Phishing_URL_detection_.ipynb

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        "<h1>Phishing URL detection using KNN, SVM and XG boost classifier</h1>"
      ]
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        "Initial preparation and PreProcessing\n",
        "\n"
      ]
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      "execution_count": 1,
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        "id": "Kjy9VT_Bb0Pz"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "from sklearn import metrics \n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
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              "   Index  UsingIP  LongURL  ShortURL  Symbol@  Redirecting//  PrefixSuffix-  \\\n",
              "0      0        1        1         1        1              1             -1   \n",
              "1      1        1        0         1        1              1             -1   \n",
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              "\n",
              "   SubDomains  HTTPS  DomainRegLen  ...  UsingPopupWindow  IframeRedirection  \\\n",
              "0           0      1            -1  ...                 1                  1   \n",
              "1          -1     -1            -1  ...                 1                  1   \n",
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              "3           1      1            -1  ...                -1                  1   \n",
              "4           1      1            -1  ...                 1                  1   \n",
              "\n",
              "   AgeofDomain  DNSRecording  WebsiteTraffic  PageRank  GoogleIndex  \\\n",
              "0           -1            -1               0        -1            1   \n",
              "1            1            -1               1        -1            1   \n",
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              "3           -1            -1               0        -1            1   \n",
              "4            1             1               1        -1            1   \n",
              "\n",
              "   LinksPointingToPage  StatsReport  class  \n",
              "0                    1            1     -1  \n",
              "1                    0           -1     -1  \n",
              "2                   -1            1     -1  \n",
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              "[5 rows x 32 columns]"
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          "metadata": {},
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      ],
      "source": [
        "data = pd.read_csv(\"https://raw.githubusercontent.com/sandyram19/Phishing-Url-Detection/main/phishing.csv\")\n",
        "data.head()"
      ]
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          "output_type": "execute_result",
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              "Index(['Index', 'UsingIP', 'LongURL', 'ShortURL', 'Symbol@', 'Redirecting//',\n",
              "       'PrefixSuffix-', 'SubDomains', 'HTTPS', 'DomainRegLen', 'Favicon',\n",
              "       'NonStdPort', 'HTTPSDomainURL', 'RequestURL', 'AnchorURL',\n",
              "       'LinksInScriptTags', 'ServerFormHandler', 'InfoEmail', 'AbnormalURL',\n",
              "       'WebsiteForwarding', 'StatusBarCust', 'DisableRightClick',\n",
              "       'UsingPopupWindow', 'IframeRedirection', 'AgeofDomain', 'DNSRecording',\n",
              "       'WebsiteTraffic', 'PageRank', 'GoogleIndex', 'LinksPointingToPage',\n",
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              "      dtype='object')"
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          "execution_count": 3
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          "data": {
            "text/plain": [
              "32"
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          "metadata": {},
          "execution_count": 4
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      "source": [
        "len(data.columns)"
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            "text/plain": [
              "Index                  0\n",
              "UsingIP                0\n",
              "LongURL                0\n",
              "ShortURL               0\n",
              "Symbol@                0\n",
              "Redirecting//          0\n",
              "PrefixSuffix-          0\n",
              "SubDomains             0\n",
              "HTTPS                  0\n",
              "DomainRegLen           0\n",
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              "HTTPSDomainURL         0\n",
              "RequestURL             0\n",
              "AnchorURL              0\n",
              "LinksInScriptTags      0\n",
              "ServerFormHandler      0\n",
              "InfoEmail              0\n",
              "AbnormalURL            0\n",
              "WebsiteForwarding      0\n",
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              "PageRank               0\n",
              "GoogleIndex            0\n",
              "LinksPointingToPage    0\n",
              "StatsReport            0\n",
              "class                  0\n",
              "dtype: int64"
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        "Splitting"
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        "X = data.drop([\"class\",\"Index\"],axis =1)\n",
        "y = data[\"class\"]"
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              "   UsingIP  LongURL  ShortURL  Symbol@  Redirecting//  PrefixSuffix-  \\\n",
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              "0                 1                  1           -1            -1   \n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
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              "<p>5 rows × 30 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-113888ea-0d92-48a4-b46e-4fd524441857')\"\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-113888ea-0d92-48a4-b46e-4fd524441857 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-113888ea-0d92-48a4-b46e-4fd524441857');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "k2qTKidcD81W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "8e4f3259-2346-483c-94dd-c2544086a97a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "             Index   UsingIP   LongURL  ShortURL   Symbol@  Redirecting//  \\\n",
              "Index     1.000000 -0.388620  0.006441 -0.006221 -0.169437      -0.003771   \n",
              "UsingIP  -0.388620  1.000000 -0.052159  0.403547  0.158766       0.397220   \n",
              "LongURL   0.006441 -0.052159  1.000000 -0.097976 -0.075205      -0.080788   \n",
              "ShortURL -0.006221  0.403547 -0.097976  1.000000  0.104433       0.843149   \n",
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              "\n",
              "          PrefixSuffix-  SubDomains     HTTPS  DomainRegLen  ...  \\\n",
              "Index         -0.007402    0.233936 -0.006899     -0.001290  ...   \n",
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              "LongURL        0.055334    0.004249  0.049033     -0.221807  ...   \n",
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              "\n",
              "          UsingPopupWindow  IframeRedirection  AgeofDomain  DNSRecording  \\\n",
              "Index             0.006593           0.002583     0.115175      0.400751   \n",
              "UsingIP           0.096953           0.054739    -0.010580     -0.050929   \n",
              "LongURL          -0.049486          -0.013902     0.179678     -0.040549   \n",
              "ShortURL          0.036599           0.016570    -0.052562      0.436163   \n",
              "Symbol@           0.290880           0.284401    -0.005459     -0.047820   \n",
              "\n",
              "          WebsiteTraffic  PageRank  GoogleIndex  LinksPointingToPage  \\\n",
              "Index          -0.015147  0.065034    -0.012466             0.002623   \n",
              "UsingIP         0.002728 -0.091857     0.029206            -0.338971   \n",
              "LongURL         0.009296  0.183672     0.002825            -0.023216   \n",
              "ShortURL       -0.047025  0.014612     0.155832            -0.198463   \n",
              "Symbol@         0.032981 -0.064714     0.037047            -0.006124   \n",
              "\n",
              "          StatsReport     class  \n",
              "Index        0.163479  0.000802  \n",
              "UsingIP     -0.019420  0.094033  \n",
              "LongURL     -0.066708  0.057661  \n",
              "ShortURL     0.085572 -0.067931  \n",
              "Symbol@     -0.080286  0.052994  \n",
              "\n",
              "[5 rows x 32 columns]"
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          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",
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              "  "
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        "corr=data.corr()\n",
        "corr.head()"
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          "metadata": {},
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      "source": [
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              "      <td>0.261366</td>\n",
