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    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bU8xsnpmdNrU",
        "outputId": "d34ffaf7-20c0-4c7b-c170-4ac9a1671d18"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "FM2lR-hPdi7s"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "for root, dirs, files in os.walk('/content/drive/MyDrive'):\n",
        "    if 'Iris.csv' in files:\n",
        "        print(os.path.join(root, 'Iris.csv'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVJ5K6ySd0jo",
        "outputId": "859aa8fa-21a9-4ebc-ecc4-142e401baaee"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/ML Maya Assignment/Iris.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/drive/MyDrive/ML Maya Assignment/Iris.csv\"\n",
        "\n",
        "df = pd.read_csv(path)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NMYBlVYfeLp-",
        "outputId": "588ea6eb-31f1-4df8-de95-844e3205c756"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
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              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
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            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(\"Id\",axis=1)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "8abpUoore_Bm",
        "outputId": "5c44ddcb-e0e5-4af1-948a-fed81d695e72"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4            5.0           3.6            1.4           0.2  Iris-setosa"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
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        "df.head(10)"
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      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4            5.0           3.6            1.4           0.2  Iris-setosa\n",
              "5            5.4           3.9            1.7           0.4  Iris-setosa\n",
              "6            4.6           3.4            1.4           0.3  Iris-setosa\n",
              "7            5.0           3.4            1.5           0.2  Iris-setosa\n",
              "8            4.4           2.9            1.4           0.2  Iris-setosa\n",
              "9            4.9           3.1            1.5           0.1  Iris-setosa"
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              "      <td>Iris-setosa</td>\n",
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              "      <th>1</th>\n",
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              "      <th>6</th>\n",
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              "      <td>Iris-setosa</td>\n",
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              "      <th>8</th>\n",
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              "      <th>9</th>\n",
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              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 63
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      "cell_type": "code",
      "source": [
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      "metadata": {
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          "height": 300
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        "id": "ozwTuTaWfE8P",
        "outputId": "cec70906-3f04-4272-c2f2-cbd1f987b332"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count     150.000000    150.000000     150.000000    150.000000\n",
              "mean        5.843333      3.054000       3.758667      1.198667\n",
              "std         0.828066      0.433594       1.764420      0.763161\n",
              "min         4.300000      2.000000       1.000000      0.100000\n",
              "25%         5.100000      2.800000       1.600000      0.300000\n",
              "50%         5.800000      3.000000       4.350000      1.300000\n",
              "75%         6.400000      3.300000       5.100000      1.800000\n",
              "max         7.900000      4.400000       6.900000      2.500000"
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              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
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              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.763161</td>\n",
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              "      <th>min</th>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
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              "\n",
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              "        document.querySelector('#df-ac52b380-ecc9-41d1-9bbf-f4ce5a81e3a3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 64
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    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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      "metadata": {
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          "height": 300
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        "id": "3B68_xkofIKC",
