Fraud_Detection.ipynb

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      "name": "B1.6 - Fraud Detection/Imbalaced Data - Teachers' sheet.ipynb.ipynb",
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGWbBXoHCO-H"
      },
      "source": [
        "# Credit Card Fraud Detection\n",
        "### Content\n",
        "The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n",
        "\n",
        "It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n",
        "\n",
        "** Run the following code to import the librairies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXcFkVpkCO-I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c58c351-676c-48e6-8668-091c09d66fd0"
      },
      "source": [
        "# Imported Libraries\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.decomposition import PCA, TruncatedSVD\n",
        "import matplotlib.patches as mpatches\n",
        "import time\n",
        "\n",
        "# Classifier Libraries\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import collections\n",
        "\n",
        "\n",
        "# Other Libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import NearMiss\n",
        "from imblearn.metrics import classification_report_imbalanced\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
        "from collections import Counter\n",
        "from sklearn.model_selection import KFold, StratifiedKFold\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9oIeuXcpMwY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad90e39b-2410-41ac-b321-2cf3b8fbafaf"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjS2F8icOwn5"
      },
      "source": [
        "# Object\n",
        "In this notebook, we will be focusing on dealing with the **imbalanced dataset**, which is our case. \n",
        "\n",
        "The obvious challenge of solving class imbalance imbalance is the lack of data for one of the classes. For instance, many datasets are such that there are 95% samples for class 1 and the rest for class 0 and since data is what algorithm needs to learn, fails to recognize the minority. In this case, choice of loss function is necessary and it should be more sensitive to class 1 than class 0.\n",
        "\n",
        "Most classification datasets don’t have exactly equal numbers of records in each class, but a small difference doesn’t matter as much. This class imbalance problem can occur in binary classification problems as well as multi-class classification problems, but most techniques can be used on either."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tEgZNJeDCO-N"
      },
      "source": [
        "## In this first step you are going to analyze the dataset\n",
        "#### 1. Import your dataset and visualize the first five rows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_XgOWX_YpXqg"
      },
      "source": [
        "path = '/content/drive/MyDrive/Course v2/B1. Machine Learning Deep Learning/B1.6 Fraud Detection/creditcard.csv'\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BtY2o9rCO-N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "7c0679d0-6f50-476b-9130-86a2070b54a8"
      },
      "source": [
        "df = pd.read_csv(path)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
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              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
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              "      <td>0</td>\n",
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              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
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              "      <td>69.99</td>\n",
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            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQEMgUi7CO-S"
      },
      "source": [
        "#### 2. Is there any Null value in your dataset?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akA7O1eOCO-T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5045cd43-79f7-4495-c288-3f8e072725a7"
      },
      "source": [
        "# Good No Null Values!\n",
        "df.isnull().sum().max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gtQcRN0MCO-W"
      },
      "source": [
        "#### 3. Display the columns of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLMg_v2CCO-X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb793bdd-709e-4c09-fc74-917a39a916dd"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
              "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
              "       'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z4uQm_JcCO-f"
      },
      "source": [
        "Note: Notice how imbalanced is our original dataset! Most of the transactions are non-fraud. If we use this dataframe as the base for our predictive models and analysis we might get a lot of errors and our algorithms will probably overfit since it will \"assume\" that most transactions are not fraud. But we don't want our model to assume, we want our model to detect patterns that give signs of fraud!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJ2IMpBrCO-f"
      },
      "source": [
        "#### 4. Calculate amounts of each class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "heBhLVI8qqel",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3be4fab1-38f6-4f07-9a22-7a79fdd6b788"
      },
      "source": [
        "df['Class'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    284315\n",
              "1       492\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_PwU22Br-e9G"
