajQWBgoEF5aGgocnNzTW6vsLAQhYWFyvP8/HwAQFFREYqKiozq68u0bmK2nabe6+z039kVvztwZ7EBs6///z5TUv+4ar8ROaq7Zw+8+uqrSrl+9sCKFSvQrVs3AEBWVhaaNm2KnTt34v7777dXk4kcgrkBgnXr1mHVqlUICAjAmDFj0L9/f3z77bd2aimRbVmU6Pzxxx947rnnkJ2dDU9PT6s0ICMjA+np6UblmzZtgre3d4nvm95OZ3a769evr3Dbqqrs7Gx7N8EuyrqiWkn9U1BQYMXWEFFF3T174O5Ep7TZAyUlOpYMrHFQzTxXHlgrbVANMD+wZus+4wAB0V8sSnR2796Nc+fO4b777lPKiouL8dVXX2Hu3LnYuHEjbt26hStXrhic1cnLy0NYWJjJbU6aNAlpaWnK8/z8fERGRiIhIQH+/v5G9YuKipCdnY3JP7ihUKcqsa0HpiZa8tWcgr5vevToAbVabe/mVLrmUzeafV3rJpjeTldi/+h/9BCR/Vl79gBQvoE1DqqZ54oDa5bcpsBU/9h6UM3aAwRE1mCvW4BYlOh0794dP/30k0HZ8OHD0aRJE0ycOBGRkZFQq9XYsmULkpKSAABHjhzB77//jtjYWJPb1Gq10Gq1RuVqtdrsj/VCnQqFxSUnOq74Q1+vtL5zVub2h7uV1D+u2GdEjsgWswcAywbWOKhmnisPrJU2qAaYH1iz5aCaPS8vKOtZ0LvruhJXPgsKVOzygor0mUWJjp+fH5o3b25Q5uPjg6CgIKV85MiRSEtLQ/Xq1eHv74+xY8ciNjaWIwVERFQmtpg9AJRvYI2Daua54sBaWQfVANP9Y6v+cpTLC0o7Cwq49plQVzwLClTs8oKKnAW1eDGC0syaNQtubm5ISkpCYWEhEhMTMW/ePGt/DFGVMXXqVKMDRePGjXH48GEAd1bBef7557Fy5UqDmAkNDbVHc4nszhazB4icnb0vLyjrWVDANc+EuvJZUKBilxdU5CxohROdbdu2GTz39PREZmYmMjMzK7ppIqfRrFkzbN68WXnu4fFX6HEVHCJDnD1AZDlHubygtLOg+ve7Klc8CwpU7PKCivSX1c/oEJExDw8PkyNmXAWHqHw4e4DIEAcIiIwx0SGqBEePHkVERAQ8PT0RGxuLjIwM1K5d26GWyb27ritx9QtES2Oufyqzzzh7gKjiOEBAroaJDpGNxcTEYOnSpWjcuDHOnj2L9PR0PPjggzhw4IBDLZML8AJRKpk9lskloorhAAG5OiY6RDbWq1cv5d8tW7ZETEwMoqKi8NFHH8HLy6tc2+QFotbj6heIlsZc//DeU0RE5MiY6BBVssDAQDRq1AjHjh1Djx49HGaZXP37XZWrXiBaVpW5TC4REZE1uNm7AUSu5vr16zh+/DjCw8PRtm1bZRUcPS6TS0RERFRxPKNDZGMTJkxA3759ERUVhTNnzmDKlClwd3fHkCFDEBAQwFVwiIiIiGyAiQ6RjZ06dQpDhgzBxYsXERwcjAceeAA7d+5EcHAwAK6CQ0RERGQLTHSIbGzlypVmX+cqOERERETWx2t0iIiIiIjI6TDRISIiIiIip8NEh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOkw0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6THSIiIiIiMjpMNEhIiIiIiKnw0SHiIiIiIicDhMdIiIiIiJyOkx0iIiIiIjI6TDRISIiIiIip8NEh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOkw0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6THSIiIiIiMjpWJToZGRkoH379vDz80NISAj69euHI0eOGNS5efMmUlNTERQUBF9fXyQlJSEvL8+qjSYiIiIiIjLHokRn+/btSE1Nxc6dO5GdnY2ioiIkJCTgxo0bSp3x48fj888/x6pVq7B9+3acOXMG/fv3t3rDiYjIOXFQjYiIrMGiRGfDhg0YNmwYmjVrhlatWmHp0qX4/fffsXv3bgDA1atXsWTJEsycORPdunVD27ZtkZWVhR07dmDnzp02+QJERORcOKhGZDkOEBAZq9A1OlevXgUAVK9eHQCwe/duFBUVIT4+XqnTpEkT1K5dGzk5ORX5KCIichEcVCOyHAcIiIx5lPeNOp0O48aNQ6dOndC8eXMAQG5uLjQaDQIDAw3qhoaGIjc31+R2CgsLUVhYqDzPz88HABQVFaGoqMiovr5M6yZm22fqvc5O/51d8bsDgNbd/D6h32dK6h9X7TciR2fpoNr9999vcjuWHG94rDHPlY83pR1rAPPHG1v12YYNGwyeL126FCEhIdi9ezc6d+6sDBCsWLEC3bp1AwBkZWWhadOm2LlzZ4lxQ1SVlTvRSU1NxYEDB/DNN99UqAEZGRlIT083Kt+0aRO8vb1LfN/0djqz212/fn2F2lWVZWdn27sJdvFGh7LVK6l/CgoKrNiav2RkZGDNmjU4fPgwvLy80LFjR7z++uto3LixUqdLly7Yvn27wftGjx6NBQsW2KRNRFWFtQbVgPIdb3isMc8VjzdlPdYApvvHVseae1ljgMAWgwN313Ulrjw4AFRsMLoifVauRGfMmDH44osv8NVXX6FWrVpKeVhYGG7duoUrV64YHIDy8vIQFhZmcluTJk1CWlqa8jw/Px+RkZFISEiAv7+/Uf2ioiJkZ2dj8g9uKNSpSmzjgamJ5fhmVZu+b3r06AG1Wm3v5lS65lM3mn1d6yaY3k5XYv/o/4Bbm346Qfv27XH79m289NJLSEhIwMGDB+Hj46PUS0lJwbRp05Tn5hJ9IldhrUE1wLLjDY815rny8aa0Yw1g/nhjq2PN3aw1QGCLwQHAtQcIXHFwAKjYYHRFBgcsSnREBGPHjsUnn3yCbdu2oW7dugavt23bFmq1Glu2bEFSUhIA4MiRI/j9998RGxtrcptarRZardaoXK1Wm/3jWahTobC45IOPq/3hvVtpfeeszO0Pdyupf2zVZ6VNJ9Dz9vYucUCAyBVZc1ANKN/xhsca81zxeFPWYw1gun8qo7+sNUBgi8EBwDUHCFx5cACo2GB0RQYHLEp0UlNTsWLFCnz66afw8/NTRgACAgLg5eWFgIAAjBw5EmlpaahevTr8/f0xduxYxMbGcu4n0f+7dzqB3vLly7Fs2TKEhYWhb9++mDx5cokjZpxOYD2uPp2gNOb6x1Z9ZotBNSJXYc0BAlsMDujf76pccXAAqNhgdEX6y6JEZ/78+QDuXE9wt6ysLAwbNgwAMGvWLLi5uSEpKQmFhYVITEzEvHnzyt1AImdiajoBAAwdOhRRUVGIiIjA/v37MXHiRBw5cgRr1qwxuR1OJ7A+V51OUFaVea0BB9WILMcBAiJjFk9dK42npycyMzORmZlZ7kYROauSphOMGjVK+XeLFi0QHh6O7t274/jx46hfv77RdjidwHpcfTpBacz1j62uNeCgGpHlOEBAZKzcq64RkWVKmk5gSkxMDADg2LFjJhMdTiewPledTlBWlXmtAQfViCzHAQIiY0x0iGystOkEpuzbtw8AEB4ebuPWERGRM+AAAZExJjpENlbadILjx49jxYoV6N27N4KCgrB//36MHz8enTt3RsuWLe3ceiIiIqKqiYkOkY2VNp1Ao9Fg8+bNmD17Nm7cuIHIyEgkJSXh5ZdftkNriYiIiJwDEx0iGyttOkFkZCS2b99eSa0hIiIicg1MdIiIXFSdF9eVWkfrLmW+ozUREZEjcbN3A4iIiIiIiKyNiQ4RERERETkdJjpEREREROR0mOgQEREREZHTYaJDREREREROh4kOERERERE5HSY6RERERETkdJjoEBERERGR02GiQ0REREREToeJDhEREREROR0mOkRERERE5HSY6BARERERkdNhokNERERERE6HiQ4RERERETkdJjpEREREROR0mOgQEREREZHTYaJDREREREROh4kOERERERE5HSY6RERERETkdJjoEBERERGR02GiQ0REREREToeJDhEREREROR0mOkRERERE5HSY6BARERERkdOxWaKTmZmJOnXqwNPTEzExMfj+++9t9VFEToExQ2Q5xg2R5Rg35Cpskuh8+OGHSEtLw5QpU7Bnzx60atUKiYmJOHfunC0+jqjKY8wQWY5xQ2Q5xg25EpskOjNnzkRKSgqGDx+O6OhoLFiwAN7e3njnnXds8XFEVR5jhshyjBsiyzFuyJV4WHuDt27dwu7duzFp0iSlzM3NDfHx8cjJyTGqX1hYiMLCQuX51atXAQCXLl1CUVGRUf2ioiIUFBTAo8gNxTpVie24ePFiRb5GlaTvm4sXL0KtVtu7OZXO4/YN86/rBAUFuhL759q1awAAEbFJ+0piacwAlsVNWWMGYNy4WtyUFjOA+bixV8wAjhM3rhgzAOOm1DpOEjc81liPK8cMULHfaBWJGasnOhcuXEBxcTFCQ0MNykNDQ3H48GGj+hkZGUhPTzcqr1u3boXaUeOtCr2dnNTQMtS5du0aAgICbN4WPUtjBmDcUOUqLW4qO2YAx4kbxgyVxBnihscaqky2iBmrJzqWmjRpEtLS0pTnOp0Oly5dQlBQEFQq4xGB/Px8REZG4o8//oC/v39lNtXhsW/MK61/RATXrl1DRESEHVpnGUvihvuFeewf88z1T1WKGYBxY03sH/OcJW4YM9bD/jHPVjFj9USnRo0acHd3R15enkF5Xl4ewsLCjOprtVpotVqDssDAwFI/x9/fnztKCdg35pnrn8oeXQMsjxmgfHHD/cI89o95JfWPPWIGYNw4CvaPeVU9bhgz1sf+Mc/aMWP1xQg0Gg3atm2LLVu2KGU6nQ5btmxBbGystT+OqMpjzBBZjnFDZDnGDbkam0xdS0tLQ3JyMtq1a4cOHTpg9uzZuHHjBoYPH26LjyOq8hgzRJZj3BBZjnFDrsQmic7gwYNx/vx5vPLKK8jNzUXr1q2xYcMGo4vfykOr1WLKlClGp1KJfVMaR+4fxoz9sH/Mc+T+YdzYD/vHPEfuH1vFjSN/Z0fA/jHPVv2jEnusb0hERERERGRDNrlhKBERERERkT0x0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6DpfoZGZmok6dOvD09ERMTAy+//57s/VXrVqFJk2awNPTEy1atMD69esrqaX2YUn/LF26FCqVyuDh6elZia2tXF999RX69u2LiIgIqFQqrF27ttT3bNu2Dffddx+0Wi0aNGiApUuX2rydtsC4MY9xY5orxwzAuCkN48Y0V44bxox5jJmS2StuHCrR+fDDD5GWloYpU6Zgz549aNWqFRITE3Hu3DmT9Xfs2IEhQ4Zg5MiR2Lt3L/r164d+/frhwIEDldzyymFp/wB37jB79uxZ5XHy5MlKbHHlunHjBlq1aoXMzMwy1T9x4gT69OmDrl27Yt++fRg3bhyeeuopbNy40cYttS7GjXmMm5K5aswAjJvSMG5K5qpxw5gxjzFjnt3iRhxIhw4dJDU1VXleXFwsERERkpGRYbL+oEGDpE+fPgZlMTExMnr0aJu2014s7Z+srCwJCAiopNY5FgDyySefmK3zwgsvSLNmzQzKBg8eLImJiTZsmfUxbsxj3JSNK8WMCOOmNIybsnGluGHMmMeYKbvKjBuHOaNz69Yt7N69G/Hx8UqZm5sb4uPjkZOTY/I9OTk5BvUBIDExscT6VVl5+gcArl+/jqioKERGRuKRRx7Bzz//XBnNrRKcYf9h3JjHuLEuZ9l3GDfmMW6syxn2HcaMeYwZ67PW/uMwic6FCxdQXFxsdGfe0NBQ5ObmmnxPbm6uRfUdxbZt26BSqbBt27Yy1a9Tpw569+5tcf80btwY77zzDj799FMsW7YMOp0OHTt2xKlTpyr6FZxCSftPfn4+/vzzTzu1yjKuFDflUZ7+qUjcDBs2DHXq1LFG0x2SM8QMwLgpjbn++eWXX0wev3i8KZkzxA1jxjxL+2fbtm0YPnw4/v73v9s8ZqZOnQqVSmXVbVYGa8WNwyQ6ZH2xsbF48skn0bp1a8TFxWHNmjUIDg7GwoUL7d00IodVWtycOXMGU6dOxb59++zbUCIHUpbjzfr16zF16lT7NZLo/+3YsQNTp07FlStXbPo5Bw8eNJvYxcXF8TeajTlMolOjRg24u7sjLy/PoDwvLw9hYWEm3xMWFmZRfUfRuXNn/Pnnn+jcuXOZ36PRaCzun3up1Wq0adMGx44ds6i9zqqk/cff3x9eXl52apVlXCluyqM8/XOve+PmzJkzSE9PN5noLF68GEeOHKlwux2VM8QMwLgpjbn+qV69epm2Yep4s379eqSnp1u1rVWBM8SNs8XMjh07kJ6ebrVEp6T+2bdvH86fP1+mbfA3miFrxY3DJDoajQZt27bFli1blDKdToctW7YgNjbW5HtiY2MN6gNAdnZ2ifUdhZubGzw9PeHmZr77CwoKDN5jaf/cq7i4GD/99BPCw8ONXrt58yZ0Ol0Zv4FzqKr7z91cKW7Kozz9cy9zcXMvtVoNrVZb7vY6OmfZd1wxbnQ6HW7evFmmuub6p1mzZmXahiVxUxGWfC97qer7DuCaMWOJkvrnwoULZT4mVFbMVBVW238sXSnBllauXClarVaWLl0qBw8elFGjRklgYKDk5uaKiMgTTzwhL774olL/22+/FQ8PD/n3v/8thw4dkilTpoharZaffvrJLu3/7bff5O9//7s0atRIPD09pXr16jJgwAA5ceKEQb2tW7cKANm6datSFhcXJ82aNZMffvhBHnzwQfHy8pLnnntORESioqKkT58+MmnSJFGpVOLh4SH16tWTHj16GPXP008/LQMGDJBq1aqJWq2WJk2ayH//+1/ZvXu3PProo+Lp6SlZWVkCQD744AP55z//KREREaJSqeTy5cuSnJwsPj4+curUKXnkkUfEx8dHatSoIc8//7zcvn3b4HsUFxfLrFmzJDo6WrRarYSEhMioUaPk0qVLtuzmEl27dk327t0re/fuFQAyc+ZM2bt3r5w8eVJERF588UV54oknlPq//vqreHt7yz/+8Q85dOiQZGZmiru7u2zYsMEu7S+vqh43dytrDImIHDhwQLp27Sqenp5Ss2ZNmT59uixZskQAGNQHIO7u7kb9U6tWLUlOTlb65+LFi/L8889LSEiIeHp6iq+vr3Ts2FESExPF09NTfv75ZyV2731kZWWJiEhycrJERUUZtLO4uFhmz54tzZs3F61WKzVq1JDExETZtWuX7TqyjFw1ZkSqbtxMmTJFAMihQ4dk4MCB4ufnJ9WrV5dnn31W/vzzT6UeAElNTZVly5ZJdHS0eHh4KKscnTp1SoYPHy4hISGi0WgkOjpalixZYvA5K1euFI1GI23atBEvLy/x9PQUrVYrH3zwgQCQHj16KP3z1VdfSXR0tAQHB4tGo5HQ0FBp1KiRaLVa+fnnn0XkTmyYih2969evS1pamtSqVUs0Go00atRI3nzzTdHpdAbtMve9Kourxk1VjZl76WPo3seJEyekqKhIpk2bJvXq1RONRiNRUVEyadIkuXnzptF2MjMzJTo6WjQajYSHh0tCQoJoNBqlf8LDw40+w9/fX0T++h340ksvSVpamoSEhIibm5u4ubnJ+vXrjT5r586dkpiYKP7+/uLl5SWdO3eWb775xqje119/Le3atROtViv16tWTBQsWKN/X3uwVN/b/5veYM2eO1K5dWzQajXTo0EF27typvBYXFyfJyckG9T/66CNp1KiRaDQaadasmaxbt66SW/yXVatWSatWreSVV16RRYsWyUsvvSTVqlWTqKgouXHjhlKvpEQnLCxMgoODZezYsbJw4UJZu3atiNxJdBo1aiSBgYFKcqNSqQSAvP3228o2YmNjxdPTU/z8/OSf//yndO7cWdRqtQCQgIAA6d27t+zZs0f5/OjoaGndurXMnDlTMjIy5MaNG5KcnCyenp7SrFkzGTFihMyfP1+SkpIEgMybN8/g+z711FPi4eEhKSkpsmDBApk4caL4+PhI+/bt5datW7btbBNK+hGq32eSk5MlLi7O6D2tW7cWjUYj9erVU36wVjVVOW7uVtYYOnv2rAQHB0u1atVk6tSp8uabb0rDhg2lZcuWJhOdXr16GfVPVFSUsk8kJyfLrl27pH79+tKuXTupXr26uLu7i5ubm3h4eMjGjRtFRCQ3N1emTZsmAGTUqFHy/vvvy/vvvy/Hjx8XEdOJzrBhw5Q2zJ49W/7973/LI488InPmzLF5f5bGlWNGpGrGjf5HS4sWLaRv374yd+5cefzxxwWAwY8EANK0aVMJDg6W9PR0yczMlL1790pubq7UqlVLIiMjZdq0aTJ//nx5+OGHBYDMmjVLeX9BQYGEhISISqUSNzc3qV27tjRp0kSJsVatWin9M3bsWKlTp44EBASIu7u7eHp6ikqlkvj4eGV7O3bskB49eggAJW7ef/99ERHR6XTSrVs3UalU8tRTT8ncuXOlb9++AkDGjRtn8P1L+l6VyZXjpirGzL1+/PFHGTJkiLLP6/fF69evKwn5gAEDJDMzU5588kkBIP369TPYhj4O4+PjZc6cOTJmzBhxd3eX2rVrS2RkpGg0GmncuLE0atRIatSoIe+//740adJEunbtKiJ/7UNqtVpUKpX4+vpKw4YNxdPTUzp06GDwWVu2bBGNRiOxsbHy1ltvyaxZs6Rly5ai0Wjku+++U+rt379fvLy8pHbt2pKRkSHTp0+X0NBQJWbtzV5xY/9v7kQKCgqMynJycgSAvPfee0pZSYkOAFmwYIHRNqKiogSArF69Wim7evWqhIeHS5s2bZSycePGCQD5+uuvlbJr165J3bp1pU6dOlJcXGzw+fXq1TNqsz7Ip02bZlDepk0badu2rfL866+/FgCyfPlyg3obNmwwWU5UFmWNIf2+fvcf+XPnzklAQIDJRGfKlClG29UnOno3b95UYkTvxIkTotVqDeJh165dBmdx7nZvovO///1PAMizzz5rVPfekWqistD/wHr44YcNyp955hkBID/++KOI3Nnv3dzclDMqeiNHjpTw8HC5cOGCQfmjjz4qAQEBSgzOnj1bAMhHH32k1Llx44Y0aNDA6PhlKm4zMjJEpVIpo7UiIqmpqSZ/cK1du1YAyKuvvmpQPmDAAFGpVHLs2DGlrKTvRWSJN9980+hYsW/fPgEgTz31lEHdCRMmCAD53//+JyJ3jjUajUYSEhIMjhlz584VAPLOO+8oZX369DEa/BL563dY06ZNpbCwUCl/++23BYBy1kun00nDhg0lMTHR4JhRUFAgdevWlR49eihl/fr1E09PT4OYO3jwoLi7uztEomMvDnONjjO4++KooqIiXLx4EQ0aNEBgYCD27NlT6vu1Wi2GDx9u8rWIiAj87W9/U577+/vjySefxN69e5UVPdavX48OHTrggQceUOr5+vpi1KhR+O2333Dw4EGDbSYnJ5d4QdfTTz9t8PzBBx/Er7/+qjxftWoVAgIC0KNHD1y4cEF5tG3bFr6+vti6dWup35foXmWNofXr1+P+++9Hhw4dlLLg4GA89thj5f5srVarXDdXXFyMixcvwtfXF40bNy5T/JqyevVqqFQqTJkyxei1qrjcJzmO1NRUg+djx44FcCc29OLi4hAdHa08FxGsXr0affv2hYgY/O1OTEzE1atXlX19/fr1CA8Px4ABA5T3e3t7Y9SoUUZtuTtub9y4gQsXLqBjx44QEezdu7fU77J+/Xq4u7vj2WefNSh//vnnISL48ssvDcrv/V5E1qCPnbS0NIPy559/HgCwbt06AMDmzZtx69YtjBs3zuBa65SUFPj7+yv1ymL48OHQaDTK8wcffBAAlN9b+/btw9GjRzF06FBcvHhRidcbN26ge/fu+Oqrr6DT6VBcXIyNGzeiX79+qF27trK9pk2bIjEx0ZJucDoe9m6AM/nzzz+RkZGBrKwsnD59GiKivHb16tVS31+zZk2DHf5uDRo0MPph1KhRIwDAb7/9hrCwMJw8eRIxMTFG723atCkA4OTJk2jevLlSXrduXZOf5enpieDgYIOyatWq4fLly8rzo0eP4urVqwgJCTG5jXPnzpksJzKnrDFU0r7euHHjcn+2TqfD22+/jXnz5uHEiRMoLi5WXgsKCirXNo8fP46IiIgyr1RFVFYNGzY0eF6/fn24ubnht99+U8ru/Rt//vx5XLlyBYsWLcKiRYtMblf/t/vkyZMmjzumYuz333/HK6+8gs8++8zgOAGU7dh38uRJREREwM/Pz6D87mPX3Uo6dhFVxMmTJ+Hm5oYGDRoYlIeFhSEwMFDZD/X/vTcWNBoN6tWrZ7S/mnN3UgLc+a0FQImjo0ePArgzMF2Sq1evorCwEH/++afR3wV9O+8eAHE1THSsaOzYscjKysK4ceMQGxuLgIAAqFQqPProo2Va0ayyl5ks6fPc3d1Lfa9Op0NISAiWL19u8vV7EyWisqhoDFni7kQGAF577TVMnjwZI0aMwPTp01G9enW4ublh3LhxLrciIVU9ps4Q3vs3Xr8fP/744yX+cGrZsqVFn1tcXIwePXrg0qVLmDhxIpo0aQIfHx+cPn0aw4YNs0nsVJUlmalqqsyz7SX93tIP8unj580330Tr1q1N1vX19UVhYaFN2ucMmOhY0ccff4zk5GS89dZbStnNmzetsk77sWPHICIGAfjLL78AgHIn9qioKJP38Dh8+LDyurXUr18fmzdvRqdOnXjQIaspawxFRUUpI113M7X/V6tWzej9t27dwtmzZ40+u2vXrliyZIlB+ZUrV1CjRg3luSUHwfr162Pjxo24dOkSz+qQVR09etTgzMaxY8eg0+mU44EpwcHB8PPzQ3FxMeLj481uPyoqCgcOHDA67twbYz/99BN++eUXvPvuu3jyySeV8uzsbKNtlhQ7UVFR2Lx5M65du2ZwVscWxy4iwPS+GBUVBZ1Oh6NHjypnE4E79265cuWKsh/q/3vkyBHUq1dPqXfr1i2cOHHCILYqmjTVr18fwJ3LFczFbHBwMLy8vMp8XHQlvEbHitzd3Q2m2gDAnDlzjEaOy+PMmTP45JNPlOf5+fl477330Lp1a+XmW71798b333+PnJwcpd6NGzewaNEi1KlTx6pzmgcNGoTi4mJMnz7d6LXbt2/b/G7D5JzKGkO9e/fGzp078f333ytl58+fN3mGsX79+vjqq68MyhYtWmS0TVOfvWrVKpw+fdqgzMfHBwDKtI8nJSVBREzeJPHezyKyRGZmpsHzOXPmAAB69epV4nvc3d2RlJSE1atX48CBA0av331jw969e+PMmTP4+OOPlbKCggKjKW/6Eem792cRwdtvv220/ZJip3fv3iguLsbcuXMNymfNmgWVSmX2OxGVh6l9sXfv3gCA2bNnG9SdOXMmAKBPnz4AgPj4eGg0GvznP/8x2O+XLFmCq1evKvX0n1OW6Zsladu2LerXr49///vfuH79utHr+ph1d3dHYmIi1q5di99//115/dChQ9i4cWO5P98Z8IyOFT300EN4//33ERAQgOjoaOTk5GDz5s3lnt9/t0aNGmHkyJHYtWsXQkND8c477yAvLw9ZWVlKnRdffBEffPABevXqhWeffRbVq1fHu+++ixMnTmD16tWl3qDUEnFxcRg9ejQyMjKwb98+JCQkQK1W4+jRo1i1ahXefvttg4tYicqirDH0wgsv4P3330fPnj3x3HPPwcfHB4sWLUJUVBT2799vUPepp57C008/jaSkJPTo0QM//vgjNm7caHCWRv/Z06ZNw/Dhw9GxY0f89NNPWL58ucGIHXAncQoMDMSCBQvg5+cHHx8fxMTEmLxuoGvXrnjiiSfwn//8B0ePHkXPnj2h0+nw9ddfo2vXrhgzZoyVeo5czYkTJ/Dwww+jZ8+eyMnJwbJlyzB06FC0atXK7PtmzJiBrVu3IiYmBikpKYiOjsalS5ewZ88ebN68GZcuXQJw58LquXPn4sknn8Tu3bsRHh6O999/H97e3gbba9KkCerXr48JEybg9OnT8Pf3x+rVq42u1QHu/GgDgGeffRaJiYlwd3fHo48+ir59+6Jr16745z//id9++w2tWrXCpk2b8Omnn2LcuHHKqDaRtej3xX/+85949NFHoVar0bdvXyQnJ2PRokW4cuUK4uLi8P333+Pdd99Fv3790LVrVwB3zp5MmjQJ6enp6NmzJx5++GEcOXIE8+bNQ/v27fH4448bfM6HH36ItLQ0tG/fHr6+vujbt2+Z2+nm5ob//ve/6NWrF5o1a4bhw4ejZs2aOH36NLZu3Qp/f398/vnnAID09HRs2LABDz74IJ555hncvn0bc+bMQbNmzYyOiy6l0td5c2KXL1+W4cOHS40aNcTX11cSExPl8OHDRsvYmrthqCn6G4Zu3LhRWrZsKVqtVpo0aSKrVq0yqnv8+HEZMGCABAYGKuuxf/HFFwZ19J9v6v36G4beq6QbTi1atEjatm0rXl5e4ufnJy1atJAXXnhBzpw5U1I3EZWorDEkcueeAXFxcaXeMLS4uFgmTpwoNWrUEG9vb0lMTJRjx46ZXF76+eefl/DwcPHy8pJOnTpJTk6OxMXFGa3t/+mnnyo3K0QpNwy9ffu2vPnmm9KkSRPRaDQSHBwsvXr1kt27d1uv48hl6P8WHzx4UAYMGCB+fn5SrVo1GTNmjMkbhpqSl5cnqampEhkZKWq1WsLCwqR79+6yaNEig3onT56Uhx9+WLy9vaVGjRry3HPPKbcQuPv4dfDgQYmPjxdfX1+pUaOGpKSkyI8//mi0DPvt27dl7NixEhwcrNwLTu/atWsyfvx4iYiIELVaLQ0bNjR7w1Ciipo+fbrUrFlT3NzcDG4Ymp6eLnXr1hW1Wi2RkZEl3jB07ty50qRJE1Gr1RIaGip///vf5fLlywZ1rl+/LkOHDpXAwEABoBwfSvodduLECZO3L9i7d6/0799fgoKCRKvVSlRUlAwaNEi2bNliUG/79u3Stm1b5b4zjnTDUHtRiXD+BBE5h6VLl2L48OE4ceKE2WsViKqqqVOnIj09HefPnzc6K0lERIZ4jQ4RERERETkdJjpEREREROR0mOgQEREREZHT4TU6RERERETkdHhGh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOl42LsB99LpdDhz5gz8/PygUqns3RxyISKCa9euISIiAm5uVWsMgHFD9lCVYwZg3JB9VOW4YcyQPVQkZhwu0Tlz5gwiIyPt3QxyYX/88Qdq1apl72ZYhHFD9lQVYwZg3JB9VcW4YcyQPZUnZhwu0fHz8wNw58v4+/sbvV5UVIRNmzYhISEBarW6spvn0Ng35pXWP/n5+YiMjFT2warEXNxwvzCP/WOeuf6pyjEDMG4qgv1jnrPGDWOm/Ng/5tkqZhwu0dGfCvX39y8x0fH29oa/vz93lHuwb8wra/9UxdPx5uKG+4V57B/zytI/VTFmAMZNRbB/zHPWuGHMlB/7xzxbxUzVmhxKRERERCadPn0ajz/+OIKCguDl5YUWLVrghx9+UF4XEbzyyisIDw+Hl5cX4uPjcfToUTu2mMi2LEp06tSpA5VKZfRITU0FAHTp0sXotaefftomDSciIiKiOy5fvoxOnTpBrVbjyy+/xMGDB/HWW2+hWrVqSp033ngD//nPf7BgwQJ899138PHxQWJiIm7evGnHlhPZjkVT13bt2oXi4mLl+YEDB9CjRw8MHDhQKUtJScG0adOU597e3lZoJhERERGV5PXXX0dkZCSysrKUsrp16yr/FhHMnj0bL7/8Mh555BEAwHvvvYfQ0FCsXbsWjz76aKW3mcjWLEp0goODDZ7PmDED9evXR1xcnFLm7e2NsLAw67TOjOZTN6KwuOS5er/N6GPzNhBVJaXFDMC4ISIqTZ0X15VaR+sueKNDJTTmLp999hkSExMxcOBAbN++HTVr1sQzzzyDlJQUAMCJEyeQm5uL+Ph45T0BAQGIiYlBTk4OEx2yqdLixlYxU+7FCG7duoVly5YhLS3N4OKg5cuXY9myZQgLC0Pfvn0xefJkntUhIiIisqFff/0V8+fPR1paGl566SXs2rULzz77LDQaDZKTk5GbmwsACA0NNXhfaGio8tq9CgsLUVhYqDzPz88HcOfC8aKiIoO6+uf3ltMdrt4/Wncx/7rbnddN9U9F+qzcic7atWtx5coVDBs2TCkbOnQooqKiEBERgf3792PixIk4cuQI1qxZU+J2LAkifTnwV4eUxBV3JFcPotKU1j/sNyIiqqp0Oh3atWuH1157DQDQpk0bHDhwAAsWLEBycnK5tpmRkYH09HSj8k2bNpU4iJ2dnV2uz3IVrto/ZT1bY6p/CgoKyv255U50lixZgl69eiEiIkIpGzVqlPLvFi1aIDw8HN27d8fx48dRv359k9spTxABwPR2OrPtW79+fWlfwWm5ahCVVUn9U5FAIiIisqfw8HBER0cblDVt2hSrV68GAOWygry8PISHhyt18vLy0Lp1a5PbnDRpEtLS0pTn+vuZJCQkmFxeOjs7Gz169ODyySa4ev80n7rR7OtaN8H0djqT/aM/CVIe5Up0Tp48ic2bN5s9UwMAMTExAIBjx46VmOhYEkTAXzvK5B/cUKgr+XqDA1MTy/JVnIqrB1FpSuufigRSaU6fPo2JEyfiyy+/REFBARo0aICsrCy0a9cOwJ2LRKdMmYLFixfjypUr6NSpE+bPn4+GDRvarE1EROQ8OnXqhCNHjhiU/fLLL4iKigJwZ2GCsLAwbNmyRUls8vPz8d133+Hvf/+7yW1qtVpotVqjcrVaXeLvDHOvkev2T2nXCOuZ6p+K9Fe5Ep2srCyEhISgTx/zFy7v27cPAAxGDu5VniACgEKdymynueJOpOeqQVRWJfWPrfpMv+Rn165d8eWXXyI4OBhHjx41ueTnu+++i7p162Ly5MlITEzEwYMH4enpaZN2ERGR8xg/fjw6duyI1157DYMGDcL333+PRYsWYdGiRQDu3Gxx3LhxePXVV9GwYUPlWBMREYF+/frZt/FENmJxoqPT6ZCVlYXk5GR4ePz19uPHj2PFihXo3bs3goKCsH//fowfPx6dO3dGy5YtrdpooqqES34SEZGttW/fHp988gkmTZqEadOmoW7dupg9ezYee+wxpc4LL7yAGzduYNSoUbhy5QoeeOABbNiwgQNq5LQsumEoAGzevBm///47RowYYVCu0WiwefNmJCQkoEmTJnj++eeRlJSEzz//3GqNJaqKPvvsM7Rr1w4DBw5ESEgI2rRpg8WLFyuvl7bkJxERUVk89NBD+Omnn3Dz5k0cOnRIWVpaT6VSYdq0acjNzcXNmzexefNmNGrUyE6tJbI9i8/oJCQkQMR4xbPIyEhs377dKo0iciaOsuRnaSsV3l3XlXC1QvPM9Q/7jIiIHFm5V10jorJxlCU/S1upEOBqhVQyay/5SUREZGtMdIhszFGW/CxtpUKAqxVyEQ9j5vrHlisVEhERVRQTHSIbc5QlP0tbqVD/flfF1QrNs/aSn0RERLbGRIfIxrjkJxEREVHlY6JDZGNc8pOIiIio8jHRIaoEDz30EB566KESX9cv+Tlt2rRKbBURERGR87L4PjpERERERESOjokOERERERE5HSY6RERERETkdJjoEBERERGR02GiQ0REREREToeJDhEREREROR0mOkRERERE5HSY6BARERERkdPhDUOJiIhK0HzqRhQWq0p8/bcZfSqxNUREZAme0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6THSIiIiIiMjpMNEhIiIiIiKnw0SHiIgc2owZM6BSqTBu3Dil7ObNm0hNTUVQUBB8fX2RlJSEvLw8+zWSiIgcDhMdIiJyWLt27cLChQvRsmVLg/Lx48fj888/x6pVq7B9+3acOXMG/fv3t1MriYjIETHRISIih3T9+nU89thjWLx4MapVq6aUX716FUuWLMHMmTPRrVs3tG3bFllZWdixYwd27txpxxYTEZEj4Q1DiYjIIaWmpqJPnz6Ij4/Hq6++qpTv3r0bRUVFiI+PV8qaNGmC2rVrIycnB/fff7/J7RUWFqKwsFB5np+fDwAoKipCUVGRQV39c62bmG3jve9zFfrv7YrfX+tufp8A/tpvTPWPK/YZkb0w0SEiIoezcuVK7NmzB7t27TJ6LTc3FxqNBoGBgQbloaGhyM3NLXGbGRkZSE9PNyrftGkTvL29Tb5nejud2XauX7/e7OvOLjs7295NqHRvdCh7XVP9U1BQYMXWEJE5THSIiMih/PHHH3juueeQnZ0NT09Pq2130qRJSEtLU57n5+cjMjISCQkJ8Pf3N6hbVFSE7OxsTP7BDYU6VYnbPDA10Wrtq0r0/dOjRw+o1Wp7N6dSNZ+6sdQ6WjfB9HY6k/2jP5NIRLbHRIeIiBzK7t27ce7cOdx3331KWXFxMb766ivMnTsXGzduxK1bt3DlyhWDszp5eXkICwsrcbtarRZardaoXK1Wl/hjvVCnQmFxyYmOq/3Iv5e5vnNW5vaHe5nqH1frLyJ74mIERETkULp3746ffvoJ+/btUx7t2rXDY489pvxbrVZjy5YtynuOHDmC33//HbGxsXZsOZHj4LLsRBYmOlOnToVKpTJ4NGnSRHmdAURERBXl5+eH5s2bGzx8fHwQFBSE5s2bIyAgACNHjkRaWhq2bt2K3bt3Y/jw4YiNjS1xIQIiV8Jl2YnusPiMTrNmzXD27Fnl8c033yivMYCIiKgyzJo1Cw899BCSkpLQuXNnhIWFYc2aNfZuFpHdcVl2or9YfI2Oh4eHyTnQ+gBasWIFunXrBgDIyspC06ZNsXPnTo6yERFRuW3bts3guaenJzIzM5GZmWmfBhE5KGsuy16eJdm5fLZprt4/pS3Lbqsl2S1OdI4ePYqIiAh4enoiNjYWGRkZqF27dqXc10BfDvDeBqa4ehCVprT+Yb8REVFVZu1l2cuzJLsrLjluCVftn7Iuy27tJdktSnRiYmKwdOlSNG7cGGfPnkV6ejoefPBBHDhwoFLvawDw3gbmuGoQlVVJ/VMZ9zaYMWMGJk2ahOeeew6zZ88GcOfatueffx4rV65EYWEhEhMTMW/ePISGhtq8PURE5BxssSy7LZZkB1xzWXZXXpIdKH1ZdlstyW5RotOrVy/l3y1btkRMTAyioqLw0UcfwcvLq1wNsCSIAN7bwBxXD6LSlNY/tr63gbmLQ9etW4dVq1YhICAAY8aMQf/+/fHtt9/atD1EROQ8bLEsuy2WZNe/31W54pLsQNmXZbf2kuwVuo9OYGAgGjVqhGPHjqFHjx6Vdl8DgPc2MMdVg6isSuofW/bZ3ReH3j1nmte2ERGRNeiXZb/b8OHD0aRJE0ycOBGRkZHKsuxJSUkAuCw7Ob8KJTrXr1/H8ePH8cQTT6Bt27YMIKISWPPiUKB8F4iWdl3b3XVdCa9tM89c/7DPiByHfln2u929LDsAZVn26tWrw9/fH2PHjuWy7OTULEp0JkyYgL59+yIqKgpnzpzBlClT4O7ujiFDhhjc14ABRPQXa18cCpTv2rbSrmsDeG0blczaF4gSUeWbNWsW3NzckJSUZHBNKJGzsijROXXqFIYMGYKLFy8iODgYDzzwAHbu3Ing4GAADCCie9ni4lCAF4haE69tM89c/9j6ujYiqhguy06uzqJEZ+XKlWZfZwARGbLFxaEALxC1BV7bZp61LxAlIiKytQpdo0NE5vHiUCIiIiL7YKJDZEO8OJSIiIjIPpjoENkZr20jIiIisj4mOkSVjBeHEhEREdkeEx0iIhdV58V1pdbRugve6FAJjSEiIrIyN3s3gIiIiIiIyNqY6BARERERkdNhokNERERERE6HiQ4RERERETkdJjpEREREROR0mOgQEREREZHTYaJDREREREROh4kOERERERE5HSY6RERERETkdJjoEBERERGR02GiQ0REREREToeJDhEREREROR0mOkRERERE5HSY6BARERERkdNhokNERERERE6HiQ4RERERETkdJjpEREREROR0mOgQEREREZHTYaJDREQOJSMjA+3bt4efnx9CQkLQr18/HDlyxKDOzZs3kZqaiqCgIPj6+iIpKQl5eXl2ajERETkiJjpERORQtm/fjtTUVOzcuRPZ2dkoKipCQkICbty4odQZP348Pv/8c6xatQrbt2/HmTNn0L9/fzu2moiIHI2HvRtARER0tw0bNhg8X7p0KUJCQrB792507twZV69exZIlS7BixQp069YNAJCVlYWmTZti586duP/+++3RbCIicjA8o0NERA7t6tWrAIDq1asDAHbv3o2ioiLEx8crdZo0aYLatWsjJyfHLm0kIiLHY9EZnYyMDKxZswaHDx+Gl5cXOnbsiNdffx2NGzdW6nTp0gXbt283eN/o0aOxYMEC67SYiIhchk6nw7hx49CpUyc0b94cAJCbmwuNRoPAwECDuqGhocjNzS1xW4WFhSgsLFSe5+fnAwCKiopQVFRkUFf/XOsmZtt37/tchf57u+L317qb3yeAv/YbU/1jqz4ry2+0mzdv4vnnn8fKlStRWFiIxMREzJs3D6GhoTZpE5G9WZTo6OdNt2/fHrdv38ZLL72EhIQEHDx4ED4+Pkq9lJQUTJs2TXnu7e1tvRYTEZHLSE1NxYEDB/DNN99UeFsZGRlIT083Kt+0aVOJx6np7XRmt7l+/foKt6sqy87OtncTKt0bHcpe11T/FBQUWLE1fynLb7Tx48dj3bp1WLVqFQICAjBmzBj0798f3377rU3aRGRvFiU6pc2b1vP29kZYWJh1WkhUxXGUjah8xowZgy+++AJfffUVatWqpZSHhYXh1q1buHLlisFZnby8PLPHnkmTJiEtLU15np+fj8jISCQkJMDf39+gblFREbKzszH5BzcU6lQlbvPA1MRyfLOqT98/PXr0gFqttndzKlXzqRtLraN1E0xvpzPZP/ozidbGa9uIjFVoMYJ7503rLV++HMuWLUNYWBj69u2LyZMn86wOuSyOshFZRkQwduxYfPLJJ9i2bRvq1q1r8Hrbtm2hVquxZcsWJCUlAQCOHDmC33//HbGxsSVuV6vVQqvVGpWr1eoSf6wX6lQoLC450XG1H/n3Mtd3zsrc/nAvU/1TWf1l6bVtphIdW0z3vLuuK3Hl6Z5A6VM+bTXds9yJjql50wAwdOhQREVFISIiAvv378fEiRNx5MgRrFmzxuR2LAkifTnAedOmuHoQlaa0/rFVv3GUjcgyqampWLFiBT799FP4+fkp190EBATAy8sLAQEBGDlyJNLS0lC9enX4+/tj7NixiI2NZbwQwXrXttliuifg2lM+XXG6J1D2KZ/Wnu5Z7kSnpHnTo0aNUv7dokULhIeHo3v37jh+/Djq169vtJ3yBBHAedPmuGoQlVVJ/WOredP3ssYoG5Ezmz9/PoA7i9vcLSsrC8OGDQMAzJo1C25ubkhKSjKY7klE1ru2zRbTPQHXnPLpytM9gdKnfNpqume5Ep2S5k2bEhMTAwA4duyYyUTHkiACOG/aHAZR+YMIsN286btZa5SN0wmsx5XPhDrq6lEipbfL09MTmZmZyMzMtEkbiKoqa17bZovpnvr3uypXnO4JlH3Kp7Wne1qU6JQ2b9qUffv2AQDCw8NNvl6eIAI4b9ocBpF5JfVPZfSZtUbZOJ3A+lzxTKijrh5FRJaz1bVtRFWZRYlOafOmjx8/jhUrVqB3794ICgrC/v37MX78eHTu3BktW7a0yRcgqiqsOcrG6QTW48pnQh119SgishyvbSMyZlGiU9q8aY1Gg82bN2P27Nm4ceMGIiMjkZSUhJdfftlqDSaqamwxysbpBNbnimdCq8rqUURUOl7bRmTM4qlr5kRGRmL79u0VahCRs+EoGxER2RqvbSMyVqH76BBR6TjKRkRERFT5mOgQ2RhH2YiIiIgqn5u9G0BERERERGRtTHSIiIiIiMjpMNEhIiIiIiKnw0SHiIiIiIicDhMdIiIiIiJyOkx0iIiIiIjI6TDRISIiIiIip8NEh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOkw0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6THSIiIiIiMjpMNEhIiIiIiKnw0SHiIiIiIicDhMdIiIiIiJyOkx0iIiIiIjI6TDRISIiIiIip8NEh