Anomaly_Detection_Pycaret.ipynb

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    "colab": {
      "name": "Anomaly Detection.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/parasgulati8/Fraud-Detection/blob/master/Anomaly_Detection_Pycaret.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "oMwX8JaoPunp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "48533782-47af-49ef-b58e-1f3fae0b67cf"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1jPxtOQRNNh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d71fa02c-7a23-4f46-fed5-73e11a867ccd"
      },
      "source": [
        "cd drive/'My Drive'/'Colab Notebooks'/dataset/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks/dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQGAfwQqbt40",
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        "colab": {
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          "height": 1000
        },
        "outputId": "de6bf0e0-6bdd-495b-c609-820aca4aee29"
      },
      "source": [
        "!pip install pycaret"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Collecting funcy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ce/4b/6ffa76544e46614123de31574ad95758c421aae391a1764921b8a81e1eae/funcy-1.14.tar.gz (548kB)\n",
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            "Collecting zope.interface\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/57/33/565274c28a11af60b7cfc0519d46bde4125fcd7d32ebc0a81b480d0e8da6/zope.interface-5.1.0-cp36-cp36m-manylinux2010_x86_64.whl (234kB)\n",
            "\u001b[K     |████████████████████████████████| 235kB 44.7MB/s \n",
            "\u001b[?25hCollecting combo\n",
            "  Downloading https://files.pythonhosted.org/packages/78/52/e880bd923eba122515307d29ab43c1c356bad60610c27bed2cdec25d0240/combo-0.1.0.tar.gz\n",
            "Collecting suod\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a1/87/9170cabe1b5e10a7d095c0e28f2e30e7c1886a13f063de85d3cfacc06f4b/suod-0.0.4.tar.gz (2.1MB)\n",
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            "\u001b[?25hCollecting chart-studio>=1.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/ce/330794a6b6ca4b9182c38fc69dd2a9cbff60fd49421cb8648ee5fee352dc/chart_studio-1.1.0-py3-none-any.whl (64kB)\n",
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            "Building wheels for collected packages: pandas-profiling, shap, pyLDAvis, pyod, cufflinks, htmlmin, confuse, funcy, combo, suod\n",
            "  Building wheel for pandas-profiling (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/f2/67/a4/ff47e0efaf4a93617047d2be52862dda80a848ff5c67bfb18d\n",
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            "  Created wheel for cufflinks: filename=cufflinks-0.17.0-cp36-none-any.whl size=67744 sha256=a59dbda0dbc187bf908dde1eb308356f7d2e402c782d816e82bfaac5fb1dc7c5\n",
            "  Stored in directory: /root/.cache/pip/wheels/44/d7/dc/e830ab00bc2dd3b2731295103baa070f8cbdda8891f71a7a8d\n",
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            "  Created wheel for htmlmin: filename=htmlmin-0.1.12-cp36-none-any.whl size=27084 sha256=b39aff789b182e53bb1b17302ed9d36cf23683a84b5e989b0dbf1383f1568530\n",
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            "  Created wheel for confuse: filename=confuse-1.1.0-cp36-none-any.whl size=17574 sha256=1986b64fecebb37ace4f4d2d75532a79823d32fa3e14d94a4d5516fe68a99820\n",
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            "  Created wheel for funcy: filename=funcy-1.14-py2.py3-none-any.whl size=32042 sha256=840b3b95dca227b130940859a2ac8bb95548fed39a65d34a686bb495d38bad62\n",
            "  Stored in directory: /root/.cache/pip/wheels/20/5a/d8/1d875df03deae6f178dfdf70238cca33f948ef8a6f5209f2eb\n",
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            "  Created wheel for combo: filename=combo-0.1.0-cp36-none-any.whl size=42043 sha256=ddf6c9c1824f8a424005d68354ae6a70cf5da0b93ad70a04680fde9286d7ceb4\n",
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            "  Created wheel for suod: filename=suod-0.0.4-cp36-none-any.whl size=2167157 sha256=b97a2488bb110d3227cc1dcffcfb01b8ae8d4b10d694aaf25c5afbbf4ee2af13\n",
            "  Stored in directory: /root/.cache/pip/wheels/57/55/e5/a4fca65bba231f6d0115059b589148774b41faea25b3f2aa27\n",
            "Successfully built pandas-profiling shap pyLDAvis pyod cufflinks htmlmin confuse funcy combo suod\n",
            "Installing collected packages: scikit-learn, kmodes, catboost, lightgbm, yellowbrick, htmlmin, phik, confuse, pandas-profiling, shap, botocore, colorama, rsa, awscli, funcy, pyLDAvis, datefinder, zope.interface, datetime, combo, suod, pyod, chart-studio, cufflinks, pycaret\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: lightgbm 2.2.3\n",
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            "  Found existing installation: yellowbrick 0.9.1\n",
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            "  Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "  Found existing installation: botocore 1.16.4\n",