              "      <td>0.267531</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.193730</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.012945</td>\n",
              "      <td>-0.002734</td>\n",
              "      <td>0.162700</td>\n",
              "      <td>0.050797</td>\n",
              "      <td>0.258625</td>\n",
              "      <td>0.074479</td>\n",
              "      <td>0.096110</td>\n",
              "      <td>-0.011569</td>\n",
              "      <td>0.063126</td>\n",
              "      <td>0.714704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AnchorURL</th>\n",
              "      <td>-0.005275</td>\n",
              "      <td>0.099701</td>\n",
              "      <td>-0.023153</td>\n",
              "      <td>0.000607</td>\n",
              "      <td>0.057968</td>\n",
              "      <td>-0.005341</td>\n",
              "      <td>0.348854</td>\n",
              "      <td>0.229374</td>\n",
              "      <td>0.535712</td>\n",
              "      <td>-0.160355</td>\n",
              "      <td>...</td>\n",
              "      <td>0.041211</td>\n",
              "      <td>0.013441</td>\n",
              "      <td>0.075393</td>\n",
              "      <td>0.093131</td>\n",
              "      <td>0.326172</td>\n",
              "      <td>0.099201</td>\n",
              "      <td>0.038866</td>\n",
              "      <td>0.018788</td>\n",
              "      <td>0.077115</td>\n",
              "      <td>0.692895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PrefixSuffix-</th>\n",
              "      <td>-0.007402</td>\n",
              "      <td>-0.005306</td>\n",
              "      <td>0.055334</td>\n",
              "      <td>-0.080458</td>\n",
              "      <td>-0.011711</td>\n",
              "      <td>-0.085709</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.087852</td>\n",
              "      <td>0.261366</td>\n",
              "      <td>-0.096826</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.014716</td>\n",
              "      <td>-0.036894</td>\n",
              "      <td>0.074083</td>\n",
              "      <td>-0.016610</td>\n",
              "      <td>0.110555</td>\n",
              "      <td>-0.006855</td>\n",
              "      <td>0.067796</td>\n",
              "      <td>0.067469</td>\n",
              "      <td>-0.002851</td>\n",
              "      <td>0.348588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WebsiteTraffic</th>\n",
              "      <td>-0.015147</td>\n",
              "      <td>0.002728</td>\n",
              "      <td>0.009296</td>\n",
              "      <td>-0.047025</td>\n",
              "      <td>0.032981</td>\n",
              "      <td>-0.062761</td>\n",
              "      <td>0.110555</td>\n",
              "      <td>-0.005948</td>\n",
              "      <td>0.258625</td>\n",
              "      <td>-0.134571</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.043126</td>\n",
              "      <td>-0.022037</td>\n",
              "      <td>0.089814</td>\n",
              "      <td>0.048450</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.030905</td>\n",
              "      <td>-0.012529</td>\n",
              "      <td>-0.019701</td>\n",
              "      <td>0.008878</td>\n",
              "      <td>0.346003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43307d7a-1d6d-4c23-a1ca-5309c0298174')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-43307d7a-1d6d-4c23-a1ca-5309c0298174 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-43307d7a-1d6d-4c23-a1ca-5309c0298174');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "incCorr=corr.sort_values(by='class',ascending=False)\n",
        "incCorr.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "GI-vWiymFQmF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b120a40-954a-477e-fb3b-0e5d383580e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class                  1.000000\n",
              "HTTPS                  0.714704\n",
              "AnchorURL              0.692895\n",
              "PrefixSuffix-          0.348588\n",
              "WebsiteTraffic         0.346003\n",
              "SubDomains             0.298231\n",
              "RequestURL             0.253478\n",
              "LinksInScriptTags      0.248415\n",
              "DomainRegLen           0.225879\n",
              "ServerFormHandler      0.221380\n",
              "GoogleIndex            0.129000\n",
              "AgeofDomain            0.121402\n",
              "PageRank               0.104593\n",
              "UsingIP                0.094033\n",
              "StatsReport            0.079632\n",
              "DNSRecording           0.075579\n",
              "ShortURL               0.067931\n",
              "AbnormalURL            0.060751\n",
              "LongURL                0.057661\n",
              "Symbol@                0.052994\n",
              "StatusBarCust          0.041878\n",
              "HTTPSDomainURL         0.040096\n",
              "Redirecting//          0.038885\n",
              "NonStdPort             0.036461\n",
              "LinksPointingToPage    0.032694\n",
              "WebsiteForwarding      0.020151\n",
              "InfoEmail              0.018039\n",
              "DisableRightClick      0.012675\n",
              "IframeRedirection      0.003362\n",
              "Index                  0.000802\n",
              "Favicon                0.000231\n",
              "UsingPopupWindow       0.000136\n",
              "Name: class, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "incCorr['class']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Gg6SZ1b73u0o"
      },
      "outputs": [],
      "source": [
        "tenfeatures=incCorr[1:11].index\n",
        "twenfeatures=incCorr[1:21].index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OlQJFt9XL0sq"
      },
      "source": [
        "Structutre to Store metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "--o2wRAAL2aS"
      },
      "outputs": [],
      "source": [
        "ML_Model = []\n",
        "accuracy = []\n",
        "f1_score = []\n",
        "precision = []\n",
        "\n",
        "def storeResults(model, a,b,c):\n",
        "  ML_Model.append(model)\n",
        "  accuracy.append(round(a, 3))\n",
        "  f1_score.append(round(b, 3))\n",
        "  precision.append(round(c, 3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYR270k3pP_3"
      },
      "source": [
        "**KNN model (Non Learning Based)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Q75d15R4o9Ao"
      },
      "outputs": [],
      "source": [
        "def KNN(X):\n",
        "  x=[a for a in range(1,10,2)]\n",
        "  knntrain=[]\n",
        "  knntest=[]\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 = 0.2, random_state = 42)\n",
        "  X_train.shape, y_train.shape, X_test.shape, y_test.shape\n",
        "  for i in range(1,10,2):\n",
        "    from sklearn.neighbors import KNeighborsClassifier\n",
        "    knn = KNeighborsClassifier(n_neighbors=i)\n",
        "    knn.fit(X_train,y_train)\n",
        "    y_train_knn = knn.predict(X_train)\n",
        "    y_test_knn = knn.predict(X_test)\n",
        "    acc_train_knn = metrics.accuracy_score(y_train,y_train_knn)\n",
        "    acc_test_knn = metrics.accuracy_score(y_test,y_test_knn)\n",
        "    print(\"K-Nearest Neighbors with k={}: Accuracy on training Data: {:.3f}\".format(i,acc_train_knn))\n",
        "    print(\"K-Nearest Neighbors with k={}: Accuracy on test Data: {:.3f}\".format(i,acc_test_knn))\n",
        "    knntrain.append(acc_train_knn)\n",
        "    knntest.append(acc_test_knn)\n",
        "    print()\n",
        "  import matplotlib.pyplot as plt\n",
        "  plt.plot(x,knntrain,label=\"Train accuracy\")\n",
        "  plt.plot(x,knntest,label=\"Test accuracy\")\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "CysX7YsG1Aey",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "ef0a0d32-b22e-4266-cb32-6748e33d9b1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K-Nearest Neighbors with k=1: Accuracy on training Data: 0.989\n",
            "K-Nearest Neighbors with k=1: Accuracy on test Data: 0.956\n",
            "\n",
            "K-Nearest Neighbors with k=3: Accuracy on training Data: 0.976\n",
            "K-Nearest Neighbors with k=3: Accuracy on test Data: 0.946\n",
            "\n",
            "K-Nearest Neighbors with k=5: Accuracy on training Data: 0.965\n",
            "K-Nearest Neighbors with k=5: Accuracy on test Data: 0.941\n",
            "\n",
            "K-Nearest Neighbors with k=7: Accuracy on training Data: 0.958\n",
            "K-Nearest Neighbors with k=7: Accuracy on test Data: 0.936\n",
            "\n",
            "K-Nearest Neighbors with k=9: Accuracy on training Data: 0.953\n",
            "K-Nearest Neighbors with k=9: Accuracy on test Data: 0.938\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Xmain=X\n",
        "Xten=X[tenfeatures]\n",
        "Xtwen=X[twenfeatures]\n",
        "\n",
        "KNN(Xmain)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "k23I_EUQ7CVe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "c7bcc203-b399-4c72-f28a-a44145d3225f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K-Nearest Neighbors with k=1: Accuracy on training Data: 0.939\n",
