ML_22_Handling_Imbalanced_Data.ipynb

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        "<a href=\"https://colab.research.google.com/github/rajeshmore1/DataScience_Mentorship/blob/main/ML_22_Handling_Imbalanced_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Methods for Dealing with Imbalanced Data\n",
        "Imbalanced classes are a common problem in machine learning classification where there are a disproportionate ratio of observations in each class. Class imbalance can be found in many different areas including medical diagnosis, spam filtering, and fraud detection.\n",
        "\n",
        "In this guide, we'll look at five possible ways to handle an imbalanced class problem using credit card data. Our objective will be to correctly classify the minority class of fraudulent transactions.\n",
        "\n",
        "Important Note: This guide will focus soley on addressing imbalanced classes and will not addressing other important machine learning steps including, but not limited to, feature selection or hyperparameter tuning."
      ],
      "metadata": {
        "id": "yaEtjEmypOG9"
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    {
      "cell_type": "markdown",
      "source": [
        "Dataset: https://www.kaggle.com/code/tboyle10/methods-for-dealing-with-imbalanced-data/data"
      ],
      "metadata": {
        "id": "QDvKq-OLpDto"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, recall_score"
      ],
      "metadata": {
        "id": "ZtqEoQ-7pFmi"
      },
      "execution_count": 1,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# setting up default plotting parameters\n",
        "%matplotlib inline\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [20.0, 7.0]\n",
        "plt.rcParams.update({'font.size': 22,})\n",
        "\n",
        "sns.set_palette('viridis')\n",
        "sns.set_style('white')\n",
        "sns.set_context('talk', font_scale=0.8)"
      ],
      "metadata": {
        "id": "CwsnnpR1qvqm"
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      "execution_count": 3,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/creditcard.csv\")"
      ],
      "metadata": {
        "id": "MTHFrJkypX18"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.shape)\n",
        "df.head()"
      ],
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      "outputs": [
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          "text": [
            "(284807, 31)\n"
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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",
              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "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",
              "\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",
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              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# Class Imbalance\n",
        "print(df.Class.value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Flpb8nXvq1cD",
        "outputId": "27a7617c-a64b-4ecc-b754-425eb7ee9fbf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    284315\n",
            "1       492\n",
            "Name: Class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using seaborns countplot to show distribution of questions in dataset\n",
        "fig, ax = plt.subplots()\n",
        "g = sns.countplot(df.Class, palette='viridis')\n",
        "g.set_xticklabels(['Not Fraud', 'Fraud'])\n",
        "g.set_yticklabels([])\n",
        "\n",
        "# function to show values on bars\n",
        "def show_values_on_bars(axs):\n",
        "    def _show_on_single_plot(ax):        \n",
        "        for p in ax.patches:\n",
        "            _x = p.get_x() + p.get_width() / 2\n",
        "            _y = p.get_y() + p.get_height()\n",
        "            value = '{:.0f}'.format(p.get_height())\n",
        "            ax.text(_x, _y, value, ha=\"center\") \n",
        "\n",
        "    if isinstance(axs, np.ndarray):\n",
        "        for idx, ax in np.ndenumerate(axs):\n",
        "            _show_on_single_plot(ax)\n",
        "    else:\n",
        "        _show_on_single_plot(axs)\n",
        "show_values_on_bars(ax)\n",
        "\n",
        "sns.despine(left=True, bottom=True)\n",
        "plt.xlabel('')\n",
        "plt.ylabel('')\n",
        "plt.title('Distribution of Transactions', fontsize=30)\n",
        "plt.tick_params(axis='x', which='major', labelsize=15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
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      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print percentage of questions where target == 1\n",
        "(len(df.loc[df.Class==1])) / (len(df.loc[df.Class == 0])) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQrs4r12rSqv",
        "outputId": "11c60674-7bb9-4811-bd8a-50c362e1705a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.17304750013189596"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the plot above, we can see we have a very imbalanced class - just 0.17% of our dataset belong to the target class!\n",
        "\n",
        "This is a problem because many machine learning models are designed to maximize overall accuracy, which especially with imbalanced classes may not be the best metric to use. Classification accuracy is defined as the number of correct predictions divided by total predictions times 100. For example, if we simply predicted all transactions are not fraud, we would get a classification acuracy score of over 99%!\n",
        "\n",
        "# Create Train and Test Sets\n",
        "\n",
        "The training set is used to build and validate the model, while the test set is reserved for testing the model on unseen data."
