Heart_Disease_Prediction_ML.ipynb

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        "<a href=\"https://colab.research.google.com/github/sakshisahu612/Heart-Disease-Prediction-using-Machine-Learning/blob/main/Heart_Disease_Prediction_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "**Importing essential libraries**"
      ],
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      "outputs": [
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          "name": "stdout",
          "text": [
            "['.config', 'heart.csv', 'sample_data']\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import os\n",
        "print(os.listdir())\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing and understanding our dataset**"
      ],
      "metadata": {
        "id": "pyF-jAGNKzRx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"heart.csv\")"
      ],
      "metadata": {
        "id": "AkMgjvHc57YN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verifying it as a 'dataframe' object in pandas"
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    {
      "cell_type": "code",
      "source": [
        "type(dataset)"
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        "outputId": "26fb8af1-f816-4264-ea8d-43b9f95ed1e4"
      },
      "execution_count": null,
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          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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    {
      "cell_type": "markdown",
      "source": [
        "Shape of dataset"
      ],
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        "id": "jl_y0r30MIbC"
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    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
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          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 7
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Printing out a few columns"
      ],
      "metadata": {
        "id": "LDV8J2imML-G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head(5)"
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
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              "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
              "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   0     1       1  \n",
              "1   0     2       1  \n",
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            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "103   42    1   2       120   240    1        1      194      0      0.8   \n",
              "143   67    0   0       106   223    0        1      142      0      0.3   \n",
              "277   57    1   1       124   261    0        1      141      0      0.3   \n",
              "271   61    1   3       134   234    0        1      145      0      2.6   \n",
              "90    48    1   2       124   255    1        1      175      0      0.0   \n",
              "\n",
              "     slope  ca  thal  target  \n",
              "103      0   0     3       1  \n",
              "143      2   2     2       1  \n",
              "277      2   0     3       0  \n",
              "271      1   2     2       0  \n",
              "90       2   2     2       1  "
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              "      <th>103</th>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>240</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>194</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>67</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>106</td>\n",
              "      <td>223</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>142</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>124</td>\n",
              "      <td>261</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>134</td>\n",
              "      <td>234</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>145</td>\n",
              "      <td>0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>124</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8c83a27c-d819-4dc2-b24f-540b44d87c36');\n",
              "        const dataTable =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Description"
      ],
      "metadata": {
        "id": "zpkOE4L_MUIM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "MsG4qvJX6HJM",
        "outputId": "1bb35ad5-ef7e-4f6e-ab85-2dc8d962ae3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
              "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
              "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
              "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
              "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
              "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
              "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
              "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
              "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
              "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
              "\n",
              "             thal      target  \n",
              "count  303.000000  303.000000  \n",
              "mean     2.313531    0.544554  \n",
              "std      0.612277    0.498835  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    1.000000  \n",
              "75%      3.000000    1.000000  \n",
              "max      3.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a2a2b14-0407-429a-a0c0-0d96aec953da\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</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-5a2a2b14-0407-429a-a0c0-0d96aec953da')\"\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",
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              "  </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-5a2a2b14-0407-429a-a0c0-0d96aec953da 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-5a2a2b14-0407-429a-a0c0-0d96aec953da');\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-163e9c65-baa1-4b6c-9157-8af879612a41\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-163e9c65-baa1-4b6c-9157-8af879612a41')\"\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-163e9c65-baa1-4b6c-9157-8af879612a41 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DrlGWbpe6HfZ",
        "outputId": "33554c2e-fdfc-4273-b50c-b3a9f1de6410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's understand our columns better:"
      ],
      "metadata": {
        "id": "X0amEhucMaiE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info = [\"age\",\"1: male, 0: female\",\"chest pain type, 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic\",\"resting blood pressure\",\" serum cholestoral in mg/dl\",\"fasting blood sugar > 120 mg/dl\",\"resting electrocardiographic results (values 0,1,2)\",\" maximum heart rate achieved\",\"exercise induced angina\",\"oldpeak = ST depression induced by exercise relative to rest\",\"the slope of the peak exercise ST segment\",\"number of major vessels (0-3) colored by flourosopy\",\"thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\"]\n",
        "\n",
        "\n",
        "\n",
        "for i in range(len(info)):\n",
        "    print(dataset.columns[i]+\":\\t\\t\\t\"+info[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kyoo2Tfe6H92",
        "outputId": "d46c8e33-8a1c-4a1f-bb5f-b332d6d7194c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age:\t\t\tage\n",
            "sex:\t\t\t1: male, 0: female\n",
            "cp:\t\t\tchest pain type, 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic\n",
            "trestbps:\t\t\tresting blood pressure\n",
            "chol:\t\t\t serum cholestoral in mg/dl\n",
            "fbs:\t\t\tfasting blood sugar > 120 mg/dl\n",
            "restecg:\t\t\tresting electrocardiographic results (values 0,1,2)\n",
            "thalach:\t\t\t maximum heart rate achieved\n",
            "exang:\t\t\texercise induced angina\n",
            "oldpeak:\t\t\toldpeak = ST depression induced by exercise relative to rest\n",
            "slope:\t\t\tthe slope of the peak exercise ST segment\n",
            "ca:\t\t\tnumber of major vessels (0-3) colored by flourosopy\n",
            "thal:\t\t\tthal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysing the 'target' variable"
      ],
      "metadata": {
        "id": "PJEAzf00MdjU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"target\"].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y377zEph6hmO",
        "outputId": "e2de562a-ff0e-4b96-f44d-3a5dbf049dde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    303.000000\n",
              "mean       0.544554\n",
              "std        0.498835\n",
              "min        0.000000\n",
              "25%        0.000000\n",
              "50%        1.000000\n",
              "75%        1.000000\n",
              "max        1.000000\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"target\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAwweCyS6ihs",
        "outputId": "dd24c1bf-dea1-46f1-c92d-6bd05011091f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Clearly, this is a classification problem, with the target variable having values '0' and '1'"
      ],
      "metadata": {
        "id": "Pqh2drtYMos4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratory Data Analysis (EDA)**"
      ],
      "metadata": {
        "id": "9IR8Q4OpNIzj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, analysing the target variable:"
      ],
      "metadata": {
        "id": "eTbzDyYBNPJ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = dataset[\"target\"]\n",
        "\n",
        "sns.countplot(y)\n",
        "\n",
        "\n",
        "target_temp = dataset.target.value_counts()\n",
        "\n",
        "print(target_temp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "2dvMINYq6jMs",
        "outputId": "aec69fcc-9017-4425-c537-49519d59c0f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    165\n",
            "0    138\n",
            "Name: target, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Percentage of patience without heart problems: \"+str(round(target_temp[0]*100/303,2)))\n",
        "print(\"Percentage of patience with heart problems: \"+str(round(target_temp[1]*100/303,2)))\n",
        "\n",
        "#Alternatively,\n",
        "# print(\"Percentage of patients with heart problems: \"+str(y.where(y==1).count()*100/303))\n",
        "# print(\"Percentage of patients with heart problems: \"+str(y.where(y==0).count()*100/303))\n",
        "\n",
        "# #Or,\n",
        "# countNoDisease = len(df[df.target == 0])\n",
        "# countHaveDisease = len(df[df.target == 1])"
      ],
      "metadata": {
        "id": "YrHRqtwf6jh-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54c4675a-140c-4314-b510-058f898f3e63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of patience without heart problems: 45.54\n",
            "Percentage of patience with heart problems: 54.46\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**We'll analyse 'sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca' and 'thal' features**"
      ],
      "metadata": {
        "id": "ZnAmnlMfNXTC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysing the 'Sex' feature"
      ],
      "metadata": {
        "id": "yzI8LI8INcgo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"sex\"].unique()"
      ],
      "metadata": {
        "id": "6ZFf9FFq6j6H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d86c370a-13d0-42b3-9142-b7987453fc37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We notice, that as expected, the 'sex' feature has 2 unique features"
      ],
      "metadata": {
        "id": "S89soKh6NlFV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=dataset[\"sex\"],y=y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "QIyeTTdh9Xmw",
        "outputId": "1c22b1e8-a9e7-40b3-dfae-07906015429b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sex', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We notice, that females are more likely to have heart problems than males"
      ],
      "metadata": {
        "id": "TocxYflmNvxu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"cp\"].unique()"
      ],
      "metadata": {
        "id": "N0NoLyh264RY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eae5c833-4b19-46e5-c5a7-dda29c593527"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 2, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As expected, the CP feature has values from 0 to 3"
      ],
      "metadata": {
        "id": "D1dOWNnMNyDd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=dataset[\"cp\"],y=y)"
      ],
      "metadata": {
        "id": "kq6Lhggx7D1i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "d4a7773b-dcbb-42cb-c49f-488a939a7c78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='cp', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We notice, that chest pain of '0', i.e. the ones with typical angina are much less likely to have heart problems"
      ],
      "metadata": {
        "id": "GGH-WuDYN5_q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analysing the FBS feature**"
      ],
      "metadata": {
        "id": "aXyK9Rj5N7Mh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"fbs\"].describe()"
      ],
      "metadata": {
        "id": "IVr1aqpu7EIu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db05d5a1-e186-4376-a818-3b0c76092db0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    303.000000\n",
              "mean       0.148515\n",
              "std        0.356198\n",
              "min        0.000000\n",
              "25%        0.000000\n",
              "50%        0.000000\n",
              "75%        0.000000\n",
              "max        1.000000\n",
              "Name: fbs, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"fbs\"].unique()"
      ],
      "metadata": {
        "id": "ADuFsv9x7Ecl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e36ecb79-b704-42eb-e99f-13c5c4e1167c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=dataset[\"fbs\"],y=y)"
      ],
      "metadata": {
