credit_card_fraud_features_ensemble_learning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Bambillo1/elecomsCoding_academy/blob/main/credit_card_fraud_features_ensemble_learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtJIc_bY97SE"
      },
      "source": [
        " ## **Credit Card Fraud Classification with Features Selection and Ensemble Learning**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eYSKhh3o97S3"
      },
      "source": [
        "This is my first Kaggle Contribution, using a very well-rated dataset in the community: the **Credit Card Fraud Dataset**. Sometimes, use all variables in a classification can be time consuming, and create overfitting. The objective of this work is to find, through Feature Selection, the most significant variables for the recognition of credit card fraud patterns, and subsequently to test the efficiency of some Machine Learning models (using this variables) working both separately and together, through Ensemble Learning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I42SLslB97S7"
      },
      "source": [
        "First, the libraries and the dataset for this Notebook are imported[](http://)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "fPR9vOIE97TA"
      },
      "outputs": [],
      "source": [
        "# Base Libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import set_printoptions\n",
        "from numpy import mean\n",
        "# Models\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "# Metrics\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import auc\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqnkT7SN-JfK",
        "outputId": "8428ccd0-b365-4073-9258-5d213a5bf0d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3J5yIu2-Jqc",
        "outputId": "6740eb62-2bbd-4cb6-879b-e2adc14b2937"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1I9NixTkJqP_VikSnCN8AiHAdFHgrABaa\n",
            "To: /content/creditcard.csv\n",
            "100% 151M/151M [00:03<00:00, 40.2MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown --id 1I9NixTkJqP_VikSnCN8AiHAdFHgrABaa"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "0KbxT90T97TJ",
        "outputId": "b7732f6d-209b-4d73-e78f-d1f61d969713"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
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              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
              "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
              "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
              "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
              "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
              "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
              "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
              "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
              "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
              "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
              "1  0.125895 -0.008983  0.014724    2.69      0  \n",
              "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
              "4  0.502292  0.219422  0.215153   69.99      0  \n",
              "5  0.105915  0.253844  0.081080    3.67      0  \n",
              "6 -0.257237  0.034507  0.005168    4.99      0  \n",
              "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
              "8 -0.384157  0.011747  0.142404   93.20      0  \n",
              "9  0.094199  0.246219  0.083076    3.68      0  \n",
              "\n",
              "[10 rows x 31 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
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              "      <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",
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              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
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              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
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              "      <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>...</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",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <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",
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              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</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",
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              "      <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",
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              "      <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",
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              "      <th>5</th>\n",
              "      <td>2.0</td>\n",
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              "      <td>0.960523</td>\n",
