outbreak_detect_case_study.ipynb

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        "<a href=\"https://colab.research.google.com/github/dheeraj0000/2216177_AIML_Bootcamp_2023/blob/main/outbreak_detect_case_study.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "qk92XjDY0jTo"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv('/content/outbreak_detect.csv')"
      ],
      "metadata": {
        "id": "_d_902SI0mAF"
      },
      "execution_count": 22,
      "outputs": []
    },
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        "dataset.head()"
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      "outputs": [
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            "text/plain": [
              "   maxTemp  minTemp  avgHumidity  Rainfall  Positive   pf Outbreak\n",
              "0     29.0     18.0        49.74      0.00      2156  112       No\n",
              "1     34.0     23.0        83.27     15.22     10717  677      Yes\n",
              "2     40.0     23.0        50.74      0.00      1257  127       No\n",
              "3      NaN     24.0        84.57     11.15      9693  482       No\n",
              "4     34.0     24.0        59.16      9.06      4198  211       No"
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    {
      "cell_type": "code",
      "source": [
        "print(dataset.isnull().any())"
      ],
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        "id": "CzEq3UUA06SO",
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      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "maxTemp         True\n",
            "minTemp         True\n",
            "avgHumidity    False\n",
            "Rainfall       False\n",
            "Positive       False\n",
            "pf             False\n",
            "Outbreak        True\n",
            "dtype: bool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.dtypes)"
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      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "maxTemp        float64\n",
            "minTemp        float64\n",
            "avgHumidity    float64\n",
            "Rainfall       float64\n",
            "Positive         int64\n",
            "pf               int64\n",
            "Outbreak        object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
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        "id": "NWLiGpP508o9",
        "outputId": "a11f22c1-a657-40fe-ea9b-e2789524a017"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 26 entries, 0 to 25\n",
            "Data columns (total 7 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   maxTemp      24 non-null     float64\n",
            " 1   minTemp      25 non-null     float64\n",
            " 2   avgHumidity  26 non-null     float64\n",
            " 3   Rainfall     26 non-null     float64\n",
            " 4   Positive     26 non-null     int64  \n",
            " 5   pf           26 non-null     int64  \n",
            " 6   Outbreak     25 non-null     object \n",
            "dtypes: float64(4), int64(2), object(1)\n",
            "memory usage: 1.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=dataset.dropna()"
      ],
      "metadata": {
        "id": "8RwNHiQT2zbG"
      },
      "execution_count": 27,
      "outputs": []
    },
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        "dataset"
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        "id": "w9sWs8FQ3US_",
        "outputId": "c6b95230-8387-48b5-c520-e6b166cdc4da"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    maxTemp  minTemp  avgHumidity  Rainfall  Positive   pf Outbreak\n",
              "0      29.0     18.0        49.74      0.00      2156  112       No\n",
              "1      34.0     23.0        83.27     15.22     10717  677      Yes\n",
              "2      40.0     23.0        50.74      0.00      1257  127       No\n",
              "4      34.0     24.0        59.16      9.06      4198  211       No\n",