      },
      "source": [
        "Do you have any Comments:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_SmZmZr-jAx"
      },
      "source": [
        "Note: Notice how imbalanced is our original dataset! Most of the transactions are non-fraud. If we use this dataframe as the base for our predictive models and analysis we might get a lot of errors and our algorithms will probably overfit since it will \"assume\" that most transactions are not fraud. But we don't want our model to assume, we want our model to detect patterns that give signs of fraud!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZgK0R9o-vpg"
      },
      "source": [
        "#### 5. Let's visualize our results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EcZOG3vTo8Xu"
      },
      "source": [
        "def visualize_data(data):\n",
        "  colors = [\"#0101DF\", \"#DF0101\"]\n",
        "  sns.countplot('Class', data=df, palette=colors)\n",
        "  plt.title('Class Distributions \\n (0: No Fraud || 1: Fraud)', fontsize=14)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErHrtOlNpHks",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "94ad41b2-f92e-4016-ce35-af74f4f65571"
      },
      "source": [
        "visualize_data(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SvwDNxNr_UE_"
      },
      "source": [
        "Check the ratio of both classes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrD0Hk3QOqR1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4108495b-7163-4fb7-d108-09eb768e899a"
      },
      "source": [
        "from sklearn.utils import compute_class_weight\n",
        "\n",
        "# calculate the class weight by providing the 'balanced' parameter.\n",
        "class_weight = compute_class_weight('balanced', df.Class.unique() , df.Class)\n",
        "\n",
        "# print the result\n",
        "print(class_weight)\n",
        "print(\"No.No_Fraud : No.Fraud ---\", round(class_weight[1] / class_weight[0]) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[  0.50086524 289.43800813]\n",
            "No.No_Fraud : No.Fraud --- 578\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPZzkj_YQRko"
      },
      "source": [
        "As you can see here, we have a highly imbalanced data: the amount of no-fraud cases is nearly 578 times the amount of fraud cases. If we don't deal with it, it will post a big problem to our machine learning model, as it can't detect the fraud cases, because we feed it with the vast majority of non-fraud data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nwu5VwyTUUiI"
      },
      "source": [
        "# Scaling\n",
        "\n",
        "Before we deal with the imblanace problem, let's scale the data first"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tl9VCo1EVOE_"
      },
      "source": [
        "# Since most of our data has already been scaled we should scale the columns that are left to scale (Amount and Time)\n",
        "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
        "\n",
        "# RobustScaler is less prone to outliers.\n",
        "\n",
        "std_scaler = StandardScaler()\n",
        "rob_scaler = RobustScaler()\n",
        "\n",
        "df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
        "df['scaled_time'] = std_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
        "\n",
        "# drop the original columns that we put our scalers on\n",
        "df.drop(['Time','Amount'], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W9CUt9G9WAnG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "ebad5e39-a84c-44b9-b647-531d3a07cd45"
      },
      "source": [
        "scaled_amount = df['scaled_amount']\n",
        "scaled_time = df['scaled_time']\n",
        "\n",
        "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n",
        "df.insert(0, 'scaled_amount', scaled_amount)\n",
        "df.insert(1, 'scaled_time', scaled_time)\n",
        "\n",
        "# Amount and Time are Scaled!\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>scaled_amount</th>\n",
              "      <th>scaled_time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.783274</td>\n",
              "      <td>-1.996583</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.269825</td>\n",
              "      <td>-1.996583</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.983721</td>\n",
              "      <td>-1.996562</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.418291</td>\n",
              "      <td>-1.996562</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.670579</td>\n",
              "      <td>-1.996541</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   scaled_amount  scaled_time        V1  ...       V27       V28  Class\n",
              "0       1.783274    -1.996583 -1.359807  ...  0.133558 -0.021053      0\n",
              "1      -0.269825    -1.996583  1.191857  ... -0.008983  0.014724      0\n",
              "2       4.983721    -1.996562 -1.358354  ... -0.055353 -0.059752      0\n",
              "3       1.418291    -1.996562 -0.966272  ...  0.062723  0.061458      0\n",
              "4       0.670579    -1.996541 -1.158233  ...  0.219422  0.215153      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMupo36-Ykav",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "8a63a860-62dd-45fc-b37c-43d93a001ac4"
      },
      "source": [
        "# let's shuffule our dataset first:\n",
        "df = df.sample(frac=1)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>scaled_amount</th>\n",