              "      <td>1.141109</td>\n",
              "      <td>-0.168252</td>\n",
              "      <td>0.420987</td>\n",
              "      <td>-0.029728</td>\n",
              "      <td>0.476201</td>\n",
              "      <td>0.260314</td>\n",
              "      <td>-0.568671</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.208254</td>\n",
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              "      <td>-0.026398</td>\n",
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              "      <td>0.105915</td>\n",
              "      <td>0.253844</td>\n",
              "      <td>0.081080</td>\n",
              "      <td>3.67</td>\n",
              "      <td>0</td>\n",
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              "      <th>6</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1.229658</td>\n",
              "      <td>0.141004</td>\n",
              "      <td>0.045371</td>\n",
              "      <td>1.202613</td>\n",
              "      <td>0.191881</td>\n",
              "      <td>0.272708</td>\n",
              "      <td>-0.005159</td>\n",
              "      <td>0.081213</td>\n",
              "      <td>0.464960</td>\n",
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              "      <td>-0.167716</td>\n",
              "      <td>-0.270710</td>\n",
              "      <td>-0.154104</td>\n",
              "      <td>-0.780055</td>\n",
              "      <td>0.750137</td>\n",
              "      <td>-0.257237</td>\n",
              "      <td>0.034507</td>\n",
              "      <td>0.005168</td>\n",
              "      <td>4.99</td>\n",
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              "      <td>7.0</td>\n",
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              "      <td>1.074380</td>\n",
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              "      <td>1.120631</td>\n",
              "      <td>-3.807864</td>\n",
              "      <td>0.615375</td>\n",
              "      <td>...</td>\n",
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              "      <td>40.80</td>\n",
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              "      <th>8</th>\n",
              "      <td>7.0</td>\n",
              "      <td>-0.894286</td>\n",
              "      <td>0.286157</td>\n",
              "      <td>-0.113192</td>\n",
              "      <td>-0.271526</td>\n",
              "      <td>2.669599</td>\n",
              "      <td>3.721818</td>\n",
              "      <td>0.370145</td>\n",
              "      <td>0.851084</td>\n",
              "      <td>-0.392048</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.073425</td>\n",
              "      <td>-0.268092</td>\n",
              "      <td>-0.204233</td>\n",
              "      <td>1.011592</td>\n",
              "      <td>0.373205</td>\n",
              "      <td>-0.384157</td>\n",
              "      <td>0.011747</td>\n",
              "      <td>0.142404</td>\n",
              "      <td>93.20</td>\n",
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              "      <td>-0.246761</td>\n",
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              "      <td>-0.120794</td>\n",
              "      <td>-0.385050</td>\n",
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              "      <td>0.094199</td>\n",
              "      <td>0.246219</td>\n",
              "      <td>0.083076</td>\n",
              "      <td>3.68</td>\n",
              "      <td>0</td>\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2b8d4b1b-e63b-46a7-b0bf-4169170f37a9 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-2b8d4b1b-e63b-46a7-b0bf-4169170f37a9');\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",
              "\n",
              "\n",
              "<div id=\"df-1d79eb9f-761b-4fc8-8fac-630c229e16b9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1d79eb9f-761b-4fc8-8fac-630c229e16b9')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1d79eb9f-761b-4fc8-8fac-630c229e16b9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "creditcard = pd.read_csv('creditcard.csv')\n",
        "\n",
        "creditcard.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlbTeahT97TR"
      },
      "source": [
        "\n",
        "As we can see, the data set contains the time, 28 references regarding the card's consumption behavior, the amount spent and the binary classification (fraudulent or not). Below, the data is splitted between train and test sets (70/30 classic distribution)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
        "import seaborn as sns\n",
        "rob_scaler = RobustScaler()\n",
        "\n",
        "creditcard['scaled_amount'] = rob_scaler.fit_transform(creditcard['Amount'].values.reshape(-1,1))\n",
        "creditcard['scaled_time'] = rob_scaler.fit_transform(creditcard['Time'].values.reshape(-1,1))\n",
        "\n",