            "    Uninstalling botocore-1.16.4:\n",
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            "  Found existing installation: rsa 4.0\n",
            "    Uninstalling rsa-4.0:\n",
            "      Successfully uninstalled rsa-4.0\n",
            "  Found existing installation: cufflinks 0.17.3\n",
            "    Uninstalling cufflinks-0.17.3:\n",
            "      Successfully uninstalled cufflinks-0.17.3\n",
            "Successfully installed awscli-1.18.61 botocore-1.16.11 catboost-0.20.2 chart-studio-1.1.0 colorama-0.4.3 combo-0.1.0 confuse-1.1.0 cufflinks-0.17.0 datefinder-0.7.0 datetime-4.3 funcy-1.14 htmlmin-0.1.12 kmodes-0.10.1 lightgbm-2.3.1 pandas-profiling-2.3.0 phik-0.9.12 pyLDAvis-2.1.2 pycaret-1.0.0 pyod-0.7.9 rsa-3.4.2 scikit-learn-0.22 shap-0.32.1 suod-0.0.4 yellowbrick-1.0.1 zope.interface-5.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "rsa",
                  "sklearn"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zs57oTvOCn-p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import itertools\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import cohen_kappa_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import VotingClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0s5VkWgzcoem",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"\n",
        "Created on Mon Feb  4 13:25:04 2019\n",
        "\n",
        "@author: paras\n",
        "\"\"\"\n",
        "dataset = pd.read_csv('creditcard.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, 30].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuS62wQycGcj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "94e20f5f-a205-4474-a09e-c5b8de67147a"
      },
      "source": [
        "from pycaret.classification import *\n",
        "clf1 = setup(data = dataset, target = 'Class')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-f0e24636043c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpycaret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassification\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mclf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Class'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pycaret'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
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              "        }</style><table id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row0_col0\" class=\"data row0 col0\" >Extra Trees Classifier</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.999600</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.954700</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.788300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.968600</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.863500</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.863300</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row1_col0\" class=\"data row1 col0\" >CatBoost Classifier</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.999600</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.976600</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.808800</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.962600</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.874700</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.874500</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row2_col0\" class=\"data row2 col0\" >Random Forest Classifier</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.999500</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.933200</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.767700</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.954300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.847600</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.847400</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row3_col0\" class=\"data row3 col0\" >Extreme Gradient Boosting</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.999500</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.978400</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.783800</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.945700</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.853000</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.852800</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row4_col0\" class=\"data row4 col0\" >Linear Discriminant Analysis</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.999300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.980400</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.734500</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.868700</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.792700</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.792400</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row5_col0\" class=\"data row5 col0\" >Decision Tree Classifier</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.999200</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.898000</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.796300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.767400</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.778300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.777900</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row6_col0\" class=\"data