MachineLearningCLassificationWIthBest6Features.ipynb

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      "name": "MachineLearningCLassificationWIthBest6Features.ipynb",
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      "source": [
        "# MACHINE LEARNING CLASSIFICATION AND COMPARISONS\n",
        "\n",
        "This notebook we have used 6 different ML classifiers and compared them to find the best one that can accurately classify our malicious dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SUPmMKPDWlmp"
      },
      "source": [
        "## Installing some libraries."
      ]
    },
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        "id": "qr2UdY33jeUQ",
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          "height": 1000
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      },
      "source": [
        "pip install smote_variants"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "text": [
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            "  Downloading https://files.pythonhosted.org/packages/bb/3a/ae99a15e65636559d936dd2159d75af1619491e8cb770859fbc8aa62cef6/statistics-1.0.3.5.tar.gz\n",
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            "Building wheels for collected packages: statistics, minisom\n",
            "  Building wheel for statistics (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Building wheel for minisom (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for minisom: filename=MiniSom-2.2.5-cp36-none-any.whl size=8474 sha256=eac16fcc81a82e272e0bcf7df10c51edd35f4f52275787b35b7a7c04cac18e7d\n",
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            "Successfully built statistics minisom\n",
            "Installing collected packages: statistics, minisom, smote-variants\n",
            "Successfully installed minisom-2.2.5 smote-variants-0.3.13 statistics-1.0.3.5\n"
          ],
          "name": "stdout"
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7czY2ZsRLL2q",
        "outputId": "e8fd7c9b-5957-4c46-a018-cb319a90ae83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "pip install imbalanced_databases"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting imbalanced_databases\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/44/5bc6bf4d05a19e711fcdbceb0f4fcc48b46b85d3f140285bacafee2a40d8/imbalanced_databases-0.1.1-py3-none-any.whl (4.3MB)\n",
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            "Installing collected packages: imbalanced-databases\n",
            "Successfully installed imbalanced-databases-0.1.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "b4FUTDEnT0qp",
        "outputId": "50ca8a94-05ed-4f73-c79d-a00abded398a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "pip install imbalanced-learn"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.6/dist-packages (0.4.3)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "msNPOM1mWlnH"
      },
      "source": [
        "## Importing libraries for our needs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JnKWN9B2LHP4",
        "outputId": "05d7218a-2bfc-4ffe-db5c-b8612c98afaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "import smote_variants as sv\n",
        "import pandas  as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import imbalanced_databases as imbd\n",
        "from sklearn import metrics\n",
        "from sklearn.datasets import load_wine\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "%matplotlib inline\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yMcZTmIOWlnP"
      },
      "source": [
        "## Reading the dataset to a dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "K6gyDfH5PcxL",
        "outputId": "464ebab9-404b-4091-c5cc-a057fbdb0677",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5i6xdA4nJ_Me",
        "colab": {}
      },
      "source": [
        "df_train = pd.read_csv('/content/drive/My Drive/Major Project Works/dataset/ml_dataset.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rL13VhV0kb78",
        "colab": {}
      },
      "source": [
        "df = df_train.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lEIV2KpEXKWA",
        "outputId": "df488519-2521-4c2c-fe18-b93e085e52ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'pkts', 'bytes', 'dur', 'average_dur', 'stddev_dur',\n",
              "       'total_dur', 'min_dur', 'max_dur', 'spkts', 'dpkts', 'sbytes', 'dbytes',\n",
              "       'rate', 'srate', 'drate', 'TnBPSrcIP', 'TnBPDstIP', 'TnP_PSrcIP',\n",
              "       'TnP_PDstIP', 'TnP_PerProto', 'TnP_Per_Dport', 'AR_P_Proto_P_SrcIP',\n",
              "       'AR_P_Proto_P_DstIP', 'N_IN_Conn_P_DstIP', 'N_IN_Conn_P_SrcIP',\n",
              "       'AR_P_Proto_P_Sport', 'AR_P_Proto_P_Dport',\n",
              "       'Pkts_P_State_P_Protocol_P_DestIP', 'Pkts_P_State_P_Protocol_P_SrcIP',\n",
              "       'attack'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "b5Xjb7hKWlnu",
        "colab": {}
      },
      "source": [
        "df.drop([ 'Unnamed: 0', 'pkts', 'bytes', 'dur', \n",
        "       'total_dur',  'spkts', 'dpkts', 'sbytes', 'dbytes',\n",
        "       'rate', 'TnBPSrcIP', 'TnBPDstIP', 'TnP_PSrcIP',\n",
        "       'TnP_PDstIP', 'TnP_PerProto', 'TnP_Per_Dport', 'AR_P_Proto_P_SrcIP',\n",
        "       'AR_P_Proto_P_DstIP', \n",
        "       'AR_P_Proto_P_Sport', 'AR_P_Proto_P_Dport',\n",
        "       'Pkts_P_State_P_Protocol_P_DestIP', 'Pkts_P_State_P_Protocol_P_SrcIP','N_IN_Conn_P_SrcIP','N_IN_Conn_P_DstIP'],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7McXAJU-B5OJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv(r'dataset_ddos_six.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ASmcr4G1Wlnz"
      },
      "source": [
        "## Getting genral idea about the weight of available classification packets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "j8XjHHFUbd4t"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ILzX8RoAJ6vd",