        "id": "guyz_lBp7FDv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "efb23fd6-b456-4d86-a7ce-1970ec355a44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='fbs', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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nTp2qzZs3S9r/0tVX3zkFAAAwnMKOm9TUVC1fvlw9PT2htS+++ELLly9XamqqJOmjjz6S0+kcvCkBAAAGKOyXpaqqqnTuuedqypQpmjdvnqT9Z3MCgYDWr18vSdq+fbuuuOKKwZ0UAABgAMKOm/nz5+uDDz7QY489pq1bt0qSfv7zn+sXv/iFxo4dK0m65JJLBndKAACAAQorbr744gulpqZq/fr1+u1vfztUMwEAABy2sK65GT16tD7//POhmgUAAOCIhX1B8aJFi3T77bfryy+/HIp5AAAAjkjY19y88cYb8nq9euGFF3TyySdrzJgxfW5ft27doA0HAAAQrrDjZty4cbrggguGYhYAAIAjFnbcPPTQQ0MxBwAAwKAI+5obAACASBb2mRtJeuqpp/TnP/9ZLS0tfT6pWJKam5sHZTAAAIDDEfaZm7vuuktut1tOp1MbN25UVlaWjjvuOG3fvl0/+tGPhmJGAACAAQs7bu69916tXr1ad999t2JiYnT99dfrxRdf1FVXXaWOjo6hmBEAAGDAwo6blpYWzZ8/X5J0zDHHaO/evZL2f+XCE088MbjTAQAAhCnsuElMTFR7e7sk6YQTTtBrr70mSfrggw/U29s7uNMBAACEKey4+f73v69nn31WkuR2u3XNNddowYIFysvL0/nnnz/oAwIAAIQj7LhZsmSJiouLJe3/KoaamhrNnj1by5Yt04033nhYQ1RVVSklJUUOh0PZ2dlqbGw86L7r1q1TZmamxo0bpzFjxig9PV2PPPLIYT0uAAAwT9hvBZ8xY4Z2796tiRMnSpIuuugiXXTRRfrPf/6jiRMnKhAIhHV/tbW18ng8qq6uVnZ2tioqKpSbm6stW7aEHuN/TZgwQUuWLFFqaqpiYmK0fv16ud1uTZw4Ubm5ueE+HQAAYJiwz9wc7Lqaffv2yeFwhD1AeXm5CgsL5Xa7NWfOHFVXVys2NlY1NTX97n/mmWfq/PPP1+zZszV9+nQtXrxY8+bN0yuvvBL2YwMAAPMM+MyNx+ORJNlsNpWUlCg2NjZ0WyAQ0Ouvv6709PSwHrynp0dNTU2hl7kkKSoqSi6XSw0NDd94fG9vr1566SVt2bJFt99+e7/7dHd3q7u7O/RzZ2dnWDMCAICRZcBxs3HjRkn7g2Lz5s2KiYkJ3RYTE6O0tDRdd911YT14W1ubAoGAnE5nn3Wn06n33nvvoMd1dHQoKSlJ3d3dio6O1r333qsFCxb0u29ZWZluueWWsOYCAAAj14Dj5uWXX5a0/x1SlZWViouLG7KhvsnYsWO1adMm7du3T16vVx6PR9OmTdOZZ555wL7FxcWhs07S/jM3ycnJwzgtAAAYTpZ+K3hCQoKio6Pl9/v7rPv9fiUmJh70uKioKM2YMUOSlJ6ernfffVdlZWX9xo3dbpfdbh+0mQEAQGSz9FvBY2JilJGRIa/XG1oLBoPyer3KyckZ8P0Eg8E+19UAAICj12F9K/hg8ng8KigoUGZmprKyslRRUaGuri653W5JUn5+vpKSklRWViZp/zU0mZmZmj59urq7u7VhwwY98sgjuu+++6x8GgAAIEJYHjd5eXlqbW1VSUmJfD6f0tPTVVdXF7rIuKWlRVFRX59g6urq0hVXXKEPP/xQxxxzjFJTU/Xoo48qLy/PqqcAAAAiiK33KPtCqM7OTsXHx6ujo2NIL4rO+N2fhuy+gZGq6Y58q0cYFC3LTrZ6BCDinFCyeUjvP5x/vy295gYAAGCwETcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjBIRcVNVVaWUlBQ5HA5lZ2ersbHxoPuuWbNGp59+usaPH6/x48fL5XIdcn8AAHB0sTxuamtr5fF4VFpaqubmZqWlpSk3N1d79uzpd//6+npdfPHFevnll9XQ0KDk5GT94Ac/0EcffTTMkwMAgEhkedyUl5ersLBQbrdbc+bMUXV1tWJjY1VTU9Pv/o899piuuOIKpaenKzU1VQ888ICCwaC8Xm+/+3d3d6uzs7PPBgAAzGVp3PT09KipqUkulyu0FhUVJZfLpYaGhgHdx6effqovvvhCEyZM6Pf2srIyxcfHh7bk5ORBmR0AAEQmS+Omra1NgUBATqezz7rT6ZTP5xvQfdxwww2aPHlyn0D6X8XFxero6Ahtu3btOuK5AQBA5Bpl9QBHYvny5XryySdVX18vh8PR7z52u112u32YJwMAAFaxNG4SEhIUHR0tv9/fZ93v9ysxMfGQx65cuVLLly/X3/72N82bN28oxwQAACOIpS9LxcTEKCMjo8/FwF9dHJyTk3PQ41asWKFbb71VdXV1yszMHI5RAQDACGH5y1Iej0cFBQXKzMxUVlaWKioq1NXVJbfbLUnKz89XUlKSysrKJEm33367SkpK9PjjjyslJSV0bc6xxx6rY4891rLnAQAAIoPlcZOXl6fW1laVlJTI5/MpPT1ddXV1oYuMW1paFBX19Qmm++67Tz09Pbrwwgv73E9paaluvvnm4RwdAABEIMvjRpKKiopUVFTU72319fV9ft6xY8fQDwQAAEYsyz/EDwAAYDARNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwiuVxU1VVpZSUFDkcDmVnZ6uxsfGg+77zzju64IILlJKSIpvNpoqKiuEbFAAAjAiWxk1tba08Ho9KS0vV3NystLQ05ebmas+ePf3u/+mnn2ratGlavny5EhMTh3laAAAwElgaN+Xl5SosLJTb7dacOXNUXV2t2NhY1dTU9Lv/qaeeqjvuuEMXXXSR7Hb7ME8LAABGAsvipqenR01NTXK5XF8PExUll8ulhoaGQXuc7u5udXZ29tkAAIC5LIubtrY2BQIBOZ3OPutOp1M+n2/QHqesrEzx8fGhLTk5edDuGwAARB7LLygeasXFxero6Ahtu3btsnokAAAwhEZZ9cAJCQmKjo6W3+/vs+73+wf1YmG73c71OQAAHEUsO3MTExOjjIwMeb3e0FowGJTX61VOTo5VYwEAgBHOsjM3kuTxeFRQUKDMzExlZWWpoqJCXV1dcrvdkqT8/HwlJSWprKxM0v6LkP/1r3+F/vzRRx9p06ZNOvbYYzVjxgzLngcAAIgclsZNXl6eWltbVVJSIp/Pp/T0dNXV1YUuMm5paVFU1Ncnlz7++GOdcsopoZ9XrlyplStX6owzzlB9ff1wjw8AACKQpXEjSUVFRSoqKur3tv8/WFJSUtTb2zsMUwEAgJHK+HdLAQCAowtxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoERE3VVVVSklJkcPhUHZ2thobGw+5/1/+8helpqbK4XDo5JNP1oYNG4ZpUgAAEOksj5va2lp5PB6VlpaqublZaWlpys3N1Z49e/rd/9VXX9XFF1+sSy+9VBs3btTChQu1cOFC/fOf/xzmyQEAQCSyPG7Ky8tVWFgot9utOXPmqLq6WrGxsaqpqel3/8rKSv3whz/U7373O82ePVu33nqrvv3tb+uee+4Z5skBAEAkGmXlg/f09KipqUnFxcWhtaioKLlcLjU0NPR7TENDgzweT5+13NxcPfPMM/3u393dre7u7tDPHR0dkqTOzs4jnP7QAt2fDen9AyPRUP/eDZe9nwesHgGIOEP9+/3V/ff29n7jvpbGTVtbmwKBgJxOZ591p9Op9957r99jfD5fv/v7fL5+9y8rK9Mtt9xywHpycvJhTg3gcMXf/VurRwAwVMrih+Vh9u7dq/j4Qz+WpXEzHIqLi/uc6QkGg2pvb9dxxx0nm81m4WQYDp2dnUpOTtauXbsUFxdn9TgABhG/30eX3t5e7d27V5MnT/7GfS2Nm4SEBEVHR8vv9/dZ9/v9SkxM7PeYxMTEsPa32+2y2+191saNG3f4Q2NEiouL4y8/wFD8fh89vumMzVcsvaA4JiZGGRkZ8nq9obVgMCiv16ucnJx+j8nJyemzvyS9+OKLB90fAAAcXSx/Wcrj8aigoECZmZnKyspSRUWFurq65Ha7JUn5+flKSkpSWVmZJGnx4sU644wzdOedd+onP/mJnnzySb355ptavXq1lU8DAABECMvjJi8vT62trSopKZHP51N6errq6upCFw23tLQoKurrE0zz58/X448/rptuukm///3vNXPmTD3zzDOaO3euVU8BEcxut6u0tPSAlyYBjHz8fuNgbL0DeU8VAADACGH5h/gBAAAMJuIGAAAYhbgBAABGIW4AAIBRiBsYraqqSikpKXI4HMrOzlZjY6PVIwE4Qn//+991zjnnaPLkybLZbAf9bkEcvYgbGKu2tlYej0elpaVqbm5WWlqacnNztWfPHqtHA3AEurq6lJaWpqqqKqtHQYTireAwVnZ2tk499VTdc889kvZ/+nVycrKuvPJK3XjjjRZPB2Aw2Gw2Pf3001q4cKHVoyCCcOYGRurp6VFTU5NcLldoLSoqSi6XSw0NDRZOBgAYasQNjNTW1qZAIBD6pOuvOJ1O+Xw+i6YCAAwH4gYAABiFuIGREhISFB0dLb/f32fd7/crMTHRoqkAAMOBuIGRYmJilJGRIa/XG1oLBoPyer3KycmxcDIAwFCz/FvBgaHi8XhUUFCgzMxMZWVlqaKiQl1dXXK73VaPBuAI7Nu3T9u2bQv9/MEHH2jTpk2aMGGCTjjhBAsnQ6TgreAw2j333KM77rhDPp9P6enpuuuuu5SdnW31WACOQH19vc4666wD1gsKCrR27drhHwgRh7gBAABG4ZobAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAjRm9vry677DJNmDBBNptN48aN09VXX231WAAiDHEDYMSoq6vT2rVrtX79eu3evVtz5861eiQAEYgvzgQwYrz//vuaNGmS5s+fL0kaNYq/wgAciDM3AEaEX/3qV7ryyivV0tIim82mlJQUSdKXX36poqIixcfHKyEhQUuXLtX/fmXevffeq5kzZ8rhcMjpdOrCCy+06BkAGC7EDYARobKyUsuWLdOUKVO0e/duvfHGG5Kkhx9+WKNGjVJjY6MqKytVXl6uBx54QJL05ptv6qqrrtKyZcu0ZcsW1dXV6Xvf+56VTwPAMOCcLoARIT4+XmPHjlV0dLQSExND68nJyVq1apVsNptmzZqlzZs3a9WqVSosLFRLS4vGjBmjn/70pxo7dqymTp2qU045xcJnAWA4cOYGwIh22mmnyWazhX7OycnRv//9bwUCAS1YsEBTp07VtGnTdMkll+ixxx7Tp59+auG0AIYDcQPAWGPHjlVzc7OeeOIJTZo0SSUlJUpLS9Mnn3xi9WgAhhBxA2BEe/311/v8/Nprr2nmzJmKjo6WtP8dVS6XSytWrNDbb7+tHTt26KWXXrJiVADDhGtuAIxoLS0t8ng8+s1vfqPm5mbdfffduvPOOyVJ69ev1/bt2/W9731P48eP14YNGxQMBjVr1iyLpwYwlIgbACNafn6+PvvsM2VlZSk6OlqLFy/WZZddJkkaN26c1q1bp5tvvlmff/65Zs6cqSeeeELf+ta3LJ4awFCy9f7vB0IAAACMcFxzAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCj/D08GL2AYfq36AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nothing extraordinary here"
      ],
      "metadata": {
        "id": "u0Y4RA49OBDQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analysing the restecg feature**"
      ],
      "metadata": {
        "id": "f7zy9u2BOEYI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"restecg\"].unique()"
      ],
      "metadata": {
        "id": "8MvJaGxk7aA-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d405480-195c-42c3-e435-88eed2e22aa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=dataset[\"restecg\"],y=y)"
      ],
      "metadata": {
        "id": "xTR75-hm7aXP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "8ab643eb-86f2-44eb-c097-1fcbfa283a9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='restecg', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We realize that people with restecg '1' and '0' are much more likely to have a heart disease than with restecg '2'"
      ],
      "metadata": {
        "id": "haAgJw3OOk72"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analysing the 'exang' feature**"
      ],
      "metadata": {
        "id": "hJYUTsfbOnkc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"exang\"].unique()"
      ],
      "metadata": {
        "id": "HlPXfCyT7avO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a536d001-fe8d-47e7-b71f-c06fa506de5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=dataset[\"exang\"],y=y)"
      ],
      "metadata": {
        "id": "YxEJicQu7bIu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "17232c6d-0456-4a33-8cbf-d005e2cfe0b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='exang', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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dOv3pT3/Sn/70J61bt05z587Vxo0btX79ev3ud7/Tbbfd9rH31dfXp9bWVnm93o8Gstvl9XrV3Nw85HF33nmnZsyYoaVLl37sYwQCAaWlpcW27Ozs4T1RAAAwLsV95uYnP/mJ1q5dq+Li4ti+hQsXaubMmbr99tvV0tKiSZMm6YYbbtDq1atPeF8dHR2KRCJyu90D9rvdbr3yyiuDHvPMM89o/fr1euGFF4Y1b2Vlpfx+f+zn7u5uAgcAAIPFHTe7d+/WOeecc9z+c845R7t375Z07KWrD79zaiQdOnRI3/rWt/Tggw8qPT19WMc4nU4+XBAAgNNI3HEzb948rVy5UuvWrZPD4ZAkHT16VCtXrtS8efMkSW+//fZxZ2MGk56erqSkJIXD4QH7w+GwMjIyjlv/6quv6o033tAXv/jF2L5oNHrsiUyYoD179vB2dAAATnNxx01tba2+9KUvaebMmTr//PMlHTubE4lE9Nvf/laS9Nprr+naa6/92PtyOBzKy8tTMBiMvZ07Go0qGAyqoqLiuPXz5s2LnR360G233aZDhw5p7dq1vNwEAADij5tFixbp9ddf16OPPqq9e/dKkq688kp94xvf0OTJkyVJ3/rWt4Z9f36/X2VlZcrPz1dBQYFqamrU09MTe/dUaWmpsrKyFAgE5HK5dN555w04fsqUKZJ03H4AAHB6iitujh49qnnz5um3v/2trrnmmhEZoKSkRO3t7aqqqlIoFJLH41FjY2PsZa22tjbZ7Sf1WYMAAOA0FFfcJCcn6+9///uID1FRUTHoy1CS1NTUdMJjH3rooRGfBwAAjF9xnxJZtmyZfvazn+mDDz4YjXkAAABOSdzX3Dz//PMKBoN68skntXDhQk2aNGnA7Y8//viIDQcAABCvuONmypQp+upXvzoaswAAAJyyuOPmF7/4xWjMAQAAMCJ4GxIAADBK3GduJGnz5s361a9+pba2NvX19Q24befOnSMyGAAAwMmI+8zNvffeK5/PJ7fbrV27dqmgoEDTp0/Xa6+9ps9//vOjMSMAAMCwxR03//7v/65169bpvvvuk8Ph0E033aSnnnpKP/jBD9TV1TUaMwIAAAxb3HHT1tamRYsWSZImTpyoQ4cOSTr2lQv/+Z//ObLTAQAAxCnuuMnIyFBnZ6ck6eyzz9Yf/vAHSdLrr7+u/v7+kZ0OAAAgTnHHzec+9zn95je/kST5fD5df/31uuyyy1RSUqIrrrhixAcEAACIR9zvlrr11luVlZUl6dhXMUyfPl07duzQl770JV1++eUjPiAAAEA84o6b2bNn68CBA5oxY4Yk6aqrrtJVV12lv/3tb5oxY4YikciIDwkAADBccb8sNdR1NYcPH5bL5TrlgQAAAE7FsM/c+P1+SZLNZlNVVZVSUlJit0UiET333HPyeDwjPiAAAEA8hh03u3btknTszM3u3bvlcDhitzkcDuXm5mrFihUjPyEAAEAchh0327dvl3TsHVJr165VamrqqA0FAABwsvhWcAAAYBS+FRwAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABglLg/xA8AAKstX75c7e3tkqQzzzxTa9eutXgiJBLiBgAw7rS3tyscDls9BhIUL0sBAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMEpCxE1tba1ycnLkcrlUWFiolpaWIdc+/vjjys/P15QpUzRp0iR5PB49/PDDYzgtAABIZJbHTUNDg/x+v6qrq7Vz507l5uaquLhYBw8eHHT9tGnTdOutt6q5uVkvvviifD6ffD6ftm3bNsaTAwCARGR53KxZs0bl5eXy+XxasGCB6urqlJKSovr6+kHXX3LJJbriiis0f/58nXvuuVq+fLnOP/98PfPMM2M8OQAASESWxk1fX59aW1vl9Xpj++x2u7xer5qbmz/2+P7+fgWDQe3Zs0ef+cxnBl3T29ur7u7uARsAADCXpXHT0dGhSCQit9s9YL/b7VYoFBryuK6uLp1xxhlyOBxavHix7rvvPl122WWDrg0EAkpLS4tt2dnZI/ocAABAYrH8ZamTMXnyZL3wwgt6/vnn9ZOf/ER+v19NTU2Drq2srFRXV1ds279//9gOCwAAxpSl3wqenp6upKSk477ZNRwOKyMjY8jj7Ha7Zs+eLUnyeDz6y1/+okAgoEsuueS4tU6nU06nc0TnBgAAicvSMzcOh0N5eXkKBoOxfdFoVMFgUEVFRcO+n2g0qt7e3tEYEQAAjDOWnrmRJL/fr7KyMuXn56ugoEA1NTXq6emRz+eTJJWWliorK0uBQEDSsWto8vPzde6556q3t1dbt27Vww8/rAceeMDKpwEAABKE5XFTUlKi9vZ2VVVVKRQKyePxqLGxMXaRcVtbm+z2j04w9fT06Nprr9Vbb72liRMnat68eXrkkUdUUlJi1VMAAAAJxNbf399v9RBjqbu7W2lpaerq6lJqauqoPU7ejRtG7b6B8ap1VanVI8AQ3/jGN2LXa7rdbm3cuNHiiTDa4vn3e1y+WwoAAGAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjTLB6AAAYb9ruXGj1CKe9D96bLinp///5Hf6bJICzq3ZbPUIMZ24AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYJSHipra2Vjk5OXK5XCosLFRLS8uQax988EFddNFFmjp1qqZOnSqv13vC9QAA4PRiedw0NDTI7/erurpaO3fuVG5uroqLi3Xw4MFB1zc1NenrX/+6tm/frubmZmVnZ+uf//mf9fbbb4/x5AAAIBFZHjdr1qxReXm5fD6fFixYoLq6OqWkpKi+vn7Q9Y8++qiuvfZaeTwezZs3T//xH/+haDSqYDA46Pre3l51d3cP2AAAgLksjZu+vj61trbK6/XG9tntdnm9XjU3Nw/rPo4cOaKjR49q2rRpg94eCASUlpYW27Kzs0dkdgAAkJgsjZuOjg5FIhG53e4B+91ut0Kh0LDu44c//KHOOuusAYH0v1VWVqqrqyu27d+//5TnBgAAiWuC1QOcipUrV2rTpk1qamqSy+UadI3T6ZTT6RzjyQAAgFUsjZv09HQlJSUpHA4P2B8Oh5WRkXHCY1evXq2VK1fq6aef1vnnnz+aYwIAgHHE0pelHA6H8vLyBlwM/OHFwUVFRUMed/fdd+uuu+5SY2Oj8vPzx2JUAAAwTlj+spTf71dZWZny8/NVUFCgmpoa9fT0yOfzSZJKS0uVlZWlQCAgSfrZz36mqqoqbdy4UTk5ObFrc8444wydccYZlj0PAACQGCyPm5KSErW3t6uqqkqhUEgej0eNjY2xi4zb2tpkt390gumBBx5QX1+f/uVf/mXA/VRXV+tHP/rRWI4OAAASkOVxI0kVFRWqqKgY9LampqYBP7/xxhujPxAAABi3LP8QPwAAgJFE3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMMoEqwcAACBe05yRQf8MSMQNAGAcuuWC96weAQmMl6UAAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFEsj5va2lrl5OTI5XKpsLBQLS0tQ659+eWX9dWvflU5OTmy2WyqqakZu0EBAMC4YGncNDQ0yO/3q7q6Wjt37lRubq6Ki4t18ODBQdcfOXJE//AP/6CVK1cqIyNjjKcFAADjgaVxs2bNGpWXl8vn82nBggWqq6tTSkqK6uvrB13/yU9+UqtWrdJVV10lp9M5xtMCAIDxwLK46evrU2trq7xe70fD2O3yer1qbm4escfp7e1Vd3f3gA0AAJjLsrjp6OhQJBKR2+0esN/tdisUCo3Y4wQCAaWlpcW27OzsEbtvAACQeCy/oHi0VVZWqqurK7bt37/f6pEAAMAommDVA6enpyspKUnhcHjA/nA4PKIXCzudTq7PAQDgNGLZmRuHw6G8vDwFg8HYvmg0qmAwqKKiIqvGAgAA45xlZ24kye/3q6ysTPn5+SooKFBNTY16enrk8/kkSaWlpcrKylIgEJB07CLkP//5z7E/v/3223rhhRd0xhlnaPbs2ZY9DwAAkDgsjZuSkhK1t7erqqpKoVBIHo9HjY2NsYuM29raZLd/dHLpnXfe0QUXXBD7efXq1Vq9erUuvvhiNTU1jfX4AAAgAVkaN5JUUVGhioqKQW/7v8GSk5Oj/v7+MZgKAACMV8a/WwoAAJxeiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGSYi4qa2tVU5OjlwulwoLC9XS0nLC9Y899pjmzZsnl8ulhQsXauvWrWM0KQAASHSWx01DQ4P8fr+qq6u1c+dO5ebmqri4WAcPHhx0/Y4dO/T1r39dS5cu1a5du7RkyRItWbJEL7300hhPDgAAEpHlcbNmzRqVl5fL5/NpwYIFqqurU0pKiurr6wddv3btWl1++eW68cYbNX/+fN1111268MILdf/994/x5AAAIBFNsPLB+/r61NraqsrKytg+u90ur9er5ubmQY9pbm6W3+8fsK+4uFi//vWvB13f29ur3t7e2M9dXV2SpO7u7lOc/sQive+P6v0D49Fo/96NlUN/j1g9ApBwRvv3+8P77+/v/9i1lsZNR0eHIpGI3G73gP1ut1uvvPLKoMeEQqFB14dCoUHXBwIB3XHHHcftz87OPsmpAZystPuusXoEAKMlkDYmD3Po0CGlpZ34sSyNm7FQWVk54ExPNBpVZ2enpk+fLpvNZuFkGAvd3d3Kzs7W/v37lZqaavU4AEYQv9+nl/7+fh06dEhnnXXWx661NG7S09OVlJSkcDg8YH84HFZGRsagx2RkZMS13ul0yul0Dtg3ZcqUkx8a41Jqair/8wMMxe/36ePjzth8yNILih0Oh/Ly8hQMBmP7otGogsGgioqKBj2mqKhowHpJeuqpp4ZcDwAATi+Wvyzl9/tVVlam/Px8FRQUqKamRj09PfL5fJKk0tJSZWVlKRAISJKWL1+uiy++WPfcc48WL16sTZs26Y9//KPWrVtn5dMAAAAJwvK4KSkpUXt7u6qqqhQKheTxeNTY2Bi7aLitrU12+0cnmBYtWqSNGzfqtttu0y233KI5c+bo17/+tc477zyrngISmNPpVHV19XEvTQIY//j9xlBs/cN5TxUAAMA4YfmH+AEAAIwk4gYAABiFuAEAAEYhbgAAgFGIGxittrZWOTk5crlcKiwsVEtLi9UjAThFv//97/XFL35RZ511lmw225DfLYjTF3EDYzU0NMjv96u6ulo7d+5Ubm6uiouLdfDgQatHA3AKenp6lJubq9raWqtHQYLireAwVmFhoT75yU/q/vvvl3Ts06+zs7P1/e9/XzfffLPF0wEYCTabTU888YSWLFli9ShIIJy5gZH6+vrU2toqr9cb22e32+X1etXc3GzhZACA0UbcwEgdHR2KRCKxT7r+kNvtVigUsmgqAMBYIG4AAIBRiBsYKT09XUlJSQqHwwP2h8NhZWRkWDQVAGAsEDcwksPhUF5enoLBYGxfNBpVMBhUUVGRhZMBAEab5d8KDowWv9+vsrIy5efnq6CgQDU1Nerp6ZHP57N6NACn4PDhw9q3b1/s59dff10vvPCCpk2bprPPPtvCyZAoeCs4jHb//fdr1apVCoVC8ng8uvfee1VYWGj1WABOQVNTkz772c8et7+srEwPPfTQ2A+EhEPcAAAAo3DNDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAsF41GFQgENGvWLE2cOFG5ubnavHmz+vv75fV6VVxcrA+/Bq+zs1MzZ85UVVWVJCkSiWjp0qWxY+fOnau1a9cOuP9vf/vbWrJkiVavXq3MzExNnz5dy5Yt09GjR2NrDhw4oMWLF2vixImaNWuWNm7cqJycHNXU1IzZ3wOAkTHB6gEAIBAI6JFHHlFdXZ3mzJmj3//+9/rmN7+pM888U7/85S+1cOFC3XvvvVq+fLmuueYaZWVlxeImGo1q5syZeuyxxzR9+nTt2LFD3/nOd5SZmamvfe1rscfYvn27MjMztX37du3bt08lJSXyeDwqLy+XJJWWlqqjo0NNTU1KTk6W3+/XwYMHLfn7AHBq+FZwAJbq7e3VtGnT9PTTT6uoqCi2/+qrr9aRI0e0ceNGPfbYYyotLdV1112n++67T7t27dKcOXOGvM+KigqFQiFt3rxZ0rEzN01NTXr11VeVlJQkSfra174mu92uTZs26ZVXXtH8+fP1/PPPKz8/X5K0b98+zZkzR//2b/+m6667bvT+AgCMOM7cALDUvn37dOTIEV122WUD9vf19emCCy6QJF155ZV64okntHLlSj3wwAPHhU1tba3q6+vV1tam999/X319ffJ4PAPW/OM//mMsbCQpMzNTu3fvliTt2bNHEyZM0IUXXhi7ffbs2Zo6depIPlUAY4S4AWCpw4cPS5K2bNmirKysAbc5nU5J0pEjR9Ta2qqkpCT99a9/HbBm06ZNWrFihe655x4VFRVp8uTJWrVqlZ577rkB65KTkwf8bLPZFI1GR/rpAEgAxA0ASy1YsEBOp1NtbW26+OKLB11zww03yG6367//+7/1hS98QYsXL9bnPvc5SdKzzz6rRYsW6dprr42tf/XVV+OaYe7cufrggw+0a9cu5eXlSTp2Rundd989yWcFwErEDQBLTZ48WStWrND111+vaDSqT3/60+rq6tKzzz6r1NRUpaenq76+Xs3Nzbrwwgt14403qqysTC+++KKmTp2qOXPmaMOGDdq2bZtmzZqlhx9+WM8//7xmzZo17BnmzZsnr9er73znO3rggQeUnJysG264QRMnTpTNZhvFZw9gNPBWcACWu+uuu3T77bcrEAho/vz5uvzyy7Vlyxbl5ORo6dKl+tGPfhS7HuaOO+6Q2+3WNddcI0n67ne/q6985SsqKSlRYWGh/va3vw04izNcGzZskNvt1mc+8xldccUVKi8v1+TJk+VyuUb0uQIYfbxbCgAG8dZbbyk7O1tPP/20Lr30UqvHARAH4gYAJP3ud7/T4cOHtXDhQh04cEA33XST3n77be3du/e4i5EBJDauuQEASUePHtUtt9yi1157TZMnT9aiRYv06KOPEjbAOMSZGwAAYBQuKAYAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAY5f8Bz6SybK1bnNgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "People with exang=1 i.e. Exercise induced angina are much less likely to have heart problems"
      ],
      "metadata": {
        "id": "3C3aQgIkOsxp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analysing the Slope feature**"
      ],
      "metadata": {
        "id": "FIu8Ugi4OwfI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"slope\"].unique()"
      ],
      "metadata": {
        "id": "uBWCdGrL7kkP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69d536ca-be55-4096-c68b-a025d99b1c6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=dataset[\"slope\"],y=y)"
      ],
      "metadata": {