            "K-Nearest Neighbors with k=1: Accuracy on test Data: 0.930\n",
            "\n",
            "K-Nearest Neighbors with k=3: Accuracy on training Data: 0.948\n",
            "K-Nearest Neighbors with k=3: Accuracy on test Data: 0.935\n",
            "\n",
            "K-Nearest Neighbors with k=5: Accuracy on training Data: 0.947\n",
            "K-Nearest Neighbors with k=5: Accuracy on test Data: 0.936\n",
            "\n",
            "K-Nearest Neighbors with k=7: Accuracy on training Data: 0.944\n",
            "K-Nearest Neighbors with k=7: Accuracy on test Data: 0.934\n",
            "\n",
            "K-Nearest Neighbors with k=9: Accuracy on training Data: 0.944\n",
            "K-Nearest Neighbors with k=9: Accuracy on test Data: 0.937\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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H7T6d1NRUunTpwosvvghAixYt+Mtf/gLAHXfcwWeffcb111/P0KFDGTt2LAMHDuTEiRNkZ2czYsQIXn75ZW688UYOHTrEokWLmDp1ajF+sUVjyaEKOZqWyb3T4gjwE968vaNN01kJBPj7cU3r+lzTuj4JOw8yaeFWJv+0jUkLt9K/VX3u7hFDx8ZV47mVtLQ0Vq1aRd++fQHnLr9+/foAtGnThqFDh3LjjTdy4403nvFcpxrqu7ChuFeuXFnosN2n4+/vz6BBg3Lfz5s3jxdeeIFjx46RkpJCy5Yt6d27N0lJSQwcOBAgd2TWXr16MXr0aJKTk/noo48YNGhQqcwcZ8mhilBVHp+5nM3JR3lnRJeyvas0ZaJdw9q8NqQ9T/RvxtTF23j/lx18vnI37RrW5u4eMfRvdX7pjZVVjDv80qKqtGzZksWLFxfY9vnnn7NgwQLmzJnD008/zcqVpy/lFHWo79PxHZ4b8g7RHRISktvOcOLECUaPHk1cXBwNGzZk3LhxZxwO/M4772T69Ol88MEHTJkypdixFYW1OVQRb/6whS9W/sbY/s3o3qTwsVdM5XBB7VCe6N+cxU/0YfwNLTl0PIOH3l9Grxfm8eYPmzl0rPAJbyq64OBgkpOTc5NDRkYGq1evJjs7m507d3L55Zfz/PPPc+jQIY4ePUqNGjU4cuRIoec61VDfOUNx58z3kJKSkmfYboAjR46QmZlJdHQ0CQkJuZ9/qgl4chJB3bp1OXr0KDNnzgSgRo0aREVF5bYvpKWlcezYMcCZDvWVV14BnCqp0mDJoQpYsCGZf3y9juva1Gdkzwu9DseUkerBAdzZNZq5j/birTtjaRxRnee+XEfX5+byl09XsXVfqtchlig/Pz9mzpzJH//4R9q2bUu7du1YtGgRWVlZ3H777bRu3Zr27dvz0EMPUbt2ba6//npmzZpVaIP0448/zhNPPEH79u3zTPxzzz330KhRI9q0aUPbtm1577338gzb3bZtW/r27cuJEyfo3r07MTExtGjRgoceeogOHToUGnft2rUZOXIkrVq14uqrr86tngJ45513ePXVV2nTpg3dunXjt99+A6BevXo0b968RKYDPRUbeK+S25lyjOteW0j9WiF8PLob1YKsJrEqW73rEJMXbmPO8l1kZDtDh9/dI4auFxZ/6HAbeM87x44do3Xr1ixdupRatYr2rEtxB96zkkMldjw9i3vfiUdV+fcdHS0xGFpeUIsXf9eWhWMv58HLm7B0x0Fu+88vXPvqQmbGJ5KWaUN0lHffffcdzZs358EHHyxyYjgbVnKopFSVR2YkMHv5LiYP78Tll5zndUimHDqRkcUny5KY/NNWNuw5SmQNd+jwLo3OOG+4lRwqFhuy2wAw+adtfJqwi8euutgSgzmlkEB/BnduxK2dGvLjxn1MWriVl77dwBvzNjGwvTN0+MX1Tj10uKraszIVwNkUAiw5VEKLN+/nmS/WclWLeozu3cTrcEwFICJcdnEkl10cyaa9R5i0cBsfL03kgyXO0OEjesTQ6+LIPIkgJCSE/fv3ExFRcac6rQpUlf379+c+J1FUVq1Uyew6eJzrX1tI7WqBfPJAd2qEBHodkqmgUlLTef/XHUxdtI29R9Jocl4Yd3eP4aYODQgJ9CcjI4PExMQz9sk33gsJCSEqKorAwLzfB+c8TaiI9AP+CfgDb6nqc/m2NwYmA5FACnC7qib6bK8JrAE+UdUx7rr5QH3guLvbVaq6V0SCgWlAR2A/cKuqbjtdfJYcHCcysvjdvxezJTmVT8d056LIMK9DMpVAemY2n690pjRdlXSYOtUCGdqlMXd2bcx5NW2e8YrsnHoriYg/8AbQH2gBDBGR/E9dTACmqWobYDzwbL7tfwMWFHL6oarazn3tddeNAA6oahPgZeD5M8VonKLjk5+sYkXiIV76XVtLDKbEBAX4MbB9FHPG9GDGvZfSKTqcN+Zvovvz3/PojARWJR3yOkRTCorS5tAZ2KSqWwBE5APgBpySQI4WwKPu8jzgk5wNItIRqAd8BRSaofK5ARjnLs8EXhcR0cpQ/1WKpv+yg//GJ/LQFU24quX5XodjKiERocuFEXS5MILt+1OZ8tM2/hu3k4+XJdHFHTq8TxUcOryyKspzDg2AnT7vE911vpYDN7nLA4EaIhIhIn7Ai8CpBlmfIiIJIvKknGzRyv08Vc0EDgEFJhwQkXtFJE5E4pKTk4twGZVX3LYUxs9ZzeWXRPLIlRd7HY6pAhpHVGfcgJYseqIPf76mOYkHjnPvO/Fc8eJ83v5pK6lVfOjwyqCkHoJ7DOglIsuAXkASkAWMBr7wbX/wMVRVWwM93dcdxflAVZ2oqrGqGpsz+1NVtOfwCe5/dykNaofyyuD2NhG9KVO1QgMZedmF/PCH3rxxWwciqgcxbs4aLn12Ls98sZakg8fPfBJTLhWlWikJaOjzPspdl0tVd+GWHEQkDBikqgdFpCvQU0RGA2FAkIgcVdWxqprkHntERN7Dqb6a5vN5iSISANTCaZg2+aRnZnP/9HhS0zKZPqILtUKtZ5LxRoC/H9e2qc+1beqzbMcBJi3cmvvq1+p8RvSIoUOjqjF0eGVRlOSwBGgqIjE4X9yDgdt8dxCRukCKqmYDT+D0XEJVh/rsMxyIVdWx7pd+bVXdJyKBwHXAd+6us4FhwGLgZuB7a28o3FNzVrN0x0HeuK0Dl5x/6geVjClL7RvV4fXb6pB08DjTFm3jvV938PmK3bRvVJsRPWLo1/J8Akpr6HBTYs74F3Lr/ccAXwNrgQ9VdbWIjBeRAe5uvYH1IrIBp/H56TOcNhj4WkRWAAk4Sec/7rZJQISIbMJp5B5brCuqIj5cspN3f9nBfb0u5No29b0Ox5gCGtQO5YlrmvPzE314akBLDqSmM+a9ZfT6x3wmLtjMoeOVc+jwysIegquAEnYe5HdvLqZzTDhv39XJ7sJMhZCVrXy/bi+TFm7h5y0pVAvy53exDbmrezSNI6p7HV6VdM4PwZV3VSk57DuaxvWvLcTfT5gzpgd1qgd5HZIxxbYq6RCTf9rKnOW7yMxWrmxejxE9YugSE25DcZQhSw6VREZWNre/9QsJOw/y0f3daNWg9IbrNaYs7D18gnd+3s70n7dz4FgGzc6vQXREdYID/QgO8CMk0D/Pz+AAf0ICnZ/BPj9Dct8Xvm+gv1jSKYSNylpJPPPFWn7ZmsLLt7a1xGAqhfNqhvD7qy7hgcubMGtZErOWJrF1XyppmVmkZWZzIuPkz+xzuI/1E4qcSHKTju92n+NCfPYJKWRf32OCA/wr7EOBlhwqiFnLEpny0zbu6h7NwPZRXodjTIkKCfRnSOdGDOncqNDtqkpmtuZJGGkZWZzIyC40kaRlZufuk7vvaY45npHFgWPp7nHuPu4x6ZnZ53Rtgf5SIJEE5yaSc0tEwYH+NI6oRt0zzL1xNiw5VACrkg7xxMcr6RITzp+usclVTNUjIgT6C4H+foQFl+3XVna2kp6VTdppE5FP0snIm6AKS1q+Ce7g8Yw8Scx3e2YRikt/v7EVt1/auMSv25JDOXcgNZ1R0+OpUy2I12/rQKD1TDKmTPn5CSF+/oQE+gNl+6BpZlb2yVKQTwLKKdmkZWZz0XmlM8imJYdyLDMrmwffX8bew2l8OKorkTVKvuhojCm/Avz9CPD3o7oH//UtOZRj//hmPQs37eOFQW1o17C21+EYY6oQq6Mopz5fsZt//7CFoV0a8btODc98gDHGlCBLDuXQ+t+O8IeZy+nQqDZ/vb6l1+EYY6ogSw7lzKHjGdz3ThzVgwP41+0dCQqwP5ExpuzZN085kp2tPPLBMhIPHOdfQztQz+bnNcZ4xJJDOfLKdxuYtz6Zvw5oSWx0uNfhGGOqMEsO5cQ3q3/j1e83cUvHKG7vUvhTosYYU1YsOZQDm/Ye5dEPl9MmqhZ/u7GVDRBmjPGcJQePHTnhNEAHB/jx5u0d3acwjTHGW/YQnIeys5Xff7icbfuPMX1EFy6oHep1SMYYA1jJwVP/N38T36zZw5+uaU7XiyK8DscYY3IVKTmISD8RWS8im0SkwJzOItJYROaKyAoRmS8iUfm21xSRRBF53X1fTUQ+F5F1IrJaRJ7z2Xe4iCSLSIL7uudcL7I8mrd+Ly9+u4Eb2l3A3d2jvQ7HGGPyOGNyEBF/4A2gP9ACGCIiLfLtNgGYpqptgPHAs/m2/w1YkP8YVW0GtAe6i0h/n20zVLWd+3qr6JdTMWzfn8rD7y+j2fk1ee6mNtYAbYwpd4pScugMbFLVLaqaDnwA3JBvnxbA9+7yPN/tItIRqAd8k7NOVY+p6jx3OR1YClSJGWyOpWdy3zvx+PkJE+/oSGiQNUAbY8qfoiSHBsBOn/eJ7jpfy4Gb3OWBQA0RiRARP+BF4LFTnVxEagPXA3N9Vg9yq6hmikiho86JyL0iEiciccnJyUW4DO+pKo/PXMGGPUd4dXB7GoZX8zokY4wpVEk1SD8G9BKRZUAvIAnIAkYDX6hqYmEHiUgA8D7wqqpucVfPAaLdKqpvgamFHauqE1U1VlVjIyMjS+gyStdbP27lsxW7+cPVzbjs4ooRszGmaipKV9YkwPfuPcpdl0tVd+GWHEQkDBikqgdFpCvQU0RGA2FAkIgcVdWcRu2JwEZVfcXnXPt9Tv0W8ELxLql8+mnTPp79ci3XtD6fUb0u9DocY4w5raIkhyVAUxGJwUkKg4HbfHcQkbpAiqpmA08AkwFUdajPPsOB2JzEICJ/B2oB9+Q7V31V3e2+HQCsLf5llS+JB44x5r2lXBQZxgs3t7UGaGNMuXfGaiVVzQTGAF/jfFF/qKqrRWS8iAxwd+sNrBeRDTiNz0+f7pxuV9c/4zRkL83XZfUht3vrcuAhYHjxL6v8OJGRxX3vxJOZrUy8M7bMJ0c3xpizIarqdQznLDY2VuPi4rwOowBV5ff/Xc7HS5OYNCyWPs3reR2SMcbkEpF4VY0tbJs9IV2Kpi3ezsdLk/ifKy+2xGCMqVAsOZSSX7em8LfP1nBl83o8eEUTr8MxxphiseRQCnYfOs7od+NpFF6Nl25ti5+fNUAbYyoWax0tYWmZWdw/fSnH07N4f+Sl1AwJ9DokY4wpNksOJWzc7NUk7DzIm7d3oGm9Gl6HY4wxZ8WqlUrQe7/s4P1fd/LA5RfRr1V9r8MxxpizZsmhhCzdcYC/zl7FZRdH8mjfS7wOxxhjzoklhxKw98gJ7p8eT/1aobw6uB3+1gBtjKngrM3hHKVnZvPAu0s5fDyTj0d3pna1IK9DMsaYc2bJ4Rw9/fkalmw7wKtD2tO8fk2vwzHGmBJh1UrnYGZ8IlMXb2dkzxgGtL3A63CMMabEWHI4SysTD/GnWSvpdlEEf+zXzOtwjDGmRFlyOAv7j6Yxano8kWHBvDakPQH+9ms0xlQu1uZQTJlZ2Tz4/jKSj6bx0ahuRIQFex2SMcaUOLvlLabnv1rHos37eWZga1pH1fI6HGOMKRWWHIph9vJd/OfHrQzr2pibO0Z5HY4xxpQaSw5FtHb3YR6fuZxO0XX43+taeB2OMcaUKksORXDwWDr3vRNPrdBA3hjagUBrgDbGVHLWIH0GWdnKQx8ksPvQcWbc15XzaoR4HZIxxpS6It0Ci0g/EVkvIptEZGwh2xuLyFwRWSEi80UkKt/2miKSKCKv+6zrKCIr3XO+KiLirg8XkW9FZKP7s865XuS5eOnb9SzYkMxTA1rRoZGnoRhjTJk5Y3IQEX/gDaA/0AIYIiL5K90nANNUtQ0wHng23/a/AQvyrfsXMBJo6r76uevHAnNVtSkw133via9W7eaNeZsZ0rkht3Vp5FUYxhhT5opScugMbFLVLaqaDnwA3JBvnxbA9+7yPN/tItIRqAd847OuPlBTVX9WVQWmATe6m28AprrLU33Wl6mNe47w+w+X065hbcYNaOlFCMYY45miJIcGwE6f94nuOl/LgZvc5YFADRGJEBE/4EXgsULOmXiKc9ZT1d3u8m84iaUAEblXROJEJC45ObkIl1F0h09kcN878YQG+fOv2zsQHOBfouc3xpjyrqS63TwG9BKRZUAvIAnIAkYDX6hq4ukOPhW3VKGn2DZRVWNVNTYyMvIswy4oO1t5dEYCO1KO8cZtHahfK7TEzm2MMRVFUXorJQENfd5Huetyqeou3JKDiIQBg1T1oIh0BXqKyGggDAgSkaPAP93zFHbOPSJSX1V3u9VPe8/ius7aa99v4ru1exl3fQu6XBhRlh9tjDHlRlFKDkuApiISIyJBwGBgtu8OIlLXrUICeAKYDKCqQ1W1kapG45QupqnqWLfa6LCIXOr2UroT+NQ9fjYwzF0e5rO+1M1du4dX5m7gpg4NGNYtuqw+1hhjyp0zJgdVzQTGAF8Da4EPVXW1iIwXkQHubr2B9SKyAaeN4OkifPZo4C1gE7AZ+NJd/xzQV0Q2Ale670vd1n2pPDIjgZYX1OSZga1xe9YaY0yVJE61fsUWGxurcXFxZ3380bRMBr7xE/uOpjHnwR5E1alWgtEZY0z5JCLxqhpb2LYq/4S0qvL4zOVsTj7KOyO6WGIwxhhsbCXe/GELX6z8jbH9m9G9SV2vwzHGmHKhSieHBRuS+cfX67iuTX1G9rzQ63CMMabcqNLJ4WhaJm0b1uaFm9tYA7Qxxvio0m0O17SuT7+W5+PnZ4nBGGN8VemSA2CJwRhjClHlk4MxxpiCLDkYY4wpwJKDMcaYAiw5GGOMKcCSgzHGmAIsORhjjCnAkoMxxpgCLDkYY4wpwJKDMcaYAiw5GGOMKcCSgzHGmAIsORhjjCmgSMlBRPqJyHoR2SQiYwvZ3lhE5orIChGZLyJRPuuXikiCiKwWkVHu+hruupzXPhF5xd02XESSfbbdU4LXa4wxpgjOOGS3iPgDbwB9gURgiYjMVtU1PrtNAKap6lQRuQJ4FrgD2A10VdU0EQkDVrnH7gLa+XxGPPCxz/lmqOqYc7w2Y4wxZ6koJYfOwCZV3aKq6cAHwA359mkBfO8uz8vZrqrpqprmrg8u7PNE5GLgPODH4odvjDGmNBQlOTQAdvq8T3TX+VoO3OQuDwRqiEgEgIg0FJEV7jmed0sNvgbjlBTUZ90gt4pqpog0LCwoEblXROJEJC45ObkIl2GMMaaoSqpB+jGgl4gsA3oBSUAWgKruVNU2QBNgmIjUy3fsYOB9n/dzgGj3mG+BqYV9oKpOVNVYVY2NjIwsocswxhgDRUsOSYDv3XuUuy6Xqu5S1ZtUtT3wZ3fdwfz7AKuAnjnrRKQtEKCq8T777fepinoL6FjkqzHGGFMiipIclgBNRSRGRIJw7vRn++4gInVFJOdcTwCT3fVRIhLqLtcBegDrfQ4dQt5SAyJS3+ftAGBt0S/HGGNMSThjbyVVzRSRMcDXgD8wWVVXi8h4IE5VZwO9gWdFRIEFwAPu4c2BF931AkxQ1ZU+p/8dcE2+j3xIRAYAmUAKMPxsL84YY8zZkbztwBVTbGysxsXFeR2GMcZUKCISr6qxhW2zJ6SNMcYUYMnBGGNMAZYcjDGmIkrdB4teh/2bS+X0Z2yQNsYYU05kZ8PW+RA/FdZ9DtkZ4BcAEReV+EdZcjDGmPLuUBIkvAfLpsHBHRAaDp3vhQ53wHnNS+UjLTkYY0x5lJUBG7+BpdOcn5oNF/aGK8dBs+sgILhUP96SgzHGlCcpW2DpO5DwLhzdA2HnQ49Hof3tEB5TZmFYcjDGGK9lnIB1n0H827DtRxA/aHo1dBwGTfqCf9l/VVtyMMYYr+xZ41QbrfgAjh+A2o3hiieh3W1Q8wJPQ7PkYIwxZSntKKz+2EkKiUvAP8hpQ+g4DKIvA7/y8YSBJQdjjCltqpC0FJZOhVUfQfpRiGwGVz8DbQZD9QivIyzAkoMxxpSWYymw8r9OKWHPKgisBi1vckoJUZ1AxOsIT8mSgzElKTsLDifBgW1wYDsc2wcXtIeGl0JgiNfRmbKgCtsWOglhzaeQleb8G7juZWh1M4TU9DrCIrHkYExxqDp3gwe3nUwAB7bBQffnoUTIzix4nH8wNOoCMb2cvur123nSA8WUoqN7ne6nS6c53VGDa0GHO51X/TZeR1dsNmS3MfllHHeeQi3sy//Adkg/knf/anWhTmOnp0mdaGe5TrTzPrQ27PwVtvwAW39wqhYAgmtCdA83WfRy6p/LcRWDOYXsLNg012lL2PCVc2PQuLuTEFrcAIGhXkd4WqcbsttuXUzVk50Fh3fl/cLPTQDb4ehvefcPCD35hd+4e74E0AiCa5z+8y6+2nkBHE2GbQtOJov1Xzjrw+pBzGXuq5dzflN+HdwBy6Y7r8NJzg3CpaOdpFC3qdfRlQgrOZjKR9XpM17ol/82OLjTGbAsh/hBzSj3C78x1I7Oe/cfdl7p3dUf2O4kia1uwkjd66yvE32yVBHTC6rXLZ3PN0WXmQ4bvnQGvdv8vbOuSR8nIVzcHwKCvI3vLJyu5GDJwVRMGSecu7fcBLDNZ3kHpB3Ku39oeN4vfN+7/5pR5eM/tiokrztZqti2ENIOO9vqtTqZLBp3O3NpxZScfRudaqOE950OBjWjnKEs2g91So4VmCUHU/FkZ8OR3ae++z+yO+/+ASHul34hCaB24wrTQySPrEzYnQBb5jvJYscvTs8XvwBo0PFksojqVOqDsFU56cdg7WynlLBjkfM7v6Q/dBgGF10Bfv5eR1gizjk5iEg/4J+AP/CWqj6Xb3tjYDIQCaQAt6tqort+Fs6kQoHAa6r6pnvMfKA+cNw9zVWquldEgoFpQEdgP3Crqm47XXyWHCqo4wdPU/WzA7LSfXYWqNng1Hf/1c8rN0+WlpqM47Dzl5Mli13LnJE6A0KhcdeTyeL8NpXmy6vM7V7hDmfxoVP6DL/IqTZqOwRq1PM6uhJ3TslBRPyBDUBfIBFYAgxR1TU++/wX+ExVp4rIFcBdqnqHiAS5n5EmImHAKqCbqu5yk8NjqhqX7/NGA21UdZSIDAYGquqtp4vRkkM5lZnm1O8X2u1zO5w4mHf/kNoFe/vUiXZetaLs7ji/4wdh+08nk0XyOmd9SG2nJ9SFvZ2EUbep9YQ6nROHYdVMp5SwO8HpdtzyRicpNO5eqX9359pbqTOwSVW3uCf7ALgBWOOzTwvgUXd5HvAJgKr63voFU7RpSW8AxrnLM4HXRUS0MtR/VTbZ2c6Qwqe6+z+8C/D5s/kHO3W0daKdqpD8VT+htT24iAostDY0u9Z5ARz5Dbb+6MwUtmWBM8onQI0LnF5QOY3btRp4FXH5oep0MV46FVbPgoxjTrtO/39Am1sgtI7XEXquKMmhAbDT530i0CXfPsuBm3CqngYCNUQkQlX3i0hD4HOgCfAHVd3lc9wUEckCPgL+7iaA3M9T1UwROQREAPt8P1BE7gXuBWjUqGI3ClUY+zfDrxOdB3xyqn4yT/jsIFCjvtvT5rKC1T9h51f+qh8v1Tjf+WJrc4vz5Xdg68lSxaZvnZE/ASKanKyCiu4J1cK9jbsspe53fg9LpzklraAwaPM7p5RwQYdKXUoorpJ6zuExnDv84cACIAnIAlDVnUAbEbkA+EREZqrqHmCoqiaJSA2c5HAHTltDkajqRGAiONVKJXQdpjCqzpOfXzzu1HHXbeo8tHXx1e6Xf4zz5V+roQ0RUV6IQPiFziv2LqeUt3f1yWSxYgbETQLEeXo3J1k06gpB1b2OvmRlZzvXvHSaU5rKSndKrgNeh5YDITjM6wjLpaIkhySgoc/7KHddLrc0cBOA27YwSFUP5t9HRFYBPYGZqprkrj8iIu/hVF9N8/m8RBEJAGrhNEwbLxw/CJ894hS9o3vCwH9btURF5OcH57d2Xt3GOFNQJsWfTBY//wsWvQp+gdCws5MsYi6DqFjwD/Q6+rNzeJc7nMU7TlVnaB2IHeGUEuq18Dq6cq8oyWEJ0FREYnC+uAcDt/nuICJ1gRRVzQaewOm5hIhEAftV9biI1AF6AC+7X/q1VXWfiAQC1wHfuaebDQwDFgM3A99be4NHti+Gj0c63Ub7/BW6P2y9YCoL/0BodKnz6v1HSE+FHYvdZLEA5j8L85+BwOrOcxU57RX1WpXvqsGsTJ95l792Sroxl0GfvzhzJljJtsjOmBzcev8xwNc4XVknq+pqERkPxKnqbKA38KyIKE610gPu4c2BF931AkxQ1ZUiUh342k0M/jiJ4T/uMZOAd0RkE0632MEldK2mqLIyYcELsOAfTrXR3d9AVEevozKlKag6NLnSeYEzuOC2hU6pYssP8M23zvpqEU4JMidZhF9YPurpU7a4w1m86wx/ElYPuj8CHe5wYjTFZg/BmbwObHdKCzt/cfp2X/MPexrXwKEkp0SRkyyOuP1KajX0GebjMqdRvKxkpsHaOU4pYesP7rzLVzkPqjW9yka9LQJ7QtoUzcqZ8Nn/OMvXvuT0ejEmP1XYv+nkk9tbfzz5zEpkM59hPrqXTvfkvWudhLD8fXfe5UbQ/k5nOAuP512uaCw5mNNLO+L0RFr+HkR1hkH/cbqfGlMU2Vnw28qTpYodi53nBsTPmbcipwqq0aVnP4R12lGnU8TSaZD4q9Nw3vw6p3E5pnf5bgcpxyw5mFNLjIePRji9OS77A1z2uBXHzbnJTIPEuJPJIinOmefAP9jpCXVhL+cL/YL2p/+3pgq7ljoJYeVHzjwadS9xh7MYbCPVlgBLDqag7Cz46Z8w72nn4bRB/3F6pRhT0tKOOD3fcpLFnpXO+uCaTtVTTsnivOZO4/bxA7AiZ97llc7YUa1ucpJCwy7lowG8krDJfkxeh3fBx/fCth+hxY1w/Ss2XIApPcE14OKrnBdA6r68jdsbvnTWVz8Pzm8F2xc5T97Xb+e0fbW+GUJqeRZ+VWXJoapZ+xnMHuNMXHLDG9BuqN2JmbJVva5TEmh1k/P+4I6Tkx3tTnDmSuhwJ9Rv62mYVZ0lh6oi/Rh882eIm+zckQ2aBHWbeB2VMW5vo9udlyk3LDlUBb+thJkjYN966PYQXPFk+Zj5zBhTbllyqMxU4Zc34du/ONNk3vEJXHS511EZYyoASw6V1dFk+OR+Z6jmi/vDDa9b1z9jTJFZcqiMNn7nJIa0w3DNBOh0jzU6G2OKxZJDZZKZBt89BT+/Aee1gDs/taGJjTFnxZJDZZG83ml03rMSOt8Lfcef/VAFxpgqz5JDRacK8W/DV09AUDUYMgMu6ed1VMaYCs6SQ0V2LAVmP+hMfXjh5TDwzbIdMtkYU2lZcqioti6Aj++D1GS46u9w6QM2MqUxpsRYcqhosjJg3jOw8GWIuAiGfAcXtPM6KmNMJWPJoSJJ2QIf3eNMDN/+Duj3HASHeR2VMaYSKlI9hIj0E5H1IrJJRMYWsr2xiMwVkRUiMl9EonzWLxWRBBFZLSKj3PXVRORzEVnnrn/O51zDRSTZPSZBRO4pqYutsFRh+QfwZk9nBq5b3nYearPEYIwpJWcsOYiIP/AG0BdIBJaIyGxVXeOz2wRgmqpOFZErgGeBO4DdQFdVTRORMGCViMwGDgITVHWeiAQBc0Wkv6q6Y/cyQ1XHlNRFVmgnDsHnv4eV/4VG3eCmiVC7oddRGWMquaJUK3UGNqnqFgAR+QC4AfBNDi2AR93lecAnAKqa7rNPMG5JRVWPufuhqukishSIOuurqKx2/urM0nYoCS7/X+j5KPj5ex2VMaYKKEq1UgNgp8/7RHedr+WAOzg7A4EaIhIBICINRWSFe47nVXWX74EiUhu4Hpjrs3qQW0U1U0QKvU0WkXtFJE5E4pKTk4twGRVIdhb88AJMdp9XuPsr6PUHSwzGmDJTUn0fHwN6icgyoBeQBGQBqOpOVW0DNAGGiUi9nINEJAB4H3g1p2QCzAGi3WO+BaYW9oGqOlFVY1U1NjIysoQuoxw4uBPevs6ZvrPVTTBqoTPvrjHGlKGiVCslAb5371HuulxuaeAmALdtYZCqHsy/j4isAnoCM93VE4GNqvqKz377fQ57C3ihKBdSKayeBXMedkoOAydC21u9jsgYU0UVpeSwBGgqIjFu4/FgYLbvDiJSV0RyzvUEMNldHyUioe5yHaAHsN59/3egFvBIvnPV93k7AFhbzGuqeNKOwqcPwH+HQ0QTGPWjJQZjjKfOWHJQ1UwRGQN8DfgDk1V1tYiMB+JUdTbQG3hWRBRYADzgHt4ceNFdLzg9lFa6XV3/DKwDlooznPTrqvoW8JCIDAAygRRgeIldbXm0a5nz7ML+zdDz99D7CfAP9DoqY0wVJ6rqdQznLDY2VuPi4rwOo3iys2Hx6zB3PFSPdLqoxvT0OipjTBUiIvGqGlvYNntC2gtHfoNZ98GW+dD8erj+VagW7nVUxhiTy5JDWVv/pdO+kH4Mrv8ndBhms7QZY8odSw5lJeM4fPMkLPkPnN8aBk2GyIu9jsoYYwplyaEs7FntzNKWvBa6joE+f4GAYK+jMsaYU7LkUJpU4df/wDf/CyG14PaPoMmVXkdljDFnZMmhtKTuc9oWNnwFTa+CG/4PwirRk9zGmErNkkNp2Pw9zBoFxw9Av+ehy33W6GyMqVAsOZSkzHT4fjwseg0im8HtH8P5rbyOyhhjis2SQ0nZtwk+uht2L4fYEc68zkHVvI7KGGPOiiWHc6UKy6bDl487PZAGvwfNrvU6KmOMOSeWHM7F8QMw5xFY8wnEXAYD/w01L/A6KmOMOWeWHM7W9kXw0Ug4+htcOQ66PQx+JTU9hjHGeMuSQ3FlZcIPz8OPE6BONIz4Bhp09DoqY4wpUZYciuPANqe0kPgrtBsK/Z+H4BpeR2WMMSXOkkNRrfgvfP6oszxoErS+2dt4jDGmFFlyOJMTh+GLP8CKD6BhF7jpP1CnsddRGWNMqbLkcDqJ8c6zCwd3QK+xcNkfwN9+ZcaYys++6QqTnQU/vQLznoEa9WH4F9C4q9dRGWNMmbHkkN+hJGeWtm0/QsuBcN0rEFrb66iMMaZMFaljvoj0E5H1IrJJRMYWsr2xiMwVkRUiMl9EonzWLxWRBBFZLSKjfI7pKCIr3XO+KuKMTCci4SLyrYhsdH/WKamLPaM1s+Ff3SBpqTOK6s1TLDEYY6qkMyYHEfEH3gD6Ay2AISLSIt9uE4BpqtoGGA88667fDXRV1XZAF2CsiOQ8QvwvYCTQ1H31c9ePBeaqalNgrvu+dKWnwpyH4cM7IDwGRv0I7YfaSKrGmCqrKCWHzsAmVd2iqunAB8AN+fZpAXzvLs/L2a6q6aqa5q4Pzvk8EakP1FTVn1VVgWnAje5+NwBT3eWpPutLx+4VMLE3xE+F7o/A3d9AxEWl+pHGGFPeFSU5NAB2+rxPdNf5Wg7c5C4PBGqISASAiDQUkRXuOZ5X1V3u8YmnOGc9Vd3tLv8G1CssKBG5V0TiRCQuOTm5CJdRiGXvwlt9nO6qd34CfZ+CgKCzO5cxxlQiJTUY0GNALxFZBvQCkoAsAFXd6VY3NQGGiUihX/aFcUsVeoptE1U1VlVjIyPPcoa1iIvg4qvh/kVwYe+zO4cxxlRCRemtlAQ09Hkf5a7L5ZYGbgIQkTBgkKoezL+PiKwCegI/uecp7Jx7RKS+qu52q5/2Fv1yiqnRpc7LGGNMHkUpOSwBmopIjIgEAYOB2b47iEhdEck51xPAZHd9lIiEust1gB7Aerfa6LCIXOr2UroT+NQ9fjYwzF0e5rPeGGNMGTljclDVTGAM8DWwFvhQVVeLyHgRGeDu1htYLyIbcNoInnbXNwd+EZHlwA/ABFVd6W4bDbwFbAI2A1+6658D+orIRuBK970xxpgyJE61fsUWGxurcXFxXodhjDEViojEq2psYdtsdhpjjDEFWHIwxhhTgCUHY4wxBVhyMMYYU4AlB2OMMQVUit5KIpIMbD/Lw+sC+0ownJJicRWPxVV85TU2i6t4ziWuxqpa6BATlSI5nAsRiTtVVy4vWVzFY3EVX3mNzeIqntKKy6qVjDHGFGDJwRhjTAGWHGCi1wGcgsVVPBZX8ZXX2Cyu4imVuKp8m4MxxpiCrORgjDGmAEsOxhhjCqiyyUFEJovIXncConLDnVZ1noisEZHVIvKw1zEBiEiIiPwqIsvduJ7yOiZfIuIvIstE5DOvY8khIttEZKWIJIhIuRk2WERqi8hMEVknImtFpGs5iOkS9/eU8zosIo94HReAiPyP+29+lYi8LyIhXscEICIPuzGtLo3fVZVtcxCRy4CjwDRVbeV1PDnc2e/qq+pSEakBxAM3quoaj+MSoLqqHhWRQGAh8LCq/uxlXDlE5FEgFqipqtd5HQ84yQGIVdVy9eCUiEwFflTVt9wJvKrln7nRSyLijzMzZBdVPduHW0sqlgY4/9ZbqOpxEfkQ+EJV3/Y4rlbAB0BnIB34ChilqptK6jOqbMlBVRcAKV7HkZ+q7lbVpe7yEZwJlhp4G5Uzn7eqHnXfBrqvcnFnISJRwLU4k0eZ0xCRWsBlwCQAVU0vT4nB1QfY7HVi8BEAhIpIAFAN2OVxPOBOpKaqx9wJ2X7Anaq5pFTZ5FARiEg00B74xeNQgNyqmwSceb2/VdVyERfwCvA4kO1xHPkp8I2IxIvIvV4H44oBkoEpbjXcWyJS3eug8hkMvO91EACqmgRMAHYAu4FDqvqNt1EBsAroKSIRIlINuAZoWJIfYMmhnBKRMOAj4BFVPex1PACqmqWq7YAooLNbtPWUiFwH7FXVeK9jKUQPVe0A9AcecKsyvRYAdAD+partgVRgrLchneRWcw0A/ut1LAAiUge4ASepXgBUF5HbvY0KVHUt8DzwDU6VUgKQVZKfYcmhHHLr9D8C3lXVj72OJz+3GmIe0M/jUAC6AwPc+v0PgCtEZLq3ITncu05UdS8wC6d+2GuJQKJPqW8mTrIoL/oDS1V1j9eBuK4EtqpqsqpmAB8D3TyOCQBVnaSqHVX1MuAAsKEkz2/JoZxxG34nAWtV9SWv48khIpEiUttdDgX6Aus8DQpQ1SdUNUpVo3GqI75XVc/v7ESkutuhALfa5iqcqgBPqepvwE4RucRd1QfwtLNDPkMoJ1VKrh3ApSJSzf2/2QenHdBzInKe+7MRTnvDeyV5/oCSPFlFIiLvA72BuiKSCPxVVSd5GxXg3AnfAax06/cB/qSqX3gXEgD1galuTxI/4ENVLTfdRsuhesAs5/uEAOA9Vf3K25ByPQi861bhbAHu8jgeIDeJ9gXu8zqWHKr6i4jMBJYCmcAyys8wGh+JSASQATxQ0h0LqmxXVmOMMadm1UrGGGMKsORgjDGmAEsOxhhjCrDkYIwxpgBLDsYYYwqw5GCMMaYASw7GGGMK+H9OdDD+s3R1RgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "KNN(Xten)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "XdTtPGqN7Eph",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "c278268e-bd7d-4aba-fd11-d5d22453b2b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "K-Nearest Neighbors with k=1: Accuracy on training Data: 0.981\n",
            "K-Nearest Neighbors with k=1: Accuracy on test Data: 0.951\n",
            "\n",
            "K-Nearest Neighbors with k=3: Accuracy on training Data: 0.970\n",
            "K-Nearest Neighbors with k=3: Accuracy on test Data: 0.940\n",
            "\n",
            "K-Nearest Neighbors with k=5: Accuracy on training Data: 0.960\n",
            "K-Nearest Neighbors with k=5: Accuracy on test Data: 0.942\n",
            "\n",
            "K-Nearest Neighbors with k=7: Accuracy on training Data: 0.956\n",
            "K-Nearest Neighbors with k=7: Accuracy on test Data: 0.939\n",
            "\n",
            "K-Nearest Neighbors with k=9: Accuracy on training Data: 0.950\n",
            "K-Nearest Neighbors with k=9: Accuracy on test Data: 0.938\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "KNN(Xtwen)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECwnfVBBJ-jh"
      },
      "source": [
        "K=5, X[ten] has given best accuracy. But it didnt exploit most of the features which may lead to underfit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lXKwx6A52L_Y"
      },
      "source": [
        "***The value of k in the KNN algorithm is related to the error rate of the model. A small value of K could lead to overfitting as well as a big value of k can lead to underfitting***<hr>\n",
        "Here the accuracy is being inversely proportional to K value. Hence KNN is not most suitable though it is simple and easy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Njphz6agMxrW"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
        "X_train.shape, y_train.shape, X_test.shape, y_test.shape\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train,y_train)\n",
        "\n",
        "y_train_knn = knn.predict(X_train)\n",
        "y_test_knn = knn.predict(X_test)\n",
        "\n",
        "acc_train_knn = metrics.accuracy_score(y_train,y_train_knn)\n",
        "acc_test_knn = metrics.accuracy_score(y_test,y_test_knn)\n",
        "\n",
        "f1_score_train_knn = metrics.f1_score(y_train,y_train_knn)\n",