        "outputId": "75487662-7b47-41bf-9c50-3a3106242b89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
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              "        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>average_dur</th>\n",
              "      <th>stddev_dur</th>\n",
              "      <th>min_dur</th>\n",
              "      <th>max_dur</th>\n",
              "      <th>srate</th>\n",
              "      <th>drate</th>\n",
              "      <th>attack</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.679473</td>\n",
              "      <td>0.544126</td>\n",
              "      <td>0.135347</td>\n",
              "      <td>1.223599</td>\n",
              "      <td>0.780467</td>\n",
              "      <td>0.468280</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.679572</td>\n",
              "      <td>0.544197</td>\n",
              "      <td>0.135375</td>\n",
              "      <td>1.223769</td>\n",
              "      <td>0.780536</td>\n",
              "      <td>0.468322</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.110847</td>\n",
              "      <td>1.110847</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.221694</td>\n",
              "      <td>0.624899</td>\n",
              "      <td>0.900214</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.113328</td>\n",
              "      <td>1.113328</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.226655</td>\n",
              "      <td>0.624931</td>\n",
              "      <td>0.898208</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.113098</td>\n",
              "      <td>1.113098</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.226195</td>\n",
              "      <td>0.624954</td>\n",
              "      <td>0.898394</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.113392</td>\n",
              "      <td>1.113392</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.226783</td>\n",
              "      <td>0.624985</td>\n",
              "      <td>0.898157</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.612942</td>\n",
              "      <td>1.612942</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.225885</td>\n",
              "      <td>0.625035</td>\n",
              "      <td>0.619985</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.613153</td>\n",
              "      <td>1.613153</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.226305</td>\n",
              "      <td>0.625056</td>\n",
              "      <td>0.619904</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.611953</td>\n",
              "      <td>1.611953</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.223905</td>\n",
              "      <td>0.625353</td>\n",
              "      <td>0.620366</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.612818</td>\n",
              "      <td>1.612818</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.225637</td>\n",
              "      <td>0.625374</td>\n",
              "      <td>0.620033</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   average_dur  stddev_dur   min_dur   max_dur     srate     drate  attack\n",
              "0     0.679473    0.544126  0.135347  1.223599  0.780467  0.468280       1\n",
              "1     0.679572    0.544197  0.135375  1.223769  0.780536  0.468322       1\n",
              "2     1.110847    1.110847  0.000000  2.221694  0.624899  0.900214       1\n",
              "3     1.113328    1.113328  0.000000  2.226655  0.624931  0.898208       1\n",
              "4     1.113098    1.113098  0.000000  2.226195  0.624954  0.898394       1\n",
              "5     1.113392    1.113392  0.000000  2.226783  0.624985  0.898157       1\n",
              "6     1.612942    1.612942  0.000000  3.225885  0.625035  0.619985       1\n",
              "7     1.613153    1.613153  0.000000  3.226305  0.625056  0.619904       1\n",
              "8     1.611953    1.611953  0.000000  3.223905  0.625353  0.620366       1\n",
              "9     1.612818    1.612818  0.000000  3.225637  0.625374  0.620033       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwlYBapFVWDU",
        "colab_type": "code",
        "outputId": "d71ea476-86d7-48a5-a1b9-86c561156307",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "df[df.attack == 0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>average_dur</th>\n",
              "      <th>stddev_dur</th>\n",
              "      <th>min_dur</th>\n",
              "      <th>max_dur</th>\n",
              "      <th>srate</th>\n",
              "      <th>drate</th>\n",
              "      <th>attack</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1926624</th>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000836</td>\n",
              "      <td>0.000836</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1926625</th>\n",
              "      <td>0.000028</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.002751</td>\n",
              "      <td>0.002751</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1926626</th>\n",
              "      <td>0.048565</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.048565</td>\n",
              "      <td>0.048565</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1926627</th>\n",
              "      <td>0.000238</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000199</td>\n",
              "      <td>0.000261</td>\n",
              "      <td>0.002751</td>\n",
              "      <td>0.002751</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1926628</th>\n",
              "      <td>0.098505</td>\n",
              "      <td>0.080150</td>\n",
              "      <td>0.018356</td>\n",
              "      <td>0.178655</td>\n",
              "      <td>0.001755</td>\n",
              "      <td>0.001755</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1927096</th>\n",
              "      <td>0.293476</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.293476</td>\n",