              "      <th>scaled_time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>65419</th>\n",
              "      <td>-0.097813</td>\n",
              "      <td>-0.910521</td>\n",
              "      <td>-0.362240</td>\n",
              "      <td>0.772002</td>\n",
              "      <td>1.850464</td>\n",
              "      <td>0.792450</td>\n",
              "      <td>0.185240</td>\n",
              "      <td>0.201619</td>\n",
              "      <td>0.702099</td>\n",
              "      <td>-0.116111</td>\n",
              "      <td>0.169838</td>\n",
              "      <td>-0.283189</td>\n",
              "      <td>-0.666117</td>\n",
              "      <td>0.244096</td>\n",
              "      <td>-0.065730</td>\n",
              "      <td>-0.517103</td>\n",
              "      <td>-0.373276</td>\n",
              "      <td>-0.859058</td>\n",
              "      <td>0.164418</td>\n",
              "      <td>-0.736311</td>\n",
              "      <td>-0.249592</td>\n",
              "      <td>-0.079161</td>\n",
              "      <td>-0.016425</td>\n",
              "      <td>0.335484</td>\n",
              "      <td>-0.250948</td>\n",
              "      <td>0.100263</td>\n",
              "      <td>0.159995</td>\n",
              "      <td>-0.350899</td>\n",
              "      <td>-0.210759</td>\n",
              "      <td>-0.202297</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5484</th>\n",
              "      <td>-0.307413</td>\n",
              "      <td>-1.880280</td>\n",
              "      <td>-1.317304</td>\n",
              "      <td>0.123519</td>\n",
              "      <td>2.457654</td>\n",
              "      <td>2.716571</td>\n",
              "      <td>0.814807</td>\n",
              "      <td>0.191741</td>\n",
              "      <td>-0.999622</td>\n",
              "      <td>0.384125</td>\n",
              "      <td>0.928563</td>\n",
              "      <td>0.548064</td>\n",
              "      <td>1.206366</td>\n",
              "      <td>-2.258819</td>\n",
              "      <td>1.186063</td>\n",
              "      <td>1.203716</td>\n",
              "      <td>-1.488013</td>\n",
              "      <td>0.526272</td>\n",
              "      <td>0.211213</td>\n",
              "      <td>0.960991</td>\n",
              "      <td>0.193159</td>\n",
              "      <td>-0.123614</td>\n",
              "      <td>-0.165697</td>\n",
              "      <td>0.045674</td>\n",
              "      <td>-0.213357</td>\n",
              "      <td>-0.036051</td>\n",
              "      <td>-0.692173</td>\n",
              "      <td>-0.075011</td>\n",
              "      <td>0.434823</td>\n",
              "      <td>0.163794</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254230</th>\n",
              "      <td>-0.027947</td>\n",
              "      <td>1.301825</td>\n",
              "      <td>-0.479119</td>\n",
              "      <td>-0.552405</td>\n",
              "      <td>-0.336637</td>\n",
              "      <td>-3.086234</td>\n",
              "      <td>1.316347</td>\n",
              "      <td>3.850850</td>\n",
              "      <td>-1.605479</td>\n",
              "      <td>1.705125</td>\n",
              "      <td>-0.326634</td>\n",
              "      <td>-0.203615</td>\n",
              "      <td>-0.478357</td>\n",
              "      <td>-0.489649</td>\n",
              "      <td>0.054793</td>\n",
              "      <td>-0.467950</td>\n",
              "      <td>-0.103685</td>\n",
              "      <td>1.716843</td>\n",
              "      <td>-0.168154</td>\n",
              "      <td>-1.108581</td>\n",
              "      <td>-0.527823</td>\n",
              "      <td>0.142023</td>\n",
              "      <td>0.264523</td>\n",
              "      <td>0.520943</td>\n",
              "      <td>0.078099</td>\n",
              "      <td>0.695098</td>\n",
              "      <td>-0.145361</td>\n",
              "      <td>-0.320328</td>\n",
              "      <td>0.145715</td>\n",
              "      <td>-0.090780</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125958</th>\n",
              "      <td>2.962342</td>\n",
              "      <td>-0.356676</td>\n",
              "      <td>0.850054</td>\n",
              "      <td>-1.201491</td>\n",
              "      <td>-0.237813</td>\n",
              "      <td>-0.607297</td>\n",
              "      <td>-0.627241</td>\n",
              "      <td>0.100217</td>\n",
              "      <td>-0.097422</td>\n",
              "      <td>0.010931</td>\n",
              "      <td>-1.379790</td>\n",
              "      <td>0.674089</td>\n",
              "      <td>1.702221</td>\n",
              "      <td>0.833149</td>\n",
              "      <td>0.395394</td>\n",
              "      <td>0.499841</td>\n",
              "      <td>0.512581</td>\n",
              "      <td>-1.836788</td>\n",
              "      <td>0.359775</td>\n",
              "      <td>0.326139</td>\n",
              "      <td>-1.178905</td>\n",
              "      <td>-0.061747</td>\n",
              "      <td>-0.196874</td>\n",
              "      <td>-0.551026</td>\n",
              "      <td>-0.103988</td>\n",
              "      <td>-0.243243</td>\n",
              "      <td>0.062726</td>\n",
              "      <td>1.027707</td>\n",
              "      <td>-0.087820</td>\n",