row6 col0\" >Ada Boost Classifier</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.999200</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.964300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.697000</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.829600</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.752900</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.752500</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row7_col0\" class=\"data row7 col0\" >Gradient Boosting Classifier</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.999200</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.803800</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.642300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.851300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.713800</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.713400</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row8_col0\" class=\"data row8 col0\" >Logistic Regression</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.999100</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.918100</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.713700</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.734000</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.721700</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.721200</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row9_col0\" class=\"data row9 col0\" >Ridge Classifier</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.999000</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row9_col3\" class=\"data row9 col3\" >0.481300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row9_col4\" class=\"data row9 col4\" >0.866700</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row9_col5\" class=\"data row9 col5\" >0.611800</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row9_col6\" class=\"data row9 col6\" >0.611300</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row10_col0\" class=\"data row10 col0\" >K Neighbors Classifier</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.998400</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.600400</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.054000</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.700000</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.099500</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.099300</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row11_col0\" class=\"data row11 col0\" >SVM - Linear Kernel</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.998200</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.000000</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.029200</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.058300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.038900</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.038700</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row12_col0\" class=\"data row12 col0\" >Light Gradient Boosting Machine</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row12_col1\" class=\"data row12 col1\" >0.995300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row12_col2\" class=\"data row12 col2\" >0.663800</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row12_col3\" class=\"data row12 col3\" >0.486500</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row12_col4\" class=\"data row12 col4\" >0.243600</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row12_col5\" class=\"data row12 col5\" >0.299400</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row12_col6\" class=\"data row12 col6\" >0.297700</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row13_col0\" class=\"data row13 col0\" >Naive Bayes</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row13_col1\" class=\"data row13 col1\" >0.993500</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row13_col2\" class=\"data row13 col2\" >0.965300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row13_col3\" class=\"data row13 col3\" >0.647300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row13_col4\" class=\"data row13 col4\" >0.159800</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row13_col5\" class=\"data row13 col5\" >0.255800</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row13_col6\" class=\"data row13 col6\" >0.253700</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row14_col0\" class=\"data row14 col0\" >Quadratic Discriminant Analysis</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row14_col1\" class=\"data row14 col1\" >0.978600</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row14_col2\" class=\"data row14 col2\" >0.966100</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row14_col3\" class=\"data row14 col3\" >0.875300</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row14_col4\" class=\"data row14 col4\" >0.066800</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row14_col5\" class=\"data row14 col5\" >0.124100</td>\n",