      ],
      "metadata": {
        "id": "uSuGETdarX3t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for modeling\n",
        "# Separate input features and target\n",
        "y = df.Class\n",
        "X = df.drop('Class', axis=1)\n",
        "\n",
        "# setting up testing and training sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=27)"
      ],
      "metadata": {
        "id": "DO3wOdGprVOm"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Baseline Models¶\n"
      ],
      "metadata": {
        "id": "UyWdBt3IrgjN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# DummyClassifier to predict only target 0\n",
        "dummy = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
        "dummy_pred = dummy.predict(X_test)\n",
        "\n",
        "# checking unique labels\n",
        "print('Unique predicted labels: ', (np.unique(dummy_pred)))\n",
        "\n",
        "# checking accuracy\n",
        "print('Test score: ', accuracy_score(y_test, dummy_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KK9wzgm7rfxr",
        "outputId": "aa8be405-745d-4bcf-b2b0-c280f2e8f21f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique predicted labels:  [0]\n",
            "Test score:  0.9981461194910255\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As predicted our accuracy score for classifying all transactions as not fraud is 99.8%!\n",
        "\n",
        "As the Dummy Classifier predicts only Class 0, it is clearly not a good option for our objective of correctly classifying fraudulent transactions.\n",
        "\n",
        "Let's see how logistic regression performs on this dataset."
      ],
      "metadata": {
        "id": "xlkuS_wKroch"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Modeling the data as is\n",
        "# Train model\n",
        "lr = LogisticRegression(solver='liblinear').fit(X_train, y_train)\n",
        " \n",
        "# Predict on training set\n",
        "lr_pred = lr.predict(X_test)"
      ],
      "metadata": {
        "id": "Xtsmx-DMrlwy"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking accuracy\n",
        "accuracy_score(y_test, lr_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GakFjfeJrruv",
        "outputId": "cf9a16ea-90a1-464b-d5be-0be9cd8fcb1d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9992135052386169"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking unique values\n",
        "predictions = pd.DataFrame(lr_pred)\n",
        "predictions[0].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DR7vepujrt9e",
        "outputId": "9369cffe-56b2-4359-e02f-3cb58246e4d1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    71108\n",
              "1       94\n",
              "Name: 0, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic Regression outperformed the Dummy Classifier! We can see that it predicted 94 instances of class 1, so this is definitely an improvement. But can we do better?\n",
        "\n",
        "Let's see if we can apply some techniques for dealing with class imbalance to improve these results."
      ],
      "metadata": {
        "id": "ezGBCF5xr0Ax"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Change the performance metric\n",
        "Accuracy is not the best metric to use when evaluating imbalanced datasets as it can be misleading. Metrics that can provide better insight include:\n",
        "\n",
        "* Confusion Matrix: a talbe showing correct predictions and types of incorrect predictions.\n",
        "* Precision: the number of true positives divided by all positive predictions. Precision is also called Positive Predictive Value. It is a measure of a classifier's exactness. Low precision indicates a high number of false positives.\n",
        "* Recall: the number of true positives divided by the number of positive values in the test data. Recall is also called Sensitivity or the True Positive Rate. It is a measure of a classifier's completeness. Low recall indicates a high number of false negatives.\n",
        "* F1: Score: the weighted average of precision and recall.\n",
        "\n",
        "Since our main objective with the dataset is to prioritize accuraltely classifying fraud cases the recall score can be considered our main metric to use for evaluating outcomes."