              "      <td>0.023980</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237520</th>\n",
              "      <td>1.228953</td>\n",
              "      <td>1.146923</td>\n",
              "      <td>1.762348</td>\n",
              "      <td>-0.650060</td>\n",
              "      <td>-0.326182</td>\n",
              "      <td>0.595607</td>\n",
              "      <td>-0.820942</td>\n",
              "      <td>-0.016060</td>\n",
              "      <td>-0.948533</td>\n",
              "      <td>0.134690</td>\n",
              "      <td>1.408019</td>\n",
              "      <td>-0.566714</td>\n",
              "      <td>-0.544818</td>\n",
              "      <td>0.370916</td>\n",
              "      <td>0.858618</td>\n",
              "      <td>-1.782596</td>\n",
              "      <td>1.029762</td>\n",
              "      <td>1.213302</td>\n",
              "      <td>0.108219</td>\n",
              "      <td>0.876322</td>\n",
              "      <td>-0.695667</td>\n",
              "      <td>0.136428</td>\n",
              "      <td>0.206264</td>\n",
              "      <td>0.558685</td>\n",
              "      <td>0.158936</td>\n",
              "      <td>0.696037</td>\n",
              "      <td>-0.535421</td>\n",
              "      <td>0.466539</td>\n",
              "      <td>0.006780</td>\n",
              "      <td>0.018848</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        scaled_amount  scaled_time        V1  ...       V27       V28  Class\n",
              "65419       -0.097813    -0.910521 -0.362240  ... -0.210759 -0.202297      0\n",
              "5484        -0.307413    -1.880280 -1.317304  ...  0.434823  0.163794      0\n",
              "254230      -0.027947     1.301825 -0.479119  ...  0.145715 -0.090780      0\n",
              "125958       2.962342    -0.356676  0.850054  ... -0.087820  0.023980      0\n",
              "237520       1.228953     1.146923  1.762348  ...  0.006780  0.018848      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZeDDFlbm1mp"
      },
      "source": [
        "# Know the right metric to choose\n",
        "\n",
        "In this case, we care more about catching most of the fraud cases (class 1), so the cost of **false negatives** will cost us more. As such, we rely more on **recall** score. Overall accuracy is a bad metric for this example. \n",
        "\n",
        "Here is the case when to use precision or recall:\n",
        "- Precision: When you have a higher cost related to false postive.\n",
        "- Recall: When you have a higher cost related to false negative"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PYV_El4tAS3I"
      },
      "source": [
        "# Split the data\n",
        "\n",
        "Before we apply resampling methods on our data, let's split the data into train / test set, so that we could use our test set as the unseen data to test each method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjrtn6zgAjtM",
        "outputId": "78510c9a-47d7-4388-c25b-15b6bd061fb4"
      },
      "source": [
        "X = df.drop('Class', axis =1)\n",
        "y = df['Class']\n",
        "\n",
        "# split the data and check the shape, with 30% of the test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)   #stratified\n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(199364, 30)\n",
            "(199364,)\n",
            "(85443, 30)\n",
            "(85443,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6lfiO18Rm4y"
      },
      "source": [
        "# Dealing with imbalanced data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0RX2uCmgcnn"
      },
      "source": [
        "Resampling is widely-used when it comes to dealing with imbalanced datset. It has a few advantages :\n",
        "- Easy to implement\n",
        "- Fast to run\n",
        "- Have an excellent starting point\n",
        "\n",
        "Resampling changes the dataset into a more balanced one by adding instances to the minority class or deleting ones from the majority class, that way we build better machine learning models.\n",
        "\n",
        "Resampling is achieved using 2 different ways: Oversampling and Undersampling.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cuwavPPJiGye"
      },
      "source": [
        "- **Oversampling**:All the observations from the minority class are kept and sampling without replacement is performed in the majority class where the number of observations sampled is equal to the sample size of the minority class This method adds copies of instances from the under-represented class (minority class) to obtain a balanced dataset. There are multiple ways you can oversample a dataset, like random oversampling.\n",
        "\n",
        "Below is the visualized process for how oversampling works:\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvH-vdKHirv2"
      },
      "source": [
        "- **Undersampling**: Decide how many times the sample size of the minority class wants to be used and perform sampling with replacement in the minority class and sampling without replacement in the majority class. These methods delete instances from the over-represented class (majority class) in different ways. The most obvious way is to do delete instances randomly.\n",
        "\n",
        "Below is a visualized process of how undersampling works:\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmtI94eFitUZ"
      },
      "source": [
        "Although these techniques generally do work well, they do have some disadvantages:\n",
