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      "display_name": "Python 3"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "E3cGLGocHue8"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn import metrics\n",
        "from sklearn.svm import SVC\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/train.csv')\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NoYrTl7cHz2A",
        "outputId": "1e8e1d08-8e59-4056-850c-8f82915faafe"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   ID  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
            "0   1         1         0         1         0         1         0         1   \n",
            "1   2         0         0         0         0         0         0         0   \n",
            "2   3         1         1         1         1         1         1         1   \n",
            "3   4         0         0         0         0         0         0         0   \n",
            "4   5         0         0         0         0         0         0         0   \n",
            "\n",
            "   A8_Score  A9_Score  ...  gender       ethnicity jaundice austim  \\\n",
            "0         0         1  ...       f               ?       no     no   \n",
            "1         0         0  ...       m               ?       no     no   \n",
            "2         1         1  ...       m  White-European       no    yes   \n",
            "3         0         0  ...       f               ?       no     no   \n",
            "4         0         0  ...       m               ?       no     no   \n",
            "\n",
            "   contry_of_res used_app_before     result     age_desc  relation Class/ASD  \n",
            "0        Austria              no   6.351166  18 and more      Self         0  \n",
            "1          India              no   2.255185  18 and more      Self         0  \n",
            "2  United States              no  14.851484  18 and more      Self         1  \n",
            "3  United States              no   2.276617  18 and more      Self         0  \n",
            "4   South Africa              no  -4.777286  18 and more      Self         0  \n",
            "\n",
            "[5 rows x 22 columns]\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmmxAk6SH05r",
        "outputId": "6385628c-7840-439c-e1b5-9bc636d19788"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(800, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7X1lDC1lIJ2O",
        "outputId": "e942b527-4b9a-4768-9381-ab60dd63ec67"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 800 entries, 0 to 799\n",
            "Data columns (total 22 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   ID               800 non-null    int64  \n",
            " 1   A1_Score         800 non-null    int64  \n",
            " 2   A2_Score         800 non-null    int64  \n",
            " 3   A3_Score         800 non-null    int64  \n",
            " 4   A4_Score         800 non-null    int64  \n",
            " 5   A5_Score         800 non-null    int64  \n",
            " 6   A6_Score         800 non-null    int64  \n",
            " 7   A7_Score         800 non-null    int64  \n",
            " 8   A8_Score         800 non-null    int64  \n",
            " 9   A9_Score         800 non-null    int64  \n",
            " 10  A10_Score        800 non-null    int64  \n",
            " 11  age              800 non-null    float64\n",
            " 12  gender           800 non-null    object \n",
            " 13  ethnicity        800 non-null    object \n",
            " 14  jaundice         800 non-null    object \n",
            " 15  austim           800 non-null    object \n",
            " 16  contry_of_res    800 non-null    object \n",
            " 17  used_app_before  800 non-null    object \n",
            " 18  result           800 non-null    float64\n",
            " 19  age_desc         800 non-null    object \n",
            " 20  relation         800 non-null    object \n",
            " 21  Class/ASD        800 non-null    int64  \n",
            "dtypes: float64(2), int64(12), object(8)\n",
            "memory usage: 137.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T\n"
      ],
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          "height": 488
        },
        "id": "Ue8z-s_NIKpQ",
        "outputId": "1514831c-e073-4346-fbc9-186fa849fee6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           count        mean         std       min         25%         50%  \\\n",
              "ID         800.0  400.500000  231.084400  1.000000  200.750000  400.500000   \n",
              "A1_Score   800.0    0.560000    0.496697  0.000000    0.000000    1.000000   \n",
              "A2_Score   800.0    0.530000    0.499411  0.000000    0.000000    1.000000   \n",
              "A3_Score   800.0    0.450000    0.497805  0.000000    0.000000    0.000000   \n",
              "A4_Score   800.0    0.415000    0.493030  0.000000    0.000000    0.000000   \n",
              "A5_Score   800.0    0.395000    0.489157  0.000000    0.000000    0.000000   \n",
              "A6_Score   800.0    0.303750    0.460164  0.000000    0.000000    0.000000   \n",
              "A7_Score   800.0    0.397500    0.489687  0.000000    0.000000    0.000000   \n",
              "A8_Score   800.0    0.508750    0.500236  0.000000    0.000000    1.000000   \n",
              "A9_Score   800.0    0.495000    0.500288  0.000000    0.000000    0.000000   \n",
              "A10_Score  800.0    0.617500    0.486302  0.000000    0.000000    1.000000   \n",
              "age        800.0   28.452118   16.310966  2.718550   17.198153   24.848350   \n",
              "result     800.0    8.537303    4.807676 -6.137748    5.306575    9.605299   \n",
              "Class/ASD  800.0    0.201250    0.401185  0.000000    0.000000    0.000000   \n",
              "\n",
              "                  75%         max  \n",
              "ID         600.250000  800.000000  \n",
              "A1_Score     1.000000    1.000000  \n",
              "A2_Score     1.000000    1.000000  \n",
              "A3_Score     1.000000    1.000000  \n",
              "A4_Score     1.000000    1.000000  \n",
              "A5_Score     1.000000    1.000000  \n",
              "A6_Score     1.000000    1.000000  \n",
              "A7_Score     1.000000    1.000000  \n",
              "A8_Score     1.000000    1.000000  \n",
              "A9_Score     1.000000    1.000000  \n",
              "A10_Score    1.000000    1.000000  \n",
              "age         35.865429   89.461718  \n",
              "result      12.514484   15.853126  \n",
              "Class/ASD    0.000000    1.000000  "
            ],
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              "      <th>A4_Score</th>\n",
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              "    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-d58e8d95-dade-43bd-9a59-c96e0473b820 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 800.0,\n        \"max\": 800.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106.44877685977218,\n        \"min\": 0.20125,\n        \"max\": 400.5,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          0.495\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.365832951154644,\n        \"min\": 0.4011852745685732,\n        \"max\": 231.08440016582685,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          0.5002877770091719\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8737955366901204,\n        \"min\": -6.137748048,\n        \"max\": 2.718549681,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.39569828782531,\n        \"min\": 0.0,\n        \"max\": 200.75,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106.46453842602422,\n        \"min\": 0.0,\n        \"max\": 400.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 159.51073357463736,\n        \"min\": 0.0,\n        \"max\": 600.25,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 212.72805726263888,\n        \"min\": 1.0,\n        \"max\": 800.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ethnicity'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "v83kFsTQILug",
        "outputId": "e884688a-cc3e-4895-955c-77d1b7e5f12a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ethnicity\n",
              "White-European     257\n",
              "?                  203\n",
              "Middle Eastern      97\n",
              "Asian               67\n",
              "Black               47\n",
              "South Asian         34\n",
              "Pasifika            32\n",
              "Others              29\n",
              "Latino              17\n",
              "Hispanic             9\n",
              "Turkish              5\n",
              "others               3\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ethnicity</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>White-European</th>\n",
              "      <td>257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>?</th>\n",
              "      <td>203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Middle Eastern</th>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asian</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Black</th>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Asian</th>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pasifika</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Others</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latino</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hispanic</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkish</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>others</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['relation'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "B485SFYdIMzg",
        "outputId": "efb1f4c1-8c3f-4fb8-d60d-58eee4e3dca6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "relation\n",
              "Self                        709\n",
              "?                            40\n",
              "Parent                       29\n",
              "Relative                     18\n",
              "Others                        2\n",
              "Health care professional      2\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>relation</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Self</th>\n",
              "      <td>709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>?</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parent</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Relative</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Others</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health care professional</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.replace({'yes':1, 'no':0, '?':'Others', 'others':'Others'})\n"
      ],
      "metadata": {
        "id": "-1jyyKEfIN7Q"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(df['Class/ASD'].value_counts().values, autopct='%1.1f%%')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "ZS1wn6wvIPJq",
        "outputId": "7efa6a19-fba7-4e59-af17-db0e06de87bb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "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": [
        "ints = []\n",
        "objects = []\n",
        "floats = []\n",
        "\n",
        "for col in df.columns:\n",
        "  if df[col].dtype == int:\n",
        "    ints.append(col)\n",
        "  elif df[col].dtype == object:\n",
        "    objects.append(col)\n",
        "  else:\n",
        "    floats.append(col)\n"
      ],
      "metadata": {
        "id": "Wtrs1SkSIQKv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ints.remove('ID')\n",
        "ints.remove('Class/ASD')\n"
      ],
      "metadata": {
        "id": "_8HUmuWnIRnR"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "source": [
        "# Convert the data to long-form using melt\n",
        "df_melted = df.melt(id_vars=['ID', 'Class/ASD'], value_vars=ints, var_name='col', value_name='value')\n",
        "\n",
        "plt.subplots(figsize=(15,15))\n",
        "\n",
        "for i, col in enumerate(ints):\n",
        "  plt.subplot(5,3,i+1)\n",
        "  # Use the melted DataFrame and specify x and hue\n",
        "  sb.countplot(x='value', hue='Class/ASD', data=df_melted[df_melted['col'] == col])\n",
        "  plt.title(f'Distribution of {col}')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GlWSKWy0JR07",
        "outputId": "566b728b-35dd-41fe-a89e-098c798cf16d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 14 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.subplots(figsize=(15, 15))  # Adjust figure size as needed\n",
        "\n",
        "for i, col in enumerate(objects):\n",
        "    plt.subplot(5, 1, i + 1)  # Adjust subplot grid as needed\n",
        "    sb.countplot(x=col, hue='Class/ASD', data=df)\n",
        "    plt.title(f'Distribution of {col}')\n",
        "    plt.xticks(rotation=45, ha='right') # Rotates x-axis labels for better readability\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oj69j1JdPplX",
        "outputId": "968a121f-596f-494e-a04f-faa07a8640ae"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 6 Axes>"
            ],