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOkw0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6Nkt0MjMzUadOHXh6eiImJgbff/+9rT6KyCkwZogsx7ghshzjhlyFTRKdDz/8EGlpaZgyZQr27NmDVq1aITExEefOnbPFxxFVeYwZIssxbogsx7ghV2KTRGfmzJlISUnB8OHDER0djQULFsDb2xvvvPOOLT6OqMpjzBBZjnFDZDnGDbkSqyc6t27dwu7duxEfH//Xh7i5IT4+Hjk5Odb+OKIqjzFDZDnGDZHlGDfkajysvcELFy6guLgYoaGhBuWhoaE4fPiwUf3CwkIUFhYqz69evQoAuHTpEoqKiozqFxUVoaCgAB5FbijWqUpsx8WLF8v7Faosfd9cvHgRarXa3s2pdB63b5h/XScoKNCV2D/Xrl0DAIiITdpXEktjBrAsbsoaMwDjxtXiprSYAczHjb1iBnCcuHHFmAEYN6XWcZK44bHGelw5ZoCK/UarSMxYPdGxVEZGBtLT043K69atW6Ht1nirQm8nJzW0DHWuXbuGgIAAm7elIhg3VJlKi5uqEDOAbeKGMUMlcYa44bGGKpMtYsbqiU6NGjXg7u6OvLw8g/K8vDyEhYUZ1Z80aRLS0tKU5zqdDpcuXUJQUBBUKuMRgfz8fERGRuKPP/6Av7+/tZtfpbFvzCutf0QE165dQ0RERKW2y9KYASyLG+4X5rF/zDPXP/aKGYBxY2/sH/OcJW4YM9bD/jHPVjFj9URHo9Ggbdu22LJlC/r16wfgTmBs2bIFY8aMMaqv1Wqh1WoNygIDA0v9HH9/f+4oJWDfmGeuf+wxumZpzADlixvuF+axf8wrqX/sNSLNuHEM7B/zqnrcMGasj/1jnrVjxiZT19LS0pCcnIx27dqhQ4cOmD17Nm7cuIHhw4fb4uOIqjzGDJHlGDdElmPckCuxSaIzePBgnD9/Hq+88gpyc3PRunVrbNiwwejiNyK6gzFDZDnGDZHlGDfkSmy2GMGYMWNKnD5QEVqtFlOmTDE6lUrsm9I4ev8wZuyD/WOeo/cP48Y+2D/mOXr/2CJuHP072xv7xzxb9Y9K7LG+IRERERERkQ1Z/YahRERERERE9sZEh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOk4XKKTmZmJOnXqwNPTEzExMfj+++/N1l+1ahWaNGkCT09PtGjRAuvXr6+kltqHJf2zdOlSqFQqg4enp2cltrZyffXVV+jbty8iIiKgUqmwdu3aUt+zbds23HfffdBqtWjQoAGWLl1q83baAuPGPMaNaa4cMwDjpjSMG9NcOW4YM+YxZkpmr7hxqETnww8/RFpaGqZMmYI9e/agVatWSExMxLlz50zW37FjB4YMGYKRI0di79696NevH/r164cDBw5Ucssrh6X9A9y5w+zZs2eVx8mTJyuxxZXrxo0baNWqFTIzM8tU/8SJE+jTpw+6du2Kffv2Ydy4cXjqqaewceNGG7fUuhg35jFuSuaqMQMwbkrDuCmZq8YNY8Y8xox5dosbcSAdOnSQ1NRU5XlxcbFERERIRkaGyfqDBg2SPn36GJTFxMTI6NGjbdpOe7G0f7KysiQgIKCSWudYAMgnn3xits4LL7wgzZo1MygbPHiwJCYm2rBl1se4MY9xUzauFDMijJvSMG7KxpXihjFjHmOm7CozbhzmjM6tW7ewe/duxMfHK2Vubm6Ij49HTk6Oyffk5OQY1AeAxMTEEutXZeXpHwC4fv06oqKiEBkZiUceeQQ///xzZTS3SnCG/YdxYx7jxrqcZd9h3JjHuLEuZ9h3GDPmMWasz1r7j8MkOhcuXEBxcTFCQ0MNykNDQ5Gbm2vyPbm5uRbVt5apU6dCpVLhwoULNv2cu3Xp0qVM/aNSqZS7HTdu3BjvvPMOPv30Uyxbtgw6nQ4dO3bEqVOnKq3djqyk/Sc/Px9//vmnnVplmaoUN/eqjDgqT/+Yips2bdrAx8fHZu2sKpwhZoCqHTeVwVpxw+PNHc4QN4wZ03bt2oWOHTsiMDAQxcXFmDlzJlQqlfI6Y6b8rBU3HtZuGDmO2NhYxMbGKs87duyIpk2bYuHChZg+fbodW0bkeAoKCvDGG2+gS5cuePLJJ5Xyjh07IigoCDdv3rRj64gcG4835GqKioowcOBAeHp6YurUqZg4cSLCwsLK/H7GTOVwmDM6NWrUgLu7O/Ly8gzK8/LyStxxwsLCLKpflanVagCo0PdVq9Vo06YNjh07ZvX2VUUl7T/+/v7w8vKyU6ssw7gxz5L+KSgoQHp6OrZt22ZQrlarUb16ddyZVuzanCFmAMZNacrTP/fi8eYvzhA3jBljx48fx8mTJzFhwgSMGzcO7u7uGDx4sMHZBsZM+Vkrbhwm0dFoNGjbti22bNmilOl0OmzZssUg471bbGysQX0AyM7OLrF+VaZSqeDl5WVR/9yruLgYP/30E8LDw23VzCrFGfYfV4wbnU5X5rMr5emfexUXF+Py5csG0xFcVVXfd/RcMW4sYa244fHmDmfYdxgzxvSrqQUGBir9s23bNmWJaMZMxVht/7FwoQSbWrlypWi1Wlm6dKkcPHhQRo0aJYGBgZKbmysiIk888YS8+OKLSv1vv/1WPDw85N///rccOnRIpkyZImq1Wn766SebtnPKlCkCQA4dOiQDBw4UPz8/qV69ujz77LPy559/KvXeeecd6dq1qwQHB4tGo5GmTZvKvHnzTG5z/fr10rlzZ/H19RU/Pz9p166dLF++XHk9Li5OatWqZdA/vXv3FgDSr18/KSoqkieeeEIASGpqqixbtkyCgoJErVZLs2bNZPHixfLoo4+Kp6en/PzzzyIismfPHunZs6f4+fmJj4+PdOvWTXJycgzalZWVJQDkm2++kfHjx0uNGjXE29tb+vXrJ+fOnTP5PR544AHx9vYWX19f6d27txw4cMAa3V6qa9euyd69e2Xv3r0CQGbOnCl79+6VkydPiojIiy++KE888YRS/9dffxVvb2/5xz/+IYcOHZLMzExxd3eXDRs2VEp7raWqxM29yhpHd+/T0dHR4uHhoazWcurUKRk+fLiEhISIRqOR6OhoWbJkicHnLFu2TNzd3SUqKkp8fX3Fw8ND3N3d5eOPPxaRO/3z9NNPCwCjx6hRo+TRRx8VNzc38fLyklOnTskjjzwiPj4+UqNGDXn++efl9u3bBp9XXFwss2bNkujoaNFqtRISEiKjRo2SS5cuGdTbtWuXJCQkSFBQkHh6ekqdOnVk+PDhNujpkrlqzIg4btysWrVKAMi2bduMXluwYIEAUD7z0KFDkpSUJNWqVROtVitt27aVTz/91OA9lv4Nz8zMVOIMgHTr1k127txp0D9xcXESEBAgI0aMkM6dO4uXl5dUr15dXn75ZTl+/LgsWrRIqlevLgCkTp06kp2dbfQ5ZTn+6P9G3Ev/nU6cOKGUVWY8uWrcOGrM2ENycrLR8aJp06bi7u4uAAz6R38Mi4uLkxo1aijHqscff1w2btwox48fl927d0tSUpK4u7tLRESEaDQaCQ4Olvj4eNm9e7e9v65V2CtuHCrRERGZM2eO1K5dWzQajXTo0EF27typvBYXFyfJyckG9T/66CNp1KiRaDQaadasmaxbt87mbdT/8W3RooX07dtX5s6dK48//rgAMPif1L59exk2bJjMmjVL5syZIwkJCQJA5s6da7C9rKwsUalU0rx5c/nXv/4lmZmZ8tRTTxlsKy4uTpo1a6b0j4eHh6hUKundu7fyQysuLk4ASPPmzaVGjRoSGxurBBkAefDBB2XPnj0iInLgwAHx8fGR8PBwmT59usyYMUPq1q0rWq3WoM/1B5Q2bdpIt27dZM6cOfL888+Lu7u7DBo0yOB7vPfee6JSqaRnz54yZ84cef3116VOnToSGBhocECyla1bt5r8sarfZ5KTkyUuLs7oPa1btxaNRiP16tWTrKwsm7fTFqpC3NyrrHGkP4AEBwdLenq6ZGZmyt69eyU3N1dq1aolkZGRMm3aNJk/f748/PDDAkBmzZqlvP/8+fPi7+8vfn5+4u7uLpGRkRIVFSVqtVr27t0rcXFxMnToUJk/f74AkPr160tQUJC4u7tL9erVpXfv3tK3b1/x9PSUZs2ayYgRI2T+/PmSlJQkAIwGL5566inx8PCQlJQUWbBggUycOFF8fHykffv2cuvWLRERycvLk2rVqkmjRo3kzTfflMWLF8s///lPadq0aaX0vZ4rx4yIY8ZNQUGB+Pr6yjPPPGP0WteuXZXlVg8cOCABAQESHR0tr7/+usydO1c6d+4sKpVK1qxZo7zHkr/h+piMj4+XOXPmSOfOnQWAqFQqadeundI/cXFxotFoxMfHR/7xj3/InDlzlMTG3d1d3NzcpGHDhjJhwgSpWbOmBAQESH5+vvI5ZT3+lDXRqex4cuW4ccSYsYcdO3bISy+9JADk2Weflffff182bdokvXr1EgAG/QNAWrVqJRqNRlq3bi2zZ8+WevXqiYeHh9SsWVM0Go2EhoZKRESEqNVqSUtLk//+97/y+uuvS9++fWXZsmX2/rpWYa+4cbhEpyrQ//F9+OGHDcqfeeYZASA//vijiNw5YN0rMTFR6tWrpzy/cuWK+Pn5SUxMjMEotoiITqdT/q1PdEREVq9eLWq1WlJSUqS4uNjgPfod54cfflDKTp48KZ6envK3v/1NKevXr59oNBo5fvy4UnbmzBnx8/OTzp07K2X6A0p8fLxBe8aPHy/u7u5y5coVEbmTqQcGBkpKSopBe3JzcyUgIMConKiscQRA3NzclDOReiNHjpTw8HC5cOGCQfmjjz4qAQEBSvzdvn1bCgsLDepcvnxZQkNDZcSIEUrZ+fPnBYBMmTLFqK360btp06YZlLdp00batm2rPP/6668FgMHZWBGRDRs2GJR/8sknAkB27dpVYv+Q6xoyZIiEhIQYnC08e/asuLm5Kftg9+7dpUWLFnLz5k2ljk6nk44dO0rDhg2VsrL+DT937pxoNBpJSEgwOK7MnTtXAMg777yjlOkH1VasWKGUHT58WInVu3/8bty4UQAY/EAp6/GnrIkO44nsQf/DfdWqVUqZqX1Wn/gcO3ZMKfvxxx8FgMyZM0cpCwgIMLgPD1mHw1yjUxWlpqYaPB87diwAYP369QBgcLHU1atXceHCBcTFxeHXX3/F1atXAdyZb3jt2jW8+OKLyrxOPVPXBHzwwQcYPHgwRo8ejYULF8LNzfh/YWxsLNq2bas8r127Nh555BFs3LgRxcXFKC4uxqZNm9CvXz/Uq1dPqRceHo6hQ4fim2++QX5+vsE2R40aZdCeBx98EMXFxcpdfLOzs3HlyhUMGTIEFy5cUB7u7u6IiYnB1q1bzfQkubLS4ggA4uLiEB0drTwXEaxevRp9+/aFiBjsc4mJibh69Sr27NkDAHB3d4dGowFwZ870pUuXcPv2bbRr106pU1ZPP/20wfMHH3wQv/76q/J81apVCAgIQI8ePQza1LZtW/j6+ipxEBgYCAD44osvUFRUZFEbyPkNHjwY586dM1gY4+OPP4ZOp8PgwYNx6dIl/O9//8OgQYNw7do1ZT+7ePEiEhMTcfToUZw+fdpgm6X9Dd+8eTNu3bqFcePGGRxXUlJS4O/vj3Xr1hlsz9fXF48++qjyvHHjxggMDETTpk0RExOjlOv/rY+T8hx/SsN4IkcXHx+P+vXrK89btmwJf39/g+NHYGAgvvvuO5w5c8YeTXRaTHQqoGHDhgbP69evDzc3N/z2228AgG+//Rbx8fHw8fFBYGAggoOD8dJLLwGAkugcP34cANC8efNSP+/EiRN4/PHHkZSUhDlz5pR4cfS97QKARo0aoaCgAOfPn8f58+dRUFCAxo0bG9Vr2rQpdDod/vjjD4Py2rVrGzyvVq0aAODy5csAgKNHjwIAunXrhuDgYIPHpk2blIv2iO5VWhwBQN26dQ3qnD9/HleuXMGiRYuM9rfhw4cDgME+9+6776Jly5bw9PREUFAQgoODsW7dOiUOy8LT0xPBwcEGZdWqVVNiALgTB1evXkVISIhRu65fv660KS4uDklJSUhPT0eNGjXwyCOPICsrC4WFhWVuDzmvnj17IiAgAB9++KFS9uGHH6J169Zo1KgRjh07BhHB5MmTjfazKVOmAIDR39zS/obrE557jwsajQb16tVTXterVauW0TEoICAAkZGRRmV3f055jj+lYTyRo7s3/gDj48cbb7yBAwcOIDIyEh06dMDUqVMNEiEqH95Hx4ru/qN//PhxdO/eHU2aNMHMmTMRGRkJjUaD9evXY9asWdDpdBZvPzw8HOHh4Vi/fj1++OEHtGvXzprNN8vd3d1kufz/krv67/P++++bXErRw4O7GpWNqQT+3qUk9fvb448/juTkZJPbadmyJQBg2bJlGDZsGPr164d//OMfCAkJgbu7OzIyMpSBhrIoKQbubVdISAiWL19u8nV9oqRSqfDxxx9j586d+Pzzz7Fx40aMGDECb731Fnbu3AlfX98yt4ucj1arRb9+/fDJJ59g3rx5yMvLw7fffovXXnsNwF/7/4QJE5CYmGhyGw0aNDB4XtrfcEuVtD1rfk5Jg3nFxcVG9RhP5MjKEheDBg3Cgw8+iE8++QSbNm3Cm2++iddffx1r1qxBr169KqupToe/Pivg6NGjBiPNx44dg06nQ506dfD555+jsLAQn332mUEmf+8ULv2pzAMHDhgdmO7l6emJL774At26dUPPnj2xfft2NGvWzGS77vXLL7/A29tb+aHl7e2NI0eOGNU7fPgw3NzcjEblSqP/HiEhIYiPj7foveTazMVRSYKDg+Hn54fi4uJS97ePP/4Y9erVw5o1awx+OOlHvvWssXx0/fr1sXnzZnTq1KlM6/zff//9uP/++/Gvf/0LK1aswGOPPYaVK1fiqaeeqnBbqGobPHgw3n33XWzZsgWHDh2CiGDw4MEAoEz5UqvVVvt7GxUVBQA4cuSIwZSyW7du4cSJE1b7nODg4DIff/Rnna5cuaJMTwNgdHZJj/FEVV14eDieeeYZPPPMMzh37hzuu+8+/Otf/2KiUwGculYBmZmZBs/nzJkDAOjVq5eSvd+drV+9ehVZWVkG70lISICfnx8yMjKM7g1iagQsICAAGzduREhICHr06GFyRDonJ8fg2oM//vgDn376KRISEuDu7g53d3ckJCTg008/NZgelJeXhxUrVuCBBx6Av79/GXvhjsTERPj7++O1114zOUf6/PnzFm2PXIe5OCqJu7s7kpKSsHr1ahw4cMDo9bv3N1Ox+N133yEnJ8fgPd7e3gDu/Kgqr0GDBqG4uNjkXa1v376tbPvy5ctG8d26dWsA4HQbAnBnTn/16tXx4Ycf4sMPP0SHDh2UAYGQkBB06dIFCxcuxNmzZ43eW56/t/Hx8dBoNPjPf/5jsG8uWbIEV69eRZ8+fcr/Ze5iyfFHP4D21VdfKfVu3LiBd99912CbjCeq6oqLi42mUoeEhCAiIoL7cAXxjE4FnDhxAg8//DB69uyJnJwcLFu2DEOHDkWrVq3g6ekJjUaDvn37YvTo0bh+/ToWL16MkJAQgwOTv78/Zs2ahaeeegrt27fH0KFDUa1aNfz4448oKCgw+oMO3LlDcXZ2Nh544AHEx8fjm2++Qc2aNZXXmzdvjsTERDz77LPQarWYN28eACA9PV2p8+qrryrbeOaZZ+Dh4YGFCxeisLAQb7zxhsV94e/vj/nz5+OJJ57Afffdh0cffRTBwcH4/fffsW7dOnTq1Alz5861eLvk/MzFkTkzZszA1q1bERMTg5SUFERHR+PSpUvYs2cPNm/ejEuXLgEAHnroIaxZswZ/+9vf0KdPH5w4cQILFixAdHQ0rl+/rmzPy8sL0dHR+PDDD9GoUSNUr14dzZs3L9P1c3pxcXEYPXo0MjIysG/fPiQkJECtVuPo0aNYtWoV3n77bQwYMADvvvsu5s2bh7/97W+oX78+rl27hsWLF8Pf3x+9e/cuX0eSU1Gr1ejfvz9WrlyJGzdu4N///rfB65mZmXjggQfQokULpKSkoF69esjLy0NOTg5OnTqFH3/80aLPCw4OxqRJk5Ceno6ePXvi4YcfxpEjRzBv3jy0b98ejz/+uNW+W1mPPwkJCahduzZGjhyJf/zjH3B3d8c777yjHFv0GE9U1V27dg21atXCgAED0KpVK/j6+mLz5s3YtWsX3nrrLXs3r2qzx1JvVZ1++cCDBw/KgAEDxM/PT6pVqyZjxowxWCL6s88+k5YtWyo3L3v99dflnXfeMbrRmb5ux44dxcvLS/z9/aVDhw7ywQcfKK/fvby03rFjxyQ8PFyaNm0q58+fFxHDmys2bNhQtFqttGnTRrZu3Wr0Pfbs2SOJiYni6+sr3t7e0rVrV9mxY4dBHf0ynvcu26lfVvHe7W7dulUSExMlICBAPD09pX79+jJs2DCD5a6JRMoeR/p92pS8vDxJTU2VyMhIUavVEhYWJt27d5dFixYpdXQ6nbz22msSFRWlxMMXX3whycnJEhUVZbC9HTt2SNu2bUWj0RgsNZ2cnCw+Pj4lfod7LVq0SNq2bSteXl7i5+cnLVq0kBdeeEHOnDkjIndib8iQIVK7dm3lpqIPPfQQ44QMZGdnK/ex+eOPP4xeP378uDz55JMSFhYmarVaatasKQ899JByM1wRy/+Gz507V5o0aSJqtVpCQ0Pl73//u1y+fNmgjqnjkYhIVFSU9OnTx6jcVAyX5fgjIrJ7926JiYkRjUYjtWvXlpkzZxotL814InuwZHlpU8ewqKgo5R4yhYWF8o9//ENatWql3ES3VatWJd5knspOJVLOKxGJiIiIiIgcFK/RISIiIiIip8NEh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOkw0SEiIiIiIqfDRIeIiIiIiJyOw90wVKfT4cyZM/Dz84NKpbJ3c8iFiAiuXbuGiIgIuLlVrTEAxg3ZQ1WOGYBxQ/ZRleOGMUP2UJGYcbhE58yZM4iMjLR3M8iF/fHHH6hVq5a9m2ERxg3ZU1WMGYBxQ/ZVFeOGMUP2VJ6YcbhEx8/PD8CdL+Pv72/0elFRETZt2oSEhASo1erKbp5DY9+YV1r/5OfnIzIyUtkHqxJzccP9wjz2j3nm+qcqxwzAuKkI9o95zho3jJnyY/+YZ6uYcbhER38q1N/fv8REx9vbG/7+/txR7sG+Ma+s/VMVT8ebixvuF+axf8wrS/9UxZgBGDcVwf4xz1njhjFTfuwf82wVM1VrcigREREREVEZMNEhIiIiIiKnw0SHiIiIiIicjsNdo0NUXnVeXGf2da274I0OldQYoiqgtJgBGDfNp25EYXHJ88J/m9GnEltD5PhKixmAcUOVh2d0iIiIiIjI6TDRISIiIiIip8NEh8jG6tSpA5VKZfRITU0FAHTp0sXotaefftrOrSYiIiKq2niNDpGN7dq1C8XFxcrzAwcOoEePHhg4cKBSlpKSgmnTpinPvb29K7WNRERERM6GiQ6RjQUHBxs8nzFjBurXr4+4uDilzNvbG2FhYZXdNCIiIiKnxUSHqBLdunULy5YtQ1pamsEdfpcvX45ly5YhLCwMffv2xeTJk82e1SksLERhYaHyPD8/H8CdOwsXFRUZ1NU/v7ec7nDl/tG6S+l13O7UMdU/rthnRERUdViU6NSpUwcnT540Kn/mmWeQmZmJLl26YPv27QavjR49GgsWLKhYK4mcxNq1a3HlyhUMGzZMKRs6dCiioqIQERGB/fv3Y+LEiThy5AjWrFlT4nYyMjKQnp5uVL5p06YSE6Ts7OwKt9+ZuWL/WLJstKn+KSgosGJriIiIrMuiRIfXGhBVzJIlS9CrVy9EREQoZaNGjVL+3aJFC4SHh6N79+44fvw46tevb3I7kyZNQlpamvI8Pz8fkZGRSEhIgL+/v0HdoqIiZGdno0ePHlCr1Vb+RlWfK/dP86kbS62jdRNMb6cz2T/6M4lEZH8cjCYyZlGiw2sNiMrv5MmT2Lx5s9kzNQAQExMDADh27FiJiY5Wq4VWqzUqV6vVJf5YN/cauWb/lHZTv7uZ6h9X6y8iR8bBaCJj5b5Gxx7XGujL7/4v/cXV+6a06w3MXWtgrtxasrKyEBISgj59zN8Ret++fQCA8PBwm7aHiIicBwejiYyVO9Gx57UGgGvOpy8rV+2bsl5vUFL/2PJ6A51Oh6ysLCQnJ8PD46+wO378OFasWIHevXsjKCgI+/fvx/jx49G5c2e0bNnSZu0hIiLnZa3BaKKqrtyJjj2uNQBcez59aVy9b0q73sDctQaAba832Lx5M37//XeMGDHCoFyj0WDz5s2YPXs2bty4gcjISCQlJeHll1+2WVuIiMi5WWswujwrfOpnT5jjijNPXH3WTWnM9U9F+qxciY69rzUoy+uuzFX7pqzXG5TUP7bss4SEBIgY//GPjIw0ujiUiIioIqw1GF2eWTfT2+lKbd/69etLreOsXHXWTVlZe4XPciU6vNaAiIiIyPFYczC6PCt8Tv7BDYU68wOPB6YmluWrOBVXn3VTGnP9U5EZNxYnOrzWgIiIiMgxWXMwujyzbgp1qlJnWLjyD31XnXVTVtZe4dPiRIfXGhARERE5Hg5GExmyONHhtQZEREREjoeD0USGyr3qGhERERE5Dg5GExlys3cDiIiI7lanTh2oVCqjR2pqKgCgS5cuRq89/fTTdm41ERE5Gp7RISIih7Jr1y4UFxcrzw8cOIAePXpg4MCBSllKSgqmTZumPOdND4mI6F5MdIiIyKEEBwcbPJ8xYwbq16+PuLg4pczb2xthYWGV3TQiIqpCmOgQEZHDunXrFpYtW4a0tDSoVH8tWbt8+XIsW7YMYWFh6Nu3LyZPnlzqWR1b3OXdVe9yzru8m2eru7wTkWWY6BARkcNau3Ytrly5gmHDhillQ4cORVRUFCIiIrB//35MnDgRR44cKfUGiba4y7sr3+Ed4F3eS2Ptu7wTkWWY6BARkcNasmQJevXqhYiICKVs1KhRyr9btGiB8PBwdO/eHcePHy/xDu+Abe7y7op3eAd4l/fS2Oou70RkGSY6RETkkE6ePInNmzeXeqYmJiYGAHDs2DGziY4t7vLu6j/yeZd386x9l3cisgyXlyYiIoeUlZWFkJAQ9OnTx2y9ffv2AQDCw8MroVVERFRV8IwOERE5HJ1Oh6ysLCQnJ8PD469D1fHjx7FixQr07t0bQUFB2L9/P8aPH4/OnTujZcuWdmwxERE5GiY6RETkcDZv3ozff/8dI0aMMCjXaDTYvHkzZs+ejRs3biAyMhJJSUl4+eWX7dRSIiJyVEx0iIjI4SQkJEDEeGnnyMhIbN++3Q4tIiKiqobX6BARERERkdNhokNERERERE6HiQ4RERERETkdJjpEREREROR0mOgQEREREZHTYaJDREREREROh4kOERERERE5HSY6RERERETkdJjoEBERERGR02GiQ0REREREToeJDpGNTZ06FSqVyuDRpEkT5fWbN28iNTUVQUFB8PX1RVJSEvLy8uzYYiIiIqKqj4kOUSVo1qwZzp49qzy++eYb5bXx48fj888/x6pVq7B9+3acOXMG/fv3t2NriYiIiKo+D3s3gMgVeHh4ICwszKj86tWrWLJkCVasWIFu3boBALKystC0aVPs3LkT999/f2U3lYiIiMgpWJToTJ06Fenp6QZljRs3xuHDhwHcmYLz/PPPY+XKlSgsLERiYiLmzZuH0NBQ67WYqAo6evQoIiIi4OnpidjYWGRkZKB27drYvXs3ioqKEB8fr9Rt0qQJateujZycnBITncLCQhQWFirP8/PzAQBFRUUoKioyqKt/fm853eHK/aN1l9LruN2pY6p/XLHPiIio6rD4jE6zZs2wefPmvzbg8dcmxo8fj3Xr1mHVqlUICAjAmDFj0L9/f3z77bfWaS1RFRQTE4OlS5eicePGOHv2LNLT0/Hggw/iwIEDyM3NhUajQWBgoMF7QkNDkZubW+I2MzIyjAYdAGDTpk3w9vY2+Z7s7OwKfQ9n54r980aHstc11T8FBQVWbA0RVQQHo4mMWZzocAoOkWV69eql/Ltly5aIiYlBVFQUPvroI3h5eZVrm5MmTUJaWpryPD8/H5GRkUhISIC/v79B3aKiImRnZ6NHjx5Qq9Xl+xJOzJX7p/nUjaXW0boJprfTmewf/ZlEInIMHIwmMmRxomPPKTj68rv/S39x9b4pbRqOuSk45sqtLTAwEI0aNcKxY8fQo0cP3Lp1C1euXDE4q5OXl2dyQEFPq9VCq9UalavV6hJ/rJt7jVyzfwqLVWWua6p/XK2/iBwdB6OJDFmU6DjKFBzANaeZlJWr9k1Zp+GU1D+VNQ3n+vXrOH78OJ544gm0bdsWarUaW7ZsQVJSEgDgyJEj+P333xEbG1sp7SEiIufgCNeD6gcVzXHFAVlXH4wujbn+qUifWZTo2HsKDuDa00xK4+p9U9o0HHNTcADbTcOZMGEC+vbti6ioKJw5cwZTpkyBu7s7hgwZgoCAAIwcORJpaWmoXr06/P39MXbsWMTGxnKEjYiIysxRBqOnt9OV2tb169eXWsdZuepgdFlZ+3rQCi0vba8pOGV53ZW5at+UdRpOSf1jqz47deoUhgwZgosXLyI4OBgPPPAAdu7cieDgYADArFmz4ObmhqSkJIMLRImIiMrK3oPR+sHWyT+4oVBn/nh8YGpiudpTlbn6YHRpzPVPRQaiK5TocAoOUelWrlxp9nVPT09kZmYiMzOzklpE5Ni4ehRRxdlrMLpQpyp14NGVf+i76mB0WVn7elA3SypPmDAB27dvx2+//YYdO3bgb3/7m8kpOFu3bsXu3bsxfPhwTsEhIiKLNWvWDGfPnlUe33zzjfLa+PHj8fnnn2PVqlXYvn07zpw5g/79+9uxtUSORz8YHR4ebjAYrcfBaHIFFp3R4RQcIiKqDFw9isgyvB6UyJhFiQ6n4BARUWWw9upRRM6Og9FExip0jQ4REZG12WL1KMA2S+W66lKxXCrXPFstlWsOB6OJjDHRISIih2KL1aMA2yyV68rL5AJcKrc01l4ql4gsw0SHiIgcmjVWjwJss1SuKy6TC3Cp3NLYaqlcIrIMEx0iInJo1rqVgS2WynX1H/lcKtc8ay+VS0SWYaJDREQOhatHERGRNTDRISIih8LVo4iIyBqY6BARkUPh6lFERGQNbvZuABERERERkbUx0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6THSIiIiIiMjpMNEhIiIiIiKnw0SHiIiIiIicDhMdIiIiIiJyOkx0iIiIiIjI6TDRISIiIiIip8NEh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOkw0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6THSIbCwjIwPt27eHn58fQkJC0K9fPxw5csSgTpcuXaBSqQweTz/9tJ1aTERERFT1WZTo8AcbkeW2b9+O1NRU7Ny5E9nZ2SgqKkJCQgJu3LhhUC8lJQVnz55VHm+88YadWkxERERU9VmU6PAHG5HlNmzYgGHDhqFZs2Zo1aoVli5dit9//x27d+82qOft7Y2wsDDl4e/vb6cWExFRVcPBaCJjHpZU3rBhg8HzpUuXIiQkBLt370bnzp2Vcv0PNiIydvXqVQBA9erVDcqXL1+OZcuWISwsDH379sXkyZPh7e1tchuFhYUoLCxUnufn5wMAioqKUFRUZFBX//zecrrDlftH6y6l13G7U8dU/7hinxE5Kv1gdPv27XH79m289NJLSEhIwMGDB+Hj46PUS0lJwbRp05TnJR1niJyBRYnOvazxg43Ileh0OowbNw6dOnVC8+bNlfKhQ4ciKioKERER2L9/PyZOnIgjR45gzZo1JreTkZGB9PR0o/JNmzaVGGvZ2dnW+RJOyhX7540OZa9rqn8KCgqs2BoiqggORhMZK3eiY60fbJaMTOvL7/4v/cXV+6a00WlzI9Pmyq0pNTUVBw4cwDfffGNQPmrUKOXfLVq0QHh4OLp3747jx4+jfv36RtuZNGkS0tLSlOf5+fmIjIxEQkKC0ZS3oqIiZGdno0ePHlCr1Vb+RlWfK/dP86kbS62jdRNMb6cz2T/6v9fWlpGRgTVr1uDw4cPw8vJCx44d8frrr6Nx48ZKnS5dumD79u0G7xs9ejQWLFhgkzYRVTX2mj2gP9aa44q/U1z9N1ppzPVPRfqs3ImOtX6wlWdkGnDN0deyctW+KevodEn9Y+vR6TFjxuCLL77AV199hVq1apmtGxMTAwA4duyYybjRarXQarVG5Wq1usQf6+ZeI9fsn8JiVZnrmuofW/UXp+AQVYw9Zw9Mb6crtX3r168v4zdxPq76G62srD17oFyJjjV/sFkyMg249uhraVy9b0obnTY3Mg3YbnRaRDB27Fh88skn2LZtG+rWrVvqe/bt2wcACA8Pt0mbiBwZp+AQVYw9Zw9M/sENhTrzgygHpiaW52tVaa7+G6005vqnIr/PLEp0bPGDrTwj02V53ZW5at+UdXS6pP6xVZ+lpqZixYoV+PTTT+Hn54fc3FwAQEBAALy8vHD8+HGsWLECvXv3RlBQEPbv34/x48ejc+fOaNmypU3aRFSV8HpQorKz9+yBQp2q1OOxK/5G0XPV32hlZe3ZAxYlOvzBRmS5+fPnA7hzTcHdsrKyMGzYMGg0GmzevBmzZ8/GjRs3EBkZiaSkJLz88st2aC2RY7HWFBzANtcbuOp8e15vYJ6trjcwh7MHiIxZlOjwBxuR5UTM/1CKjIw0uqjaFppP3VjqKNtvM/rYvB1ElrDWFBzANtcbuPK1BgCvNyhNZa5WyMFoImMWT10zp7J+sBERkfOz5hQcwDbXG7jitQYArzcoja2uNzCHg9FExip0Hx0iIiJrs9UUHFtcb+DqP/J5vYF5lblaIQejiYwx0SEiIofCKThERGQNTHSIiMihcAoOERFZAxMdIiJyKJyCQ0RE1lBlE53SVpDi6lFERERERK6ryiY6RERERETk+Oq8uM7s61p3wRsdrP+5btbfJBERERERkX0x0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6THSIiIiIiMjpMNEhIiIiIiKnw0SHiIiIiIicDhMdIiIiIiJyOkx0iIiIiIjI6TDRISIiIiIip8NEh4iIiIiInA4THSIiIiIicjpMdIiIiIiIyOl42LsBRERERFVFnRfXlVpH6y54o0MlNIaIzOIZHSIiIiIicjpMdIiIiIiIyOkw0SEiIiIiIqfDRIeIiIiIiJwOEx0iIiIiInI6Nkt0MjMzUadOHXh6eiImJgbff/+9rT6KyCkwZogsx7ghshzjhlyFTRKdDz/8EGlpaZgyZQr27NmDVq1aITExEefOnbPFxxFVeYwZIssxbogsx7ghV2KTRGfmzJlISUnB8OHDER0djQULFsDb2xvvvPOOLT6OqMpjzBBZjnFDZDnGDbkSq98w9NatW9i9ezcmTZqklLm5uSE+Ph45OTlG9QsLC1FYWKg8v3r1KgDg0qVLKCoqMqpfVFSEgoICeBS5oVinKrEdFy9erMjXqJL0fXPx4kWo1Wp7N6fSedy+Yf51naCgQFdi/1y7dg0AICI2aV9JLI0ZwLK4KWvMAIwbV4ub0mIGMB839ooZwHHixhVjBmDclFrHSeKGxxrrceWYASr2G60iMWP1ROfChQsoLi5GaGioQXloaCgOHz5sVD8jIwPp6elG5XXr1q1QO2q8VaG3k5MaWoY6165dQ0BAgM3bomdpzACMG6pcpcVNZccM4Dhxw5ihkjhD3PBYQ5XJFjFj9UTHUpMmTUJaWpryXKfT4dKlSwgKCoJKZTwikJ+fj8jISPzxxx/w9/evzKY6PPaNeaX1j4jg2rVriIiIsEPrLGNJ3HC/MI/9Y565/qlKMQMwbqyJ/WOes8QNY8Z62D/m2SpmrJ7o1KhRA+7u7sjLyzMoz8vLQ1hYmFF9rVYLrVZrUBYYGFjq5/j7+3NHKQH7xjxz/VPZo2uA5TEDlC9uuF+Yx/4xr6T+sUfMAIwbR8H+Ma+qxw1jxvrYP+ZZO2asvhiBRqNB27ZtsWXLFqVMp9Nhy5YtiI2NtfbHEVV5jBkiyzFuiCzHuCFXY5Opa2lpaUhOTka7du3QoUMHzJ49Gzdu3MDw4cNt8XFEVR5jhshyjBsiyzFuyJXYJNEZPHgwzp8/j1deeQW5ublo3bo1NmzYYHTxW3lotVpMmTLF6FQqsW9K48j9w5ixH/aPeY7cP4wb+2H/mOfI/WOruHHk7+wI2D/m2ap/VGKP9Q2JiIiIiIhsyCY3DCUiIiIiIrInJjpEREREROR0mOgQEREREZHTYaJDREREREROx+ESnczMTNSpUweenp6IiYnB999/b7b+qlWr0KRJE3h6eqJFixZYv359JbXUPizpn6VLl0KlUhk8PD09K7G1leurr75C3759ERERAZVKhbVr15b6nm3btuG+++6DVqtFgwYNsHTpUpu30xYYN+Yxbkxz5ZgBGDelYdyY5spxw5gxjzFTMnvFjUMlOh9++CHS0tIwZcoU7NmzB61atUJiYiLOnTtnsv6OHTswZMgQjBw5Env37kW/fv3Qr18/HDhwoJJbXjks7R/gzh1mz549qzxOnjxZiS2uXDdu3ECrVq2QmZlZpvonTpxAnz590LVrV+zbtw/jxo3DU089hY0bN9q4pdbFuDGPcVMyV40ZgHFTGsZNyVw1bhgz5jFmzLNb3IgD6dChg6SmpirPi4uLJSIiQjIyMkzWHzRokPTp08egLCYmRkaPHm3TdtqLpf2TlZUlAQEBldQ6xwJAPvnkE7N1XnjhBWnWrJlB2eDBgyUxMdGGLbM+xo15jJuycaWYEWHclIZxUzauFDeMGfMYM2VXmXHjMGd0bt26hd27dyM+Pl4pc3NzQ3x8PHJycky+Jycnx6A+ACQmJpZYvyorT/8AwPXr1xEVFYXIyEg88sgj+PnnnyujuVWCM+w/jBvzGDfW5Sz7DuPGPMaNdTnDvsOYMY8xY33W2n8cJtG5cOECiouLje7MGxoaitzcXJPvyc3Ntah+VaGft/nbb78pZeXpn8aNG+Odd97Bp59+imXLlkGn06Fjx444deqULZtfZZS0/+Tn5+PPP/+0U6ssw7gxryJxExwcjKCgICVucnJyoFKpquzcemtwhpgBnCtupk6dCpVKZdVtOtLxRqVSYerUqRXahr05Q9w4U8zYgjViJjs7G23atOFvtP9nrbhxmESHrC82NhZPPvkkWrdujbi4OKxZswbBwcFYuHChvZtGZHM7duzAW2+9ZfH79HHj6+uLwMBAJW5WrFhhg1YSOYeKHG/Wr19f5ZMZIgD473//i3379pWp7r0xExISAk9PT/5GszKHSXRq1KgBd3d35OXlGZTn5eUhLCzM5HvCwsIsql+Vlad/7qVWq9GmTRscO3bMFk2sckraf/z9/eHl5WWnVlmGcVOyHTt2YObMmXBzcyvX9920aRM2bdqkxM3dZ1hdlTPEDMC4KU1lH2/Wr1+P9PR0k6/9+eefePnll8v0mY7KGeKGMWNejRo14Obmhnfeeccg0bHk+x45cgQ9evTgb7T/Z624cZhER6PRoG3bttiyZYtSptPpsGXLFsTGxpp8T2xsrEF9AMjOzi6xflVWnv65V3FxMX766SeEh4fbqplVijPsP4yb0rVo0aJccaPRaKDRaJS4CQkJsXVTHZ6z7DuMG/Mc6Xjj6ekJDw+PCm3D3pxh32HMmKfRaNCkSRODMktjxsPDAz///DN/o/0/q+0/lq6UYEsrV64UrVYrS5culYMHD8qoUaMkMDBQcnNzRUTkiSeekBdffFGp/+2334qHh4f8+9//lkOHDsmUKVNErVbLTz/9ZK+vYBVZWVkCQE6cOKGUrV+/Xho3biwAxNPTUzp37iwDBgww6p9+/fpJ06ZNRavVSkhIiLzyyivSv39/CQ8Pl0cffVQ8PT3l559/lg8++EDuu+8+8fX1FT8/P2nevLnMnj3bTt/YOq5duyZ79+6VvXv3CgCZOXOm7N27V06ePCkiIi+++KI88cQTSv1ff/1VvL295R//+IccOnRIMjMzxd3dXTZs2GCvr1AujBtjU6ZMEQBGj+zsbImLixMPDw8JCgoSjUYjAQEBkpCQoLw3PT1dNm7cKDExMdK2bVslbjZt2iQAJCsry35fzMpcNWZEqmbcfP3119KuXTvRarVSr149WbBggbKv6xUVFcm0adOkXr16otFoJCoqSiZNmiQ3b9402FZUVJT06dNHtm7dKm3bthVPT09p3ry5bN26VURE0tLSRKVSiYeHh0RHR0v//v2N+iclJUWSkpKkWrVq4uHhIQ0bNpSFCxfK7t27lbjZt2+fTJ06VRo0aCBarVaqV68unTp1kk2bNomISHJysslY1QMgU6ZMMWj7qVOnZMSIERIeHi4ajUbq1KkjTz/9tBQWFtqg1425atxUxZgpK3P71MWLF+X555+X5s2bi4+Pj/j5+UnPnj1l3759yvu3bt1qcj/28vKS3Nxc+eWXX6R27dri4+MjWq1WatasKc2bN5c1a9bI8ePHZffu3eLt7S1ubm7y888/i4iY3J7+cffvw0OHDilxqNVqpW3btvLpp59WdheWyl5x41CJjojInDlzpHbt2qLRaKRDhw6yc+dO5bW4uDhJTk42qP/RRx9Jo0aNRKPRSLNmzWTdunWV3GLruzfRee+990SlUknPnj2V5AaAuLu7y5o1a5T3NW/eXABIy5YtZebMmRITEyNubm6iUqnEzc1NevfuLXv27FF+sHXv3l0yMzMlMzNTxowZIwMHDrTTN7aOkv7Q6PeZ5ORkiYuLM3pP69atRaPRSL169arsj1jGjaEff/xRhgwZIgBk1qxZ8uSTT0pQUJCo1Wrx8fGRPn36yKxZs2TOnDlSrVo1ASBz584VEZFx48ZJ7dq1RaVSiVqtVuLmxIkTTpfouHLMiFStuNm/f794eXlJ7dq1JSMjQ6ZPny6hoaHSsmVLg8RAnzgMGDBAMjMz5cknnxQA0q9fP4PtRUVFSePGjSU8PFymTp0qs2bNkpo1a4qvr68sW7ZMateuLQ8//LByvNFqtbJjxw7l/e3atRO1Wi3R0dHy+uuvS9euXUWr1QoACQgIUOLmpZdeEpVKJSkpKbJ48WJ56623ZMiQITJjxgwREdmxY4f06NFDAMj777+vPPTuTXROnz4tERER4u3tLePGjZMFCxbI5MmTpWnTpnL58mXbdP49XDluqlLMlFVp+9SuXbukfv368uKLL8rChQtl2rRpUrNmTQkICJDTp0+LiEhubq5MmzZNAIivr6+4u7tLvXr1ZPXq1VJYWCh169YVjUYjbdq0kf/+97+Snp4uoaGhEhERIRqNRkJDQ8XLy0v69u2rtOvueNA/oqKixMvLS86fPy8iIgcOHJCAgAAlDufOnSudO3cWlUpl8PvQEdgrbhwu0SHDROfatWsSGBgoKSkpBnVyc3MlICDAoLxFixZSq1YtuXbtmlK2bds2ASBRUVFK2XPPPSf+/v5y+/Ztm3+X/2vv3uOqqvP9j78BYeMNCEWQxFupeLc0CS01Q3mYeWTkTGVNXjLrTGgp01R2Mm9NWs2oMxOp0xhWjqeyk1ppIlniKTENszTLUfPWKHgLUMztDtbvD3/saQds2LAvsPbr+XjsR67vXpfP+rY/7PXZ6/IFfOWFF16o8MuXYRjGxYsXK8ybnJxsdOzY0aFt8ODBDn90zVjooOFISUkxQkND7b9+GoZh7Nu3zwgKCrIXOrt37zYkGffff7/Dso8++qghyfjoo4/sbe3atTMkORQvWVlZ9l+hf76dZcuWGZLsZ3sMwzBuvfVWo2fPng5nisrKyowBAwYYnTp1srf17t27wlgqv5SWluZQrP3cLwudcePGGYGBgcbOnTsrzFtWVuZ0O0BlqvtMXbp0ySgtLXVoP3z4sGGxWIy5c+fa23bu3Fnpd0T5GYzVq1c7jaNdu3YVCsWfe/755w1JxmuvvWZvq2ke+rN6c48OKpedna3CwkKNHTtWZ86csb+CgoKUkJCgjz/+WJJ04sQJ7dmzx/60qHKDBw9Wz549HdYZERGhkpISZWdne3VfgPrg5zcxFhUV6cyZMxo8eLC+++47FRUV+TAyoHKlpaXKyspSSkqK2rZta2/v2rWrkpOT7dMbNmyQJKWnpzss/