        "f1_score_test_knn = metrics.f1_score(y_test,y_test_knn)\n",
        "\n",
        "precision_score_train_knn = metrics.precision_score(y_train,y_train_knn)\n",
        "precision_score_test_knn = metrics.precision_score(y_test,y_test_knn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "3DVBy6JLPdhF"
      },
      "outputs": [],
      "source": [
        "storeResults('K-Nearest Neighbors',acc_test_knn,f1_score_test_knn,precision_score_train_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYCkwUkfc4cg"
      },
      "source": [
        " **SVM Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Kua-EMRcc3fg"
      },
      "outputs": [],
      "source": [
        "def SVM(X, y):\n",
        "    x=[a for a in range(1,10,2)]\n",
        "    svmtrain=[]\n",
        "    svmtest=[]\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 = 0.2, random_state = 42)\n",
        "    X_train.shape, y_train.shape, X_test.shape, y_test.shape\n",
        "    from sklearn.svm import SVC\n",
        "    for i in range(1,10,2):\n",
        "        svm = SVC(kernel='linear', C=i)\n",
        "        svm.fit(X_train, y_train)\n",
        "        y_train_svm = svm.predict(X_train)\n",
        "        y_test_svm = svm.predict(X_test)\n",
        "        acc_train_svm = metrics.accuracy_score(y_train, y_train_svm)\n",
        "        acc_test_svm = metrics.accuracy_score(y_test, y_test_svm)\n",
        "        print(\"SVM with C={}: Accuracy on training Data: {:.3f}\".format(i,acc_train_svm))\n",
        "        print(\"SVM with C={}: Accuracy on test Data: {:.3f}\".format(i,acc_test_svm))\n",
        "        svmtrain.append(acc_train_svm)\n",
        "        svmtest.append(acc_test_svm)\n",
        "        print()\n",
        "    import matplotlib.pyplot as plt\n",
        "    plt.plot(x,svmtrain,label=\"Train accuracy\")\n",
        "    plt.plot(x,svmtest,label=\"Test accuracy\")\n",
        "    plt.legend()\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "8HpJLj68dtAK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "0d189f01-2be8-450d-fac9-dc69ec3db315"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM with C=1: Accuracy on training Data: 0.926\n",
            "SVM with C=1: Accuracy on test Data: 0.935\n",
            "\n",
            "SVM with C=3: Accuracy on training Data: 0.926\n",
            "SVM with C=3: Accuracy on test Data: 0.935\n",
            "\n",
            "SVM with C=5: Accuracy on training Data: 0.926\n",
            "SVM with C=5: Accuracy on test Data: 0.935\n",
            "\n",
            "SVM with C=7: Accuracy on training Data: 0.926\n",
            "SVM with C=7: Accuracy on test Data: 0.935\n",
            "\n",
            "SVM with C=9: Accuracy on training Data: 0.926\n",
            "SVM with C=9: Accuracy on test Data: 0.935\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Xmain=X\n",
        "Xten=X[tenfeatures]\n",
        "Xtwen=X[twenfeatures]\n",
        "\n",
        "SVM(Xmain, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "nnjlsyDqeiLy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "b5ff0e09-6e8f-4376-f9ee-478035855c2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM with C=1: Accuracy on training Data: 0.919\n",
            "SVM with C=1: Accuracy on test Data: 0.925\n",
            "\n",
            "SVM with C=3: Accuracy on training Data: 0.919\n",
            "SVM with C=3: Accuracy on test Data: 0.925\n",
            "\n",
            "SVM with C=5: Accuracy on training Data: 0.919\n",
            "SVM with C=5: Accuracy on test Data: 0.925\n",
            "\n",
            "SVM with C=7: Accuracy on training Data: 0.919\n",
            "SVM with C=7: Accuracy on test Data: 0.925\n",
            "\n",
            "SVM with C=9: Accuracy on training Data: 0.919\n",
            "SVM with C=9: Accuracy on test Data: 0.925\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "SVM(Xten,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "FCP11jPSemvn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "0e568b32-4840-44cf-feba-16e2108660e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM with C=1: Accuracy on training Data: 0.920\n",
            "SVM with C=1: Accuracy on test Data: 0.929\n",
            "\n",
            "SVM with C=3: Accuracy on training Data: 0.920\n",
            "SVM with C=3: Accuracy on test Data: 0.929\n",
            "\n",
            "SVM with C=5: Accuracy on training Data: 0.920\n",
            "SVM with C=5: Accuracy on test Data: 0.929\n",
            "\n",
            "SVM with C=7: Accuracy on training Data: 0.920\n",
            "SVM with C=7: Accuracy on test Data: 0.929\n",
            "\n",
            "SVM with C=9: Accuracy on training Data: 0.920\n",
            "SVM with C=9: Accuracy on test Data: 0.929\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "SVM(Xtwen, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "8Fz7xgnBiTUX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "967194ed-ffc6-41b5-b121-44d062565051"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM with C=1: Accuracy on training data: 0.926\n",
            "SVM with C=1: Accuracy on test data: 0.935\n",
            "SVM with C=1: F1 score on training data: 0.934\n",
            "SVM with C=1: F1 score on test data: 0.942\n",
            "SVM with C=1: Precision on training data: 0.926\n",
            "SVM with C=1: Precision on test data: 0.930\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn import metrics\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "svm = SVC(kernel='linear', C=1, random_state=42)\n",
        "svm.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_train_svm = svm.predict(X_train)\n",
        "y_test_svm = svm.predict(X_test)\n",
        "\n",
        "\n",
        "acc_train_svm = metrics.accuracy_score(y_train, y_train_svm)\n",
        "acc_test_svm = metrics.accuracy_score(y_test, y_test_svm)\n",
        "\n",
        "f1_score_train_svm = metrics.f1_score(y_train, y_train_svm)\n",
        "f1_score_test_svm = metrics.f1_score(y_test, y_test_svm)\n",
        "\n",
        "precision_score_train_svm = metrics.precision_score(y_train, y_train_svm)\n",
        "precision_score_test_svm = metrics.precision_score(y_test, y_test_svm)\n",
        "\n",
        "print(\"SVM with C={}: Accuracy on training data: {:.3f}\".format(1, acc_train_svm))\n",
        "print(\"SVM with C={}: Accuracy on test data: {:.3f}\".format(1, acc_test_svm))\n",
        "print(\"SVM with C={}: F1 score on training data: {:.3f}\".format(1, f1_score_train_svm))\n",
        "print(\"SVM with C={}: F1 score on test data: {:.3f}\".format(1, f1_score_test_svm))\n",
        "print(\"SVM with C={}: Precision on training data: {:.3f}\".format(1, precision_score_train_svm))\n",
        "print(\"SVM with C={}: Precision on test data: {:.3f}\".format(1, precision_score_test_svm))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "_oE9YnoU2Y-f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "93d7a44b-a00c-4e27-ce6f-7ff1df3f6865"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm\n",
        "\n",
        "def plot_decision_boundary(X, y):\n",
        "    if isinstance(X, pd.DataFrame):\n",
        "        X = X.values\n",
        "    if isinstance(y, pd.DataFrame):\n",
        "        y = y.values\n",
        "\n",
        "    # Fit SVM model\n",
        "    clf = svm.SVC(kernel='linear')\n",
        "    clf.fit(X, y)\n",
        "\n",
        "    # Get the separating hyperplane\n",
        "    w = clf.coef_[0]\n",
        "    a = -w[0] / w[1]\n",
        "    xx = np.linspace(np.min(X[:,0]), np.max(X[:,0]))\n",
        "    yy = a * xx - (clf.intercept_[0]) / w[1]\n",
        "\n",
        "    # Plot the decision boundary\n",
        "    plt.plot(xx, yy, 'k-')\n",
        "\n",
        "    # Plot the support vectors\n",
        "    plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
        "                s=80, facecolors='none', edgecolors='b')\n",
        "\n",
        "    # Plot the data points\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1)\n",
        "\n",
        "    # Set the x-axis and y-axis limits\n",
        "    plt.xlim(np.min(X[:,0]), np.max(X[:,0]))\n",
        "    plt.ylim(np.min(X[:,1]), np.max(X[:,1]))\n",
        "\n",
        "    # Set the x-axis and y-axis labels\n",
        "    plt.xlabel('Feature 1')\n",
        "    plt.ylabel('Feature 2')\n",
        "\n",
        "    # Show the plot\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "plot_decision_boundary(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "xW8T_IQlwFJi"
      },
      "outputs": [],
      "source": [
        "storeResults('Support Vector Machines',acc_test_svm,f1_score_test_svm,precision_score_train_svm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POWGwd5rDDT7"
      },
      "source": [
        "# Gradient Boost (Boosting Based)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzz8_O_bshRe"
      },
      "source": [
        "**Gradient Booster Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "MaDVXLPODQgO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5490737d-2de9-44ea-e77b-3c72e55bdba0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8843, 30), (8843,), (2211, 30), (2211,))"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "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 = 0.2, random_state = 42)\n",