              "      <td>0.293476</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1927097</th>\n",
              "      <td>0.292003</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.292003</td>\n",
              "      <td>0.292003</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1927098</th>\n",
              "      <td>0.277896</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.277896</td>\n",
              "      <td>0.277896</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1927099</th>\n",
              "      <td>0.280742</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.280742</td>\n",
              "      <td>0.280742</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1927100</th>\n",
              "      <td>0.280513</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.280513</td>\n",
              "      <td>0.280513</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>477 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         average_dur  stddev_dur   min_dur  ...     srate     drate  attack\n",
              "1926624     0.000006    0.000002  0.000004  ...  0.000836  0.000836       0\n",
              "1926625     0.000028    0.000008  0.000022  ...  0.002751  0.002751       0\n",
              "1926626     0.048565    0.000000  0.048565  ...  0.000000  0.000000       0\n",
              "1926627     0.000238    0.000022  0.000199  ...  0.002751  0.002751       0\n",
              "1926628     0.098505    0.080150  0.018356  ...  0.001755  0.001755       0\n",
              "...              ...         ...       ...  ...       ...       ...     ...\n",
              "1927096     0.293476    0.000000  0.293476  ...  0.000000  0.000000       0\n",
              "1927097     0.292003    0.000000  0.292003  ...  0.000000  0.000000       0\n",
              "1927098     0.277896    0.000000  0.277896  ...  0.000000  0.000000       0\n",
              "1927099     0.280742    0.000000  0.280742  ...  0.000000  0.000000       0\n",
              "1927100     0.280513    0.000000  0.280513  ...  0.000000  0.000000       0\n",
              "\n",
              "[477 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gq1OUFUMmbxT",
        "outputId": "1e7244e4-b2b9-4d57-f3f6-550ee1ce9cfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "pd.value_counts(df['attack']).plot.bar()\n",
        "plt.title('Attack histogram')\n",
        "plt.xlabel('attack')\n",
        "plt.ylabel('Value')\n",
        "df['attack'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1926624\n",
              "0        477\n",
              "Name: attack, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "h6Pf_-VcWloC"
      },
      "source": [
        " #### Here we can find that there is a lot of imbalance in the dataset, so we can tell the data is highly-imballanced. Thus we need to synthtically oversample the minority class to get a balanced dataset for training and testing."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RCg4KirBWloC"
      },
      "source": [
        "## Defining some methods which are later used:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qD0Yxvv2qQGR",
        "colab": {}
      },
      "source": [
        "# Used to plot the roc curve.\n",
        "def plot_roc_curve(fpr, tpr):\n",
        "    plt.plot(fpr, tpr, color='orange', label='ROC')\n",
        "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    \n",
        "# Used for classification of dataset.\n",
        "def classif_results(): \n",
        "  conf_mat = confusion_matrix(y_true=y_test, y_pred=y_pred)\n",
        "  print('Confusion matrix:\\n', conf_mat)\n",
        "\n",
        "  labels = ['Class 0', 'Class 1']\n",
        "  fig = plt.figure()\n",
        "  ax = fig.add_subplot(111)\n",
        "  cax = ax.matshow(conf_mat, cmap=plt.cm.Blues)\n",
        "  fig.colorbar(cax)\n",
        "  ax.set_xticklabels([''] + labels)\n",
        "  ax.set_yticklabels([''] + labels)\n",
        "  plt.xlabel('Predicted')\n",
        "  plt.ylabel('Expected')\n",
        "  plt.show()\n",
        "\n",
        "  print(\"Accuracy\", metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "  from sklearn.metrics import classification_report\n",
        "  print(classification_report(y_test, y_pred))\n",
        "\n",
        "\n",
        "  auc = roc_auc_score(y_test, y_pred)\n",
        "  print(\"AUC Score: \")\n",
        "  print(auc)\n",
        "\n",
        "  fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "  plot_roc_curve(fpr, tpr)\n",
        "\n",
        "# Used for splitting and normalizing dataset.\n",
        "def test_scale():\n",
        "  from sklearn.model_selection import train_test_split\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 0)\n",
        "\n",
        "  from sklearn.preprocessing import StandardScaler\n",
        "  sc_X = StandardScaler()\n",
        "  X_train = sc_X.fit_transform(X_train)\n",
        "  X_test = sc_X.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8_F_K1RnWloI"
      },
      "source": [
        "### Here, we are applying SMOTE method, and applying it to dataset. We use the daataset by applying attack packets to X and normal to Y and oversample Y sythetically to length of X"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4I_gzBXwBJTp",
        "outputId": "01b3cf73-5be0-49c1-fe3c-786ea1148405",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "X = df.iloc[:, df.columns != 'attack']\n",
        "y = df.iloc[:, df.columns == 'attack']\n",
        "X, y = SMOTE().fit_sample(X, y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FCCKBLFBeK98",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 0)\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "WaBPuri0Sk4O"
      },
      "source": [
        "# Logistic Regression:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZCxyQRcQNnbB",
        "colab": {}
      },
      "source": [
        "\n",