7vf/U6StH79eof2bt26OVzvnpCQIEkaOnSow3bK27/77jtJ0rlz5/TRRx/pjjvu0Pnz5+3fS2fPnlVycrIOHDigf/3rX5KufN98/fXXOnDgQN06QVfueVi7dq1GjRqlfv36VXjf3Y/ZhvnV5DNlsVgUGHjlcLm0tFRnz55Vs2bN1KVLF+3atavabYSHh0uSsrKydPHixVrF+fHHH2vGjBmaOnWq7r33Xkmu5aE/o9Cp58q/HIYOHaqoqCiH16ZNm3Tq1ClJ0tGjRyVJ1157bYV1/LLtoYceUufOnTVixAi1adNG9913nzZu3OjhPQHqh08//VRJSUlq2rSpIiIiFBUVpSeffFKSKHRQL50+fVo//vijOnXqVOG9Ll262P999OhRBQYGVvibHxMTo4iICPv3RLmfFzPSvw/I4uLiKm3/4YcfJEkHDx6UYRiaOXNmhe+lWbNmSZL9u2nu3LkqLCxU586d1bNnT/3+97/XV1995XIfSFf6obi4WD169KjV8sAv1eQzVVZWpkWLFqlTp06yWCxq2bKloqKi9NVXX9XoO6NDhw5KT0/X3//+d7Vs2VLJycnKyMio8ffN999/rzvvvFMDBw7UwoUL7e2u5KE/a9iPMvEDZWVlkqTXX3+90kcU1uZpNK1atdLu3buVlZWlDz74QB988IEyMzM1btw4vfrqq3WOGaivDh06pFtvvVXx8fFauHCh4uLiFBISog0bNmjRokX2fAMaspqe2QgKCnKp3TAMSf/+Xnr00Ucdzij9XHmxNWjQIB06dEjr1q3Tpk2b9Pe//12LFi3S0qVLdf/999coTsCXnn32Wc2cOVP33Xef5s2bp8jISAUGBmratGk1/s7405/+pAkTJtjz4OGHH9b8+fO1fft2tWnTpsrlLl++rP/8z/+UxWLRW2+95XDM50oe+jMKnXrummuukXSlOElKSqpyvnbt2klSpc9fr6wtJCREo0aN0qhRo1RWVqaHHnpIy5Yt08yZM0kMmEJlB3vvvfeerFar3n33XYdfs8svAQXqo6ioKDVu3LjSy7/2799v/3e7du1UVlamAwcOqGvXrvb2goICFRYW2r8n6qpjx46SroyV4+x7qVxkZKQmTpyoiRMn6sKFCxo0aJBmz55tL3RqWphFRUUpLCxMe/furX3wwM/U5DP19ttv65ZbbtHy5csd2gsLC9WyZUv7dHWf4549e6pnz5566qmntG3bNg0cOFBLly7VM888U+UyDz/8sHbv3q2tW7cqOjra4T1X89BfcelaPZecnKywsDA9++yzstlsFd4/ffq0JCk2NlY9evTQa6+9pgsXLtjfz8nJ0Z49exyWOXv2rMN0YGCgevXqJUmyWq3u3gXAJ5o2bSrpypdRufJfqst/mZauXK6WmZnp1dgAVwQFBSk5OVlr167VsWPH7O3ffPONsrKy7NO33XabJGnx4sUOy5df7jJy5Ei3xNOqVSsNGTJEy5Yt08mTJyu8X/69JFX8vmnWrJmuvfZah++aynK1MoGBgUpJSdF7772nzz//vML7P89roCZq8pkKCgqq8NlavXp1hftfqvocFxcX66effnJo69mzpwIDA50ec2VmZmrZsmXKyMhQ//79K7zvSh76M87o1HNhYWFasmSJ7r33Xl1//fW66667FBUVpWPHjmn9+vUaOHCgXnzxRUlXTq+OHj1aAwcO1MSJE/XDDz/oxRdfVI8ePRyKn/vvv1/nzp3T0KFD1aZNGx09elR//etf1adPH4dfAYGGrG/fvpKk//7v/9Zdd92l4OBgDRo0yH4288EHH9SFCxf08ssvq1WrVpV+UQD1xZw5c7Rx40bdfPPNeuihh/TTTz/pr3/9q7p3726/56V3794aP368/va3v6mwsFCDBw/Wjh079OqrryolJUW33HKL2+LJyMjQTTfdpJ49e2ry5Mnq2LGjCgoKlJubq++//15ffvmlpCsPPBgyZIj69u2ryMhIff7553r77bc1ZcoU+7rKc/Xhhx9WcnKygoKCdNddd1W63WeffVabNm3S4MGD9cADD6hr1646efKkVq9erU8++UQRERFu20f4h+o+U7fffrvmzp2riRMnasCAAdqzZ4/+8Y9/2M+olLvmmmsUERGhpUuXqnnz5mratKkSEhL05ZdfasqUKfr1r3+tzp0766efftLrr7+uoKAgpaamVhrTmTNn9NBDD6lbt26yWCxauXKlw/u/+tWv1LRp0xrnoV/z4RPfUIXKBgz9+OOPjeTkZCM8PNwIDQ01rrnmGmPChAnG559/7rDsG2+8YcTHxxsWi8Xo0aOH8e677xqpqalGfHy8fZ63337bGD58uNGqVSsjJCTEaNu2rfHggw8aJ0+e9NYuAl4xb9484+qrrzYCAwPtOfXuu+8avXr1MkJDQ4327dsbzz33nPHKK69UyDkeL436Jicnx+jbt699TImqBgydM2eO0aFDByM4ONiIi4tzOmDoL0ky0tLSHNrKP/svvPCCQ/uhQ4eMcePGGTExMUZwcLBx9dVXG7fffrvx9ttv2+d55plnjP79+xsRERFG48aNjfj4eOMPf/iDcfnyZfs8P/30kzF16lQjKirKCAgIqHbA0KNHjxrjxo0zoqKi7IOnpqWleW3AUJiPs8/UpUuXjN/97ndG69atjcaNGxsDBw40cnNzK3xHGIZhrFu3zujWrZvRqFEj+/fFd999Z9x3333GNddcY4SGhhqRkZHGLbfcYnz44YcOy/788dLlOVfV6+ffVTXJQ38WYBic6zW7Pn36KCoqisdJAwAAwG9wj46J2Gy2CteBbtmyRV9++aWGDBnim6AAAAAAH+CMjokcOXJESUlJ+s1vfqPY2Fh9++23Wrp0qcLDw7V37161aNHC1yECAAAAXsHDCEzkqquuUt++ffX3v/9dp0+fVtOmTTVy5EgtWLCAIgcAAAB+hTM6AAAAAEyHe3QAAAAAmA6FDgAAAADTodABAAAAYDr17mEEZWVlOnHihJo3b66AgABfhwM/YhiGzp8/r9jYWAUGNqzfAMgb+EJDzhmJvIFvNOS8IWfgC3XJmXpX6Jw4cUJxcXG+DgN+7Pjx42rTpo2vw3AJeQNfaog5I5E38K2GmDfkDHypNjlT7wqd5s2bS7qyM2FhYRXet9ls2rRpk4YPH67g4GBvh+cX/LWPi4uLFRcXZ/8MNiTO8sZf/3/WFP3jnLP+acg5I5E3dUH/OGfWvCFnao/+cc5TOVPvCp3yU6FhYWFVFjpNmjRRWFgYHxQP8fc+boin453ljb///6wO/eNcTfqnIeaMRN7UBf3jnFnzhpypPfrHOU/lTMO6OBQAAAAAaoBCBwAAAIDpUOgAAAAAMJ16d48OXNP+ifXVznNkwUgvRAKgoanJ3w9LkKHn+3shmHqqx+wsWUurvi6cv6+Ao+pyRiJv4D2c0QEAAABgOhQ6AAAAAEyHQgcAAACA6VDoAAAAADAdCh0AAAAApkOhAwAAAMB0KHQAAAAAmA6FDgAAAADTodABAAAAYDoUOgAAAABMh0IHAAAAgOlQ6AAAAAAwHQodAEC90r59ewUEBFR4paWlSZKGDBlS4b3/+q//8nHUAID6ppGvAwAA4Od27typ0tJS+/TevXs1bNgw/frXv7a3TZ48WXPnzrVPN2nSxKsxAgDqPwodAEC9EhUV5TC9YMECXXPNNRo8eLC9rUmTJoqJifF2aACABoRCBwBQb12+fFkrV65Uenq6AgIC7O3/+Mc/tHLlSsXExGjUqFGaOXNmtWd1rFarrFarfbq4uFiSZLPZZLPZHOYtn7YEGk7X+cvl/EX5fvvr/lfHWf/QZ4D3UOgAAOqttWvXqrCwUBMmTLC33X333WrXrp1iY2P11Vdf6fHHH9f+/fv1zjvvOF3X/PnzNWfOnArtmzZtqrJImtevzOk6N2zYUP1OmFh2dravQ6jXKuufixcv+iASwD9R6AAA6q3ly5drxIgRio2Ntbc98MAD9n/37NlTrVu31q233qpDhw7pmmuuqXJdM2bMUHp6un26uLhYcXFxGj58uMLCwhzmtdlsys7O1szPA2UtC/jlquz2zk6uzW41eOX9M2zYMAUHB/s6nHrHWf+Un0kE4HkUOoAX/Otf/9Ljjz+uDz74QBcvXtS1116rzMxM9evXT5JkGIZmzZqll19+WYWFhRo4cKCWLFmiTp06+ThywHeOHj2qDz/8sNozNQkJCZKkgwcPOi10LBaLLBZLhfbg4OAqD9atZQGyllZd6Pj7Qb6zvkPl/UN/Ad7j0uOllyxZol69eiksLExhYWFKTEzUBx98YH//0qVLSktLU4sWLdSsWTOlpqaqoKDA7UEDDckPP/yggQMHKjg4WB988IH27dunP/3pT7rqqqvs8zz//PP6y1/+oqVLl+qzzz5T06ZNlZycrEuXLvkwcsC3MjMz1apVK40cOdLpfLt375YktW7d2gtRAQAaCpfO6LRp00YLFixQp06dZBiGXn31VY0ePVpffPGFunfvrunTp2v9+vVavXq1wsPDNWXKFI0ZM0affvqpp+IH6r3nnntOcXFxyszMtLd16NDB/m/DMLR48WI99dRTGj16tCTptddeU3R0tNauXau77rrL6zEDvlZWVqbMzEyNHz9ejRr9+6vq0KFDWrVqlW677Ta1aNFCX331laZPn65BgwapV69ePowYAFDfuFTojBo1ymH6D3/4g5YsWaLt27erTZs2Wr58uVatWqWhQ4dKuvJrXNeuXbV9+3bdeOON7osaaEDeffddJScn69e//rVycnJ09dVX66GHHtLkyZMlSYcPH1Z+fr6SkpLsy4SHhyshIUG5ubmVFjq1eXoUT/qpnD/3jyXI+RPFpH8/dczbT4/68MMPdezYMd13330O7SEhIfrwww+1ePFilZSUKC4uTqmpqXrqqac8FgsAoGGq9T06paWlWr16tUpKSpSYmKi8vDzZbDaHg7X4+Hi1bdtWubm5VRY6rhywlbf//L/+riYHKq72lb/2saf297vvvtOSJUuUnp6uJ598Ujt37tTDDz+skJAQjR8/Xvn5+ZKk6Ohoh+Wio6Pt7/1SbZ4exdORnPPH/nm+f83n9fbTo4YPHy7DqPj3LS4uTjk5OR7bLgDAPFwudPbs2aPExERdunRJzZo105o1a9StWzft3r1bISEhioiIcJjf2cGaVLsDNsk/D0oqU5MDldo+/tTf+thTB21lZWXq16+fnn32WUnSddddp71792rp0qUaP358rdZZm6dH8XSkyvlz//SYnVXtPJZAQ/P6lfH0KABAg+NyodOlSxft3r1bRUVFevvttzV+/Pg6/brmygGb5N8HJZWpyYGKq48/9dc+9tRBW+vWrdWtWzeHtq5du+p///d/Jck+untBQYHDzdQFBQXq06dPpeuszdOjeDqSc/7YP86eJvZLPD0KANDQuFzohISE6Nprr5Uk9e3bVzt37tSf//xn3Xnnnbp8+bIKCwsdzuoUFBTYD+QqU5sDtpq87y9qcqBS237ytz721L4OHDhQ+/fvd2j75z//qXbt2km68mCCmJgYbd682V7YFBcX67PPPtNvf/tbj8QEAABgdnUeR6esrExWq1V9+/ZVcHCwNm/erNTUVEnS/v37dezYMSUmJtY5UKChmj59ugYMGKBnn31Wd9xxh3bs2KG//e1v+tvf/iZJCggI0LRp0/TMM8+oU6dO6tChg2bOnKnY2FilpKT4NngAAIA6av/EeqfvW4IMl+4brSmXCp0ZM2ZoxIgRatu2rc6fP69Vq1Zpy5YtysrKUnh4uCZNmqT09HRFRkYqLCxMU6dOVWJiIk9cg1+74YYbtGbNGs2YMUNz585Vhw4dtHjxYt1zzz32eR577DGVlJTogQceUGFhoW666SZt3LhRoaGhPowcAACg4XKp0Dl16pTGjRunkydPKjw8XL169VJWVpaGDRsmSVq0aJECAwOVmpoqq9Wq5ORkvfTSSx4JHGhIbr/9dt1+++1Vvh8QEKC5c+dq7ty5XowKAADAvAJdmXn58uU6cuSIrFarTp06pQ8//NBe5EhSaGioMjIydO7cOZWUlOidd95xen8OAAAA3G/BggX2S6PLXbp0SWlpaWrRooWaNWum1NRUFRQU+C5IwMNcKnQAAABQv+3cuVPLli1Tr169HNqnT5+u9957T6tXr1ZOTo5OnDihMWPG+ChKwPModAAAAEziwoULuueee/Tyyy/rqquusrcXFRVp+fLlWrhwoYYOHaq+ffsqMzNT27Zt0/bt230YMeA5FDoAAAAmkZaWppEjRyopKcmhPS8vTzabzaE9Pj5ebdu2VW5urrfDBLyizo+X9gfVPRKv3JEFIz0cCQAAQOXeeOMN7dq1Szt37qzwXn5+vkJCQhzGOpSk6Oho5efnV7o+q9Uqq9Vqny4fWNtms8lmsznMWz5tCTSqjfOXy/qD8n32x32Xrjw+2un7//9zU1n/1KXPKHQAAAAauOPHj+uRRx5Rdna224YmmD9/vubMmVOhfdOmTWrSpEmly8zrV1btejds2FDn2Bqq7OxsX4fgEzUdI6ey/rl48WKtt0uhAwAA0MDl5eXp1KlTuv766+1tpaWl2rp1q1588UVlZWXp8uXLKiwsdDirU1BQUOUTcmfMmKH09HT7dHFxseLi4jR8+HCFhYU5zGuz2ZSdna2ZnwfKWhbgNNa9s5NrsYcNW3n/DBs2TMHBwb4Ox+t6zM5y+r4l0NC8fmWV9k/5mcTaoNABAABo4G699Vbt2bPHoW3ixImKj4/X448/rri4OAUHB2vz5s1KTU2VJO3fv1/Hjh1TYmJipeu0WCyyWCwV2oODg6s8WLeWBcha6rzQ8ccD/XLO+s7MqvtMlKusf+rSXxQ6AAAADVzz5s3Vo0cPh7amTZuqRYsW9vZJkyYpPT1dkZGRCgsL09SpU5WYmKgbb7zRFyEDHkehAwAA4AcWLVqkwMBApaamymq1Kjk5WS+99JKvwwI8hkIHAADAhLZs2eIwHRoaqoyMDGVkZPgmIMDLGEcHAAAAgOlQ6AAAAAAwHQodAAAAAKZDoQMAAADAdCh0AAAAAJhOg33qWo/ZWU4HHzqyYKQXowEAuMvs2bM1Z84ch7YuXbro22+/lSRdunRJv/vd7/TGG284PCI3OjraF+ECAOopzugAAOqd7t276+TJk/bXJ598Yn9v+vTpeu+997R69Wrl5OToxIkTGjNmjA+jBQDURw32jA4AwLwaNWqkmJiYCu1FRUVavny5Vq1apaFDh0qSMjMz1bVrV23fvp0R3gEAdpzRAQDUOwcOHFBsbKw6duyoe+65R8eOHZMk5eXlyWazKSkpyT5vfHy82rZtq9zcXF+FCwCohzijA3jRggULNGPGDD3yyCNavHixJO43AH4pISFBK1asUJcuXXTy5EnNmTNHN998s/bu3av8/HyFhIQoIiLCYZno6Gjl5+c7Xa/VapXVarVPFxcXS5JsNptsNpvDvOXTlkDD6Tp/uZy/KN9vf93/6jjrH/oM8B6XCp358+frnXfe0bfffqvGjRtrwIABeu6559SlSxf7PBy0AZXbuXOnli1bpl69ejm0T58+XevXr9fq1asVHh6uKVOmaMyYMfr00099FCngWyNGjLD/u1evXkpISFC7du301ltvqXHjxrVe7/z58ys85ECSNm3apCZNmlS6zLx+ZU7XuWHDhlrHYwbZ2dm+DqFeq6x/Ll686INIAP/kUqGTk5OjtLQ03XDDDfrpp5/05JNPavjw4dq3b5+aNm0qiYM2oDIXLlzQPffco5dfflnPPPOMvZ37DYDqRUREqHPnzjp48KCGDRumy5cvq7Cw0OGsTkFBQaX39PzcjBkzlJ6ebp8uLi5WXFychg8frrCwMId5bTabsrOzNfPzQFnLqn7C597ZybXbqQauvH+GDRum4OBgX4dT7zjrn/IziQA8z6VCZ+PGjQ7TK1asUKtWrZSXl6dBgwZx0AZUIS0tTSNHjlRSUpJDoVPd/QbkDHDlh4JDhw7p3nvvVd++fRUcHKzNmzcrNTVVkrR//34dO3ZMiYmJTtdjsVhksVgqtAcHB1d5sG4tC3A6lIG/H+Q76ztU3j/0F+A9dbpHp6ioSJIUGRkpiYM2oDJvvPGGdu3apZ07d1Z4r7b3G9TmXgOuC6+cP/ePJcj5/SfSv+9R8ea9Bo8++qhGjRqldu3a6cSJE5o1a5aCgoI0duxYhYeHa9KkSUpPT1dkZKTCwsI0depUJSYm8h0DAHBQ60KnrKxM06ZN08CBA9WjRw9JtTtoc+WArbxd8u4NojU5GHD3NmuqJrG5Gpe/Hvh5Yn+PHz+uRx55RNnZ2QoNDXXbemtzrwHX0jvnj/3zfP+az+vNew2+//57jR07VmfPnlVUVJRuuukmbd++XVFRUZKkRYsWKTAwUKmpqQ73ggIA8HO1LnTS0tK0d+9eh0HcaqM2B2ySd28QrenBgC9uSq1JbLWNy98O/Dxx0JaXl6dTp07p+uuvt7eVlpZq69atevHFF5WVlVWr+w1qc68B19JXzp/7p8fsrGrnsQQamtevzKv3GrzxxhtO3w8NDVVGRoYyMjI8sn0AgDnUqtCZMmWK3n//fW3dulVt2rSxt8fExLh80ObKAZvkmxtEa3Iw4O5t1lRNYnM1Ln898PPEQdutt96qPXv2OLRNnDhR8fHxevzxxxUXF1er+w1qc68B19I754/94+zek1/iXgMAQEPjUqFjGIamTp2qNWvWaMuWLerQoYPD+7W5SbQ2B2ySd28QrenBgC++9GsSW23j8rcDP0/sa/Pmze2XdpZr2rSpWrRoYW/nfgMAAAD3c6nQSUtL06pVq7Ru3To1b97cft9NeHi4GjduzE2iQC1wvwEAAID7uVToLFmyRJI0ZMgQh/bMzExNmDBBEgdtQHW2bNniMM39BgAAAO7n8qVr1eGgDQAAAICvBfo6AAAAAABwNwodAAAAAKZDoQMAAADAdCh0AAAAAJgOhQ4AAAAA06HQAQAAAGA6FDoAAAAATIdCBwAAAIDpUOgAAAAAMB0KHQAAAACmQ6EDAADQwM2fP1833HCDmjdvrlatWiklJUX79+93mOfSpUtKS0tTixYt1KxZM6WmpqqgoMBHEQOeR6EDAADQwOXk5CgtLU3bt29Xdna2bDabhg8frpKSEvs806dP13vvvafVq1crJydHJ06c0JgxY3wYNeBZjXwdAAAAAOpm48aNDtMrVqxQq1atlJeXp0GDBqmoqEjLly/XqlWrNHToUElSZmamunbtqu3bt+vGG2/0RdiAR1HoAAAAmExRUZEkKTIyUpKUl5cnm82mpKQk+zzx8fFq27atcnNzKy10rFarrFarfbq4uFiSZLPZZLPZHOYtn7YEGtXG9stl/UH5PvvjvkuSJcj556L8c1NZ/9Slzyh0AAAATKSsrEzTpk3TwIED1aNHD0lSfn6+QkJCFBER4TBvdHS08vPzK13P/PnzNWfOnArtmzZtUpMmTSpdZl6/smrj27BhQ7XzmFV2dravQ/CJ5/vXbL7K+ufixYu13i6FDuAneszOkrU0wOk8RxaM9FI0AABPSUtL0969e/XJJ5/UaT0zZsxQenq6fbq4uFhxcXEaPny4wsLCHOa12WzKzs7WzM8DZS1z/l2zd3ZyneJqiMr7Z9iwYQoODvZ1OF7XY3aW0/ctgYbm9SurtH/KzyTWBoUOAACASUyZMkXvv/++tm7dqjZt2tjbY2JidPnyZRUWFjqc1SkoKFBMTEyl67JYLLJYLBXag4ODqzxYt5YFVPujmj8e6Jdz1ndmVt1nolxl/VOX/uKpawAAAA2cYRiaMmWK1qxZo48++kgdOnRweL9v374KDg7W5s2b7W379+/XsWPHlJiY6O1wAa+g0AEA1Cs1GQ9kyJAhCggIcHj913/9l48iBnwvLS1NK1eu1KpVq9S8eXPl5+crPz9fP/74oyQpPDxckyZNUnp6uj7++GPl5eVp4sSJSkxM5IlrMC0uXQMA1Cvl44HccMMN+umnn/Tkk09q+PDh2rdvn5o2bWqfb/LkyZo7d659uqqbowF/sGTJEklXfgT4uczMTE2YMEGStGjRIgUGBio1NVVWq1XJycl66aWXvBwp4D0un9HZunWrRo0apdjYWAUEBGjt2rUO7xuGoaefflqtW7dW48aNlZSUpAMHDrgrXqDBYbRqwDUbN27UhAkT1L17d/Xu3VsrVqzQsWPHlJeX5zBfkyZNFBMTY3/98uZowJ8YhlHpq7zIkaTQ0FBlZGTo3LlzKikp0TvvvFPl/TmAGbh8RqekpES9e/fWfffdV+lous8//7z+8pe/6NVXX1WHDh00c+ZMJScna9++fQoNDXVL0EBDUpNfp6dPn67169dr9erVCg8P15QpUzRmzBh9+umnPo4e8L1fjgdS7h//+IdWrlypmJgYjRo1SjNnznR6VscTY4L465gY/j4mSHWc9Q99BniPy4XOiBEjNGLEiErfMwxDixcv1lNPPaXRo0dLkl577TVFR0dr7dq1uuuuu+oWLdAAMVo1UHuVjQciSXfffbfatWun2NhYffXVV3r88ce1f/9+vfPOO1WuyxNjgvjzeCCS/44JUlPuHhMEgGvceo/O4cOHlZ+f7zDqbnh4uBISEpSbm0uhA4jRqusbf/5lurqRqiXPjVZdU1WNB/LAAw/Y/92zZ0+1bt1at956qw4dOqRrrrmm0nV5YkwQfxwPRGJMkOo465+6jAkCwDVuLXTKR9aNjo52aHc26q4rB2zl7ZJ3LyeoycGAu7dZUzWJzdW4/PXAzxv7y2jV9Zc//jJd05GqJd/8Ml3VeCCVSUhIkCQdPHiwykLHE2OC+PtBvr+OCVJT7h4TBIBrfP7UtdocsEnevZygpgcDvjhIrElstY3L3w78vHE5AaNV1z/+/Mt0dSNVS54brdoZwzA0depUrVmzRlu2bKkwHkhldu/eLUlq3bq1R2ICADQ8bi10yp/cUVBQ4PBlU1BQoD59+lS6jCsHbJJvLieoycGAu7dZUzWJzdW43HHgV5/7rCqevpyA0arrN3/8ZbqmI1VL3v1lOi0tTatWrdK6devs44FIVy6Fbty4sQ4dOqRVq1bptttuU4sWLfTVV19p+vTpGjRokHr16uWRmAAADY9bC50OHTooJiZGmzdvthc2xcXF+uyzz/Tb3/620mVqc8AmefdygpoeDPjiIKkmsdU2rroc+NXnPquKp2Kp7tfpn49WnZqaKonRquHfqhsPJCQkRB9++KEWL16skpISxcXFKTU1VU899ZQPogUA1FcuFzoXLlzQwYMH7dOHDx/W7t27FRkZqbZt22ratGl65pln1KlTJ/vjpWNjY5WSkuLOuIEGo7pfp38+WnVkZKTCwsI0depURquG3zIM5/cexsXFKScnx0vRAAAaKpcLnc8//1y33HKLfbr8srPx48drxYoVeuyxx1RSUqIHHnhAhYWFuummm7Rx40bG0IHfYrRqAAAA73O50BkyZIjTX9sCAgI0d+5czZ07t06BAWZR3a/T0r9Hq87IyPBCRAAAAOYX6OsAAAAAAMDdKHQAAAAAmA6FDgAAAADTodABAAAAYDoUOgAAAABMh0IHAAAAgOlQ6AAAAAAwHZfH0QEAAPBX7Z9YX+08liBDz/f3QjAAnOKMDgAAAADTodABAAAAYDoUOgAAAABMh0IHAAAAgOlQ6AAAAAAwHQodAAAAAKZDoQMAAADAdCh0AAAAAJgOhQ4AAAAA06HQAQAAAGA6FDoAAAAATKeRrwMA3KX9E+urnefIgpFeiAQAAAC+5rEzOhkZGWrfvr1CQ0OVkJCgHTt2eGpTgCmQM4DryBvAdeQN/IVHCp0333xT6enpmjVrlnbt2qXevXsrOTlZp06d8sTmgAaPnAFcR94AriNv4E88UugsXLhQkydP1sSJE9WtWzctXbpUTZo00SuvvOKJzcFN2j+xXu2fWK8es7MkST1mZ9nbyl/wDHIGcB15A7iOvIE/cXuhc/nyZeXl5SkpKenfGwkMVFJSknJzc929OaDBI2cA15E3gOvIG/gbtz+M4MyZMyotLVV0dLRDe3R0tL799tsK81utVlmtVvt0UVGRJOncuXOy2WwV5rfZbLp48aIa2QJVWhZQZRxnz56t7S5U0OinkhrN585t1lRNYqtpXOXralRm6OLFskr72NV1uSs2d23T2fbOnz8vSTIMw20x1YSrOSO5ljc1zRnJN59hXyvvn7Nnzyo4ONjX4XhVTXKm/O9BZf3jq5yR6k/e+GPOSORNtfOYJG/4rnEff84Zqfq88VTO+Pypa/Pnz9ecOXMqtHfo0KFO6235pzot3mC2WRO1ietuN67LGW/3WU22d/78eYWHh3s+mDowU96g/qvq70G5hpAzkmfyhpxBVcyQN3zXwJs8kTNuL3RatmypoKAgFRQUOLQXFBQoJiamwvwzZsxQenq6fbqsrEznzp1TixYtFBBQ8ReB4uJixcXF6fjx4woLC3N3+JD/9rFhGDp//rxiY2O9ul1Xc0ZyLW/89f9nTdE/zjnrH1/ljETe+Br945xZ8oaccR/6xzlP5YzbC52QkBD17dtXmzdvVkpKiqQribF582ZNmTKlwvwWi0UWi8WhLSIiotrthIWF8UHxMH/sY1/8uuZqzki1yxt//P/pCvrHuar6x1e/SJM39QP941xDzxtyxv3oH+fcnTMeuXQtPT1d48ePV79+/dS/f38tXrxYJSUlmjhxoic2BzR45AzgOvIGcB15A3/ikULnzjvv1OnTp/X0008rPz9fffr00caNGyvc/AbgCnIGcB15A7iOvIE/8djDCKZMmVLl5QN1YbFYNGvWrAqnUuE+9LFvkDO+Qf84V9/7h7zxDfrHufreP57Im/q+z75G/zjnqf4JMHzxfEMAAAAA8CC3DxgKAAAAAL5GoQMAAADAdCh0AAAAAJgOhQ4AAAAA02lQhU5GRobat2+v0NBQJSQkaMeOHb4OyVRmz56tgIAAh1d8fLyvw0INuJobq1evVnx8vEJDQ9WzZ09t2LDBS5H6hiv9s2LFigp5EBoa6sVovWfr1q0aNWqUYmNjFRAQoLVr11a7zJYtW3T99dfLYrHo2muv1YoVKzwep6eQN86RN5Xz57whZ5wjZ6rmq7xpMIXOm2++qfT0dM2aNUu7du1S7969lZycrFOnTvk6NFPp3r27Tp48aX998sknvg4J1XA1N7Zt26axY8dq0qRJ+uKLL5SSkqKUlBTt3bvXy5F7R23+doSFhTnkwdGjR70YsfeUlJSod+/eysjIqNH8hw8f1siRI3XLLbdo9+7dmjZtmu6//35lZWV5OFL3I2+cI2+q5q95Q844R84457O8MRqI/v37G2lpafbp0tJSIzY21pg/f74PozKXWbNmGb179/Z1GHCRq7lxxx13GCNHjnRoS0hIMB588EGPxukrrvZPZmamER4e7qXo6g9Jxpo1a5zO89hjjxndu3d3aLvzzjuN5ORkD0bmGeSNc+RNzfhT3pAzzpEzNefNvGkQZ3QuX76svLw8JSUl2dsCAwOVlJSk3NxcH0ZmPgcOHFBsbKw6duyoe+65R8eOHfN1SHCiNrmRm5vrML8kJScnmzKXavu348KFC2rXrp3i4uI0evRoff31194It94zy2eHvHGOvHEvM3x2yBnnyBn3c9fnp0EUOmfOnFFpaamio6Md2qOjo5Wfn++jqMwnISFBK1as0MaNG7VkyRIdPnxYN998s86fP+/r0FCF2uRGfn6+3+RSbfqnS5cueuWVV7Ru3TqtXLlSZWVlGjBggL7//ntvhFyvVfXZKS4u1o8//uijqFxH3jhH3riXGfKGnHGOnHE/d+VNI3cHhtrbtm2bNm3apGnTpikiIsLr2x8xYoT937169VJCQoLatWunt956S5MmTfJ6PIAvJCYmKjEx0T49YMAAde3aVcuWLdO8efN8GBlQf5E3gGvIGe9oEGd0WrZsqaCgIBUUFDi0FxQUKCYmxkdRud+2bds0Z84cFRYW+joUSVJERIQ6d+6sgwcP+joUVKE2uRETE2P6XCrnjr8dwcHBuu6668gDVf3ZCQsLU+PGjX0UlevIG+fIG/cyQ96QM86RM+7nrrxpEIVOSEiI+vbtq82bN9vbysrKtHnzZodqGO514cIFHTp0SK1bt/Z1KKhCbXIjMTHRYX5Jys7ONmUuueNvR2lpqfbs2UMeyDyfHfLGOfLGvczw2SFnnCNn3M9tnx9Xn5TgK2+88YZhsViMFStWGPv27TMeeOABIyIiwsjPz/d1aG4xa9YsQ1KFV9u2bY1evXpVukznzp2N4cOHG4ZhGIcPHzYkGS+88IKxcOFCo23btkZoaKgxaNAgY8+ePRWW/eabb4zU1FTjqquuMiwWi9G3b19j9OjRxpYtW4zDhw8bn376qZGUlGS0bNnSOHXqlEf3HXVTXW7ce++9xhNPPGGf/9NPPzUaNWpk/PGPfzS++eYbY9asWUZwcHClnxMzcLV/5syZY2RlZRmHDh0y8vLyjLvuussIDQ01vv76a1/tgsecP3/e+OKLL4wvvvjCkGQsXLjQ+OKLL4yjR48ahmEYTzzxhHHvvffa5//uu++MJk2aGL///e+Nb775xsjIyDCCgoKMjRs3+moXao28cY68qZq/5g054xw545yv8qbBFDqGYRh//etfjbZt2xohISFG//79je3bt/s6JLf58ssvjbFjxxqSjEWLFhmvv/668frrrxuLFy82JFX4w7Bjxw5DkvHaa68ZhvHvQqdnz55G+/btjeeee86YM2eOERkZaURFRTkUhHv37jXCw8ONbt26Gc8995zx4osvGoMGDTIkGVdddZUREhJiXH311cadd95pHDx40Kv9gNpxlhuDBw82xo8f7zD/W2+9ZXTu3NkICQkxunfvbqxfv97LEXuXK/0zbdo0+7zR0dHGbbfdZuzatcsHUXvexx9/XOkPLOX9MX78eGPw4MEVlunTp48REhJidOzY0cjMzPR63O5C3jhH3lTOn/OGnHGOnKmar/ImwDAMw7VzQPCUP/7xj/r973+vw4cPq3379pKkoqIixcTE6JFHHtGCBQvs8z7yyCNavny5CgoK1LRpUx05ckQdOnRQ48aNdeDAAV199dWSpB07dighIUHTp0/XwoULJUlJSUk6deqUdu7cKYvFIkkyDEM33XSTTp8+rX/+85/e3XEAAADAzRrEPTr+LDw8XKNHj9b//M//qLwmLS0t1ZtvvqmUlBQ1bdrUYf6UlBR7kSNJ/fv3V0JCgjZs2CBJOnfunD766CPdcccdOn/+vM6cOaMzZ87o7NmzSk5O1oEDB/Svf/3LezsIAAAAeACFTgMwbtw4HTt2TP/3f/8nSfrwww9VUFCge++9t8K8nTp1qtDWuXNnHTlyRJJ08OBBGYahmTNnKioqyuE1a9YsSdKpU6c8tzMAAACAFzCOTgOQnJys6OhorVy5UoMGDdLKlSsVExNTYcTYmigrK5MkPfroo0pOTq50nmuvvbZO8QIAAAC+RqFTjwQEBFTaHhQUpLvvvlsrVqzQc889p7Vr12ry5MkKCgqqMO+BAwcqtP3zn/+03/PTsWNHSVee116bQgkAAABoCLh0rR4pv9+msgFD7733Xv3www968MEHdeHCBf3mN7+pdB1r1651uMdmx44d+uyzzzRixAhJUqtWrTRkyBAtW7ZMJ0+erLD86dOn3bAnAAAAgG/x1LV6ZOfOnerfv79uu+023XXXXQoODtaoUaPsBVDPnj21d+9ede3aVfv27XNYtvypaz179tT58+f129/+VlarVYsXL1ZAQIDDIFT79u3TTTfdpMDAQE2ePFkdO3ZUQUGBcnNz9f333+vLL7/0+r4DAAAA7sSla/XIDTfcoHnz5mnp0qXauHGjysrKdPjwYXuhM27cOD322GOVPoSg3Lhx4xQYGKjFixfr1KlT6t+/v1588UWHkXa7deumzz//XHPmzNGKFSt09uxZtWrVStddd52efvppj+8nAAAA4Gmc0WlA/vznP2v69Ok6cuSI2rZt6/Be+RmdF154QY8++qiPIgQAAADqB+7RaSAMw9Dy5cs1ePDgCkUOAAAAAEdculbPlZSU6N1339XHH3+sPXv2aN26db4OCQAAAKj3KHTqudOnT+vuu+9WRESEnnzySf3Hf/yHr0MCAAAA6j3u0QEAAABgOtyjAwAAAMB0KHQAAAAAmE69u0enrKxMJ06cUPPmzRUQEODrcOBHDMPQ+fPnFRsbq8BAfgMAAABoyOpdoXPixAnFxcX5Ogz4sePHj6tNmza+DgMAAAB1UO8KnebNm0u6crAZFhbm42ics9ls2rRpk4YPH67g4GBfh+NxZt/f4uJixcXF2T+DAAAAaLjqXaFTfrlaWFhYgyh0mjRporCwMFMe+P+Sv+wvl0wCAAA0fNyIAAAAAMB06lToLFiwQAEBAZo2bZq97dKlS0pLS1OLFi3UrFkzpaamqqCgoK5xAgAAAECN1brQ2blzp5YtW6ZevXo5tE+fPl3vvfeeVq9erZycHJ04cUJjxoypc6AAAAAAUFO1ukfnwoULuueee/Tyyy/rmWeesbcXFRVp+fLlWrVqlYYOHSpJyszMVNeuXbV9+3bdeOONbgm6/RPrq53nyIKRbtkWAAAAgIanVmd00tLSNHLkSCUlJTm05+XlyWazObTHx8erbdu2ys3NrVukAAAAAFBDLp/ReeONN7Rr1y7t3Lmzwnv5+fkKCQlRRESEQ3t0dLTy8/MrXZ/VapXVarVPFxcXS7ryhC+bzVbpMpYgo9o4q1rWncq34Y1t1Qdm31+z7hcAAIA/cqnQOX78uB555BFlZ2crNDTULQHMnz9fc+bMqdC+adMmNWnSpNJlnu9f/Xo3bNhQ19BqLDs722vbqg/Mur8XL170dQgAAABwkwDDMKo/PfL/rV27Vr/61a8UFBRkbystLVVAQIACAwOVlZWlpKQk/fDDDw5nddq1a6dp06Zp+vTpFdZZ2RmduLg4nTlzpspxdHrMzqo21r2zk2u6W7Vms9mUnZ2tYcOGmXpcmXJm39/i4mK1bNlSRUVF9X4MJwAAADjn0hmdW2+9VXv27HFomzhxouLj4/X4448rLi5OwcHB2rx5s1JTUyVJ+/fv17Fjx5SYmFjpOi0WiywWS4X24ODgKg+mraXVD+jozQNxZ7GakVn314z7BAAA4K9cKnSaN2+uHj16OLQ1bdpULVq0sLdPmjRJ6enpioyMVFhYmKZOnarExES3PXENAAAAAKpTq8dLO7No0SIFBgYqNTVVVqtVycnJeumll9y9GQAAAACoUp0LnS1btjhMh4aGKiMjQxkZGXVdNQAAAADUSq3G0QEAAACA+oxCBwAAAIDpUOgAAAAAMB0KHQAAAACmQ6EDAAAAwHQodAAAAACYDoUOAAAAANOh0AEAAABgOhQ6AAAAAEyHQgcAAACA6VDoAAAAADAdCh0AAAAApkOhAwAAAMB0KHQAAAAAmA6FDgAAAADTodABAAAAYDoUOgAAAABMh0IHAAAAgOlQ6AAAAAAwHQodAAAAAKZDoQMAAADAdCh0AAAAAJgOhQ4AAAAA06HQAQAAAGA6FDoAAAAATIdCBwAAAIDpUOgAAAAAMB0KHQAAAACmQ6EDAAAAwHQodAAAAACYDoUOAAAAANOh0AEAAABgOhQ6AAAAAEyHQgcAAACA6VDoAAAAADAdCh0AAAAApkOhAwAAAMB0KHQAAAAAmA6FDgAAAADTodABAAAAYDoUOgAAAABMh0IHAAAAgOlQ6AAAAAAwHQodAAAAAKZDoQMAAADAdCh0AAAAAJgOhQ4AAAAA03Gp0Jk/f75uuOEGNW/eXK1atVJKSor279/vMM+lS5eUlpamFi1aqFmzZkpNTVVBQYFbgwYAAAAAZ1wqdHJycpSWlqbt27crOztbNptNw4cPV0lJiX2e6dOn67333tPq1auVk5OjEydOaMyYMW4PHAAAAACq0siVmTdu3OgwvWLFCrVq1Up5eXkaNGiQioqKtHz5cq1atUpDhw6VJGVmZqpr167avn27brzxRvdFDgAAAABVqNM9OkVFRZKkyMhISVJeXp5sNpuSkpLs88THx6tt27bKzc2ty6YAAAAAoMZcOqPzc2VlZZo2bZoGDhyoHj16SJLy8/MVEhKiiIgIh3mjo6OVn59f6XqsVqusVqt9uri4WJJks9lks9kqXcYSZFQbX1XLulP5NryxrfrA7Ptr1v0CAADwR7UudNLS0rR371598skndQpg/vz5mjNnToX2TZs2qUmTJpUu83z/6te7YcOGOsXliuzsbK9tqz4w6/5evHjR1yEAAADATWpV6EyZMkXvv/++tm7dqjZt2tjbY2JidPnyZRUWFjqc1SkoKFBMTEyl65oxY4bS09Pt08XFxYqLi9Pw4cMVFhZW6TI9ZmdVG+Pe2ck12pe6rMtmsyk7O1vDhg1TcHBwjbbXkJl9f8vPJgIAAKDhc6nQMQxDU6dO1Zo1a7RlyxZ16NDB4f2+ffsqODhYmzdvVmpqqiRp//79OnbsmBITEytdp8VikcViqdAeHBxc5cG0tTSg2lhreiDujnU5i9WMzLq/ZtwnAAAAf+VSoZOWlqZVq1Zp3bp1at68uf2+m/DwcDVu3Fjh4eGaNGmS0tPTFRkZqbCwME2dOlWJiYk8cQ0AAACA17hU6CxZskSSNGTIEIf2zMxMTZgwQZK0aNEiBQYGKjU1VVarVcnJyXrppZfcEiwAAAAA1ITLl65VJzQ0VBkZGcrIyKh1UAAAAABQF3UaRwcAAAAA6iMKHQAAAACmQ6EDAAAAwHQodAAAAACYDoUOAAAAANOh0AEAAABgOhQ6AAAAAEyHQgcAAACA6VDoAAAAADAdCh0AAAAApkOhAwAAAMB0KHQAAAAAmA6FDgAAAADTodABAAAAYDqNfB0A6qb9E+urnefIgpFeiAQAAACoPzijAwAAAMB0KHQAAAAAmA6FDgAAAADTodABAAAAYDoUOgAAAABMh0IHAAAAgOlQ6AAAAAAwHcbRgWkwphAAAADKcUYHAAAAgOlQ6AAAAAAwHQodAAAAAKZDoQMAAADAdCh0AAAAAJgOT12DT/GkNAAAAHgCZ3QAAAAAmA6FDgAAAADTodABAAAAYDoUOgAAAABMh0IHAAAAgOlQ6AAAAAAwHQodAAAAAKZDoQMAAADAdBgw1MtqMkCmxCCZAAAAQF1wRgcAAACA6VDoAAAAADAdLl2Dy3rMzpK1NMDpPFx6BwAAAF/ijA4AAAAA06HQAQAAAGA6FDoAAAAATIdCBwAAAIDpUOgAAAAAMB0KHQAAAACm47FCJyMjQ+3bt1doaKgSEhK0Y8cOT20KAAAAABx4pNB58803lZ6erlmzZmnXrl3q3bu3kpOTderUKU9sDgAAAAAceKTQWbhwoSZPnqyJEyeqW7duWrp0qZo0aaJXXnnFE5sDAAAAAAeN3L3Cy5cvKy8vTzNmzLC3BQYGKikpSbm5uRXmt1qtslqt9umioiJJ0rlz52Sz2SrdRqOfSqqN4+zZszWKty7rstlsunjxos6ePavg4GC3bc/ZNmuzvpquqzrl+9vIFqjSsgC3bLO+/L+UpPPnz0uSDMOo0fYAAABQfwUYbj6qO3HihK6++mpt27ZNiYmJ9vbHHntMOTk5+uyzzxzmnz17tubMmePOEIA6OX78uNq0aePrMAAAAFAHbj+j46oZM2YoPT3dPl1WVqZz586pRYsWCghwftbA14qLixUXF6fjx48rLCzM1+F4nNn31zAMnT9/XrGxsb4OBQAAAHXk9kKnZcuWCgoKUkFBgUN7QUGBYmJiKsxvsVhksVgc2iIiItwdlkeFhYWZ8sC/Kmbe3/DwcF+HAAAAADdw+8MIQkJC1LdvX23evNneVlZWps2bNztcygYAAAAAnuKRS9fS09M1fvx49evXT/3799fixYtVUlKiiRMnemJzAAAAAODAI4XOnXfeqdOnT+vpp59Wfn6++vTpo40bNyo6OtoTm/MZi8WiWbNmVbj0zqz8bX8BAADQcLn9qWsAAAAA4GseGTAUAAAAAHyJQgcAAACA6VDoAAAAADAdCh0AAAAApkOh48T8+fN1ww03qHnz5mrVqpVSUlK0f/9+p8usWLFCAQEBDq/Q0FAvRVw3s2fPrhB7fHy802VWr16t+Ph4hYaGqmfPntqwYYOXogUAAACqRqHjRE5OjtLS0rR9+3ZlZ2fLZrNp+PDhKikpcbpcWFiYTp48aX8dPXrUSxHXXffu3R1i/+STT6qcd9u2bRo7dqwmTZqkL774QikpKUpJSdHevXu9GDEAAABQEY+XdsHp06fVqlUr5eTkaNCgQZXOs2LFCk2bNk2FhYXeDc4NZs+erbVr12r37t01mv/OO+9USUmJ3n//fXvbjTfeqD59+mjp0qUeihIAAACoHmd0XFBUVCRJioyMdDrfhQsX1K5dO8XFxWn06NH6+uuvvRGeWxw4cECxsbHq2LGj7rnnHh07dqzKeXNzc5WUlOTQlpycrNzcXE+HCQAAADhFoVNDZWVlmjZtmgYOHKgePXpUOV+XLl30yiuvaN26dVq5cqXKyso0YMAAff/9916MtnYSEhK0YsUKbdy4UUuWLNHhw4d188036/z585XOn5+fr+joaIe26Oho5efneyNcAAAAoEqNfB1AQ5GWlqa9e/c6vWdFkhITE5WYmGifHjBggLp27aply5Zp3rx5ng6zTkaMGGH/d69evZSQkKB27drprbfe0qRJk3wYGQAAAOAaCp0amDJlit5//31t3bpVbdq0cWnZ4OBgXXfddTp48KCHovOciIgIde7cucrYY2JiVFBQ4NBWUFCgmJgYb4QHAAAAVIlL15wwDENTpkzRmjVr9NFHH6lDhw4ur6O0tFR79uxR69atPRChZ124cEGHDh2qMvbExERt3rzZoS07O9vhjBYAAADgCxQ6TqSlpWnlypVatWqVmjdvrvz8fOXn5+vHH3+0zzNu3DjNmDHDPj137lxt2rRJ3333nXbt2qXf/OY3Onr0qO6//35f7IJLHn30UeXk5OjIkSPatm2bfvWrXykoKEhjx46VVHFfH3nkEW3cuFF/+tOf9O2332r27Nn6/PPPNWXKFF/tAgAAACCJS9ecWrJkiSRpyJAhDu2ZmZmaMGGCJOnYsWMKDPx3vfjDDz9o8uTJys/P11VXXaW+fftq27Zt6tatm7fCrrXvv/9eY8eO1dmzZxUVFaWbbrpJ27dvV1RUlKSK+zpgwACtWrVKTz31lJ588kl16tRJa9eudfqwBgAAAMAbGEcHAAAAgOlw6RoAAAAA06HQAQAAAGA6FDoAAAAATIdCBwAAAIDpUOgAAAAAMB0KHQAAAACmQ6EDAAAAwHQodAAAAACYDoUOAAAAANOh0AEAAABgOhQ6AAAAAEyHQgcAAACA6fw/HOKIGr6fhe8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = zoo.iloc[:,1:].corr()\n",