        "creditcard.drop(['Time','Amount'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "aOC6bPUOVqT8"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_amount = creditcard['scaled_amount']\n",
        "scaled_time = creditcard['scaled_time']\n",
        "\n",
        "creditcard.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
        "creditcard.insert(0, 'scaled_amount', scaled_amount)\n",
        "creditcard.insert(1, 'scaled_time', scaled_time)"
      ],
      "metadata": {
        "id": "nzhSyw1pWa_E"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target = 'Class'\n",
        "ax = sns.countplot(x=target,data=creditcard)\n",
        "print(creditcard[target].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "JCxavBoxXKOR",
        "outputId": "d8fc0bc8-4ad9-41d7-ac0b-8eb268f239a1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    284315\n",
            "1       492\n",
            "Name: Class, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Positive transactions %\n",
        "\n",
        "percentage_P_Tr = 100*(284315/float(creditcard.shape[0]))\n",
        "percentage_P_Tr\n",
        "\n",
        "#Fraudulent transactions %\n",
        "\n",
        "percentage_F_Tr = 100*(492/float(creditcard.shape[0]))\n",
        "percentage_F_Tr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ysRD7-9Xrhb",
        "outputId": "a3929dac-7d79-4eb0-bee6-4727b0247031"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1727485630620034"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = creditcard.loc[:,creditcard.columns!=target]\n",
        "y = creditcard.loc[:,creditcard.columns==target]"
      ],
      "metadata": {
        "id": "bloUmGFgXraS"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "from sklearn.datasets import make_classification\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "smt = SMOTE(random_state=0)\n",
        "X, y = smt.fit_resample(X, y)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2)"
      ],
      "metadata": {
        "id": "ARWP9TTCXrUJ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.countplot(x=target,data=y)\n",
        "print(y[target].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "OQmzy-vPXrN8",
        "outputId": "74ae3cc4-6e00-4351-ffe5-41267d1ac827"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    284315\n",
            "1    284315\n",
            "Name: Class, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check correlation\n",
        "data_num = creditcard.drop('Class', axis=1)\n",
        "corr = data_num.corr()\n",
        "\n",
        "#correlation map\n",
        "f,ax = plt.subplots(figsize=(25, 25))\n",
        "sns.heatmap(data_num.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "D9W8y_YeXrF-",
        "outputId": "7dbe8340-e2af-4156-ece5-ee6134b97aac"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-URCEIC97TX"
      },
      "source": [
        "## Feature Selection (Importance)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_plvd4g97Td"
      },
      "source": [
        "Tree-based estimators can be used to compute impurity-based feature importances, which in turn can be used to discard irrelevant features (https://scikit-learn.org/stable/modules/feature_selection.html). This technique will help find the best variables to be used in the models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mISjD0s597Tf",
        "outputId": "135ba7dc-f4c4-44ab-a321-5dad020b9d30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-f648f2c210d9>:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  fi.fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.0097512  0.01305429 0.00788514 0.01911845 0.0832813  0.05498864\n",
            " 0.00689644 0.00657133 0.05975248 0.00885117 0.0421445  0.03877952\n",
            " 0.07495089 0.1208921  0.01244779 0.15386751 0.00723155 0.06497163\n",
            " 0.11298862 0.01574608 0.01620126 0.00778178 0.00667188 0.00972846\n",
            " 0.00559947 0.01061396 0.00513726 0.00904425 0.01091799 0.00413307]\n"
          ]
        }
      ],
      "source": [
        "# feature extraction\n",
        "fi = ExtraTreesClassifier(n_estimators=10)\n",
        "fi.fit(X, y)\n",
        "print(fi.feature_importances_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1t1pi8IV97Tl"
      },
      "source": [
        "## Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YadfVG8A97Tn"
      },
      "source": [
        "For this notebook, three classification models were tested: **K-Nearest Neighbor**, **Random Forest** and **Logistic Regression**, all using the sklearn library. Initially, X and y receive new values, based on the most representative variables found by Feature Selection."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "kFz8cks897Tp"
      },
      "outputs": [],
      "source": [
        "array = creditcard.values\n",
        "X = array[:, [17,14,10,12,11]]\n",
        "y = array[:,30]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H-BdE46oVJwL"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHDqPG1Q97Tr"