            "image/png": 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WN998o6VLl6p79+6Kjo5OcZmXSkxM1Ny5c1WyZEkNHz7cNWdbjRo1lCFDhlTPE5eQkKD//e9/at68uYYOHepa7nx4aN26dfXUU0+5tpEnjqlff/1Vw4cP15NPPql3331XH374oR588EGNGDEiRWXGx8frzTffVNasWVW5cmVJUuvWrTVy5Ei9+OKLatOmjbp27eqRh6Fu2LDBdR6977779Msvv6ho0aI6cOCAXn/9dQ0fPlxS8rfT2bNn1aBBA3Xr1k0vvPCCJKlDhw6qXr26Ro8erfj4eI/M1xcUFKSTJ08qV65crmU///yzNm7cqKpVq+qBBx7QsGHDUtQG6eJ5+9SpU679cO7cORUsWFCNGzfWnXfeqW+++UbfffddqtoQFxen+Ph4Vz/snGPwpZdeUqNGjfT222+75oG0FMx7lzFjRt15553q0KGDGjdurDlz5qhIkSKqV6+e/v33X4/Mj5ktWzbXNpIubrd69epp//79io2NTfWcq8HBwWrVqpXat2/vWjZhwgQtXbpUn376qWbOnKlGjRq5Ptcp2U7O47FZs2basGGDypUrp59//lnz5s1T+fLlNWXKlFRtp9y5c6tZs2bKkiWLxo4dq5o1a+rIkSN68skn9dZbbykuLk6tW7fW3r17U9yGdOnSKV++fIqMjNTo0aMVFBSkefPm6cSJE3r66ae1detWzZo1K8XlZ8yYUTNmzNCECRNcZdSuXVvVq1fX4sWLJaV+bteoqCgdO3ZMe/fuVUxMjCZPnqwvvvhCMTExOnnypN5++21Nnjw5VdetBw8e1P79+5UnTx4FBgZKks6cOaPatWurQoUKevfdd3Xs2LEUlZ0pUyadOnVKefLkkXRxe1StWlUfffSR4uPj9e6772rz5s0pjj0xMVErVqxQ0aJF9cYbb+j999/Xe++9p2eeeUbSxfNcavZBfHy8Zs2apcqVK2vMmDEKCgpSnjx5VLt2bR05csQjDyaLj4/XjBkz1KRJE40ePdr12Tt//rzWr1+v5s2ba+TIkanaTpf68ssv9dJLL6lr16566qmntHjxYvXq1UtvvPFGisuMi4vT8OHDlSVLFrVq1UqS9OCDD+rZZ59Vr169dM8996hv374pPo6cNm7c6OoTmjRposWLF+v8+fOaP3++Ro0alarr48jISN19993q0aOH6zPdu3dvlS5dWs8//7xH9rXD4VBUVJRKly7tWrZ582Zt2LBBlSpVUrt27VJ9jR8XF6fz58+rSZMmruvwUqVKqU6dOkpMTNRHH32kbdu2paodMTExXuunM2bMqHLlynm1j3Y4HMqSJYvX+ukcOXKoZcuWXu2jL+WNfjp37txq0qSJV/toybv9dMaMGfXWW295tY+WpP/++8+r/fTff//ttT5auvn76bi4OK/202amuLi4m7qfNjPFxsamST/tVR5Ni9+E9uzZY7lz5zZJrlsxnL9yHDx40BwOh3333XepquPy0YFz5841h8NhAwcOdM0NlJCQkOJbcy79xc85onLkyJFX3Cpz+a/77oqOjnb9/2effWYOh8PmzZtnJ0+etJUrV1qNGjU8Ni1B586dbejQoWZm1q1bN8uZM6eVK1fOnnjiiVRNcXH48GHr3LmzZcqUye69994kczJ98sknliNHDvv2229TXH5iYqJ16NDB+vbtay1btkxy680///xjjz32mPXq1cvi4+M98itaYmKiRUZGWuvWra19+/YeKXffvn1Wp04dK1mypLVt29YcDoctXLjQEhMT7ejRo/bMM89Yw4YN7cSJE6mqa/Xq1RYQEGD169e3Tp06WZYsWax79+5mdvH2x2zZstnOnTuTXUd8fLzFxsZa165drV27dnbhwgVLTEx0/Uq6d+9eq127tj388MMpjv3YsWNJfrVfs2aN5cqVyx566CGLjIx0LU/N9lm5cqXrM+CUkJBgxYoV88iIgX/++cfWrFnj+vv555+3dOnS2YgRI2zq1KlWo0YNa9y4scemuGjSpInrtsZJkyZZlixZLDg42L7//vsUl/nXX39Zz5497a677rLChQvbfffdZ3/++aedO3fO/vjjDytYsKB17tw51bH/+OOP1rhxY3vvvffs3nvvtX///dfMLo5wGjdunN111122ffv2FJV96a2ezlsPZ86caaVLl3Y9HyA1I40SEhIsKirKmjZtam3atLHp06fbsGHDLFOmTDZr1iz77rvvbNy4cVa1alVbtGhRsst3nhcKFCiQ5C6Kf/75x8qVK2cffvihVaxY0fXZTo0aNWpYo0aNXH9fuHDB9f/Vq1e3Dh06pLoOp8TERNu7d69r1Jdzn1+4cME2bNiQ7FFlCQkJSUZAOc8N8+bNswoVKiRZNyUj1q52rlm1apWVKFHCFi9e7CqzVatWVr169WSXf3kdS5cutTvuuMM1cqx58+YWEBBgzZs3T/E549LjfMCAARYSEmItWrSwiIiIJOuVL1/eunTpkqo6evXqZZUrV7aOHTvaO++8k2SdgQMHWtmyZT06Z6mZ2ejRoy1nzpyu647U9tWNGjWy/Pnz2z333GNBQUFJrlFfeuklK1KkSKqmNHPexdapUyfbs2ePrV692jJnzmwvvfSSmZndcccd9vzzz6eo7NOnT1ujRo2sV69eZnbx3BcXF2dmF0d1FipUKMkdJClx8OBBW7x4sevvzz77zDJlyuSxEV+ff/75FXebREZGWuHChW3Lli0pLvdSu3fvthUrVrj+njBhggUEBFifPn1s3LhxlidPHmvTpo1r2yWXs/1RUVFWv35913liwIABli5dOsuRI4etW7cuVW1Yv369NW3a1Jo0aWJlypSxZs2a2YYNG+zAgQP2zTffWGBgYKqnbZg1a5a1bt3a5s6da+Hh4a5zxpEjR6xLly4WHh6equd/XP68h/j4eBs+fLiVL1/eNV1Gao6liIgIK126tHXp0sUWL15so0ePtsyZM9vUqVPt008/tccee8zuvvtu27BhQ4rr2Lt3r2XKlMk++eQT17IDBw5YzZo17bPPPrNcuXK5popIjsOHDye5/qlevbpH++nDhw/b1q1bXX87t7Mn++jDhw+7prD0Rj99+PDhq54TPNlHX74fzDzbTx8+fDjJ3M79+/f3eB99eRuefPJJj/bTl+5nsys/s57ooy9vQ8OGDT3aT19a/pEjRywkJMTjfbTzOik6OtoaNWrkmi7Vk/208/vOgQMHvNJPO8v3Zj/trOOvv/7ySj/tLP/S/eHpftpZxx9//GFNmza1pk2beq2fNmNqEa/66quvTJL16NEjyZQfR44csUqVKtmvv/7qkXouTTY6E8KDBg2yQ4cOWf/+/e3BBx9M1S3Sl34ZHDFiRJKHVLz44ov26quvpvii1+nvv/9O8hBGM7MWLVrY/fffn6pyndtl9uzZNmbMGHvqqacsf/78tm/fPvviiy+sRIkS1qtXr1TdKnPo0CEbNmyYLV26NEmdZmYlS5a87jy47li3bp1lyZLFHA5HkpOzmdlzzz1n9evX9/iDDP73v/+Zw+FwTUeQWvv27bN58+bZmDFjrF27dklee+mll6xSpUqp2gdOv//+uz322GPWvXt3mz59umv5okWLrGzZskmSwjdy+UNHV6xYYenSpUsyjYhznRUrVlhAQECSC5rklu/k/LytXbvWlcyOioqy2NhYe+utt+yHH35IdR3O4yUhIcHCwsKSlPnTTz+5Pe/gtco/ceKE9evXL8kF1o4dO1L0A9616mjYsKHrFsFu3bpZ9uzZLTQ01CZNmpSsedwuL3/Pnj3WqVMna9Gihe3cuTPJa4sXLzaHw2G7du1KVRv+/PNPK1CggJUrV87Cw8OTvHbw4EHLnDmzffrppykq/2rngtOnT1vhwoWT3M6XXJe3Ye3atdasWTN79NFH7Y477rD333/f9VpERIQVKVLEJk6cmOLy33zzTXM4HK7bubNmzeqa2mf+/PlWrFgxO3HihNtJ+TNnzlh0dHSSi50NGzZYvnz5ksyh7+zLBgwYkOz+52p1mCXtQ/fs2eP6orxv3z7r06ePVa9e3a2pcdwpf/78+Va+fHnX3wMGDLCWLVu6/RDla9VhdnF6l71795rZ/22nyZMnW61atdz+8net8g8fPmwtW7Y0s4u3FRcqVMhmz55tWbNmtVatWrmSCqmp45VXXrH//e9/rs+Ic5u0bdv2in4pueWfPXvWKlWqZA6Hw3UrpdMPP/xglSpVStb0R9fbD874jx8/bmXLlrWhQ4cm+xrgWuWvXr3aFi5caNWqVbMTJ0649vOvv/5qJUuWTNa572p1fPnll1a4cGHLly+f5cqVK8mzCOrVq3fFj63Xc/LkSfvzzz9dMX311VfmcDjsf//7n5ld/Fw4j8tPP/3UcubMmexE/MmTJ23Hjh1XbXd8fLzNnTs3yZfk+Ph4++ijj9z+Qussf/fu3Um2k3N/njlzxgoXLpzk+njt2rUpasPl/dn+/futY8eOSfrktWvXmsPhSFYdzv3w119/JXk+TI0aNVwPwerRo4dlzZrVgoKCbO7cucme6uDyNvz5559Wt25du/fee6+Yp/fNN9+0PHny2D///OP25+LSNphdnAM1Y8aMVqVKFdcUI047d+40h8OR7B/NnW24fDs5Yzx69Khly5bNxo8fn6xyL2+D81j9+eefrVSpUtamTRsLDQ1NknDev3+/Zc6cOdkPEHPW4dwPo0ePtsDAQBsxYoS98cYbFhwcbD179jQzs9dee83q1KljZ86ccXs/OKcQa9OmjWtgxMaNGy1Pnjwe6acvLf/SQUyX9o+p6aMvraN169ZJPreX1pGafvrSNvz+++9XvJbaPvryOi5NaB06dMgj/fTV9rPZxUEpnuijL6/Dua/Pnj1rFStW9Eg/fa1tZOaZPvryOi7NGf3888+2aNGiVPfTV9tGX375pRUqVMgjfbTZxc9vy5YtXYMv58+f7/F+euPGjdaiRYurTv/oiX7a2YZz584lyVV4sp++Vhs81U8723B5PtCT/bSzDc4Brps2bfJoP301JLK9yLlxg4KC7MEHH7S5c+fajh07bOjQoZY/f37XPFyecOkI0blz51qGDBnsjjvusPTp03vk4YzOg2zEiBHWvHlzMzMbNWqUORwO27RpU6rLv1RCQoKdP3/eHn74YY/MhWp2cTSqw+Gw0NDQJHOtffnllx55mERUVNQVF6YnTpyw2rVrJ7l4TKlVq1aZw+Gwli1bJkmWPvPMM9a9e3ePj/SKiYmxJk2aWMeOHVM9t9qlZs6caS1atEiyrfr3728PPPCAx+YfvtoJceDAgdawYUO3T3S7du2yV155xQ4fPpxk+SuvvGIBAQFXXPyvX7/eypYt6/YDyq5V/uV+++03y5Url7Vv3966du1qGTJksD179qS4jku3TVxcnJ05c8ZKlizp6gyHDRtmDofDrUTwjdrg7Cyd56YtW7ZY1apVk/WL9dXqcB7rQ4YMsY8++siefvppK1CggO3bt89efPFFy5w5s7366qtufSG4VhsOHDhg3333nasu53ZbsGCBlSlTJlkJqWvV8fXXX1v69OktX758SS5QY2JirHHjxknuvkhJ+U7O7TB9+nQrUaLEFXNNpqaOM2fOWHx8vNWuXdvmzZvnWh4bG2v33nuv68ekG12kXK38hIQEmz17ttWoUcOaNWuW5CHJ06ZNsypVqrh98bN9+3Zr0qSJValSxQoUKOB6RsX58+fts88+szx58li7du0sNjbW1Y8+9thj1qFDB4uLi3OrnmvVcbX37t271xo2bGgOh8OyZMlyxZfR1JT/zTff2B133GFm5hopf+kXRU+1walbt272xBNPuPVj9rXKN7t4nDZo0MDy589vISEhri+Gq1atspCQELd/nLpaHZeeCy5/EHNiYqK1a9cuyUOmUlr+2rVr7c4777SwsDBbsmSJq0977rnnrEGDBm4PKHB3P8TFxVnXrl2tdu3ayboGuFr5l+6/77777orRggMHDrTq1avbqVOnUlTHRx995Hrt9OnT9ttvvyUZjXfhwgVr1qyZ2+eMrVu3WpUqVaxChQqWIUMGGzdunF24cMGefvppCwoKsq+++irJ+t9++62VLVs2WaNoL60jMDDQnn/++SvOB3FxcTZv3jzXl+RnnnnG0qdP79YXcWf5d955pwUFBdnzzz9vCQkJrnNQXFycRUREWIECBVzPW3H20e7+2Hy1Nlz6eXDuT2c//fPPP1vFihXdnu/20vKdbXBeMz766KP27bff2jPPPGMFChSwAwcO2LPPPuu6+9Jdl+9r552aO3futAULFlzRT7/55ptWoUIFtwdHXL6NnOW/8847lj59eqtcubIrOWh28Yf6OnXqJGvE2tW206X7wfn/AwcOtDp16iQ7kXP5NhozZoyZXUw8R0VFWY0aNeznn382s/+7q6pevXr2+eefp6iOwMBAe+GFF+zgwYM2YcIEK168uNWuXTtJEn7EiBFWu3btZLVj+fLllj59emvcuLF17tzZNWJ87ty5ljNnTmvdunWq+unLy79WojklffTldTRq1Mg6d+6c5JrLGXdq+unrteHydpglr4++UR3Oeabz5s2bqn768vIvTchd/p0zuX30tepwJmrXrl1rZcuWtcKFC6eqn77efnCex1PaR1+rjku301dffWVly5ZNsn5y++lLj9VOnTq5vp+dOXPGfv311yRtSm4fbfZ/D7QdMmSIa1lcXJz17dvXgoKCrhicl5J++mp1XJoTc9aZ0n7aWf6lz+wx+7/PWWxsbKr76Wu1wenkyZNJ2pXcfvpq5cfExFhiYqI9+uij9s0336S6n768Dmf8O3bssAULFriuvVPaT18LiWwvcm7clStXWoMGDaxo0aJWokQJK126dKpu57qWxMRE1wHSuHFjy5Url8duR3SeEMaMGWM9e/a0yZMnW1BQ0BUdqKeMGjXKihQp4pGH0JldPNG8//77ri9Onh7BfDWjR4+2UqVKpepBVZdauXKlFShQwGrWrGndunWzTp06WXBwcJJb5Dxp4sSJlj17do9NBWF28cttcHCwTZo0yebMmWODBw+2HDlyeOw4vdyWLVusd+/elj17drd/cNm9e7flypXLHA6HDRs2LMndFGfPnrVx48aZw+GwkSNH2oYNG+zkyZM2dOhQK1mypFud1vXKv5rVq1ebw+GwXLlyuf15c6cO5w9GzuTm+PHj3b5gv175l96meanhw4dbrVq13O7Yb9SGDz74wPW05Uu/UL788stunTduVP61fhBp2rSp2x3mjer47LPPLCAgwJo2bWqfffaZ7d6924YOHWoFChRw64EtyTmWnFOjXHqnQmrbkJCQYGfOnLFatWrZqFGj7L///rPTp0/bqFGjXHe+pLYN58+fT3IrsZlZ3759rV27dnb+/Hm3Eo+5c+e2/v372yeffGIDBgywDBkyuPrgs2fP2uLFi61QoUJWpkwZ17RKWbJkcfvceq06rvUjckxMjHXo0MFy5crl1hQyySl/0aJFdtddd9nw4cMtMDDQ7XNGctsQGxtrI0eOtDx58rj1UOMblR8XF2cjR460hg0bJvnSbGZuX+gmtw3OOvPnz+96yFpKynceSwkJCbZt2zarUqWKFSlSxCpVqmT333+/5ciRw+3+x902OI/7ffv2mcPhuOI26dSUHxkZaQULFrS7777bRo0aZd26dbPcuXOnug3Xuu6Njo62oUOHWr58+ZIkDG9U/sCBA2379u32yiuvuH6APXTokPXo0cMyZMhgb7/9th05csTOnz9vQ4cOtUqVKiUrEX+1Oq52Xo6Pj7dPP/3UHA6H5cyZ060fC90pPzEx0Y4dO+b6oXb8+PGWNWtWt5Nq7tZxqaFDh1qDBg3c2k7XKt95zTt58mRzOBxJEl5mF/tRdx+EfqM6rnZHzrPPPmtt27Z1KyF1rfIPHDhg58+ft5dfftkCAgKsc+fOtmrVKouIiLCRI0dasWLFkvXjmrvH0g8//GDZsmWzL7/80q2yr1e+cxsdP37cSpQo4TpHxMbG2pgxY6xQoUJuJ8wvr2Py5MkWEBDgGpD133//XXHHY8+ePa1bt24WGxubrJHxrVq1snfeeceqVq1qjz76qOt6buHChVauXDm74447UtRPX638jh07ugYHXXosJbePTm4dCxcuTFE/nZw2JLePvlEdzu/Pw4YNs1atWrnOc8ntp69VvvN74OUJyOT00TeqY8eOHWZ28YHxDRs2tMKFC6eon75W+VfbD8nto92t47///rOyZctavXr1UtRPX638Rx999Kp5kuT20WYXt3GWLFmuuCs9Pj7eTpw4YX369El1P32tOi4fsOCsN7n9tDvlJyYm2vHjx1PcT7tbx6WS009fq3zn96o33njDNdAzpf30teq43jkhOf309ZDI9qJLN25UVJTt37/ftmzZcsPEVWrEx8db//79zeFwJBnt4ikTJkwwh8NhwcHBqZ7n7mo+//xz69Onj+XOndvjyX5PPIHaHZ999pn17NnTcubM6fE27Ny500aOHGnh4eH21FNPeSWJ7Txhnjp1yqpVq+b2KGN3LVu2zEqUKGGlSpWyhg0beuU4Nbt4kv7iiy+sQ4cObtdx5swZe+KJJ+zxxx+36dOnu6bpuTT5mpCQYB9++KGFhoZawYIFrUyZMlagQAG3LkSvVf61zgkxMTHWq1cvy5Ytm9sX08mto0qVKlajRg0LDAx06zOd3PK3b99uI0eOtOzZs6d6P1xax65du2zkyJGuBExyPt/ulH/phcO2bdtsxIgRlj17drd/dHF3O/30009Wu3ZtCwkJsTJlyrj9Q2dy94OZWZcuXeyOO+5w+4ulu3XMmzfPHA6HlS5d2mrVqmVFixb1WBsujfPPP/+0fv36WbZs2dzaDydPnrQmTZrYM888k2R5w4YNk8yXZ3bxQn3w4MHWvXt369u3r9ufN3fquLQNCQkJNm3aNEuXLp1b2yi55Tv3hbsX6imp46effrK2bdtaoUKFPNYGs4vTrl3tzgJ3jtXktuGHH36w+++/30JDQ72yH959910bPXq0vfTSS27f5puSYyk6Otqefvppt368c6f8S+erbNSokdWuXdseeughj34eLj1Xb9y40Xr16uV2H3r8+HGrX79+knk0ExMTrWnTprZ27VrbsmWL/f777/bWW29ZYGCghYWFWcWKFS1v3rxuX49dq45mzZrZr7/+ahs3bkyShIyPj7du3bpZtmzZXImS1JbvTBBeuHDBypcvb+Hh4RYYGOj2Zzq5bdizZ4+NHDnS7XPr9cr/5ZdfbPPmzfbRRx/Z4MGDXYkVd6c3crcO53ybl7Zh1KhRliNHDremebvesfTrr7/apk2b7O+//7ZvvvnGChYsaCEhIVa2bFm3+7cbteFq+8Hs4rzDd999tyUkJNzw3Hej8tevX2///fefvf/+++ZwOKxatWrWoEEDK1iwYKrb0LRpU/vll19s/fr1Se4w3rVrlw0ePNiyZ8+e7On2jh07ZqVLl7Z///3XvvjiC6tRo4Z169bNGjRoYO3bt7fo6GgbOHBgsvvp65Xfo0cPq1OnjrVt29bMLp4Dk9NHJ6cO5zQ1yU2oJbcNP/zwQ7L6aHfq6N69u917771Wp06dq/6I4+6PFe62YcmSJcnqo91tQ506dVzP+poxY0ay++nktCEuLi5ZfbS7ddSqVcseeeQR2759uzVo0CDZ/XRy2rB+/fpk9dFm//c8DOdUtPHx8davXz9r3ry5lStXzqZNm2bLly+3qVOnprifvlYdLVq0sDJlytjrr7+eJBGb3H7anfKdUyxduHDB7rzzzmT308ltw969e5PVT1+v/DvuuMPeeOMNmzFjhvXv39/1XTq5/bQ7bbh0e+/duzdZ/fSNkMj2Im9t3OuJj4+39957zyPTiVzNunXrzOFwpPhBZDeybds2a9++vVsnGX+1efNma9GihUc+oNdy6e2n3pKYmOix6T4ud/LkSYuIiEjWFA0pceHChWS14dy5czZ9+nSbO3eumf1fUujyZLbZxXm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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sb.countplot(data=df, x='contry_of_res', hue='Class/ASD')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "LqgkwS4ZIVVB",
        "outputId": "44cbf944-89b3-46ac-f687-f8cb2c0befe1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(15,5))\n",
        "\n",
        "for i, col in enumerate(floats):\n",
        "  plt.subplot(1,2,i+1)\n",
        "  sb.distplot(df[col])\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "6vUzjCceIW49",
        "outputId": "77aff49b-4286-40fa-9a9c-395bb98b5787"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(15,5))\n",
        "\n",
        "for i, col in enumerate(floats):\n",
        "  plt.subplot(1,2,i+1)\n",
        "  sb.boxplot(df[col])\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "0q1DLFhVIYdb",
        "outputId": "62a37de9-e381-4b37-d28d-0b8d7f6d2ab3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": 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HPfbu3TtHHXVU3v/+9+eoo45K7969Cw98fGMeAAAAQCko6h3ZY8eOzYwZMzJ48OAMHz48Dz74YC677LJ87nOfS/KXOwbPPvvsXHjhhdl9990zdOjQnH/++Rk4cGCOO+64YkYH2CK9+uqrSf6ydMiKFSuyYMGCwrGKior069cvy5cvL8wDAAAAKAVFLbL//d//Peeff34mTJiQF198MQMHDsz//t//O1OnTi3MOeecc7JmzZqcfvrpWbFiRY444ojcfvvtqa6uLmJygC3Tu971riTJ8uXLM2rUqOyyyy5pbW1NVVVV/vCHP2TRokWd5gEAAACUgqIW2b169cqsWbMya9asN51TVlaW6dOnZ/r06V0XDGArtffee+eWW25JkixevLhQXCfp9HyBvffeu8uzAQAAALyZoq6RDUDXeuWVVwo/t7a2djr2t/t/Ow8AAACg2BTZAN1I7969k3S++/oNZWVlhfE35gEAAACUAkU2QDeyatWqJOvfjZ0kHR0dhfE35gEAAACUAkU2QDeyww47FH4uL+/8fwF/u/+38wAAAACKragPewSga/1tQX3wwQfn0EMPTVVVVVpbW3PPPfcUHv6oyAYAAABKiSIboBt56qmnkiQ1NTV5+umnC8V1ktTW1qampiYtLS156qmncvDBBxcrJgAAAEAnimyAbqS5uTlJ0tLSkrq6unzqU58q3JF97733pqmpqdM8AAAAgFKgyAboRgYOHJgk+chHPtKpuE6SAQMG5CMf+UhuueWWwjwAAACAUuBhjwDdyLHHHpuKiorMnz8/bW1tnY69/vrrmT9/fioqKnLssccWKSEAAADA+tyRDdCNVFZW5tBDD82vfvWrtLa25phjjslee+2VRx99NAsXLszrr7+eww8/PJWVlcWOCgAlo6WlJStXrix2DKCEPPPMM53+C/CGPn36pKamptgxtkqKbIBupK2tLU8++WT69u2bP/3pT7nrrrty1113FY737ds3Tz31VNra2lJRUVHEpABQGlpaWvKZE0/Ka+taix0FKEEzZswodgSgxPSsrMp//ee1yuzNQJEN0I0sWbIkzc3NKSsry6GHHpqdd94569atS2VlZZ577rksWrQoHR0dWbJkSUaOHFnsuABQdCtXrsxr61rz52H/kvbqPsWOAwCUsPK1K5OnfpGVK1cqsjcDRTZAN/Lyyy8nSQ455JBccMEFeeihh7J8+fL069cv++67b6ZMmZJFixYV5gEAf9Fe3Sft2/UvdgwAgG6ryx/2OHv27AwZMiTV1dUZNWpU7r333recP2vWrOy5557ZZpttMmjQoEyaNClr167torQAW5cVK1YkSWpqavKZz3wmkyZNygUXXJBJkyblM5/5THbaaadO8wAAAABKQZcW2TfeeGMaGhrS2NiYBx54IPvvv3/GjBmTF198cYPzr7/++nz1q19NY2Njfve73+Wqq67KjTfemHPPPbcrYwNsNXbYYYckyS233JLly5d3OrZ8+fL85Cc/6TQPAAAAoBR0aZF92WWX5bTTTsv48eOzzz77ZO7cudl2221z9dVXb3D+3XffncMPPzyf/vSnM2TIkLzvfe/L8ccf/w/v4gZgw/r161f4ua2trdOxv93/23kAAAAAxdZlRfa6dety//33p76+/q9vXl6e+vr6NDU1bfCcww47LPfff3+huH7qqady22235YMf/GCXZAbY2rS3txd+7tGj82MS/nb/b+cBAAAAFFuXPezx5ZdfTltb23pP7Kypqcmjjz66wXM+/elP5+WXX84RRxyRjo6OvP766/n85z//lkuLtLa2prW1tbC/evXqTfMBALYCixcvLvw8cuTI7LLLLmltbU1VVVX+8Ic/ZNGiRYV5Bx98cJFSAgAAAHTWZUX2xliwYEEuuuiizJkzJ6NGjcoTTzyRs846KxdccEHOP//8DZ4zc+bMTJs2rYuTAmwZ3ngmwXve8578+te/LhTXSVJRUZGRI0fmwQcffNNnFwAAAAAUQ5ctLdK/f/9UVFSkpaWl03hLS0tqa2s3eM7555+fE088Maeeemr222+/fPSjH81FF12UmTNnvumvvU+ePDkrV64sbI888sgm/ywAW6qddtopSfLAAw+st7RIRUVFHnzwwU7zAAAAAEpBlxXZlZWVOfDAAzN//vzCWHt7e+bPn5+6uroNnvPqq6+mvLxzxIqKiiRJR0fHBs+pqqpK7969C1uvXr020ScA2PIdcMABhZ+32267fPnLX85///d/58tf/nK22267Dc4DAAAAKLYuXVqkoaEhJ598cg466KAccsghmTVrVtasWZPx48cnSU466aTsvPPOmTlzZpJk7NixueyyyzJy5MjC0iLnn39+xo4dWyi0Adg4r7zySr7xjW8U9nv27FnENAAAAABvrkuL7HHjxuWll17K1KlT09zcnAMOOCC333