      ],
      "metadata": {
        "id": "dBjJrGzwr3dx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score\n",
        "f1_score(y_test, lr_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmUHxhTKrxMJ",
        "outputId": "622a8791-029f-4608-dc00-efb98efe9ef2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7522123893805309"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "pd.DataFrame(confusion_matrix(y_test, lr_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "AbVWXISssDpK",
        "outputId": "7f7f8f7d-3904-4a1a-cc91-fe66b62eaf2e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       0   1\n",
              "0  71061   9\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c18dce7-99e5-4550-96ac-678a62bab8e0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-8c18dce7-99e5-4550-96ac-678a62bab8e0 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-8c18dce7-99e5-4550-96ac-678a62bab8e0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall_score(y_test, lr_pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNM2pbCgsF1s",
        "outputId": "95abd623-d402-4a95-dfce-f7fe99e886fc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6439393939393939"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have a very high accuracy score of 0.999 but a F1 score of only 0.752. And from the confusion matrix, we can see we are misclassifying several observations leading to a recall score of only 0.64."
      ],
      "metadata": {
        "id": "zmC0OhAlsKd1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Change the algorithm\n",
        "While in every machine learning problem, its a good rule of thumb to try a variety of algorithms, it can be especially beneficial with imbalanced datasets. Decision trees frequently perform well on imbalanced data. They work by learning a hierachy of if/else questions. This can force both classes to be addressed."
      ],
      "metadata": {
        "id": "I7UNbd87sNJP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "# train model\n",
        "rfc = RandomForestClassifier(n_estimators=10).fit(X_train, y_train)\n",
        "\n",
        "# predict on test set\n",
        "rfc_pred = rfc.predict(X_test)\n",
        "\n",
        "accuracy_score(y_test, rfc_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IpC3l2JsIIU",
        "outputId": "98673a28-a3bc-4c60-bb5f-17cffcfdd6b5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9995646189713772"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score\n",
        "f1_score(y_test, rfc_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AvqS6prIsSsL",
        "outputId": "aabb9e81-d4aa-4138-c560-fa7cfe5c02fb"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8691983122362869"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "pd.DataFrame(confusion_matrix(y_test, rfc_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Mn-Ii45qsU39",
        "outputId": "d7ca8445-b4f1-44f4-c84a-c41cd6332f32"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       0    1\n",
              "0  71068    2\n",
              "1     29  103"
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "      <td>103</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a85cf99e-f6fc-4f1e-8886-e5c613e38300')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a85cf99e-f6fc-4f1e-8886-e5c613e38300 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-a85cf99e-f6fc-4f1e-8886-e5c613e38300');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# recall score\n",
        "recall_score(y_test, rfc_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QamokQgrsXVM",
        "outputId": "385ad9b2-a76e-404e-804a-94c92cf45d18"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7803030303030303"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Resampling Techniques\n",
        "\n",
        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)\n",
        "# 3. Oversampling Minority Class\n",
        "Oversampling can be defined as adding more copies of the minority class. Oversampling can be a good choice when you don't have a ton of data to work with. A con to consider when undersampling is that it can cause overfitting and poor generalization to your test set.\n",
        "\n",
        "We will use the resampling module from Scikit-Learn to randomly replicate samples from the minority class.\n",
        "\n",
        "### Important Note\n",
        "\n",
        "Always split into test and train sets BEFORE trying any resampling techniques! Oversampling before splitting the data can allow the exact same observations to be present in both the test and train sets! This can allow our model to simply memorize specific data points and cause overfitting."
      ],
      "metadata": {
        "id": "p46FQDrEsc40"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Note\n",
        "The simplest implementation of over-sampling is to duplicate random records from the minority class, which can cause overfishing.\n",
        "\n",
        "In under-sampling, the simplest technique involves removing random records from the majority class, which can cause loss of information."