        "plt.figure(figsize = (10,8))\n",
        "sns.heatmap(correlation_matrix ,cmap = 'coolwarm' , annot = True,fmt = '.2f')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "HaQop43tfjBi",
        "outputId": "5e21b2b0-f795-43bd-c115-8a2454e21037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "_822Xfhnfipm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "zoo['type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufc8zwUOfpI2",
        "outputId": "3b109a9c-4ee2-419a-ab58-b8992e25b27e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4, 2, 7, 6, 5, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_outliers(data):\n",
        "  numerical_columns = data.select_dtypes(include = {'int64','float64'})\n",
        "  for i in numerical_columns:\n",
        "    Q1 = data[i].quantile(0.25)\n",
        "    Q3 = data[i].quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "    UL = Q3 + (1.5 * IQR)\n",
        "    LL = Q1 - (1.5 * IQR)\n",
        "    data = data[(data[i] >= LL) & (data[i] <= UL)]\n",
        "  return data\n",
        "\n",
        "out_data = remove_outliers(zoo)\n",
        "out_data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "collapsed": true,
        "id": "P_TjXrfnfx9U",
        "outputId": "7c4df277-c22b-41ef-c982-482d1183f09a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   animal name  hair  feathers  eggs  milk  airborne  aquatic  predator  \\\n",
              "1     antelope     1         0     0     1         0        0         0   \n",
              "4         boar     1         0     0     1         0        0         1   \n",
              "5      buffalo     1         0     0     1         0        0         0   \n",
              "10     cheetah     1         0     0     1         0        0         1   \n",
              "17        deer     1         0     0     1         0        0         0   \n",
              "22    elephant     1         0     0     1         0        0         0   \n",
              "28     giraffe     1         0     0     1         0        0         0   \n",
              "36        hare     1         0     0     1         0        0         0   \n",
              "44     leopard     1         0     0     1         0        0         1   \n",
              "45        lion     1         0     0     1         0        0         1   \n",
              "47        lynx     1         0     0     1         0        0         1   \n",
              "48        mink     1         0     0     1         0        1         1   \n",
              "49        mole     1         0     0     1         0        0         1   \n",
              "50    mongoose     1         0     0     1         0        0         1   \n",
              "54     opossum     1         0     0     1         0        0         1   \n",
              "55        oryx     1         0     0     1         0        0         0   \n",
              "64     polecat     1         0     0     1         0        0         1   \n",
              "67        puma     1         0     0     1         0        0         1   \n",
              "69     raccoon     1         0     0     1         0        0         1   \n",
              "94        vole     1         0     0     1         0        0         0   \n",
              "98        wolf     1         0     0     1         0        0         1   \n",
              "\n",
              "    toothed  backbone  breathes  venomous  fins  legs  tail  domestic  \\\n",
              "1         1         1         1         0     0     4     1         0   \n",
              "4         1         1         1         0     0     4     1         0   \n",
              "5         1         1         1         0     0     4     1         0   \n",
              "10        1         1         1         0     0     4     1         0   \n",
              "17        1         1         1         0     0     4     1         0   \n",
              "22        1         1         1         0     0     4     1         0   \n",
              "28        1         1         1         0     0     4     1         0   \n",
              "36        1         1         1         0     0     4     1         0   \n",
              "44        1         1         1         0     0     4     1         0   \n",
              "45        1         1         1         0     0     4     1         0   \n",
              "47        1         1         1         0     0     4     1         0   \n",
              "48        1         1         1         0     0     4     1         0   \n",
              "49        1         1         1         0     0     4     1         0   \n",
              "50        1         1         1         0     0     4     1         0   \n",
              "54        1         1         1         0     0     4     1         0   \n",
              "55        1         1         1         0     0     4     1         0   \n",
              "64        1         1         1         0     0     4     1         0   \n",
              "67        1         1         1         0     0     4     1         0   \n",
              "69        1         1         1         0     0     4     1         0   \n",
              "94        1         1         1         0     0     4     1         0   \n",
              "98        1         1         1         0     0     4     1         0   \n",
              "\n",
              "    catsize  type  \n",
              "1         1     1  \n",
              "4         1     1  \n",
              "5         1     1  \n",
              "10        1     1  \n",
              "17        1     1  \n",
              "22        1     1  \n",
              "28        1     1  \n",
              "36        0     1  \n",
              "44        1     1  \n",
              "45        1     1  \n",
              "47        1     1  \n",
              "48        1     1  \n",
              "49        0     1  \n",
              "50        1     1  \n",
              "54        0     1  \n",
              "55        1     1  \n",
              "64        1     1  \n",
              "67        1     1  \n",
              "69        1     1  \n",
              "94        0     1  \n",
              "98        1     1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d8188d55-fec4-4ee6-ac5f-76621e608c92\" 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>animal name</th>\n",
              "      <th>hair</th>\n",
              "      <th>feathers</th>\n",
              "      <th>eggs</th>\n",
              "      <th>milk</th>\n",
              "      <th>airborne</th>\n",
              "      <th>aquatic</th>\n",
              "      <th>predator</th>\n",
              "      <th>toothed</th>\n",
              "      <th>backbone</th>\n",
              "      <th>breathes</th>\n",
              "      <th>venomous</th>\n",
              "      <th>fins</th>\n",
              "      <th>legs</th>\n",
              "      <th>tail</th>\n",
              "      <th>domestic</th>\n",
              "      <th>catsize</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>antelope</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>boar</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>buffalo</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>cheetah</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>deer</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>elephant</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>giraffe</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>hare</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>leopard</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>lion</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>lynx</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>mink</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>mole</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>mongoose</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>opossum</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>oryx</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>polecat</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>puma</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>raccoon</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>vole</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>wolf</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8188d55-fec4-4ee6-ac5f-76621e608c92')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d8188d55-fec4-4ee6-ac5f-76621e608c92 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-d8188d55-fec4-4ee6-ac5f-76621e608c92');\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",
              "\n",
              "\n",
              "<div id=\"df-90e63a70-7471-470f-a0ba-ca82bb2f1dbd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-90e63a70-7471-470f-a0ba-ca82bb2f1dbd')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-90e63a70-7471-470f-a0ba-ca82bb2f1dbd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fcdfee21-d0be-4e7c-bb4d-4f1b42b18dc2\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('out_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_fcdfee21-d0be-4e7c-bb4d-4f1b42b18dc2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('out_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "out_data",