        "outputId": "bd23e475-4928-4551-a137-64ffb41d7ea7"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count     150.000000    150.000000     150.000000    150.000000\n",
              "mean        5.843333      3.054000       3.758667      1.198667\n",
              "std         0.828066      0.433594       1.764420      0.763161\n",
              "min         4.300000      2.000000       1.000000      0.100000\n",
              "25%         5.100000      2.800000       1.600000      0.300000\n",
              "50%         5.800000      3.000000       4.350000      1.300000\n",
              "75%         6.400000      3.300000       5.100000      1.800000\n",
              "max         7.900000      4.400000       6.900000      2.500000"
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              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>3.054000</td>\n",
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              "      <td>1.198667</td>\n",
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              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Species\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "HoyqbQ5SfQCV",
        "outputId": "dd8c2283-62dc-4818-9100-1cd0bcfaac7c"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Species\n",
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "Name: count, dtype: int64"
            ],
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              "      <th>Species</th>\n",
              "      <th></th>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "execution_count": 66
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      "execution_count": 67,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "SepalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "PetalWidthCm     0\n",
              "Species          0\n",
              "dtype: int64"
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      "source": [
        "df.hist(figsize=(10,8))\n",
        "\n",
        "plt.show()"
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          "height": 699
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        "id": "ac1X6soffVDo",
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      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colors={\n",
        "    \"Iris-setosa\":\"blue\",\n",
        "    \"Iris-versicolor\":\"orange\",\n",
        "    \"Iris-virginica\":\"red\"\n",
        "}\n",
        "\n",
        "plt.figure(figsize=(6,5))\n",
        "\n",
        "for species,color in colors.items():\n",
        "    subset=df[df[\"Species\"]==species]\n",
        "\n",
        "    plt.scatter(subset[\"SepalLengthCm\"],\n",
        "                subset[\"SepalWidthCm\"],\n",
        "                color=color,\n",
        "                label=species)\n",
        "\n",
        "plt.xlabel(\"Sepal Length\")\n",
        "plt.ylabel(\"Sepal Width\")\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "k94bBkYmfbwT",
        "outputId": "1743a1c1-1f77-49ac-ce51-09a86ef88427"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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Sgv79+2PNmjUAgDVr1iApKcnYX1lZGYYPH27yvpycHJw5cwZ9+/atNS6dTodTp04hPT0dPj4+mDp1Kvr27Ytbt27ViNnHx6de26Kh7H13Cy+XENlRZiaQnAxcuON/EMPCgDfeAEaOdFxcRDX4hNq2nWABAQEYM2YMxowZg1GjRmHw4MH49ddfERQUhKCgoBrtk5KSMHv2bIwbNw7nzp3D2LFjja/FxsZi48aNiIiIgLu7vJ9JHx8fDB8+HMOHD8e0adPQvn17nDhxArGxsSbtunbtigsXLphcfrlThw4dkJ2dbbIsOzsb0dHRxnEb1dsXFBSgoKDAeDbj+++/R1FRETp27ChrHWyJZzKI7CQzExg1yjTBAICLFw3LM+0zho7IOs37AL5hADS1NNAAvjpDOwdbunQpPvzwQ/zwww84ffo01q9fj5CQEDRq1KjW94wcORKlpaV49tln0a9fP7Ro0cL42rRp0/Drr79i3LhxOHToEHJzc7Fz505MmjTJ7KWKKhkZGfi///s/fPvttzh37hxWrVoFHx8fs2NE4uLi0LdvXzz66KPYtWsX8vLy8Omnn2LHjh0AgL/+9a/Ys2cPFi1ahNOnT2PlypVYvnw5nnvuObN/OyEhAV26dEFSUhKOHj2Kr7/+GuPHj0dcXBx69Ohh5Za0PSYZRHag1xvOYEhmxtBVLZs5k5dOSEHctED3N35/Uj3R+P1592UOmy/jTv7+/khLS0OPHj3Qs2dPnD9/Htu3b4ebW+0/cf7+/hg+fDiOHz9ucqkEAFq0aIHs7Gzo9Xo8+OCD6NKlC2bOnIlGjRpZ7LNRo0Z45513cP/996Nr167YvXs3tm3bhqZNm5ptv3HjRvTs2RPjxo1Dx44dMXv2bGMSExsbi3Xr1mHt2rXo3Lkz/vd//xcLFy40O+gTMNzSu2XLFjRu3Bh9+/ZFQkIC2rRpg48++qiOrSeWRpLMfe05r5KSEgQGBqK4uBgBAQGODodcxL59QL9+dbfbuxeIjxcdDbmC3377DXl5eQ2fD6Eg03CXyZ2DQH11hgRDx2t8zsjSviP3N5RjMojsoNDKsXHWtiOyG91Iw22qCpnxk9SFSQaRHYRaOTbO2nZEduWmBYLjHR0FqRDHZBDZQZ8+hrtINLWModNoAJ3O0I6IyFkwySCyA63WcJsqUDPRqHq+bJmhHRGRs2CSQWQnI0cCGzYALVuaLg8LMyznPBlE5Gw4JoPIjkaOBBITgawswyDP0FDDJRKewSAiZ8Qkg8jOtFrepkpEroGXS4iIiEgIJhlEREQkBJMMIiJSHY1Gg82bNwvpe9++fdBoNCgqKmpwX3LjzMjIsFhzRW04JoNcgl7PwZZE9WbnA2jixIkoKiqy+ONcWFiIxo0bC/n7vXv3RmFhIQIDAxvcl9w4x4wZg6FDhzb47yoFkwxyeiyvTtQACjuAKioq4OnpiZCQEGF/o67+9Xo9NBqNxWJpVeTG6ePjAx8fH1nvUTJeLiGnxvLqRA2ggAMoPj4e06dPx8yZM9GsWTMMGjQIgOlliIqKCkyfPh2hoaHw9vZGeHg4UlNTzfZ3+vRpaDQa/PDDDybLX3/9dURGRgKoebmk6hLG1q1b0bFjR3h5eSE/Px+FhYUYNmwYfHx80Lp1a6xZswYRERFYtmyZsd874zx//jw0Gg0yMzPRr18/+Pr64u6778aBAweM7c1dLtm2bRt69uwJb29vNGvWDI888ojxtf/85z/o0aMH/P39ERISgscffxxXrlyRu5mFYZJBTovl1YkaQEEH0MqVK+Hp6Yns7Gz8+9//rvH6m2++ia1bt2LdunU4deoUVq9ejYiICLN9RUdHo0ePHli9erXJ8tWrV+Pxxx+vNYby8nIsXrwY7777Lr777jsEBQVh/Pjx+Omnn7Bv3z5s3LgRb7/9tlU/8H//+9/x3HPPIScnB9HR0Rg3bhxu375ttu0nn3yCRx55BEOHDsWxY8ewZ88e3HPPPcbXb926hUWLFuH48ePYvHkzzp8/X2