        "from sklearn.linear_model import LogisticRegression \n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(X_train,y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zq3cY5zdNAij",
        "outputId": "d4fbb098-274a-43e4-a525-8baa62c7ea93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "source": [
        "classif_results()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix:\n",
            " [[753354  18220]\n",
            " [147195 622531]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.8926782586128593\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.98      0.90    771574\n",
            "           1       0.97      0.81      0.88    769726\n",
            "\n",
            "    accuracy                           0.89   1541300\n",
            "   macro avg       0.90      0.89      0.89   1541300\n",
            "weighted avg       0.90      0.89      0.89   1541300\n",
            "\n",
            "AUC Score: \n",
            "0.8925777736464021\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1_oX5TSiZlYN",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "XgAdLKxwYz4a"
      },
      "source": [
        "# Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "u8Eq9ptJY17w",
        "colab": {}
      },
      "source": [
        "test_scale()\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier()\n",
        "classifier.fit(X_train,y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mc1cZCHAY1-0",
        "outputId": "ee8045ca-21f7-4b70-c064-466efe83f436",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "source": [
        "classif_results()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix:\n",
            " [[771563     11]\n",
            " [ 34080 735646]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.977881658340362\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98    771574\n",
            "           1       1.00      0.96      0.98    769726\n",
            "\n",
            "    accuracy                           0.98   1541300\n",
            "   macro avg       0.98      0.98      0.98   1541300\n",
            "weighted avg       0.98      0.98      0.98   1541300\n",
            "\n",
            "AUC Score: \n",
            "0.9778551239961651\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4U1rSQSJY2Bq",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "F7eoKyO8gvU0"
      },
      "source": [
        "# Random Forest:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "v0xYdJZGgugZ",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "test_scale()\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "SxnUXQK6gulj",
        "outputId": "c7967cc6-be1a-43b0-a059-3985e1ae34dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "source": [
        "classif_results()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix:\n",
            " [[771567      7]\n",
            " [ 33836 735890]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.9780425614740803\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98    771574\n",
            "           1       1.00      0.96      0.98    769726\n",
            "\n",
            "    accuracy                           0.98   1541300\n",
            "   macro avg       0.98      0.98      0.98   1541300\n",
            "weighted avg       0.98      0.98      0.98   1541300\n",
            "\n",
            "AUC Score: \n",
            "0.978016214059112\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Q7-wWafQguo8",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "k3RQYB8WSSSY"
      },
      "source": [
        "# KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uJNUH0aVSd4P",
        "colab": {}
      },
      "source": [
        "test_scale()\n",
        "\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier()\n",
        "classifier.fit(X_train,y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UPjbK4lIV__f",
        "outputId": "1d4c327e-8261-4214-b783-fc0876cf08ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "source": [
        "classif_results()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix:\n",
            " [[771526     48]\n",
            " [ 33964 735762]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.9779329137740869\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98    771574\n",
            "           1       1.00      0.96      0.98    769726\n",
            "\n",
            "    accuracy                           0.98   1541300\n",
            "   macro avg       0.98      0.98      0.98   1541300\n",
            "weighted avg       0.98      0.98      0.98   1541300\n",
            "\n",
            "AUC Score: \n",
            "0.9779064985234964\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C1+brWyb1ZM5sk+TL+iCZv/g5HFSzUT5CD755gaoyZcpqHnjgW557rhU9ezbg+utr5Nr6vbxq6JYs5itwn1frNzlE1edA6PGZbVbLzLGDb9TfB7+MDqD5i0JUmcAOpNltnvAtHxWdM9tkQta2bQfp3XsOs2Zt4rLLLqJFi3K5HkNIdBZHHFXnzDezg2xOtutmeqA+kTPbJFFZt/nmKwIFSnnU3ODzOMo+9iZvmDp1NffcM4eUlFRGjGhL376NiI7O/ZqZfSPSqDpnnHmhzTf1eA5tlGR9kIwu7B58vWzzjbGDrzF+lCwZQ9OmFzF2bHuqVCkRtDgi59uZuBAW3g/JSRkfqHNKVgfQ6BgoUOLsLzELtHlC8oFIzm2XMeacJCen8sorCzlxIpWBAy+jU6cqdOwYiwT5exo5iWDPD5D4M5S72m3/zeHmhlMH4vx28DXGnGHp0t0kJMxm0aI/ufnmWqgqIhL0JACRlAjSNJ8CBYoHOwpjTIQ4fjyZoUN/5rnnfqVUqRg++OAabrihZp5IAGkiLxEYY0wuWr9+H88//yu33lqbl19uS+nShYId0hksERhjTA5LSjrBJ59soHv3OOrVK8uaNf+iatXgdQZnxX5BYowxOWjOnM3Urz+R22+fxerViQB5OgmAJQJjjMkR+/YdIyHhSzp0mEGBAtF8/3036tQpHeywAmJNQ8YYc45SUlJp0eI91q3bx2OPNWXQoGbExITO4TV0IjXGmDxm794jlCpViOjoKIYNa0mlSsW45JILgh1WtlnTkDHGZJOq8s47K6lZczxvv70MgOuuqxGSSQCsRmCMMdmyZcsB7rlnDrNnb6Z583K0alUh2CGdM0sExhgToMmTV3HvvXNQhddeu4I+fRoRFZV3fhh2tiwRGGNMgMqWLUSLFuUZM6Y9lSuHzwgFlgiMMSYDJ0+mMHz4Qk6eTOXJJ5vRsWMVOnQI/iBxOc06i40xxo/ffvuTpk2n8NhjP7BqVSLOvbQIuyQAlgiMMeY0x44l8/jjP9C48WR27kziww+vZerULmGZANJY05AxxvjYsGEfL720gDvuqMvw4W0oWTIm2CF5zhKBMSbiJSWd4KOP1nP77XWpV68sa9f+K6h3DMtt1jRkjIlos2dvom7dCdx55xenBomLpCQAlgiMMREqMfEod945i06dPqRw4fz88MMtITNIXE6zpiFjTMRxBombyoYN+xg48DKeeOKykBokLqdF7pYbYyLOnj1HKF3aGSTu+edbUblyMRo2PD/YYQWdNQ0ZY8KeqjJhwnJq1hzHW285g8R17VrdkoDLagTGmLC2efMBevX6ijlzttCyZQXatq0Y7JDyHEsExpiw9e67K7n33q8RgdGjr+Seey4Oi0HicpolAmNM2LrggiK0alWBN99sT6VKxYIdTp5licAYEzZOnkzhhRcWkJKSyqBBzenQIZYOHWKDHVaeZ53FxpiwsHjxnzRuPJknnvgfa9fuOzVInMmaJQJjTEg7evQkAwbMo0mTyfz55xE++qgrU6ZcHdaDxOU0TxOBiHQSkbUiskFEBviZX0lEvhOR30RkmYhc5WU8xpjws3HjAV5+eSE9etRj1aq7uO66GsEOKeR4lghEJBoYBXQG4oBbRCQuXbEngPdVtRHQDRjtVTzGmPBx8OBxJk5cAUDdumVYvz6Bt9/uGBEjhXrByxpBE2CDqm5U1RPANKBrujIKpHXlFwd2ehiPMSYMzJq1kXr1JpKQMPvUIHHhdNvIYPAyEZQHtvlMb3ef8zUYuE1EtgOzgPv9LUhEeonIQhFZuGfPHi9iNcbkcXv3HuH222dx9dX/pWjRAvz4Y+QOEpfTgt1ZfAswUVUrAFcB74rIGTGp6lhVjVfV+LJly+Z6kMaY4EobJG7atDUMGtSMxYtv57LLygU7rLDh5e8IdgC+v+Wu4D7nKwHoBKCqP4lIDFAG2O1hXMaYEPHnn4cpW7Yw0dFRvPRSGypXLkaDBnYymNO8rBEsAGqISBURKYDTGTwzXZmtQDsAEakDxADW9mNMhFNVxo1bTq1a4xk7dikA11xTzZKARzyrEahqsoj0BWYD0cB4VV0pIkOAhao6E3gIeEtE/o3TcdxD7VcgxkS0jRv307PnV3z77VZat67AlVdWDnZIYc/TISZUdRZOJ7Dvc4N8Hq8CWngZgzEmdEyatII+fb4mOjqKN99sT8+eDWyQuFwQOWMNWUXDmDyvXLnzuOKKSrzxRnsqVCga7HAiRuQkAmNMnnPiRArPPfcLqanK4MEtaN8+lvbtY4MdVsQJ9uWjuc/GHzEmT1iw4A8uvfRdnnpqPhs3HrBB4oIo8hKBMSaojhw5ycMPz+Wyy95j375jzJx5Pe+8c5UNEhdE1jRkjMlVmzYd4LXXfqNnzwY8/3wrihcvGOyQIp4lAmOM5w4cOM5//7uOu+6qT926ZdiwIYGKFe2OYXmFNQ0ZYzz1+ee/U7fuBO6++yvWrHEGibMkkLdYIjDGeGLPniN07/45Xbp8RMmSMfz0063Urm2DxOVF1jRkjMlxKSmpXH75VDZtOsDTTzdnwICmFCgQHeywTAYsERhjcsyuXYc5/3xnkLjhw9sQG1uMevVsfKC8LuCmIREp7GUgxpjQlZqqjBmzlJo1xzFmjDNIXJcu1SwJhIgsE4GINBeRVcAad/piEbFbShpjANiwYR/t2r1P795zaNz4Qjp2jA12SCabAmkaegXoiDuEtKouFZFWnkZljAkJEyYsp0+fbyhQIIq33upAQkJ9+2FYCAqoj0BVt6V7c1O8CccYE0oqVSpGx46xjBrVjvLlbZC4UBVIItgmIs0BFZH8wAPAam/DMsbkRcePJ/Pss84gcUOGXE67dpVp187uFxDqAuks7g3ch3Pj+R1AQ6CPl0EZY/KeX35xBol7+umf2Lr1kA0SF0YCqRHUUtXuvk+ISAvgR29CMsbkJYcPn+DJJ39kxIhFlC9flM8+u56rr64W7LBMDgqkRvBagM8ZY8LQli0HGT16Cb17X8zKlT0sCYShDGsEItIMaA6UFZH+PrOK4dyD2BgTpvbvP8aMGeu4++4GxMWVYcOGu+2OYWEss6ahAsB5bhnfT8BB4EYvgzLGBM8nn2zg3nvnsHv3ES6/vDy1a5e2JBDmMkwEqvo98L2ITFTVLbkYkzEmCHbvPky/ft8yffpaGjQoy8yZ19sgcREikM7iIyLyIlAXiEl7UlWv8CwqY0yuSklJpUWLqWzdeoihQy/n0Ucbkz+/tQBHikASwRRgOtAF51LSO4E9XgZljMkdO3cmceGFRYiOjuLVV68gNrYYcXFlgh2WyWWBXDVUWlXHASdV9XtV/RdgtQFjQlhqqvLGG0uoXXs8b765BICrrqpqSSBCBVIjOOn+/0NErgZ2AqW8C8kY46V16/6iZ8+vmDdvO1deWZnOnasEOyQTZIEkgqEiUhx4COf3A8WABz2NyhjjiXHjltO37zfExEQzfnxHevSoZ4PEmawTgap+5j48ALSFU78sNsaEmNjYYnTuXIVRo9px0UXnBTsck0dk9oOyaOBmnDGGvlTVFSLSBXgcKAQ0yp0QjTFn6/jxZJ555mcAhg61QeKMf5nVCMYBFYFfgZEishOIBwao6se5EZwx5uzNn7+DhITZrFnzF//6Vz1U1ZqBjF+ZJYJ4oIGqpopIDLALqKaqibkTmjHmbCQlnWDgwP/x2muLqVixKF9+eQMdO1qHsMlYZpePnlDVVABVPQZszG4SEJFOIrJWRDaIyIAMytwsIqtEZKWIvJed5RtjzrR160HGjFnKffc1YsWKuywJmCxlViOoLSLL3McCVHOnBVBVbZDZgt0+hlFAe2A7sEBEZqrqKp8yNYDHgBaquk9Ezj+HbTEmYu3bd4wPPlhLr14XExdXho0be1KunHUGm8BklgjqnOOymwAbVHUjgIhMA7oCq3zK9ARGqeo+AFXdfY7rNCbifPTRevr0+Zo9e47QunVFatUqZUnAZEtmg86d60Bz5YFtPtPbgabpytQEEJEfcYa2HqyqX6ZfkIj0AnoBVKpU6RzDMiY87Np1mPvv/4YZM9bRsOH5fP75P6hVy37rabIvoJvXe7z+GkAboAIwT0Tqq+p+30KqOhYYCxAfH2/3xzMRLyUllZYtp7Jt2yGGDWvJww/H2yBx5qx5mQh24Fx+mqaC+5yv7cAvqnoS2CQi63ASwwIP4zImZG3ffohy5c4jOjqKkSOvoEqV4jZUtDlngQw6h4gUEpFa2Vz2AqCGiFQRkQJAN2BmujIf49QGEJEyOE1FG7O5HmPCXmqq8tpri6ldezxvvOEMEte5c1VLAiZHZJkIROQaYAnwpTvdUETSH9DPoKrJQF9gNrAaeF9VV4rIEBG51i02G0gUkVXAd8Aj9jsFY063Zk0irVpNo1+/b7n88vJ06VI12CGZMBNI09BgnCuA5gKo6hIRCejCZFWdBcxK99wgn8cK9Hf/PGZdCyb0vP32Mvr2/YbChfMzaVJnbr89zn4dbHJcQMNQq+qBdB8+O6oakwuqVSvBNddU4/XX23HBBUWCHY4JU4EkgpUicisQ7f4ArB8w39uwvGRnUybvOnYsmSFDfgJg2LCWtG1bibZt7ZJp461AOovvx7lf8XHgPZzhqO1+BMbksB9/3EHDhu/w7LO/sGfPEZyWU2O8F0iNoLaqDgQGeh2MMZHo0KETPP74D4wa9RuVKxdj9uwb6dAhNthhmQgSSI1guIisFpFnRKSe5xEZE2G2bz/E228v5/77L2H58h6WBEyuyzIRqGpbnDuT7QHGiMhyEXnC88iMCWOJiUdP/R6gTp3SbNx4N6++egXnnVcgyJGZSBTQD8pUdZeqjgR64/ymYFAWLzHG+KGqzJixlri4CfTr9y1r1/4FYLeNNEEVyA/K6ojIYBFZjnPz+vk4w0UYY7Lhjz+SuOGGmdx006dUrFiUhQtvs0HiTJ4QSGfxeGA60FFVd3ocjzFhyRkkbho7diTxwgut+Pe/48mXL6AKuTGeyzIRqGqz3AjEmHC0bdtBypcvSnR0FKNGtaNKleLUrGm1AJO3ZHhKIiLvu/+Xi8gyn7/lPncuM8b4kZKSysiRpw8S17FjFUsCJk/KrEbwgPu/S24EYky4WL06kYSE2fz00046d67CNddUC3ZIxmQqwxqBqv7hPuyjqlt8/4A+uROeMaFl7NilNGz4DuvW7ePdd6/i88//QaVKxYIdljGZCqS3qr2f5zrndCDGhIMaNUpy/fXVWbWqB7fdZiOFmtCQYdOQiNyLc+ZfNV2fQFHgR68DMyYUHD16ksGD5yMiPPdcKxskzoSkzPoI3gO+AJ4FBvg8f0hV//I0KmNCwLx527j77q9Yv34fvXtfjKpaDcCEpMyahlRVNwP3AYd8/hARu/TBRKyDB4/Tp88cWreeTkpKKt98czNvvNHekoAJWVnVCLoAi3BuROP7KVfA7pdnItLOnUlMnLiS/v0vZciQFhQpYuMDmdCWYSJQ1S7u/4BuS2lMONu79wjvv7+WPn0aUbt2aTZt6ml3DDNhI5CxhlqISBH38W0i8rKIWG+YiQiqyvTpa4iLm8CDD37HunVO95glARNOArl89A3giIhcDDwE/A6862lUxuQBO3cmcd11H9Ot22dUrlyMRYtut18Gm7AUyKBzyaqqItIVeF1Vx4lIgteBGRNMKSmptGrlDBL30kuteeCBS22QOBO2AkkEh0TkMeB2oKWIRAH5vQ3LmODYsuUAFSo4g8SNHn0lVasWp3r1ksEOyxhPBXKK80+cG9f/S1V34dyL4EVPozIml6WkpPLyywupU2fCqUHiOnSItSRgIkIgt6rcBUwBiotIF+CYqr7jeWTG5JIVK/bQvPl7PPTQXNq1q8R119UIdkjG5KpArhq6GfgVuAm4GfhFRG70OjBjcsObby7hkkveZePGA7z33tXMnHk9FSoUDXZYxuSqQPoIBgKNVXU3gIiUBb4GZngZmDFeShsOok6d0tx0Uy1GjGhL2bKFgx2WMUERSCKISksCrkQCvOm9MXnNkSMnGTToR6Kjheefb03r1hVp3bpisMMyJqgCOaB/KSKzRaSHiPQAPgdmeRuWMTlv7tytNGgwieHDF5KUdBJVDd1qk3QAABfSSURBVHZIxuQJgdyz+BER+QdwufvUWFX9yNuwjMk5Bw4c59FHv2fs2GVUq1aCb7+92YaKNsZHZvcjqAG8BFQDlgMPq+qO3ArMmJzyxx9JTJ68iocfjufpp1tQuLD9DMYYX5k1DY0HPgNuwBmB9LXsLlxEOonIWhHZICIDMil3g4ioiMRndx3G+LNnzxFee20xALVrl2bz5l68+GIbSwLG+JFZ01BRVX3LfbxWRBZnZ8EiEg2MwrnV5XZggYjMVNVV6coVBR4AfsnO8o3xR1WZOnUN/fp9y8GDx+nYMZaaNUvZFUHGZCKzGkGMiDQSkUtE5BKgULrprDQBNqjqRlU9AUwDuvop9wzwPHAs29Eb42PbtoNcc81HdO/+OdWrl+C33+6wQeKMCUBmNYI/gJd9pnf5TCtwRRbLLg9s85neDjT1LeAmlIqq+rmIPJLRgkSkF9ALoFIl6+QzZ0pOTqVNm+ns2nWYV15py/33NyI62q5yNiYQmd2Ypq2XK3YHr3sZ6JFVWVUdC4wFiI+Pt2v+zCmbNx+gYsWi5MsXxZgxHahatThVq5YIdljGhBQvT5l2AL6/1KngPpemKFAPmCsim4HLgJnWYWwCkZycyksvLaBOnQmMHu0MEnfllZUtCRhzFgL5ZfHZWgDUEJEqOAmgG3Br2kxVPQCUSZsWkbk4l6gu9CYcq0iEi2XL9pCQ8CULF/5J167VueGGmsEOyZiQ5lkiUNVkEekLzAaigfGqulJEhgALVXWmV+s24Wv06N944IHvKFmyINOnd+Gmm2ohIsEOy5iQlmUiEOdb1h2oqqpD3PsVX6iqv2b1WlWdRbrhKFR1UAZl2wQU8Tmzg0YoShskrl69MnTrVptXXmlDmTJ2SagxOSGQGsFoIBXnKqEhwCHgQ6Cxh3EZA8Dhwyd44okfyZdPePHFNrRqVZFWrWyQOGNyUiCdxU1V9T7c6/xVdR9QwNOojAG++WYL9etPYsSIRRw/nmKDxBnjkUBqBCfdXwkrnLofQaqnUZmItn//MR5++HvGjVtOjRolmTevGy1bVgh2WMaErUBqBCOBj4DzReQ/wP+AYZ5GZSLan38eYdq0Nfzf/zVh6dI7LAkY47FAhqGeIiKLgHY4Pa3XqepqzyMzEeXPPw8zbdoaHnjgUmrVKsXmzT2tM9iYXBLIVUOVgCPAp77PqepWLwMzkUFVmTJlNQ888C1JSSe56qqq1KhR0pKAMbkokD6Cz3H6BwSIAaoAa4G6HsZlIsDWrQfp3XsOX3yxiWbNyjFuXEdq1CgZ7LCMiTiBNA3V9512B4rr41lEJiKkDRK3e/cRRo68gj59GtogccYESbZ/Wayqi0WkadYljTnTxo37qVy5GPnyRfHWWx2oVq0EsbHFgx2WMREtkD6C/j6TUcAlwE7PIjJhKTk5leHDF/DUU/N54YXW9Ot3Ce3aVQ52WMYYAqsRFPV5nIzTZ/ChN+GYcLRkyW4SEmazePGfXH99DW66yQaJMyYvyTQRuD8kK6qqD+dSPCbMvP76Yv7977mULh3DjBnX2kihxuRBGSYCEcnnjiDaIjcDMuEhbZC4Bg3K0r17HV5+uQ2lShUKdljGGD8yqxH8itMfsEREZgIfAIfTZqrqfz2OzYSgpKQTDBz4P/Lnj+Kll2yQOGNCQSDX68UAiTijj3YBrnH/G3Oar77aTL16E3nttcWcPJlqg8QZEyIyqxGc714xtIK/f1CWxr7h5pR9+47Rv/93TJy4klq1SjFvXjcuv9zGBzImVGSWCKKB8/B/JxdLBOaU3buPMGPGOh57rCmDBjUjJsbLO6AaY3JaZt/YP1R1SK5FYkLKrl2HmTp1Nf/+d7w7SFwvSpe2zmBjQlFmfQR2T0dzBlVl0qQVxMVN4LHHfmD9+n0AlgSMCWGZJYJ2uRaFCQmbNx+gU6cP6dHjS+LiSrNkyR02SJwxYSDDpiFV/Ss3AzF5W3JyKm3bTmfv3qOMGtWO3r0bEhVllUZjwoH16plMbdiwjypVipMvXxTjx3eiatXiVK5sg8QZE05s3F/j18mTKQwb9jN1605k1KglALRtW8mSgDFhyGoE5gyLF/9JQsJslizZzU031eSf/6wV7JCMMR6yRGBOM3LkYvr3/46yZQvz3/925frrawQ7JGOMxywRGODvQeIaNTqfO+6oy/DhbShZMibYYRljcoElggh36NAJHntsHgULRjN8eFtatqxAy5Y2PIQxkcQ6iyPYl19uol69CYwevQRVbJA4YyKU1QgiUGLiUfr3/4533llFnTql+PHHW2nWrFywwzLGBIklggiUmHiUjz7awJNPXsbAgZdRsKB9DIyJZJ42DYlIJxFZKyIbRGSAn/n9RWSViCwTkW9ExO5m7pE//kjipZcWoKrUrFmKLVt6MWTI5ZYEjDHeJQL3fsejgM5AHHCLiMSlK/YbEK+qDYAZwAtexROpVJXx45dTp84EnnzyRzZs2A9gVwQZY07xskbQBNigqhtV9QQwDejqW0BVv1PVI+7kz4BdrpKDNm3aT4cOM0hImM3FF5dl6VIbJM4YcyYv2wXKA9t8prcDTTMpnwB84W+GiPQCegFUqlQpp+ILa8nJqVxxxfskJh7jjTeupFevi22QOGOMX3migVhEbgPigdb+5qvqWGAsQHx8vF3jmIn16/dRtaozSNyECZ2oVq0EFSsWC3ZYxpg8zMumoR1ARZ/pCu5zpxGRK4GBwLWqetyzaML8GvmTJ1MYOvQn6tWbyOuv/wZAmzaVLAkYY7LkZY1gAVBDRKrgJIBuwK2+BUSkETAG6KSquz2MxXelubKa3LRw4S4SEmazbNkeunWrzS231A52SMaYEOJZIlDVZBHpC8wGooHxqrpSRIYAC1V1JvAicB7wgTgH6K2qeq1XMYWjV19dRP/+c7nwwiJ88sl1XHtt9WCHZIwJMZ72EajqLGBWuucG+Ty+0sv1h7O0QeLi4y8kIaE+L7zQihIl7JJQY0z25YnOYhO4gweP83//N4+YmHy88kpbWrQoT4sW5YMdljEmhNmgcyFk1qyN1K07kbFjl5Evn9ggccaYHGE1ghCwd+8RHnzwO6ZMWU3duqWZMeNWmja9KNhhGWPChCWCELBv33E+/fR3nnqqGY8/fhkFCkQHOyRjTBixRJBH7dhxiClTVvPII42pUaMkW7b0ss5gY4wnrI8gj1FV3nprGXFxExg8eD6//+4MEmdJwBjjFUsEecjvv++nXbv36dXrKy655AKWLbuT6tVtkDhjjLesaSiPSE5OpV279/nrr2OMGdOeu+9uYIPEGWNyhSWCIFu79i+qVStBvnxRTJrUmWrVSlChQtFgh2WMiSDWNBQkJ06k8PTT86lffyKjRjmDxLVuXdGSgDEm11mNIAh+/fUPEhJms2LFXm69tQ7du9cJdkjGmAhmiSCXjRixiIcemstFFxXh00+vp0uXasEOyRgT4SwR5JK0QeKaNLmQnj0b8PzzrShevGCwwzLGGEsEXjtw4DiPPvo9hQrlY8SIK2jevDzNm9sgccaYvMM6iz306ae/Exc3gbffXk7BgtE2SJwxJk+yGoEH9uw5wgMPfMvUqWuoX78MH3/clcaNbZA4Y0zeZInAAwcOHGfWrE08/XRzBgxoaoPEGWPyNEsEOWTbtoNMnryaAQOaUL26M0icdQYbY0KB9RGco9RU5c03l1C37kSGDv3p1CBxlgSMMaHCEsE5WL9+H1dcMZ177/2aJk0uZPnyHjZInDEm5FjT0FlKTk6lffsP2L//OOPGdeSuu+ohYoPEGWNCjyWCbFq9OpEaNUqSL18U7757FdWqlaBcufOCHZYxJgAnT55k+/btHDt2LNiheCYmJoYKFSqQP3/+gF9jiSBAx48nM2zYLwwb9gsvvtiaBx+8lJYtKwQ7LGNMNmzfvp2iRYsSGxsbljV4VSUxMZHt27dTpUqVgF9niSAAP/+8k4SE2axalcjtt8dx++1xwQ7JGHMWjh07FrZJAEBEKF26NHv27MnW6ywRZGH48AU88sj3VKhQlFmz/kHnzlWDHZIx5hyEaxJIczbbZ4kgA6mpSlSU0KxZOXr3vpjnnmtFsWJ2SagxJvzY5aPp7N9/jISEL3nggW8BaN68PKNHt7ckYIzJEdHR0TRs2JB69epxzTXXsH///lPzVq5cyRVXXEGtWrWoUaMGzzzzzGljlH3xxRfEx8cTFxdHo0aNeOihh3IkJksEPj7+eD1xcROYNGklRYsWsEHijDE5rlChQixZsoQVK1ZQqlQpRo0aBcDRo0e59tprGTBgAGvXrmXp0qXMnz+f0aNHA7BixQr69u3L5MmTWbVqFQsXLqR69eo5EpM1DQG7dx+mb99v+OCDdTRseD6fffYPLrnkgmCHZYzx0qIHYd+SnF1myYZw6YiAizdr1oxly5YB8N5779GiRQs6dOgAQOHChXn99ddp06YN9913Hy+88AIDBw6kdu3agFOzuPfee3MkbKsRAAcPnmDOnC385z+X8+uv3S0JGGM8l5KSwjfffMO1114LOM1Cl1566WllqlWrRlJSEgcPHmTFihVnzM8pEVsj2Lr1IO++u4rHH29K9eol2br1HooWLRDssIwxuSUbZ+456ejRozRs2JAdO3ZQp04d2rdvH5Q4fHlaIxCRTiKyVkQ2iMgAP/MLish0d/4vIhLrZTzgXA00evRv1K07gWHDfj41SJwlAWNMbkjrI9iyZQuqeqqPIC4ujkWLFp1WduPGjZx33nkUK1aMunXrnjE/p3iWCEQkGhgFdAbigFtEJP0vsRKAfapaHXgFeN6reADW7ixLm3afcN9939CsWTlWrrzLBokzxgRF4cKFGTlyJMOHDyc5OZnu3bvzv//9j6+//hpwag79+vXj0UcfBeCRRx5h2LBhrFu3DoDU1FTefPPNHInFyxpBE2CDqm5U1RPANKBrujJdgUnu4xlAO/Ho1x7JydDx+btZviKRCRM6MXv2jcTGFvdiVcYYE5BGjRrRoEEDpk6dSqFChfjkk08YOnQotWrVon79+jRu3Ji+ffsC0KBBA0aMGMEtt9xCnTp1qFevHhs3bsyROLzsIygPbPOZ3g40zaiMqiaLyAGgNLDXt5CI9AJ6AVSqVOmsgslXqiaTB39Mta4PcVGF0me1DGOMOVdJSUmnTX/66aenHtevX5+5c+dm+NouXbrQpUuXHI8pJDqLVXUsMBYgPj7+7C7ur9CVy+9LXyExxhjjZdPQDqCiz3QF9zm/ZUQkH1AcSPQwJmOMMel4mQgWADVEpIqIFAC6ATPTlZkJ3Ok+vhH4Vu3nvMYYD4X7IeZsts+zRKCqyUBfYDawGnhfVVeKyBARudYtNg4oLSIbgP7AGZeYGmNMTomJiSExMTFsk0Ha/QhiYmKy9ToJtR0SHx+vCxcuDHYYxpgQFMl3KBORRaoa7+81IdFZbIwxOSF//vzZunNXpLCxhowxJsJZIjDGmAhnicAYYyJcyHUWi8geYMtZvrwM6X61HAFsmyODbXNkOJdtrqyqZf3NCLlEcC5EZGFGvebhyrY5Mtg2RwavttmahowxJsJZIjDGmAgXaYlgbLADCALb5shg2xwZPNnmiOojMMYYc6ZIqxEYY4xJxxKBMcZEuLBMBCLSSUTWisgGETljRFMRKSgi0935v4hIbO5HmbMC2Ob+IrJKRJaJyDciUjkYceakrLbZp9wNIqIiEvKXGgayzSJys/terxSR93I7xpwWwGe7koh8JyK/uZ/vq4IRZ04RkfEisltEVmQwX0RkpLs/lonIJee8UlUNqz8gGvgdqAoUAJYCcenK9AHedB93A6YHO+5c2Oa2QGH38b2RsM1uuaLAPOBnID7YcefC+1wD+A0o6U6fH+y4c2GbxwL3uo/jgM3Bjvsct7kVcAmwIoP5VwFfAAJcBvxyrusMxxpBE2CDqm5U1RPANCD9PSq7ApPcxzOAdiIiuRhjTstym1X1O1U94k7+jHPHuFAWyPsM8AzwPBAO4w4Hss09gVGqug9AVXfncow5LZBtVqCY+7g4sDMX48txqjoP+CuTIl2Bd9TxM1BCRC46l3WGYyIoD2zzmd7uPue3jDo30DkAhPId7QPZZl8JOGcUoSzLbXarzBVV9fPcDMxDgbzPNYGaIvKjiPwsIp1yLTpvBLLNg4HbRGQ7MAu4P3dCC5rsft+zZPcjiDAichsQD7QOdixeEpEo4GWgR5BDyW35cJqH2uDU+uaJSH1V3R/UqLx1CzBRVYeLSDPgXRGpp6qpwQ4sVIRjjWAHUNFnuoL7nN8yIpIPpzqZmCvReSOQbUZErgQGAteq6vFcis0rWW1zUaAeMFdENuO0pc4M8Q7jQN7n7cBMVT2pqpuAdTiJIVQFss0JwPsAqvoTEIMzOFu4Cuj7nh3hmAgWADVEpIqIFMDpDJ6ZrsxM4E738Y3At+r2woSoLLdZRBoBY3CSQKi3G0MW26yqB1S1jKrGqmosTr/Itaoayvc5DeSz/TFObQARKYPTVLQxN4PMYYFs81agHYCI1MFJBHtyNcrcNRO4w7166DLggKr+cS4LDLumIVVNFpG+wGycKw7Gq+pKERkCLFTVmcA4nOrjBpxOmW7Bi/jcBbjNLwLnAR+4/eJbVfXaoAV9jgLc5rAS4DbPBjqIyCogBXhEVUO2thvgNj8EvCUi/8bpOO4Ryid2IjIVJ5mXcfs9ngLyA6jqmzj9IFcBG4AjwF3nvM4Q3l/GGGNyQDg2DRljjMkGSwTGGBPhLBEYY0yEs0RgjDERzhKBMcZEOEsEJk8SkRQRWeLzF5tJ2aQcWN9EEdnkrmux+wvV7C7jbRGJcx8/nm7e/HON0V1O2n5ZISKfikiJLMo3DPXROI337PJRkyeJSJKqnpfTZTNZxkTgM1WdISIdgJdUtcE5LO+cY8pquSIyCVinqv/JpHwPnFFX++Z0LCZ8WI3AhAQROc+9j8JiEVkuImeMNCoiF4nIPJ8z5pbu8x1E5Cf3tR+ISFYH6HlAdfe1/d1lrRCRB93niojI5yKy1H3+n+7zc0UkXkSeAwq5cUxx5yW5/6eJyNU+MU8UkRtFJFpEXhSRBe4Y8/cEsFt+wh1sTESauNv4m4jMF5Fa7i9xhwD/dGP5pxv7eBH51S3rb8RWE2mCPfa2/dmfvz+cX8Uucf8+wvkVfDF3XhmcX1Wm1WiT3P8PAQPdx9E44w2VwTmwF3Gf/z9gkJ/1TQRudB/fBPwCXAosB4rg/Cp7JdAIuAF4y+e1xd3/c3HveZAWk0+ZtBivBya5jwvgjCJZCOgFPOE+XxBYCFTxE2eSz/