        "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "cz8eUTMesm_A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "707e79bb-3570-4ece-eeee-114eae9a83a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(learning_rate=0.7, max_depth=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.7, max_depth=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.7, max_depth=4)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "gbc = GradientBoostingClassifier(max_depth=4,learning_rate=0.7)\n",
        "gbc.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "dfGJSfEosyoo"
      },
      "outputs": [],
      "source": [
        "y_train_gbc = gbc.predict(X_train)\n",
        "y_test_gbc = gbc.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "rr9_PTz2s7K5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b14e49c-496c-4f40-b63e-c14ed6e7dd10"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting Classifier : Accuracy on training Data: 0.989\n",
            "Gradient Boosting Classifier : Accuracy on test Data: 0.974\n",
            "\n"
          ]
        }
      ],
      "source": [
        "acc_train_gbc = metrics.accuracy_score(y_train,y_train_gbc)\n",
        "acc_test_gbc = metrics.accuracy_score(y_test,y_test_gbc)\n",
        "print(\"Gradient Boosting Classifier : Accuracy on training Data: {:.3f}\".format(acc_train_gbc))\n",
        "print(\"Gradient Boosting Classifier : Accuracy on test Data: {:.3f}\".format(acc_test_gbc))\n",
        "print()\n",
        "\n",
        "f1_score_train_gbc = metrics.f1_score(y_train,y_train_gbc)\n",
        "f1_score_test_gbc = metrics.f1_score(y_test,y_test_gbc)\n",
        "\n",
        "precision_score_train_gbc = metrics.precision_score(y_train,y_train_gbc)\n",
        "precision_score_test_gbc = metrics.precision_score(y_test,y_test_gbc)\n",
        "\n",
        "storeResults('Gradient Boosting Classifier',acc_test_gbc,f1_score_test_gbc,precision_score_train_gbc)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRgnhMFEtHnu"
      },
      "source": [
        "<h3>Gradient Boost classifier has higher accuracy hence this model will be used hereafter</h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "c2T5OAolSgUH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "a7a79352-5552-4cec-80e0-f764a08670dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df = pd.DataFrame({\n",
        "    'Modelname': ML_Model,\n",
        "    'Accuracy Score': accuracy,\n",
        "    'F1 Score': f1_score,\n",
        "    'Precision Score': precision\n",
        "})\n",
        "df.set_index('Modelname', inplace=True)\n",
        "\n",
        "# plot the scores for each model\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "df.plot(kind='bar', ax=ax)\n",
        "ax.set_xticklabels(df.index, rotation=0)\n",
        "ax.set_ylim([0.9, 1])\n",
        "ax.set_yticks([0.9,0.91,0.92,0.93,0.94,0.95,0.96,0.97,0.98,0.99,1])\n",
        "ax.set_xlabel('Model')\n",
        "ax.set_ylabel('Score')\n",
        "ax.set_title('Model Scores')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qaTTijpvvg_u"
      },
      "source": [
        "Decoding URL to variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "rlrbFWFrwSW-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "084f8f0a-4127-48cc-8428-044c1fb2f5a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting python-whois\n",
            "  Downloading python-whois-0.8.0.tar.gz (109 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109.6/109.6 KB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.9/dist-packages (from python-whois) (0.16.0)\n",
            "Building wheels for collected packages: python-whois\n",
            "  Building wheel for python-whois (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-whois: filename=python_whois-0.8.0-py3-none-any.whl size=103262 sha256=d3d533b75a24ccbb332ab7a84a11544aa90b06d59fe295fdb231304f17a12a7e\n",
            "  Stored in directory: /root/.cache/pip/wheels/e6/e9/d3/1e41a6c95b398de12c5a332ff28805aa44e68aa317ea60266d\n",
            "Successfully built python-whois\n",
            "Installing collected packages: python-whois\n",
            "Successfully installed python-whois-0.8.0\n"
          ]
        }
      ],
      "source": [
        "pip install python-whois"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "wfGNFluJtGZL"
      },
      "outputs": [],
      "source": [
        "import ipaddress\n",
        "import re\n",
        "import urllib.request\n",
        "from bs4 import BeautifulSoup\n",
        "import socket\n",
        "import requests\n",
        "from googlesearch import search\n",
        "import whois\n",
        "from datetime import date, datetime\n",
        "import time\n",
        "from dateutil.parser import parse as date_parse\n",
        "from urllib.parse import urlparse\n",
        "\n",
        "class FeatureExtraction:\n",
        "    features = []\n",
        "    def __init__(self,url):\n",
        "        self.features = []\n",
        "        self.url = url\n",
        "        self.domain = \"\"\n",
        "        self.whois_response = \"\"\n",
        "        self.urlparse = \"\"\n",
        "        self.response = \"\"\n",
        "        self.soup = \"\"\n",
        "\n",
        "        try:\n",
        "            self.response = requests.get(url)\n",
        "            self.soup = BeautifulSoup(response.text, 'html.parser')\n",
        "        except:\n",
        "            pass\n",
        "\n",
        "        try:\n",
        "            self.urlparse = urlparse(url)\n",
        "            self.domain = self.urlparse.netloc\n",
        "        except:\n",
        "            pass\n",
        "\n",
        "        try:\n",
        "            self.whois_response = whois.whois(self.domain)\n",
        "        except:\n",
        "            pass\n",
        "\n",
        "\n",
        "        \n",
        "\n",
        "        self.features.append(self.UsingIp())\n",
        "        self.features.append(self.longUrl())\n",
        "        self.features.append(self.shortUrl())\n",
        "        self.features.append(self.symbol())\n",
        "        self.features.append(self.redirecting())\n",
        "        self.features.append(self.prefixSuffix())\n",
        "        self.features.append(self.SubDomains())\n",
        "        self.features.append(self.Hppts())\n",
        "        self.features.append(self.DomainRegLen())\n",
        "        self.features.append(self.Favicon())\n",
        "        \n",
        "\n",
        "        self.features.append(self.NonStdPort())\n",
        "        self.features.append(self.HTTPSDomainURL())\n",
        "        self.features.append(self.RequestURL())\n",
        "        self.features.append(self.AnchorURL())\n",
        "        self.features.append(self.LinksInScriptTags())\n",
        "        self.features.append(self.ServerFormHandler())\n",
        "        self.features.append(self.InfoEmail())\n",
        "        self.features.append(self.AbnormalURL())\n",
        "        self.features.append(self.WebsiteForwarding())\n",
        "        self.features.append(self.StatusBarCust())\n",
        "\n",
        "        self.features.append(self.DisableRightClick())\n",
        "        self.features.append(self.UsingPopupWindow())\n",
        "        self.features.append(self.IframeRedirection())\n",
        "        self.features.append(self.AgeofDomain())\n",
        "        self.features.append(self.DNSRecording())\n",
        "        self.features.append(self.WebsiteTraffic())\n",
        "        self.features.append(self.PageRank())\n",
        "        self.features.append(self.GoogleIndex())\n",
        "        self.features.append(self.LinksPointingToPage())\n",
        "        self.features.append(self.StatsReport())\n",
        "\n",
        "\n",
        "     # 1.UsingIp\n",
        "    def UsingIp(self):\n",
        "        try:\n",
        "            ipaddress.ip_address(self.url)\n",
        "            return -1\n",
        "        except:\n",
        "            return 1\n",
        "\n",
        "    # 2.longUrl\n",
        "    def longUrl(self):\n",
        "        if len(self.url) < 54:\n",
        "            return 1\n",
        "        if len(self.url) >= 54 and len(self.url) <= 75:\n",
        "            return 0\n",
        "        return -1\n",
        "\n",
        "    # 3.shortUrl\n",
        "    def shortUrl(self):\n",
        "        match = re.search('bit\\.ly|goo\\.gl|shorte\\.st|go2l\\.ink|x\\.co|ow\\.ly|t\\.co|tinyurl|tr\\.im|is\\.gd|cli\\.gs|'\n",
        "                    'yfrog\\.com|migre\\.me|ff\\.im|tiny\\.cc|url4\\.eu|twit\\.ac|su\\.pr|twurl\\.nl|snipurl\\.com|'\n",
        "                    'short\\.to|BudURL\\.com|ping\\.fm|post\\.ly|Just\\.as|bkite\\.com|snipr\\.com|fic\\.kr|loopt\\.us|'\n",
        "                    'doiop\\.com|short\\.ie|kl\\.am|wp\\.me|rubyurl\\.com|om\\.ly|to\\.ly|bit\\.do|t\\.co|lnkd\\.in|'\n",
        "                    'db\\.tt|qr\\.ae|adf\\.ly|goo\\.gl|bitly\\.com|cur\\.lv|tinyurl\\.com|ow\\.ly|bit\\.ly|ity\\.im|'\n",
        "                    'q\\.gs|is\\.gd|po\\.st|bc\\.vc|twitthis\\.com|u\\.to|j\\.mp|buzurl\\.com|cutt\\.us|u\\.bb|yourls\\.org|'\n",