              "5      34.0     27.0        73.23      0.00     11808  712      Yes\n",
              "6      31.0     24.0        88.77     41.40     10881  648      Yes\n",
              "7      33.0     24.0        77.94     23.88      8830  459      Yes\n",
              "8      31.0     24.0        84.57     11.15      9693  482       No\n",
              "9      36.0     24.0        53.40      2.12      9310  549       No\n",
              "10     32.0     23.0        57.50      0.00     13154  838      Yes\n",
              "11     34.0     18.0        59.40      0.00      2197  136       No\n",
              "12     42.0     24.0        49.43      2.19      3362  213       No\n",
              "13     45.0     32.0        34.74      0.38       416   26       No\n",
              "14     43.0     28.0        69.07      4.65      7514  410       No\n",
              "15     33.0     23.0        80.97      6.92     10990  390      Yes\n",
              "16     32.0     24.0        87.32     11.92      6536  338       No\n",
              "17     40.0     27.0        63.97      0.00     11169  776      Yes\n",
              "19     39.0     25.0        47.52      0.00      8131  312       No\n",
              "20     36.0     26.0        72.78      3.54      5138  213       No\n",
              "21     31.0     23.0        73.35      4.97     10659  612      Yes\n",
              "22     30.0     23.0        86.81      7.21      9041  418       No\n",
              "23     30.0     22.0        78.80      3.12     11265  404      Yes\n",
              "24     33.0     22.0        73.71      1.75      9233  212       No"
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              "      <th></th>\n",
              "      <th>maxTemp</th>\n",
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              "      <td>84.57</td>\n",
              "      <td>11.15</td>\n",
              "      <td>9693</td>\n",
              "      <td>482</td>\n",
              "      <td>No</td>\n",
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              "      <th>9</th>\n",
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              "      <td>24.0</td>\n",
              "      <td>53.40</td>\n",
              "      <td>2.12</td>\n",
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              "      <td>549</td>\n",
              "      <td>No</td>\n",
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              "      <td>No</td>\n",
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              "      <td>24.0</td>\n",
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              "      <td>No</td>\n",
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              "      <td>No</td>\n",
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              "      <td>Yes</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>32.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>87.32</td>\n",
              "      <td>11.92</td>\n",
              "      <td>6536</td>\n",
              "      <td>338</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>40.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>63.97</td>\n",
              "      <td>0.00</td>\n",
              "      <td>11169</td>\n",
              "      <td>776</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>39.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>47.52</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8131</td>\n",
              "      <td>312</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>36.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>72.78</td>\n",
              "      <td>3.54</td>\n",
              "      <td>5138</td>\n",
              "      <td>213</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>31.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>73.35</td>\n",
              "      <td>4.97</td>\n",
              "      <td>10659</td>\n",
              "      <td>612</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>30.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>86.81</td>\n",