s5eEfg5RJyWllZNf8H7E6SBBQUGNpx3gqiahR0AEVFRSEtLa3W1/Pz8xEVFYUHHngAGo0G4eHhFvtLSkrC8uXLsWjRIgCGsxtHjhzBqlWran3PrVu3kJ6ejrvvvhsA8MMPP2D37t04dOgQevToAQB49913ERUVVef6PPfccxg2bBgAYMGCBejUqRPOnj2L9u3b12j78ssvY+zYsViwYIFxWVUMAPCnP/3J+O82bdrgzTffRM+ePVFWVgY/P786YxGNZzLIabG8OlEDKOgA6t69u8XXJ06ciJycHLRr1w4zZszAZ599ZnztmWeegZ+fn/EBAGPHjsX58+dx8OBBAIazGLGxsWZ/5Kt4enqia9euxuenTp2Cu7s7YmNjjcvatm1r1SDPO/sJ/b30cm1nQHJycjBgwIBa+zpy5AiGDx+OVq1awd/fH3FxcQAMiZcSMMkgp8Xy6kQNoKAD6K677rL4emxsLPLy8rBo0SLcuHEDo0ePxqhRowAACxcuRE5OjvEBGAZj9u/fH2vWrAEArFmzBklJSRb/ho+PDzS1lVGWycPDw/jvqj4rKytr/bu1uX79OgYNGoSAgACsXr0ahw4dwqZNmwAYxqkoAZMMclosr07UACo7gAICAjBmzBi88847+Oijj7Bx40b8+uuvCAoKQtu2bY2PKklJSfjoo49w4MABnDt3DmPHjpX199q1a4fbt2/j2LFjxmVnz57FtWvXbLZOgOGsx549e8y+9sMPP+Dq1at49dVX0adPH7Rv315Rgz4BJhnkxFhenagBVHQALV26FB9++CF++OEHnD59GuvXr0dISIjFSa1GjhyJ0tJSPPvss+jXrx9atGgh62+2b98eCQkJePrpp/H111/j2LFjePrpp216xgMA5s2bhw8//BDz5s3DyZMnceLECSxevBgA0KpVK3h6euKf//wnzp07h61btxrHmSgFkwxyaiyvTtQAKjmA/P39kZaWhh49eqBnz544f/48tm/fbnEeC39/fwwfPhzHjx+v81JJbT744AMEBwejb9++eOSRR/DUU0/B398f3t7e9V2VGuLj47F+/Xps3boV3bp1Q//+/fH1118DAJo3b46MjAysX78eHTt2xKuvvorXXnvNZn/bFjSSZO7+JOdVUlKCwMBAFBcXIyAgwNHhkJ1wxk9yNb/99hvy8vLQunXrhv/o8QCyyoULF6DT6bB7926LgzWVztK+I/c3lLewkktgeXWiBuABZNbnn3+OsrIydOnSBYWFhZg9ezYiIiLQt29fR4emGEwyiIiI6uHWrVv429/+hnPnzsHf3x+9e/fG6tWrTe4ecXVMMoiIiOph0KBBxmnOyTwO/CQiIiIheCaDyIVxPJ/zc7Gx/WQDttxnFHMm49VXX4VGo8HMmTMttlu/fj3at28Pb29vdOnSBdu3b7dPgEROJjMTiIgA+vUDHn/c8N+ICFamdRZV4wLKy8sdHAmpTdU+Y4uxJYo4k3Ho0CGsWLHCZD53c7788kuMGzcOqampeOihh7BmzRqMGDECR48eRefOne0ULZH6VVXwrv4/LFUVvBU0BQLVk1arRaNGjYwzQPr6+tp0kihyPpIkoby8HFeuXEGjRo2gtcFpTYfPk1FWVobY2Fikp6fjpZdeQrdu3bBs2TKzbceMGYPr16/j448/Ni6777770K1bN7Plf83hPBnk6vR6wxmL2gpsajSGuZby8njpRO0kScKlS5dQVFTk6FBIRRo1aoSQkBCzSanq5smYNm0ahg0bhoSEBLz00ksW2x44cACzZs0yWTZo0CBs3ry51vfcvHkTN2/eND4vKSlpULxEaqegCt4kmEajQWhoKIKCgnDr1i1Hh0Mq4OHhYZMzGFUcmmSsXbsWR48exaFDh6xqf+nSJQQHB5ssCw4OxqVLl2p9T2pqKhYsWNCgOImciYIqeJOdaLVam/5wEFnLYQM/CwoKkJycjNWrV9t0nvfq5s6di+LiYuOjoKBA2N8iUgMFVfAmIifnsDMZR44cwZUrVxAbG2tcptfr8d///hfLly/HzZs3a2TeISEhuHz5ssmyy5cvIyQkpNa/4+XlBS8vL9sGT6RiVRW8L16sOfAT+GNMhkIqeBORijnsTMaAAQNw4sQJ5OTkGB89evRAUlIScnJyzJ7a69WrF/bs2WOybNeuXejVq5e9wiZSPRVV8CYilXPYmQx/f/8at53eddddaNq0qXH5+PHj0bJlS6SmpgIAkpOTERcXh3/84x8YNmwY1q5di8OHD+Ptt9+2e/xEalZVwTs52XQQaFiYIcHg7atEZAsOv7vEkvz8fLi5/XGypXfv3lizZg1eeOEF/O1vf0NUVBQ2b97MOTKI6mHkSCAxkTN+EpE4Dp8nw944TwYREVH9yP0NVcy04kRERORcmGQQERGREIoek0GkBhUVQHo6kJsLREYCU6cCnp6OjoqIyPGYZBA1wOzZwNKlhnogVZ57Dpg1C0hLc1xcRERKwCSDqJ5mzwaWLKm5XK//YzkTDSJyZby7hKgeKioAX1/TMxjVabVAeTkvnRCR8+DdJUR2kJ5uOcEADK+np9snHiIiJWKSQVQPubm2bUdE5IyYZBDVQ2SkbdsRETkjjskgqgeOySAiV8QxGUR24OlpuE3VklmzmGAQkWvjLaxE9VR1e2r1eTK0Ws6TQUQE8HKJo8MhJ8AZP4nIVcj9DeWZDKIG8vQEZs50dBRERMrDMRlEREQkBJMMIiIiEoJJBhEREQnBMRmkSjduACkpwJkzQFSUoSCZj4+jo1IGvR7IygIKC4HQUKBPH8MdL0SkMCIPVoV8EfBMBqnOiBGGibD+9S/gs88M//X1NSx3dZmZQEQE0K8f8Pjjhv9GRBiWE5GCiDxYFfRFwCSDVGXECGDLFvOvbdni2olGZiYwahRw4YLp8osXDcuZaBAphMiDVWFfBJwng1Tjxg3DGYu6lJe73qUTvd7wPyrVv1eqaDRAWBiQl8dLJ0QOJfJgtcMXAacVJ6eVkmLbds4kK6v27xUAkCSgoMDQjogcSOTBqsAvAiYZpBpnzti2nTMpLLRtOyISROTBqsAvAiYZpBpRUbZt50xCQ23bjogEEXmwKvCLgGMySDU4JqN2VZdiL140nBGtjmMyiBRC5MFqhy8Cjskgp+XjAyQmWm6TmOh6CQZg+L544w3DvzUa09eqni9bxgSDyOFEHqwK/CJgkkGqsnlz7YlGYqLhdVc1ciSwYQPQsqXp8rAww/KRIx0TFxFVI/JgVdgXAS+XkCpxxs/aKWSiPyKqiwpn/JT7G8okg4iIiKzCMRlERESkCEwyiIiISAhWYSVVEj3uQE7/HANBRGQekwxSncxMIDnZdPbcsDDDnVu2GDgtp3/RsRARqRkvl5CqiC4wKKd/hRU7JCJSHN5dQqohusCgnP4BVj0lItfDu0vIaYkuMCinfwUWOyQiUhyOySDVEF1gUET/rHpKRK6MSQaphugCgyL6Z9VTInJlvFxCqtGnj2GcQ/W6P1U0GkCnM7QT3b/oWIiInAGTDFIN0QUG5fSvwGKHRESKwySDVEV0gUE5/Sus2CERkeLwFlZSJc74SURkf6zCWgcmGURERPXDeTKIiIhIEZhkEBERkRBMMoiIiEgITsblZNQ6CFGtcRORjfHLwKkwyXAiai07rta4icjG+GXgdHi5xEmotey4WuMmIhvjl4FT4i2sTkB0CXRR1Bo3EdkYvwxUg7ewuiC1lh1Xa9xEZGP8MnBaTDKcgOgS6KKoNW4isjF+GTgtJhlOQHQJdFHUGjcR2Ri/DJwWkwwnoNay42qNm4hsjF8GTotJhhNQa9lxtcZNRDbGLwOnxSTDSai17Lha4yYiG+OXgVPiLaxORq2T5ak1biKyMX4ZKBpLvdfB2ZMMIiIiUThPBhERESkCkwwiIiISggXSSBEqKoD0dCA3F4iMBKZOBTw9bddeSZd5lRQLEZFIHJNBDjd7NrB0qeHHt4pWC8yaBaSlNby9kgo7KikWIiK5VDUm46233kLXrl0REBCAgIAA9OrVC59++mmt7TMyMqDRaEwe3t7edoyYbG32bGDJEtOEATA8X7LE8HpD2iupsKOSYiEisgeHnsnYtm0btFotoqKiIEkSVq5ciSVLluDYsWPo1KlTjfYZGRlITk7GqVOnjMs0Gg2Cg4Ot/ps8k6EcFRWAr2/NhOFOWi1QXm64FCK3vZIKOyopFiKi+lLVmYzhw4dj6NChiIqKQnR0NF5++WX4+fnh4MGDtb5Ho9EgJCTE+Kgrwbh58yZKSkpMHqQM6emWEwbA8Hp6ev3aK6mwo5JiISKyF8XcXaLX67F27Vpcv34dvXr1qrVdWVkZwsPDodPpkJiYiO+++85iv6mpqQgMDDQ+dDqdrUOnesrNlddObnslFXZUUixERPbi8CTjxIkT8PPzg5eXF5555hls2rQJHTt2NNu2Xbt2eO+997BlyxasWrUKlZWV6N27Ny5Y+F/EuXPnori42PgoKCgQtSokU2SkvHZy2yupsKOSYiEisheH311SUVGB/Px8FBcXY8OGDXj33Xexf//+WhONO926dQsdOnTAuHHjsGjRIqv+HsdkKIe9xmRcvGi4HFGdI8ZkKCEWIqL6UtWYDADw9PRE27Zt0b17d6SmpuLuu+/GG1XV+Org4eGBmJgYnD17VnCUJIKnp+G2U0tmzfpj/gu57ZVU2FFJsRAR2YvDk4zqKisrcfPmTava6vV6nDhxAqE8x6xaaWlASkrNH1et1rC8+rwXctsrqbCjkmIhIrKHel0uKSoqwtdff40rV66gsrLS5LXx48db3c/cuXMxZMgQtGrVCqWlpVizZg0WL16MnTt3YuDAgRg/fjxatmyJ1NRUAMDChQtx3333oW3btigqKsKSJUuwefNmHDlyxKrLKwAvlygVZ/x0TCxERHLI/Q2VPa34tm3bkJSUhLKyMgQEBEBzx7lfjUYjK8m4cuUKxo8fj8LCQgQGBqJr167GBAMA8vPz4eb2x8mWa9eu4amnnsKlS5fQuHFjdO/eHV9++aXVCQYpl6cnMHOmuPZaLRAfLzMoQZQUCxGRSLLPZERHR2Po0KF45ZVX4OvrKyouYXgmg4iIqH6ED/y8ePEiZsyYocoEg4iIiOxHdpIxaNAgHD58WEQsRERE5ESsGpOxdetW47+HDRuGlJQUfP/99+jSpQs8PDxM2j788MO2jZBkETmoUO5gS5F9y11PtW4X1avUAz9nATcKAZ9QoHkfwM1GG15JOwERmSdZQaPRWPVwc3OzpjuHKi4ulgBIxcXFjg7F5jZulKSwMEkyTPdkeISFGZY3VEqKJGm1pn1rtYbl9u5b7nqqdbuoXv5GSdoUJkmr8cdjU5hheUMpaScgciFyf0OtSjKcibMmGRs3SpJGY/odChiWaTQN+y5NSanZ752Phvygyu1b7nqqdbuoXv5GSVqtMU0wVuP3ZZqGJRpK2gmIXIzc31DZd5d88MEHGDNmDLy8vEyWV1RUYO3atbJuYXUEZ7y7RGQZcblTeYvsW+56qnW7qF6lHtgaAZTXVlNIA/iGAQ/nyb90oqSdgMgFCb+7ZNKkSSguLq6xvLS0FJMmTZLbHdmAyDLicsuri+xb7nqqdbuo3s9ZFhIMAJCA8gJDO7mUtBMQUZ1kJxmSJJlMwFXlwoULCAwMtElQJI/IMuJyy6uL7Fvueqp1u6jeDSs3qLXt7qSknYCI6mT1jJ8xMTHQaDTQaDQYMGAA3N3/eKter0deXh4GDx4sJEiyTGQZcbnl1UX2LXc91bpdVM/Hyg1qbbs7KWknIKI6WT0mY8GCBcb//vWvf4Wfn5/xNU9PT0RERODRRx+Fp8IvQDvzmAwRZcSVOCbD2vVU63ZRPeOYjIsAzH292GBMhhJ2AiIXJPs3VO7I0oyMDOnGjRty36YYzn53SfVB9Eq/i6K+d5dYu55q3S6qZ7y7pPodJja8u0QJOwGRi+EtrHVw1iRDksxPBaDTKX8+CFvMk2FpPdW6XVTP7DwZOnHzZDhqJyByIUJuYW3cuLHZwZ7m/Prrr1a1cxRnvFxyJ7XObMkZP50UZ/wkcipyf0OtSjJWrlxp/PfVq1fx0ksvYdCgQejVqxcA4MCBA9i5cydefPFF/OUvf2lA+OI5e5JBREQkipAk406PPvoo+vXrh+nTp5ssX758OXbv3o3NmzfLCtjemGQQERHVj/DJuHbu3Gn2VtXBgwdj9+7dcrsjIiIiJyU7yWjatCm2bNlSY/mWLVvQtGlTmwRFRERE6mf1ZFxVFixYgMmTJ2Pfvn249957AQBfffUVduzYgXfeecfmAZJr4Bg+Eu5WBbAtHbiQC4RFAsOnAh4qGaHL0cWkUrLHZACGpOLNN9/EyZMnAQAdOnTAjBkzjEmHknFMhvJkZgLJyaYlJsLCgDfeAEaObHh7Irw9G/jbUuDqHbOnNdUCr8wCnk5zXFzWmD0bWLrUdOY3rRaYNQtIU3js5HSED/xUOyYZypKZCYwaVXMyxqo7pjdsME0c5LYnwtuzgSlLan99RYpyE43Zs4ElFmJPSWGiQXYlJMkoKSkxdlZSUmKxrdJ/uJlkKAerdpNwtyqAUF/TMxjVNdMCP5Ur79IJ564nBRJyd0njxo1x5coVAECjRo3QuHHjGo+q5UTWYtVuEm5buuUEAwB+0RvaKU16uuUEAzC8nq7A2Il+Z9XAz88//xxNmjQx/tva2T+JLGHVbhLuQq5t29lTrpUxWduOyAGsSjLi4uKQl5eH1q1bIz4+XnBI5CpYtZuEC4u0bTt7irQyJmvbETmA1QM/3dzcEB4ejn79+qF///6Ij49HWFiY6PhsjmMylINVu0k4jskgsilhM35+/vnnmDBhAs6dO4ennnoK4eHhiIqKwpQpU7B27Vpcvny5QYGT69FqDbedAn/cHVKl6vmyZX8kDHLbE8HD03CbqiUvz1JeggEYEodZdcQ+axYTDFK2+pR6vXHjhrRnzx7pxRdflPr06SN5eXlJbm5uUseOHevTnV05c6l3tWLVbhJuRYokNdWa7jTNtIblSpeSIknaarFrtYblRHYmpNR7bSoqKpCdnY1PP/0UK1asQFlZGfR1jYZ2MF4uUSbO+EnCccZPogYTOhlXRUUFDh48iL1792Lfvn346quvoNPp0LdvX/Tt2xdxcXFo1apVg1ZANCYZRERE9SP3N9Tq2iX9+/fHV199hdatWyMuLg5TpkzBmjVrEMqh/ERERGSG1UlGVlYWQkNDjXeWxMXFseoqERER1crqJKOoqAhZWVnYt28fFi9ejHHjxiE6OhpxcXHGpKN58+YiY3UKoscSyOlfSZd5OcZCJSr1wM9ZwI1CwCcUaN4HcHPSD0ruGA4l7cQiBzmJ3geUtB2p4eo7wrSkpETavn27lJKSIvXs2VPy9PSUOnXqVN/u7MaRd5eYuysiLMx2d0XI6V9JA9ZFbxeykfyNkrQpTJJW44/HpjDDcmdj7m6UphbuRlHSTiw3FjntRe8DStqOZJbc39B6Jxl6vV46ePCglJqaKj344IOSr6+v5ObmVt/u7MZRScbGjZKk0ZgeO4BhmUbT8GNITv8pKTXb3fmwZ6IheruQjeRvlKTVGtMfl9X4fZnGuRKNFXUcINUTDSXtxHJjkdNe9D6gpO1ItRJ2C2tlZSUOHz6Mffv2Ye/evcjOzsb169fRsmVL9OvXz/gIDw8XeeKlwRxxd4no6qFy+tfrlTOJIKuqqkSlHtgaAZTXVp1OA/iGAQ/nqf/SidwZQpW0E4ssa6yB2H1ASduRLBI242ejRo3Qq1cvvPHGG2jatClef/11nD59Gvn5+Vi5ciUmTpyo+ATDUURXD5XTv5IKO7Kqqkr8nGXhxwUAJKC8wNBO7eRWbVXSTiyyrLHofUBJ25FsyuqBn0uWLEG/fv0QHR0tMh6nJLp6qJz+lVTYkVVVVeKGlR+Ate2UTG7VViXtxCLLGkdYGUN99wElbUeyKauTjClTpoiMw6mJrh4qp38lFXZkVVWV8LHyA7C2nZLJrdqqpJ1YZFljHytjqO8+oKTtSDbVoGnF1ciRYzJEVQ+V078Sx2SwqqrCGcdkXARg7uuCYzIUsROLLGtsHJMhaB9Q0nYki4SNyaD6E109VE7/SirsyKqqKuGmBbr//kGh2gdV9bz7MvUnGID8qq1K2olFljUWvQ8oaTuSbQm800WRlDZPhi2rh8rpX+nzZLCqqgKZnSNB51y3r1aRW7VVSTuxyLLGovcBJW1HMsuuVVjVyNEF0jjjp3mc5E8lOONn7e2VtBNzxk8SREgV1q1bt1odwMMPP2x1W0dwdJJBRESkVkKqsI4YMcKqP67RaKCvaxIGIiIicglWJRmVlZWi4yAiIiInw7tLiIiISAirJ+O60/Xr17F//37k5+ejoqLC5LUZM2bYJDCqH5FjpuT2feMGkJICnDkDREUBS5YAPtZO6kPkaEoagHjzBvBRCpB/BmgVBYxZAnjZ8GD67QaQlgLkngEio4DZSwBvHqxkA3JvXzl69KgUEhIiBQQESFqtVmrevLmk0Wiku+66S2rdurXc7uzOkbewiiaySrLcvhMTzRewTExseCxEwimp5PiSRElqUu1AagLDcluYkihJbtX6d4NhOVE1wku9x8XFSU899ZSk1+slPz8/KTc3V8rPz5f69u0rbVTBvczOmmSIrJIst+/aEgwmGqQKSio5viTR8sHU0ERjSh39M9GgaoTPk9GoUSN89dVXaNeuHRo1aoQDBw6gQ4cO+OqrrzBhwgT88MMPIk642Iwz3sIqskqy3L5v3DBMW16X8nJeOiEFUlLJ8Zs3gBa+wK8W2jQFcLG8fpdOfrsB3OULWBrX7wbgejkvnZCR8GnFPTw84OZmeFtQUBDy8/MBAIGBgSgoKJDbHdmAyCrJcvtOSbGuX2vbEdmVkkqOf5RiOcEAgKu/t6uPtBTLCQZgeD2NByvVn+yBnzExMTh06BCioqIQFxeH//3f/8Uvv/yC//znP+jcubOIGKkOIqsky+37zBnr2lvbjsiulFRyPN/Kg8TadtXlWvk+a9sRmSH7TMYrr7yC0N/L7b788sto3Lgxnn32Wfz88894++23bR4g1U1klWS5fUdFWdfe2nZEdqWkkuOtrDxIrG1XXaSV77O2HZEZrF3iBERWSZbbN8dkkKopqeQ4x2SQAtmt1PuVK1eQlZWFrKws/Pzzz/XthmxAZJVkuX37+ACJiZb7TExkgkEKpaSS414+wNw6DqbnE+s/X4a3D/BUHf0/lcgEgxpG7u0rJSUl0hNPPCG5u7tLGo1G0mg0kru7u5SUlCQVFRXJ7c7unPUWVkkSWyVZbt+cJ4NUTUklx83Nk9GU82SQYwi/hXXMmDE4duwY/vnPf6JXr14AgAMHDiA5ORndunXD2rVrBaRCtuOMl0vuxBk/iWyEM37arn9yGkJKvd/prrvuws6dO/HAAw+YLM/KysLgwYNx/fp1eRHbmbMnGURERKIIH5PRtGlTBAYG1lgeGBiIxo0by+2OiIiInJTsJOOFF17ArFmzcOnSJeOyS5cuISUlBS+++KJNgyMiIiL1kn25JCYmBmfPnsXNmzfRqlUrAEB+fj68vLwQVW3yg6NHj9ouUhux9eUSuZdtlXSZV66KCiA9HcjNBSIjgalTAU/P2tvLWVc1bxdU6oGfs4AbhYBPKNC8D+Bmw+BvVwBn04HSXMA/Emg7FXC3sOFFxnOrAtiWDlzIBcIigeFTAQ8b7QT16V9J5AxCkvsZid7uIrnSl6QLrKvs31C5I0vnz59v9UOJbHl3idxCjUoq7ChXSookabWmsWu1huXmyFlXNW8XKX+jJG0Kk6TV+OOxKcyw3BaOpkjSGq1p/2u0huX2jmdFiiQ1rbYTNNUalpsj94OV27+SyLmdSu5nJHq7i+RKX5Iusq7Cq7DaUnp6utSlSxfJ399f8vf3l+677z5p+/btFt+zbt06qV27dpKXl5fUuXNn6ZNPPpH1N22VZMgt1Kikwo5ypaRYLtRYPdGQs65q3i5S/kZJWq0x/bFYjd+XaRr+w340xUzfdzyqJxoi41lRx05Q/QdP7gcrt38lkVN2WO5nJHq7i+RKX5IutK7Cb2EFgKKiImzYsAG5ublISUlBkyZNcPToUQQHB6Nly5ZW97Nt2zZotVpERUVBkiSsXLkSS5YswbFjx9CpU6ca7b/88kv07dsXqampeOihh7BmzRosXrwYR48etbpuii0ul8gt1Kikwo5yVVQYZvDU62