        "- If we duplicate random records from the minority class to do oversampling, this will cause overfitting. (Oversampling)\n",
        "- By undersampling and removing random records from the majority class, you risk losing some important information for the machine learning algorithm to use while training and predicting. (Undersampling)\n",
        "\n",
        "Let's start with undersampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2zFKTLBr8Pv"
      },
      "source": [
        "## Undersampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IoGbnVfYSYOf"
      },
      "source": [
        "### Random Undersampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AALFHpyBn0t1"
      },
      "source": [
        "We could use RandomUnderSampler() method to do the undersampling job. Here we can specify a few parameters inside the Object:\n",
        "- **sampling_strategy**: this parameter can be used to tell the object how to perform undersampling on our dataset. It can be \"majority\" to resample only the majority class, \"not_minority\" to resample all classes but the minority class. If we leave it empty, it will be set default, which stands for \"not minority\".\n",
        "\n",
        "\n",
        "- **return_indices**: A boolean parameter on whether to return the indices of the remove instances or not. By default, it is set to False.\n",
        "- **random_state**:  An integer that controls the randomness of the procedure, which allow us to reproduce the results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okr8FzHBOqq6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "026e3db3-947c-49f4-969e-0b662decaceb"
      },
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "# create the object.\n",
        "under_sampler = RandomUnderSampler() \n",
        "\n",
        "\n",
        "# fit the object to the training data.\n",
        "x_under, y_under = under_sampler.fit_sample(X_train, y_train)\n",
        "\n",
        "# See how data ia being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_under))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({0: 328, 1: 328})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-uKyMx8Urz2d"
      },
      "source": [
        "### NearMiss Undersampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1TBPxuJsLdJ"
      },
      "source": [
        "The basics of the NearMiss algorithms include the following:\n",
        "1. The method starts by calculating the distances between all instances of the majority class and the instances of the minority class.\n",
        "2. Then k instances of the majority class that have the smallest distances to those in the minority class are selected to be retained.\n",
        "3. If there are n instances in the minority class, NearMiss will result in k × n instances of the majority class.\n",
        "\n",
        "Below is a visualization process of Near Miss Undersampling:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ur6L0Ksgsgdi"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQctdOrGs_8I"
      },
      "source": [
        "We could use NearMiss() method to do the undersampling job. Here we can specify a few parameters inside the Object:\n",
        "\n",
        "- **version**: Note that we could set parameter **version** to 1, 2 or 3, which have different versions of this algorithm:\n",
        " - NearMiss-1 chooses instances of the majority class where their average distances to the three closest instances of the minority class are the smallest.\n",
        "\n",
        " - NearMiss-2 uses the three farthest samples of the minority class.\n",
        "\n",
        " - NearMiss-3 picks a given number of the closest samples of the majority class for each sample of the minority class.\n",
        "\n",
        "- **n_neighbors**: NearMiss-3 algorithm start by a phase of re-sampling. This parameter correspond to the number of neighbours selected create the subset in which the selection will be performed. By default, it's 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_1y3VE8OqvS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5d69fb1-568b-419a-e30a-265a2b0e9559"
      },
      "source": [
        "from imblearn.under_sampling import NearMiss\n",
        "\n",
        "# create the object with auto\n",
        "near = NearMiss(version=1) # let's choose the version 1 here, but you could switch it to different versions\n",
        "\n",
        "\n",
        "# fit the object to the training data.\n",
        "x_under_near, y_under_near = near.fit_sample(X_train, y_train)\n",
        "\n",
        "# See how data is being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_under_near))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({0: 328, 1: 328})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dw1_hmLLu5Vz"
      },
      "source": [
        "### Tomek links Undersampling\n",
        "Tomek links are pairs of very close instances that belong to different classes. They’re samples near the borderline between classes. By removing the examples of the majority class of each pair, we increase the space between the two classes and move toward balancing the dataset by deleting those points.\n",
        "\n",
        "Below is a visualization process of Tomek links:\n",
        "![image.png](data:image/png;base64,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)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QB8R-FjKvu1D"
      },
      "source": [