              "                        <td id=\"T_4fae1c5a_9889_11ea_b593_0242ac1c0002row14_col6\" class=\"data row14 col6\" >0.121200</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f15a182a748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDXFglI9CZcY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "7a24c604-00dc-4f41-fec0-320a2d2f74cb"
      },
      "source": [
        "plt.hist(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([284315.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
              "             0.,      0.,    492.]),\n",
              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGrDgbZ3CvKV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "d51a2a3e-6c4c-4647-c7b1-e38228f55266"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqGu74mECZYi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 967
        },
        "outputId": "5d262596-9189-4c6b-cb6f-b03cd3378d04"
      },
      "source": [
        "def plotCorrelationMatrix(df, graphWidth):\n",
        "    filename = 'People Analytics'\n",
        "    df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
        "    # print(df.columns)\n",
        "    corr = df.corr()\n",
        "    # print(corr)\n",
        "    plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')\n",
        "    corrMat = plt.matshow(corr, fignum = 1)\n",
        "    plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\n",
        "    plt.yticks(range(len(corr.columns)), corr.columns)\n",
        "    plt.gca().xaxis.tick_bottom()\n",
        "    plt.colorbar(corrMat)\n",
        "    plt.title(f'Correlation Matrix for {filename}', fontsize=15)\n",
        "    plt.show()\n",
        "\n",
        "plotCorrelationMatrix(dataset, 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zBB-YU2SDYmf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_folds(X,y, k=5):\n",
        "    return list(StratifiedKFold(n_splits=k, shuffle=True, random_state=1).split(X, y))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHJ1ICp-DYjD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "folds = get_folds(X, y)\n",
        "fold = folds[0]\n",
        "X_train, X_test, y_train, y_test = X[fold[0]], X[fold[1]], y[fold[0]], y[fold[1]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FS-0YGcEIM8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "0ce3669c-b853-4049-b6c4-156c0d0f7010"
      },
      "source": [
        "plt.hist(y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([227452.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
              "             0.,      0.,    393.]),\n",
              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUsN8ZYVCZT1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "bea46bc1-a502-43e7-e112-495b5cb4ec2c"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTZOeuP4CZP5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "4d2f286d-e14c-4a25-8a61-fa1415a79b0d"
      },
      "source": [
        "smote = SMOTE('minority')\n",
        "X_train_sampled, y_train_sampled = smote.fit_sample(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSoMN3HkEYXD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "83cbb89d-894b-4346-89a9-56201de6f415"
      },
      "source": [
        "y_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlWWgjLtCZJs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "dfa1d2da-8882-47db-8e73-66afe312fc5a"
      },
      "source": [
        "plt.hist(y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([227452.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
              "             0.,      0.,    393.]),\n",
              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eG0ncnqqCZFF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.decomposition import PCA"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9Ki_IAUCZAQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pca = PCA(n_components=2)\n",
        "X_train_pca = pca.fit_transform(X_train)\n",
        "X_test_pca = pca.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2OQjEK8yCY6n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "1a977313-7209-48fa-eb24-332b56186d35"
      },
      "source": [
        "pd.Series(pca.explained_variance_ratio_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.999973\n",
              "1    0.000027\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkXynBFVF0AW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_pca = pd.DataFrame(X_train_pca)\n",
        "X_test_pca = pd.DataFrame(X_test_pca)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nP4ZR78CYzH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "0534cf8b-27d6-4b78-9f00-35c87d71c7f5"
      },
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "plt.scatter(X_train_pca.iloc[np.where(y_train == 1)][0], X_train_pca.iloc[np.where(y_train == 1)][1], c = 'r', marker='o')\n",