        "                    'x\\.co|prettylinkpro\\.com|scrnch\\.me|filoops\\.info|vzturl\\.com|qr\\.net|1url\\.com|tweez\\.me|v\\.gd|tr\\.im|link\\.zip\\.net', self.url)\n",
        "        if match:\n",
        "            return -1\n",
        "        return 1\n",
        "\n",
        "    # 4.Symbol@\n",
        "    def symbol(self):\n",
        "        if re.findall(\"@\",self.url):\n",
        "            return -1\n",
        "        return 1\n",
        "    \n",
        "    # 5.Redirecting//\n",
        "    def redirecting(self):\n",
        "        if self.url.rfind('//')>6:\n",
        "            return -1\n",
        "        return 1\n",
        "    \n",
        "    # 6.prefixSuffix\n",
        "    def prefixSuffix(self):\n",
        "        try:\n",
        "            match = re.findall('\\-', self.domain)\n",
        "            if match:\n",
        "                return -1\n",
        "            return 1\n",
        "        except:\n",
        "            return -1\n",
        "    \n",
        "    # 7.SubDomains\n",
        "    def SubDomains(self):\n",
        "        dot_count = len(re.findall(\"\\.\", self.url))\n",
        "        if dot_count == 1:\n",
        "            return 1\n",
        "        elif dot_count == 2:\n",
        "            return 0\n",
        "        return -1\n",
        "\n",
        "    # 8.HTTPS\n",
        "    def Hppts(self):\n",
        "        try:\n",
        "            https = self.urlparse.scheme\n",
        "            if 'https' in https:\n",
        "                return 1\n",
        "            return -1\n",
        "        except:\n",
        "            return 1\n",
        "\n",
        "    # 9.DomainRegLen\n",
        "    def DomainRegLen(self):\n",
        "        try:\n",
        "            expiration_date = self.whois_response.expiration_date\n",
        "            creation_date = self.whois_response.creation_date\n",
        "            try:\n",
        "                if(len(expiration_date)):\n",
        "                    expiration_date = expiration_date[0]\n",
        "            except:\n",
        "                pass\n",
        "            try:\n",
        "                if(len(creation_date)):\n",
        "                    creation_date = creation_date[0]\n",
        "            except:\n",
        "                pass\n",
        "\n",
        "            age = (expiration_date.year-creation_date.year)*12+ (expiration_date.month-creation_date.month)\n",
        "            if age >=12:\n",
        "                return 1\n",
        "            return -1\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 10. Favicon\n",
        "    def Favicon(self):\n",
        "        try:\n",
        "            for head in self.soup.find_all('head'):\n",
        "                for head.link in self.soup.find_all('link', href=True):\n",
        "                    dots = [x.start(0) for x in re.finditer('\\.', head.link['href'])]\n",
        "                    if self.url in head.link['href'] or len(dots) == 1 or domain in head.link['href']:\n",
        "                        return 1\n",
        "            return -1\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 11. NonStdPort\n",
        "    def NonStdPort(self):\n",
        "        try:\n",
        "            port = self.domain.split(\":\")\n",
        "            if len(port)>1:\n",
        "                return -1\n",
        "            return 1\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 12. HTTPSDomainURL\n",
        "    def HTTPSDomainURL(self):\n",
        "        try:\n",
        "            if 'https' in self.domain:\n",
        "                return -1\n",
        "            return 1\n",
        "        except:\n",
        "            return -1\n",
        "    \n",
        "    # 13. RequestURL\n",
        "    def RequestURL(self):\n",
        "        try:\n",
        "            for img in self.soup.find_all('img', src=True):\n",
        "                dots = [x.start(0) for x in re.finditer('\\.', img['src'])]\n",
        "                if self.url in img['src'] or self.domain in img['src'] or len(dots) == 1:\n",
        "                    success = success + 1\n",
        "                i = i+1\n",
        "\n",
        "            for audio in self.soup.find_all('audio', src=True):\n",
        "                dots = [x.start(0) for x in re.finditer('\\.', audio['src'])]\n",
        "                if self.url in audio['src'] or self.domain in audio['src'] or len(dots) == 1:\n",
        "                    success = success + 1\n",
        "                i = i+1\n",
        "\n",
        "            for embed in self.soup.find_all('embed', src=True):\n",
        "                dots = [x.start(0) for x in re.finditer('\\.', embed['src'])]\n",
        "                if self.url in embed['src'] or self.domain in embed['src'] or len(dots) == 1:\n",
        "                    success = success + 1\n",
        "                i = i+1\n",
        "\n",
        "            for iframe in self.soup.find_all('iframe', src=True):\n",
        "                dots = [x.start(0) for x in re.finditer('\\.', iframe['src'])]\n",
        "                if self.url in iframe['src'] or self.domain in iframe['src'] or len(dots) == 1:\n",
        "                    success = success + 1\n",
        "                i = i+1\n",
        "\n",
        "            try:\n",
        "                percentage = success/float(i) * 100\n",
        "                if percentage < 22.0:\n",
        "                    return 1\n",
        "                elif((percentage >= 22.0) and (percentage < 61.0)):\n",
        "                    return 0\n",
        "                else:\n",
        "                    return -1\n",
        "            except:\n",
        "                return 0\n",
        "        except:\n",
        "            return -1\n",
        "    \n",
        "    # 14. AnchorURL\n",
        "    def AnchorURL(self):\n",
        "        try:\n",
        "            i,unsafe = 0,0\n",
        "            for a in self.soup.find_all('a', href=True):\n",
        "                if \"#\" in a['href'] or \"javascript\" in a['href'].lower() or \"mailto\" in a['href'].lower() or not (url in a['href'] or self.domain in a['href']):\n",
        "                    unsafe = unsafe + 1\n",
        "                i = i + 1\n",
        "\n",
        "            try:\n",
        "                percentage = unsafe / float(i) * 100\n",
        "                if percentage < 31.0:\n",
        "                    return 1\n",
        "                elif ((percentage >= 31.0) and (percentage < 67.0)):\n",
        "                    return 0\n",
        "                else:\n",
        "                    return -1\n",
        "            except:\n",
        "                return -1\n",
        "\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 15. LinksInScriptTags\n",
        "    def LinksInScriptTags(self):\n",
        "        try:\n",
        "            i,success = 0,0\n",
        "        \n",
        "            for link in self.soup.find_all('link', href=True):\n",
        "                dots = [x.start(0) for x in re.finditer('\\.', link['href'])]\n",
        "                if self.url in link['href'] or self.domain in link['href'] or len(dots) == 1:\n",
        "                    success = success + 1\n",
        "                i = i+1\n",
        "\n",
        "            for script in self.soup.find_all('script', src=True):\n",
        "                dots = [x.start(0) for x in re.finditer('\\.', script['src'])]\n",
        "                if self.url in script['src'] or self.domain in script['src'] or len(dots) == 1:\n",
        "                    success = success + 1\n",
        "                i = i+1\n",
        "\n",
        "            try:\n",
        "                percentage = success / float(i) * 100\n",
        "                if percentage < 17.0:\n",
        "                    return 1\n",
        "                elif((percentage >= 17.0) and (percentage < 81.0)):\n",
        "                    return 0\n",
        "                else:\n",
        "                    return -1\n",
        "            except:\n",
        "                return 0\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 16. ServerFormHandler\n",
        "    def ServerFormHandler(self):\n",
        "        try:\n",
        "            if len(self.soup.find_all('form', action=True))==0:\n",
        "                return 1\n",
        "            else :\n",
        "                for form in self.soup.find_all('form', action=True):\n",
        "                    if form['action'] == \"\" or form['action'] == \"about:blank\":\n",
        "                        return -1\n",
        "                    elif self.url not in form['action'] and self.domain not in form['action']:\n",
        "                        return 0\n",
        "                    else:\n",
        "                        return 1\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 17. InfoEmail\n",
        "    def InfoEmail(self):\n",
        "        try:\n",