        "We could use TomekLinks() method to do the undersampling job. Here we can specify a few parameters inside the Object:\n",
        "- **sampling_strategy**: this parameter can be used to tell the object how to perform undersampling on our dataset. It can be \"majority\" to resample only the majority class, \"not_minority\" to resample all classes but the minority class. \"not majority\" resample all classes but the majority class, and \"all\" to resample all classes. If we leave it empty, it will be set default, which stands for \"not minority\". "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94YjkeVEOqzq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d4c7741-cfa8-438c-c73a-42827d554d57"
      },
      "source": [
        "# import the TomekLinks object.\n",
        "from imblearn.under_sampling import TomekLinks\n",
        "\n",
        "# set sampling_strategy = 'majority' when initialize the object\n",
        "tomek_links = TomekLinks(sampling_strategy = 'majority')\n",
        "\n",
        "\n",
        "# fit the object to the training data.\n",
        "x_tomek, y_tomek = tomek_links.fit_sample(X_train, y_train)\n",
        "\n",
        "# See how data is being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_tomek))   #199364 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({0: 199016, 1: 328})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-5K5TTkzamD"
      },
      "source": [
        "### Cluster Centroids Undersampling\n",
        "The idea of udersampling with cluster centroids is that we basically remove the unimportant instance from the majority class. To decide whether an instance is important or not, we use the concept of clustering on the geometry of the feature space.\n",
        "\n",
        "Clustering is an unsupervised learning approach, in which clusters are creating encircling data points belonging.\n",
        "\n",
        "We will use it only to find cluster centroid that are obtained by averaging feature vector for all the features over the data points.\n",
        "\n",
        "After finding the cluster centroid of the majority class, we decide the following:\n",
        "\n",
        "- The instance belonging to the cluster (majority class), which is farthest from the cluster centroid in the feature space, is considered to be the most unimportant instance.\n",
        "- The instance belonging to the majority class, which is nearest to the cluster centroid in the feature space, is considered to be the most important instance.\n",
        "\n",
        "Below is the visualization process of how it works:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9e79M1n0QIn"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ae151sxK04MQ"
      },
      "source": [
        "We could use ClusterCentroids() method to do the undersampling job. Here we can specify a few parameters inside the Object:\n",
        "\n",
        "- **sampling_strategy**: this parameter can be used to tell the object how to perform undersampling on our dataset. It can be \"majority\" to resample only the majority class, \"not minority\" to resample all classes but the minority class. \"not majority\" resample all classes but the majority class, and \"all\" to resample all classes. If we leave it empty, it will be set default, which stands for \"not minority\". Note that the number of samples will not be equal in each. We could also use the a dictionary to set keys corresponding to the targeted classes, and the values corresponding to the desired number of samples for each targeted class.\n",
        "- **estimator**: An object that performs the clustering process for this method—K-Means is the default here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQNpnqsBOq2j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b4209db-5562-446b-a5df-5b88747aaf1f"
      },
      "source": [
        "# import the ClusterCentroids object.\n",
        "from imblearn.under_sampling import ClusterCentroids\n",
        "\n",
        "# instantiate the object with the right ratio strategy.\n",
        "cluster_centroids  = ClusterCentroids(sampling_strategy='auto') \n",
        "\n",
        "\n",
        "# fit the object to the training data.\n",
        "x_cluster, y_cluster = cluster_centroids.fit_sample(X_train, y_train)\n",
        "\n",
        "# See how data is being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_cluster))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({0: 328, 1: 328})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avM3jrkH98FZ"
      },
      "source": [
        "### ENN Undersampling\n",
        "\n",
        "ENN stands for Edited Nearest Neighbor Rule. This technique removes any instance from the majority class whose class label is different from the class label of at least two of its three nearest neighbors. In general terms, it’s near or around the borderline of different classes.\n",
        "\n",
        "The point here is to increase the classification accuracy of minority instances rather than majority instances."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iBeI50H-OUt"
      },
      "source": [
        "We could use EditedNearestNeighbours(). Note that we could also pass a dictionary to sampling_strategy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lt9TTp37-T_b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "560655a9-7ce0-4b1b-9ec7-c3d691516dcf"
      },
      "source": [
        "# import the EditedNearestNeighbours object.\n",
        "from imblearn.under_sampling import EditedNearestNeighbours\n",