tvo9UaZvD09JS3roB6twsq9cDWCKC8tmJaGsA3DHg4r36XKm5XAOt9AcnChtdogcfKDZdORMZzqwII9QWuWoilmRb4qdxwCl/uDi+3fyWRM8Wtl6e8z0j0dhfJlb4kXWldYYe7S7755htER0dj8eLFeO2111BUVAQAyMzMxNy5c2X1NXz4cAwdOhRRUVGIjo7Gyy+/DD8/Pxw8eNBs+zfeeAODBw9GSkoKOnTogEWLFiE2NhbLly+v9W/cvHkTJSUlJo+GkluoUUmFHeVKT7ecYACG19PTDf+Ws65q3i74OcvCjwUASEB5gaFdfZxNt5xgAIbXz6aLj2dbuuUfOgD4RW9oB8j/YOX2ryRyyg7L/YxEb3eRXOlL0pXWtR5kJxmzZs3CxIkTcebMGXh7exuXDx06FP/973/rHYher8fatWtx/fp14yRf1R04cAAJCQkmywYNGoQDBw7U2m9qaioCAwOND51OV+8Yq8gt1Kikwo5y5ebKaydnXdW8XXDDyqCsbVddqZUbvqqdyHguWBnLhXrsBPXpX0nklB2W+xmJ3u4iudKXpCutaz3ITjIOHTqEKVOm1FjesmVLk9tarXXixAn4+fnBy8sLzzzzDDZt2oSOHTuabXvp0iUEBwebLAsODrb4d+fOnYvi4mLjo6CgQHaM1ckt1Kikwo5yRUbKaydnXdW8XeBjZVDWtqvO38oNX9VOZDxhVsYSVo+doD79K4mcssNyPyPR210kV/qSdKV1rQfZYzKCgoKwc+dOxMTEwN/fH8ePH0ebNm2wa9cu/OlPf5L9I15RUYH8/HwUFxdjw4YNePfdd7F//36ziYanpydWrlyJcePGGZelp6djwYIFuHz5slV/z5ZjMqwt1Kikwo5y1XdMhjXrCqh3u/wxBuIiAHOHkKPGZAiIp75jA6z9YF1uTIaVn5Ho7S6SK31JutK6wg5jMh5++GEsXLgQt27dAgBoNBrk5+djzpw5ePTRR2UH7OnpibZt26J79+5ITU3F3XffjTeqqiBWExISUiOZuHz5MkJCQmT/3YaQW6hRSYUd5fL0BGbNstxm1qw/5suQs65q3i5w0wLdq/bTasFXPe++rP7zU7h7Au3r2PDtZ/0xX4bIeDw8gVfqiOXlWX8kAHI/WLn9K4mcssNyPyPR210kV/qSdKV1rQ+5t68UFRVJCQkJUqNGjSStVivpdDrJw8ND6tu3r1RWVia3uxr69esnTZgwwexro0ePlh566CGTZb169ZKmTJlidf+i58mwVKhRSYUd5bLFPBm1rauat4v5OQ90Cpsnw0bxmJuvoZnM+RosfbBy+1eSBs+TYeEzEr3dRXKlL0kXWVe73MIKANnZ2Th+/DjKysoQGxtbY0CmNebOnYshQ4agVatWKC0tNd6SunPnTgwcOBDjx49Hy5YtkZqaCsBwC2tcXBxeffVVDBs2DGvXrsUrr7xi91tY7+QCE7wZccbPWnDGz9rbc8ZP820546ft2iuJC6yr8CqstvTnP/8Ze/bsQWFhIQIDA9G1a1fMmTMHAwcOBADEx8cjIiICGRkZxvesX78eL7zwAs6fP4+oqCikpaVh6NChVv9NVmElIiKqH2FJxoEDB3D16lU89NBDxmUffPAB5s2bh+vXr2PEiBH45z//CS8vr/pHbwdMMoiIiOpH2MDPhQsX4rvvvjM+P3HiBP785z8jISEBzz//PLZt22a8rEFERERkdZKRk5ODAQMGGJ+vXbsW9957L9555x3MmjULb775JtatWyckSCIiIlIfd2sbXrt2zWQirP3792PIkCHG5z179rTJRFfOToXjfMjR5A4SFD0QVQ6RscvtW+7BJ3I7KukzUjt+qSqa1UlGcHAw8vLyoNPpUFFRgaNHj2LBggXG10tLS+Hh4SEkSGeRmQkkJ5tOWx8WZrhleuRIx8VFClaQCRxJNq154RtmmG9BZ2ankdteJJGxy+1b7sEncjsq6TNSO36pKp7Vl0uGDh2K559/HllZWZg7dy58fX3Rp08f4+vffPMNIq2dg9oFZWYCo0bVrItz8aJheWamY+IiBSvIBLJG1SyqVX7RsLwgs2HtRRIZu9y+5R58Irejkj4jteOXqipYfXfJL7/8gpEjR+KLL76An58fVq5ciUceecT4+oABA3Dffffh5ZdfFhasLTji7hKVV/YlR5Bbul106Xk5RMYOyOtb7sEncjsq6TNSO36pOoywu0uaNWuG//73v7h27RquXbtmkmAAhvkr5s2bJz9iF+BilX3JFuSWBRddel4OkbHL7VvuwSdyOyrpM1I7fqmqhtVjMqoEBgaaXd6kSZMGB+OsXKyyL9mC3LLgokvPy6GE2Kvayj34RG5HJX1GascvVdWQnWSQfC5W2ZdsQW5ZcNGl5+VQQuxVbeUefCK3o5I+I7Xjl6pqyK7CSvL16WO4PFi94F4VjQbQ6QztiAAYbmn0DUPNap1VNICvztCuPu1FEhm73L7lHnwit6OSPiO145eqajDJsANXq+xLNiC3LLjo0vNyiIxdbt9yDz6R21FJn5Ha8UtVNZhk2MnIkcCGDUDLlqbLw8IMy3lLN9WgGwn02QD4VttpfMMMy6vPqSC3vUgiY5fbt9yDT+R2VNJnpHb8UlUFh1ZhdQRHF0jj5HQkG2f8tE3fnPHTOfFL1a5UVerdERydZBAREamVsHkyiIiIiORgkkFERERCMMkgIiIiITgZF5HSyR3YdrsCOJsOlOYC/pFA26mAu6dt+hZNZOxy21dUAOnpQG4uEBkJTJ0KeNYSC6CswZxKikVJlLa/Ky0eESQXU1xcLAGQiouLHR0KUd02bpSksDBJMlRjMDzCwgzLzTmaIklrtJK0Gn881mgNyxvat2giY5fbPiVFkrRa0/ZarWG5OfkbJWlTmGnsm8IMy+1NSbEoidL2d6XFYyW5v6G8u4RIqapKWVc/RKsmG6o+F8Cx2cDJJbX31yEFiEmrX9+iiYxdbvvZs4ElFmJJSQHS0v54XlW+HdW/Sn/v357zXygpFiVR2v6utHhk4C2sdWCSQaogt5T17QpgvS8g6WvvU6MFHis3/FdJZbJFxi53O1ZUAL6+hvfVRqsFyssNl06UVL5dSbEoidLKwistHpl4CyuRM5BbyvpsuuUfacDw+tl05ZXJFhm73Pbp6ZYTDMDwenq64d9KKt+upFiURGn7u9LiEYwDP4mUSG4p69Jc69qX5gKFwbaNoaFExi53O+ZaGUtVOyWVb1dSLEqitLLwSotHMJ7JIFIiuaWs/SOta+8fqbwy2SJjl9s+0spYqtopqXy7kmJREqXt70qLRzCOySBSoqrrthcv1hwcBthmTIa1fYsmMna527HeYzIuouZgS8AxYzIUEIuSyN0HXC0emTgmg8gZyC1l7e4JtJ9luc/2swztlFYmW2Tsctt7egKz6ohl1qw/5stQUvl2JcWiJErb35UWj2BMMoiUSm4p65g0w62emmpfThqt6S2g9elbNJGxy22flma4TbX6l7xWW/P2VUBZ5duVFIuSKG1/V1o8AvFyCZHSccZP820546c6YlESpe3vSovHCpwnow5MMoiIiOqHYzKIiIhIEZhkEBERkRCcjItcg5qvUYuMvbwMWPTkH+MOXvwP4Otnm74BZW130WM4iKgGJhnk/AoygSPJplMu+4YZbvdT+mh7kbFPuAf4z6E7plQ4ASz2B57sCaz8umF9A8ra7pmZQHKy6XTOYWGGWwnNjeSX256IzOLAT3Juaq5KKTL2CfcAHxyq/fXxDUw0lLTdRVdtJXIhvLukDkwyXIiaq1KKjL28DPDzNz8pZBU3AKWl9bt0oqTtLrfipcorZBKJxrtLiKqouSqlyNgXPWk5wQCAyt/b1YeStrvoqq1EZBGTDHJeaq5KKTJ2uZVG5VLSdpdb8dLFKmQSicYkg5yXmqtSioxdbqVRuZS03UVXbSUiizgmg5yXmqtSiozdbmMyFLDd5Va8VHmFTCLROCaDqIqaq1KKjN3Xz3CbqiVP9Kz/fBlK2u6iq7YSkUVMMsi5qbkqpcjYV35tuE21eg7ghobfvgooa7uLrtpKRLXi5RJyDUqaeVIuzvhpG5zxk6jBOE9GHZhkEBER1Q/HZBAREZEiMMkgIiIiIZhkEBERkRCswkpkb6IHQ8rpX0kDMwEOtiTX4gL7O5MMInsSXf5cTv9KKsUOsLw6uRYX2d95dwmRvYgufy6nfyWVYgdYXp1ci4r3d97CWgcmGeQQosufy+kfUE4pdoDl1cm1qHx/5y2sREokuvy5nP6VVIodYHl1ci0utr9zTAaRPYgufy6if3uUYgdYXp1ci4vt70wyiOxBdPlzEf3boxQ7wPLq5FpcbH/n5RIie2jexzDOoUZFsioawFdnaCe6f9GxyNWnj+EadPWqp8ZwNIBOZ2hHpHYutr8zySCyB9Hlz+X0r6RS7ADLq5NrcbH9nUkGkb2ILn8up38llWIHWF6dXIsL7e+8hZXI3jjjZ+1cYAZEIiMV7u+cJ6MOTDKIiIjqh/NkEBERkSIwySAiIiIhOE8GiSPyeqPSxhLIITd2ue1VeJ3XLm5VANvSgQu5QFgkMHwq4OHp6Kisw8+UVIpJBokhssKg0qqHyiE3drntXaSyo2xvzwb+thS4qv9jWdPngFdmAU+nOS4ua/AzJRVz6OWS1NRU9OzZE/7+/ggKCsKIESNw6tQpi+/JyMiARqMxeXh7e9spYrJKVYXB6vPzX7xoWJ6ZWf++q6qHVq+9UX7RsLygAX2LJjd2ue1Fbnc1e3s2MGWJaYIBGJ5PWWJ4Xan4mZLKOfTuksGDB2Ps2LHo2bMnbt++jb/97W/49ttv8f333+Ouu+4y+56MjAwkJyebJCMajQbBwcFW/U3eXSKYyAqDoiuZiiQ3drntVV7ZUZhbFUCob80E407NtMBP5cq7dMLPlBRIVXeX7NixAxMnTkSnTp1w9913IyMjA/n5+Thy5IjF92k0GoSEhBgflhKMmzdvoqSkxORBAomsMKi06qFyyI1dbnsXq+xotW3plhMMAPhFb2inNPxMyQko6u6S4uJiAECTJk0stisrK0N4eDh0Oh0SExPx3Xff1do2NTUVgYGBxodOp7NpzFSNyAqDoiuZiiQ3drntXayyo9Uu5Nq2nT3xMyUnoJgko7KyEjNnzsT999+Pzp0719quXbt2eO+997BlyxasWrUKlZWV6N27Ny7UkvHPnTsXxcXFxkdBQYGoVSBAbIVB0ZVMRZIbu9z2LlbZ0WphkbZtZ0/8TMkJKGbGz2effRaffvopvvjiC4SFhVn9vlu3bqFDhw4YN24cFi1aVGd7jskQrOo68sWLhtO51dlkTMZFAOZ2WzWMybAydrntRW53NXOGMRn8TElBVDUmo8r06dPx8ccfY+/evbISDADw8PBATEwMzp49Kyg6kkVkhUGlVQ+VQ27sctu7WGVHq3l4Gm5TteTlWcpLMAB+puQUHJpkSJKE6dOnY9OmTfj888/RunVr2X3o9XqcOHECoTxlqBwiKwwqrXqoHHJjl9vehSo7yvJ0GrAiBWha7ce4mdawXMnzZPAzJZVz6OWSqVOnYs2aNdiyZQvatWtnXB4YGAgfHx8AwPjx49GyZUukpqYCABYuXIj77rsPbdu2RVFREZYsWYLNmzfjyJEj6NixY51/k5dL7IgzfprHGT8dgzN+EjWYqqqwaqqfAvzd+++/j4kTJwIA4uPjERERgYyMDADAX/7yF2RmZuLSpUto3LgxunfvjpdeegkxMTFW/U0mGURERPWjqiTDEZhkEBER1Y8qB34SERGR82GSQUREREKwCiuJo+bBmSLdrgDOpgOluYB/JNB2KuCukgGIREQyMMkgMdRcjl2kY7OBH5YC0h2TQx17Dmg/C4hR8K2URET1wMslZHtqLscu0rHZwMklpgkGYHh+conhdSIiJ8Ikg2yrUm84g2F2Kuzflx2ZaWjnSm5XGM5gWPLDUkM7IiInwSSDbEvN5dhFOpte8wxGdZLe0I6IyEkwySDbUnM5dpFKrSwlbm07IiIVYJJBtqXmcuwi+VtZStzadkREKsAkg2yreR/DXSQ1KodW0QC+OkM7V9J2KqCp4/ZdjdbQjojISTDJINtSczl2kdw9DbepWtJ+FufLICKnwiSDbE/N5dhFikkDOqTUPKOh0RqWc54MInIyLJBG4nDGT/M44ycRqZTc31DO+EniuGmB4HhHR6E87p5A+5mOjoKISDheLiEiIiIhmGQQERGRELxconB6PZCVBRQWAqGhQJ8+gNYZhzW40vgNV1pXJeF2J7I7JhkKlpkJJCcDF+6YpTssDHjjDWCkM92g4UoVW11pXZWE253IIXi5RKEyM4FRo0wTDAC4eNGwPNNZCpm6UsVWV1pXJeF2J3IYJhkKpNcbzmCYu7m4atnMmYZ2quZKFVtdaV2VhNudyKGYZChQVlbNMxh3kiSgoMDQTtVcqWKrK62rknC7EzkUkwwFKrSyQKm17RTLlSq2utK6Kgm3O5FDMclQoFArC5Ra206xXKliqyutq5JwuxM5FJMMBerTx3AXiaaWQqYaDaDTGdqpmitVbHWldVUSbncih2KSoUBareE2VaBmolH1fNkyJ5gvw5UqtrrSuioJtzuRQzHJUKiRI4ENG4CW1QqZhoUZljvNPBmuVLHVldZVSbjdiRyGVVgVjjN+OiFXWlcl4XYnajC5v6FMMoiIiMgqcn9DebmEiIiIhGCSQUREREIwySAiIiIhWIWViMS5VQFsSwcu5AJhkcDwqYCHp2365kBOIsVjkkFEYrw9G/jbUuDqHcXHmj4HvDILeDqtYX2zdDuRKvByCRHZ3tuzgSlLTBMMwPB8yhLD6/XF0u1EqsFbWInItm5VAKG+NROMOzXTAj+Vy790UqkHtkZYqKyqMZzReDiPl06IBOAtrETkWNvSLScYAPCL3tBOLpZuJ1IVJhlEZFsXcm3b7k4s3U6kKkwyiMi2wiJt2+5OLN1OpCpMMojItoZPBZrWMR6imdbQTi6WbidSFSYZRGRbHp6G21QteXlW/ebLYOl2IlVhkkFEtvd0GrAipeYZjWZaw/KGzJPB0u1EqsFbWIlIHM74SeRU5P6GcsZPIhLHwxMYOVNM325aIDheTN9EZBO8XEJERERCMMkgIiIiIZhkEBERkRBMMoiIiEgIJhlEREQkBJMMIiIiEoJJBhEREQnBJIOIiIiEYJJBREREQjDJICIiIiGYZBAREZEQTDKIiIhICCYZREREJASTDCIiIhKCSQYREREJwSSDiIiIhGCSQUREREK4OzoAIgBApR74OQu4UQj4hALN+wBuWkdHRUREDcAkgxyvIBM4kgyUX/hjmW8Y0P0NQDfScXEREVGDOPRySWpqKnr27Al/f38EBQVhxIgROHXqVJ3vW79+Pdq3bw9vb2906dIF27dvt0O0JERBJpA1yjTBAIDyi4blBZmOiYuIiBrMoUnG/v37MW3aNBw8eBC7du3CrVu38OCDD+L69eu1vufLL7/EuHHj8Oc//xnHjh3DiBEjMGLECHz77bd2jJxsolJvOIMBycyLvy87MtPQjoiIVEcjSZK5b3iH+PnnnxEUFIT9+/ejb9++ZtuMGTMG169fx8cff2xcdt9996Fbt27497//XaP9zZs3cfPmTePzkpIS6HQ6FBcXIyAgwPYrQda7vA/Y06/udgP2AsHxoqMhIqI6lJSUIDAw0OrfUEXdXVJcXAwAaNKkSa1tDhw4gISEBJNlgwYNwoEDB8y2T01NRWBgoPGh0+lsFzA1zI1C27YjIiJFUUySUVlZiZkzZ+L+++9H586da2136dIlBAcHmywLDg7GpUuXzLafO3cuiouLjY+CggKbxk0N4BNq23ZERKQoirm7ZNq0afj222/xxRdf2LRfLy8veHl52bRPspHmfQx3kZRfhPlxGRrD68372DsyIiKyAUWcyZg+fTo+/vhj7N27F2FhYRbbhoSE4PLlyybLLl++jJCQEJEhkghuWsNtqgAATbUXf3/efRnnyyAiUimHJhmSJGH69OnYtGkTPv/8c7Ru3brO9/Tq1Qt79uwxWbZr1y706tVLVJgkkm4k0GcD4NvSdLlvmGE558kgIlIth14umTZtGtasWYMtW7bA39/fOK4iMDAQPj4+AIDx48ejZcuWSE1NBQAkJycjLi4O//jHPzBs2DCsXbsWhw8fxttvv+2w9aAG0o0EWiZyxk8iIifj0FtYNZrqp8gN3n//fUycOBEAEB8fj4iICGRkZBhfX79+PV544QWcP38eUVFRSEtLw9ChQ636m3JvvyEiIiIDub+hiponwx6YZBAREdWPqufJICIiIufBJIOIiIiEYJJBREREQjDJICIiIiGYZBAREZEQTDKIiIhICCYZREREJASTDCIiIhKCSQYREREJoZhS7/ZSNcFpSUmJgyMhIiJSl6rfTmsnC3e5JKO0tBQAoNPpHBwJERGROpWWliIwMLDOdi5Xu6SyshI//fQT/P39ay3QpjQlJSXQ6XQoKChw6norrrKeANfVWbnKurrKegJc1+okSUJpaSlatGgBN7e6R1y43JkMNzc3hIWFOTqMegkICHD6nRxwnfUEuK7OylXW1VXWE+C63smaMxhVOPCTiIiIhGCSQUREREIwyVABLy8vzJs3D15eXo4ORShXWU+A6+qsXGVdXWU9Aa5rQ7ncwE8iIiKyD57JICIiIiGYZBAREZEQTDKIiIhICCYZREREJASTDIV59dVXodFoMHPmzFrbZGRkQKPRmDy8vb3tF2Q9zZ8/v0bc7du3t/ie9evXo3379vD29kaXLl2wfft2O0Vbf3LXU62fZ5WLFy/iiSeeQNOmTeHj44MuXbrg8OHDFt+zb98+xMbGwsvLC23btkVGRoZ9gm0gueu6b9++Gp+tRqPBpUuX7Bi1fBEREWbjnjZtWq3vUeOxKnc91Xys6vV6vPjii2jdujV8fHwQGRmJRYsW1VmDpKHHqsvN+Klkhw4dwooVK9C1a9c62wYEBODUqVPG52qZIr1Tp07YvXu38bm7e+274Jdffolx48YhNTUVDz30ENasWYMRI0bg6NGj6Ny5sz3CrTc56wmo9/O8du0a7r//fvTr1w+ffvopmjdvjjNnzqBx48a1vicvLw/Dhg3DM888g9WrV2PPnj2YPHkyQkNDMWjQIDtGL0991rXKqVOnTGZQDAoKEhlqgx06dAh6vd74/Ntvv8XAgQPx2GOPmW2v1mNV7noC6j1WFy9ejLfeegsrV65Ep06dcPjwYUyaNAmBgYGYMWOG2ffY5FiVSBFKS0ulqKgoadeuXVJcXJyUnJxca9v3339fCgwMtFtstjJv3jzp7rvvtrr96NGjpWHDhpksu/fee6UpU6bYODLbkrueav08JUmS5syZIz3wwAOy3jN79mypU6dOJsvGjBkjDRo0yJah2Vx91nXv3r0SAOnatWtigrKT5ORkKTIyUqqsrDT7ulqP1erqWk81H6vDhg2T/vSnP5ksGzlypJSUlFTre2xxrPJyiUJMmzYNw4YNQ0JCglXty8rKEB4eDp1Oh8TERHz33XeCI7SNM2fOoEWLFmjTpg2SkpKQn59fa9sDBw7U2B6DBg3CgQMHRIfZYHLWE1Dv57l161b06NEDjz32GIKCghATE4N33nnH4nvU+rnWZ12rdOvWDaGhoRg4cCCys7MFR2pbFRUVWLVqFf70pz/V+n/tav1M72TNegLqPVZ79+6NPXv24PTp0wCA48eP44svvsCQIUNqfY8tPlcmGQqwdu1aHD16FKmpqVa1b9euHd577z1s2bIFq1atQmVlJXr37o0LFy4IjrRh7r33XmRkZGDHjh146623kJeXhz59+qC0tNRs+0uXLiE4ONhkWXBwsOKvZ8tdT7V+ngBw7tw5vPXWW4iKisLOnTvx7LPPYsaMGVi5cmWt76ntcy0pKcGNGzdEh1xv9VnX0NBQ/Pvf/8bGjRuxceNG6HQ6xMfH4+jRo3aMvGE2b96MoqIiTJw4sdY2aj1W72TNeqr5WH3++ecxduxYtG/fHh4eHoiJicHMmTORlJRU63tscqzKO+FCtpafny8FBQVJx48fNy6r63JJdRUVFVJkZKT0wgsvCIhQnGvXrkkBAQHSu+++a/Z1Dw8Pac2aNSbL/vWvf0lBQUH2CM9m6lrP6tT0eXp4eEi9evUyWfY///M/0n333Vfre6KioqRXXnnFZNknn3wiAZDKy8uFxGkL9VlXc/r27Ss98