      },
      "source": [
        "### K-Nearest Neighbor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LG6hxqdz97Ts"
      },
      "source": [
        "Classifier implementing the k-nearest neighbors vote (https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html). First, a new kNN model is created, then we create a dictionary of all values we want to test for n_neighbors. GridSearch is used to test all values for n_neighbors, and finally we fit the model to the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "2HW4oynH97Tt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65f4488e-2f96-4c31-ce37-1b706db716e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 5.5 s, sys: 1.13 s, total: 6.64 s\n",
            "Wall time: 14min 11s\n"
          ]
        }
      ],
      "source": [
        "knn = KNeighborsClassifier(algorithm='auto')\n",
        "params_knn = {\n",
        "    'n_neighbors': (1,30, 1),\n",
        "    'leaf_size': (20,40,1),\n",
        "    'p': (1,2),\n",
        "    'weights': ('uniform', 'distance'),\n",
        "    'metric': ('minkowski', 'chebyshev')}\n",
        "knn_gs = GridSearchCV(\n",
        "    estimator=knn,\n",
        "    param_grid=params_knn,\n",
        "    scoring = 'accuracy',\n",
        "    n_jobs = -1,\n",
        "    cv = 5)\n",
        "\n",
        "%time knn_gs.fit(X_train, y_train)\n",
        "y_pred = knn_gs.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsD5asfc97Tv"
      },
      "source": [
        "Here, the best model is saved and the best parameters are shown, together with model's Area Under the Curve (AUC - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "jzi0ZjTY97Tz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d64caa14-0b5e-4027-dab7-3e08a3b209e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'leaf_size': 20, 'metric': 'minkowski', 'n_neighbors': 30, 'p': 2, 'weights': 'distance'}\n"
          ]
        }
      ],
      "source": [
        "knn_best = knn_gs.best_estimator_\n",
        "print(knn_gs.best_params_)\n",
        "fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
        "\n",
        "knn_auc = auc(fpr, tpr)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_best_1 = KNeighborsClassifier(leaf_size =  20, metric = 'minkowski', n_neighbors = 30, p =  2,  weights = 'distance')"
      ],
      "metadata": {
        "id": "oEIbphcgIC1v"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_best_1.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "sYg0leZCLC09",
        "outputId": "863b5084-db40-4c33-d14d-8731e904704d"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(leaf_size=20, n_neighbors=30, weights='distance')"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(leaf_size=20, n_neighbors=30, weights=&#x27;distance&#x27;)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(leaf_size=20, n_neighbors=30, weights=&#x27;distance&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_knn_1 = knn_best_1.predict(X_test)"
      ],
      "metadata": {
        "id": "5P4ZFbm4LOvc"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, y_pred_knn_1)\n",
        "\n",
        "knn_auc = auc(fpr, tpr)\n",
        "\n",
        "print(knn_auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GefFeEGDLWHQ",
        "outputId": "eefe3e0a-def0-4575-fd34-e90c037cbae1"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8681846231600027\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VdcyPzFE97T1"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84DtSEwx97T3"
      },
      "source": [
        "A Random Forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control overfitting (https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html). Below, we create a new Random Forest Classifier, a dictionary of all values we want to test for n_estimators and we use gridsearch to test all values for n_estimators, finally fitting the model to training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "qapqhmjh97T4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aeebec23-ed92-4a0d-9359-ed5946be5bc5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 20min 53s, sys: 1.11 s, total: 20min 55s\n",
            "Wall time: 21min 23s\n"
          ]
        }
      ],
      "source": [
        "rf = RandomForestClassifier()\n",
        "params_rf = {\"n_estimators\": np.arange(1,30,1)}\n",
        "rf_gs = GridSearchCV(rf, params_rf, cv=5)\n",
        "\n",
        "%time rf_gs.fit(X_train, y_train)\n",
        "y_pred_rf = rf_gs.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUuv2eeb97T8"
      },
      "source": [
        "Here, the best model is saved and the best parameters are shown, together with model's Area Under the Curve (AUC - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "tlDH09wj97T9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf0c5867-2b96-4b83-9be9-f5400184f3b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_estimators': 20}\n"
          ]
        }
      ],
      "source": [