      ],
      "metadata": {
        "id": "Dw-U3Jp5udbC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import resample\n",
        "# Separate input features and target\n",
        "y = df.Class\n",
        "X = df.drop('Class', axis=1)\n",
        "\n",
        "# setting up testing and training sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=27)"
      ],
      "metadata": {
        "id": "q9UAwWUpsZW2"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# concatenate our training data back together\n",
        "X = pd.concat([X_train, y_train], axis=1)\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "NyIli6rhsoVM",
        "outputId": "57951693-151d-406e-a410-533abff356b0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Time        V1        V2        V3        V4        V5        V6  \\\n",
              "264873  161634.0 -0.395578  1.489129 -0.833442 -0.224271  0.369444 -1.453886   \n",
              "163821  116237.0  1.950487  0.002312 -1.761814  1.232470  0.523175 -0.650657   \n",
              "72083    54557.0  1.105167 -0.166253  0.569520  0.681043 -0.259189  0.642792   \n",
              "196949  131771.0  1.805238  0.961264 -1.717212  4.094625  0.938666 -0.227785   \n",
              "126213   77959.0  0.835421 -1.191847  0.578455  0.586101 -1.236663  0.194617   \n",
              "\n",
              "              V7        V8        V9  ...       V21       V22       V23  \\\n",
              "264873  0.796593 -0.060403  0.338270  ...  0.231624  0.955194 -0.172092   \n",
              "163821  0.504231 -0.200857  0.116805  ...  0.086306  0.326297 -0.068839   \n",
              "72083  -0.437034  0.356746  0.441417  ...  0.009073  0.293023 -0.028688   \n",
              "196949  0.152911  0.066753 -1.073784  ... -0.137875 -0.450959  0.098530   \n",
              "126213 -0.532404  0.061561 -0.734344  ... -0.072349 -0.109154 -0.308356   \n",
              "\n",
              "             V24       V25       V26       V27       V28  Amount  Class  \n",
              "264873 -0.041050 -0.313444 -0.174301  0.064657 -0.036960    2.74      0  \n",
              "163821 -0.416589  0.426044 -0.486299 -0.031266 -0.072543   38.44      0  \n",
              "72083  -0.242206  0.389813  0.482852  0.010705 -0.008399    1.00      0  \n",
              "196949 -0.662272 -0.150154 -0.098852 -0.000030  0.017622   37.89      0  \n",
              "126213  0.011968  0.461350 -0.244810  0.031845  0.060910  237.00      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
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              "  <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>...</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>264873</th>\n",
              "      <td>161634.0</td>\n",
              "      <td>-0.395578</td>\n",
              "      <td>1.489129</td>\n",
              "      <td>-0.833442</td>\n",
              "      <td>-0.224271</td>\n",
              "      <td>0.369444</td>\n",
              "      <td>-1.453886</td>\n",
              "      <td>0.796593</td>\n",
              "      <td>-0.060403</td>\n",
              "      <td>0.338270</td>\n",
              "      <td>...</td>\n",
              "      <td>0.231624</td>\n",
              "      <td>0.955194</td>\n",
              "      <td>-0.172092</td>\n",
              "      <td>-0.041050</td>\n",
              "      <td>-0.313444</td>\n",
              "      <td>-0.174301</td>\n",
              "      <td>0.064657</td>\n",
              "      <td>-0.036960</td>\n",
              "      <td>2.74</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163821</th>\n",
              "      <td>116237.0</td>\n",
              "      <td>1.950487</td>\n",
              "      <td>0.002312</td>\n",
              "      <td>-1.761814</td>\n",
              "      <td>1.232470</td>\n",
              "      <td>0.523175</td>\n",
              "      <td>-0.650657</td>\n",
              "      <td>0.504231</td>\n",
              "      <td>-0.200857</td>\n",
              "      <td>0.116805</td>\n",
              "      <td>...</td>\n",
              "      <td>0.086306</td>\n",
              "      <td>0.326297</td>\n",
              "      <td>-0.068839</td>\n",
              "      <td>-0.416589</td>\n",
              "      <td>0.426044</td>\n",
              "      <td>-0.486299</td>\n",
              "      <td>-0.031266</td>\n",
              "      <td>-0.072543</td>\n",
              "      <td>38.44</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72083</th>\n",
              "      <td>54557.0</td>\n",
              "      <td>1.105167</td>\n",
              "      <td>-0.166253</td>\n",
              "      <td>0.569520</td>\n",
              "      <td>0.681043</td>\n",
              "      <td>-0.259189</td>\n",
              "      <td>0.642792</td>\n",
              "      <td>-0.437034</td>\n",
              "      <td>0.356746</td>\n",
              "      <td>0.441417</td>\n",
              "      <td>...</td>\n",
              "      <td>0.009073</td>\n",
              "      <td>0.293023</td>\n",
              "      <td>-0.028688</td>\n",