574QGQy5Yt63QH9pQpU1JWVpYpU6bkueeey4477pixY8dmxowZXRkbYKuxZMmSws+vvfZap2N/u79kyRIPewQAAABKRpc/7PHMM8/MmWeeucFjCxYs6LTfo0ePNDY2prGxsQuSAXQvVVVVaW1tfdN9AAAAgFLR5UU2AMUzYsSIJEmvXr1y44035tZbb83zzz+fgQMH5kMf+lDGjRuX1atXF+YBAAAAlAJFNkA38sbyTatXr85HP/rRTndgX3nllYX9v3/QLgAAAEAxaSoAupEVK1YUfl63bl2nY3+7/7fzAAAAAIpNkQ3Qjeywww5Jkv79+6esrKzTsfLy8vTv37/TPAAAAIBSYGkRgG7o5ZdfzqGHHppRo0aluro6a9euzaJFi3LPPfcUOxoAAADAetyRDdCNLF++vPBzWVlZ9thjj/zLv/xL9thjj053aP/tPAAAAIBic0c2QDfyxtrXH/nIR3Lvvfdm4sSJhWMDBgzI2LFj85Of/MQa2QAAAEBJUWQDdCNvrH3d0tKS//zP/8xDDz2U5cuXp1+/ftl3330zZcqUTvMAAAAASoGlRQC6kTce5rho0aI0NjamsrIydXV1qaysTGNjYxYtWtRpHgAAAEApcEc2QDcyYsSI1NbWpk+fPnnyySc7LS1SW1ubPffcM6tWrcqIESOKmBIAAACgM3dkA3QjFRUVmTBhQh5//PH86U9/6nRs+fLlefzxx3PGGWekoqKiSAkBAAAA1qfIBuiGOjo6UlZW1mmsrKwsHR0dRUoEAAAA8OYU2QDdSFtbW+bMmZPDDjssP/7xjzNx4sR89KMfzcSJE/PjH/84hx12WC6//PK0tbUVOyoAAABAgTWyAbqRJUuWpLm5OWPHjs1nP/vZNDc3F47993//dz784Q/n7rvvzpIlSzJy5MgiJgUAAAD4K0U2QDeyfPnyJMkVV1yRww47LOeff36GDh2apUuX5rrrrsuVV17ZaR4AAABAKbC0CEA3ssMOOyRJ9ttvv1x44YUZPnx4tt122wwfPjwXXnhh9ttvv07zAAAAAEqBIhsAAAAAgJKmyAboRlasWJEkeeihhzJlypQ8/PDDefXVV/Pwww9nypQpeeihhzrNAwAAACgF1sgG6Eb69euXJDn11FNzyy23ZOLEiYVjtbW1OfXUU3PFFVcU5gEAAACUAndkA3QjI0aMSG1tbW699da89NJLnY69+OKLufXWWzNgwICMGDGiSAkBAAAA1qfIBuhGKioqsttuu+X5559PRUVFPv3pT+e//uu/8ulPfzoVFRV5/vnnM2zYsFRUVBQ7KgAAAECBpUUAupF169blnnvuyXbbbZdtttkm119/fa6//vokyY477phXX30199xzT9atW5fKysoipwUAAAD4C3dkA3QjP/7xj9PW1pa99947f/rTnzodW758efbee++0tbXlxz/+cZESAgAAAKzPHdkA3cjzzz+fJLnvvvvSt2/f/Ou//mt23nnnPPfcc7nzzjtz3333dZoHAAAAUAoU2QDdyE477ZQkqaysTM+ePfODH/yg07HKysqsW7euMA8AAACgFCiyAbqRsrKyJH9ZK/vvlxb505/+lNdee63TPAAAAIBSYI1sgG6kubm58HNHR0eOP/74/Od//meOP/74dHR0bHAeAAAAQLEpsgG6kfb29iTJdtttl/b29nz/+9/PiSeemO9///tpb2/Pdttt12keAKVr4cKFGTt2bAYOHJiysrLcfPPNnY53dHRk6tSpGTBgQLbZZpvU19fn97//fXHCAgDAP0mRDdCNvFFUt7W1Zccdd+x0bMcdd0xbW1uneQCUrjVr1mT//ffP7NmzN3j8kksuybe//e3MnTs3ixYtynbbbZcxY8Zk7dq1XZwUAAD+edbIBuhGevT4y2V/7dq1aWtry/HHH58PfvCDue222/KjH/2osEb2G/MAKF0f+MAH8oEPfGCDxzo6OjJr1qxMmTIlxx57bJLk2muvTU1NTW6++eZ86lOf6sqoAADwT3NHNkA3MmLEiCRJZWVl2traOi0t0tbWlsrKyk7zANgyLV26NM3Nzamvry+M9enTJ6NGjUpTU1MRkwEAwMZxyx1AN1Je/pd/v1y3bl1GjRqVXXbZJa2tramqqsof/vCHLFq0qNM8ALZMbzy0t6amptN4TU3Nmz7Qt7W1Na2trYX9VatWbb6AAADwDimyAbqRFStWFH5evHhxobhOkqqqqg3OA6B7mDlzZqZNm1bsGAAAsEFuuQPoRvr165ckOe2007LDDjt0Ota3b9+ceuqpneYBsGWqra1NkrS0tHQab2lpKRz7e5MnT87KlSsL27PPPrvZcwIAwNvljmyAbmTEiBGpra3Nww8/nP/6r//KQw89lOXLl6dfv37Zd99909jYmAEDBlgjG2ALN3To0NTW1mb+/Pk54IADkvxlqZBFixbljDPO2OA5VVVVnX47BwAASok7sgG6kYqKikyYMCFNTU1pbGxMZWVl6urqUllZmcbGxjQ1NeWMM85IRUVFsaMC8A+88sorWbx4cRYvXpzkLw94XLx4cZYtW5aysrKcffbZufDCC3PLLbfkt7/9bU466aQMHDgwxx13XFFzAwDAxnBHNkA3M3r06EybNi2zZ8/OxIkTC+O1tbWZNm1aRo8eXcR0ALxd9913X44++ujCfkNDQ5Lk5JNPzjXXXJNzzjkna9asyemnn54VK1bkiCOOyO23357q6upiRQYAgI2myAbohh555JG89NJLncZefPHFPPLII4psgC3EUUcdlY6Ojjc9XlZWlunTp2f69OldmAoAADYPRTZANzN37tzccMMN6du3b0455ZTU1dWlqakpV111VW644YYkyec///kipwSA0lL+5xXFjgAAlDjfFzYvRTZAN7Ju3br88Ic/TN++ffPDH/4wPXr85f8GPvzhD+f9739/PvnJT+aHP/xhPve5z6WysrLIaQGgdGyzdGGxIwAAdGuKbIBu5Mc//nHa2tpyyimnFErsN/To0SOf+9zncumll+bHP/5xPvnJTxYpJQCUnj8PHZ32bXYodgwAoISV/3mFf/zejBTZAN3I888/nySpq6vb4PE3xt+YBwD8Rfs2O6R9u/7FjgEA0G2VFzsAAF1n4MCBSZKmpqYNHn9j/I15AAAAAKXAHdnQzaxduzbLli0rdgyKZPjw4SkvL89//Md/ZNiwYZ2WF3n99dczb968lJeXZ/jw4Xn88ceLmJRiGTx4cKqrq4sdAwAAADpRZEM3s2zZspx++unFjkGRrV69OhMmTHjT4291jK3bvHnzssceexQ7BgAAAHSiyIZuZvDgwZk3b16xY1BkP/rRj/Kzn/0s7e3thbHy8vLU19fnE5/4RBGTUWyDBw8udgQAAABYjyIbupnq6mp3W5Jzzz03X/7yl3PllVfmBz/4Qf7X//pfOfXUU1NZWVnsaAAAAADr8bBHgG6qsrIy9fX1SZL6+nolNgAAAFCyFNkAAAAAAJQ0RTYAAAAAACVNkQ0AAAAAQElTZAMAAAAAUNIU2QAAAAAAlDRFNgAAAAAAJU2RDQAAAABASVNkAwAAAABQ0opeZD/33HP5zGc+k3e9613ZZpttst9+++W+++4rHO/o6MjUqVMzYMCAbLPNNqmvr8/vf//7IiYGAAAAAKArFbXI/tOf/pTDDz88PXv2zP/9v/83jzzySC699NL07du3MOeSSy7Jt7/97cydOzeLFi3KdtttlzFjxmTt2rVFTA4AAAAAQFfpUcw3//rXv55Bgwblu9/9bmFs6NChhZ87Ojoya9asTJkyJccee2yS5Nprr01NTU1uvvnmfOpTn+ryzAAAAAAAdK2i3pF9yy235KCDDsonP/nJ7LTTThk5cmSuuOKKwvGlS5emubk59fX1hbE+ffpk1KhRaWpqKkZkAAAAAAC6WFGL7KeeeiqXX355dt9999xxxx0544wz8sUvfjHf+973kiTNzc1Jkpqamk7n1dTUFI79vdbW1qxataqwrV69evN+CAAAAAAANquiLi3S3t6egw46KBdddFGSZOTIkXnooYcyd+7cnHzyyRv1mjNnzsy0adM2ZUwAAAAAAIqoqHdkDxgwIPvss0+nsb333jvLli1LktTW1iZJWlpaOs1paWkpHPt7kydPzsqVKwvbI488shmSAwAAAADQVYpaZB9++OF57LHHOo09/vjj2XXXXZP85cGPtbW1mT9/fuH4qlWrsmjRotTV1W3wNauqqtK7d+/C1qtXr833AQAAAAAA2OyKurTIpEmTcthhh+Wiiy7K//pf/yv33ntv5s2bl3nz5iVJysrKcvbZZ+fCCy/M7rvvnqFDh+b888/PwIEDc9xxxxUzOgAAAAAAXaSoRfbBBx+cm266KZMnT8706dMzdOjQzJo1KyeccEJhzjnnnJM1a9bk9NNPz4oVK3LEEUfk9ttvT3V1dRGTAwAAAADQVYpaZCfJhz/84Xz4wx9+0+NlZWWZPn16pk+f3oWpAAAAAAAoFUVdIxsAAAAAAP4RRTYAAAAAACVNkQ0AAAAAQElTZAMAAAAAUNIU2QAAAAAAlDRFNgAAAAAAJU2RDQAAAABASVNkAwAAAABQ0hTZAAAAAACUtC4vsmfPnp0hQ4akuro6o0aNyr333vuW81esWJGJEydmwIABqaqqyh577JHbbruti9ICAAAAAFBsPbryzW688cY0NDRk7ty5GTVqVGbNmpUxY8bksccey0477bTe/HXr1uVf//Vfs9NOO+VHP/pRdt555zzzzDPZYYcdujI2AAAAAABF1KVF9mWXXZbTTjst48ePT5LMnTs3t956a66++up89atfXW/+1VdfneXLl+fuu+9Oz549kyRDhgzpysgAAAAAABRZly0tsm7dutx///2pr6//65uXl6e+vj5NTU0bPOeWW25JXV1dJk6cmJqamuy777656KKL0tbW1lWxAQAAAAAosi67I/vll19OW1tbampqOo3X1NTk0Ucf3eA5Tz31VO66666ccMIJue222/LEE09kwoQJee2119LY2LjBc1pbW9Pa2lrYX7169ab7EAAAAAAAdLkuf9jjO9He3p6ddtop8+bNy4EHHphx48blvPPOy9y5c9/0nJkzZ6ZPnz6FbZ999unCxAAAAAAAbGpdVmT3798/FRUVaWlp6TTe0tKS2traDZ4zYMCA7LHHHqmoqCiM7b333mlubs66des2eM7kyZOzcuXKwvbII49sug8BAAAAAECX67Iiu7KyMgceeGDmz59fGGtvb8/8+fNTV1e3wXMOP/zwPPHEE2lvby+MPf744xkwYEAqKys3eE5VVVV69+5d2Hr16rVpPwgAAAAAAF2qS5cWaWhoyBVXXJHvfe97+d3vfpczzjgja9asyfjx45MkJ510UiZPnlyYf8YZZ2T58uU566yz8vjjj+fWW2/NRRddlIkTJ3ZlbAAAAAAAiqjLHvaYJOPGjctLL72UqVOnprm5OQcccEBuv/32wgMgly1blvLyv3brgwYNyh133JFJkyZlxIgR2XnnnXPWWWflK1/5SlfGBgAAAACgiLq0yE6SM888M2eeeeYGjy1YsGC9sbq6utxzzz2bORUAAAAAAKWqS5cWAQAAAACAd0qRDQAAAABASVNkAwAAAABQ0hTZAAAAAACUNEU2AAAAAAAlTZENAAAAAEBJU2QDAAAAAFDSFNkAAAAAAJQ0RTYAAAAAACVNkQ0AAAAAQElTZAMAAAAAUNIU2QAAAAAAlDRFNgAAAAAAJU2RDQAAAABASVNkAwAAAABQ0hTZAAAAAACUNEU2AAAAAAAlTZENAAAAAEBJU2QDAAAAAFDSFNkAAAAAAJQ0RTYAAAAAACVNkQ0AAJvZsGHD8sc//nG98RUrVmTYsGFFSAQAAFsWRTYAAGxmTz/9dNra2tYbb21tzXPPPVeERAAAsGXpUewAAACwtbrlllsKP99xxx3p06dPYb+trS3z58/PkCFDipAMAAC2LIpsAADYTI477rgkSVlZWU4++eROx3r27JkhQ4bk0ksvLUIyAADYsiiyAQBgM2lvb0+SDB06NL/+9a/Tv3//IicCAIAt0z+1RvYTTzyRO+64I3/+85+TJB0dHZskFAAAbE2WLl2qxAYAgH/CRt2R/cc//jHjxo3LXXfdlbKysvz+97/PsGHDcsopp6Rv375+PRIAgG7v29/+9tue+8UvfnEzJgEAgC3fRhXZkyZNSo8ePbJs2bLsvffehfFx48aloaFBkQ0AQLf3zW9+823NKysrU2RvAcrXrix2BACgxPm+sHltVJH9P//zP7njjjuyyy67dBrffffd88wzz2ySYAAAsCVbunRpsSOwCfTp0yc9K6uSp35R7CgAwBagZ2VV+vTpU+wYW6WNKrLXrFmTbbfddr3x5cuXp6qq6p8OBQAA/HO+9rWvZdq0aZ3G9txzzzz66KNFSrRlqqmpyX/957VZudIdVsBfPfPMM5kxY0bOO++87LrrrsWOA5SQPn36pKamptgxtkobVWQfeeSRufbaa3PBBRck+cuvQ7a3t+eSSy7J0UcfvUkDAgDAlu5zn/vcWx6/+uqrN8v7Dh8+PD/72c8K+z16bNTX/26vpqbGX0iBDdp1112zxx57FDsGQLewUd9kL7nkkrz3ve/Nfffdl3Xr1uWcc87Jww8/nOXLl+dXv/rVps4IAABbtD/96U+d9l977bU89NBDWbFiRY455pjN9r49evRIbW3