Z9AHRyp4sB+dzHVwIfuo97AK/7vH4YcJv7uATOWERFgv1+219w/8JuiAkTNo6qasO0CRHJDwwTkVZAKs6Z8AXALp/XLADGu2U/VtUlItIa52YlP7pDaxTAOZP250UReQJnnJoEnPFrPlLVw24M/wVaAl8Cw0XkeZzmpB+ysV1fAK+KSEGgEzBPVY+6zVENRORGt1xxnMHiNqV7fSERWeJu/2pgjk/5SSJSA2eYhfwZrL8DcK2IPOxOxwCV3GWZCGWJwISK7kBZ4FJVPSnOiKIxvgVUdZ6bKK4GJorIy8A+YI6q3hLAOh5R1RlpEyLSzl8hVV0nzr0OrgKGisg3qjokkI1Q1WMiMhfoCPwT50Yr4Nxt6n5VnZ3FIo6qakMRKYwz/s59wEicG/B8p6rXux3rczN4vQA3qOraQOI1kcH6CEyoKA7sdpNAW+CMey6Lcx/mP1X1LeBtnNv9/Qy0EJG0Nv8iIlIzwHX+AFwnIoVFpAhOs84PIlIOOKKqk3EG8/N3z9iTbs3En+k4A4Wl1S7AOajfm/YaEanprtMvde421w94SP4eSj1tKOIePkUP4TSRpZkN3C9u9UicUWlNhLNEYELFFCBeRJYDdwBr/JRpAywVkd9wzrZfVdU9OAfGqSKyDKdZqHYgK1TVxTh9B7/i9Bm8raq/AfWBX90mmqeAoX5ePhZYltZZnM5XODcG+lqd2y+Ck7hWAYvFuWn5GLKosbuxLMO5McsLwLPutvu+7jsgLq2zGKfmkN+NbaU7bSKcXT5qjDERzmoExhgT4SwRGGNMhLNEYIwxEc4SgTHGRDhLBMYYE+EsERhjTISzRGCMMRHu/wHq27HVQEXQdQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RtcmLQ1rVewF",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Q802V5OiXG8F"
      },
      "source": [
        "# Support Vector Machines:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dZB9pl0bVwwU",
        "colab": {}
      },
      "source": [
        "test_scale()\n",
        "\n",
        "# Fitting SVM to the Training set\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "classifier = SVC()\n",
        "\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rBko4G0GXaw7",
        "outputId": "3345bcff-5ed3-4806-a82a-ba8f37cbbde0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 812
        }
      },
      "source": [
        "\n",
        "\n",
        "classif_results()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix:\n",
            " [[754603  16971]\n",
            " [147757 621969]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.8931239862453773\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.98      0.90    771574\n",
            "           1       0.97      0.81      0.88    769726\n",
            "\n",
            "    accuracy                           0.89   1541300\n",
            "   macro avg       0.90      0.89      0.89   1541300\n",
            "weighted avg       0.90      0.89      0.89   1541300\n",
            "\n",
            "AUC Score: \n",
            "0.8930220931283096\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zLo3JLgWOPQF",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uC67IAVcYtOz"
      },
      "source": [
        "# Naive Bayes Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zBwG-YYncej7",
        "colab": {}
      },
      "source": [
        "test_scale()\n",
        "\n",
        "# Fitting SVM to the Training set\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "#Create a Gaussian Classifier\n",
        "classifier = GaussianNB()\n",
        "\n",
        "# Train the model using the training sets\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hLkdNWmZksZC",
        "outputId": "99329095-1b27-4cf4-bc2a-6a183e78b17c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "source": [
        "classif_results()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix:\n",
            " [[106236 665338]\n",
            " [  3371 766355]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vZEPMzOpReh5HX0s/ztcp6WeSfpi2T5b0oKQuSbdKOirFj07bXWn/SblzzE3xn0s6LxefnmJdkq7OxQvrqKVugpb0KUkbgXdJ2pBbngQ2lv6bMDN7nVRy6YfPAk/ktr8MXB8RpwB7gNkpPhvYk+LXp3JIOg24GHg3MB34Zkr6ncA3yDqxpwEfTWXr1VGorx70d8meXHdX+uxZJkXEf+3jWDOzhpCgs0OllnLn02jgT0mTHZR1vT8I3J6KLCF7pAXADA4+2uJ2YGoqPwO4JSJ+l27a6wLOSEtXRGyJiJeAW4AZfdRRqG6Cjoi9EfEU8FWgOyK2RsRW4ICkM/v+azAza4x+DHEMl7Qut8wpON1XgL8HXk3bJwDPR8SBtL0NGJXWRwFPA6T9e1P538d7HVMrXq+OQmWn2S0AJua2XyiImZk1TT+Gl3dFxOTa59F/AXZGxMOSzmlA05qmbIJWRETPRkS8KsmvyzKzw0Kokc/ieD/w55IuAN4AHEc2SjBE0hGphzsa2J7KbwfGANtS3jse2J2L98gfUxTfXaeOQmVncWyR9LeSjkzLZ4EtJY81M3t9dPCJdn0tfYmIuRExOiJOIrvId1+6pvZj4KJUbBbZtTfI3iQ1K61flMpHil+cZnmcDIwle9PUWmBsmrFxVKpjWTqmVh2FyvaCPwnMBz4PBLAKKBrXaTlHdXYwatgxA90M648tftJtOzoMN6pcBdwi6YvAz4CbUvwm4DuSuoBusoRLRGySdBvwOHAAuDwiXklt/RtgBdAJLIqITX3UUajsnYQ7explZna4CehsQoKOiPuB+9P6FrIZGL3L7Af+qsbx1wHXFcSXA8sL4oV11FJqiEPSqZJWSXosbZ8u6fNlKzEze706VG4ZTMqOQd8AzAVeBoiIDbhHbWaHUTsm6LJj0MdGxEO9xoAO1CpsZtZI2QXAQZZ9SyiboHdJegfZBUIkXQQ807RWmZn1Mth6x2WUTdCXAwvJnsmxHXgS8K3eZnbYtGEHuvQsji3An6Q3qXRExK+b2ywzs4MEHNGGGbrsLI4TJM0HHgDul/RVSSc0t2lmZgc16kaVVlJ2FsctwHPAX5LdBfMccGuzGmVmlidlt3qXWQaTsmPQIyPi2tz2FyV9pBkNMjMrMshybylle9D3SrpYUkdaPkx2G6OZ2WHhedC1XQZcAXwnbXcCL0r6BBARcVwzGmdmBulW78GWfUsoO4vjzc1uiJlZTYOwd1xG2Vkcs3ttd0qa15wmmZn9Ryr5ZzApOwY9VdJySSMljQPWAO5Vm9lhITwGXVNEXJJmbWwEXgQuiYifNrVlZmY5gy35llF2iGMs2SvK7wC2Ah+TdGwzG2ZmltePl8YOGmVncfyA7G0Bq9Krw/+O7LUu725ay8zMEgk6yw7IDiJlE/QZEbEPsjl1wP+R9IPmNcvM7LUG212CZdT9nSTp7wEiYp+k3q98+XizGmVmlteuFwn7+k9D/q0pc3vtm97gtpiZ1dSohyVJeoOkhyQ9KmmTpC+k+MmSHpTUJenW9EZu0lu7b03xByWdlDvX3BT/uaTzcvHpKdYl6epcvLCOWvpK0KqxXrRtZtYkoqPkUsLvgA9GxHhgAjBd0hTgy8D1EXEKsAfouf9jNrAnxa9P5ZB0Glkn9t1kHdZvpntEOoFvAOcDpwEfTWWpU0ehvhJ01Fgv2jYzawrRuB50ZF5Im0emJYAPAren+BLgwrQ+I22T9k9NkyVmALdExO8i4kmgi+yN3WcAXRGxJSJeInsa6Ix0TK06CvV1kXC8pH1kfz/HpHXS9hv6ONbMrDEER5QfYB4uaV1ue2FELHzN6bJe7sPAKWS93V8Az0dEz7tWtwGj0voo4GmAiDggaS9wQoqvyZ02f8zTveJnpmNq1VGoboKOiM56+83MDoeeHnRJuyJicr0CEfEKMEHSEOBO4F2vq4FNUnaanZnZgGrGNLuIeF7Sj4E/BoZIOiL1cEcD21Ox7cAYYJukI4Djgd25eI/8MUXx3XXqKNSGU7/NrBU1cBbHiannjKRjgHOBJ4Afk70xCmAWcFdaX5a2SfvvS/eDLAMuTrM8TgbGAg+R3cQ3Ns3YOIrsQuKydEytOgq5B21mlSca2pscCSxJ49AdwG0R8UNJjwO3SPoi8DPgplT+JuA7krqAbtL044jYJOk24HHgANnd1q8ASPobspeadAKLImJTOtdVNeoo5ARtZtWnxg1xRMQG4D0F8S1kMzB6x/cDvW/U69l3HXBdQXw5sLxsHbU4QZtZ5WV3ErbfrRdO0GbWEtovPTtBm1mLaMMOtBO0mbWCwfes5zKcoM2s8ho8i6NlOEGbWUvwRUIzsyoSHuIwM6siD3GYmVWYe9BmZhXVfunZCdrMWoCATvegzcyqqQ3zsxO0mbUCoTYc5HCCNrOW4B60mVkFZdPs2i9DO0GbWfWVfFvKYOMEbWYtwbd6m5lVUPbA/oFuxeHnBG1mLcGzOMzMKqoNRzja8vkjZtaCVPJPn+eRxkj6saTHJW2S9NkUHyZppaTN6XNoikvSfEldkjZImpg716xUfrOkWbn4JEkb0zHzlR4kUquOWpqWoCW9RdItkn4h6WFJyyWdKukkSY81qc6zJT0i6YCki5pRh5kdfj1j0GWWEg4A/z0iTgOmAJdLOg24GlgVEWOBVWkb4HxgbFrmAAsgS7bAPOBMsjd1z8sl3AXAZbnjpqd4rToKNSVBp98WdwL3R8Q7ImISMBcY0Yz6cn4JfBz4bpPrMbPDSaKj5NKXiHgmIh5J678GngBGATOAJanYEuDCtD4DWBqZNcAQSSOB84CVEdEdEXuAlcD0tO+4iFgTEQEs7XWuojoKNasH/QHg5Yj4Vk8gIh6NiAfyhVJv+oHU631E0vtSfKSk1ZLWS3pM0lmSOiUtTtsbJV3Zu9KIeCoiNgCvNunnMrMBopILMFzSutwyp+Y5pZOA9wAPAiMi4pm061kOdihHAU/nDtuWYvXi2wri1KmjULMuEo4DHi5RbidwbkTslzQWuBmYDFwCrIiI6yR1AscCE4BRETEOQNKQQ21c+sLmAIx561sP9TRmdphkQxylrxLuiojJfZ5TehNwB3BFROzLP286IkJSHEpbyypTx0BfJDwSuEHSRuDfgNNSfC1wqaR/AP4o/TdkC/B2SV+TNB3Yd6iVRsTCiJgcEZNPHH7i6/sJzOyw6EcPuu9zSUeSJed/jYjvpfCONDxB+tyZ4tuBMbnDR6dYvfjogni9Ogo1K0FvAiaVKHclsAMYT9ZzPgogIlYDZ5P9UIslzUxjPOOB+4FPAjc2vtlmVlkNytDpGtlNwBMR8U+5XcuAnpkYs4C7cvGZaTbHFGBvGqZYAUyTNDRdHJxG9j//Z4B9kqakumb2OldRHYWaNcRxH/C/JM2JiIUAkk4Hjue1YzbHA9si4tU0RaUzlX1bit8g6WhgoqTlwEsRcYeknwP/0qS2m1kFNfBW7/cDHwM2SlqfYv8D+BJwm6TZwFbgw2nfcuACoAv4DXApQER0S7qW7H/8ANdERHda/zSwGDgGuDst1KmjUFMSdBpb+RDwFUlXAfuBp4ArehX9JnCHpJnAPcCLKX4O8DlJLwMvkP0GGgV8W1JPr39u73olvZds9shQ4M8kfSEi3t3In83MBkaj0nNE/KTO6aYWlA/g8hrnWgQsKoivI7sW1zu+u6iOWpp2J2FE/Iravx3GpTKbgdNz8atSfAkHp6LkTSyI5etcy2vHfsxssGjDOwl9q7eZVV42vNx+GdoJ2syqz8+DNjOrrjbMz07QZtYKhNqwC+0EbWYtoQ3zsxO0mVVff+4SHEycoM2sNbRhhnaCNrOW4Gl2ZmYV5TFoM7Mq8jxoM7Pq8hCHmVkFCfegzcwqqw3zsxO0mbWINszQTtBm1hIa+MD+luEEbWYtof3SsxO0mbWKNszQTtBmVnnt+sD+Zr3V28yscdKNKmWWPk8lLZK0U9JjudgwSSslbU6fQ1NckuZL6pK0QdLE3DGzUvnN6aXXPfFJkjamY+anN3vXrKMeJ2gzawkquZSwGJjeK3Y1sCoixgKr0jbA+cDYtMwBFkCWbIF5wJnAGcC8XMJdAFyWO256H3XU5ARtZi0ge2B/maUvEbEa6O4VnsHBF1UvAS7MxZdGZg0wRNJI4DxgZUR0R8QeYCUwPe07LiLWpLeBL+11rqI6avIYtJm1hH7MshsuaV1ue2FELOzjmBER8UxafxYYkdZHAU/nym1LsXrxbQXxenXU5ARtZpXXzwf274qIyYdaV0SEpDjU4xtZh4c4zKw1NHAQusCONDxB+tyZ4tuBMblyo1OsXnx0QbxeHTU5QZtZS1DJP4doGdAzE2MWcFcuPjPN5pgC7E3DFCuAaZKGpouD04AVad8+SVPS7I2Zvc5VVEdNHuIws5bQqDu9Jd0MnEM2Vr2NbDbGl4DbJM0GtgIfTsWXAxcAXcBvgEsBIqJb0rXA2lTumojoufD4abKZIscAd6eFOnXU5ARtZtUn6GhQgo6Ij9bYNbWgbACX1zjPImBRQXwdMK4gvruojnqcoM2sRbTfnYRO0GZWeX5gv5lZhbVhfnaCNrPW4B60mVlFlbmNe7BxgjazltB+6dkJ2sxaQNlHiQ42TtBm1hLa8YH9TtBm1hraLz87QZtZa2jD/OwEbWatQHS04SC0E7SZVV673knox42amVWUe9Bm1hLasQftBG1mLcHT7MzMqsg3qpiZVVO7XiR0gjazluAhDjOzinIP2sysotowPztBm1mLaMMM7QRtZpUnaMtbvZW9Vbx9SXoO2DrQ7WiC4cCugW6E9ctg/s7eFhEnHurBku4h+/spY1dETD/Uuqqk7RP0YCVpXURMHuh2WHn+zqw3P4vDzKyinKDNzCrKCXrwWjjQDbB+83dmr+EEPUhFxID+Y5f0iqT1kh6T9G+Sjn0d51os6aK0fqOk0+qUPUfS+w6hjqcklb0I1RQD/Z1Z9ThBW7P8NiImRMQ44CXgk/mdkg5pimdE/HVEPF6nyDlAvxO0WRU5Qdvh8ABwSurdPiBpGfC4pE5J/1vSWkkbJH0CQJmvS/q5pP8L/EHPiSTdL2lyWp8u6RFJj0paJekksl8EV6be+1mSTpR0R6pjraT3p2NPkHSvpE2SbqQtb4OwqvONKtZUqad8PnBPCk0ExkXEk5LmAHsj4r2SjgZ+Kule4D3AO4HTgBHA48CiXuc9EbgBODuda1hEdEv6FvBCRPxjKvdd4PqI+ImktwIrgP8EzAN+EhHXSPpTYHZT/yLMDoETtDXLMZLWp/UHgJvIhh4eiognU3wacHrP+DJwPDAWOBu4OSJeAX4l6b6C808BVvecKyK6a7TjT4DTdPAutOMkvSnV8Rfp2B9J2nOIP6dZ0zhBW7P8NiIm5AMpSb6YDwGfiYgVvcpd0MB2dABTImJ/QVvMKs1j0DaQVgCfknQkgKRTJb0RWA18JI1RjwQ+UHDsGuBsSSenY4el+K+BN+fK3Qt8pmdDUs8vjdXAJSl2PjC0YT+VWYM4QdtAupFsfPkRSY8B/0z2v7o7gc1p31Lg//U+MCKeA+YA35P0KHBr2vUD4EM9FwmBvwUmp4uQj3NwNskXyBL8JrKhjl826Wc0O2R+FoeZWUW5B21mVlFO0GZmFeUEbWZWUU7QZmYV5QRtZlZRTtBmZhXlBG1mVlH/H7eRqYmJgjOuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.5661396223966781\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.14      0.24    771574\n",
            "           1       0.54      1.00      0.70    769726\n",
            "\n",
            "    accuracy                           0.57   1541300\n",
            "   macro avg       0.75      0.57      0.47   1541300\n",
            "weighted avg       0.75      0.57      0.47   1541300\n",
            "\n",
            "AUC Score: \n",
            "0.5666539481112982\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "M6HUBYufk7zw",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RFhSu26Epqrp"
      },
      "source": [
        "# Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LhH1Fh49ptq6",
        "outputId": "3840b1a3-97fc-47ff-acab-59342ff8d9ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "\n",
        "classifier = Sequential()\n",
        "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 6))\n",
        "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n",
        "classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
        "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
        "\n",
        "\n",
        "# Fitting the ANN to the Training set\n",
        "classifier.fit(X_train, y_train, batch_size = 10, epochs = 3)\n",
        "\n",
        "# Part 3 - Making predictions and evaluating the model\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "y_pred = (y_pred > 0.5)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "2311948/2311948 [==============================] - 260s 113us/step - loss: 0.2312 - accuracy: 0.9180\n",
            "Epoch 2/3\n",
            "2311948/2311948 [==============================] - 264s 114us/step - loss: 0.2177 - accuracy: 0.9243\n",
            "Epoch 3/3\n",
            "2311948/2311948 [==============================] - 261s 113us/step - loss: 0.2155 - accuracy: 0.9250\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JoAWOUZNpvr2",
        "outputId": "79a20609-e458-4eb3-ac4b-2971eb131848",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "source": [
        "classif_results()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix:\n",
            " [[748417  23157]\n",
            " [ 90857 678869]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.9260273794848505\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.97      0.93    771574\n",
            "           1       0.97      0.88      0.92    769726\n",
            "\n",
            "    accuracy                           0.93   1541300\n",
            "   macro avg       0.93      0.93      0.93   1541300\n",
            "weighted avg       0.93      0.93      0.93   1541300\n",
            "\n",
            "AUC Score: \n",
            "0.9259746087725486\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MszcdtWl0KcK",
        "colab": {}
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      "source": [
        ""
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      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "m8XYR-eRhE5q",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}