              "summary": "{\n  \"name\": \"out_data\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"animal name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"antelope\",\n          \"puma\",\n          \"oryx\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hair\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feathers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eggs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"milk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airborne\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"aquatic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toothed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"backbone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"breathes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venomous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"legs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 4,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tail\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"domestic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"catsize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(len(out_data) / len(zoo)) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pzuyWaL0h33L",
        "outputId": "09121b74-6f24-45a9-e684-c9d2d6f1c36a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.792079207920793"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(out_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OBlwPQmieO8",
        "outputId": "6dd36103-194f-4b21-b98e-8503c83fb4d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(zoo)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3qi4fQyir21",
        "outputId": "26c778d7-c6bd-49bf-82a0-f8b7873de924"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "101"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(98 / 101)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqIEu3ZciRXP",
        "outputId": "f76b0be4-5d53-4d21-af9f-388cd7bb6aeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9702970297029703"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = zoo.iloc[:,1:-1]\n",
        "y = zoo.iloc[:,-1]"
      ],
      "metadata": {
        "id": "I_4hPr0mkwgg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "collapsed": true,
        "id": "0J99LnBLlDRM",
        "outputId": "2454f580-fa3f-4840-f4b5-f7b9c3da49b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     hair  feathers  eggs  milk  airborne  aquatic  predator  toothed  \\\n",
              "0       1         0     0     1         0        0         1        1   \n",
              "1       1         0     0     1         0        0         0        1   \n",
              "2       0         0     1     0         0        1         1        1   \n",
              "3       1         0     0     1         0        0         1        1   \n",
              "4       1         0     0     1         0        0         1        1   \n",
              "..    ...       ...   ...   ...       ...      ...       ...      ...   \n",
              "96      1         0     0     1         0        0         0        1   \n",
              "97      1         0     1     0         1        0         0        0   \n",
              "98      1         0     0     1         0        0         1        1   \n",
              "99      0         0     1     0         0        0         0        0   \n",
              "100     0         1     1     0         1        0         0        0   \n",
              "\n",
              "     backbone  breathes  venomous  fins  legs  tail  domestic  catsize  \n",
              "0           1         1         0     0     4     0         0        1  \n",
              "1           1         1         0     0     4     1         0        1  \n",
              "2           1         0         0     1     0     1         0        0  \n",
              "3           1         1         0     0     4     0         0        1  \n",
              "4           1         1         0     0     4     1         0        1  \n",
              "..        ...       ...       ...   ...   ...   ...       ...      ...  \n",
              "96          1         1         0     0     2     1         0        1  \n",
              "97          0         1         1     0     6     0         0        0  \n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hair</th>\n",
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              "      <th>2</th>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <th>4</th>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>101 rows × 16 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 101,\n  \"fields\": [\n    {\n      \"column\": \"hair\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feathers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eggs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"milk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airborne\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"aquatic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"toothed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"backbone\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"breathes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"venomous\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"legs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tail\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"domestic\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"catsize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train , x_test , y_train , y_test = train_test_split(x,y ,test_size = 0.2 , random_state = 0)\n"
      ],
      "metadata": {
        "id": "Fe0-lV5pkauK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import KFold ,cross_val_score,GridSearchCV"
      ],
      "metadata": {
        "id": "bn-gCSNMoX9G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_neighbors = np.array(range(0,80))\n",
        "parameters = dict(n_neighbors = n_neighbors)\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48YHnj7ooHja",
        "outputId": "47f478cb-463c-4120-e921-209580570889"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
              "        51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
              "        68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79])}"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "Qb2hCqlrpIjp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "KNN = KNeighborsClassifier()\n",
        "grid = GridSearchCV(estimator = KNN , param_grid = parameters)\n",
        "grid.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "BTXAeLiKlO5g",
        "outputId": "6869c882-517a-4017-dfa9-e7eb05af079f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={'n_neighbors': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
              "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
              "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79])})"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
              "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
              "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
              "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
              "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = grid.predict(x_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oqa9ISE5tmqM",
        "outputId": "5269ce4a-3566-4bff-a5d6-7c76a4e56f22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 4, 4, 1, 1, 1, 2, 4, 1, 1, 7, 1, 2, 7, 4, 6, 1, 6, 2, 4, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYPOEV3ylwJD",
        "outputId": "b1aa0ab6-ae0c-42f2-cd98-f14db7f604fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.95"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oB7l_u4tqNQS",
        "outputId": "a0ec26e1-abfe-4776-c3fe-62613628d62c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "--------------------------------------*------------------------------------------"
      ],
      "metadata": {
        "id": "oX68VNBPqo6y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kfold = KFold(n_splits = 4)\n",
        "KNN = KNeighborsClassifier(n_neighbors = 1)\n",
        "results = cross_val_score(KNN  ,x , y, cv = kfold)\n",
        "print(results)\n",
        "print(results.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vsT-lQLkqq-m",
        "outputId": "ba5e54b6-46b9-4fbe-e7cd-8be3975e09ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.   1.   0.96 0.88]\n",
            "0.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate the classifier's performance on the testing set using accuracy, precision, recall, and F1-score metrics."
      ],
      "metadata": {
        "id": "iUyEKTiQs4qj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score , precision_score , recall_score , f1_score\n",
        "\n",
        "accuracy_score = accuracy_score(y_test,y_pred)\n",
        "print('accuracy_score : ',accuracy_score)\n",
        "\n",
        "precision_score = precision_score(y_test,y_pred , average = 'macro')\n",
        "print('precision_score' , precision_score)\n",
        "\n",
        "recall_score = recall_score(y_test, y_pred , average = 'macro')\n",
        "print('recall score : ' , recall_score)\n",
        "\n",
        "f1_score = f1_score(y_test, y_pred , average = 'macro')\n",
        "print('f1_score : ' , f1_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9WV29Fpos50d",
        "outputId": "a5aef912-63f2-450c-ce32-600232d1daa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy_score :  1.0\n",
            "precision_score 1.0\n",
            "recall score :  1.0\n",
            "f1_score :  1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the model performance is absoultely good"
      ],
      "metadata": {
        "id": "njCcv0A2AFFo"
      }
    }
  ]
}