        "id": "aoMjxh2g7lIk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "2bcd06d0-8e7b-4e1c-a8ee-b02863f6c6bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='slope', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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4U6f9IX5ut1uSFBERoRUrVigmJiZ4W1dXl3bt2qW0tLR+HxAAACAUpx03u3fvlvTFmZu9e/fKZrMFb7PZbEpNTdU999zT/xMCAACE4LTj5uWXX5YkuVwurV69mk/3BQAAYSnka26effZZxcbG6tChQ9q2bZv+8pe/SFKf76ACAAAYbCHHTWtrq6666ip985vf1He/+10dOXJEknTbbbfpRz/6Ub8PCAAAEIqQ4+auu+7SiBEj1NjY2OOi4tzcXFVXV/frcAAAAKE67WtuvvTSSy9p27ZtmjhxYo/9F154oT788MN+GwwAAOBMhHzmxu/39zhj86XW1lbZ7fZ+GQoAAOBMhRw38+bN0y9/+cvg7xEREQoEAnriiSd05ZVX9utwAAAAoQr5ZaknnnhCV111lf74xz+qs7NT9913n95++221trbqtddeG4gZAQAATlvIZ24uuugiHTx4UJdddpluuOEG+f1+ff/739fu3bs1ZcqUgZgRAADgtIV85kaS4uLitHz58v6eBQAA4KyFHDdvvfVWn/sjIiIUHR2tSZMmcWExAACwTMhxk5aWFvxW8C8/lfj/fkv4iBEjlJubq2eeeYZvCQcAAIMu5GtuNm/erAsvvFBr167Vm2++qTfffFNr167VtGnTtHHjRq1fv15/+MMf9NBDDw3EvADQp2XLlmnRokVatGiRli1bZvU4ACwU8pmbH//4x1q9erVycnKC+2bNmqWJEydqxYoVqqur06hRo/SjH/1ITz75ZL8OCwAn09zcLJ/PZ/UYAMJAyHGzd+9eTZ48udf+yZMna+/evZK+eOnqy++cMlX6vb/8+kUGi/34ePC035GPjw/7f4/6VYutHgEA8P+F/LLU9OnTtXLlSnV2dgb3nThxQitXrtT06dMlSYcPH5bD4ei/KQEAAE5TyGduysvLdf3112vixImaPXu2pC/O5nR1denFF1+UJL333nu64447+ndSAACA0xBy3MyZM0fvv/++fvOb3+jgwYOSpJtuukmLFi3SmDFjJEm33HJL/04JAABwmkKKmxMnTmj69Ol68cUX9YMf/GCgZgIAADhjIV1zM2LECH322WcDNQsAAMBZC/mC4qVLl+onP/mJPv/884GYBwAA4KyEfM3NG2+8IY/Ho5deekmzZs3SqFGjetz+/PPP99twAAAAoQo5bsaOHasbb7xxIGYBAAA4ayHHzbPPPtvvQ5SXl2vVqlXyer1KTU3VmjVrlJmZ+bXHbdq0SQsXLtQNN9ygF154od/nAgAAQ0/I19z0t6qqKrndbhUXF6uhoUGpqanKycnR0aNHT3ncBx98oHvuuUfz5s0bpEkBAMBQEPKZG0l67rnn9O///u9qbGzs8UnFktTQ0BDSfZWWlio/P18ul0uSVFFRoS1btqiyslIPPPBAn8d0dXXp7//+7/Xwww/rv//7v/XJJ5+c9P47OjrU0dER/L29vT2k+QAAwNAS8pmbn/70p3K5XHI4HNq9e7cyMzM1YcIEvffee/rOd74T0n11dnaqvr5eTqfzq4EiI+V0OlVbW3vS4x555BElJCTotttu+9rHKCkpUVxcXHBLTk4OaUYAADC0hBw3P/vZz7R27VqtWbNGNptN9913n7Zv364777xTbW1tId1XS0uLurq6en0PlcPhkNfr7fOYHTt2aP369Vq3bt1pPUZhYaHa2tqCW1NTU0gzAgCAoSXkuGlsbNScOXMkSSNHjtSxY8ckffGVC//2b//Wv9P9lWPHjumWW27RunXrFB8ff1rH2O12xcbG9tgAAIC5Qr7mJjExUa2trZo8ebImTZqk119/XampqXr//ffV3d0d0n3Fx8crKipKPp+vx36fz6fExMRe699991198MEHmj9/fnBfIBD44omcc44OHDigKVOmhPqUAACAQUI+c/M3f/M3+v3vfy9Jcrlcuvvuu3X11VcrNzdX3/ve90K6L5vNpvT0dHk8nuC+QCAgj8ej7OzsXuunT5+uvXv3as+ePcHt+uuv15VXXqk9e/ZwPQ0AAAj9zM3y5cuVlJQk6YuvYpgwYYJ27typ66+/Xtdee23IA7jdbuXl5SkjI0OZmZkqKyuT3+8Pvntq8eLFSkpKUklJiaKjo3XRRRf1OH7s2LGS1Gs/AAAYnkKOm6lTp+rIkSNKSEiQJN188826+eab9b//+79KSEhQV1dXSPeXm5ur5uZmFRUVyev1Ki0tTdXV1cGLjBsbGxUZafnH8QAAgCEi5Lg52XU1x48fV3R09BkNUVBQoIKCgj5vq6mpOeWxGzZsOKPHBAAAZjrtuHG73ZKkiIgIFRUVKSYmJnhbV1eXdu3apbS0tH4fEAAAIBSnHTe7d++W9MWZm71798pmswVvs9lsSk1N1T333NP/EwIAAITgtOPm5ZdflvTFO6RWr17N58UAAICwFBbfCg4AANBfeBsSAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjBLy1y8ACE+Nj8yyegRLff7JBElR///nj4b9v8ekor1WjwBYhjM3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoYRE35eXlSklJUXR0tLKyslRXV3fStc8//7wyMjI0duxYjRo1SmlpafrVr341iNMCAIBwZnncVFVVye12q7i4WA0NDUpNTVVOTo6OHj3a5/rx48dr+fLlqq2t1VtvvSWXyyWXy6Vt27YN8uQAACAcWR43paWlys/Pl8vl0syZM1VRUaGYmBhVVlb2uf6KK67Q9773Pc2YMUNTpkzRsmXLNHv2bO3YsaPP9R0dHWpvb++xAQAAc1kaN52dnaqvr5fT6Qzui4yMlNPpVG1t7dce393dLY/HowMHDujb3/52n2tKSkoUFxcX3JKTk/ttfgAAEH4sjZuWlhZ1dXXJ4XD02O9wOOT1ek96XFtbm0aPHi2bzabrrrtOa9as0dVXX93n2sLCQrW1tQW3pqamfn0OAAAgvJxj9QBnYsyYMdqzZ4+OHz8uj8cjt9utb3zjG7riiit6rbXb7bLb7YM/JAAAsISlcRMfH6+oqCj5fL4e+30+nxITE096XGRkpKZOnSpJSktL0/79+1VSUtJn3AAAgOHF0pelbDab0tPT5fF4gvsCgYA8Ho+ys7NP+34CgYA6OjoGYkQAADDEWP6ylNvtVl5enjIyMpSZmamysjL5/X65XC5J0uLFi5WUlKSSkhJJX1wgnJGRoSlTpqijo0Nbt27Vr371K/385z+38mkAAIAwYXnc5Obmqrm5WUVFRfJ6vUpLS1N1dXXwIuPGxkZFRn51gsnv9+uOO+7Qn//8Z40cOVLTp0/Xr3/9a+Xm5lr1FAAAQBixPG4kqaCgQAUFBX3eVlNT0+P3xx57TI899tggTAUAAIYiyz/EDwAAoD8RNwAAwCjEDQAAMEpYXHMDAGdrvL2rz58BDD/EDQAjPHjxJ1aPACBM8LIUAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjnGP1ABiaAiNG9fkzAABWI25wRo5P+47VIwAA0CdelgIAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRwiJuysvLlZKSoujoaGVlZamuru6ka9etW6d58+Zp3LhxGjdunJxO5ynXAwCA4cXyuKmqqpLb7VZxcbEaGhqUmpqqnJwcHT16tM/1NTU1WrhwoV5++WXV1tYqOTlZ11xzjQ4fPjzIkwMAgHBkedyUlpYqPz9fLpdLM2fOVEVFhWJiYlRZWdnn+t/85je64447lJaWpunTp+tf//VfFQgE5PF4BnlyAAAQjiyNm87OTtXX18vpdAb3RUZGyul0qra29rTu49NPP9WJEyc0fvz4Pm/v6OhQe3t7jw0AAJjL0rhpaWlRV1eXHA5Hj/0Oh0Ner/e07uP+++/X+eef3yOQ/q+SkhLFxcUFt+Tk5LOeGwAAhC/LX5Y6GytXrtSmTZu0efNmRUdH97mmsLBQbW1twa2pqWmQpwQAAIPpHCsfPD4+XlFRUfL5fD32+3w+JSYmnvLYJ598UitXrtR//dd/afbs2SddZ7fbZbfb+2VeAAAQ/iw9c2Oz2ZSent7jYuAvLw7Ozs4+6XFPPPGEHn30UVVXVysjI2MwRgUAAEOEpWduJMntdisvL08ZGRnKzMxUWVmZ/H6/XC6XJGnx4sVKSkpSSUmJJOknP/mJioqKtHHjRqWkpASvzRk9erRGjx5t2fMAAADhwfK4yc3NVXNzs4qKiuT1epWWlqbq6urgRcaNjY2KjPzqBNPPf/5zdXZ26m//9m973E9xcbH+5V/+ZTBHBwAAYcjyuJGkgoICFRQU9HlbTU1Nj98/+OCDgR8IAAAMWUP63VIAAAB/jbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTL46a8vFwpKSmKjo5WVlaW6urqTrr27bff1o033qiUlBRFRESorKxs8AYFAABDgqVxU1VVJbfbreLiYjU0NCg1NVU5OTk6evRon+s//fRTfeMb39DKlSuVmJg4yNMCAIChwNK4KS0tVX5+vlwul2bOnKmKigrFxMSosrKyz/WXXHKJVq1apZtvvll2u32QpwUAAEOBZXHT2dmp+vp6OZ3Or4aJjJTT6VRtbW2/PU5HR4fa29t7bAAAwFyWxU1LS4u6urrkcDh67Hc4HPJ6vf32OCUlJYqLiwtuycnJ/XbfAAAg/Fh+QfFAKywsVFtbW3BramqyeiQAADCAzrHqgePj4xUVFSWfz9djv8/n69eLhe12O9fnAAAwjFh25sZmsyk9PV0ejye4LxAIyOPxKDs726qxAADAEGfZmRtJcrvdysvLU0ZGhjIzM1VWVia/3y+XyyVJWrx4sZKSklRSUiLpi4uQ33nnneDPhw8f1p49ezR69GhNnTrVsucBAADCh6Vxk5ubq+bmZhUVFcnr9SotLU3V1dXBi4wbGxsVGfnVyaWPPvpIF198cfD3J598Uk8++aQuv/xy1dTUDPb4AAAgDFkaN5JUUFCggoKCPm/762BJSUlRd3f3IEwFAACGKuPfLQUAAIYX4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUcIibsrLy5WSkqLo6GhlZWWprq7ulOt/+9vfavr06YqOjtasWbO0devWQZoUAACEO8vjpqqqSm63W8XFxWpoaFBqaqpycnJ09OjRPtfv3LlTCxcu1G233abdu3drwYIFWrBggfbt2zfIkwMAgHBkedyUlpYqPz9fLpdLM2fOVEVFhWJiYlRZWdnn+tWrV+vaa6/VvffeqxkzZujRRx/Vt771LT399NODPDkAAAhH51j54J2dnaqvr1dhYWFwX2RkpJxOp2pra/s8pra2Vm63u8e+nJwcvfDCC32u7+joUEdHR/D3trY2SVJ7e/tZzd7V8ZezOh5mOdu/p/5w7LMuq0dAGAmHv8nP//K51SMgzJzN3+WXx3Z3d3/tWkvjpqWlRV1dXXI4HD32OxwO/elPf+rzGK/X2+d6r9fb5/qSkhI9/PDDvfYnJyef4dRAb3FrfmD1CEBPJXFWTwD0Enf/2f9dHjt2THFxp74fS+NmMBQWFvY40xMIBNTa2qoJEyYoIiLCwsmGvvb2diUnJ6upqUmxsbFWjwPwN4mwxN9l/+ju7taxY8d0/vnnf+1aS+MmPj5eUVFR8vl8Pfb7fD4lJib2eUxiYmJI6+12u+x2e499Y8eOPfOh0UtsbCz/wSKs8DeJcMTf5dn7ujM2X7L0gmKbzab09HR5PJ7gvkAgII/Ho+zs7D6Pyc7O7rFekrZv337S9QAAYHix/GUpt9utvLw8ZWRkKDMzU2VlZfL7/XK5XJKkxYsXKykpSSUlJZKkZcuW6fLLL9dTTz2l6667Tps2bdIf//hHrV271sqnAQAAwoTlcZObm6vm5mYVFRXJ6/UqLS1N1dXVwYuGGxsbFRn51QmmOXPmaOPGjXrooYf04IMP6sILL9QLL7ygiy66yKqnMGzZ7XYVFxf3etkPsAp/kwhH/F0Ovoju03lPFQAAwBBh+Yf4AQAA9CfiBgAAGIW4AQAARiFuAACAUYgbnJHy8nKlpKQoOjpaWVlZqqurs3okDGOvvvqq5s+fr/PPP18REREn/a45YLCUlJTokksu0ZgxY5SQkKAFCxbowIEDVo81bBA3CFlVVZXcbreKi4vV0NCg1NRU5eTk6OjRo1aPhmHK7/crNTVV5eXlVo8CSJJeeeUVLV26VK+//rq2b9+uEydO6JprrpHf77d6tGGBt4IjZFlZWbrkkkv09NNPS/riU6WTk5P1wx/+UA888IDF02G4i4iI0ObNm7VgwQKrRwGCmpublZCQoFdeeUXf/va3rR7HeJy5QUg6OztVX18vp9MZ3BcZGSmn06na2loLJwOA8NXW1iZJGj9+vMWTDA/EDULS0tKirq6u4CdIf8nhcMjr9Vo0FQCEr0AgoLvuuktz587l0/QHieVfvwAAgMmWLl2qffv2aceOHVaPMmwQNwhJfHy8oqKi5PP5euz3+XxKTEy0aCoACE8FBQV68cUX9eqrr2rixIlWjzNs8LIUQmKz2ZSeni6PxxPcFwgE5PF4lJ2dbeFkABA+uru7VVBQoM2bN+sPf/iDLrjgAqtHGlY4c4OQud1u5eXlKSMjQ5mZmSorK5Pf75fL5bJ6NAxTx48f16FDh4K/v//++9qzZ4/Gjx+vSZMmWTgZhqulS5dq48aN+t3vfqcxY8YEr0mMi4vTyJEjLZ7OfLwVHGfk6aef1qpVq+T1epWWlqaf/vSnysrKsnosDFM1NTW68sore+3Py8vThg0bBn8gDHsRERF97n/22Wd16623Du4wwxBxAwAAjMI1NwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAhoRbb71VCxYssHoMAEMAcQMAAIxC3AAAAKMQNwDCynPPPadZs2Zp5MiRmjBhgpxOp/x+f691HR0duvPOO5WQkKDo6GhddtlleuONN4K319TUKCIiQlu2bNHs2bMVHR2tSy+9VPv27etxPzt27NC8efM0cuRIJScn68477+zz8QAMHcQNgLBx5MgRLVy4UP/4j/+o/fv3q6amRt///vfV1/f73nffffqP//gP/eIXv1BDQ4OmTp2qnJwctba29lh377336qmnntIbb7yhc889V/Pnz9eJEyckSe+++66uvfZa3XjjjXrrrbdUVVWlHTt2qKCgYFCeL4CBwbeCAwgbDQ0NSk9P1wcffKDJkyf3uO3WW2/VJ598ohdeeEF+v1/jxo3Thg0btGjRIknSiRMnlJKSorvuukv33nuvampqdOWVV2rTpk3Kzc2VJLW2tmrixInasGGD/u7v/k5LlixRVFSUnnnmmeDj7NixQ5dffrn8fr+io6MH78kD6DecuQEQNlJTU3XVVVdp1qxZuummm7Ru3Tp9/PHHvda9++67OnHihObOnRvcN2LECGVmZmr//v091mZnZwd/Hj9+vKZNmxZc8+abb2rDhg0aPXp0cMvJyVEgEND7778/QM8SwEA7x+oBAOBLUVFR2r59u3bu3KmXXnpJa9as0fLly7Vr164Bebzjx4/rn//5n3XnnXf2um3SpEkD8pgABh5nbgCElYiICM2dO1cPP/ywdu/eLZvNps2bN/dYM2XKFNlsNr322mvBfSdOnNAbb7yhmTNn9lj7+uuvB3/++OOPdfDgQc2YMUOS9K1vfUvvvPOOpk6d2muz2WwD+CwBDCTO3AAIG7t27ZLH49E111yjhIQE7dq1S83NzZoxY4beeuut4LpRo0bp9ttv17333qvx48dr0qRJeuKJJ/Tpp5/qtttu63GfjzzyiCZMmCCHw6Hly5crPj4++GGA999/vy699FIVFBRoyZIlGjVqlN555x1t375dTz/99GA+dQD9iLgBEDZiY2P16quvqqysTO3t7Zo8ebKeeuopfec731FVVVWPtStXrlQgENAtt9yiY8eOKSMjQ9u2bdO4ceN6rVu2bJn+53/+R2lpafrP//zP4FmZ2bNn65VXXtHy5cs1b948dXd3a8qUKcELkAEMTbxbCoCRvny31Mcff6yxY8daPQ6AQcQ1NwAAwCjEDQAAMAovSwEAAKNw5gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABglP8HORvPWBoaMn0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We observe, that Slope '2' causes heart pain much more than Slope '0' and '1'"
      ],
      "metadata": {
        "id": "fK4MjZZnO6Ya"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analysing the 'ca' feature**"
      ],
      "metadata": {
        "id": "yMz3mDkoO9ny"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#number of major vessels (0-3) colored by flourosopy"
      ],
      "metadata": {
        "id": "XZBXZdWCPCii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"ca\"].unique()"
      ],
      "metadata": {
        "id": "PBE--IMP7leC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0467016-7e07-4107-9879-f1038f9947af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 1, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(dataset[\"ca\"])"
      ],
      "metadata": {
        "id": "_ttR8cgy79Z2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "3ec40ded-d9b1-46e7-8abd-1037ad43ccd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=dataset[\"ca\"],y=y)"
      ],
      "metadata": {
        "id": "2V2Je4Ka79wR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "ad6cbc17-98ba-4258-9147-ba542152170c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='ca', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ca=4 has astonishingly large number of heart patients"
      ],
      "metadata": {
        "id": "ek3-FKg8POPt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Analysing the 'thal' feature"
      ],
      "metadata": {
        "id": "OKrN-V3nPT1w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"thal\"].unique()"
      ],
      "metadata": {
        "id": "S7YHlBg07-Gn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "860a51f0-76ce-48ba-fb37-590410df840d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x=dataset[\"thal\"],y=y)"
      ],
      "metadata": {
        "id": "eiGNJpse7-dj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "3ed889cf-269d-4042-acdf-cb93129bd841"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='thal', ylabel='target'>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(dataset[\"thal\"])"
      ],
      "metadata": {
        "id": "NxW9csEK7-2i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "edc2a937-3d59-4854-af2b-f435c89566c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='thal', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train Test split**"
      ],
      "metadata": {
        "id": "zEEQ3P9oPYrr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "predictors = dataset.drop(\"target\",axis=1)\n",
        "target = dataset[\"target\"]\n",
        "\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(predictors,target,test_size=0.20,random_state=0)"
      ],
      "metadata": {
        "id": "Aqys9GZ88fcX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "id": "aFYDyyWJ9EP1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdecad33-88cb-49d8-ec70-70e1dd2fdf40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(242, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "id": "tsozPNAo9Epv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d42e8b87-7874-4aaa-8e90-2659166c4c88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train.shape"
      ],
      "metadata": {
        "id": "aKoPI-l89FBp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83407c98-76fc-44ec-9d24-b6da004e3d27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(242,)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test.shape"
      ],
      "metadata": {
        "id": "qF-ky7gv9FVF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "103b73ba-c111-456c-e7cd-e24beaf38c4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Fitting**"
      ],
      "metadata": {
        "id": "ma-g7UoBPj-R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "X4n-2WE39R5T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Logistic Regression**"
      ],
      "metadata": {
        "id": "irw0Cyy4PoC6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "\n",
        "lr.fit(X_train,Y_train)\n",
        "\n",
        "Y_pred_lr = lr.predict(X_test)"
      ],
      "metadata": {
        "id": "qxn6iqxg9SVZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_lr.shape"
      ],
      "metadata": {
        "id": "NglJP_mp9TVL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3600e54-2c26-4a83-f453-716c2d29b46d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_lr = round(accuracy_score(Y_pred_lr,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Logistic Regression is: \"+str(score_lr)+\" %\")"
      ],
      "metadata": {
        "id": "JhIbu_vc9cCc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3339dd1e-6937-42e2-f575-c463489838b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Logistic Regression is: 85.25 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy score achieved using Logistic Regression is: 85.25 %"
      ],
      "metadata": {
        "id": "2WlYMXRTPxHc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Naive Bayes**"
      ],
      "metadata": {
        "id": "ftKfH62tPyhX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "nb = MultinomialNB()\n",
        "\n",
        "nb.fit(X_train,Y_train)\n",
        "\n",
        "Y_pred_nb = nb.predict(X_test)"
      ],
      "metadata": {
        "id": "TfZslp-K9cbB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Y_pred_nb.shape"
      ],
      "metadata": {
        "id": "rmIN1DzS99DL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a311e5c-8819-46c5-dda6-2d39d6e6ab53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_nb = round(accuracy_score(Y_pred_nb,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Naive Bayes is: \"+str(score_nb)+\" %\")"
      ],
      "metadata": {
        "id": "2f0z2wZ-9838",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34c9285c-06f3-4b9e-e94b-488e90a9c0d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Naive Bayes is: 77.05 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy score achieved using Naive Bayes is: 77.05 %"
      ],
      "metadata": {
        "id": "KwuKPLT0P8ZC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**K Nearest Neighbors**"
      ],
      "metadata": {
        "id": "8_m4OZmqQMwp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=13)\n",
        "knn.fit(X_train,Y_train)\n",
        "Y_pred_knn=knn.predict(X_test)"
      ],
      "metadata": {
        "id": "X2YmznpP-Syh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_knn.shape"
      ],
      "metadata": {
        "id": "jFQ2kRCW-Xw1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8ad5d1c-3dde-4fb6-d19d-aa725d67fe94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_knn = round(accuracy_score(Y_pred_knn,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using KNN is: \"+str(score_knn)+\" %\")"
      ],
      "metadata": {
        "id": "4khO3whj-Xj5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c49f05c3-b454-4cd7-dcfd-e2d2a4ab3c83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using KNN is: 72.13 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy score achieved using KNN is: 72.13 %"
      ],
      "metadata": {
        "id": "cb1yFUd0QV_O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Decision Tree**"
      ],
      "metadata": {
        "id": "yEFCLcDrQhYL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model=DecisionTreeClassifier(criterion='entropy')\n",
        "model.fit(X_train,Y_train)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "krWlwW_UHbxQ",
        "outputId": "1a320402-09cd-48d1-c25e-677667825cb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRpKlG8hIWIM",
        "outputId": "13bd9fab-618a-4fef-d61f-75d371aa08b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
              "       1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
              "       1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAOgtXz9IdHb",