        "\n",
        "\n",
        "# create the object to resample the majority class.\n",
        "enn = EditedNearestNeighbours(sampling_strategy= 'majority')\n",
        "\n",
        "\n",
        "# fit the object to the training data.\n",
        "x_enn, y_enn = enn.fit_sample(X_train, y_train)\n",
        "\n",
        "# See how data is being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_enn))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({0: 198921, 1: 328})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6jVsY1HnSsu"
      },
      "source": [
        "## Test each undersampling method"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j926hV9XltRy"
      },
      "source": [
        "Next, let's apply logistic regression to test the classification report results for each under sampling methods we applied"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZj8BmtPloPG"
      },
      "source": [
        "def logistic_regression(X_train,y_train, method):\n",
        "  # create the model\n",
        "  lr = LogisticRegression()\n",
        "\n",
        "  # fit the data\n",
        "  lr.fit(X_train, y_train)\n",
        "\n",
        "  # make predictions\n",
        "  lr_pred = lr.predict(X_test)\n",
        "\n",
        "  # get the classification report\n",
        "  print(f'{method}: ')\n",
        "  print('\\n')\n",
        "  labels = ['No Fraud', 'Fraud']\n",
        "  print(classification_report(y_test, lr_pred, target_names=labels))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fWOGlJr1fML-",
        "outputId": "42744071-41c4-4ef0-e0e1-7bc8ab4c8931"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(199364, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2_N1XSJl3dt",
        "outputId": "cf05c454-45fb-4161-c06a-8b6e615314fa"
      },
      "source": [
        "undersampling_methods = ['Without Sampling','Random Undersampling', 'NearMiss Undersampling', 'Tomek Links Undersampling', 'Cluster Centroids Undersampling', 'Edited Nearest Neighbor']\n",
        "\n",
        "# X data from all methods\n",
        "X_train_data = [X_train,x_under, x_under_near, x_tomek, x_cluster, x_enn]\n",
        "\n",
        "# y data from all methods\n",
        "y_train_data = [y_train, y_under, y_under_near, y_tomek, y_cluster, y_enn]\n",
        "\n",
        "\n",
        "# loop into these lists and run the method:\n",
        "for X, y, method in zip(X_train_data, y_train_data, undersampling_methods):\n",
        "  logistic_regression(X,y, method)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Without Sampling: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      1.00      1.00     85279\n",
            "       Fraud       0.87      0.57      0.69       164\n",
            "\n",
            "    accuracy                           1.00     85443\n",
            "   macro avg       0.93      0.79      0.85     85443\n",
            "weighted avg       1.00      1.00      1.00     85443\n",
            "\n",
            "Random Undersampling: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      0.95      0.97     85279\n",
            "       Fraud       0.03      0.88      0.06       164\n",
            "\n",
            "    accuracy                           0.95     85443\n",
            "   macro avg       0.52      0.91      0.52     85443\n",
            "weighted avg       1.00      0.95      0.97     85443\n",
            "\n",
            "NearMiss Undersampling: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      0.43      0.61     85279\n",
            "       Fraud       0.00      0.96      0.01       164\n",
            "\n",
            "    accuracy                           0.43     85443\n",
            "   macro avg       0.50      0.70      0.31     85443\n",
            "weighted avg       1.00      0.43      0.60     85443\n",
            "\n",
            "Tomek Links Undersampling: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      1.00      1.00     85279\n",
            "       Fraud       0.87      0.58      0.70       164\n",
            "\n",
            "    accuracy                           1.00     85443\n",
            "   macro avg       0.94      0.79      0.85     85443\n",
            "weighted avg       1.00      1.00      1.00     85443\n",
            "\n",
            "Cluster Centroids Undersampling: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      0.96      0.98     85279\n",
            "       Fraud       0.04      0.88      0.08       164\n",
            "\n",
            "    accuracy                           0.96     85443\n",
            "   macro avg       0.52      0.92      0.53     85443\n",
            "weighted avg       1.00      0.96      0.98     85443\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guGWgEM473Qw"
      },
      "source": [
        "**What do you find?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSsNwg2tprOa"
      },
      "source": [
        "We can see that for each method, we increase the recall score comparing to the original model, with nearmiss achieving 93% for minority class"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iNUkSk82k-2"
      },
      "source": [
        "## Oversampling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQRgC8Ag3b4k"
      },
      "source": [
        "### Random Oversampling\n",
        "Random oversampling randomly duplicate records from the minority class. Try this technique when you don’t have a lot of data.\n",