              "      <td>-0.242206</td>\n",
              "      <td>0.389813</td>\n",
              "      <td>0.482852</td>\n",
              "      <td>0.010705</td>\n",
              "      <td>-0.008399</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196949</th>\n",
              "      <td>131771.0</td>\n",
              "      <td>1.805238</td>\n",
              "      <td>0.961264</td>\n",
              "      <td>-1.717212</td>\n",
              "      <td>4.094625</td>\n",
              "      <td>0.938666</td>\n",
              "      <td>-0.227785</td>\n",
              "      <td>0.152911</td>\n",
              "      <td>0.066753</td>\n",
              "      <td>-1.073784</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.137875</td>\n",
              "      <td>-0.450959</td>\n",
              "      <td>0.098530</td>\n",
              "      <td>-0.662272</td>\n",
              "      <td>-0.150154</td>\n",
              "      <td>-0.098852</td>\n",
              "      <td>-0.000030</td>\n",
              "      <td>0.017622</td>\n",
              "      <td>37.89</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126213</th>\n",
              "      <td>77959.0</td>\n",
              "      <td>0.835421</td>\n",
              "      <td>-1.191847</td>\n",
              "      <td>0.578455</td>\n",
              "      <td>0.586101</td>\n",
              "      <td>-1.236663</td>\n",
              "      <td>0.194617</td>\n",
              "      <td>-0.532404</td>\n",
              "      <td>0.061561</td>\n",
              "      <td>-0.734344</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.072349</td>\n",
              "      <td>-0.109154</td>\n",
              "      <td>-0.308356</td>\n",
              "      <td>0.011968</td>\n",
              "      <td>0.461350</td>\n",
              "      <td>-0.244810</td>\n",
              "      <td>0.031845</td>\n",
              "      <td>0.060910</td>\n",
              "      <td>237.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separate minority and majority classes\n",
        "not_fraud = X[X.Class==0]\n",
        "fraud = X[X.Class==1]\n",
        "\n",
        "# upsample minority\n",
        "fraud_upsampled = resample(fraud,\n",
        "                          replace=True, # sample with replacement\n",
        "                          n_samples=len(not_fraud), # match number in majority class\n",
        "                          random_state=27) # reproducible results\n",
        "# combine majority and upsampled minority\n",
        "upsampled = pd.concat([not_fraud, fraud_upsampled])\n",
        "\n",
        "# check new class counts\n",
        "upsampled.Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqqxqFBdsqnh",
        "outputId": "4ef3e686-e1c1-444e-8478-e3dda562e3e3"
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      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    213245\n",
              "1    213245\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# trying logistic regression again with the balanced dataset\n",
        "y_train = upsampled.Class\n",
        "X_train = upsampled.drop('Class', axis=1)\n",
        "\n",
        "upsampled = LogisticRegression(solver='liblinear').fit(X_train, y_train)\n",
        "\n",
        "upsampled_pred = upsampled.predict(X_test)"
      ],
      "metadata": {
        "id": "KpLpPHuoswdy"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking accuracy\n",
        "accuracy_score(y_test, upsampled_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZ6au5d7sy_8",
        "outputId": "a320922c-dd5b-4063-c0d4-a508955d00bf"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9807589674447347"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score\n",
        "f1_score(y_test, upsampled_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kz9fqGXIs1Ht",
        "outputId": "5b570864-3591-431e-b996-72cb35f448ba"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.14375000000000002"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "pd.DataFrame(confusion_matrix(y_test, upsampled_pred))"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
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      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
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              "       0     1\n",
              "0  69717  1353\n",
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              "                                                     [key], {});\n",
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      "source": [
        "recall_score(y_test, upsampled_pred)\n"
      ],
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          "base_uri": "https://localhost:8080/"
        },
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              "0.8712121212121212"
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          "metadata": {},
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      "cell_type": "markdown",
      "source": [