tZnt9AADoKhtVZO+77755/PHH853vfCe9evXKK6+8ko997GOZOHFiBgwYsKkzAgDAFu2mm25ab6y9vT1nnHFGdtttt832vr///e8zcODAVFdXp66uLjNnzszgwYM32/sBAMDmstG/W9inT5+cd955mzILAAB0G+Xl5WloaMhRRx2Vc845Z5O//qhRo3LNNddkzz33zAsvvJBp06blyCOPzEMPPZRevXqtN7+1tTWtra2F/VWrVm3yTAAAsLE2qshesmTJBsfLyspSXV2dwYMHe+gjAAD8A08++WRef/31zfLaH/jABwo/jxgxIqNGjcquu+6aH/zgBznllFPWmz9z5sz1Hg4JAAClYqOK7AMOOCBlZWVJko6OjiQp7CdJz549M27cuPzHf/xHqqurN0FMAADYcjU0NHTa7+joyAsvvJBbb701J598cpdk2GGHHbLHHnvkiSee2ODxyZMnd8q5atWqDBo0qEuyAQDAP1K+MSfddNNN2X333TNv3rz85je/yW9+85vMmzcve+65Z66//vpcddVVueuuuzJlypRNnRcAALY4Dz74YKftjd9wvPTSSzNr1qwuyfDKK6/kySeffNNn2lRVVaV3796dNgAAKBUbdUf2jBkz8q1vfStjxowpjO23337ZZZddcv755+fee+/Ndtttly996Uv5xje+scnCAgDAlujnP/95l7/nl7/85YwdOza77rprnn/++TQ2NqaioiLHH398l2cBAIB/1kYV2b/97W+z6667rje+66675re//W2Svyw/8sILL/xz6QAAYCvw5z//OR0dHdl2222TJM8880xuuumm7LPPPnnf+963Wd7zD3/4Q44//vj88Y9/zI477pgjjjgi99xzT3bcccfN8n4AALA5bVSRvddee+Xiiy/OvHnzUllZmSR57bXXcvHFF2evvfZKkjz33HOpqanZdEkBAGALdeyxx+ZjH/tYPv/5z2fFihU55JBDUllZmZdffjmXXXZZzjjjjE3+njfccMMmf00AACiWjVoje/bs2fnpT3+aXXbZJfX19amvr88uu+ySn/70p7n88suTJE899VQmTJiwScMCAMCW6IEHHsiRRx6ZJPnRj36U2traPPPMM7n22mvz7W9/u8jpAACg9G3UHdmHHXZYli5dmuuuuy6PP/54kuSTn/xkPv3pT6dXr15JkhNPPHHTpQQAgC3Yq6++Wvie/D//8z/52Mc+lvLy8hx66KF55plnipwOAABK30YV2UnSq1evjB49OkOGDMm6deuS/PUhNh/5yEc2TToAANgKvPvd787NN9+cj370o7njjjsyadKkJMmLL76Y3r17FzkdAACUvo0qsp966ql89KMfzW9/+9uUlZWlo6MjZWVlheNtbW2bLCAAAGzppk6dmk9/+tOZNGlSjjnmmNTV1SX5y93ZI0eOLHI6AAAofRu1RvZZZ52VoUOH5sUXX8y2226bhx56KL/4xS9y0EEHZcGCBZs4IgAAbNk+8YlPZNmyZbnvvvtyxx13FMbf+9735pvf/GYRkwEAwJZho4rspqamTJ8+Pf379095eXkqKipyxBFHZObMmfniF7+4qTMCAMAWr7a2Nr169cqdd96ZP//5z0mSgw8+OHvttVeRkwEAQOnbqCK7ra2t8LCa/v375/nnn0+S7Lrrrnnsscc2XToAANgK/PGPf8x73/ve7LHHHvngBz+YF154IUlyyimn5Etf+lKR0wEAQOnbqCJ73333zW9+85skyahRo3LJJZfkV7/6VaZPn55hw4Zt0oAAALClmzRpUnr27Jlly5Zl2223LYyPGzcut99+exGTAQDAlmGjHvY4ZcqUrFmzJkkyffr0fPjDH86RRx6Zd73rXbnxxhs3aUAAANjS/c///E/uuOOO7LLLLp3Gd9999zzzzDNFSgUAAFuOjSqyx4wZU/j53e9+dx599NEsX748ffv2TVlZ2SYLBwAAW4M1a9Z0uhP7DcuXL09VVVUREgEAwJZlo5YW2ZB+/fopsQEAYAOOPPLIXHvttYX9srKytLe355JLLsnRRx9dxGQAALBl2Kg7sgEAgLfv3/7t33LMMcfkvvvuy7p163LOOefk4YcfzvLly/OrX/2q2PEAAKDkKbIBAGAzeu211/LFL34xP/nJT3LnnXemV69eeeWVV/Kxj30sEydOzIABA4odEQAASp4iGwAANqOePXtmyZIl6du3b84777xixwEAgC3SJlsjGwAA2LDPfOYzueqqq4odAwAAtljuyAYAgM3s9ddfz9VXX52f/exnOfDAA7Pddtt1On7ZZZcVKRkAAGwZFNkAALCZPfTQQ3nPe96TJHn88cc7HSsrKytGJAAA2KIosgEAYDP7+c9/XuwIAACwRbNGNgAAAAAAJU2RDQAAAABASSuZIvviiy9OWVlZzj777MLY2rVrM3HixLzrXe/K9ttvn49//ONpaWkpXkgAAAAAALpcSRTZv/71r/Mf//EfGTFiRKfxSZMm5Sc/+Ul++MMf5he/+EWef/75fOxjHytSSgAAAAAAiqHoRfYrr7ySE044IVdccUX69u1bGF+5cmWuuuqqXHbZZTnmmGNy4IEH5rvf/W7uvvvu3HPPPUVMDAAAAABAVyp6kT1x4sR86EMfSn19fafx+++/P6+99lqn8b322iuDBw9OU1PTm75ea2trVq1aVdhWr1692bIDAAAAALD59Sjmm99www154IEH8utf/3q9Y83NzamsrMwOO+zQabympibNzc1v+pozZ87MtGnTNnVUAAAAAACKpGh3ZD/77LM566yzct1116W6unqTve7kyZOzcuXKwvbII49sstcGAAAAAKDrFa3Ivv/++/Piiy/mPe95T3r06JEePXrkF7/4Rb797W+nR48eqampybp167JixYpO57W0tKS2tvZNX7eqqiq9e/cubL169drMnwQAAAAAgM2paEuLvPe9781vf/vbTmPjx4/PXnvtla985SsZNGhQevbsmfnz5+fjH/94kuSxxx7LsmXLUldXV4zIAAAAAAAUQdGK7F69emXfffftNLbddtvlXe96V2H8lFNOSUNDQ/r165fevXvnC1/4Qurq6nLooYcWIzIAAAAAAEVQ1Ic9/iPf/OY3U15eno9//ONpbW3NmDFjMmfOnGLHAgAAAACgC5VUkb1gwYJO+9XV1Zk9e3Zmz55dnEAAAAAAABRd0R72CAAAAAAAb4ciGwAAAACAkqbIBgAAAACgpCmyAQAAAAAoaYpsAAAAAABKWpcX2bNnz86QIUNSXV2dUaNG5d57731b591www0pKyvLcccdt3kDAgAAAABQUrq0yL7xxhvT0NCQxsbGPPDAA9l///0zZsyYvPjii2953tNPP50vf/nLOfLII7soKQAAAAAApaJLi+zLLrssp512WsaPH5999tknc+fOzbbbbpurr776Tc9pa2vLCSeckGnTpmXYsGFdmBYAAAAAgFLQZUX2unXrcv/996e+vv6vb15envr6+jQ1Nb3pedOnT89OO+2UU0455W29T2tra1atWlXYVq9e/U9nBwAAAACgeLqsyH755ZfT1taWmpqaTuM1NTVpbm7e4Dm//OUvc9VVV+WKK6542+8zc+bM9OnTp7Dts88+/1RuAAAAAACKq8sf9vh2rV69OieeeGKuuOKK9O/f/22fN3ny5KxcubKwPfLII5sxJQAAAAAAm1uPrnqj/v37p6KiIi0tLZ3GW1paUltbu978J598Mk8//XTGjh1bGGtvb0+S9OjRI4899lh222239c6rqqpKVVVVYX/VqlWb6iNs8VpaWrJy5cpixwBKyDPPPNPpvwBv6NOnz3q/SQcAAFAsXVZkV1ZW5sADD8z8+fNz3HHHJflLMT1//vyceeaZ683fa6+98tvf/rbT2JQpU7J69ep861vfyqBBg7oi9lajpaUlnznxpLy2rrXYUYASNGPGjGJHAEpMz8qq/Nd/XqvMBgAASkKXFdlJ0tDQkJNPPjkHHXRQDjnkkMyaNStr1qzJ+PHjkyQnnXRSdt5558ycOTPV1dXZd999O52/ww47JMl64/xjK1euzGvrWvPnYf+S9uo+xY4DAJSw8rUrk6d+kZUrVyqyAQCAktClRfa4cePy0ksvZerUqWlubs4BBxyQ22+/vfAXpGXLlqW8vGSX7d4qtFf3Sft2b3/NcQAAAACAYuvSIjtJzjzzzA0uJZIkCxYseMtzr7nmmk0fCAAAAACAkub2ZwAAAAAASpoiGwAAAACAkqbIBgAAAACgpCmyAQAAAAAoaYpsAAAAAABKmiIbAAAAAICSpsgGAAAAAKCkKbIBAAAAAChpimwAAAAAAEqaIhsAAAAAgJKmyAYAAAAAoKQpsgEAAAAAKGmKbAAAAAAASpoiGwAAAACAkqbIBgAAAACgpCmyAQAAAAAoaYpsAAAAAABKmiIbAAAAAICSpsgGAAAAAKCkKbIBAAAAAChpimwAAAAAAEqaIhsAAAAAgJKmyAYAAAAAoKQpsgEAAAAAKGmKbAAAAAAASpoiGwAAAACAkqbIBgAAAACgpCmyAQAAAAAoaYpsAAAAAABKmiIbAAAAAICSpsgGAAAAAKCkKbIBAAAAAChpimwAANiKzZ49O0OGDEl1dXVGjRqVe++9t9iRAADgHVNkAwDAVurGG29MQ0NDGhsb88ADD2T//ffPmDFj8uKLLxY7GgAAvCOKbAAA2EpddtllOe200zJ+/Pjss88+mTt3brbddttcffXVxY4GAADviCIbAAC2QuvWrcv999+f+vr6wlh5eXnq6+vT1NRUxGQAAPDO9Sh2AAAAYNN7+eWX09bWlpqamk7jNTU1efTRR9eb39ramtbW1sL+qlWrNntGAAB4u9yRDQAAZObMmenTp09hGzRoULEjAQBAgSIbAAC2Qv37909FRUVaWlo6jbe0tKS2tna9+ZMnT87KlSsL27PPPttVUQEA4B9SZAMAwFaosrIyBx54YObPn18Ya29vz/z581NXV7fe/KqqqvTu3bvTBgAApcIa2QAAsJVqaGjIySefnIMOOiiHHHJIZs2alTVr1mT8+PHFjgYAAO+IIhsAALZS48aNy0svvZSpU6emubk5BxxwQG6//fb1HgAJAAClTpENAABbsTPPPDNnnnlmsWMAAMA/xRrZAAAAAACUNEU2AAAAAAAlzdIi3Uz5n1cUOwIAUOJ8XwAAAEqNIrub2WbpwmJHAAAAAAB4RxTZ3cyfh45O+zY7FDsGAFDCyv+8wj9+AwAAJUWR3c20b7ND2rfrX+wYAAAAAABvm4c9AgAAAABQ0hTZAAAAAACUNEU2AAAAAAAlTZENAAAAAEBJU2QDAAAAAFDSFNkAAAAAAJQ0RTYAAAAAACVNkQ0AAAAAQElTZAMAAAAAUNIU2QAAAAAAlDRFNgAAAAAAJU2RDQAAAABASVNkAwAAAABQ0hTZAAAAAACUNEU2AAAAAAAlrahF9syZM3PwwQenV69e2WmnnXLcccflscce6zRn7dq1mThxYt71rndl++23z8c//vG0tLQUKTEAAAAAAF2tqEX2L37xi0ycODH33HNP7rzzzrz22mt53/velzVr1hTmTJo0KT/5yU/ywx/+ML/4xS/y/PPP52Mf+1gRUwMAAAAA0JV6FPPNb7/99k7711xzTXbaaafcf//9GT16dFauXJmrrroq119/fY455pgkyXe/+93svffeueeee3LooYcWIzYAAAAAAF2opNbIXrlyZZKkX79+SZL7778/r732Wurr6wtz9tprrwwePDhNTU1FyQgAAAAAQNcq6h3Zf6u9vT1nn312Dj/88Oy7775Jkubm5lRWVmaHHXboNLempibNzc0bfJ3W1ta0trYW9levXr3ZMgMAAAAAsPmVzB3ZEydOzEMPPZQbbrjhn3qdmTNnpk+fPoVtn3322UQJAQAAAAAohpIoss8888z89Kc/zc9//vPssssuhfHa2tqsW7cuK1as6DS/paUltbW1G3ytyZMnZ+XKlYXtkUce2ZzRAQAAAADYzIpaZHd0dOTMM8/MTTfdlLvuuitDhw7tdPzAAw9Mz549M3/+/MLYY489lmXLlqWurm6Dr1lVVZXevXsXtl69em3WzwAAAAAAwOZV1DWyJ06cmOuvvz4//vGP06tXr8K613369Mk222yTPn365JRTTklDQ0P69euX3r175wtf+ELq6upy6KGHFjM6AAAAAABdpKhF9uWXX54kOeqoozqNf/e7381nP/vZJMk3v/nNlJeX5+Mf/3haW1szZsyYzJkzp4uTAgAAAABQLEUtsjs6Ov7hnOrq6syePTuzZ8/ugkQAAAAAAJSaknjYIwAAAAAAvJkuL7Jnz56dIUOGpLq6OqNGjcq99977pnOvuOKKHHnkkenbt2/69u2b+vr6t5wPAAAAAMDWp0uL7BtvvDENDQ1pbGzMAw88kP333z9jxozJiy++uMH5CxYsyPHHH5+f//znaWpqyqBBg/K+970vzz33XFfGBgAAAACgiLq0yL7sssty2mmnZfz48dlnn30yd+7cbLvttrn66qs3OP+6667LhAkTcsABB2SvvfbKlVdemfb29syfP78rYwMAAAAAUERdVmSvW7cu999/f+rr6//65uXlqa+vT1NT09t6jVdffTWvvfZa+vXr96ZzWltbs2rVqsK2evXqfzo7AAAAAADF06Or3ujll19OW1tbampqOo3X1NTk0UcffVuv8ZWvfCUDBw7sVIb/vZkzZ2batGn/VNatWfnalcWOAACUON8XAACAUtNlRfY/6+KLL84NN9yQBQsWpLq6+k3nTZ48OQ0NDYX95557Lvvss09XRCxpffr0Sc/KquSpXxQ7CgCwBehZWZU+ffoUOwYAAECSLiyy+/fvn4qKirS0tHQab2lpSW1t7Vue+41vfCMXX3xxfvazn2XEiBFvObeqqipVVVWF/VWrVm186K1ITU1N/us/r83Kle6wAv7qmWeeyYwZM3Leeedl1113LXYcoIT06dNnvd+kAwAAKJYuK7IrKytz4IEHZv78+TnuuOOSpPDgxjPPPPNNz7vkkksyY8aM3HHHHTnooIO6KO3Wqaamxl9IgQ3adddds8ceexQ7BgAAAMAGdenSIg0NDTn55JNz0EEH5ZBDDsmsWbOyZs2ajB8/Pkly0kknZeedd87MmTOTJF//+tczderUXH/99RkyZEiam5uTJNtvv3223377rowOAAAAAECRdGmRPW7cuLz00kuZOnVqmpubc8ABB+T2228v3CW8bNmylJeXF+ZffvnlWbduXT7xiU90ep3GxsZ87Wtf68roAAAAAAAUSZc/7PHMM89806VEFixY0Gn/6aef3vyBAAAAAAAoaeX/eAoAAAAAABSPIhsAAAAAgJKmyAYAAAAAoKQpsgEAAAAAKGmKbAAAAAAASpoiGwAAAACAkqbIBgAAAACgpCmyAQAAAAAoaYpsAAAAAABKmiIbAAAAAICSpsgGAAAAAKCkKbIBAAAAAChpimwAAAAAAEqaIhsAAAAAgJKmyAYAAAAAoKQpsgEAAAAAKGmKbAAAAAAASpoiGwAAAACAkqbIBgAAAACgpCmyAQAAAAAoaYpsAAAAAABKmiIbAAAAAICSpsgGAAAAAKCkKbIBAAAAAChpimwAAAAAAEqaIhsAAAAAgJKmyAYAAAAAoKQpsgEAAAAAKGmKbAAAAAAASpoiGwAAAACAkqbIBgAAAACgpCmyAQAAAAAoaYpsAAAAAABKmiIbAAAAAICSpsgGAAAAAKCkKbIBAAAAAChpimwAANgKDRkyJGVlZZ22iy++uNixAABgo/QodgAAAGDzmD59ek477bTCfq9evYqYBgAANp4iGwAAtlK9evVKbW1tsWMAAMA/zdIiAACwlbr44ovzrne9KyNHjsy//du/5fXXX3/Tua2trVm1alWnDQAASoU7sgEAYCv0xS9+Me95z3vSr1+/3H333Zk8eXJeeOGFXHbZZRucP3PmzEybNq2LUwIAwNvjjmwAANhCfPWrX13vAY5/vz366KNJkoaGhhx11FEZMWJEPv/5z+fSSy/Nv//7v6e1tXWDrz158uSsXLmysD377LNd+dEAAOAtuSMbAAC2EF/60pfy2c9+9i3nDBs2bIPjo0aNyuuvv56nn346e+6553rHq6qqUlVVtSliAgDAJqfIBgCALcSOO+6YHXfccaPOXbx4ccrLy7PTTjtt4lQAALD5KbIBAGAr09TUlEWLFuXoo49Or1690tTUlEmTJuUzn/lM+vbtW+x4sMVau3Ztli1bVuwYlIBnnnmm038hSQYPHpzq6upix4CtliIbAAC2MlVVVbnhhhvyta99La2trRk6dGgmTZqUhoaGYkeDLdqyZcty+umnFzsGJWTGjBnFjkAJmTdvXvbYY49ix4CtliIbAAC2Mu95z3tyzz33FDsGbHUGDx6cefPmFTsGUKIGDx5c7AiwVVNkAwAAwNtQXV3tbksAKJLyYgcAAAAAAIC3osgGAAAAAKCkKbIBAAAAAChpimwAAAAAAEqaIhsAAAAAgJKmyAYAAAAAoKQpsgEAAAAAKGk9ih0AAAAAYEvR1taWJUuWZPny5enXr19GjBiRioqKYscC2OopsgEAAADehoULF2bOnDlpbm4ujNXW1mbChAkZPXp0EZMBbP0sLQIAAADwDyxcuDCNjY0ZNmxYZs+endtuuy2zZ8/OsGHD0tjYmIULFxY7IsBWTZENAAAA8Bba2toyZ86c1NXV5cILL8zw4cOz7bbbZvjw4bnwwgtTV1eXyy+/PG1tbcWOCrDVsrQIdDNr167NsmXLih2DEvHMM890+i8MHjw41dXVxY4BAFBSlixZkubm5px//vkpL+98T2B5eXlOOOGETJw4MUuWLMnIkSOLlBJg66bIhm5m2bJlOf3004sdgxIzY8aMYkegRMybNy977LFHsWMAAJSU5cuXJ0mGDh26weNvjL8xD4BNT5EN3czgwYMzb968YscAStTgwYOLHQEAoOT069cvSbJ06dIMHz58veNLly7tNA+ATU+RDd1MdXW1uy0BAADegREjRqS2tjbXXXddLrzwwk7Li7S3t+e6667LgAEDMmLEiCKmBNi6edgjAAAAwFuoqKjIhAkT0tTUlClTpuThhx/Oq6++mocffjhTpkxJU1NTzjjjjFRUVBQ7KsBWyx3ZAAAAAP/A6NGjM23atMyZMycTJ04sjA8YMCDTpk3L6NGji5gOYOunyAYAAAB4G0aPHp3DDz88S5YsyfLly9OvX7+MGDHCndgAXUCRDQAAAPA2VVRUZOTIkcWOAdDtWCMbAAAAAICSpsgGAAAAAKCkbRFF9uzZszNkyJBUV1dn1KhRuffee4sdCQAAAACALlLyRfaNN96YhoaGNDY25oEHHsj++++fMWPG5MUXXyx2NAAAAAAAukDJF9mXXXZZTjvttIwfPz777LNP5s6dm2233TZXX311saMBAAAAANAFSrrIXrduXe6///7U19cXxsrLy1NfX5+mpqYNntPa2ppVq1YVttWrV3dVXAAAAAAANoOSLrJffvnltLW1paamptN4TU1NmpubN3jOzJkz06dPn8K2zz77dEVUAAAAAAA2k5IusjfG5MmTs3LlysL2yCOPFDsSAAAAAAD/hB7FDvBW+vfvn4qKirS0tHQab2lpSW1t7QbPqaqqSlVVVWF/1apVmzUjAAAAAACbV5ffkT179uwMGTIk1dXVGTVqVO699943nVtZWZmhQ4dm/Pjxqa6uzn777Zef/vSnmT9/furq6rowNQAAAAAAxdKlRfaNN96YhoaGNDY25oEHHsj++++fMWPG5MUXX9zg/LvvvjtPPvlkXnnllTQ2NuaII47Isccem1WrVmX8+PFdGR0AAAAAgCLp0iL7sssuy2mnnZbx48dnn332ydy5c7Ptttvm6quv3uD8b33rW/ngBz+Yb37zm5k7d26uvvrqbLPNNnnve9+73gMgAQAAAADYOnXZGtnr1q3L/fffn8mTJxfGysvLU19fn6ampg2e09TUlIaGhpx55pk588wzkySNjY25+eab3/R9Wltb09raWthfuXJlEmtlAwDwzrzx/fGN75Pdje/RAABsjDe+P7a3t2/S1+2yIvvll19OW1vbendS19TU5NFHH93gOc3NzRuc39zc/KbvM3PmzEybNm298UGDBm1EagAAurvBgwcXO0JR+R4NAMDGePbZZzfpd+kuK7K7yuTJk9PQ0FDYX758eYYOHZqHHnooffr0KWIyYEu3evXq7LPPPnnkkUfSq1evYsfZJLbGzwRbAn/2tgwdHR155ZVXMmDAgJSXd/kz0ouuvb09L7zwQrbffvuUlZUVOw5vwvUEimNr/LO3NX4moDhWrlyZfffdN3vvvfcmfd0uK7L79++fioqKtLS0dBpvaWlJbW3tBs+pra19R/OTpKqqKlVVVeuNDxo0KL17996I5AB/8cavxuy8885bzfVka/xMsCXwZ48txQ477FDsCPwDridQHFvjn72t8TMBxfHGNaRHj01bPXfZrSWVlZU58MADM3/+/MJYe3t75s+fn7q6ug2eU1dX12l+ktx5551vOh8AAAAAgK1Ply4t0tDQkJNPPjkHHXRQDjnkkMyaNStr1qzJ+PHjkyQnnXRSdt5558ycOTNJctZZZ+Vf/uVfcumll+ZDH/pQbrjhhtx3332ZN29eV8YGAAAAAKCIurTIHjduXF566aVMnTo1zc3NOeCAA3L77bcXHui4bNmyTusPHnbYYbn++uszZcqUnHvuudl9991z8803Z999933b71lVVZXGxsYNLjcC8E5sjdeTrfEzwZbAnz1gU3E9geLYGv/sbY2fCSiOzXU9Kevo6OjYpK8IAAAAAACbUPd7/DoAAAAAAFsURTYAAAAAACVNkQ0AAAAAQElTZAMAAAAAUNK2iiJ79uzZGTJkSKqrqzNq1Kjce++9bzn/hz/8Yfbaa69UV1dnv/32y2233dZFSYFS906uJ1dccUWOPPLI9O3bN3379k19ff0/vP4A3cc7/X7yhhtuuCFlZWU57rjjNm9AYIvxTq8nK1asyMSJEzNgwIBUVVVljz328HceIMk7v57MmjUre+65Z7bZZpsMGjQokyZNytq1a7soLVCqFi5cmLFjx2bgwIEpKyvLzTff/A/PWbBgQd7znvekqqoq7373u3PNNde84/fd4ovsG2+8MQ0NDWlsbMwDDzyQ/fffP2PGjMmLL764wfl33313jj/++Jxyyil58MEHc9xxx+W4447LQw891MXJgVLzTq8nCxYsyPHHH5+f//znaWpqyqBBg/K+970vzz33XBcnB0rNO72evOHpp5/Ol7/85Rx55JFdlBQode/0erJu3br867/+a55++un86Ec/ymOPPZYrrrgiO++8cxcnB0rNO72eXH/99fnqV7+axsbG/O53v8tVV12VG2+8Meeee24XJwdKzZo1a7L//vtn9uzZb2v+0qVL86EPfShHH310Fi9enLPPPjunnnpq7rjjjnf0vmUdHR0dGxO4VIwaNSoHH3xwvvOd7yRJ2tvbM2jQoHzhC1/IV7/61fXmjxs3LmvWrMlPf/rTwtihhx6aAw44IHPnzu2y3EDpeafXk7/X1taWvn375jvf+U5OOumkzR0XKGEbcz1pa2vL6NGj87nPfS7/7//9v6xYseJt3dkAbN3e6fVk7ty5+bd/+7c8+uij6dmzZ1fHBUrYO72enHnmmfnd736X+fPnF8a+9KUvZdGiRfnlL3/ZZbmB0lZWVpabbrrpLX+j9Ctf+UpuvfXWTjcSf+pTn8qKFSty++23v+332qLvyF63bl3uv//+1NfXF8bKy8tTX1+fpqamDZ7T1NTUaX6SjBkz5k3nA93DxlxP/t6rr76a1157Lf369dtcMYEtwMZeT6ZPn56ddtopp5xySlfEBLYAG3M9ueWWW1JXV5eJEyempqYm++67by666KK0tbV1VWygBG3M9eSwww7L/fffX1h+5Kmnnsptt92WD37wg12SGdh6bKo+tsemDNXVXn755bS1taWmpqbTeE1NTR599NENntPc3LzB+c3NzZstJ1D6NuZ68ve+8pWvZODAgetdnIHuZWOuJ7/85S9z1VVXZfHixV2QENhSbMz15Kmnnspdd92VE044IbfddlueeOKJTJgwIa+99loaGxu7IjZQgjbmevLpT386L7/8co444oh0dHTk9ddfz+c//3lLiwDv2Jv1satWrcqf//znbLPNNm/rdbboO7IBSsXFF1+cG264ITfddFOqq6uLHQfYgqxevTonnnhirrjiivTv37/YcYAtXHt7e3baaafMmzcvBx54YMaNG5fzzjvPMorAO7ZgwYJcdNFFmTNnTh544IH8n//zf3LrrbfmggsuKHY0oJvaou/I7t+/fyoqKtLS0tJpvKWlJbW1tRs8p7a29h3NB7qHjbmevOEb3/hGLr744vzsZz/LiBEjNmdMYAvwTq8nTz75ZJ5++umMHTu2MNbe3p4k6dGjRx577LHstttumzc0UJI25vvJgAED0rNnz1RUVBTG9t577zQ3N2fdunWprKzcrJmB0rQx15Pzzz8/J554Yk499dQkyX777Zc1a9bk9NNPz3nnnZfycvdGAm/Pm/WxvXv3ftt3Yydb+B3ZlZWVOfDAAzs9eKC9vT3z589PXV3dBs+pq6vrND9J7rzzzjedD3QPG3M9SZJLLrkkF1xwQW6//fYcdNBBXREVKHHv9Hqy11575be//W0WL15c2D7ykY8Unug9aNCgrowPlJCN+X5y+OGH54knnij8g1iSPP744xkwYIASG7qxjbmevPrqq+uV1W/8I1lHR8fmCwtsdTZVH7tF35GdJA0NDTn55JNz0EEH5ZBDDsmsWbOyZs2ajB8/Pkly0kknZeedd87MmTOTJGeddVb+5V/+JZdeemk+9KEP5YYbbsh9992XefPmFfNjACXgnV5Pvv71r2fq1Km5/vrrM2TIkMJa+9tvv3223377on0OoPjeyfWkuro6++67b6fzd9hhhyRZbxzoft7p95Mzzjgj3/nOd3LWWWflC1/4Qn7/+9/noosuyhe/+MVifgygBLzT68nYsWNz2WWXZeTIkRk1alSeeOKJnH/++Rk7dmyn3/oAup9XXnklTzzxRGF/6dKlWbx4cfr165fBgwdn8uTJee6553LttdcmST7/+c/nO9/5Ts4555x87nOfy1133ZUf/OAHufXWW9/R+27xRfa4cePy0ksvZerUqWlubs4BBxyQ22+/vbCA+LJlyzr9C+Jhhx2W66+/PlOmTMm5556b3XffPTfffLO/KALv+Hpy+eWXZ926dfnEJz7R6XUaGxvzta99rSujAyXmnV5PAN7MO72eDBo0KHfccUcmTZqUESNGZOedd85ZZ52Vr3zlK8X6CECJeKfXkylTpqSsrCxTpkzJc889lx133DFjx47NjBkzivURgBJx33335eijjy7sNzQ0JElOPvnkXHPNNXnhhReybNmywvGhQ4fm1ltvzaRJk/Ktb30ru+yyS6688sqMGTPmHb1vWYffBwEAAAAAoIS5FQgAAAAAgJKmyAYAAAAAoKQpsgEAAAAAKGmKbAAAAAAASpoiGwAAAACAkqbIBgAAAACgpCmyAQAAAAAoaYpsAAAAAABKmiIbAAAAAICSpsgGAAAAAKCkKbIBAAAAAChpimwAAAAAAEra/w9jp6WD98sz1gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df['result']>-5]\n",