        "plt.scatter(X_train_pca.iloc[np.where(y_train == 0)][0], X_train_pca.iloc[np.where(y_train == 0)][1], c = 'b', marker='.')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbNe-rhnHGMG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.utils import class_weight\n",
        "weights = class_weight.compute_class_weight('balanced', np.unique(y_train), y_train)\n",
        "nlabels = len(np.unique(y_train))\n",
        "weights_dict = { i: weights[i] for i in range(nlabels)}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9A-lkGjvHGIf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf1 = LogisticRegression(C=0.1, class_weight=weights_dict, random_state=0)\n",
        "clf2 = RandomForestClassifier(500, class_weight=weights_dict ,random_state=0)\n",
        "clf3 = XGBClassifier(n_estimators=250, n_jobs=-1)\n",
        "clf4 = GaussianNB()\n",
        "clf5 = SVC(C=0.1, kernel='rbf',random_state=0, probability=True, class_weight=weights_dict)\n",
        "clf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('xgb', clf3), ('gnb', clf4), ('svc', clf5)], voting='soft')\n",
        "\n",
        "clf.fit(X_train_pca, y_train)\n",
        "y_pred = clf.predict(X_test_pca)\n",
        "print (confusion_matrix(y_test, y_pred))\n",
        "\n",
        "target_names = [0,1]\n",
        "print(classification_report(y_test, y_pred, target_names))\n",
        "\n",
        "cohen_kappa_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Axk76ZZ6HGFw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urQ1pYXbHGC0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jkcxs3w9HF_j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wArwUZLvHF7X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sqm-gjKZHF4U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lH-Q8pjGCYpR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2RFmJudCW0g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# #separating positive examples\n",
        "# positive_x = []\n",
        "# positive_y = []\n",
        "# negative_x = []\n",
        "# negative_y = []\n",
        "# for i in range(len(y)):\n",
        "#     if y[i] == 1 :\n",
        "#         positive_x.append(X[i])\n",
        "#         positive_y.append(y[i])\n",
        "#     else :\n",
        "#         negative_x.append(X[i])\n",
        "#         negative_y.append(y[i])\n",
        "\n",
        "# #splitting dataset\n",
        "# from sklearn.model_selection import train_test_split\n",
        "# X_train, X_test, y_train, y_test = train_test_split(\n",
        "#         negative_x, negative_y, test_size=0.2, random_state=42)\n",
        "\n",
        "# #Adding the positive examples to test set\n",
        "# X_test.extend(positive_x)\n",
        "# y_test.extend(positive_y)\n",
        "\n",
        "# #Feature Scaling\n",
        "# from sklearn.preprocessing import StandardScaler\n",
        "# sc_X = StandardScaler()\n",
        "# X_train = sc_X.fit_transform(X_train)\n",
        "# X_test = sc_X.transform(X_test)\n",
        "\n",
        "# #Calculating mean for each feature\n",
        "# Mean_X_train = []\n",
        "# for i in range(X_train.shape[1]):\n",
        "#   Mean_X_train.append(X_train[:, i].mean()) \n",
        "\n",
        "# #Calculating standard deviation for each feature\n",
        "# std_X_train = []\n",
        "# for j in range(X_train.shape[1]):\n",
        "#   std_X_train.append(np.std(X_train[:, j], axis=0)) \n",
        "  \n",
        "# #Calculating the probability of each example in test set\n",
        "# import math\n",
        "# y_pred = []\n",
        "# def norm(x, mean, std):\n",
        "#     variance = float(std)**2\n",
        "#     denom = (2*math.pi*variance)**.5\n",
        "#     numer = math.exp(-(float(x)-float(mean))**2/(2*variance))\n",
        "#     return numer/denom\n",
        "  \n",
        "  \n",
        "# for i in range(len(X_test)):\n",
        "#      p = 1\n",
        "#      for j in range(len(Mean_X_train)):\n",
        "#          p *= norm(X_test[i, j], Mean_X_train[j], std_X_train[j])\n",
        "#      y_pred.append(p)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FC0vbk_OVh_L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# print ('Max probability', max(y_pred))\n",
        "# print ('Min probability', min(y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lu8PsMjkmRu1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "# from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-O0VRbllqza",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# y_out = []\n",
        "# for i in range(len(y_pred)):\n",
        "#     if y_pred[i] < 9.914460408105776e-28 :\n",
        "#         y_out.append(1) \n",
        "#     else:\n",
        "#         y_out.append(0)\n",
        "        \n",
        "# #checking efficiency with confusion matrix\n",
        "# print (confusion_matrix(y_test, y_out))\n",
        "# print ('Precision Score = ', precision_score(y_test, y_out))\n",
        "# print ('Recall Score = ', recall_score(y_test, y_out))\n",
        "# print ('F1 Score = ', f1_score(y_test, y_out))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mbiu-9hKs9FA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# y_out = []\n",