        "Our accuracy score decreased after upsampling, but the model is now predicting both classes more equally, making it an improvement over our plain logistic regression above.\n",
        "\n",
        "# 4. Undersampling Majority Class\n",
        "Undersampling can be defined as removing some observations of the majority class. Undersampling can be a good choice when you have a ton of data -think millions of rows. But a drawback to undersampling is that we are removing information that may be valuable.\n",
        "\n",
        "We will again use the resampling module from Scikit-Learn to randomly remove samples from the majority class.\n",
        "\n",
        "###  Under-sampling: Tomek links\n",
        "Tomek links are pairs of very close instances but of opposite classes. Removing the instances of the majority class of each pair increases the space between the two classes, facilitating the classification process.\n",
        "\n",
        "Tomek’s link exists if the two samples are the nearest neighbors of each other"
      ],
      "metadata": {
        "id": "z80LQPvms-Aq"
      }
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      "cell_type": "markdown",
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)"
      ],
      "metadata": {
        "id": "L-ZiZ4oku6Wg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# still using our separated classes fraud and not_fraud from above\n",
        "\n",
        "# downsample majority\n",
        "not_fraud_downsampled = resample(not_fraud,\n",
        "                                replace = False, # sample without replacement\n",
        "                                n_samples = len(fraud), # match minority n\n",
        "                                random_state = 27) # reproducible results\n",
        "# combine minority and downsampled majority\n",
        "downsampled = pd.concat([not_fraud_downsampled, fraud])\n",
        "\n",
        "# checking counts\n",
        "downsampled.Class.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZM0m7Zgs7TD",
        "outputId": "59237de8-f80c-47fc-d768-ee2845a10a34"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    360\n",
              "1    360\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# trying logistic regression again with the undersampled dataset\n",
        "\n",
        "y_train = downsampled.Class\n",
        "X_train = downsampled.drop('Class', axis=1)\n",
        "\n",
        "undersampled = LogisticRegression(solver='liblinear').fit(X_train, y_train)\n",
        "\n",
        "undersampled_pred = undersampled.predict(X_test)"
      ],
      "metadata": {
        "id": "tRlKriqHtFRC"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking accuracy\n",
        "accuracy_score(y_test, undersampled_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxyY7SbVtIGG",
        "outputId": "b82f4857-875a-44fd-b729-e242bcf00276"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9758574197354007"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score\n",
        "f1_score(y_test, undersampled_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rzj4RQ-ntKT3",
        "outputId": "1c6187d2-a3f0-46a8-8932-cb31368573fd"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11710323574730355"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "pd.DataFrame(confusion_matrix(y_test, undersampled_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "LmdkcF7QtMTV",
        "outputId": "19366855-2db2-4eab-fca2-a8a2ea94cc5c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       0     1\n",
              "0  69369  1701\n",
              "1     18   114"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-881a31b1-2a34-46ae-9e57-bab14802c556\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
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              "  <thead>\n",
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              "      <td>69369</td>\n",
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              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-881a31b1-2a34-46ae-9e57-bab14802c556')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-881a31b1-2a34-46ae-9e57-bab14802c556 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-881a31b1-2a34-46ae-9e57-bab14802c556');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall_score(y_test, undersampled_pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tjgi2KpMtOWV",
        "outputId": "7a7fec29-437d-4ac8-c75b-38a63677e429"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8636363636363636"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Downsampling produced a higher recall score than upsampling! My concern here is the small number of total samples we used to train the model.\n",
        "\n",
        "# 5. Generate Synthetic Samples\n",
        "SMOTE or Synthetic Minority Oversampling Technique is a popular algorithm to creates sythetic observations of the minority class.\n",
        "\n",