        "rf_best = rf_gs.best_estimator_\n",
        "print(rf_gs.best_params_)\n",
        "\n",
        "fpr, tpr, _ = roc_curve(y_test, y_pred_rf)\n",
        "\n",
        "rf_auc = auc(fpr, tpr)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_best_1 = RandomForestClassifier(n_estimators = 20)"
      ],
      "metadata": {
        "id": "AB1owKLMLcbv"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_best_1.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "JBn2snvtLtyw",
        "outputId": "3be5d91f-5cb7-46cc-aa2c-2ec6c4c6ae4d"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=20)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=20)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=20)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_rf_1 = rf_best_1.predict(X_test)"
      ],
      "metadata": {
        "id": "k5rVYbVULvac"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, y_pred_rf_1)\n",
        "\n",
        "rf_auc = auc(fpr, tpr)\n",
        "\n",
        "print(rf_auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jk3TkcL9N69h",
        "outputId": "b9a3aa3d-4550-4c19-d2da-e6300011bd27"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8749589659417316\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6z2Ph-3z97UC"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOa1kpSy97UD"
      },
      "source": [
        "This class implements regularized logistic regression using the ‘liblinear’ library, ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ solvers. Note that regularization is applied by default. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance (https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html). Here, we create a new logistic regression model and just fit the model to the training data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "wjj_FPdm97UF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f977956d-24c4-4116-956b-eb9ca2115671"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 272 ms, sys: 193 ms, total: 465 ms\n",
            "Wall time: 493 ms\n"
          ]
        }
      ],
      "source": [
        "log_reg = LogisticRegression(max_iter=1000)\n",
        "%time log_reg.fit(X_train, y_train)\n",
        "y_pred_lr = log_reg.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6v0uyOuM97UL"
      },
      "source": [
        "Here, the best model is saved and the best parameters are shown, together with model's Area Under the Curve (AUC - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "tCMDAoS897UN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "953031af-eb6e-4bfc-db3f-af4327601179"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7701940641620576\n"
          ]
        }
      ],
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, y_pred_lr)\n",
        "\n",
        "lr_auc = auc(fpr, tpr)\n",
        "\n",
        "print(lr_auc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83DZEk0k97UQ"
      },
      "source": [
        "## Ensemble (Voting/Stacking Classifiers)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oaqqwI6497UR"
      },
      "source": [
        "The goal of ensemble methods is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator (https://scikit-learn.org/stable/modules/ensemble.html). Below, we create a dictionary of our models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "roEvPXft97UT"
      },
      "outputs": [],
      "source": [
        "estimators=[(\"knn\", knn_best_1), (\"rf\", rf_best_1), (\"log_reg\", log_reg)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBplo58K97UU"
      },
      "source": [
        "### Voting Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhr1DmMq97UW"
      },
      "source": [
        "The idea behind the VotingClassifier is to combine conceptually different machine learning classifiers and use a majority vote or the average predicted probabilities (soft vote) to predict the class labels. Such a classifier can be useful for a set of equally well performing model in order to balance out their individual weaknesses (https://scikit-learn.org/stable/modules/ensemble.html). Here, we create our voting classifier, inputing our models, fit on training and test our model in data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "DIxoH4Kj97UX"
      },
      "outputs": [],
      "source": [
        "ensemble_class = VotingClassifier(estimators, voting=\"hard\")\n",
        "\n",
        "ensemble_class.fit(X_train, y_train)\n",
        "\n",
        "ensemble_class.score(X_test, y_test)\n",
        "y_pred_ec = ensemble_class.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNYB-zxE97UZ"
      },
      "source": [
        "Here, the best model is saved and the best parameters are shown, together with model's Area Under the Curve (AUC - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "cjKOoF_k97Uc"
      },
      "outputs": [],
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, y_pred_ec)\n",
        "\n",