              "      <td>7.21</td>\n",
              "      <td>9041</td>\n",
              "      <td>418</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>30.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>78.80</td>\n",
              "      <td>3.12</td>\n",
              "      <td>11265</td>\n",
              "      <td>404</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>33.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>73.71</td>\n",
              "      <td>1.75</td>\n",
              "      <td>9233</td>\n",
              "      <td>212</td>\n",
              "      <td>No</td>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=dataset.drop(['Positive', 'pf'], axis=1)"
      ],
      "metadata": {
        "id": "4MlvSokK3rSl"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "id": "c-rDPUJM34pd",
        "outputId": "d0975c63-4b2a-49a7-9083-f78d6845ea0d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    maxTemp  minTemp  avgHumidity  Rainfall Outbreak\n",
              "0      29.0     18.0        49.74      0.00       No\n",
              "1      34.0     23.0        83.27     15.22      Yes\n",
              "2      40.0     23.0        50.74      0.00       No\n",
              "4      34.0     24.0        59.16      9.06       No\n",
              "5      34.0     27.0        73.23      0.00      Yes\n",
              "6      31.0     24.0        88.77     41.40      Yes\n",
              "7      33.0     24.0        77.94     23.88      Yes\n",
              "8      31.0     24.0        84.57     11.15       No\n",
              "9      36.0     24.0        53.40      2.12       No\n",
              "10     32.0     23.0        57.50      0.00      Yes\n",
              "11     34.0     18.0        59.40      0.00       No\n",
              "12     42.0     24.0        49.43      2.19       No\n",
              "13     45.0     32.0        34.74      0.38       No\n",
              "14     43.0     28.0        69.07      4.65       No\n",
              "15     33.0     23.0        80.97      6.92      Yes\n",
              "16     32.0     24.0        87.32     11.92       No\n",
              "17     40.0     27.0        63.97      0.00      Yes\n",
              "19     39.0     25.0        47.52      0.00       No\n",
              "20     36.0     26.0        72.78      3.54       No\n",
              "21     31.0     23.0        73.35      4.97      Yes\n",
              "22     30.0     23.0        86.81      7.21       No\n",
              "23     30.0     22.0        78.80      3.12      Yes\n",
              "24     33.0     22.0        73.71      1.75       No"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ac750136-bf9d-4a2a-9bee-a142ef783c0c\">\n",
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              "      <th></th>\n",
              "      <th>maxTemp</th>\n",
              "      <th>minTemp</th>\n",
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              "      <td>15.22</td>\n",
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              "      <td>23.0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>24.0</td>\n",
              "      <td>59.16</td>\n",
              "      <td>9.06</td>\n",
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              "      <td>23.88</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
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              "      <td>24.0</td>\n",
              "      <td>84.57</td>\n",
              "      <td>11.15</td>\n",
              "      <td>No</td>\n",
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              "      <th>9</th>\n",
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              "      <td>24.0</td>\n",
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              "      <td>2.12</td>\n",
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              "      <td>23.0</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>Yes</td>\n",