        "outputId": "c29b53cc-3b6e-4083-8630-f686af2b9322"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "74     1\n",
              "153    1\n",
              "64     1\n",
              "296    0\n",
              "287    0\n",
              "      ..\n",
              "251    0\n",
              "192    0\n",
              "117    1\n",
              "47     1\n",
              "172    0\n",
              "Name: target, Length: 242, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lWCQLUBIrhX",
        "outputId": "c99caa1d-2a00-4307-9da9-30a7697fb8e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
              "       0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GABGCS52IwBY",
        "outputId": "be4a4b52-01d6-400a-b306-1397c2eb8fff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "225    0\n",
              "152    1\n",
              "228    0\n",
              "201    0\n",
              "52     1\n",
              "      ..\n",
              "146    1\n",
              "302    0\n",
              "26     1\n",
              "108    1\n",
              "89     1\n",
              "Name: target, Length: 61, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ih7FSXCI0e3",
        "outputId": "6265cbf6-4e19-49cc-ecc5-f2fbd6a01443"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLcNKI8NI-QT",
        "outputId": "2ee71acd-90ce-4781-faf6-695b99b3809f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7868852459016393"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_test,Y_test)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LlYRQNaJGZ-",
        "outputId": "2e1ad95e-61f6-426e-cf52-bfce0d77fea3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "78.68852459016394"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy score achieved using Decision Tree is: 78.68 %"
      ],
      "metadata": {
        "id": "Gghg1qPBJc_r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "max_accuracy = 0\n",
        "\n",
        "\n",
        "for x in range(200):\n",
        "    dt = DecisionTreeClassifier(random_state=x)\n",
        "    dt.fit(X_train,Y_train)\n",
        "    Y_pred_dt = dt.predict(X_test)\n",
        "    current_accuracy = round(accuracy_score(Y_pred_dt,Y_test)*100,2)\n",
        "    if(current_accuracy>max_accuracy):\n",
        "        max_accuracy = current_accuracy\n",
        "        best_x = x\n",
        "\n",
        "#print(max_accuracy)\n",
        "#print(best_x)\n",
        "\n",
        "\n",
        "dt = DecisionTreeClassifier(random_state=best_x)\n",
        "dt.fit(X_train,Y_train)\n",
        "Y_pred_dt = dt.predict(X_test)"
      ],
      "metadata": {
        "id": "aGniEMuN-Sl9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_pred_dt.shape)"
      ],
      "metadata": {
        "id": "fOcDMlSL-hjJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfc90387-ced5-441e-aa31-70f94d59f51a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(61,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_dt = round(accuracy_score(Y_pred_dt,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Decision Tree is: \"+str(score_dt)+\" %\")"
      ],
      "metadata": {
        "id": "ButEkmEE-han",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71f14782-2674-4ae2-d7df-6f8ad3153812"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Decision Tree is: 81.97 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy score achieved using Decision Tree is: 81.97 %"
      ],
      "metadata": {
        "id": "gG3e69-MQoqv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "tree.plot_tree(model,feature_names=X_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LZ1I2PLBJ6ig",
        "outputId": "ea0a6f11-f81a-41f8-f602-1928f3facb8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " Text(0.475, 0.5, 'entropy = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
              " Text(0.76875, 0.7222222222222222, 'age <= 56.5\\nentropy = 0.918\\nsamples = 78\\nvalue = [26, 52]'),\n",
              " Text(0.6125, 0.6111111111111112, 'thalach <= 148.5\\nentropy = 0.688\\nsamples = 49\\nvalue = [9, 40]'),\n",
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              " Text(0.5, 0.3888888888888889, 'entropy = 0.0\\nsamples = 4\\nvalue = [4, 0]'),\n",
              " Text(0.55, 0.3888888888888889, 'exang <= 0.5\\nentropy = 0.918\\nsamples = 3\\nvalue = [1, 2]'),\n",
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              " Text(0.575, 0.2777777777777778, 'entropy = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.7, 0.5, 'oldpeak <= 3.65\\nentropy = 0.454\\nsamples = 42\\nvalue = [4, 38]'),\n",
              " Text(0.675, 0.3888888888888889, 'trestbps <= 111.0\\nentropy = 0.378\\nsamples = 41\\nvalue = [3, 38]'),\n",
              " Text(0.625, 0.2777777777777778, 'age <= 49.5\\nentropy = 0.971\\nsamples = 5\\nvalue = [2, 3]'),\n",
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              " Text(0.9, 0.5, 'ca <= 0.5\\nentropy = 0.999\\nsamples = 25\\nvalue = [13, 12]'),\n",
              " Text(0.85, 0.3888888888888889, 'age <= 65.5\\nentropy = 0.89\\nsamples = 13\\nvalue = [4, 9]'),\n",
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              " Text(0.8, 0.16666666666666666, 'entropy = 0.0\\nsamples = 8\\nvalue = [0, 8]'),\n",
              " Text(0.85, 0.16666666666666666, 'thalach <= 143.0\\nentropy = 0.918\\nsamples = 3\\nvalue = [2, 1]'),\n",
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              " Text(0.95, 0.3888888888888889, 'oldpeak <= 1.5\\nentropy = 0.811\\nsamples = 12\\nvalue = [9, 3]'),\n",
              " Text(0.925, 0.2777777777777778, 'trestbps <= 126.5\\nentropy = 0.985\\nsamples = 7\\nvalue = [4, 3]'),\n",
              " Text(0.9, 0.16666666666666666, 'entropy = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
              " Text(0.95, 0.16666666666666666, 'fbs <= 0.5\\nentropy = 0.722\\nsamples = 5\\nvalue = [4, 1]'),\n",
              " Text(0.925, 0.05555555555555555, 'entropy = 0.0\\nsamples = 4\\nvalue = [4, 0]'),\n",
              " Text(0.975, 0.05555555555555555, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.975, 0.2777777777777778, 'entropy = 0.0\\nsamples = 5\\nvalue = [5, 0]'),\n",
              " Text(0.95, 0.5, 'entropy = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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BLT///DM2btyIefPmYebMmc1+r76+Hh999BGOHTuG1atXq2ZYSvTBLRTTCCE4ceIEvvjiCxw5cgTe3t547bXXEBsbC3Nz3eYtCoUCycnJ2LZtGzIzM9G/f3989NFHiIuLo0/QpCgAIFSbsXr1agKAfPrpp2p9vra2lowfP55YWlqS+Ph4PUdHtVUKhYIcPHiQDBgwgAAgISEhZNeuXaS+vl4v2zpw4ACJjIwkAEhoaKjetkVRxoQmA23Et99+SwCQ5cuXE4VCofb3ampqiEgkIlZWViQxMVGPEVJtjVwuJ7t37yZhYWEEAOnXrx+RSqUatU9tKRQKkpycTIYNG0YAED8/P/Lbb7+R2tpavW+boriIJgNtwPfff08AkA8++ECrgba6upqMHj2aWFtbk6SkJD1ESLUldXV1ZMeOHSQwMJAAINHR0eTIkSMGSQKacu7cOSISiQgA0qVLF/LDDz+Q6upqVmKhKLbQZMDE/fDDDwQAeeedd3QabKuqqsjIkSOJQCAgx48fZzBCqq2orq4mP/30E/H29iYAyOjRo8np06fZDkvl8uXLZOLEiYTH4xF3d3eydu1aUl5eznZYFGUQNBkwYVu3biUAyNtvv83IrKuyspIMHTqU2NracmoQp7itoqKCrF+/nnh4eBAej0defvllkpqaynZYzbp58yaZMWMGMTMzI0KhkKxYsYIUFxezHRZF6RVNBkzUr7/+Sng8Hpk7dy6jy68VFRVk8ODBxN7enpw7d46xcinTU1JSQlauXElcXFyImZkZefXVV8mNGzfYDktt2dnZZO7cucTKyoo4OjqS5cuXk/z8fLbDoii9oMmACdqxYwfh8Xhk9uzZRC6XM15+WVkZGThwIHFwcCApKSmMl08ZN5lMRj7++GPSrl07YmlpSf7xj3+QrKwstsPS2sOHD8k777xDBAIBEQgE5J133iEPHz5kOyyKYhRNBkzMn3/+Sfh8Pnn99df1kggolZaWkv79+5N27dpxesmXMpxHjx6R9957j9ja2hIbGxuyaNEikpuby3ZYjMnPzyfLly8njo6OxNLSksydO5dkZ2ezHRZFMYI+dMiE7N69G5MmTcKUKVPwyy+/wMzMTK/bKykpwYgRI5CVlYXk5GQEBwfrdXsUN+Xk5GD16tXYunUrLC0t8dZbb2HRokVwdXVlOzS9KCkpwcaNG/Htt9+iuLgY06ZNw9KlS+Hn58d2aBSlPbazEYoZ+/btI+bm5mTy5MkGfYBKYWEhCQsLI0KhkFy7ds1g26XYd+vWLfLGG28Qc3Nz0r59e/L555+ToqIitsMymPLycrJ27Vri7u5OeDwemThxIrl8+TLbYVGUVmgyYAKkUimxsLAgEyZMIHV1dQbfvkwmIyEhIcTV1dWoLhCjtHPt2jUyefJkwufzSceOHcmaNWtIWVkZ22Gxprq6mvzwww+kS5cuBAARiUT04lrK6NBkwMjFx8cTS0tLMm7cOFafnpafn0+CgoJIx44dSWZmJmtxUPqTkpJCxo4dSwAQLy8vsnHjRlJVVcV2WJxRW1tLfvvtN+Lv708AkGHDhpHk5GTWHqZEUZrgs3yWgtLBkSNHMHbsWMTExODPP/+EhYUFa7EIhUIcPXoUTk5OGDp0KLKysliLhWLWiRMnEBMTg/DwcFy/fh2//PIL7ty5g3nz5sHa2prt8DjDwsICr732GtLT0/HXX39BJpMhOjoaAwcOxMGDB0Ho5VkUh9FkwEgdO3YMYrEYw4YNw19//QVLS0u2Q4KrqyuOHj0KW1tbREdHIzs7m+2QKC0RQpCQkIBBgwZh8ODBePz4Mf78809kZGRgxowZrCaeXGdmZoaXX34ZaWlp2L9/PwghiIuLQ+/evbF7924oFAq2Q6SoF9BkwAidPHkScXFxiIqKwu7du2FlZcV2SCpubm5ISkqClZUVoqOjcf/+fbZDojSgUCiwb98+9O3bF6NGjUJNTQ0kEgkuX76MSZMm6f0OFVPC4/EQFxeH06dPIykpCU5OTnj55ZfRs2dP7NixA/X19WyHSFEqNBkwMmfOnEFsbCz69++Pffv2cXKZ1sPDA0lJSeDz+Rg6dChyc3PZDolqhVwuxx9//IHg4GCMGzcOdnZ2SExMxLlz5yASicDj8dgO0WjxeDxER0fj6NGjOHv2LLy9vfHqq6/Cz88PW7ZsQU1NDdshUhRNBozJ+fPnMWrUKPTp0wcSiQQ2NjZsh9QsT09PJCcnQy6XIzo6Gnl5eWyHRDWhtrYW27Ztg7+/P6ZMmQIvLy+cOnUKycnJGD58OE0CGNa/f39IpVKkpaWhd+/e+Mc//gEfHx+sX78elZWVbIdHtWE0GTASFy9eRExMDEJCQrB//37Y2tqyHVKrOnfujKSkJFRXV2Po0KF4/Pgx2yFR/6eqqgobNmyAr68vZs2aheDgYFy8eBEHDx7EgAED2A7P5IWGhmLXrl24ceMGhg0bhiVLlqBLly5YuXIlSktL2Q6PaoPoEwiNQFpaGoYNGwY/Pz8cPnwY9vb2bIekkTt37mDw4MFwdHTEsWPHTPbJdMagrKwMP/zwA7755hvk5+dj8uTJWLZsGXr06MF2aG1adnY2vv76a/z8888QCARYsGABFi5cCGdnZ7ZDo9oImgxw3NWrVzF06FB4e3sjMTERjo6ObIeklczMTAwZMgQuLi5ISkqCUChkO6Q2pbCwEN9//z3Wr1+P8vJyzJgxAx988AF8fHzYDo1q5OHDh/jmm2/w448/gsfjYe7cuViyZAk6duzIdmiUiaPJAIddv34dQ4YMgZeXF44cOQInJye2Q9JJRkYGhgwZAnd3dxw9ehTt27dnOyST9+TJE3z77bfYuHEj5HI5Zs+ejXfffReenp5sh0a1ID8/H+vWrcOGDRtQU1ODWbNm4f3334eXlxfboVEmiiYDHHXz5k0MGTIEHTt2xNGjR01muTA9PR3R0dHo3Lkzjhw5gnbt2rEdkkl68OABVq9ejS1btsDCwgJvvfUWFi9eTE/RGJni4mLVS5FKSkrw2muvYenSpejWrRvboVEmhiYDHHT79m0MHjwYzs7OSE5ONrkl9StXrmDo0KHw9fXF4cOHjfbUBxdlZWVh5cqV+O2332BnZ4dFixZhwYIFRr+q1NaVl5fjp59+wpo1a/DkyRNMnDgRH374IXr27Ml2aJSJoMkAx2RlZWHw4MFwcHAw6YvtUlNTMWzYMAQEBCAhIcHoLorkmuvXr+PLL7/En3/+CRcXFyxZsgRz5syh+9XEVFdX45dffsGqVatw//59vPTSS/joo48QHh7OdmiUkaO3FnJIdnY2oqOjYWtri6NHj5psIgAAYWFhOHz4MK5fv47Ro0ejvLyc7ZCM0qVLlzB+/HgEBQXh5MmT+O6775CdnY333nuPJgImyNraGnPnzsXt27fx66+/IiMjA3379sXIkSNx4sQJtsOjjBhNBjgiJycHQ4cOhaWlJZKSkuDm5sZ2SHoXHh6OhIQEXLlyBSKRiD50RQOnTp1CbGws+vTpg2vXrmHbtm24c+cO3nrrLU4/jIpihoWFBaZPn44bN25g586dePLkCQYPHoyoqCgcOnSIvhSJ0hhNBlhGCMHGjRsxaNAg8Hg8JCcnw8PDg+2wDKZ///6Ij49HSkoKYmNj6eymBYQQJCYmqgb93Nxc/PHHH7h58ybeeOMNTrysijIsMzMzTJw4EZcvX4ZUKkVdXR1iY2MRHh6OvXv30pciUWqj1wyw7OTJkxg0aBD4fD6ysrLQpUsXtkNixdGjRzF8+HAADbfDmfIpEk3J5XLs378fK1asQEpKCsLDw/HRRx9BJBKBz6f5PPU/hBAkJSVhxYoVSE5ORo8ePfDhhx9i4sSJMDMzo4+XpppFRxKWPX36FBYWFliwYAE6d+7MdjisiYqKQu/evQE0PI+AarhYzNPTE25ubhg7dixsbGyQkJCA8+fP46WXXqKJAPUCHo+HYcOGISkpCadPn0bnzp0xdepUuLm5wdHREQUFBWyHSHEUXRmgKI5asWIFli9fDgA4ceIEoqKiWI6IMkapqakIDw+HQqHAzJkzsXXrVrZDojiIJgMUxVF1dXU4dOgQAgIC4Ovry3Y4lBHLycnB5cuXMXz4cAgEArbDoTiIJgM6ysnJgUwmY7RMoVDYZh47qo/9Bxh+H5pKPSjTR8csqinmbAdgzHJychAQEMD4LXECgQAZGRkm37n0tf8Aw+5DU6kHZfromEU1hyYDOpDJZKisrMSOHTsQEBAAADhy5AgAwNnZGfn5+XBzc4O7uzuuXbuG8PBwFBYWwt3dHefPn4dQKERZWRlqamoQGRkJoOHiuWnTpkEmk5l8x2pq//3999/w8vJCTk4OCCEICwuDubk57ty5g5CQEJSWlsLT0xP79+9H165dkZmZiZEjR2LHjh2YM2cOAMPvw6bqUV5ejgsXLsDR0RHV1dUIDQ3FxYsXERAQgJqaGri6uuL27duwsrICANy7dw8jRox4pty21BYow3i+rSqf/pmfn99if9u9eze6dOmCgoICREZGstrfKP2gyQADAgICEBYWBgDw9fWFg4PDC595fqCnjw/9n8zMTHTq1AkA0LNnTwgEAsjlctU7C4qKijB37lzIZDLweDy4uLjg+PHjEIvFSEhIQOfOnREbG4v09HRERkaqfpDZrEd+fj4CAwORm5sLV1dXFBQUQCgUIjo6WlWPPn36YMOGDXjjjTewf/9+ODs748yZMzAzM0NAQABr9aBMn7KtRkZGIj8/H97e3sjNzUVNTQ2AhslMZGSkqq1++umn2LBhA+bOnYuEhAS8+uqryM7ORkFBQZPjHWV8aDLAgH379qGsrAxAw4+AQCBAbm4uHB0dYWVlhbKyMkycOPGZHzPlj0BCQgL8/PyQlpYGCwsLdO3aleXaGN7YsWNVyVRLGj+MafHixQCAiRMnAsAzt2WmpqYyHKF6dKnH1KlTAXCjHpTpY6LPKdF2ahpoMsCAsWPH4tKlS/D394etrS3S09MRGBiI6upqODk5QSgUIjExEdnZ2ViwYAHu37+PadOmobKyEjKZDP7+/ujcuTOio6PbdMfasmUL/P39UV5e/sw+FAqFMDMzQ3Fx8TP7UCAQgBCCnTt3Ijg4GGZmZhg4cKDR1cHKygq7d+9GUFAQ7O3t4e/vz2odqLZBm7bq6OiIvXv3ghACDw8PerurCaHJAENmz56t+u/Y2NgWP9t49jdv3jy9xWRM9uzZg6CgIDx48AAeHh5wd3dHRUUFfH190a1bNzx58gQRERE4deoU9u7dC1dXV9y8eRNjxozB66+/jm3btiE0NJTtasDZ2Rnm5uYoKytDZGQk7t27BwsLC9jb26vqERsbi+PHj6v+28zMDL1790ZGRgaePn2qes48RelLUlISBgwYoFZ/S0hIgLW1Na5evYqJEydi0KBB2Lt3L5ycnHDt2jVcvnyZ7epQDKDJAAO2b9+Oq1ev4vHjx3B1dUVeXh7Mzc3h6ekJLy8v1UWDly9fRk1NDYRCIW7evIkBAwbAxsYG+/btQ5cuXWBvb4+7d++yXR2Dy8jIUJ0fV95P3/h8+e3btwEAV65cgUwmU51K6dWrFx4+fAig4QmG2dnZSE1NZe0JhprWw9fXV/VvANCtWzd069aN9XpQps/NzQ1WVlYa9bcBAwao+lv//v0BAJWVlSguLjZg5JS+0GRAB0KhEAKBAOvXr2e0XIFAAKFQyGiZXKTcf9OmTWO8bEPuQ1OpB2X6LCwsYGlpyXhbpe3U+NGHDumo8QM8rl69igULFsDT0xPff/89nJycmv1efX09/vnPfyIxMRGffvopxowZo/pbW3qAR2sPQMnKysLEiRPx3XffITIyEqNHj8bQoUPx3nvvtVgu1x469PXXX+PYsWM4cOAATp8+jYULF2LXrl3w8fFpsdy21BYo/amoqMB3332HVatWoaamBlOmTMFrr70Ge3t7rcu8fv06NmzYgAsXLiAkJARr167F0KFDGYyaMihCMeLIkSPE1taWREVFkZKSErW+U19fT2bNmkUAkI0bN+o5QuP01VdfEVtbW1JVVUUIIWTu3Lmka9euRKFQsByZ+hQKBenSpQuZN28eIYSQqqoqIhAIyFdffcVyZJSpq6mpIRs3biQdO3YkFhYWZP78+eTRo0eMbuPIkSMkPDycACAjRowgKSkpjJZPGQZNBhjw999/E0tLSxITE0MqKio0+q5CoSCLFy8mAOiPQxMiIiLIuHHjVP9/8OBBAoCkp6ezGJVmrl27RgCQ+Ph41b+NHTuWREZGshgVZcrkcjn5/fffibe3N+HxeOTVV18ld+/e1dv2FAoF2bNnDwkICCAAyMsvv0xu3rypt+1RzKPJgI7+85//EDMzMzJ+/HhSXV2tVRkKhYJ88sknBABZtmyZUc169enJkyeEx+ORn3/+WfVvVVVVxNbWlnz55ZcsRqaZFStWPLO6QQgh27ZtIzwejzx9+pTFyChTo1AoyP79+0lwcDABQEQiEbl69arBtl9XV0d+/vln4unpSczMzMisWbPIgwcPDLZ9Sns0GdDBTz/9RHg8HnnttddIXV2dzuWtWbOGACDz588ncrmcgQiN2y+//EJ4PB558uTJM/8+btw4EhERwVJUmuvfvz8ZP378M//2+PFjwuPxyC+//MJOUJTJOXnyJBk4cCABQKKiosjp06dZi6Wqqop8++23RCgUEisrK7JkyRIik8lYi4dqHU0GtPTNN98QAOStt95i9If7xx9/JDwej8yYMYORBMOYNfej//PPPzeZJHCRcnWjqR/9ppIEitLUlStXSFxcHAFAQkNDycGDBzmzulhSUkI+/fRTYmdnRxwcHMhnn31GysrK2A6LagJNBjTUeEl/6dKleul0ylMPL7/8MqmpqWG8fGPQ0umApk4fcFVLicuKFSuInZ2d1qeXqLbtzp07ZMqUKYTH4xEfHx/yxx9/cHZF8enTp2TRokXE0tKSuLq6ku+++462e46hyYAGFAoFeeeddwgAvZ+zVl6UOGrUKI0vSjQF8fHxLV4o+PyFhVzV0oWCV69eJQDIoUOHDBwVZczy8vLI3Llzibm5OXFzcyObN28mtbW1bIellnv37pHXX3+d8Pl80rlzZ/Lbb7+R+vp6tsOiCE0G1Nb4NsDvv//eINtMTEwkAoGADBo0SO3bFU1Fa7cQfvnlly9clMc1rd1C+PwthxTVkqKiIrJs2TJiY2ND2rVrR1auXGm0E4UbN26Q8ePHEwCkR48eZN++fZw5tdFW0WRADbW1teSVV14hfD6f/Prrrwbd9unTp4mjoyPp06dPm7kAR6FQEE9PT/L22283+xnl7XoHDx40YGSaUec2yAULFhBPT086EFLNqqioICtXriTt2rUjNjY2ZNmyZaSwsJDtsBhx/vx5MnToUAKA9O/fnyQnJ7MdUptFk4FWVFVVEZFIRCwsLMhff/3FSgypqalEKBSSHj16kLy8PFZiMKS0tDQCgBw5cqTZzyhn1XPnzjVgZJqZM2cO8fb2bvGH/vDhwwQAuXz5sgEjo4xBbW0t+eGHH4ibmxsxNzcn8+bNM9n+n5iYSHr37k0AkJiYGHLp0iW2Q2pzaDLQgrKyMjJ06FBibW3N+gw0IyODeHh4EF9fX3Lv3j1WY9G3f/3rX8TBwaHViyfffvtt0qlTJ07OqhUKBenUqRNZuHBhi5+rqakh9vb25PPPPzdMYBTnyeVy8scffxBfX1/C4/HI1KlTyZ07d9gOS+8UCgX573//S/z8/AgAMnHiRJKZmcl2WG0GTQaaUVhYSPr370/s7e3J8ePH2Q6HEELI3bt3ibe3N+nUqZNJP92rT58+ZOLEia1+LjExkQAgqampBohKM6mpqa2ubihNmDCBhIeHGyAqissUCgU5ePAgCQ0NJQBIXFxcm1wxqqurI1u3biWdOnUiZmZm5M033yS5ublsh2Xy+IZ5A4Jxefr0KaKjo3Hr1i0cPXoUgwYNYjskAEDXrl1x8uRJODg4YNCgQbhy5QrbITEuLy8PFy9ehEgkavWzgwYNgoODA6RSqQEi04xUKoWDgwOioqJa/axIJEJKSgoePXpkgMgoLjpz5gyGDBmC0aNHw9bWFidPnsT+/fsREhLCdmgGZ25ujpkzZ+L27dv4+uuvsXv3bvj6+uL9999HYWEh2+GZLrazEa558OAB8fPzIx07diTXrl1jO5wm5efnk7CwMNKuXTty9uxZtsNh1I8//kjMzMxIQUGBWp+fOHEi6dOnj56j0lzv3r3JpEmT1PqsTCYjfD6fbNmyRc9RUVxz9epVIhKJCAASHBxMpFIpJ097sam4uJh8/PHHxNbWljg6OpIvvviClJeXsx2WyaHJQCN37twhnTt3Jl5eXuT27dtsh9Oi4uJiMmDAAGJra0uOHj3KdjiMGTNmDBk0aJDan//3v/9NAJCHDx/qMSrNPHz4kAAgO3bsUPs7UVFRRCQS6TEqikvu3r1LXn31VcLj8Yi3tzf5/fffOfvAIK548uQJWbhwIbG0tCQdOnQgGzZsaLMPZdMHmgz8n/T0dOLm5ka6d+9OcnJy2A5HLeXl5WTkyJHEysqKSKVStsPRWUVFBbG2tiarV69W+zvKWfWPP/6ox8g0o+nqBiGEfP3118TGxoZUVlbqMTKKbY8fPybz588nFhYWpGPHjmTjxo30B01D2dnZZPr06YTP55OuXbuSf//73/TBRQygyQAh5OLFi8TZ2ZkEBweTx48fsx2ORqqrq8m4ceOIubk5+fPPP9kORyd///03AaDxFcRRUVFkzJgxeopKc3FxcWTw4MEafScjI4MAMImkjnpRcXEx+eijj4hAICCOjo7kyy+/pEvdOkpPTydjx44lAEjPnj2JRCKhp1h00OaTgRMnThB7e3vSr18/o32QR11dnWrJcevWrWyHo7VZs2aR7t27a/y91atXE2tra048jU25urFmzRqNvqdQKEi3bt3Im2++qafIKDZUVlaSr7/+mrRv355YW1uTDz74QKMVI6p1Z8+eJUOGDCEASGRkJGfu/jI2bToZOHToELGxsSHR0dGktLSU7XB0IpfLydy5cwkA8u2337Idjsbkcjnp2LEjWbJkicbfvXnzJgFA/v77bz1EphltVzcIIeSdd94hbm5u9NyxCairqyM//fQT8fDwIGZmZmTOnDmcuq7F1CgUCpKQkEDCwsIIABIbG0vS0tLYDsuotNlkYPfu3cTCwoLExcWZzHlahUJB3n//fQKAfPbZZ0a1ZHbhwgUCQOusvlu3bmTWrFkMR6W5WbNmET8/P62+m5ycTACQlJQUhqOiDEUul5OdO3eSbt26EQBk8uTJnL8Y2ZTI5XKya9cu0r17dwKAvPLKK3T/q6lNJgO//fYb4fP5ZNKkSUbzti91KRQKsmLFCgKAvPvuu0aTECxfvpw4OTmRuro6rb6/ZMkS0rFjR1Zn1XK5nHTo0IG8++67Wn2/traWODk5kY8//pjhyCh9UygU5NChQ6qZ6ejRo+nMlEV1dXVky5YtxMPDg5ibm9OVGTW0uWRg48aNBACZOXOmSV+Bun79egKA/OMf/zCKeoaEhJCpU6dq/f1jx44RAOT8+fMMRqWZ8+fP67S6QQghU6ZMIaGhoQxGRekbPWfNXZWVlWT16tWkffv2xMbGhnzwwQdGe22YvrWpZGDlypUEAFm0aJHRzJh18fPPPxM+n0+mTJnC6RWQ+/fvEwA63Q1RV1dHnJycyPLlyxmMTDO6rm4QQsgff/xBABjN7a1tWXp6OnnppZcIABIUFESvZuew4uJisnz5ciIQCEi7du3IV199xYkLjrmkTSQDCoWCfPjhhwQA+fjjj9tUh921axexsLAgL730EqmqqmI7nCZt2LCBmJubk+LiYp3KmTJlCgkJCWEmKC0EBweTadOm6VRGUVERMTc3J5s2bWIoKoppyvvceTwevc/dyDx+/JgsWLBA9ZyHTZs2cXqiZEgmnwzI5XKyYMECAkCjh9mYkgMHDhBra2syfPhwTt7bHBMTQ4YNG6ZzOX/++ScBwMpbHe/du0cAkJ07d+pc1tChQ8moUaMYiIpi0pMnT8jbb79NLCws6BPwjFzjJ0D6+PjQJ0ASE08G6urqyIwZMwiPx+PUE+rYcOzYMWJnZ0ciIyNJUVER2+GolJaWEktLS7Ju3TqdyyouLiZmZmY6XXugLaZWNwgh5KuvviLm5ubk2LFjDERG6aqkpET1bHwHBweyYsUKTibVlOauXbtGxGIxAUBCQkLI/v3729TKcWMmmwxcuXKFjBs3jpiZmZHff/+d7XA44fz588TJyYn06tWLZGRksB0OIYSoniB26dIlncvKy8sjAAgAg54SuX//PnF2diZBQUGMlLdw4UICgL7WmGVVVVVkzZo1xNnZmVhbW5P333+fPjDIRJ05c4YMGjSIACADBw4kJ0+eZDskgzPJZODOnTsEAOHxeGTfvn1sh8MpV69eJU5OTgQAI7NxXSl/vJl6++KUKVOIl5cXI2WpKykpiQAgVlZWjJRXUFBAXFxcyNKlSxkpj1KfQqEgDx48IFu3biWdOnUiZmZm5M033yS5ublsh0bpmUKhIPHx8aRXr14EAImLiyNXrlwh1dXVbIdmECaZDPzwww+qZZ+2uuTTEuXMk82L7ZTee+898sEHH7Adhk6qqqpIREQESU1NZTsUSkexsbGqBHXSpEnk1q1bbIdEGZhcLid//vkn8fX1VbWF7du3sx2W3vEIIQQmqKamBlZWVmyHwVkPHjyAnZ0dnJyc2A6FojjDzs4OFRUVWL16Nd599122w6FYVFdXh1GjRiEpKQne3t7IyspiOyS9MtlkgKIoSlNyuRwKhQIWFhZsh0JxQH19PbKysuDm5gYHBwe2w9ErziUDOTk5kMlkeilbKBTCy8tLL2UbA33tW0PvV33UwxTqALSdNk7HCcqQ2kJ/NWc7gMZycnIQEBCAyspKvZQvEAiQkZHBmZ1vSPrct8/vV6Y7TuMOo696mEIdgLbRxuk4QRlSW+mvnEoGZDIZKisr8fbbb8PDwwPOzs7Iz8+Hm5sb0tPT4eXlBWtra8jlcvTq1Qvnzp1D9+7dUVtbC2dnZ1RUVCAzMxNFRUXw8fFBXl4eevTogZMnT6Jnz56YNm0aZDIZJ3a8oTXet7169UJpaSn4fD4uX74Mb29vuLi4ICUlBT169IBQKISrqyuuXbuG8PBwFBYWwt3dHefPn4e9vT1qa2tRVVWFW7duISwsDDNnzlTtV310nMYdRlmPHTt2oKCgADKZDD169EBeXh7KyspQWVkJDw8PFBcXw9raGoMGDXqhndy9exelpaXo2rVrk23DGOswceJEZGRktIk2ztQ48eTJE3Tt2hUymQw9e/ak4wTVpMb99dGjR+Dz+bC3t0dmZiYCAwMRGhqKM2fOwMPDA3w+H46OjqioqMC1a9fg6OgIKysr5OTkYOjQodixYwfmzJkDAJzrr5xKBpSmT5+OsLAwAEBpaWmz52r69ev3wr/FxMS88G9xcXFITU1lNkgj9fy+nT9/vupvs2fPfuazI0aMeOb/w8PDXyjv+f3auONkZmbC09MTAFBUVARra2s8ffoUdnZ2sLCwQGVlJUaMGIHi4mLweDy0a9cOu3btglgsxsWLF9GpUyfU19c32WECAgLUaiPAi+1k4MCBqv9uqm0YYx3aosZtGWjYh9OnT3/hc3ScoJgQEBCAqVOnAnixvzZuh0pN9ctBgwbpL0AdcTIZ2LdvH8rKygAA+fn5EAgEyM3NVWVZZWVlmDhxImQyGXg8HlxcXLBhwwa88cYbSElJgY2NDW7fvg2BQICAgAD07NmT5RpxB5P7tn///s1uJyMjAzExMSgvL0d6ejp69OiB6upqhISEwMzMDMXFxcjOzkb//v1x//59CAQCWFlZISgoCAKBAHFxcfD39292cNalHn///Tf69euHs2fPqjL5pihXB/z9/VFeXo7i4mL4+PjAw8MDQqFQVY+UlBQsWLBAVY9PPvkEu3fvRp8+fWBmZgaBQMB4HXbu3Ak/Pz/IZDJUV1dj/PjxarcBU7Jt2zb4+voC0K4dhISE4MKFCyCENJk0UJSSLv01ISEBfn5+uHv3LvLz8znZ1jiZDIwdOxZhYWHYsmWLaiAuKSmBh4cHqqur4eXlhZSUFNUPyoIFCzB+/HjU1taif//+2LZtG0JDQ2FmZkYTgefosm979+6NPXv2wN7eHrGxsbC3t2/2vPr48eNV2XJsbGyLMXXu3Fn136+//rra9bh06RL8/f1ha2uL9PR0BAYGorq6Gk5OThAKhUhMTFTV4f79+5g2bRqsrKxgZmaGGzduICQkBL169WpxNth4tUTbejRXvvJYAMCWLVvQoUMHeHp6quoiEAhw7ty5Z5KnxYsX4/79+xg3bhysrKywe/dutfeZKZo5c6aqLWvSDng8HuRyuepUYmRkJIDmjxVFaTvmKBQKlJWVIS8vDx06dMC4ceMAcK+tcTIZUNJ2IF64cKHeYjIFe/bsQVBQEB48eAAPDw+4u7ujoqICvr6+6NatG548eYKIiAicOnUKu3btQr9+/fDnn39i4sSJGD16NPbu3YvU1FQMHjy42W1s374dV69exePHj+Hq6oq8vDyYm5vD09MTXl5equsQLl++DAcHB+Tn56O8vBy9e/eGXC7HmTNn4Onp2eysGtC+fSiX+lqjSR2Uz7UIDAxEbm4uHj16hNzcXPj5+bVYB13r0pYTASW67yhDSEpKwoABA9QeN2NiYnDo0CEMGTIEU6dOxe7du8Hn83Ht2jWUlpbCxsaG7So9g5PJQEZGBpKSkiAUCtUaiGUyGVxdXeHv74+0tDTVUraPjw969+6tKpNq2A8BAQEAoFpeVf4/ANy+fRsAcOXKFchkMvj6+qKgoAADBgzAw4cPAQD9+/dHdnY2UlNTX9ivQqEQAoEA69evZyxmgUAAoVD4Qj00aSPKOvz999+IiYmBjY0N9u3b1+SPtSHrAECrevD5fNy4cQN8Ph88Hg+hoaFtro1rs/+qq6thbW2tStpu376N2tpaeHp6onfv3m1uH1Lqc3Nzg5WVldrjZlZWFrp164aHDx/i4cOHqs9XVlZCJpOhvLzc8JVoAaeeM0BvGdIfemthy0yhDkDbaON0nKAMqa30V04lA0Drg/D9+/cxfvx4rF27FlFRURg1ahRiY2OxePHiVsvm0gMe2NDavj106BA++ugjHDx4EIQQxMXFYcWKFRg1alSL5XLtgT2zZ89Wzew3b96MnTt34siRIzA3b34hjGt10Ladt5U2/vz+Kyoqwrx58/Do0SNs2LABQUFBzX6XEILvvvsO27dvx7x58/DGG2+Ax+Op/t5W9iGlvtb669q1a5GQkID4+HicOHECS5Yswd69e1ttR5xqa6y8EUEHa9asIdbW1qSiooIQQsisWbNI9+7dWY7KNLzyyiukV69eqv8PDQ0lkydPZjEizRUUFBAzMzPyww8/EEIIuXDhAgFAjh07xnJkmnm+nc+cOZO282bk5uYSf39/0qFDB3L16lW1vqNQKMjnn39OAJD333+fvtCM0km3bt3I7NmzCSGElJeXEysrK/LNN9+wHJVm+GwnI5qSSCQYPny46jyvSCTCrVu3kJmZyXJkxq2urg7x8fEQi8WqfxOLxYiPj0ddXR2LkWkmPj4ecrkcY8aMAQD07t0bbm5ukEqlLEemmefbuVgspu28CXfv3kVUVBQqKytVDw1SB4/Hw/Lly7Fu3Tp8/fXXmDdvHhQKhZ6jpUxRZmYmbt++DZFIBACwtbXF8OHDIZFIWI5MM0aVDBQWFuL06dOqnQ4Aw4cPh7W1tdEN9lxz6tQplJSUPLNvRSIRiouLcerUKRYj04xUKkXv3r3h4eEBAODz+RgzZoxRdUzaztVz48YNDBw4EObm5jh58iS6deumcRkLFy7Etm3b8OOPP2L69Omor6/XQ6SUKZNKpbCxscGwYcNU/yYSiXDq1CkUFRWxGJlmjCoZeH7WBzRcgDF8+HA6SOpIIpHA3d39mSdphYWFGdWsura2FocOHXrmRxRo6Ji3b982mll1c+182LBhRnMs9E15a6tQKMTJkyd1Ou/6xhtv4I8//sCff/6JCRMmoKamhsFIKVP3/CoeAIwZMwZyuRzx8fEsRqYZo0oGJBIJ+vTpA3d392f+XSQS4fTp0ygoKGApMuNGCIFUKoVIJHrmQio+nw+RSASpVArCretMm3Ty5MkXVjcAYNiwYbCxsTGaH9Lm2rlYLMbp06dRWFjIUmTccPr0aURHR8Pb2xvHjh1Dhw4ddC5z0qRJ2LdvH+Lj4yESiVBRUcFApJSpKygoeGEVDwA8PDzQu3dvo1qRNJpkoLlZH2CcWRiX3Lx5E1lZWU3uW5FIhDt37hjFrFoqlcLDwwO9evV65t+Vq0fG0DFpO29ZYmIiRo4cibCwMBw5cgTt27dnrOy4uDjEx8fj7NmziImJQXFxMWNlU6YpPj4eCoXimVU8JZFIhEOHDhnNNVdGkwycPHkSpaWlTQ6S7u7u6NOnj9HM/LhGec5r6NChL/xNOavm+g8pIQQSieSF1Q0lY1k9aq2dG9tsg0l79+7FmDFjMGTIEBw8eBD29vaMbyM6OhpHjhzB9evXMXToUOTn5zO+Dcp0SCQShIeHw83N7YW/iUQilJSU4OTJkyxEpjmjSQYkEgk6deqE0NDQJv+uzMJqa2sNG5gJkEgkGDlyZJOPx7SxscGIESM4n2jduHED2dnZTf6IAg2zaoVCwflZdWvtXCwWt8l2vmPHDkyYMAEvvfQS9u7dq9dHufbr1w/Hjx/Hw4cPMXjwYNWTNymqsZZW8QCgV69e8PDwMJrk3SiSgebOaTcmEolQWlqKEydOGDg64yaTyXD27NlmGzTQsG/PnDnD6Vm1VCqFQCBocnUDaHiUaHh4OKc7pibt3FhmG0z44Ycf8Nprr2H69On4448/YGlpqfdtBgcH4+TJkygvL0dUVBTu3r2r921SxuXEiRMoKytrduzk8XhGdc2VUSQDrc36ACA0NBSdOnXi/AyWaw4ePAiFQtHku7eV4uLioFAocPDgQQNGphmpVIqRI0fC2tq62c9wffWItvMXrV69GnPnzsWCBQuwZcsWmJmZGWzb3bt3x8mTJ2FmZoaoqCj63gLqGRKJBJ6enggJCWn2MyKRCHfv3jWKtmMUyYBEIoGtrS2io6Ob/YyxZWFcIZVK0a9fP3Ts2LHZz7i5uaFv376cnVU/ffq01dUNoGGJvaysjLOrR5q0c4lEYtLtnBCCf/7zn3j//fdVDwfi8w0/XHXu3BknTpxA+/btMWjQIM69dpZihzqreAAwdOhQCAQCzo6djRlFMqDOrA9oyMKys7Nx/fp1A0Vm3Gpqalo859WYSCRCQkICJ2fVyhWLllY3gIalX09PT852TE3b+Y0bNwwUmWEpFAosWrQIX3zxBVatWoXPP/+8xQFX39zc3HDs2DF07doV0dHROH36NGuxUNxw/fp13Lt3r9Wx09raGiNHjjSKlTzOJwNPnz7FuXPn1PrBio6Ohq2trVHseC44fvw4ysvL1U4GysrKcOzYMf0HpiGpVIq+ffu2er85l1ePtGnnXE1qdCGXyzF79mx8//332Lx5M95//322QwIAODs748iRIwgNDcXIkSORmJjIdkgUi5SreEOGDGn1syKRCGfPnuX8nSmcTwYOHDgAoPVZH9CQhRnDle9cIZVK4eXlpdbz3IODg+Hl5cW5fVtdXY2EhIRn3qnQEpFIhHv37nFu9UjTdm4ssw1N1NbWYvLkyfjtt9+wfft2zJkzh+2QnuHg4ID4+HgMHjwYY8aMwd9//812SBRLpFIpYmJiWl3FA/7Xp5V9nKs4nwxIpVL0798frq6uan1eLBbj3LlzePr0qZ4jM27K+/LFYrFaS7BcnVUfO3YMFRUVas2ogYZZtZ2dHedm1Zq2c5FIZFLtvKqqCuPGjcPff/+Nv/76C9OmTWM7pCYJBALs27cPIpEI/+///T/8/vvvbIdEGdiTJ09w/vx5tcecDh06oF+/fpxP3jmdDFRXV+Pw4cNq73TAeLIwtl27dg05OTka7VuRSIT79+8jPT1dj5FpRiqVonPnzi2+v74xKysrzs2qdWnnXL7DQ11lZWWIjY1FcnIypFIpxo0bx3ZILbK0tMSff/6JV199Fa+++ip+/PFHtkOiDEj52zJ69Gi1vyMSiXD48GFOv/eC08mActan7hIwALi6uhpFFsY2qVQKOzs7DB48WO3vDBkyBHZ2dpzZt8oretVd3VASiUQ4f/48Z2bV2rbz/v37c+ZYaKuwsBDDhw9HWloaDh8+jJEjR7IdklrMzc2xbds2zJ8/H3PmzMGaNWvYDokyEKlUioiICLVX8YCGFevy8nJOXnOlxOlkQCKRoGvXrggMDNToe2KxGIcPH0Z1dbWeIjN+EokEo0aNgpWVldrfsbKyQkxMDGeW2K9cuYIHDx5oNKMGuLd6pG07V97hYazt/PHjxxgyZAiysrKQnJyMgQMHsh2SRvh8PtavX48PP/wQ7733Hj7++GNOnUKjmKfNKh4A9OjRA126dOHM2NkUziYDhBDs37+/1fs4m6J861hycrKeojNujx8/xoULFzRu0EDDvr1w4QKePHmih8g0I5VKYW9vr9HqBgC4uLggIiKCEx2TiXbO5dlGc3JycjBo0CAUFBTgxIkTz7w625jweDysWLECK1euxOeff4533nmHJgQmLDk5GZWVlRqPnVy95qoxziYD2s76gP9lYca+hKovBw4cAJ/P1+icl5LyO1yYVSuv6NXm8bTKc3hsz6rbYju/ffs2Bg4ciLq6Opw8eVLjFREu+uCDD7Bx40asW7cOs2fPhlwuZzskSg+0XcUDGsacBw8e4OrVq3qITHecTQYkEgkcHBwwaNAgjb/L4/EgFouxf/9+zmZhbJJIJIiMjIRQKNT4uy4uLoiMjGR9Vp2Xl4eUlBSNzrM3JhaLUVlZyfrqERPtnMuzjeddu3YNUVFRsLW1xalTp+Dt7c12SIyZN28efvvtN/zyyy+YOnWq0by6llKPchVP02uUlAYPHgx7e3vWx87mcDYZkEqlGDVqlNYvJVFmYVeuXGE4MuNWVVWFxMRErWaiSiKRCImJiazOqnVZ3QCAgIAAeHt7sz6rbkvt/MKFCxg8eDDc3Nxw4sQJeHh4sB0S41577TX89ddf2LNnD8aPH4+qqiq2Q6IYcvnyZeTm5mo9dlpaWmLUqFGsjznN4WQykJeXh4sXL+r0gzVo0CA4ODhwNgtjS1JSEqqqqnROBiorK5GUlMRgZJqRSqWIjIyEs7OzVt/nwjk8Jts5VwcYpePHj2PYsGEICAhAcnIyXFxc2A5Jb8aPHw+pVIqjR48iLi4O5eXlbIdEMUC5ihcVFaV1GSKRCCkpKXj06BGDkTGDk8nAgQMHYGZmpvWsD2jIwmJiYjg/SBqaRCKBj48P/P39tS4jICAAPj4+rO3byspKHDlyROtTBEoikQi5ubm4fPkyM4FpiKl2zuXZBtDwLIRRo0ahf//+OHz4MNq1a8d2SHoXExODhIQEXLp0CSNGjEBRURHbIVE60nUVD2i45orP53PimqvncTIZkEgkGDBgANq3b69TOWKxGBcvXkReXh5DkRk3Xc95KbE9qz569KjOqxtAw6za0dGRtR9Sptq5crbBxXb+119/YezYsarE3NbWlu2QDCYqKgpJSUm4ffs2hgwZwok7cCjtPHz4EJcuXdJ5AuLs7IwBAwZwcsWac8mActan60APALGxseDxeFi/fj0DkRm/P/74A3l5eYzsW5FIhIcPHyItLY2ByDQjlUrh6+sLPz8/ncqxsLDAyJEjsXPnThQWFjIUnXqYbudcnG388ssveOWVVzBhwgT89ddfaj3H3dT07t0bx48fR35+PgYNGoQHDx6wHRKlhfXr14PP5yM2NlbnskQiEY4cOcK960kIx7z66qsEALl06ZLOZRUUFBAABAApKSlhIDrjptwXycnJOpdVXV1NbGxsSFhYGJHL5boHp6YzZ84QgUBApkyZwkh5AwYMIADIhx9+yEh56mKynRNCSFBQEOnUqRPJy8tjpDxdVFZWkvXr1xMA5B//+IdB2wdX3blzh3Tu3Jl07tyZpKen031iREpKSlRjZ0FBgc7lXbx4kQAgr732GgPRMYdzKwP//ve/AYCRi26cnJwQGhoKCwsL2Nvb61yesevatSscHBwQHh6uc1kVFRWoqqpCamqqQTPcnTt3orKykrElV+Urcg19ZTuT7RwAbty4gdzcXNy4cYOR8rRVU1MDgUCAhQsX4t1338XmzZvB53NumDE4Hx8fnDp1Cubm5ggKCsL06dPZDolSk729PSwsLBAaGgonJyedy1P2+e3bt+tcFpN4hHDrBuU333wTAPDTTz8xVmZNTY1Gj901VfX19eDxeDAzM2OkvA0bNmD37t0GvVf/3LlzmD17No4eParRs8Fbwkb7YLqdnzhxAnPnzsWFCxdYPS+/b98+1YuGKioqIBAIWIuFi7Zs2YI333wTAoEAFRUVbIdDqYnpMUIfv3O64lwyQFGU8ZLL5UhMTMTQoUN1uuralGVnZ6O4uBi9evViOxSKUqHJAEVRFEW1ceZsB8CEnJwcyGQyRssUCoXw8vJitEymtdV6a4up/UX3kWZMeX+pSx/7FaD7Vlu0nb/IYMmAvgbinJwcBAQEoLKyUueyGxMIBMjIyODswTVUvZnuNE11GENtg6n91VLb0HfCweS+on3JMPS1XwG6b7VhyHZuTBMQgyQD+hyIZTIZKisrsWPHDmRmZiI0NBR3797Fw4cPERgYCDMzM5w4cQJTp05FSUkJZDIZfH19UVNTAzc3N0gkEoSHh+PKlSvo1q0bfHx8kJGRgWnTpkEmk3G2kzWud0BAABISEuDq6oqcnBwQQhAWFgZzc3PcuXMHISEhKC0thaenJ86cOQNra2vIZDLVa3wnTJgAS0vLF+qtj07TVLKh720AL+6vpKQkPH36FN26dYOVlRXc3Nxw7do1hIeHo7CwEO7u7jh//jzs7e1RW1uLmpoaREZGttg29J1wML2vWupLmrSp3bt3w9vbG0VFRQgPD2+xTbVFyv369ttvo1evXigtLQWfz8fly5fh7e0NFxcXpKSkoEePHhAKhXB1dX2hLTZ+SmZVVRVu3bqFsLAwzJw5s03vW208386Bhse0Ozo6orq6GqGhobh48SICAgJQU1MDV1dXXL9+HYQQODg44N69exgxYsQzZTbVzg01AWGKQZIB5c6fNWsW+vbtCwAoKiqCtbU1nj59Cjs7O1hYWKCyshIjRoxAcXExeDwe2rVrh127dqmeJFhXV4dly5Y12fgDAgIwdepUAEBpaSkcHBxUf3vjjTeajU35HvW4uDimq20QAQEBCAsLQ1hY2Av1bsrz740fPHhws599PtHy9PQEoN2x69SpE+rr61/oMIbYhrr76/kOruktmPpu50yV39p+0mebasumT5+u2lelpaWYP3++6m+zZ89+5rPqtMXU1FQ9RNl2KNs5APj6+j7Tzp9//0BERITG5Rvid49JBr1moEOHDvD390d5eTmKi4vh4+MDDw8PCIVCmJmZobi4GCkpKViwYAHu378PgUCA5cuXY8+ePViyZAlSU1OxbNmyJsvet28fysrKAAD5+fkQCATIzc2Fo6MjrKysUFZWhokTJ0Imk4HH48HFxQUbNmzAG2+8gZSUFFhbWyM/Px91dXVG8zpYQLd6JyQkwM/PD2lpabCwsEDXrl2b3IYyu9X02K1YsQK7d+9GUFAQ3N3dW3ylKxPbaNeuHSwsLBjdX+7u7ti2bRsmTJiApKQk1NTUtHpMtGnnynq01s4BYO7cubh06ZJqG+np6YiIiEB1dbVqGzKZDNnZ2aptLF26FDY2Nrh8+TImTZrU4g+JLm1q586d6NevH27evAlra2u19ldboct+PXfuHNzc3JCZmQkHBwd07tyZ5doYP6bGToFA0OJvhrb91crKCnfv3kVISEiL4wFTDJoMjB8/XpWJtfZYx8aNfebMma2WPXbsWNUOt7W1RXp6OgIDA1FdXQ0nJycIhUIkJiY+s8OnTZsGMzMzXL9+HW5uboiNjYW9vb1RZdxjx45FWFgYtmzZompsJSUl8PDwQHV1Nby8vJCSkoLi4mJV3cePH4/a2loMGzYM//73v7Fo0SIALc80Gs9cNDl2ytWa1so31DYUCgX4fD7Ky8uRnZ2NwMBAODs7qzqlubk5fvrpJ1Ubyc/Ph0gkwm+//YYFCxao1Ta0beeN69EabffVwoULWy1blzY1Y8YM7N27F5MmTQJAZ6+N6bJfIyIisHv3bri7u8Pd3R3+/v503+pI298MhUKBsrIy3LhxA+Hh4ejRo4fexrbXX3/dYMfZoMnA9u3bcfXqVTx+/Biurq7Iy8uDubk5PD094eXl9cz5sZqaGri4uKBTp05IS0uDvb097t6922L5+hwguU7buisTgZZoctwKCgoQHByMc+fOISoqCnK5HGfOnIGnp2eLD6DRdBs9e/bE+fPnNdoGoP0P9YIFC1rdT9rWZcCAAeDz+Wq3c037kZWVFQIDA3Hjxg3w+XzweDy1HqCibZtSJgJU03T5YaCYpe2xUGeCqqRJf3VwcMCjR48QHh6u9njAFIMkA0KhEAKBgJEXBgkEAgiFwhf+PSMjA0lJSRAKhWrt9JKSEjg5OcHf3x9paWmwsbHB7du34ePjY1RPTdO03o0b2/Hjx+Ht7Y3KykqEhoYiIyPjmbKZPG5Kzx8/Q2yjMU06JgDw+Xz4+/urfkhzcnKa3a6+2znT+4qpvpSfnw9vb294enoiLS0NLi4uqKioaLJNtWW69FVl+0tNTUVoaCh69+5N962OtEna//77b8TExMDOzg5Hjx5Fu3bt4OLi0uRjwA3xu8ckgz10iN5ayCx6a6Hm26C3Fra8jbbal/SN3lrILfTWwqaZxBMIm9vhu3btwqpVq/Drr7+iZ8+eqn8vKirC2LFjMXToUHzyySdNlmkMD5BoraHNnTsXfD4fGzduxPfff499+/bh8OHDLb6bwBjqra3m9ldiYiKWLl0KqVQKQgjEYjFWrVqF4cOHN1lOW9xHQMOjhkeOHIlx48Zh/vz5z7Svlpjy/lJXa301NzcXL730Er7++msMGTIEI0eOxNixY1s9PUX3rXaaOx6ffvopjh07hr179z7zUqKrV6/i9ddfxwcffICJEyc2WabRHwvDvyjRMJ48eUIcHR3JrFmzmvz7pk2bCABy5swZA0dmGCUlJcTCwoJ8//33hBBCTp8+TQCQ06dPsxwZ97z66qskKChI9f89evTg3OtFueDUqVPP9JnvvvuOWFhY0NeDM2DdunXE0tKSlJaWEkIIee2110iPHj1YjqptUY6RmzdvbvLvM2fOJO3atSNPnjwxcGSGYbLJwPTp00n79u1Jfn5+k3+vr68nffr0IaGhoaSurs7A0enfrl27CABy7949QkhDfYVCIVm6dCnLkXFLfX09cXZ2Jh9++KHq35YtW0aEQiGpr69nMTLu+eCDD4iLi4tqv2RnZxMA5K+//mI5MuM3bNgwMnLkSNX///e//yUAyN27d1mMqu2oq6sjISEhpE+fPs32+6dPnxInJycyY8YMA0dnGCb5ovFTp07ht99+w1dffdXsRRdmZmbYtGkTrly5gh9++MHAEeqfRCJBcHCw6gpYMzMzjBkzBlKplOXIuOXs2bMoKCiASCRS/ZtIJIJMJsO5c+dYjIx7JBIJxowZozrN1KVLFwQFBdE2paOSkhIcP34cYrFY9W8jR46EpaUl3bcGsnnzZly9ehWbNm1q9jSqi4sLvvrqK/z66684ffq0gSM0ALazEabV1dWR4OBgEh4ertbM7s033ySOjo7k8ePHBojOMOrq6kj79u3JRx999My/7969mwAgWVlZLEXGPe+99x5xdXUlcrlc9W/19fXExcWFvP/++yxGxi137twhAMiePXue+fcPP/yQODs701UUHezcufOZVTylkSNHkmHDhrEUVdvx6NEj4uDgQP7xj3+0+tn6+noSHh5OgoODTW5F2eRWBjZs2IBr1661mOE19uWXX8Lc3BzvvfeeAaIzjLNnz6KwsPCZ2S5AZxtNkUqlGDNmDPj8/3UFuoryIqlUCktLyxcekysSiVBQUICzZ8+yFJnxe34VT0ksFuP48eMoKSlhKbK24f3334eFhQVWrFjR6meVK8rK3xiTwnY2wqS8vDxib29P5s6dq9H3tmzZQgCQ48eP6ykyw3r33XdJhw4dnpntKo0aNYrONv7P7du3CQCyd+/eF/62Z88eAoDcuXPH8IFxUHR0NBk1atQL/y6Xy4mrqytdRdFSXV0dcXJyIsuXL3/hb/fu3SMAyM6dO1mIrG04fvw4AUC2bt2q0ffmzJlDHBwcSF5enp4iMzyTSgamTJlChEIhKSgo0Oh7crmc9OvXjwQFBZHa2lo9RWc4fn5+ZObMmU3+bePGjcTc3JwUFxcbOCruWbt2LbGysiLl5eUv/K2srIxYWlqSb7/91vCBcUxRURExNzcnmzZtavLvr7/+OvH39zdwVKZB+WN0/vz5Jv8eHBxMpk2bZuCo2oba2loSFBRE+vfv3+TEqSUFBQVEKBSa1LExmdMEx44dw3/+8x98/fXXaN++vUbf5fP52LRpE27cuIENGzboKULDuHXrFjIzM184RaA0ZswY1NfX49ChQwaOjHskEgmGDRsGW1vbF/5mZ2eHYcOGQSKRsBAZtxw6dAj19fUYM2ZMk38Xi8W4efMm7ty5Y+DIjJ9UKkXHjh3Rp0+fJv8uFotx4MAB1NfXGzgy0/f999/jxo0b2LRp0zOnCdXRvn17rFq1Cjt27MDx48f1FKGBsZ2NMKG2tpYEBgaSiIgIjTO8xt566y1ib29PHj58yGB0hrVmzZpmZ7tKoaGhZOrUqQaMinsKCwuJmZlZs/cUE9LwLApzc3NSVFRkuMA4aPLkySQ0NLTZv5eXlxMrKyuydu1aA0ZlGrp3797ss1AIIeT8+fMmdQqTK3Jzc4mdnR2ZP3++1mXI5XISERFBevToYRIryiaxMrB+/XrcvHlTqwyvsS+++ALW1tZYsmQJg9EZllQqbXa2qyQSiXDw4ME2Pds4dOgQ5HJ5s7NdgK6iAEBdXR3i4+ObXWkCAFtbWwwdOpSuomjo1q1buHXrVov7tk+fPujYsSO9mJVh7777LgQCAT7//HOty1CuKGdkZOC7775jMDqWsJ2N6OrBgwfE1taWvP3224yU9+uvvxIA5OjRo4yUZ0gFBQXEzMyM/PDDDy1+7sKFCwQAOXbsmIEi455XXnmFhIWFtfq5Xr16kcmTJxsgIm5KTk4mAEhKSkqLn9u0aRMxMzMjhYWFBorM+K1Zs4ZYW1uTioqKFj83a9Ys0r17dwNFZfqOHDlCAJDffvuNkfIWLFhA7OzsSG5uLiPlscXok4EJEyaQDh06MHZBnEKhIAMHDiT+/v6kpqaGkTINZceOHQRAq41SLpcTNzc3smTJEgNFxi21tbXE0dGRfPLJJ61+9uOPPybt2rUziWVAbSxevJi4ubm1evrtwYMHBAD5z3/+Y6DIjN+gQYPImDFjWv3c33//TQCQzMxMA0Rl2mpqaoi/vz8ZOHAgUSgUjJRZVFREOnToQCZNmsRIeWwx6tMEiYmJ+Ouvv7BmzRo4OjoyUiaPx8PGjRtx+/ZtfPvtt4yUaShSqRRhYWHw8PBo8XN8Ph9jxoxps8u6p06dQklJSYvLs0oikQjFxcWm+cSxVhBCmnwOQ1M6deqEXr16tdk2panCwkKcPn1arTY4fPhwWFtb01MFDPj2229x+/ZtbNq0CTwej5Ey27Vrh9WrV2Pnzp04cuQII2Wygu1sRFvV1dWke/fuZNCgQYxleI0tWrSICAQCkpOTw3jZ+lBTU6P2bJcQQiQSCQFAbt68qd/AOGjx4sXE3d1drXajUCiIu7s7eeeddwwQGbdkZGQQAEQikaj1+Y8//pg4Ojq22VUUTShX8dS9WHnMmDFk8ODB+g3KxOXk5BCBQEAWL17MeNkKhYJERUURPz8/o1tRVjLalYG1a9ciKysLGzduZCzDa+zTTz+Fg4MDFi9ezHjZ+nDy5EmUlJQ883zzlgwbNgw2NjZtbrZBCIFEIoFIJFKr3fB4PNUqCjH+t31rRCKRwMbGptlXOT9PLBajpKQEp06d0nNkxk8qlaJPnz5wd3dX6/MikQinTp1CYWGhniMzXYsXL4ajoyM+/fRTxstWrijfuXMHa9euZbx8g2A7G9HGvXv3iI2Njd7Pef/+++8EADl06JBet8OEhQsXEg8PD41WSUQiEYmKitJjVNxz48YNAoDs379f7e9IpVICgGRkZOgxMu4ZOHAgEYlEan9euYqyaNEiPUZl/GpqaoiDgwP517/+pfZ3Hj58SACQHTt26DEy03Xo0CGDXNPyzjvvEIFAQO7fv6/X7eiDUSYDY8eOJe7u7qp3f+uLQqEgQ4YMId26dSPV1dV63ZYuFAoF6dq1q1ov2mjsp59+Inw+n8hkMj1Fxj2rVq0iNjY2pLKyUu3vVFZWEhsbG/L111/rMTJuyc/PJ3w+n/z0008afe/NN98kPj4+ejl1ZyqUV7OnpqZq9L3evXsb/UVqbKiuria+vr4kOjpa7+2ytLSUuLu7k/Hjx+t1O/pgdKcJDh