        "ensemble_class_auc = auc(fpr, tpr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Th6CwE-997Ud"
      },
      "source": [
        "### Stacking Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7SEnrKK597Ug"
      },
      "source": [
        "Stacked generalization is a method for combining estimators to reduce their biases. More precisely, the predictions of each individual estimator are stacked together and used as input to a final estimator to compute the prediction. This final estimator is trained through cross-validation (https://scikit-learn.org/stable/modules/ensemble.html). Here, we create our stacking classifier, inputing our models, fit on training and test our model in data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "5Izxc1or97Ui"
      },
      "outputs": [],
      "source": [
        "ensemble_stack = StackingClassifier(estimators=estimators)\n",
        "\n",
        "ensemble_stack.fit(X_train, y_train)\n",
        "\n",
        "ensemble_stack.score(X_test, y_test)\n",
        "y_pred_es= ensemble_stack.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdwO_Zbv97Va"
      },
      "source": [
        "Here, the best model is saved and the best parameters are shown, together with model's Area Under the Curve (AUC - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "vL5NZMkC97Vb"
      },
      "outputs": [],
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, y_pred_es)\n",
        "\n",
        "ensemble_stack_auc = auc(fpr, tpr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6csvKYT97Vf"
      },
      "source": [
        "## Model Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "semnubXr97Vh"
      },
      "source": [
        "In this section, all models are compared and its results shown. Considering the score, all models are very similar, so we need more metrics to see its differences."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "9LyIZHFh97Vi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7100247-c2bc-459f-902e-62d77dc5685d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Score: 0.9994265182636377\n",
            "RF Score: 0.9994499256814484\n",
            "Log_Reg Score: 0.9990519995786665\n",
            "Ensemble Voting Score: 0.9994265182636377\n",
            "Ensemble Stacking Score: 0.9994616293903538\n"
          ]
        }
      ],
      "source": [
        "print(\"KNN Score: {}\".format(knn_best.score(X_test, y_test)))\n",
        "print(\"RF Score: {}\".format(rf_best.score(X_test, y_test)))\n",
        "print(\"Log_Reg Score: {}\".format(log_reg.score(X_test, y_test)))\n",
        "print(\"Ensemble Voting Score: {}\".format(ensemble_class.score(X_test, y_test)))\n",
        "print(\"Ensemble Stacking Score: {}\".format(ensemble_stack.score(X_test, y_test)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jy0RNfM_97Vk"
      },
      "source": [
        "When using AUC, it is possible to see that the objective was achieved: to find a percentage of precision using only the variables chosen by the Feature Selection (which possibly also decreased overfitting). Of all models, RF had the best results (87.49%), while Logistic Regression was the one with the worst (77.01%)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "Gpyzm4rS97Vm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8532420b-ea69-446e-bf49-d0019549d3fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN AUC: 0.8681846231600027\n",
            "RF AUC: 0.8749589659417316\n",
            "Log_Reg AUC: 0.7701940641620576\n",
            "Ensemble Voting AUC: 0.8681846231600027\n",
            "Ensemble Stacking AUC: 0.8614571764448662\n"
          ]
        }
      ],
      "source": [
        "print(\"KNN AUC: {}\".format(knn_auc))\n",
        "print(\"RF AUC: {}\".format(rf_auc))\n",
        "print(\"Log_Reg AUC: {}\".format(lr_auc))\n",
        "print(\"Ensemble Voting AUC: {}\".format(ensemble_class_auc))\n",
        "print(\"Ensemble Stacking AUC: {}\".format(ensemble_stack_auc))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDBvU8tS97Vo"
      },
      "source": [
        "This plotted results present well the difference between the models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "_sdWqr-S97Vp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "c498c17d-bda1-43a7-a196-eda56e359fec"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "models = ['KNN', 'LR', 'Log_Reg', 'Voting', 'Stacking']\n",
        "values = [knn_auc, rf_auc, lr_auc, ensemble_class_auc, ensemble_stack_auc]\n",
        "ax.bar(models, values)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jtb9EWq97Vr"
      },
      "source": [
        "The confusion matrix shows that the classification of the model is quite efficient, with a high accuracy of true positives and negatives"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(log_reg, X_test, y_test)"
      ],
      "metadata": {