        "df.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1n9baV4AIZwL",
        "outputId": "7b53d059-ad0e-4e82-ed67-57b3aa97738c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(798, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This functions make groups by taking\n",
        "# the age as a parameter\n",
        "def convertAge(age):\n",
        "    if age < 4:\n",
        "        return 'Toddler'\n",
        "    elif age < 12:\n",
        "        return 'Kid'\n",
        "    elif age < 18:\n",
        "        return 'Teenager'\n",
        "    elif age < 40:\n",
        "        return 'Young'\n",
        "    else:\n",
        "        return 'Senior'\n",
        "\n",
        "df['ageGroup'] = df['age'].apply(convertAge)\n"
      ],
      "metadata": {
        "id": "XZ9kZWf_IbHK"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sb.countplot(x=df['ageGroup'], hue=df['Class/ASD'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "AujgnqnyIcp_",
        "outputId": "cbe89407-c6e8-4741-924e-c98d234ef17d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "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": [
        "def add_feature(data):\n",
        "\n",
        "  # Creating a column with all values zero\n",
        "  data['sum_score'] = 0\n",
        "  for col in data.loc[:,'A1_Score':'A10_Score'].columns:\n",
        "\n",
        "    # Updating the 'sum_score' value with scores\n",
        "    # from A1 to A10\n",
        "    data['sum_score'] += data[col]\n",
        "\n",
        "  # Creating a random data using the below three columns\n",
        "  data['ind'] = data['austim'] + data['used_app_before'] + data['jaundice']\n",
        "\n",
        "  return data\n",
        "\n",
        "df = add_feature(df)\n"
      ],
      "metadata": {
        "id": "fso8ebz4Idut"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sb.countplot(x=df['sum_score'], hue=df['Class/ASD'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "G1oN8YTxIe_d",
        "outputId": "27e8dde1-00c3-471e-da54-5cc90c4af364"
      },
      "execution_count": 21,
      "outputs": [
        {
          "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": [
        "# Applying log transformations to remove the skewness of the data.\n",
        "df['age'] = df['age'].apply(lambda x: np.log(x))\n"
      ],
      "metadata": {
        "id": "gDTluzi1IgMV"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sb.distplot(df['age'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "I-R5AlKSIhWy",
        "outputId": "01a4b9a0-7f1f-4019-c9b1-b5f6a7574379"
      },
      "execution_count": 23,
      "outputs": [
        {
          "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": [
        "def encode_labels(data):\n",
        "    for col in data.columns:\n",
        "\n",
        "      # Here we will check if datatype\n",
        "      # is object then we will encode it\n",
        "      if data[col].dtype == 'object':\n",
        "        le = LabelEncoder()\n",
        "        data[col] = le.fit_transform(data[col])\n",
        "\n",
        "    return data\n",
        "\n",
        "df = encode_labels(df)\n",
        "\n",
        "# Making a heatmap to visualize the correlation matrix\n",
        "plt.figure(figsize=(10,10))\n",
        "sb.heatmap(df.corr() > 0.8, annot=True, cbar=False)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "QB2QnUWSIiQ9",
        "outputId": "d0b03fee-0a11-440d-d724-83b53723b3b0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "removal = ['ID', 'age_desc', 'used_app_before', 'austim']\n",
        "features = df.drop(removal + ['Class/ASD'], axis=1)\n",
        "target = df['Class/ASD']\n"
      ],
      "metadata": {
        "id": "FbVG-30wIjas"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, Y_train, Y_val = train_test_split(features, target, test_size = 0.2, random_state=10)\n",
        "\n",
        "# As the data was highly imbalanced we will balance it by adding repetitive rows of minority class.\n",
        "ros = RandomOverSampler(sampling_strategy='minority',random_state=0)\n",
        "X, Y = ros.fit_resample(X_train,Y_train)\n",
        "X.shape, Y.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oibiPTiTIk5H",
        "outputId": "072db086-044e-4649-acfd-d686cb841ba6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1026, 20), (1026,))"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the features for stable and fast training.\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "X_val = scaler.transform(X_val)\n"
      ],
      "metadata": {
        "id": "biU0aEU6ImWD"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = [LogisticRegression(), XGBClassifier(), SVC(kernel='rbf')]\n",
        "\n",
        "for model in models:\n",
        "  model.fit(X, Y)\n",
        "\n",
        "  print(f'{model} : ')\n",
        "  print('Training Accuracy : ', metrics.roc_auc_score(Y, model.predict(X)))\n",
        "  print('Validation Accuracy : ', metrics.roc_auc_score(Y_val, model.predict(X_val)))\n",
        "  print()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRUr4237Inbn",
        "outputId": "0197b521-49a4-4e8b-ec3a-723e70775950"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression() : \n",
            "Training Accuracy :  0.8664717348927876\n",
            "Validation Accuracy :  0.782258064516129\n",
            "\n",
            "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=None, grow_policy=None, importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
            "              num_parallel_tree=None, random_state=None, ...) : \n",
            "Training Accuracy :  1.0\n",
            "Validation Accuracy :  0.7491039426523298\n",
            "\n",
            "SVC() : \n",
            "Training Accuracy :  0.9405458089668616\n",
            "Validation Accuracy :  0.8042114695340501\n",
            "\n"
          ]
        }
      ]
    },
    {
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "ConfusionMatrixDisplay.from_estimator(models[0], X_val, Y_val)\n",
        "plt.show()"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "5xfGWZbgK05Y",
        "outputId": "5b383aff-1298-4ac4-f30c-70c924b20485"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2wivrMxONFGP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}