        "            if re.findall(r\"[mail\\(\\)|mailto:?]\", self.soap):\n",
        "                return -1\n",
        "            else:\n",
        "                return 1\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 18. AbnormalURL\n",
        "    def AbnormalURL(self):\n",
        "        try:\n",
        "            if self.response.text == self.whois_response:\n",
        "                return 1\n",
        "            else:\n",
        "                return -1\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 19. WebsiteForwarding\n",
        "    def WebsiteForwarding(self):\n",
        "        try:\n",
        "            if len(self.response.history) <= 1:\n",
        "                return 1\n",
        "            elif len(self.response.history) <= 4:\n",
        "                return 0\n",
        "            else:\n",
        "                return -1\n",
        "        except:\n",
        "             return -1\n",
        "\n",
        "    # 20. StatusBarCust\n",
        "    def StatusBarCust(self):\n",
        "        try:\n",
        "            if re.findall(\"<script>.+onmouseover.+</script>\", self.response.text):\n",
        "                return 1\n",
        "            else:\n",
        "                return -1\n",
        "        except:\n",
        "             return -1\n",
        "\n",
        "    # 21. DisableRightClick\n",
        "    def DisableRightClick(self):\n",
        "        try:\n",
        "            if re.findall(r\"event.button ?== ?2\", self.response.text):\n",
        "                return 1\n",
        "            else:\n",
        "                return -1\n",
        "        except:\n",
        "             return -1\n",
        "\n",
        "    # 22. UsingPopupWindow\n",
        "    def UsingPopupWindow(self):\n",
        "        try:\n",
        "            if re.findall(r\"alert\\(\", self.response.text):\n",
        "                return 1\n",
        "            else:\n",
        "                return -1\n",
        "        except:\n",
        "             return -1\n",
        "\n",
        "    # 23. IframeRedirection\n",
        "    def IframeRedirection(self):\n",
        "        try:\n",
        "            if re.findall(r\"[<iframe>|<frameBorder>]\", self.response.text):\n",
        "                return 1\n",
        "            else:\n",
        "                return -1\n",
        "        except:\n",
        "             return -1\n",
        "\n",
        "    # 24. AgeofDomain\n",
        "    def AgeofDomain(self):\n",
        "        try:\n",
        "            creation_date = self.whois_response.creation_date\n",
        "            try:\n",
        "                if(len(creation_date)):\n",
        "                    creation_date = creation_date[0]\n",
        "            except:\n",
        "                pass\n",
        "\n",
        "            today  = date.today()\n",
        "            age = (today.year-creation_date.year)*12+(today.month-creation_date.month)\n",
        "            if age >=6:\n",
        "                return 1\n",
        "            return -1\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 25. DNSRecording    \n",
        "    def DNSRecording(self):\n",
        "        try:\n",
        "            creation_date = self.whois_response.creation_date\n",
        "            try:\n",
        "                if(len(creation_date)):\n",
        "                    creation_date = creation_date[0]\n",
        "            except:\n",
        "                pass\n",
        "\n",
        "            today  = date.today()\n",
        "            age = (today.year-creation_date.year)*12+(today.month-creation_date.month)\n",
        "            if age >=6:\n",
        "                return 1\n",
        "            return -1\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 26. WebsiteTraffic   \n",
        "    def WebsiteTraffic(self):\n",
        "        try:\n",
        "            rank = BeautifulSoup(urllib.request.urlopen(\"http://data.alexa.com/data?cli=10&dat=s&url=\" + url).read(), \"xml\").find(\"REACH\")['RANK']\n",
        "            if (int(rank) < 100000):\n",
        "                return 1\n",
        "            return 0\n",
        "        except :\n",
        "            return -1\n",
        "\n",
        "    # 27. PageRank\n",
        "    def PageRank(self):\n",
        "        try:\n",
        "            prank_checker_response = requests.post(\"https://www.checkpagerank.net/index.php\", {\"name\": self.domain})\n",
        "\n",
        "            global_rank = int(re.findall(r\"Global Rank: ([0-9]+)\", rank_checker_response.text)[0])\n",
        "            if global_rank > 0 and global_rank < 100000:\n",
        "                return 1\n",
        "            return -1\n",
        "        except:\n",
        "            return -1\n",
        "            \n",
        "\n",
        "    # 28. GoogleIndex\n",
        "    def GoogleIndex(self):\n",
        "        try:\n",
        "            site = search(self.url, 5)\n",
        "            if site:\n",
        "                return 1\n",
        "            else:\n",
        "                return -1\n",
        "        except:\n",
        "            return 1\n",
        "\n",
        "    # 29. LinksPointingToPage\n",
        "    def LinksPointingToPage(self):\n",
        "        try:\n",
        "            number_of_links = len(re.findall(r\"<a href=\", self.response.text))\n",
        "            if number_of_links == 0:\n",
        "                return 1\n",
        "            elif number_of_links <= 2:\n",
        "                return 0\n",
        "            else:\n",
        "                return -1\n",
        "        except:\n",
        "            return -1\n",
        "\n",
        "    # 30. StatsReport\n",
        "    def StatsReport(self):\n",
        "        try:\n",
        "            url_match = re.search(\n",
        "        'at\\.ua|usa\\.cc|baltazarpresentes\\.com\\.br|pe\\.hu|esy\\.es|hol\\.es|sweddy\\.com|myjino\\.ru|96\\.lt|ow\\.ly', url)\n",
        "            ip_address = socket.gethostbyname(self.domain)\n",
        "            ip_match = re.search('146\\.112\\.61\\.108|213\\.174\\.157\\.151|121\\.50\\.168\\.88|192\\.185\\.217\\.116|78\\.46\\.211\\.158|181\\.174\\.165\\.13|46\\.242\\.145\\.103|121\\.50\\.168\\.40|83\\.125\\.22\\.219|46\\.242\\.145\\.98|'\n",
        "                                '107\\.151\\.148\\.44|107\\.151\\.148\\.107|64\\.70\\.19\\.203|199\\.184\\.144\\.27|107\\.151\\.148\\.108|107\\.151\\.148\\.109|119\\.28\\.52\\.61|54\\.83\\.43\\.69|52\\.69\\.166\\.231|216\\.58\\.192\\.225|'\n",
        "                                '118\\.184\\.25\\.86|67\\.208\\.74\\.71|23\\.253\\.126\\.58|104\\.239\\.157\\.210|175\\.126\\.123\\.219|141\\.8\\.224\\.221|10\\.10\\.10\\.10|43\\.229\\.108\\.32|103\\.232\\.215\\.140|69\\.172\\.201\\.153|'\n",
        "                                '216\\.218\\.185\\.162|54\\.225\\.104\\.146|103\\.243\\.24\\.98|199\\.59\\.243\\.120|31\\.170\\.160\\.61|213\\.19\\.128\\.77|62\\.113\\.226\\.131|208\\.100\\.26\\.234|195\\.16\\.127\\.102|195\\.16\\.127\\.157|'\n",
        "                                '34\\.196\\.13\\.28|103\\.224\\.212\\.222|172\\.217\\.4\\.225|54\\.72\\.9\\.51|192\\.64\\.147\\.141|198\\.200\\.56\\.183|23\\.253\\.164\\.103|52\\.48\\.191\\.26|52\\.214\\.197\\.72|87\\.98\\.255\\.18|209\\.99\\.17\\.27|'\n",
        "                                '216\\.38\\.62\\.18|104\\.130\\.124\\.96|47\\.89\\.58\\.141|78\\.46\\.211\\.158|54\\.86\\.225\\.156|54\\.82\\.156\\.19|37\\.157\\.192\\.102|204\\.11\\.56\\.48|110\\.34\\.231\\.42', ip_address)\n",
        "            if url_match:\n",
        "                return -1\n",
        "            elif ip_match:\n",
        "                return -1\n",
        "            return 1\n",
        "        except:\n",
        "            return 1\n",
        "    \n",
        "    def getFeaturesList(self):\n",
        "        return self.features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "9CNSNZWWyNC_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "d6ff4109-cb50-43ae-8646-214a1ef3fbe8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(learning_rate=0.7, max_depth=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.7, max_depth=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.7, max_depth=4)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "gbc = GradientBoostingClassifier(max_depth=4,learning_rate=0.7)\n",
        "gbc.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "alKp13XQveql",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8577389-1b43-4a7c-a387-c2729e2f4dd8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\thttp://8csdg3iejj.lilagoraj.pl/\n",
            "Caution! Suspicious website detected\n"
          ]
        }
      ],
      "source": [
        "url=input()\n",
        "obj = FeatureExtraction(url)\n",
        "x = np.array(obj.getFeaturesList()).reshape(1,30) \n",
        "y_pred =gbc.predict(x)[0]\n",
        "if y_pred==1:\n",
        "  print(\"We guess it is a safe website\")\n",
        "else:\n",
        "  print(\"Caution! Suspicious website detected\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}