48iH379mHt2rWwt7fX67aUSz/Z2dlYs2aNXrelixs3biA7O1vtUwRKY8aMgUKhQHx8vJ4i4x6JRIKRI0fCxsZG7e/Y2NhgxIgRberiuIMHD0KhULT4HIamiMViZGVl4ebNm3qKzPhJpVJ06tQJoaGhGn1PLBYjPj4etbW1+gnMRK1evRr37t3Dhg0b9HJKuTF7e3t888032LNnj/E9n4TtbEQTVVVVxMfHhwwdOtSgM4/33nuP2NjYkOzsbINtUxNfffUVEQgEpKqqSuPvhoeHk4kTJ+ohKu5RznY1fSkJIQ0vs2pLqygvv/wyCQ8P1/h7ylWUVatW6SEq46dcxdP0ZWqEEJKamkoAkCNHjughMtN09+5dYm1tbdAXaSkUCjJ06FDi4+Oj1ZjMFqNaGfj666+Rk5Ojt4sGm/Pxxx+jffv2WLhwocG2qQmpVIoRI0bA2tpa4++KRKI2M9tQznbj4uI0/m5cXBwUCgUOHjyoh8i4pba2FgkJCWrd9va8triKognlKp42+zY0NBSdOnVqcxf96mLRokUQCoX45z//abBt8ng8bNiwATk5OVi9erXBtqsztrMRdWVlZRFra2uydOlSVra/a9cuAoBIpVJWtt+cp0+fEh6PR7Zt26bV99PS0ggAkpiYyHBk3PPyyy+Tfv36af39vn37kgkTJjAYETcdPnyYACCXL1/W6vtbt24lfD6f5OfnMxyZ8fvqq6+Ira2t1jPGuXPnkq5du9JrMtSgvPD3r7/+YmX7H3zwAbG2tiZ3795lZfuaMppkYMyYMcTT07PFF/Dok0KhIMOHDyddu3bV6OIzffvll18Ij8cjjx8/1ur7CoWCeHp6MvY4Z66qrq4mdnZ25IsvvtC6jM8//5zY29sb7X3E6po/fz7x9PTU+gfn0aNHjD7u1ZRERkaScePGaf39gwcPEgAkPT2dwahMT2VlJenatSsZOXIka4lTWVkZ6dSpk0Z35LDJKE4TSCQS7N+/H+vWrWvxBTz6pFz6yc3NxapVq1iJoSlSqRR9+/ZFhw4dtPo+j8eDSCQy+fvojx8/jvLycq2WZ5VEIhHKyspM55WlTSD/99RBdZ/D0JSOHTuib9++9FTBc/Lz83H27Fmd2mB0dDRsbW3pqYJWrFy5Eg8fPjTIRYPNsbOzw7p16yCVSo3jeLGdjbSmoqKCdO7cmcTExHBiaezDDz8kVlZW5M6dO2yHQqqqqoitra1Os11CCImPjycAyLVr1xiKjHvmz59PvLy8dGpDylWUBQsWMBgZt1y9epUAIPHx8TqV88UXXxA7OztO35JraMpVvCdPnuhUztixY0lkZCRDUZme27dvEysrK/LRRx+xHQpRKBQkJiaGdOnSpdUXUrGN8ysDX331FR49eoTvv/+etQyvsQ8//BAdOnTAggULWJ9JHzt2DBUVFRrfUvi8IUOGmPRsg/zfUwfFYrFObYjH40EsFpv0KopEIoGdnR2io6N1KkckEqG8vNykV1E0JZVK0a9fP7i6uupUjlgsxtmzZ/H06VOGIjMdhBC8/fbb6NixIz788EO2wwGPx8P333+PvLw8rFy5ku1wWsZuLtKyW7duEUtLS/LPf/6T7VCesXfvXgKA7N27l9U45s2bRzp37szIisn48eNJWFgYKSsrYyAybrly5QoBQBISEnQuS/kks6tXrzIQGff069ePkQemKBQK4uXlRaZOnUrq6uoYiMy4Xb9+ndjY2JDPP/9c57KePHlCeDwe+eWXX3QPzMTs2bOHACD79u1jO5RnLF++nFhaWpJbt26xHUqzOJsMpKWlEQcHB+Lu7s655RWFQkEGDBhA+Hw+ax1SuX+mTp3KSHmDBw8mAMjHH3/MSHlcoVAoyKBBg4i1tTUj9/xWVVURa2trMnjwYE6ctmLS9u3bCQCybt06RspzcHAgAMixY8cYKc+Y9e/fnwAgCxcuZKS8oKAg0q1bN3rHRiO//vorsbGxIcOGDeNc36yoqCBubm7EwcGBpKWlsR1Okzh7mmDr1q0oLS1Fp06dIBAI2A7nGTweD8HBwVAoFNi6dSsrMfz2228oLS3FgwcPGCnvzTffBAC9P9XR0CoqKnDixAlUV1ejoqKCkfKqq6tx/PhxRsrjEuWFsUy9DEf5PAdtL241JQEBAeDz+Yw9qyQ9PR23b9/GlStXGCnPFHz99deoqqpCz549OXFKuTGBQIBOnTqhtLSUtd+MVrGdjTTnxIkTZNy4cXp//4C2FAoFWbFiBWtPA0tKSiKdO3dm9IUYul7YxFWhoaHks88+Y6y8zz77jPTq1Yux8rjiq6++Ir169WL0oj9TbVOaUigURC6XM1be/v37iaenJykuLmasTGO3adMm8tZbb3H2tFRJSQkZN24cOXHiBNuhNIlHiIleCUVRFEVRlFo4e5qAoiiKoijDMNf3BnJyciCTyRgvVygUwsvLi/Fym8N0PQwdv5Kp1IPiBlPp35rQV52VuFx3QzH2ccoY+4Vek4GcnBwEBASgsrKS8bIFAgEyMjIMcoD1UY/m4meqETXVaAxZD31jsrM118EMsQ1D0FebMpX+rQl91lmpcd1NpQ1qwtjHKWPtF3pNBmQyGSorK7Fjxw48evQIfD4f9vb2yMzMRGBgIEJDQ3HmzBl4eHiAz+fD0dERcrkcycnJsLa2hp+fH7KyshAZGYkdO3Zgzpw5AICMjAxMmzYNMpnMIAdXWY93330XNjY26NGjB/Ly8lBWVobKykp4eHiguLgY1tbWGDRoEM6dO4fu3bujtrYWzs7OuHv3LkpLS9G1a1ecPHkSPXv2bDJ+JhtRU42m8fEICAjAnj170KFDB1hbWyMzMxOTJ09GWloabG1t4eDgAGtra9TV1eHMmTMYPnw4du3ahcmTJ8PGxgaA4Y+DEtOdral9ZYhtGII+21Tj9pSZmYnQ0FDcvXsXDx8+RGBgIMzMzHDixAlMnToVJSUlkMlk8PX1RU1NDdzc3CCRSBAeHo4rV66gW7du8PHxAcBeu1KHss5vv/02fHx8kJ2djdDQUAANb3t88uQJCCFwd3dHbW0tgoKCcPHiRQQEBKCmpgaurq64ffs2rKyscP36dfj6+sLKygoA8ODBA7i5uanqDsAk2qCmGrernJwcuLq6IicnB4QQhIWFwdzcHHfu3EFISAhKS0vh6emJ/fv3w8/PD/fu3UNERAQnfi927NiBvLw8uLi4oLS0FHw+H5cvX4a3tzdcXFyQkpKCHj16QCgUwtXVFdeuXUN4eDgKCwvh7u6Oo0ePIiIiArdu3UJNTQ2cnJz0Wg+9nyYAGhr01KlTAQClpaVwcHBQ/S0sLOyFzw8bNuyFfxs0aJD+AlTT5MmTVfE+X4/G+vXr98z/Dxw4UPXfcXFxSE1NbfJ7ykY0a9Ys9O3bFwBQVFQEa2trPH36FHZ2drCwsEBlZSVGjBiB4uJi8Hg8tGvXDrt27YJYLMbFixdRV1eHZcuWNdtoAgICEBYWhrCwsBfq0bt37xc+P2rUKADcOAYAc/upU6dOqK+vb7KDGWIbxrSvWmpTLfXvN954o9nYlH1Jm1dKs2369OnPjF0tjQdRUVHP/H9ERESz5TYeG0ylDWqrpXb1vOd/R7gwVrUW/+zZs5/5/xEjRjzz/+Hh4QCAwYMHA0CzvxtMMUgysG/fPpSVlQFoeFmHQCBAbm4uHB0dYWVlhbKyMkycOBEymQw8Hg8uLi7YsGED3njjDSQkJKBDhw7Izs5Gp06dEBQUZIiQ9VIPPz8/XLhwockf3Mbmzp2LS5cuwd/fH+Xl5UhPT0dERASqq6shFAphZmYGmUyG7OxsLFiwAPfv38fSpUthY2ODy5cvIzo6GsuWLdNLPf7++2+EhoYiMzNTNZNji677adKkSa12MENswxA6dOigqkNxcTF8fHzg4eGhqkNxcTFSUlJUdRAIBPjkk0+we/duLFmyBKmpqc22KV37hZubG54+fYqCgoIXEmku27ZtG3x9fQFoXu9jx46hS5cuuHv3LvLz89GvXz+EhIQ0uR1t26C9vT1yc3M50wY1pUu72rlzJ/r164e0tDQEBwcbZfx+fn6QyWSorq7G+PHj9R6vQZKBsWPHqhqzra0t0tPTERgYiOrqajg5OUEoFCIxMfGZxjxt2jRYWVmhrq4OlZWV6N69OyIjIwEAeXl5hgib8Xo8fvwY/fv3R11dXavbapw1xsbGtvjZzp07q/574cKFrXZ8betRU1ODkpISBAcHIzg4mBMDjC77iUvb0Lfx48erZk+a1OH1119vteyxY8ciLCwMW7ZsUf1glZSUwMPDA9XV1fDy8kJKSgqKi4tVbWr8+PGora3F6NGjsW3btmf2FRfalTpmzpypqrMm/YjH46GwsBA1NTWIjY1V60Ff2rZB5czUGCkUCvD5fJSXlyM7OxuBgYFwdnZWJUDm5ub46aefVPu2uLgYc+fOxe7du+Hn54eysjLMmDGDtfakS7947bXXsHv3bgiFQnTv3l11alafDJIMAKbTmJmoR2uNc/v27bh69SoeP34MV1dX5OXlwdzcHJ6envDy8lKdU7p8+TIAgM/nw9/fH2lpabC3t8fdu3f1Vo8FCxa0WrahaLKfampqYGVlhcDAQNy4cQN8Ph88Hk91vpaJbQAvHgu5XN7qNgxBk3o4ODjg0aNHCA8PN0ib4lLSpClt66xOkqWkybErKCjAgAEDwOfzOdcGNaVtAmuKvxmGYJBkICMjA0lJSRAKhRo36JMnTyI4OBgHDhxAaGgoevfujYyMDEOErXM98vPz4e3tDU9PT9y9exdWVlYoLy9vtmMKhUIIBAKsX79e51gFAgGEQiEj9Wh8PI4fPw5vb29UVlayNsAwuZ+ApveVIbZhCIZoU8r+qG2bSktLg4uLCyoqKhAaGspa/9aENnUG/pcsKhPS1NRU1bjWuFzAdNqgtrRNYFNTU2FjYwM+n89qe9KmjVRXV8Pa2vqFSYsh6qHXJxAa6y0Wz6O3FjaP3lqo2zYMgd5ayBx6a6H+Gfs4Zaz9Qu+PI26tMW/evBk7d+7EkSNHcOTIEXz00Uc4ePBgqy834dJDJBQKBWJiYhAXF4dFixZh9uzZrWb0XHzo0LFjx7BkyRLs3bsXhBCMHz8ea9euVV3N2hRjGWAo/TCV/q2Jlup88+ZNTJs2DQsXLsSrr76q+vf6+nq88sorcHBwwNatW8HnN//wVy7X3VBa2sdvv/02qqur8dNPP+Hbb79FfHw8Dh06xKl92lq/ePz4MeLi4vDll19i2LBhGDZsGCZPnqy6HbI5eq0Ha29F+D8hISFkypQphBBCCgsLiZmZGdm8eTPLUWnm/PnzBAA5fvw4IYSQb775hlhZWZHy8nKWI9PMG2+8Qfz8/FT/3717dzJz5kwWI6KMnSn0b3XJ5XISERFBevToQWpra1/4e3JyMgHA2mvPTUF5eTmxsrIia9euJYQQcuzYMQKAXLhwgeXINLNp0yZibm5OioqKCCGETJ48mYSGhrIaE6vvJsjJycGVK1cgEokAAE5OToiKioJEImEzLI1JpVI4OTmp7nYQiUSoqanBkSNHWI5MfQqFAgcOHFAdC6ChHvv374dCoWAxMspYNde/pVIpy5Hpx6+//oqzZ89i48aNsLCweOHvQ4YMwZQpU/D++++jqKiIhQiNX2JiImpqalRtasCAAXBycjK6NiWRSBAVFYV27doBaBhrL1++zNgr6bXBajKwf/9+mJubqx5qAwBisRhJSUlG9a54iUSC0aNHw9y84XrMbt26wc/Pz6gaaEpKCp48eQKxWKz6N7FYjCdPnuDixYssRkYZq+b699GjR42qf6ujsLAQH3zwAaZOndriabU1a9aguroay5cvN2B0pkMqlcLf31/1fAdzc3PExsYa1QSyvLwcSUlJz4y1o0aNgrm5Ofbv389aXKwmAxKJBIMGDVJlR8D/ZtWJiYnsBaaB+/fv4+rVq88cWMD4ZtUSiQTt27d/5ulokZGRcHJyMqqORnGHKfRvdX300Ueora3FmjVrWvycm5sbPvvsM2zevBmXLl0yUHSmQaFQYP/+/c+sXgINCeaVK1eQk5PDUmSaSUxMRG1t7TP14MKqOGvJQFlZGZKTk184sL6+vvD39zeaHyDl7CcmJuaZf1fOqlNSUliKTDNSqfSZ1Q2gIesePXq0Ua1wUNzQWv82pTaVkpKCH3/8EZ999hk6duzY6ufnz5+PoKAgzJs3z2gmC1xw4cIFPH369IWJFxdm1ZqQSqUICAh44QmuIpEISUlJKC8vZyUu1pKBprIjJbFYjAMHDhhFR5FIJBg8eDAcHR2f+feIiAi0b9/eKAa9+/fv49q1ay90MqDhWFy9ehX3799nITLKWLXWv41p1awlcrkc8+bNQ3BwMN566y21vmNubo5NmzbhwoUL2LZtm54jNB1SqRTOzs4vvNvB0dERgwYNMooJpFwux/79+5sda2tra1lbNWMtGZBKpQgMDGzy+fYikQhPnz7FhQsXWIhMfWVlZTh27FiTB1Y5qzaGBiqVSmFhYfHC6gYAxMTEGFXWTXGDKfRvdWzduhUXL17Exo0bn1lVa83AgQMxffp0LF26VC/vvTdFymuzzMzMXvibWCxGcnKy6l0AXHXhwgXk5+c3mST7+PggICCAtQkkK8mAMjtqaocADbNqZ2dnzs+qDx8+3OzsB2hooNeuXeP8rFq5utHUW8EcHR0xePBgo0hqKG4wlf7dmvz8fCxbtgwzZszAgAEDNP7+qlWrIJfL8eGHH+ohOtNy7949pKenNznxAhoSTDZn1eqSSqUQCoXo379/k39XXmsml8sNHBlLycD58+chk8maPbBmZmaIi4vj/A+QRCJBjx490LVr1yb/HhMTAwsLC04PeqWlpc2ubiiJxWIcO3aM81k3xQ2m0r9bs2zZMhBCsGrVKq2+36FDB6xYsQJbt27F+fPnGY7OtChXL0eOHNnk3729vREYGMj5NiWRSBAXF9fk6gbQMNbm5+ezsmrGSjKgzI5ael2pSCRCeno67t27Z7jANCCXy3Hw4MFmZz8A4ODgwPlZ9eHDh1FXV9diPZRZ9+HDhw0YGWWsTKF/t+bs2bPYtm0bVqxYAVdXV63LmTNnDkJDQzFv3jxWZoPGQiKRYMiQIU2uXiqJRCIcOHCAs/sxOzsb169fb3Gs7d+/P4RCISsTSNaSgZayIwAYOXIkp2fV586da3H2oyQSiXDs2DGUlpYaKDLNSKVSBAUFoUuXLs1+pmvXrujRowdnjwXFLabQv1uivGgwLCwM//jHP3Qqy8zMDJs2bUJqaip+/PFHhiI0LaWlpTh+/HiLP6JAw6xaJpNxdpVFKpXC0tKy2dUNoKE9sHUHl8GTgbt37+L69eut/og6ODggOjqas4OFRCKBi4sL+vbt2+LnRCIR6urqODmrlsvlOHDgQKvHAvjfHR5czbopbtC0f3N51aw5mzdvxpUrV7Bp06YWEx519e/fHzNnzsRHH32Ep0+fMhChaUlISGh19RIA+vXrB6FQyNk2JZVKER0dDXt7+xY/JxaLkZ6ejuzsbANF1sDgyYA62ZESl2fV6sx+gIZZdVBQECcb6NmzZ1FQUNBqJwMajoVMJsO5c+cMEBllrDTt38ePH0dJSYkBImPGkydPsHz5csyaNavF0yCaWrlyJfh8Pj744APGyjQVEokEPXv2bHH1EvjftShcnECWlJSotboBNKyaWVpaGrwerCQD0dHRsLOza/Wzyll1QkKCASJT3507d5CRkaHWgQUa6nHw4EHOzaqlUilcXV1bXd0AGrJuFxcXTnY0iju06d9cXDVrzvvvvw8zMzN8+eWXjJYrFArx1Vdf4ddff8Xp06cZLduY1dfXt3ptVmMikQg3btxAVlaWniPTjHJ1Y8yYMa1+1t7eHkOGDDHtZECZHamzLA0AnTt3Rs+ePTn3A6TJ7AdoWPYpKCjA2bNn9RyZZiQSCcaMGdPiqz+V+Hw+xowZw8kVDoobtOnfwcHBRtOmTp48ie3bt2PlypUQCoWMlz9z5kyEh4dj3rx5qK+vZ7x8Y3T27FkUFhaq3abYmlW3RiqVIjg4GJ07d1br88o7uAy5ambQZODQoUOor69XKztSUp6r5lLnkEqlGDp0qFqzHwDo27cvXF1dOTXo3blzBzdv3lQ74wYasu6MjAzOZd0UN2jTv5WrZlzq302pq6vDvHnz0LdvX8ycOVMv21BeTHjt2jVs3LhRL9swNhKJBB06dEB4eLhan7e3t+fctWbK1Q11ExoAGDNmDOrr6w26Km7QZEAqlSIkJAReXl5qf0ckEqGwsJAzs+ri4mKcPHlSox9RPp/PuXNZUqkUVlZWGDFihNrfGTFiBCezboobTKF/N2fDhg24fv06Nm3apNZKmrb69OmDf/zjH/jnP/+JR48e6W07xkJ5bZYm+1wkEuHEiROcuRblzJkzKCws1Og3Q7lqZsix1mDJgDbZEQCEh4ejQ4cOnPkBUs5+NDmwQMMKx82bN3Hnzh09RaYZqVSKYcOGwdbWVu3v2NnZYdiwYZw5FhR3mEr/bkpeXh4++eQTzJ07F71799b79lasWAErKyu89957et8Wl92+fRuZmZkatymRSIT6+nocOnRIT5FpRiqVomPHjujTp49G3xOLxQZdNTNYMnD69GkUFRVp/COqPFfNlcFCIpEgNDQUnp6eGn1vxIgRsLKy4kQ9ioqKcOLECY2PBfC/rLu4uJj5wCijpWv/5tIptOe9++67sLa2xhdffGGQ7bVv3x6rVq3C77//jmPHjhlkm1ykXL0cPny4Rt/z8vJCSEgIZ9qUVCpV+9qsxpSrZmfOnNFTZM8yWDKgzI60yaxFIhFu3ryJ27dv6yEy9dXV1SE+Pl6rH1FbW1sMHTqUEw300KFDkMvlGp3bVVKey+JK1k1xg679OzMzk/X+3ZSkpCT88ccf+Prrr+Hk5GSw7c6YMQMRERF46623UFdXZ7DtcolEItF49VJJJBIhPj6e9WtRbt26hczMTK1+M/r06YOOHTsabAJp0GRAm+wIAIYPH86JWfXp06dRXFys1YEFGpZ9Tp48iaKiIoYj04xUKkWvXr3QqVMnjb/r6emJ0NBQ1o8FxS2m0L+fV1tbi/nz52PAgAF47bXXDLptPp+PTZs24ebNm1i/fr1Bt80FRUVFOHXqlManCJREIhGKiopYv01TKpXC2tpa49UNwPDXmhkkGcjMzMStW7e0PrC2tracOFctkUjg5uam9XnDMWPGQC6Xszqrrqur0+rcbmPKc1ltdcZCPYuJ/j18+HBOrJo1tm7dOty6dUvvFw02JzQ0FG+99RY+/fRT5ObmGnz7bIqPj9d69RL436ya7TalvDZLIBBo9X2xWKzqX/pmkBauzI6GDRumdRlsz6oJITrNfgCgU6dO6NWrF6tJzalTp1BSUqL16gbQkHUXFxeznnVT3MBE/xaJRDh16hTrq2ZKDx48wL/+9S/Mnz8fwcHBrMXx2Wefwc7ODkuWLGEtBjZIpVKEhYXBw8NDq+9z4VqzwsJCnVY3gIZVM2tra4PUw2DJwPDhw7XOjoD/zarj4+MZjEx9mZmZuHPnjk4/ogD7s2qpVAp3d3eEhYVpXUZYWBjc3NxYX6mhuMEU+vfz3nnnHTg4OOBf//oXq3G0a9cOq1evxq5du3DkyBFWYzEU5bVZuvyIAg0JpvKOBDbouroBAAKBwGCr4npPBgoKCnD69GmdD6yHhwfCwsJY+wGSSqWwsbHRafYDNDTQkpISnDp1iqHI1EcIgUQigUgkAo/H07ocPp8PkUgEiUQCQgiDEVLGhsn+3bt3b04kmIcPH8Z///tffPPNN3B0dGQ7HEybNg1RUVF46623UFNTw3Y4enfy5EmdVy8Bw86qmyKVStG7d2+4u7vrVI5YLMapU6dQWFjIUGRN03sywER2pKR8WllBQQEDkWlGIpHoPPsBGmbV7u7u+OOPP6BQKBiKTj03b95EVlaWzp0MaDgWd+7cYS3rpriB6f4dHx/P6rUoNTU1mD9/PoYMGYLJkyezFkdjPB4PGzduRFZWFtauXct2OHqlUCjw559/wsPDA7169dKpLIFAwNq1KPfv39fonQotMdiqGdGjoqIiEhwcTIKCghgp75NPPiEAyMCBAxkpT1179+4lPB6PfPvtt4yUJxQKCQBy4sQJRspTh0KhIHFxccTKyopUVlbqXF5lZSWxsrIicXFxRKFQMBAhZWyY7t/nz58nAMjSpUsZKU9TpaWlxN/fn5iZmZHr16+zEkNL5syZQ/h8PnnvvffYDkVvjh8/TgAQFxcXRspbu3Yt4fF4ZO/evYyUp64BAwYQAGTRokWMlNejRw8SEhJCioqKGCmvKXpdGbh48SKuXr2K9PR0RsobO3YsAMDc3JyR8tS1fPlyEEIYe9e48i2Bhrxvuby8HAcOHEBNTQ0qKip0Lq+iogI1NTU4cOAAI+VRxofp/q18kNXKlSsZKU9Tp06dws2bNyGXy+Hv789KDC0JDw+HQqHA5s2b2Q5Fb5RjojpvUlVHfn4+CCH45z//yUh56lLetv3mm28yUt7169dx5coVXLp0iZHymqS3NIMQkp+fT5ydncnvv//OWJlZWVmktLSUsfLU8eGHHxIfHx9SXl7OSHkKhYJcuXKFkbI04evrSxYvXsxYeYsXLya+vr6MlUcZF6b7t0KhIEOHDiWxsbGMlKephw8fkr59+5KUlBRWtq+OdevWka+++ortMPTqypUrjK02lpeXEx8fH/Lhhx8yUp665HI5Y78XhBCyY8cO4uzsTAoKChgr83k8QugVYBRFURTVlhn+SRoURVEURXGKYU++ayAnJwcymYyRsoRCoUavVWUCk/E3xkZdKMqYmELf01cdlAxVF3osWseVMV3jZIDpHdPUjsjJyUFAQAAqKysZ2YZAIEBGRkaT22GiLs/Xgen4G3u+LoZImow9MaM0Y0xtis2+py/6rIOSIepi6GOhjzZl6GPB5lirUTKgjx3T1EGVyWSorKzEwoUL4eXlBXt7e2RmZiIwMBChoaE4c+YMPDw8wOfz4ejoCLlcjuTkZFhaWsLd3R0ymQwRERGQSCTo2bMnXn/9dchkMr0NGs/XQRn/jh078OjRI/D5fLXrYGdnB6FQiOLiYoSHh0MikWDChAmwtLRERkYGpk2bpqqLIZImQyVmFDcYW5tqqe8FBAQgISEBrq6uyMnJASEEYWFhMDc3x507dxASEoLS0lJ4enrizJkz6NChA65fv46hQ4dix44dmDNnjmo7z/c9fVLW4e2334aPjw+ys7MRGhoKoOHlSU+ePAEhBO7u7qitrUVQUBAuXryIgIAA1NTUwNXVFbdv34aVlRWysrLg5uam2lcPHjyAm5ubQery/LFISkpSayysqKjAtWvXUFZWhsjISFy7dk01nk+YMAFZWVkvxK+vNsXksbh48SK6du0KKysr2NnZ4d69e+jUqZOqLgBYHWs1SgYaH9zMzEx4enoCaHjDlLW1NZ4+fQo7OztYWFigsrISI0aMQHFxMXg8Htq1a4ddu3ZBLBbj4sWL6NSpE+rr61tslK+99prqsbmlpaVwcHAAgCYfpdvUkwEHDx6M1NTUFusya9Ys1W0s2tSjrq4Oy5Yta7IOAQEBmDp16gvxa1qH5jBVh5aOhSG2QXGHMbWp1vpeWFgYwsLCXuh7TVH2xwkTJgAABg0axNg+1db06dOfGSdaqkdUVNQz/x8REdFsuc2NifrS2rFoaiyMi4t74b+VY6GlpeULn9d3m2LiWEyaNOmFzzY+FmyPtVpdM6DMXvz9/VFeXo7i4mL4+PjAw8MDQqEQZmZmKC4uRkpKChYsWID79+9DIBBgxYoV2L17NwYNGgR7e/tWM6B9+/ahrKwMQMP9ogKBALm5uXB0dISVlRXKysowceJEyGQy8Hg8uLu7Y9u2bZgwYQKSkpLg4+PTal06dOigcT0++eQT7N69G0uWLEFqaiqWLVvGSPwuLi7YsGED3njjDSQkJMDPzw+3bt2Cg4MDOnfu3Gwd5s6di0uXLqnqkZ6ejoiICFRXV6vqIZPJkJ2drarH0qVLYW9vj9zcXEyaNKnVAULbbdTX1+P69etqbYPiDm2Pt3LWrc82pWy3AQEBeu17d+/eRWVlJcaPH8/4/lXXtm3b4Ovrq1U99u/fj6CgIFy4cAGurq7w8fFBUFCQweugy7H4+++/ERISggsXLsDLy6vFcRDQbjxfvnw59uzZ0+p4rsuxULapCxcuQKFQIC4urtlXyLM11mp9AeHs2bNV/x0bG9viZxsfQOVMGWg9Qx07dqxqp9ja2iI9PR2BgYGorq6Gk5MThEIhEhMTn9kpYrEYcrkcBQUFGDt2bKvbGD9+vCrj06Qer7/+eoufVcYfFhaGLVu2qA5sSUkJPDw8UF1dDS8vL6SkpKC4uFhVh/Hjx6O2thZRUVH4+eef8dFHH6nKa6kuTByP1mi7jXnz5qm9DYo7tD3eM2fO1Ps2pk6d2mJ/0LXv7d27lxPtdubMmao6qDsGTps2TfWDcffuXfTp00fnR/vqQpdjMXHiROzevRuOjo6IjIyEvb19i8dd2/FcnTar/Iw29Rg7diz27t37THtvCRtjrVbJwPbt23H16lU8fvwYrq6uyMvLg7m5OTw9PeHl5YXCwkK4u7vj8uXLqKmpgZWVFQIDA3Hy5ElERETg7NmzcHZ2Vus5//oekLStS25uLgoKCvDkyRO91aFxIsBUHQoKCjBgwADw+XykpaXB3t4ecrkcVlZWjG2j8X46fvw4vL29UVlZ2eo2KO7Q9ninpaXBxcUFFRUVjLapptrt3bt3W62HKSSw2tZh4cKFeotJG4aYsGjabl1cXNCpUyeDtKmmThPoWof8/Hx4e3vD09NTo/G8KRolA0KhEAKBAOvXr9d4Q80RCAQQCoVN/k3bweLkyZMYMGCA6sINfdeluTpkZGQAAJKSkiAUCjU+uDdu3ACfzwePx0NoaKiqPH3Uobl6GGIbFHcYW5tisu81/nFore8ZgjZ1aDwOKutw+/Zt+Pj4oHfv3s+Uy+V6ND4WqampsLGxAZ/Pb/ZY6LtNNd6mJvVwcHDAo0ePEB4e/kKber5ctsdajZ9ASG8tfBG9tVD3bVDcYUxtit5aqB16a2Hz2uqthZx9HHFzO0UmkyEmJgafffYZRowYgWHDhmH69OmYNWtWs2Vx8aFDW7Zswb///W8cPXoUCQkJ+OSTT5CQkNBqJkd/TCmqZa31vVOnTmHhwoXYtWsXAGDixIn47rvvMGDAgBbL5dqDbt555x2UlJRg27ZtWL16NZKTk3HgwAHweLxWy+fKQ4caj+cjR47E0KFDWx3PAe4diy1btmD79u1ISkrCoUOH8Omnn6o1ngMcGtP19tYDPdmyZQvh8/lEJpMRQgh5+eWXSXh4OMtRaa5Pnz5kwoQJhJCGF77w+XyydetWlqOiKNM3Z84c4u3tTRQKBVEoFKRr165kzpw5bIelkcrKSmJjY0NWrVpFCCEkMTGRACBpaWnsBqYhUxzPnz59apTjudG9m0AqlSIyMhLOzs4AAJFIhJSUFDx69IjlyNSXl5eHixcvQiwWA2jIDCMiIiCVSlmOjKJMGyEEUqkUIpEIPB4PPB4PIpEI+/fvB+HmImmTkpKSUFVVBZFIBKDhuQgODg5GN4Y8P56LxWKjHc+Vx8LFxUX1kCRjYlTJQFVVFRITE1U/ogAwevRo8Pl8HDhwgMXINHPgwAHw+fxnrkQVi8VITExEVVUVi5FRlGlLS0vDw4cPVQM30ND3cnNzcfnyZfYC05BUKoWPjw/8/f0BNDyIJyYmxqiSAVMbz0ePHq36N2Mcz40qGTh69Ogz2TDQMKuOjIw0qk4glUoxYMAAVTYMNKxwVFZWIikpicXIKMq0SaVSODo6PvOEwaioKKOaVStXN8Ri8TPXByhn1Xl5eSxGp76mxnNnZ2eTGc+rqqqMajw3qmRAKpXC19cXfn5+z/y7SCQymiyssrLyhWwYAPz9/eHj42NUnYCijI1UKsWoUaNgYWGh+jdLS0uMGjXKaJZ1U1NTkZeX98yPKNBwz7sxzaqbG8+NaVatHM+fPxbGOJ4bTTKgUCiwf//+F7JhoKHxVFVV4ejRoyxFp76jR4+iurr6hcbD4/EgFoshlUqN6twlRRmLhw8f4tKlSy/0PaBhDLl06ZJRzKqVqxsDBw585t+dnZ0xYMAAo/gBamk8V86qjWk8f35yZ4zjudEkA81lwwDg5+cHX19fo+gEUqkU3bp1eyEbBho6QV5eHn2GP0Xpwf79+2FmZtbkU+NiY2NhZmaG/fv3sxCZZiQSCWJjY59Z3VBSrpLq8754JtDxnHuMJhmQSqVo165dk/cCN74iWKFQsBCdehQKhepK5qYMHDgQjo6ORtEJKMrYSKVSDBw4EO3bt3/hb+3btzeKWXVubi7S0tJemIkqicViVFdXc35Wrc54LpVKOT+e79+/v8XxvF27dpxvU0pGlQyMHj26yWwYaOgEXM/CLl26hMePHzfbkS0sLDB69GijaTwUZSwqKipw9OjRZgduoGEMOXLkCKdn1crVjVGjRjX5dz8/P3Tr1o3zY4g64/mjR484P54/evSoxfE8NjbWaK5FMYpk4MGDB0hLS2uxIw8YMIDzWZhUKoWTk1OLTzoTiURITU1Fbm6uASOjKNN25MiRJs/tNiYSiVBdXY0jR44YMDLNSCQSREVFwcnJqdnPcH1W3dbG87S0NKMYz40iGdi/fz/Mzc2bzYaB/2VhXG48ynN95ubNvx9q1KhRRnPukqKMhVQqVc2am9O9e3d0796ds2NIRUUFkpKSWvwRBRp+gB4/foxLly4ZKDLNaDKec3lWLZVKTWo8N4pkQCqVYtCgQWjXrl2LnxOLxUhLS8ODBw8ME5gGcnJycOXKlVY7spOTEwYNGsTZAYmijE1r53YbU14BzsVZdWJiImpqalqtx4ABA+Dk5MTZMUTd8VwkEuHy5cucHc8vX77c4koT0DCeR0VFcfZYNMb5ZKC8vLzVc31Ko0aNgrm5OSezMHWyYSWRSISjR4+ivLzcAJFRlGm7ePEinjx5otYYIhKJ8OTJE1y8eNEAkWlGIpHA39+/xdUNgNuzak3Gc+Wsm4s/pJqM52KxGEePHkVFRYUBItMe55OBxMRE1NbWqtV42rVrx9ksTCKRqJUNAw0DUk1NDafPXVKUsZBIJGjfvj0iIyNb/WxkZCTat2/PuTFEoVDgwIEDao2DQMMYcuXKFeTk5Og5Ms2YyniuXN1wdHRs9bPK8TwxMdEAkWmP88mAVCpFYGAgfHx81Pq8WCxGUlISp2bVZWVlSE5OVrsj+/r6IiAggJOdgKKMjfLK9ZbO7SqZm5tz8o6eCxcu4OnTp60uSytxdZVU0/FcJBJxbjwvLy9HUlKS2sfC19cX/v7+nGtTz+N0MiCXy9U+16fExSxMk2xYyRiem0BRXHf//n1cvXpV7YEb+N+s+v79+3qMTDMSiQTOzs6IiIhQ6/Pt2rXDoEGDOHWqQJvxXCwWo7a2llPj+eHDhzUez8ViMefHc04nAxcuXEB+fr5GO93Hx4dzs2qJRKJRNgw0DEhPnz7FhQsX9BgZRZk2qVQKCwsLxMTEqP2dmJgYzs2qlasbZmZman9HJBIhOTkZZWVleoxMfbqM51xKapSrG97e3mp/xxjGc04nA1KpFEKhEP3799foe8pZtVwu11Nk6pPL5Rqd61OKiIiAs7Mzp5IaijI2UqkUgwcPhoODg9rfcXR0xODBgznT97Kzs5Genq7xGCISiTg1q9ZlPD9w4AAdz/WM08mARCJBXFycRtkw0LAkk5+fz4ks7Pz585DJZBo3HjMzM8TFxXEqI6YoY1JaWqrRtTqNicVizsyqtVndABpm1YGBgZwZQ7Qdz0UiEafG8/z8fI1OOwEN4zkXr0VpjLPJQHZ2Nq5fv65VR+7fvz+EQiEndry22TDQ0AnS09Nx79495gOjKBN3+PBh1NXVaTWGKGfVhw8f1kNkmpFKpRgyZIhGqxtKXJlV6zKec2lWrRzP+/Xrp/F3xWIxrl27xtnxnLPJgFQqhaWlJUaOHKnxd7mUhWmbDQPAyJEjYWFhwYl6UJSxkUqlCAoKQteuXTX+bteuXdGjRw/W+15paSmOHz+u1Y8o0JAMyGQynD9/nuHINKPreM6VVVKpVGqy4zmnk4Ho6GjY29tr9X2xWIz09HRkZ2czHJn67t69ixs3bmjdkR0cHBAdHc3ZxkNRXKXtud3GxGIx67PqhIQErVc3AO6skuo6notEIly/fp318fz69esanyJQcnBwwJAhQ1g/Fs3hZDJQUlKCY8eO6dSRR44cCUtLS1Z3vC7ZsJJIJMKxY8dQWlrKYGQUZdrOnj2LgoICrQdu4H+z6nPnzjEYmWYkEgl69uyJLl26aPV9LsyqmRjPY2JiTGI8F4vFnB3POZkMJCQkoL6+HmPGjNG6DHt7e9azMF2zYaBhQKqrq0NCQgKDkVGUaZNKpXB1dUXfvn21LqNv375wcXFhbQypr6/HwYMHdfoRBRrGkBs3buDu3bsMRaYZUxvP7ezstC6Dy+M5J5MBqVSKkJAQdO7cWadyxGIxjh8/jpKSEoYiU19JSYlO5/qUOnfujODgYM4uLVEUFymv1eHztR/izMzMMGbMGNb63tmzZ1FYWKjT6gbA/iopU+O5cpWUzfFc12PRuXNn9OzZk5PjOeeSgfr6ep3P9SmNGTOGtSzs119/1TkbVlJeEVxfX89AZBRl2q5evYqbN28yMoYoZ9VZWVkMRKYZiUSCDh06IDw8XKdy7O3tER0dzcqpAibHc5FIhPr6evz2228MRKaZQ4cOMT6eV1VVMRAZcziXDKxevRpFRUWIi4vTuazOnTsjICAA69atM+j9wtXV1Vi0aBEAwMbGRufy4uLiUFhYiNWrV+tcFkWZOuVtvB06dNC5rBEjRsDCwgLLly/XuSxNZGdn4z//+Q+GDRum0+qG0siRI3Hs2DEkJSUxEJ36mBzPra2tAQALFy5EdXW1zuWpq6ysDOvXr0dgYCC8vLx0Ls/KygqFhYWMJEhM4lwy8OGHHwIAY0tBGRkZOHv2LFJSUhgpTx1WVlbg8XgQCoVwdnbWuTzlxSbKfUNRVPOULyTS5PHfzamurkZdXR3+/PNPg74sZ9OmTcjLy2Ps6vnHjx9DoVCoJimGwuR4LhQKIRQKwefzYWVlpXN56rpw4QLOnj2LGzduMFLeiBEjAICuDLRm4MCB6NWrF4YPH85Ied9//z0EAgF69OjBSHnq4PF4uH//Ph49eqTV/ajPGz58OHr16oWBAwcyEB1FmbYzZ84gKyuLkZUBoVCIV199FW5ubjpdOKap/v37QyAQ4Pvvv2ekvGXLlsHV1RXR0dGMlKcuJsdzMzMzPHr0CPfu3QOPx2MgOvX06NGD0WMRERGB8+fPY8+ePYyUxxQeIYSwHQRFURRFUezh3MoARVEURVGGZc52AFyXk5MDmUymt/KFQiEjF6VQlKnSRx+k/Y6b9DneGvKY66se+qyDwZIBpnZOczuDyZ2v3EZOTg4CAgJQWVnJSLlNEQgEyMjIUNVJ3/uJooyJvvrg8/1OuS2mx5DGmP6B0PdY2NIYoo9t6Hu81ddYCxiuHk21W6YYJBlgcuc014mZ3PnKbchkMlRWVuLtt9+Gj48PsrOzERoaCgCora3FkydPQAiBu7s7amtrERQUhIsXLyIgIAA1NTVwdXXF7du3YWVlhevXr8PZ2RmOjo6wsbHBgwcP4ObmhmnTpkEmkzGefOiz0VCUoSj74I4dO/Do0SPw+XzY29sjMzMTgYGBCA0NxZkzZ+Dh4QE+nw9HR0dUVFTg2rVrqK+vR1hYGM6cOYMRI0ZAIpFgwoQJyMrKeqbfAfobQ/T5A6HvsbC5MURf22g83np4eMDZ2Rn5+flwc3NDeno6vLy8YG1tDblcjl69euHcuXPo3r07amtr4ezsjIqKCmRmZuLJkycIDQ3FnTt3EBwcjPPnz8PPz09vY21z9dixYwcCAgJUn0lKSsLTp0/RrVs3WFlZwc3NDdeuXUN4eDgKCwvh7u6O8+fPw97eHrW1taipqUFkZKTq+xkZGS+0WyYZJBlQ7pxZs2apHg9aVFQEa2trPH36FHZ2drCwsEBlZSVGjBiB4uJi8Hg8tGvXDrt27YJYLMbFixdRV1eHZcuWvbAzmCq/U6dOqK+vV+1wpenTpyMsLOyZOpWWljb5StGoqKhn/j8iIqLZ/ZKammrQ/URRxiogIABTp04F8GLfe75vAnjmvvbRo0cDAAYPHgwAsLS0fOHz+hpDlP2v8Q9EZmYmPD09Gd8Gk/VoaQzR9zaeH29LS0sxffr0F45ZU68RjomJabIt6GusbelYBAQEqOpRWlraZDtV3maopOsDpnRh0GsGOnToAH9/f5SXl6O4uBg+Pj7w8PCAUCiEmZkZiouLkZKSggULFuD+/fsQCARYsWIFdu/ejSVLliA1NRXLli1rtvy5c+fi0qVLqm2kp6cjIiIC1dXVqm3IZDJkZ2ertrF06VLY29sjNzcXkyZNeqHRNLZt2zb4+voCAPLz8yEQCJCbmwtHR0dYWVmhrKwMEydOhEwmA4/Hg4uLCzZs2IA33ngDf//9N0JDQ5GZmYkJEya0uJ+0rYe5uTnu3LmDXr16tbifKMrY7Nu3T/XgME37XkJCAtzc3PD06VMUFBQ0mcQr6dr3WhtDlLNRTcfBf/3rX9i2bRvc3d0RFBSEmpqaFveXvsdabbfx7rvv4ueff8ZHH33U4jZ0GWufP95NJQ2GON66tNmUlBR0795d9ZIs5YO09MmgycD48eNV2VFsbGyLn238HGvljEAds2fPVv0309uYOXMmAGDLli2qxlNSUgIPDw9UV1fDy8sLKSkpKC4uVjWe8ePHo7a2FqNGjcLOnTuxYMECvdZj5syZLQ5GFGWMxo4di7CwMK363ujRo7Ft2zYsXLgQwIsrcs/Tpe+pQ9vylfEDrdfBEGOtttv46KOPWi1bl7H2+eMNtLy/9HW8lW1WWY8OHTrA09MT6enpCAwMhEAgwLlz51SJU3Z2NhYvXoz79+8jJCQE9fX1kMlkmDdvHgDo9UJ2wMDJwPbt23H16lU8fvwYrq6uyMvLg7m5OTw9PeHl5aU6b3L58mUUFBRgwIAB4PP5SEtLg729fatv3dKk/JqaGlhZWSEwMBAnT57EgAEDVOf3W6Nt41E3EdC2HmlpaXBxcWHsSVkUxTVM/JC2RNe+V1FR0eIYom35N27cAJ/Px+3bt+Hj4wOBQMBYPbQZa3Wpy4kTJ+Dq6oqcnJxWt2Hsx1vXeigTAUMwSDIgFAohEAiwfv16ncsSCAQQCoV6K7/xNpSZWEZGhupvSUlJEAqFajWe/Px8eHt7w9PT85mEQ3kRYuNyma5HU/uJooxVRkaGRn2vpqYGLi4u6NSp0wt97/l+B+hvDNFX+U1tg+ntNDeG6HsbyuPD1PFuXKY+6sBkPRwcHPDo0SOEh4er1W6ZZLAnENJbC5tGby2kqObRWws12waT26G3FqrPFG4tpI8jboU6DYYQArFYjMjISCxbtgyvvvoq3N3dsWrVqlbLpz/aFNWy1vrg+++/j8ePH2P79u348ssvcfbsWUgkkhafX0/7HTepM95qc7wBbj10qKioCCNGjMA///lPxMTEYNiwYZg9ezZmzJjRYrl6rQOhdHbt2jUCgMTHxxNCCPn888+Jvb09qampYTkyijJt1dXVxM7OjnzxxReEEEIOHjxIAJD09HSWI6P0wVSO96+//kp4PB55/PgxIYSQl156iQwYMIDVmOi7CRgglUpha2uLIUOGAABEIhHKyspw/PhxdgOjKBN3/PhxlJeXq94NHx0dDVtbW0ilUpYjo/TBVI63VCpF3759VW/WFIlEOHPmDPLz81mLiSYDDJBKpRg5ciSsra0BAMHBwfD09DS6BkpRxkYqlcLLyws9e/YEAFhbW2PEiBG075koUzjeNTU1SEhIgFgsVv3bmDFjAAAHDx5kKyyaDOjq6dOnOHfunCpTBQAejweRSASJRAJCL8mgKL0ghEAikUAkEj1zvlgkEuHs2bN4+vQpi9FRTDOV433s2LFnVjeAhgc49e3bl9WkhiYDOjpw4ACAZx9/CgBisRj3799Heno6G2FRlMm7du0acnJynplhAf/ri2zOsijmmcrxlkql6Ny5M4KCgp75d5FIhISEhFafLqkvNBnQkVQqRf/+/eHq6vrMvw8ZMgR2dnZGtXxFUcZEKpXCzs5O9c4BpQ4dOqBfv36075kYUzjehBBIpVKIxeIX7n4Qi8X4/+3dy08TXRgG8AdoEVGkgMQbUUtbW0CNJKgpFdAYoDW2WzcujYkLN+JCExPdqHsS459A4nKmLExpNdGN3CHBkGDQcqmATWQBbRGEb0GqfAo4bWd6mXl+iSs7Z059e5xnzjmdLi0t4e3btxnpG8NACmKxGF6/fv2/6Z64PXv2oK2tDYIgZKBnROonCALa29u3fQJc/C4rFotloGekBDXUe3R0FFNTU9teM06fPo0TJ05kLNQwDKTgzZs3iEQif01bxXk8HvT29mJ+fj7NPSNSt7m5OfT29u469paXlzN2l0XyUku9RVFESUnJX7MbwO+9ZqIoZmSvGcNACkRRhNFoRG1t7bZ/H//p1Pi+AiKSR3d3N/Lz83+NsT/V1dXh5MmTOTF1TP+mlnqLogin07ntz2gDm6FmamoKo6Ojae4Zw0DS4ms/f+5s3aqyshJ2u51LBUQyEwQBdrt9x9/fyPRdFslLDfX++vUrent7t10iiGtpaUFJSUlGQg3DQJKGh4cxMzOza2GBzbUsn8+X9WtZRLkiGo3C5/NJGnvT09MYGRlJU89ICWqp979mNwCgsLAQ7e3tDAO5RBRFHDhwAM3Nzbu+zuPxIBKJIBAIpKlnROoWCAQQjUZ3XD+Oy+RdFslHLfUWRREOhwMVFRW7vi6+12xubi5NPdvEMJAkQRB2XfuJq6mpQXV1NZcKiGQiCAJMJhNsNtuuryssLITT6eTYy3FqqLfU2Q1gc69Zfn4+vF5vGnr2G8NAEkKhEAYGBiQVNr6W5fV6s3YtiyhXbGxswOv17rpXZyu3243+/n6EQqE09I7kppZ6+/1+RKNRSdeMiooKNDY2pn2Gg2EgCV6vF/n5+XC5XJJe73a7MTs7i6GhIYV7RqRug4ODCIVCkv5TBX7fZfEbPblJLfUWRRFmsxlWq1XS6+N7zaLRqMI9+41hIAmCIODSpUv/XPuJa25uRmlpaVZOXxHlEkEQUFpaiqamJkmvr6iogMPh4NjLUWqo9/r6+o5PHdyJx+NBNBqF3+9XuHe/MQwkKBKJwO/3S06qAKDX6+F0OrN2YwtRrhBFES6XC3q9XvIxbrcbPT09iEQiCvaMlKCGeg8ODuLr168JXTOsVivMZnNarxkMAwnq6elBLBZLqLDA5gd0cHAQs7OzCvWMSN1mZmYwNDSU1NiLxWJpvcui1Kml3qIowmAwwOFwSD4mE3vNGAYSJIoiTp06JXntJ87lcqGgoCDtO0SJ1MLr9aKgoEDyXp04q9UKi8XCmbkco5Z6i6KIa9euJTS7AWwuFYRCIQwODirUs/9jGEhAfO0n0aQKAOXl5bh06VJWrWUR5RJBENDU1ISysrKEjtv6dLr19XWFekdyU0O9p6enk5rdAACHwwGDwZC2awbDQAL6+/sxPz+fVGGBzekrv9+P5eVlmXtGpG7Ly8sIBAIpjb25uTkMDAzI3DNSglrq7fV6odPp4HQ6Ez5Wr9fD5XKlbYaDYSABoiiirKwsobWfrTweD1ZWVtDT0yNzz4jUzefzYWVlJemLg8PhQFlZWdZMHdPu1FJvURTR3NwMg8GQ1PEejwdDQ0OYmZmRt2PbYBhIgCAIuHbtGnQ6XVLHWywWWK1WLhUQJUgQBNhsNlgslqSOj99lcezlBjXUe2lpKeFvnv3J6XRCp9OlJdQwDEgUDAYxOjqaUmGBzemr7u7ujK9lEeWK9fV1dHd3yzL2RkZGMDU1JVPPSAlqqbfP58OPHz9Seh8GgwFNTU0MA9kklbWfrdxuN+bn59HX1ydTz4jUrbe3FwsLCylfHOJ3WfxGT3ZTS71FUURNTQ1MJlNK7bjdbgQCAcX3mjEMSCSKIlpaWlBaWppSO42NjSgvL8/4WhZRrhBFERUVFbDb7Sm1YzAY0NzczLGX5dRQ758/f8Lr9f7zlxaliO818/l8MvRsZwwDEnz48AF+vz/h77tuR6fTobW1FV1dXVn3YxpE2SYUCqGrqwutra1J79XZyuVyoaenBx8+fJChdyQ3tdT75cuX+PbtG65fv55yWyaTCadOncKLFy8Ufaoiw4AEt2/fxtraGhYWFmRpLxgMYnJyEp2dnbK0R6RWnZ2d+Pz5M4LBoCztLSwsYG1tDbdv35alPZKXWup99+5dAJDt4j0xMaF4qGEYkKChoQH79u3DvXv3ZGnv+fPnKCgoQF1dnSztEalVXV0dCgoK8OzZM1na6+jowL59+9DQ0CBLeyQvtdT73LlzOHnyJC5fvixLe0+ePIFOp4PZbJalve3kbaTrwcdERESUlTgzQEREpHEMA0RERBqX+nbNHDY1NYVwOJxyOwcPHsTx48czdg6iXMOxpy1qqbfS55Cr/d3OsaMNjQoGgxvFxcUbAFL+U1xcvBEMBjNyDqJcw7GnLWqpt9LnkLP9ZD63mp0ZCIfDiEQiuHXrFi5cuAAA+P79O4qKirCwsID9+/dDr9cjEomgtbUVi4uLyMvLg8FgwKtXr+DxeNDf34/V1VU8fPgQ4XD4rxSWjnMQ5RqOPW1RS72VPodc7VdVVWFtbQ03b95M6HOr+T0Dhw4dgs1mQ1VVFfLy8mAymVBfXw+73Y6LFy+ivr4efX19cDqdsNlsqK6uxtOnTzEzM4OOjg60tbUpco779+9jbGxM8jmIcs2dO3cAADabDWfOnMHKygrsdjssFgsaGhpw5coVhMNhTExMoL29HYcPH8aDBw9w8eJFHD58WLGx9+jRI3z69IljT2bJ1vvs2bMwGAyK1Tsd/58/fvwYk5OTks6R6ri4ceMGampqJFTkD4lP+qjDwMDABoCNgYEBxdpJxzmIcg3Hnraopd5Kn0POz1oybWl2mSAuEAhgZWUF09PTOHbsGL58+QK9Xg+z2QyLxYL5+XkYjUa8f/8esVgMRUVFOH/+PLxeL1wuF4aHh2U9RygUQm1tLSwWC969eweDwYCxsTHl/yGI0izbxl4sFsPRo0dhMpk49hSQjlpk22eqrKwMwWAQV69elfw+Un0Po6Oj2Lt3r9Sy/KL5MHDkyBHs2bPn15Odtk6vTExMAABGRkYQDodhNBoBAOPj4zCbzfj48SMWFxdlPYfVasXPnz8xPj6OyspKrK6uSjoHUa7JtrFnNBqxurrKsaeQdNQi2z5TJSUlOHbsWELvI9X3EA6HsbS09M/38ZeU5yNylBp2nxLlIo49bVFLvdX+bQJNP45YDd9LJcpFHHvaopZ6q/k5A5oOA0RERMSvFhIREWkewwAREZHGMQwQERFpHMMAERGRxjEMEBERaRzDABERkcYxDBAREWkcwwAREZHGMQwQERFpHMMAERGRxjEMEBERaRzDABERkcYxDBAREWkcwwAREZHGMQwQERFpHMMAERGRxjEMEBERaRzDABERkcYxDBAREWkcwwAREZHGMQwQERFpHMMAERGRxjEMEBERaRzDABERkcb9B8P/EnKWTq12AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Gradient Boosting Decision Tree**"
      ],
      "metadata": {
        "id": "5IaHSin9KiDK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "gbdt=GradientBoostingClassifier(n_estimators=100)\n",
        "gbdt.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "S-M7EgrMKglD",
        "outputId": "398573ec-232b-4640-9393-5a99d7490e73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbdt.score(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zRo1h-goLbls",
        "outputId": "6ee9384e-27ae-47f9-dd30-b1e8e1b26910"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbdt.score(X_test,Y_test)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpcZ3oWeLjMK",
        "outputId": "63ee8f23-68d7-439d-a21c-f605b7df4d26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.32786885245902"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy score achieved is: 80.32 %"
      ],
      "metadata": {
        "id": "Cll_MwguLwq6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest**"
      ],
      "metadata": {
        "id": "pdqIW32uQuB7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "max_accuracy = 0\n",
        "\n",
        "\n",
        "for x in range(2000):\n",
        "    rf = RandomForestClassifier(random_state=x)\n",
        "    rf.fit(X_train,Y_train)\n",
        "    Y_pred_rf = rf.predict(X_test)\n",
        "    current_accuracy = round(accuracy_score(Y_pred_rf,Y_test)*100,2)\n",
        "    if(current_accuracy>max_accuracy):\n",
        "        max_accuracy = current_accuracy\n",
        "        best_x = x\n",
        "\n",
        "#print(max_accuracy)\n",
        "#print(best_x)\n",
        "\n",
        "rf = RandomForestClassifier(random_state=best_x)\n",
        "rf.fit(X_train,Y_train)\n",
        "Y_pred_rf = rf.predict(X_test)"
      ],
      "metadata": {
        "id": "Qf7gd3RK-hOy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_rf.shape"
      ],
      "metadata": {
        "id": "3i7plHlA9lxy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00f1ffcb-4381-4d75-8fa5-942b18ebfe4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_rf = round(accuracy_score(Y_pred_rf,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Decision Tree is: \"+str(score_rf)+\" %\")"
      ],
      "metadata": {
        "id": "aCW-aenb9mHn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79dd15d2-7772-4458-e046-948e710808c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Decision Tree is: 90.16 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy score achieved using Random Forest is: 95.08 %"
      ],
      "metadata": {
        "id": "7cJ4y8SaQ2Jo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Neural Network**"
      ],
      "metadata": {
        "id": "0xLK9FBiRCr-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "model_nn=Sequential()\n",
        "model_nn.add(Dense(10,activation='relu',input_shape=(13,)))\n",
        "model_nn.add(Dense(5,activation='relu'))\n",
        "\n",
        "model_nn.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model_nn.summary()\n",
        "model_nn.compile(loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "VHzija1wAfyL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "106bf4c9-21bb-4275-c7c4-b7be25aeffd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_8 (Dense)             (None, 10)                140       \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 5)                 55        \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 1)                 6         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 201 (804.00 Byte)\n",
            "Trainable params: 201 (804.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_nn.fit(X_train,Y_train,epochs=150)"
      ],
      "metadata": {
        "id": "Ha4BgIhoAgJ_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa01ddc4-0308-43ab-dd01-5c4c55c2fef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3600 - accuracy: 0.8471\n",
            "Epoch 2/150\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3762 - accuracy: 0.8388\n",
            "Epoch 3/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3675 - accuracy: 0.8223\n",
            "Epoch 4/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3689 - accuracy: 0.8140\n",
            "Epoch 5/150\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3657 - accuracy: 0.8471\n",
            "Epoch 6/150\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3701 - accuracy: 0.8306\n",
            "Epoch 7/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3686 - accuracy: 0.8347\n",
            "Epoch 8/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3627 - accuracy: 0.8430\n",
            "Epoch 9/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3460 - accuracy: 0.8471\n",
            "Epoch 10/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3710 - accuracy: 0.8182\n",
            "Epoch 11/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3832 - accuracy: 0.8182\n",
            "Epoch 12/150\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3678 - accuracy: 0.8347\n",
            "Epoch 13/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3589 - accuracy: 0.8430\n",
            "Epoch 14/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3592 - accuracy: 0.8306\n",
            "Epoch 15/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3598 - accuracy: 0.8306\n",
            "Epoch 16/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3687 - accuracy: 0.8182\n",
            "Epoch 17/150\n",
            "8/8 [==============================] - 0s 8ms/step - loss: 0.3652 - accuracy: 0.8347\n",
            "Epoch 18/150\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3552 - accuracy: 0.8636\n",
            "Epoch 19/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3782 - accuracy: 0.8306\n",
            "Epoch 20/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3609 - accuracy: 0.8264\n",
            "Epoch 21/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3704 - accuracy: 0.8430\n",
            "Epoch 22/150\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.3677 - accuracy: 0.8388\n",
            "Epoch 23/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3642 - accuracy: 0.8347\n",
            "Epoch 24/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3599 - accuracy: 0.8306\n",
            "Epoch 25/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3731 - accuracy: 0.8140\n",
            "Epoch 26/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3873 - accuracy: 0.8347\n",
            "Epoch 27/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3631 - accuracy: 0.8471\n",
            "Epoch 28/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3714 - accuracy: 0.8512\n",
            "Epoch 29/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3737 - accuracy: 0.8264\n",
            "Epoch 30/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3635 - accuracy: 0.8058\n",
            "Epoch 31/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3695 - accuracy: 0.8388\n",
            "Epoch 32/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3846 - accuracy: 0.8306\n",
            "Epoch 33/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3735 - accuracy: 0.8306\n",