        "id": "4sjpqmsVwUR-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "b2eda454-276f-47e2-fa67-09036c2e60f0"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7dd3dec1e3e0>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(knn_best_1, X_test, y_test)"
      ],
      "metadata": {
        "id": "Cz2bq58AwjUP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "99f758b2-da4d-45ac-aae7-5efdef019461"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7dd42870dbd0>"
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(rf_best_1, X_test, y_test)"
      ],
      "metadata": {
        "id": "OoHSTfBqwnJC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "ac9dafac-a488-4355-dcbb-363409c5d3cc"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7dd3dec1e0b0>"
            ]
          },
          "metadata": {},
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(ensemble_class, X_test, y_test)"
      ],
      "metadata": {
        "id": "ictdfEsnwzU8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "e75c3ccc-9376-4d53-dc2d-cb19ae42acbc"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7dd3ddd9d3c0>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrixDisplay.from_estimator(ensemble_stack, X_test, y_test)"
      ],
      "metadata": {
        "id": "JjKt2xEBw7XS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "20e8229c-6679-4938-e7e8-3d4148282250"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7dd3ddff60e0>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_curve"
      ],
      "metadata": {
        "id": "vJ5V6QhoxB-N"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_lr)\n",
        "print(f'Precision of LR is:  {precision}')\n",
        "print(f'Recall of LR is:  {recall}')\n",
        "plt.fill_between(recall, precision)\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.title(\"LR Precision-Recall curve\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "lRSkI2kwVicJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "de52639d-9051-46d4-bcb3-e68defe2e894"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision of LR is:  [0.00173215 0.86021505 1.        ]\n",
            "Recall of LR is:  [1.         0.54054054 0.        ]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_rf_1)\n",
        "print(f'Precision of RF is:  {precision}')\n",
        "print(f'Recall of RF is:  {recall}')\n",
        "plt.fill_between(recall, precision)\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.title(\"RF Precision-Recall curve\");\n",
        "#display plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ksidCkq4VkAZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "1d4f813e-f12c-4f89-d2a8-0057e24bd778"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision of RF is:  [0.00173215 0.94067797 1.        ]\n",
            "Recall of RF is:  [1.   0.75 0.  ]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_knn_1)\n",
        "print(f'Precision of KNN is:  {precision}')\n",
        "print(f'Recall of KNN is:  {recall}')\n",
        "plt.fill_between(recall, precision)\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.title(\"KNN Precision-Recall curve\");\n",
        "#display plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "2HQFWNZFVj1-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "7aacc1e6-43d0-47ed-ca15-ce732b8d51aa"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision of KNN is:  [0.00173215 0.91596639 1.        ]\n",
            "Recall of KNN is:  [1.         0.73648649 0.        ]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_ec)\n",
        "print(f'Precision of RF is:  {precision}')\n",
        "print(f'Recall of RF is:  {recall}')\n",
        "plt.fill_between(recall, precision)\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.title(\"Ensemble Class Precision-Recall curve\");\n",
        "#display plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "YoOmnc_jVjso",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "84ebbbbf-915d-45cb-e60b-8f6912f49ea2"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision of RF is:  [0.00173215 0.91596639 1.        ]\n",
            "Recall of RF is:  [1.         0.73648649 0.        ]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_es)\n",
        "print(f'Precision of RF is:  {precision}')\n",
        "print(f'Recall of RF is:  {recall}')\n",
        "plt.fill_between(recall, precision)\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.title(\"Emsemble Stacking Precision-Recall curve\");\n",
        "#display plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "K9xOss1JHG-0",
        "outputId": "1d1030ea-fb8a-448e-9c25-89bf9f049560"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision of RF is:  [0.00173215 0.95535714 1.        ]\n",
            "Recall of RF is:  [1.         0.72297297 0.        ]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "papermill": {
      "duration": 1792.288866,
      "end_time": "2020-12-29T06:37:30.166538",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2020-12-29T06:07:37.877672",
      "version": "2.1.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}