cQTtgxNqAcffFB66KGHLLZxhmPVmvWsTk3H6ocffiiFhYVJH374ofTNN99IH3zwgdSkSRMpIyOj1vfY4ljlmQwHO3LkCK5cuYLY2Fi4u7vD3d0d+/fvx5tvvgl3d3eTQUm1qcpKz549a4eIbadRo0aIjo6uNe6QkBBcvnzZZNnly5cREhJij/Bspq71rE5Nn2doaCg6duxosqxDhw4WLw/V9rkGBATAx8dHSJy2UJ91Neeee+5RxWcLAD/++CN2796NyZMnW2yn9mPV2vWsTk3HakpKivFsRpcuXfDkk0/iL3/5i8Uz6LY4VplkONiAAQNw4sQJ5OTkGB89evRAUlIScnJyoNVq6+xDr9fjxIkTCA0NtUPEtlNWVobc3Nxa4+7Vqxf27NljsmzXrl3o1auXPcKzmbrWszo1fZ7333+/yUh7ADh9+jTCw8NrfY9aP9f6rKs5OTk5qvhsAeD9999HUFAQhg0bZrGdWj/TKtauZ3VqOlbLy8vh5mb6k6/ValFZWVnre2zyuTbo/AsJUf1yyZNPPik9//zzxucLFiyQdu7cKeXm5kpHjhyRxo4dK3l7e0vfffedA6K13l//+ldp3759Ul5enpSdnS0lJCRIzZo1k65cuSJJUs31zM7Oltzd3aXXXntNOnnypDRv3jzJw8NDOnHihKNWwSpy11Otn6ckSdLXX38tubu7Sy+//LJ05swZafXq1ZKvr6+0atUqY5vnn39eevLJJ43Pz507J/n6+kopKSnSyZMnpX/961+SVquVduzY4YhVsFp91vX111+XNm/eLJ05c0Y6ceKElJycLLm5uUm7d+92xCrIotfrpVatWklz5syp8ZqzHKuSJG891XysTpgwQWrZsqX08ccfS3l5eVJmZqbUrFkzafbs2cY2Io5VJhkKVD3JiIuLkyZMmGB8PnPmTKlVq1aSp6enFBwcLA0dOlQ6evSo/QOVacyYMVJoaKjk6ekptWzZUhozZox09uxZ4+vV11OSJGndunVSdHS05OnpKXXq1En65JNP7By1fHLXU62fZ5Vt27ZJnTt3lry8vKT27dtLb7/9tsnrEyZMkOLi4kyW7d27V+rWrZvk6ekptWnTRnr//fftF3ADyF3XxYsXS5GRkZK3t7fUpEkTKT4+Xvr888/tHHX97Ny5UwIgnTp1qsZrznKsSpK89VTzsVpSUiIlJydLrVq1kry9vaU2bdpIf//736WbN28a24g4VlnqnYiIiITgmAwiIiISgkkGERERCcEkg4iIiIRgkkFERERCMMkgIiIiIZhkEBERkRBMMoiIiEgIJhlEREQkBJMMIrIbjUaDzZs3OzqMepk4cSJGjBjh6DCIVIVJBpGT+/nnn/Hss8+iVatW8PLyQkhICAYNGoTs7GxHh1aDEn7Iz58/D41Gg5ycHIfGQeQM3B0dABGJ9eijj6KiogIrV65EmzZtcPnyZezZswdXr151dGhE5OR4JoPIiRUVFSErKwuLFy9Gv379EB4ejnvuuQdz587Fww8/bNJu8uTJaN68OQICAtC/f38cP37c+Pr8+fPRrVs3rFixAjqdDr6+vhg9ejSKi4uNbQ4dOoSBAweiWbNmCAwMRFxcHI4ePWrT9fn2228xZMgQ+Pn5ITg4GE8++SR++eUX4+vx8fGYMWMGZs+ejSZNmiAkJATz58836eOHH37AAw88AG9vb3Ts2BG7d+82uYzTunVrAEBMTAw0Gg3i4+NN3v/aa68hNDQUTZs2xbRp03Dr1i2briORM2GSQeTE/Pz84Ofnh82bN+PmzZu1tnvsscdw5coVfPrppzhy5AhiY2MxYMAA/Prrr8Y2Z8+exbp167Bt2zbs2LEDx44dw9SpU42vl5aWYsKECfjiiy9w8OBBREVFYejQoSgtLbXJuhQVFaF///6IiYnB4cOHsWPHDly+fBmjR482abdy5Urcdddd+Oqrr5CWloaFCxdi165dAAC9Xo8RI0bA19cXX331Fd5++238/e9/N3n/119/DQDYvXs3CgsLkZmZaXxt7969yM3Nxd69e7Fy5UpkZGQgIyPDJutH5JQaXD+WiBRtw4YNUuPGjSVvb2+pd+/e0ty5c6Xjx48bX8/KypICAgKk3377zeR9kZGR0ooVKyRJkqR58+ZJWq1WunDhgvH1Tz/9VHJzc5MKCwvN/l29Xi/5+/tL27ZtMy4DIG3atKnWWCdMmCAlJiaafW3RokXSgw8+aLKsoKDApEx3XFyc9MADD5i06dmzpzRnzhxjzO7u7iYx79q1yySuvLw8CYB07NixGrGFh4dLt2/fNi577LHHpDFjxtS6PkSujmcyiJzco48+ip9++glbt27F4MGDsW/fPsTGxhr/D/z48eMoKytD06ZNjWc+/Pz8kJeXh9zcXGM/rVq1QsuWLY3Pe/XqhcrKSpw6dQoAcPnyZTz11FOIiopCYGAgAgICUFZWhvz8fJusx/Hjx7F3716TGNu3bw8AJnF27drV5H2hoaG4cuUKAODUqVPQ6XQICQkxvn7PPfdYHUOnTp2g1WrN9k1ENXHgJ5EL8Pb2xsCBAzFw4EC8+OKLmDx5MubNm4eJEyeirKwMoaGh2LdvX433NWrUyOq/MWHCBFy9ehVvvPEGwsPD4eXlhV69eqGiosIm61BWVobhw4dj8eLFNV4LDQ01/tvDw8PkNY1Gg8rKSpvEILJvImfEJIPIBXXs2NE40DE2NhaXLl2Cu7s7IiIian1Pfn4+fvrpJ7Ro0QIAcPDgQbi5uaFdu3YAgOzsbKSnp2Po0KEAgIKCApNBmQ0VGxuLjRs3IiIiAu7u9fvqateuHQoKCnD58mUEBwcDMAxYvZOnpycAw/gNImoYXi4hcmJXr15F//79sWrVKnzzzTfIy8vD+vXrkZaWhsTERABAQkICevXqhREjRuCzzz7D+fPn8eWXX+Lvf/87Dh8+bOzL29sbEyZMwPHjx5GVlYUZM2Zg9OjRxksPUVFR+M9//oOTJ0/iq6++QlJSEnx8fGTHXFxcjJycHJNHQUEBpk2bhl9//RXjxo3DoUOHkJubi507d2LSpElWJwQDBw5EZGQkJkyYgG+++QbZ2dl44YUXABjOSgBAUFAQfHx8jANL77yDhojkYZJB5MT8/Pxw77334vXXX0ffvn3RuXNnvPjii3jqqaewfPlyAIYf1+3bt6Nv376YNGkSoqOjMXbsWPz444/G/9sHgLZt22LkyJEYOnQoHnzwQXTt2hXp6enG1//v//4P165dQ2xsLJ588knMmDEDQUFBsmPet28fYmJiTB4LFixAixYtkJ2dDb1ejwcffBBdunTBzJkz0ahRI7i5WfdVptVqsXnzZpSVlaFnz56YPHmy8e4Sb29vAIC7uzvefPNNrFixAi1atDAmY0Qkn0aSJMnRQRCRss2fPx+bN292ylkws7Oz8cADD+Ds2bOIjIx0dDhEToVjMojIpWzatAl+fn6IiorC2bNnkZycjPvvv58JBpEATDKIyKWUlpZizpw5yM/PR7NmzZCQkIB//OMfjg6LyCnxcgkREREJwYGfREREJASTDCIiIhKCSQYREREJwSSDiIiIhGCSQUREREIwySAiIiIhmGQQERGREEwyiIiISIj/B403N7Wz4I/vAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,5))\n",
        "\n",
        "for species,color in colors.items():\n",
        "    subset=df[df[\"Species\"]==species]\n",
        "\n",
        "    plt.scatter(subset[\"PetalLengthCm\"],\n",
        "                subset[\"PetalWidthCm\"],\n",
        "                color=color,\n",
        "                label=species)\n",
        "\n",
        "plt.xlabel(\"Petal Length\")\n",
        "plt.ylabel(\"Petal Width\")\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "0T0wdSLBfk4j",
        "outputId": "d138dc64-3f7b-422a-fdef-0f5987cbc92e"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,5))\n",
        "\n",
        "for species,color in colors.items():\n",
        "    subset=df[df[\"Species\"]==species]\n",
        "\n",
        "    plt.scatter(subset[\"SepalLengthCm\"],\n",
        "                subset[\"PetalLengthCm\"],\n",
        "                color=color,\n",
        "                label=species)\n",
        "\n",
        "plt.xlabel(\"Sepal Length\")\n",
        "plt.ylabel(\"Petal Length\")\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "9oRvGb3jfoF5",
        "outputId": "026e5c9b-f27b-4542-8611-8551531da342"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The scatterplots show that Iris-setosa is clearly separated from the other two classes. Iris-versicolor and Iris-virginica overlap slightly. Petal measurements provide better class separation than sepal measurements."