            "Epoch 34/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3835 - accuracy: 0.8223\n",
            "Epoch 35/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3717 - accuracy: 0.8347\n",
            "Epoch 36/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3681 - accuracy: 0.8264\n",
            "Epoch 37/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3680 - accuracy: 0.8388\n",
            "Epoch 38/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3653 - accuracy: 0.8388\n",
            "Epoch 39/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3699 - accuracy: 0.8388\n",
            "Epoch 40/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3811 - accuracy: 0.8306\n",
            "Epoch 41/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3521 - accuracy: 0.8471\n",
            "Epoch 42/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3644 - accuracy: 0.8595\n",
            "Epoch 43/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3570 - accuracy: 0.8430\n",
            "Epoch 44/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3612 - accuracy: 0.8347\n",
            "Epoch 45/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3766 - accuracy: 0.8306\n",
            "Epoch 46/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3603 - accuracy: 0.8347\n",
            "Epoch 47/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3683 - accuracy: 0.8347\n",
            "Epoch 48/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3569 - accuracy: 0.8388\n",
            "Epoch 49/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3683 - accuracy: 0.8388\n",
            "Epoch 50/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3744 - accuracy: 0.8347\n",
            "Epoch 51/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3669 - accuracy: 0.8347\n",
            "Epoch 52/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3628 - accuracy: 0.8306\n",
            "Epoch 53/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3716 - accuracy: 0.8347\n",
            "Epoch 54/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3696 - accuracy: 0.8099\n",
            "Epoch 55/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3821 - accuracy: 0.8058\n",
            "Epoch 56/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3646 - accuracy: 0.8471\n",
            "Epoch 57/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3655 - accuracy: 0.8430\n",
            "Epoch 58/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3507 - accuracy: 0.8264\n",
            "Epoch 59/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3680 - accuracy: 0.8471\n",
            "Epoch 60/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3649 - accuracy: 0.8264\n",
            "Epoch 61/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3746 - accuracy: 0.8347\n",
            "Epoch 62/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3583 - accuracy: 0.8430\n",
            "Epoch 63/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3615 - accuracy: 0.8430\n",
            "Epoch 64/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3567 - accuracy: 0.8306\n",
            "Epoch 65/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3679 - accuracy: 0.8430\n",
            "Epoch 66/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3857 - accuracy: 0.8347\n",
            "Epoch 67/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3632 - accuracy: 0.8430\n",
            "Epoch 68/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3838 - accuracy: 0.8223\n",
            "Epoch 69/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3619 - accuracy: 0.8347\n",
            "Epoch 70/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3741 - accuracy: 0.8182\n",
            "Epoch 71/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3563 - accuracy: 0.8388\n",
            "Epoch 72/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3736 - accuracy: 0.8347\n",
            "Epoch 73/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3699 - accuracy: 0.8306\n",
            "Epoch 74/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3774 - accuracy: 0.8099\n",
            "Epoch 75/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3719 - accuracy: 0.8306\n",
            "Epoch 76/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3657 - accuracy: 0.8347\n",
            "Epoch 77/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3738 - accuracy: 0.8140\n",
            "Epoch 78/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3571 - accuracy: 0.8347\n",
            "Epoch 79/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3568 - accuracy: 0.8388\n",
            "Epoch 80/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3749 - accuracy: 0.8182\n",
            "Epoch 81/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3803 - accuracy: 0.8306\n",
            "Epoch 82/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3681 - accuracy: 0.8306\n",
            "Epoch 83/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3581 - accuracy: 0.8430\n",
            "Epoch 84/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3490 - accuracy: 0.8471\n",
            "Epoch 85/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3665 - accuracy: 0.8264\n",
            "Epoch 86/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3769 - accuracy: 0.8182\n",
            "Epoch 87/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3684 - accuracy: 0.8099\n",
            "Epoch 88/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3580 - accuracy: 0.8388\n",
            "Epoch 89/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3628 - accuracy: 0.8347\n",
            "Epoch 90/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3682 - accuracy: 0.8347\n",
            "Epoch 91/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3854 - accuracy: 0.8347\n",
            "Epoch 92/150\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3660 - accuracy: 0.8388\n",
            "Epoch 93/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3716 - accuracy: 0.8388\n",
            "Epoch 94/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3713 - accuracy: 0.8554\n",
            "Epoch 95/150\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3691 - accuracy: 0.8264\n",
            "Epoch 96/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3633 - accuracy: 0.8347\n",
            "Epoch 97/150\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3577 - accuracy: 0.8512\n",
            "Epoch 98/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3705 - accuracy: 0.8264\n",
            "Epoch 99/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3648 - accuracy: 0.8512\n",
            "Epoch 100/150\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.3599 - accuracy: 0.8388\n",
            "Epoch 101/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3738 - accuracy: 0.8182\n",
            "Epoch 102/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3796 - accuracy: 0.8347\n",
            "Epoch 103/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3673 - accuracy: 0.8512\n",
            "Epoch 104/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3607 - accuracy: 0.8388\n",
            "Epoch 105/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3748 - accuracy: 0.8182\n",
            "Epoch 106/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3635 - accuracy: 0.8388\n",
            "Epoch 107/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3742 - accuracy: 0.8347\n",
            "Epoch 108/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3714 - accuracy: 0.8306\n",
            "Epoch 109/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3637 - accuracy: 0.8430\n",
            "Epoch 110/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3677 - accuracy: 0.8264\n",
            "Epoch 111/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3684 - accuracy: 0.8512\n",
            "Epoch 112/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3725 - accuracy: 0.8306\n",
            "Epoch 113/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3791 - accuracy: 0.8223\n",
            "Epoch 114/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3673 - accuracy: 0.8306\n",
            "Epoch 115/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3698 - accuracy: 0.8182\n",
            "Epoch 116/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3732 - accuracy: 0.8264\n",
            "Epoch 117/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3693 - accuracy: 0.8347\n",
            "Epoch 118/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3872 - accuracy: 0.8471\n",
            "Epoch 119/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3556 - accuracy: 0.8264\n",
            "Epoch 120/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3635 - accuracy: 0.8347\n",
            "Epoch 121/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3808 - accuracy: 0.8388\n",
            "Epoch 122/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3654 - accuracy: 0.8554\n",
            "Epoch 123/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3551 - accuracy: 0.8512\n",
            "Epoch 124/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3674 - accuracy: 0.8264\n",
            "Epoch 125/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3510 - accuracy: 0.8388\n",
            "Epoch 126/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3691 - accuracy: 0.8347\n",
            "Epoch 127/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3682 - accuracy: 0.8182\n",
            "Epoch 128/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3554 - accuracy: 0.8554\n",
            "Epoch 129/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3568 - accuracy: 0.8388\n",
            "Epoch 130/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3484 - accuracy: 0.8347\n",
            "Epoch 131/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3801 - accuracy: 0.8099\n",
            "Epoch 132/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3709 - accuracy: 0.8347\n",
            "Epoch 133/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3522 - accuracy: 0.8471\n",
            "Epoch 134/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3792 - accuracy: 0.8430\n",
            "Epoch 135/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3557 - accuracy: 0.8471\n",
            "Epoch 136/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3773 - accuracy: 0.8058\n",
            "Epoch 137/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3843 - accuracy: 0.8264\n",
            "Epoch 138/150\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3617 - accuracy: 0.8430\n",
            "Epoch 139/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3573 - accuracy: 0.8636\n",
            "Epoch 140/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3761 - accuracy: 0.8264\n",
            "Epoch 141/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3621 - accuracy: 0.8306\n",
            "Epoch 142/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3647 - accuracy: 0.8430\n",
            "Epoch 143/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3538 - accuracy: 0.8471\n",
            "Epoch 144/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3656 - accuracy: 0.8306\n",
            "Epoch 145/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3701 - accuracy: 0.8471\n",
            "Epoch 146/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3651 - accuracy: 0.8388\n",
            "Epoch 147/150\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 0.3717 - accuracy: 0.8471\n",
            "Epoch 148/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3650 - accuracy: 0.8264\n",
            "Epoch 149/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3644 - accuracy: 0.8264\n",
            "Epoch 150/150\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3567 - accuracy: 0.8430\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x79c17010da20>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_nn.evaluate(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_XGx2UefVj9c",
        "outputId": "bfee82b3-fc26-480a-adeb-a9c439c07ef7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 4ms/step - loss: 0.4249 - accuracy: 0.7893\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.42490094900131226, 0.78925621509552]"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_nn.evaluate(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PbVg-U29Vt-g",
        "outputId": "7e0d920e-61f0-42a5-8b93-d7b4ca3036b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 10ms/step - loss: 0.5420 - accuracy: 0.8033\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5420130491256714, 0.8032786846160889]"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_nn = model_nn.predict(X_test)"
      ],
      "metadata": {
        "id": "Us0kqG4wAyl2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11cf19e8-8631-426a-bdd6-66390e3a4622"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_nn.shape"
      ],
      "metadata": {
        "id": "BJF7zXdUAy6w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13b1d83e-db28-4942-cba1-0166f740303f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rounded = [round(x[0]) for x in Y_pred_nn]\n",
        "\n",
        "Y_pred_nn = rounded"
      ],
      "metadata": {
        "id": "QM_uxPMWAzOy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_nn = round(accuracy_score(Y_pred_nn,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Neural Network is: \"+str(score_nn)+\" %\")\n",
        "\n",
        "#Note: Accuracy of 85% can be achieved on the test set, by setting epochs=2000, and number of nodes = 11."
      ],
      "metadata": {
        "id": "adzYslHdBI9b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffea5b95-0394-4bb1-8e62-189470a3d380"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Neural Network is: 80.33 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy score achieved using Neural Network is: 80.33 %"
      ],
      "metadata": {
        "id": "LyV40b8fRLxw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Output final score**"
      ],
      "metadata": {
        "id": "zygtdI-lRPXO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scores = [score_lr,score_nb,score_svm,score_knn,score_dt,score_rf,score_xgb,score_nn]\n",
        "algorithms = [\"Logistic Regression\",\"Naive Bayes\",\"Support Vector Machine\",\"K-Nearest Neighbors\",\"Decision Tree\",\"Random Forest\",\"XGBoost\",\"Neural Network\"]\n",
        "\n",
        "for i in range(len(algorithms)):\n",
        "    print(\"The accuracy score achieved using \"+algorithms[i]+\" is: \"+str(scores[i])+\" %\")"
      ],
      "metadata": {
        "id": "jK0L-2ShBJRM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(15,8)})\n",
        "plt.xlabel(\"Algorithms\")\n",
        "plt.ylabel(\"Accuracy score\")\n",
        "\n",
        "sns.barplot(x=algorithms,y=scores)"
      ],
      "metadata": {
        "id": "0ku8EO5VBXc6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZDSQFjD1BXx3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ihwk_TtmBJ22"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jl06yuz-BKNB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}