        "# for i in range(len(y_pred)):\n",
        "#     if y_pred[i] < 9.914460408105776e-21:\n",
        "#         y_out.append(1) \n",
        "#     else:\n",
        "#         y_out.append(0)\n",
        "        \n",
        "# #checking efficiency with confusion matrix\n",
        "# from sklearn.metrics import confusion_matrix\n",
        "# confusion_matrix(y_test, y_out)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cl4n0Et-rcGA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #Calculating the probability of each exaple in training set\n",
        "# import scipy.stats\n",
        "# y_train_pred = []\n",
        "# for i in range(len(X_train)):\n",
        "#     p = 1\n",
        "#     for j in range(len(Mean_X_train)):\n",
        "#         p *= scipy.stats.norm(Mean_X_train[j], std_X_train[j]).pdf(X_train[i, j])\n",
        "#     y_train_pred.append(p)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEqI3HzGX1jA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import statsmodels.formula.api as sm\n",
        "# X = np.append(arr = np.ones((len(X), 1)).astype(int), values = X, axis =1)\n",
        "# X_opt = X[:, :]\n",
        "# regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()\n",
        "# regressor_OLS.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6DmotDtFZUYb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Visualising the Training set results\n",
        "# plt.scatter(X_train[:, 0], y_train, marker= 'x',color = 'red')\n",
        "# plt.scatter(X_train[:, 0], y_train_pred, marker= '.', color = 'blue')\n",
        "# plt.show()\n",
        "\n",
        "# # Visualising the Test set results\n",
        "# plt.scatter(X_test[:, 0], y_test, marker= 'x', color = 'red')\n",
        "# plt.scatter(X_train[:, 0], y_train_pred, marker= '.', color = 'blue')\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWVIvzQhaxIP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Visualising the Training set results\n",
        "# plt.scatter(X_train[:, 1], y_train, color = 'red')\n",
        "# plt.plot(X_train[:, 1], y_train_pred, color = 'blue')\n",
        "# # plt.title('Salary vs Experience (Training set)')\n",
        "# # plt.xlabel('Years of Experience')\n",
        "# # plt.ylabel('Salary')\n",
        "# plt.show()\n",
        "\n",
        "# # Visualising the Test set results\n",
        "# plt.scatter(X_test[:, 1], y_test, marker= 'x', color = 'red')\n",
        "# plt.plot(X_train[:, 1], y_train_pred, color = 'blue')\n",
        "# # plt.title('Salary vs Experience (Test set)')\n",
        "# # plt.xlabel('Years of Experience')\n",
        "# # plt.ylabel('Salary')\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rF8MGG6Kbumr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Visualising the Training set results\n",
        "# plt.scatter(X_train[:, 2], y_train, marker= 'x',color = 'red')\n",
        "# plt.scatter(X_train[:, 2], y_train_pred, marker= '.', color = 'blue')\n",
        "# plt.show()\n",
        "\n",
        "# # Visualising the Test set results\n",
        "# plt.scatter(X_test[:, 2], y_test, marker= 'x', color = 'red')\n",
        "# plt.scatter(X_train[:, 2], y_train_pred, marker= '.', color = 'blue')\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xy_qBIkwyNc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #Feature Scaling\n",
        "# from sklearn.preprocessing import StandardScaler\n",
        "# sc_X = StandardScaler()\n",
        "# X_train = sc_X.fit_transform(X_train)\n",
        "# X_test = sc_X.transform(X_test)\n",
        "# sc_y = StandardScaler()\n",
        "# y_train = sc_y.fit_transform(y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrlZmO3JfpK_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Mean_X_train = []\n",
        "# for i in range(len(X_train)):\n",
        "#   Mean_X_train.append(X_train[i].mean()) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDlly9TUk7JU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# std_X_train = []\n",
        "# for j in range(len(X_train)):\n",
        "#   std_X_train.append(np.std(X_train[j], axis=0)) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cz5DhhVSnO7T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import scipy.stats\n",
        "# prob = []\n",
        "# for k in range(len(Mean_X_train)):\n",
        "  \n",
        "#   l = scipy.stats.norm(Mean_X_train[k], std_X_train[k]).pdf(k)\n",
        "#   prob.append(l)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-8aassPqqcC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# MAX_X = []\n",
        "# MIN_X = []\n",
        "# for i in range(len(X_train)):\n",
        "#   MAX_X.append(max(X_train[i]))\n",
        "#   MIN_X.append(min(X_train[i])) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZjSE9XCSgrm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# X_grid = np.arange(MIN_X, MAX_X, 0.01)\n",
        "# len(MAX_X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTP1WBbEVlSQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# len(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}