      ],
      "metadata": {
        "id": "BFb19jekiCi9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,5))\n",
        "\n",
        "for species,color in colors.items():\n",
        "    subset=df[df[\"Species\"]==species]\n",
        "\n",
        "    plt.scatter(subset[\"SepalWidthCm\"],\n",
        "                subset[\"PetalWidthCm\"],\n",
        "                color=color,\n",
        "                label=species)\n",
        "\n",
        "plt.xlabel(\"Sepal Width\")\n",
        "plt.ylabel(\"Petal Width\")\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "ZNmC52e1frZ4",
        "outputId": "c301d810-0a3a-4c17-f18c-6b6166c92950"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Iris-setosa is clearly separated from the other two classes. Iris-versicolor and Iris-virginica overlap slightly. Petal measurements provide better separation than sepal measurements."
      ],
      "metadata": {
        "id": "anuZ1SB5hibv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr=df.drop(\"Species\",axis=1).corr()\n",
        "\n",
        "corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "0R8lcafTfvXu",
        "outputId": "de284797-192e-40b2-e847-8ab32fa7e3d5"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "SepalLengthCm       1.000000     -0.109369       0.871754      0.817954\n",
              "SepalWidthCm       -0.109369      1.000000      -0.420516     -0.356544\n",
              "PetalLengthCm       0.871754     -0.420516       1.000000      0.962757\n",
              "PetalWidthCm        0.817954     -0.356544       0.962757      1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f4a32fef-7595-4952-aac9-5f249fd1b34a\" 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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.109369</td>\n",
              "      <td>0.871754</td>\n",
              "      <td>0.817954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <td>-0.109369</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.420516</td>\n",
              "      <td>-0.356544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.420516</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <td>0.817954</td>\n",
              "      <td>-0.356544</td>\n",
              "      <td>0.962757</td>\n",
              "      <td>1.000000</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-f4a32fef-7595-4952-aac9-5f249fd1b34a')\"\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-f4a32fef-7595-4952-aac9-5f249fd1b34a 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-f4a32fef-7595-4952-aac9-5f249fd1b34a');\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=\"id_bd0543b2-c592-4041-bf0d-a4f440ce7a0c\">\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('corr')\"\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_bd0543b2-c592-4041-bf0d-a4f440ce7a0c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('corr');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "corr",
              "summary": "{\n  \"name\": \"corr\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5087331794989353,\n        \"min\": -0.10936924995064931,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.10936924995064931,\n          0.8179536333691642,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6614868453597539,\n        \"min\": -0.4205160964011539,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          -0.35654408961380507,\n          -0.10936924995064931\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6847985519785192,\n        \"min\": -0.4205160964011539,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.4205160964011539,\n          0.9627570970509662,\n          0.8717541573048716\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6465103986023225,\n        \"min\": -0.35654408961380507,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.35654408961380507,\n          1.0,\n          0.8179536333691642\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "\n",
        "sns.heatmap(corr,\n",
        "            annot=True,\n",
        "            cmap=\"coolwarm\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "bxMHKbCxfzju",
        "outputId": "243883e4-9bf4-486a-e16f-0624bf70580e"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Petal length and petal width have a strong positive correlation. Sepal length is positively correlated with petal measurements, while sepal width has a weaker correlation."
      ],
      "metadata": {
        "id": "QVO6pQ5BhcHf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoder=LabelEncoder()\n",
        "\n",
        "df[\"Species\"]=encoder.fit_transform(df[\"Species\"])\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FQUf_Wk1f5yU",
        "outputId": "eadead67-7bae-4f13-e6d5-8e1c67b69c6e"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0            5.1           3.5            1.4           0.2        0\n",
              "1            4.9           3.0            1.4           0.2        0\n",
              "2            4.7           3.2            1.3           0.2        0\n",
              "3            4.6           3.1            1.5           0.2        0\n",
              "4            5.0           3.6            1.4           0.2        0"
            ],
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoder.classes_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcRF3lMOf7l_",
        "outputId": "e7e7faf6-8231-48fa-f1ac-3fa7ed5478f9"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop(\"Species\",axis=1)\n",
        "\n",
        "y=df[\"Species\"]\n",
        "\n",
        "X_train,X_test,y_train,y_test=train_test_split(\n",
        "    X,\n",
        "    y,\n",
        "    test_size=0.3,\n",
        "    random_state=42\n",
        ")"
      ],
      "metadata": {
        "id": "YXhLIXj9gEg0"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LogisticRegression(max_iter=200)\n",
        "\n",
        "lr.fit(X_train,y_train)\n",
        "\n",
        "pred_lr=lr.predict(X_test)\n",
        "\n",
        "acc_lr=accuracy_score(y_test,pred_lr)\n",
        "\n",
        "print(acc_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIxMWeHFgGCt",
        "outputId": "02d736a7-f257-46c4-8e4e-b3fddc404d8b"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn=KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "knn.fit(X_train,y_train)\n",
        "\n",
        "pred_knn=knn.predict(X_test)\n",
        "\n",
        "acc_knn=accuracy_score(y_test,pred_knn)\n",
        "\n",
        "print(acc_knn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BWqxIm5ogJvZ",
        "outputId": "2f17be52-d0cb-433f-b078-e28d96d4dbe0"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tree=DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "tree.fit(X_train,y_train)\n",
        "\n",
        "pred_tree=tree.predict(X_test)\n",
        "\n",
        "acc_tree=accuracy_score(y_test,pred_tree)\n",
        "\n",
        "print(acc_tree)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4e7XiCSgMpr",
        "outputId": "dfd5642a-1a99-4627-a6e5-eeacf5460493"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results=pd.DataFrame({\n",
        "    \"Model\":[\"Logistic Regression\",\n",
        "             \"KNN\",\n",
        "             \"Decision Tree\"],\n",
        "    \"Accuracy\":[acc_lr,\n",
        "                acc_knn,\n",
        "                acc_tree]\n",
        "})\n",
        "\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "opJxTh99gPrS",
        "outputId": "005de486-3c04-4c89-a413-23b3969d27ff"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Accuracy\n",
              "0  Logistic Regression       1.0\n",
              "1                  KNN       1.0\n",
              "2        Decision Tree       1.0"
            ],
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              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Logistic Regression\",\n          \"KNN\",\n          \"Decision Tree\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr2=LogisticRegression(max_iter=500)\n",
        "\n",
        "lr2.fit(X_train,y_train)\n",
        "\n",
        "accuracy_score(y_test,lr2.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfcmEixtgRrI",
        "outputId": "97e2f375-d20d-4c5e-d91c-e0a3c3e4de35"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn2=KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "knn2.fit(X_train,y_train)\n",
        "\n",
        "accuracy_score(y_test,knn2.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smznDZIAgVQy",
        "outputId": "da8caff3-eee7-4754-be0c-6cd7403ed9d7"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tree2=DecisionTreeClassifier(max_depth=3,\n",
        "                             random_state=42)\n",
        "\n",
        "tree2.fit(X_train,y_train)\n",
        "\n",
        "accuracy_score(y_test,tree2.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uelWmaPOgXFF",
        "outputId": "c97e5562-0988-4db5-fda4-56891b02fa88"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the code of the third class?\n",
        "\n",
        "The code of the third class (Iris-virginica) is 2.\n",
        "\n",
        "Are there any changes in the accuracies?\n",
        "\n",
        "No significant changes were observed in the accuracies after changing the hyperparameters. All three models achieved an accuracy of 1.0 because the Iris dataset is simple and easy to classify."
      ],
      "metadata": {
        "id": "tqgsz3pcilBC"
      }
    }
  ]
}