        "SMOTE (Synthetic Minority Oversampling Technique) works by randomly picking a point from the minority class and computing the k-nearest neighbors for this point. The synthetic points are added between the chosen point and its neighbors.\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "fNv-jA2StSbg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "SMOTE algorithm works in 4 simple steps:\n",
        "\n",
        "1. Choose a minority class as the input vector\n",
        "2. Find its k nearest neighbors (k_neighbors is specified as an argument in the SMOTE() function)\n",
        "3. Choose one of these neighbors and place a synthetic point anywhere on the line joining the point under consideration and its chosen neighbor\n",
        "4. Repeat the steps until data is balanced"
      ],
      "metadata": {
        "id": "XMh4Mn82vJ8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Separate input features and target\n",
        "y = df.Class\n",
        "X = df.drop('Class', axis=1)\n",
        "\n",
        "# setting up testing and training sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=27)\n",
        "\n",
        "sm = SMOTE()\n",
        "X_train, y_train = sm.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "499c-qd8tQBp"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smote = LogisticRegression(solver='liblinear').fit(X_train, y_train)\n",
        "\n",
        "smote_pred = smote.predict(X_test)\n",
        "\n",
        "# Checking accuracy\n",
        "accuracy_score(y_test, smote_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b208HoTUtYr4",
        "outputId": "f038c9ad-e23d-42d0-9a23-7493d4f56ce3"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9882166231285638"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score\n",
        "f1_score(y_test, smote_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RgxIz7oKtuKL",
        "outputId": "802a6cd8-5d5e-4131-9a41-dc6083d0b469"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2136832239925023"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# confustion matrix\n",
        "pd.DataFrame(confusion_matrix(y_test, smote_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "KzolrgBJtwW-",
        "outputId": "1783937d-9bd1-4343-ef6d-d967bc88109f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       0    1\n",
              "0  70249  821\n",
              "1     18  114"
            ],
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f03ed0ed-856e-4656-83a9-24ada2116d29')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f03ed0ed-856e-4656-83a9-24ada2116d29 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-f03ed0ed-856e-4656-83a9-24ada2116d29');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall_score(y_test, smote_pred)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l87id3Umtz1u",
        "outputId": "7373df81-88ec-4e5c-ee5b-900e75a08bb4"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8636363636363636"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Penalize Algorithms (Cost-Sensitive Training)\n",
        "The next tactic is to use penalized learning algorithms that increase the cost of classification mistakes on the minority class.\n",
        "\n",
        "A popular algorithm for this technique is Penalized-SVM.\n",
        "\n",
        "During training, we can use the argument class_weight=’balanced’ to penalize mistakes on the minority class by an amount proportional to how under-represented it is.\n",
        "\n",
        "We also want to include the argument probability=True if we want to enable probability estimates for SVM algorithms.\n",
        "\n",
        "Let’s train a model using Penalized-SVM on the original imbalanced dataset:"
      ],
      "metadata": {
        "id": "qPSiUspcvWDb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "XJUwDd5ovgHE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n",
        "We covered 5 different methods for dealing with imbalanced datasets:\n",
        "\n",
        "Change the performance metric\n",
        "\n",
        "\n",
        "Oversampling minority class\n",
        "\n",
        "Undersampling majority class\n",
        "\n",
        "Change the algorithm\n",
        "\n",
        "Generate synthetic samples\n",
        "\n",
        "These are just some of the many possible methods to try when dealing with imbalanced datasets, and not an exhaustive list. Some others methods to consider are collecting more data or choosing different resampling ratios - you don't have to have exactly a 1:1 ratio! You should always try several approaches and then decide which is best for your problem."
      ],
      "metadata": {
        "id": "rv2vuUnst49K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extra Readings:\n",
        "1. https://www.analyticsvidhya.com/blog/2020/07/10-techniques-to-deal-with-class-imbalance-in-machine-learning/\n",
        "\n",
        "2. https://www.geeksforgeeks.org/ml-handling-imbalanced-data-with-smote-and-near-miss-algorithm-in-python/\n",
        "\n",
        "3. https://www.geeksforgeeks.org/how-to-handle-imbalanced-classes-in-machine-learning/"
      ],
      "metadata": {
        "id": "a5QTpd9vvksO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lyBpmqxBt1o2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}