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              "      <th>11</th>\n",
              "      <td>34.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>59.40</td>\n",
              "      <td>0.00</td>\n",
              "      <td>No</td>\n",
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              "      <th>12</th>\n",
              "      <td>42.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>49.43</td>\n",
              "      <td>2.19</td>\n",
              "      <td>No</td>\n",
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              "      <th>13</th>\n",
              "      <td>45.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>34.74</td>\n",
              "      <td>0.38</td>\n",
              "      <td>No</td>\n",
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              "      <th>14</th>\n",
              "      <td>43.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>69.07</td>\n",
              "      <td>4.65</td>\n",
              "      <td>No</td>\n",
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              "      <th>15</th>\n",
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              "      <td>23.0</td>\n",
              "      <td>80.97</td>\n",
              "      <td>6.92</td>\n",
              "      <td>Yes</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>32.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>87.32</td>\n",
              "      <td>11.92</td>\n",
              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>40.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>63.97</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Yes</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>39.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>47.52</td>\n",
              "      <td>0.00</td>\n",
              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>36.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>72.78</td>\n",
              "      <td>3.54</td>\n",
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              "      <th>21</th>\n",
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              "      <td>73.35</td>\n",
              "      <td>4.97</td>\n",
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              "      <th>22</th>\n",
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              "      <td>23.0</td>\n",
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              "      <td>7.21</td>\n",
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              "      <td>73.71</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "tvPcSwFW0-o1"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = dataset.iloc[:,:-1].values\n",
        "# output\n",
        "\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "iWFjlg_p1BR2"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.3, random_state = 0)"
      ],
      "metadata": {
        "id": "GZoxlQTX1FIK"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_x = StandardScaler()\n",
        "xtrain = sc_x.fit_transform(xtrain)#normalizing\n",
        "xtest = sc_x.transform(xtest)\n",
        "print ('train value:',xtrain[0:1, :])\n",
        "print ('test value:',xtest[0:1, :])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYebC7b91Hg2",
        "outputId": "e03b5d3a-3fce-4de1-b17d-aa976209fda9"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train value: [[-0.41907904 -0.84051051  0.53331168 -0.46893213]]\n",
            "test value: [[ 1.73618459 -0.13271219 -1.04918848 -0.42875589]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression(random_state = 0)"
      ],
      "metadata": {
        "id": "MgzYw24Q1JXf"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(xtrain, ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "3RnKC8Lj1LKP",
        "outputId": "0e72ee59-8786-40c2-f4b6-28bbf4c64f8b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ],
            "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>LogisticRegression(random_state=0)</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(xtest)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlP3FE-d1M4V",
        "outputId": "293ca86b-d037-4a0f-847c-a1c0d8dda908"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'No', 'No', 'No', 'Yes', 'No', 'No'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(ytest, y_pred)\n",
        "print (\"Confusion Matrix : \\n\", cm)\n",
        "from sklearn.metrics import accuracy_score\n",
        "print (\"Accuracy : \", accuracy_score(ytest, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_v_Ecxx1Owm",
        "outputId": "8974a6d8-2b7c-480e-98e1-229b58b6ffdb"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix : \n",
            " [[3 1]\n",
            " [3 0]]\n",
            "Accuracy :  0.42857142857142855\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mm4zsFe83d12",
        "outputId": "f4b76ed5-1bd2-4e68-fb2f-c7c557655419"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.391304347826087"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(xtest,ytest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "51foDyY34cfV",
        "outputId": "582b7428-3229-4e69-fc11-3f643eb247af"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42857142857142855"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NAIVEY'S BAYES ALGORITHM (BAYES THEOREM)"
      ],
      "metadata": {
        "id": "e-0kUupSxkSA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "dJqrwlkF44f6"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_x = StandardScaler()\n",
        "xtrain = sc_x.fit_transform(xtrain)#normalizing\n",
        "xtest = sc_x.transform(xtest)"
      ],
      "metadata": {
        "id": "Qdh533MQxo6G"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(xtrain, ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "DdRLPO8yxq_X",
        "outputId": "a22814a1-3b93-4ff1-8fd8-bd75ae5a5d85"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = gnb.predict(xtest)"
      ],
      "metadata": {
        "id": "C19M4f32xs2b"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print (\"Accuracy : \", accuracy_score(ytest, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHJgaKKJxu2J",
        "outputId": "f3929ee7-4096-4bde-8ff4-3c352170ac0b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy :  0.8333333333333334\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CORRELATION MATRIX"
      ],
      "metadata": {
        "id": "0JtVVosm2TQf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix=dataset.corr()\n",
        "print(corr_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qi8yPLC72RCb",
        "outputId": "0524548c-a36f-4165-f4c7-d1bfdde2fa97"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              maxTemp   minTemp  avgHumidity  Rainfall\n",
            "maxTemp      1.000000  0.672748    -0.624309 -0.337345\n",
            "minTemp      0.672748  1.000000    -0.168180 -0.014123\n",
            "avgHumidity -0.624309 -0.168180     1.000000  0.596079\n",
            "Rainfall    -0.337345 -0.014123     0.596079  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(corr_matrix,annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Lh8h-OhR2Y3M",
        "outputId": "70b4dfb5-0d7d-4089-a7ab-247636a15c44"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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MX1KqzEnXnEX303pSL7MB1x44pGj/qTf+gQ5HHghAnYw6NGiayYiDL6y22BPhqH+ezz59O7OjYCuTrhrD2nlLS5VpelA7+txzMbUz6rDsg9lMvTn6lvHrk7rT7crTaNyhFf8deDO5c4o/dw1a7c1ZH9zJzHv/y5ePTihVbzIlshtN0gDgPqAW8FgF77enAy8Bh5nZzKqcs8YlGzMbD4yv4PhcoDOApLHAG2b2UrUEV00KI8Yd783lkTOPoEXDupz71BR67ZfNvk0bFpX5fv0mnvhkIWPPPYpGGXVYv3krAIf9qikvDO0FwI8F2xj4fx9wZLtmSbmO6nDKicdxzuknc93Ifyc7lGrRsXdnmrXPZmTvK2jXpQNn3jaMe065oVS5+e9/wZRxE7lx8n3F9r8y8qmi9Z5DBtDmwHZhh5xQ+/Q5hMz22Tx3zNU077Ivx9w+lFdOvqVUuZ63X8CHf3uMnFnfceJT19K298EsnzyH9d+sYOLw++g1quwEe+RN57Js0pchX8WesR2JSTaSagEPAscBK4AZksab2VclyjUErgA+TcR50xJRSbwktZP0taSxkr6V9KykfpKmSlooqbukoZIeCMqPlXS/pGmSFks6o4K6+0uaLukLSS9KahDsXyrpDkmzJc2U1FXSREnfSbokKNNb0keS3pT0jaRHJFXrcxNr3uo82mbVp01WfdJrpXH8Aa2YvGhNsTL/nbOMs7q0o1FGHQCa1N+rVD3vfrOao9o3p256jftMkTDdOh9EZqOGlRf8mTio/2F89t+PAFg6ayF1G9anUbOsUuWWzlrIT7kbKqzr0JN78Pn4qSFEGZ52/Q/l25c/BiBn1nfs1ag+9ZpnFStTr3kW6Q3qkjPrOwC+fflj2h/fDYANi1bx4+LVZdd9/KFsXJ5L3rcrw7uAqojEuVSuO7DIzBab2TbgeWBQGeVGAncCW6oaOlRzsgnsB4wG9g+Wc4CjgWuA68oo3zI4/jugvKZeU+AGoJ+ZdQVmAlfFFFlmZp2BKcBY4AzgCOCfMWW6A38BOgL7AqftycUlQs6mLWQ3rFu03aJhBjkbi/+8v1+/ie/zNjPk2Y85/+kpTF2cU6qeiV+v5IQDWocer6s+mS0as2HVuqLtDWvWkZndZLfrady6KU3aNufbafMSGV7o6mc3ZlPM9W9avZ762Y1Lldm8en2FZUqqXW8vOl/6O2be+9/EBpxAFolvkTQ8+GC9cxleoqrWwPKY7RXBviKSugJtzezNRMWfjGSzxMzmmlkEmA+8b9Gb/swF2pVR/lUziwRNvBbl1HkE0SQxVdJsYAjwq5jjO7vl5gKfmtlGM8sFtkrKCo59FmT6QuA5ogmumNgf4uMfztmNS068woixLG8zj53dg1EDD+XWiV/y05btRcdzN21hUe5Gjmz/8+1Cc3vu0IE9mD3h05SfTpso3a46jbmPvc2O/K3JDqV8cbZszGyMmXWLWcbszmmCXp17gKsTGX4y+ldif5qRmO0IZccTW17l1CngXTMbXMk5Y89X8pwlX3WlXoXBD20MQMHj14T2Km3eIIM1GwuKtn/YuIXmDTOKlWnRsC6dWmWRXiuN1ln1+FXjBizL20ynllkAvPP1Kvp0yCa9VtJ6A12CHHN+f44cfCwAy778jqxWexcdy8remx/XrC/voeXqOrAHL974RMJiDNOBQ/pxwOA+AOR+uZgGMdffoGUTNq/JK1Z+85o86rdsUmGZklp02Y99T+zOEdedTZ1G9TAzdmzZzvxx7ybwSqomgd8KvRJoG7PdJti3U0OgEzBZEkA2MF7SyVWZJPBz6cz/BHhQ0n5mtkhSfaC1mX27G3V0l9Qe+B44iyCpJMOBLbNYlreZlRvyad4wg4kLVnH7wK7FyvTpkM1bC1ZyykH7kJe/le/zNtEmq17R8bcXrOTyngdUd+guBFOefocpT78DQMc+Xeg55Hi+GD+Ndl06sGVjfqVjMyU137cVdTPrs+SL3Xl5JM/8ce8xf9x7AOzTtzOdhh7Hotem07zLvmzbmE9+zoZi5fNzNrB9UwHNu+xLzqzv+M3pRzPvyXcqPMdrp48sWu925Wlsz99SoxINgO1IWFUzgA7B+91K4GyiwxnR85j9CDTduS1pMnBNVWej/Sw+9gZdYkOB5yTNAaYTHQ/aHTOAB4AFwBLglUTGuDtqp6Uxol8nLn3xE059fBLH7d+S/Zo25KEpXzN5YXSiQI/2zciqW4fTHp/EH5+fzpW9O5JVNzpZYOWP+azZuIVD99m7otP8LFx78yjOvfhKli5bwbGnnMfLr09Mdkih+mrSLNYty+GmD+/j7DuG88KNjxcd+9uEO4vWTx5xLrdOf4j0unW4dfpDnPDXXXNrDh3Ygy9en1atcSfKsg9m89OyHAZ/PJped13ElOvHFh074+3bitanXD+W3ndfxOCPR/PT9z8UzTBrN6Ab5312Py267scJY6/hpGf+Vt2XsMfiHbOptB6zHcCfgYlE3+9eMLP5km4N/vQkFEr170hIBEm9iWbu38X7mDC70VJN7UGXJTuEGuPqbv9Idgg1RsfC9GSHUGNcsvyZ8oYA4vZDn15xvee0mPRhlc8Vhp9LN5pzzv28WY3MIXHzZAOY2WRgcpLDcM65ciVwgkBSeLJxzrkUYBFv2TjnnAtZpNCTjXPOuZB5N5pzzrnQeTeac8650KX6X6l4snHOuRTgLRvnnHOh8wkCzjnnQuctG+ecc6Ezv4OAc865sPnUZ+ecc6GLeMvGOedc2LwbzTnnXOh8NppzzrnQ+Ww055xzoUv1MZufxddCO+fcz52Z4lriIWmApG8kLZI0oozjV0n6StIcSe9L+lVV4/dk45xzKcAsvqUykmoBDwInAB2BwZI6lig2C+hmZgcDLwF3VTV+TzbOOZcCIqa4ljh0BxaZ2WIz2wY8DwyKLWBmk8wsP9j8BGhT1fg92TjnXAqIRBTXImm4pJkxy/ASVbUGlsdsrwj2lWcY8FZV4/cJAs45lwLinSBgZmOAMYk4p6TzgG5Ar6rW5clmDzW89Llkh1BjXDZyeeWFfiFGz7wj2SHUGNsfH5nsEH5WEvhHnSuBtjHbbYJ9xUjqB1wP9DKzrVU9qScb55xLAQmc+jwD6CCpPdEkczZwTmwBSV2AR4EBZpaTiJP6mI1zzqUAi3OptB6zHcCfgYnAAuAFM5sv6VZJJwfF7gYaAC9Kmi1pfFXj95aNc86lgMJI4toGZjYBmFBi300x6/0SdrKAJxvnnEsBKf4NA55snHMuFRipfbsaTzbOOZcCIvEMyNRgnmyccy4FRLxl45xzLmzejeaccy50hZ5snHPOhc1noznnnAudJxvnnHOh8zEb55xzoYukdq7xZOOcc6nApz4755wLXWGyA6giTzbOOZcCIvKWjXPOuZCl+N1qPNk451wq8KnPzjnnQuez0ZxzzoXOb1fjnHMudKnesknc94w655wLTSTOJR6SBkj6RtIiSSPKOL6XpP8Exz+V1K6q8Vdby0bSWOANM3spZt8mM2uQgLpbAfeb2RllHJsMXGNmMyVNAM4JDp1jZg9V9dxhuveeWzlhQF/yCwoYNuxKZs2eV6pMeno699/3L3r16kEkEuHGm+7klVcm8NcrhnPhhYPZsWMHa3PXc9Hwq1i2bGUSriIxTr95KB37dGFbwVaeveZhVsxfUqrMSdecRffTelIvswHXHjikaP+pN/6BDkceCECdjDo0aJrJiIMvrLbYq8sNt9/DR1M/o0njLF595pFkh1Ot0tp3os6x50BaGju+/Igdn04oVabW/oeRftQgACI5y9n2+qPVHWaVJGo2mqRawIPAccAKYIak8Wb2VUyxYUCeme0n6WzgTuCsqpz3Z9GNZmargFKJpoxyJwIEWfoyoMYmmxMG9KXDfu3Zv+PRHN69Kw8+cAc9jh5Yqtx1/7ic3Nx1dDzwGCTRpEkWALNnz+PwI06goGALFw//A6PuuIFzzr20mq8iMTr27kyz9tmM7H0F7bp04MzbhnHPKTeUKjf//S+YMm4iN06+r9j+V0Y+VbTec8gA2hzYLuyQk+KUE4/jnNNP5rqR/052KNVLos5x57P1P//GNq4nY8hNFC6aja1btatI4xakH3ESW565HbbmQ72GSQx4zySwG607sMjMFgNIeh4YBMQmm0HALcH6S8ADkmRme5zz4upGk/SqpM8lzZc0XNIlku6OOT5U0gPB+o1B8+xjSc9JuiaO+ntLeiNm+wFJQ4P1pZLukDRb0kxJXSVNlPSdpEuCMu0kzQvW60p6XtICSa8AdWPqXSqpKTAK2Deo825JT0k6Jabcs5IGxfPchGXgwON5+tloI/DTz74gMyuT7OzmpcoNHXI2o+78XwDMjHXr8gCY/OE0Cgq2BI//nDatW1ZT5Il3UP/D+Oy/HwGwdNZC6jasT6NmWaXKLZ21kJ9yN1RY16En9+Dz8VNDiDL5unU+iMxGqfcmWlVpLX+NbcjBfsyFSCE7FnxGrQ5dipWpfUhPtn/xQTTRAORvTEKkVRNvN1rwHj0zZhleoqrWwPKY7RXBvjLLmNkO4Edg76rEH2/L5kIzWy+pLjADOBaYClwbHD8LuE3SYcDpwCFAOvAF8HlMPXdLKv2RtHLLzKyzpHuBscBRQAYwDyjZX3ApkG9mB0g6OIihpBFAJzPrDCCpF3Al8KqkTKAHMKSMx1Wb1q2yWbF81yezlStW07pVNmvW5BTty8xsBMCtt/yNnr2OZPHi77n8iuvJyVlbrK4Lhg7m7YmTqifwEGS2aMyGVeuKtjesWUdmdpNKE0tJjVs3pUnb5nw7rXR3pEtdatgY+2l90bZtXE9ay32Ll2mcTRpQ+9zrIC2N7R+/SmRJav0eFMbZsjGzMcCYUIPZA/FOELhc0pfAJ0BboD2wWNIRkvYG9ieafI4CXjOzLWa2EXi9RD3XmlnnnctuxDk++H8u8KmZbTSzXGCrpKwSZXsCzwCY2RxgTmWVm9mHQAdJzYDBwMtBNi8m9hNDJLJ5N8IPR+3atWjbthXTPplJ98MH8Mknn3PXnTcVK3POOafR7dBD+Pfoh5MUZc1x6MAezJ7wKRZJ9b/FdrtLaWmocQu2Pncn28Y/Qp0BF8BedSt/YA2SwAkCK4m+j+/UJthXZhlJtYFMYB1VUGnLRlJvoB9wpJnlBwPuGcDzwJnA18ArZmba83v37KB44ssocXxr8H8kZn3ndqLGnZ4CzgPOBi4oq0DsJ4badVon/B3r0kuGMGzYuQDMnDmbNm1bFR1r3aYlK1etKVZ+3bo8Nm/O55VXooOhL738BhdccHbR8WP7HsM/RlxO32NPZ9u2bYkON1THnN+fIwcfC8CyL78jq9WuFnxW9t78uGZ9eQ8tV9eBPXjxxicSFqOrGWxjHmrUpGhbDZtgm/KKlYlszCOyejFECrEf12Lr15DWOJvImtITTWqqBN5BYAbRD9ftiSaVs9k1cWqn8UR7d6YTHQ//oCrjNRBfyyaT6KyEfEn7A0cE+18hOog0mGjigWjrZqCkDEkNgN/FGcf3QMdgul0W0W66PfURwRMnqRNwcBllNgIlO7fHAn8FKDEro9o8/Mg4uh3Wn26H9Wf8+Imcf250zsPh3bvy048/FetC2+mNN9+ld68eAPTtczQLFiwEoHPnA3nowVGcetoF5OZW6QNJUkx5+h3uOvHv3HXi35nzzgy6n9YTgHZdOrBlY/5ud6E137cVdTPrs+SLb0OI1iVTZPUS1Lg5ymwKabWofUB3ChfNKlamcOEX1Gq7f3SjbgPUJJvIhtKvp5rM4lwqrSfaa/NnYCKwAHjBzOZLulXSyUGxx4G9JS0CriI69FAl8bQK3gYukbQA+IZoVxpmlhfs62hmnwX7ZkgaT7Tr6gei3V4/VnYCM1su6QWiYzBLgFmVPKQiDwNPBrEtoPiY0c7zrZM0NZhU8JaZXWtmPwSPebUK506YCW+9z4ABfflmwVTyCwq46KKrio7NnPEO3Q7rD8A/rruNcU/ez+jRt7A2dz3D/nglAHfecSMNGtTn+eei0zuXL1/JqaeV2WCr8b6aNIsD+3Thpg/vY1vBNp69dleX4N8m3MldJ/4dgJNHnEu3QUeRXrcOt05/iOn/+YC3/ic6yeLQgT344vVpSYm/ulx78yhmzJrDhg0/cewp53HZsPM5feDxyQ4rfBZh27vPsteZV4PS2DF3CrZ2FelHn0JkzVIKF80msmQe1r4TGcP+BWZsn/wf2JL8rvDdkcg/6jSzCcCEEvtuilnfAvw+cWcEVbFlVLpCqYGZbZJUj2grY7iZlTVIX6ME8c4FuppZpQkyjG60VHVZq6OTHUKNMXrmHckOocbY/vjIZIdQY9T7+5NVThX37nNeXO85Vy57pkbeayCMv7MZI6kj0XGXcSmSaPoRbTbeG0+icc656uZfnlaCmZUcaKrxzOw94FfJjsM558qT6vdG+1ncQcA5537u/PtsnHPOhS7VB4k92TjnXAqIpHi68WTjnHMpwCcIOOecC52P2TjnnAudz0ZzzjkXOh+zcc45F7rUTjWebJxzLiX4mI1zzrnQFaZ428aTjXPOpQBv2TjnnAudTxBwzjkXutRONZ5snHMuJXg3mnPOudCl+gSBtGQH4JxzrnIRLK6lqiQ1kfSupIXB/43LKNNZ0nRJ8yXNkXRWZfV6snHOuRRgcS4JMAJ438w6AO8H2yXlA38wswOBAcD/SMqqqFJPNs45lwKqq2UDDALGBevjgFNKFjCzb81sYbC+CsgBmlVUqScb55xLAZE4F0nDJc2MWYbv5qlamNnqYH0N0KKiwpK6A3WA7yoq5xMEnHMuBVicrRYzGwOMqaiMpPeA7DIOXV+iLpNU7okltQSeBoaYWYUT5jzZ7KHeLTolO4Qao2NherJDqDG2Pz4y2SHUGOnDbkx2CD8riZyNZmb9yjsm6QdJLc1sdZBMcsop1wh4E7jezD6p7Jzejeaccykg3m60BBgPDAnWhwCvlSwgqQ7wCvCUmb0UT6WebJxzLgVEzOJaEmAUcJykhUC/YBtJ3SQ9FpQ5E+gJDJU0O1g6V1Spd6M551wKqK4/6TSzdcCxZeyfCVwUrD8DPLM79Xqycc65FOA34nTOORe6eGej1VSebJxzLgXs8GTjnHMubN6ycc45Fzr/igHnnHOhs8RMa04aTzbOOZcCfDaac8650KX6l6d5snHOuRTgLRvnnHOh8zEb55xzofPZaM4550Lnf2fjnHMudD5m45xzLnSFFX8RZo3nycY551KAd6M555wLXYK+GC1pPNk451wKSO1U48nGOedSQqpPEEhLdgDOOecqF8HiWqpKUhNJ70paGPzfuIKyjSStkPRAZfXWmGQjqVDSbEnzJL0uKauS8t0k3R9HvZdLWiDp2QrK9Jb0RrA+NJ4nrjr86Z+XMm7Kk4x552H267RfmWXuePo2Hp34MI+9N4Yrbr+ctLTiP9Izhp/Oe8sn0qhxo+oIOaGO+uf5DJ4ymt+/cztNO7Urs0zTg9rx+3fvYPCU0Rz1z/OL9v/6pO6c+d4oLv7+KZod3L7U4xq02pthXz/GIRefGFb4oUtr34mMi24nY/goah9e9nXU2v8wMob9i4xh/6LOwIurOcLkueH2e+h50tmcct4lyQ4lYQotEteSACOA982sA/B+sF2ekcBH8VRaY5INUGBmnc2sE7Ae+FNFhc1sppldHke9lwHHmdm5iQiyunTvcxit27dmyDEXcO/f7+OK2/9SZrmRl97GxcdfykX9hpO1dyY9f3dM0bFmLZvRrWdXfljxQ3WFnTD79DmEzPbZPHfM1Xz498c55vahZZbrefsFfPi3x3jumKvJbJ9N294HA7D+mxVMHH4fqz/9pszHHXnTuSyb9GVY4YdPos5x57P1xXvZ8tj11O54ONq7VfEijVuQfsRJbHnmdrY8fgPb3v9/SQq2+p1y4nE8cs+/kh1GQlmc/xJgEDAuWB8HnFJWIUmHAi2Ad+KptCYlm1jTgdYAkrpLmi5plqRpkn4b7I9tjdwi6QlJkyUtlnR5sP8R4NfAW5KuLK+umqhH/yN59+X3AFgw62saNKpPk+ZNSpXL35QPQK3ataidXrvYKOKlN1/MmNseT8l7KrXrfyjfvvwxADmzvmOvRvWp1zyrWJl6zbNIb1CXnFnfAfDtyx/T/vhuAGxYtIofF68uu+7jD2Xj8lzyvl0Z3gWELK3lr7ENOdiPuRApZMeCz6jVoUuxMrUP6cn2Lz6ArdHfEfI3JiHS5OjW+SAyGzVMdhgJZWZxLZKGS5oZswzfzVO1MLOdL541RBNKMZLSgNHANfFWWuMmCEiqBRwLPB7s+ho4xsx2SOoH3A6cXsZD9wf6AA2BbyQ9bGaXSBoA9DGztZIaxVlX0jXNbkruqtyi7dzVa2mavTfrc9aXKjvqmdv47SG/ZcbkmXz05hQgmqzWrlnL4gWLqy3mRKqf3ZhNq9YVbW9avZ762Y3Jz9lQrMzm1etLlalI7Xp70fnS3/HGOaPofPFJCY+7uqhhY+ynXdduG9eT1nLf4mUaZ5MG1D73OkhLY/vHrxJZMq+aI3WJEu94jJmNAcZUVEbSe0B2GYeuL1GXSSrrxJcBE8xshaS44qpJyaaupNlEWzQLgHeD/ZnAOEkdiH5uTy/n8W+a2VZgq6Qcotl4RYky8daVUkacdz3pe6Vz3f0j6HxUZ+bPmM/gP5/NiHP/kezQapxuV53G3MfeZkf+1mSHEjqlpUHjFmx97k7UsDF7nfMPtjxxA2wtSHZobg8ksofCzPqVd0zSD5JamtlqSS2BnDKKHQkcI+kyoAFQR9ImMyt3fKcmJZsCM+ssqR4wkeiYzf1EB6AmmdmpktoBk8t5fOy7RyFlX1u8dZUpaI4OB9g/qyOtG7TZnYdX6uQhAzlx8AkAfPvltzRr1azoWLOWTVm7Zl15D2X71u1Me2c6PfofSV7uerLbZvPoxIeDxzbjkbce5E8DLycvNy+hMSfSgUP6ccDgPgDkfrmYBq32LjrWoGUTNq8pHvvmNXnUb9mkwjIlteiyH/ue2J0jrjubOo3qYWbs2LKd+ePerfBxNY1tzEONdl27GjbBNhW/9sjGPCKrF0OkEPtxLbZ+DWmNs4msWVLd4boEKKy++z6PB4YAo4L/XytZIHYMXNJQoFtFiQZqVrIBwMzygzGXVyU9RLQ1srNzfWgVq69SXbHN035tj0/4QMj4ca8zftzrABzetzuDhp7MpNcmc0CX/dm8Mb9UF1pGvQzqNajH+pz1pNVK4/BjuzP303ks+Xopv+9yVlG5Z6aN47KT/sJPeT8lOuSEmj/uPeaPi45T7dO3M52GHsei16bTvMu+bNuYX6wLDSA/ZwPbNxXQvMu+5Mz6jt+cfjTznqx4rPK100cWrXe78jS2529JuUQDEFm9BDVujjKbYhvzqH1Ad7a+/mixMoULv6D2AUdQOPdjqNsANckmsqGsD6kuFVTjHQRGAS9IGgZ8D5wJ0RnAwCVmdtGeVFrjkg2Amc2SNAcYDNxFtOvrBuDNKladyLpC9ekHn9G972E89fGTbC3Yyt1Xjy469sjbD3HJgMvIqJfByCduIb1OOkpL48tpX/L6M28kMerEWfbBbPbpewiDPx7NjoJtTL56Vxf0GW/fxksDol3LU64fS597hlMrow7LJ31ZNMOs3YBuHH3rH6jbpCEnjL2GdV99z5vn3ZWUawmFRdj27rPsdebVoDR2zJ2CrV1F+tGnEFmzlMJFs4ksmYe170TGsH+BGdsn/we2bE525NXi2ptHMWPWHDZs+IljTzmPy4adz+kDj092WFVSXfdGM7N1RMfNS+6fCZRKNGY2FhhbWb1KxZlKNUEYLZtUdQbNKi/0C/GHP/8shgETIn3YjckOocZIb/rr+EbRK3BA8+5xvecsyPmsyucKQ41s2TjnnCvO7/rsnHMudH7XZ+ecc6HzL09zzjkXOu9Gc845Fzrzlo1zzrmwpfr32Xiycc65FJDqf6biycY551KAt2ycc86FrjDiYzbOOedC5rPRnHPOhc7HbJxzzoXOx2ycc86Fzls2zjnnQucTBJxzzoXOu9Gcc86FLtW70dKSHYBzzrnKRcziWqpKUhNJ70paGPzfuJxy+0h6R9ICSV9JaldRvZ5snHMuBVic/xJgBPC+mXUA3g+2y/IUcLeZHQB0B3IqqtSTjXPOpYDqatkAg4Bxwfo44JSSBSR1BGqb2bsAZrbJzPIrqtSTjXPOpYCIReJaEqCFma0O1tcALcoo8xtgg6T/Spol6W5JtSqq1CcIOOdcCoh3goCk4cDwmF1jzGxMiTLvAdllPPz6Euc0SWWduDZwDNAFWAb8BxgKPF5eXJ5snHMuBcSbbILEMqaSMv3KOybpB0ktzWy1pJaUPRazAphtZouDx7wKHEEFyca70ZxzLgVYnEsCjAeGBOtDgNfKKDMDyJLULNjuC3xVUaVK9bnbv2SShpdsHv9S+XOxiz8Xu/hzsfsk7Q28AOwDfA+caWbrJXUDLjGzi4JyxwGjAQGfA8PNbFu59XqySV2SZppZt2THURP4c7GLPxe7+HNRc3g3mnPOudB5snHOORc6Tzapzfuid/HnYhd/Lnbx56KG8DEb55xzofOWjXPOudB5snHOORc6TzYpRtIFkmYHyzZJc4P1UcmOLZEknSypvLvNIumgmOdhvaQlwfp71Rln2CSNlXRGiX2bElR3K0kvlXNscvB3FUiaICkrWC5LxLnjjK8w+JnOk/S6pKxKyneTdH8c9V4e3Bb/2QrK9Jb0RrA+VNIDu30Brhi/XU2KMbMngScBJC0F+pjZ2qQGFQIzG0/0L5nLOz4X6AzRN2TgDTMr843Tlc3MVgFnxFHuRIDg+0ouAx4KN7IiBWbWOTj3OOBPwG3lFTazmcDMOOq9DOhnZisSEaSLj7dsQiapnaSvg0+o30p6VlI/SVODLyfqHizTg7unTpP02+CxV0p6Ilg/KPiEV6+c81wraYakOZL+Ge+5g3K3SHo6iGGhpD/WgOek6NNkUO7+4LlZXPKTfom6+wfX8YWkFyU1CPYvlXRH8El5pqSukiZK+k7SJUGZ3pI+kvSmpG8kPSJpj18jkl6V9Lmk+ZKGS7pE0t0xx2Ov8cbgnB9Lek7SNXHUX/TpO9h+QNLQ3bjedpLmBet1JT0ffOJ/BagbU+9SSU2BUcC+QZ13S3pK0ikx5Z6VNGhPn69KTAdaB+cp7/US2xq5RdITirbQFku6PNj/CPBr4K3g9VVmXS4EZuZLiAvQDtgBHEQ0uX8OPEH0Fg+DgFeBRkS/GwKgH/BysJ4GfAScSvQT21El6l4KNAX6E53iqeAxbwA94zl3UM8twJdE32CaAsuBVkl+ToYCDwTlxwIvBmU7AotK1DeW6Cf0psHzVT/Y/3fgppjn6tJg/V5gDtAQaAb8EOzvDWwh+mZUC3gXOKMK19kk+L8uMI/ordoXxRx/CzgaOAyYDWQEMS0Erom5tiXB8Z3Lpph434ip7wFg6G5cbztgXrB+FfBEsH5w8PPpVuL3rKh8sL9XzO9QZhBn7QT+nuy8zlrBz39AsF3e66Xo+SD6Oz0N2CuIfR2QHns9u1HXUILfRV/2fPFutOqxxKLdPkiaT/Rb8EzSXKIv4ExgnKQORO+llw5gZpHgk+oc4FEzm1pO/f2DZVaw3QDoQPTW35Wde6fXzKwAKJA0ieg3772agGsvT7xx7fSqmUWArySV9f0aEL3rbEdgqiSAOkQ/Ee+0s1tuLtDAzDYCGyVt1a7xgM9s151snyOaDPa0e+5ySacG622B9sBiSUcQTSj7A1OBK4g+/1uALZJeL1HPtRbTRaj4x2ziud6degL3A5jZHElzKqvczD6U9JCiN2M8negb9Y44Y4tHXUmzibZoFhBN/lDO66UMb5rZVmCrpByiyb5k11m8dbkq8mRTPbbGrEditiNEfwYjgUlmdqqi/eKTY8p3ADYBrSqoX8AdZvZosZ3Ruio7904l/+Aq7D/AijeussqrnDoFvGtmgys5Z+z5Sp4zIc+DpN5EPykfaWb5kiYTbbk8D5wJfA28EiTYPTkFRFsfsd18GSWOx3O9VfUUcB5wNnBBgurcqcDMOivadTyR6JjN/VT8eokVe82FlH3N8dblqsjHbGqGTGBlsD50505JmURfXD2BvSsYq5gIXBgzPtFaUvPdjGGQpAxF7/jam+gtxFPNJ8BRkvYDkFRf0m92s47uktoHYzVnAR/vYSyZQF6QaPYn2uoCeIVoV+FgookHoq2bgcHz3wD4XZzn+B7oKGmvoKVy7B7GCtHux3MAJHUi2pVW0kaiXXGxxgJ/BTCzCm8xv6cs+nXDlwNXS6pNOa+XPZTIulwFPNnUDHcBd0iaRfFPX/cCD5rZt8AwYFRZScTM3gH+HzA96IZ6idJvCpWZA0wi+oY90qIzlVKKmeUSfcN4LugGmk60q2p3zCA69rGA6BjEK3sYzttAbUkLiA6sfxLEmBfU/Ssz+yzYN4Nol9ccouM4c4EfKzuBmS0neiv4ecH/syp+RIUeBhoE8d5KdByt5PnWEe2inLdzooOZ/RBcz5NVOHelzGwW0ednMOW/XvZEIutyFfDb1Tgk3UJ0MPbfyY4lmYKur2vMLN6WRSLP3cDMNgVdRh8R/W6QL6o7jt0VxDsX6GpmlSZI98vlLRvnaoYxwWD4F0QH2lMh0fQj2qr5X080rjLesnHOORc6b9k455wLnScb55xzofNk45xzLnSebJxzzoXOk41zzrnQ/X8D3S2RxsQ1agAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "abQ9WDK-2cP4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}