        "Random oversampling simply replicates random minority class examples. It’s known to increase the likelihood of overfitting, which is a major drawback."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAjPXBYb4fCi"
      },
      "source": [
        "We could use RandomOverSampler() method to do the oversampling job. Here we can specify a few parameters inside the Object:\n",
        "- **sampling_strategy**: all parameters work the same way as previous, the only difference is that by default, it's \"not majority\". We could also pass a dictionary as before"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HM-MYNOgOq8D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58b53270-32ee-4652-b76a-f43a1db558e1"
      },
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "# create the object.\n",
        "over_sampler = RandomOverSampler() \n",
        "\n",
        "\n",
        "# fit the object to the training data.\n",
        "x_over, y_over = over_sampler.fit_sample(X_train, y_train)\n",
        "\n",
        "# See how data ia being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_over))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({0: 199036, 1: 199036})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbdAKpzR5pOZ"
      },
      "source": [
        "### SMOTE Oversampling\n",
        " SMOTE is effective avoiding moderl overfitting.\n",
        " To create a synthetic instance, SMOTE finds the K-nearest neighbors of each minority instance, randomly selects one of them and then calculates linear interpolations to produce a new minority instance in the neighborhood. It can be also explained by changing this instance features one at a time by a random amount. Thus, new points are added between the neighbors.\n",
        "\n",
        " Below is a visualization process of how it works:\n",
        " \n",
        " ![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wx9n9HmAOq-_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb14a252-3f94-4f30-a921-945745c98ef8"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# create the object.\n",
        "smote = SMOTE() \n",
        "\n",
        "# fit the object to the training data.\n",
        "x_smote, y_smote = smote.fit_sample(X_train, y_train)\n",
        "\n",
        "# See how data ia being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_smote))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({0: 199036, 1: 199036})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CRLQrzg7T3S"
      },
      "source": [
        "### ADASYN Oversampling\n",
        "ADASYN stands for Adaptive Synthetic sampling, and as SMOTE does, ADASYN generates samples of the minority class. But here, because of their density distributions, this technique receives wide attention.\n",
        "\n",
        "Its purpose is to generate data for minority class samples that are harder to learn, as compared to those minority samples that are easier to learn.\n",
        "\n",
        "ADASYN uses KNN to measure all minority instances, then calculates the class ratio of the minority and majority instances to create new samples. By Repeating this process, we will adaptively shift the decision boundary to focus on those samples that are hard to learn.\n",
        "\n",
        "**The major difference between SMOTE and ADASYN is the difference in the generation of synthetic sample points for minority data points. In ADASYN, we consider a density distribution which thereby decides the number of synthetic samples to be generated for a particular point, whereas in SMOTE, there is a uniform weight for all minority points.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khcyuvJxOrBb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad298ac4-81e7-4919-8fbb-d134fecb2b47"
      },
      "source": [
        "# import the ADASYN object.\n",
        "from imblearn.over_sampling import ADASYN\n",
        "\n",
        "# create the object \n",
        "adasyn = ADASYN() \n",
        "\n",
        "# Split the data into X ,y\n",
        "X = df.drop('Class', axis=1)\n",
        "y = df['Class']\n",
        "\n",
        "# fit the object to the training data.\n",
        "x_adasyn, y_adasyn = adasyn.fit_sample(X, y)\n",
        "\n",
        "# See how data ia being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_adasyn))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({0: 284315, 1: 284292})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XFFLFjlvJLW"
      },
      "source": [
        "## Test each oversampling method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vZ7dKF_wvRil",
        "outputId": "091b5cc5-db2b-4e35-9594-92fc3511d122"
      },
      "source": [
        "oversampling_methods = ['Random Oversampling', 'SMOTE Oversampling', 'ADASYN Oversampling']\n",
        "\n",
        "# X data from all methods\n",
        "X_over_data = [x_over, x_smote, x_adasyn]\n",
        "\n",
        "# y data from all methods\n",
        "y_over_data = [y_over, y_smote, y_adasyn]\n",
        "\n",
        "# run the logistice function again, and inspect the result:\n",
        "for x_train_set, y_train_set, method in zip(X_over_data, y_over_data, oversampling_methods):\n",
        "  logistic_regression(x_train_set, y_train_set, method)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Oversampling: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      0.98      0.99     85279\n",
            "       Fraud       0.07      0.88      0.13       164\n",
            "\n",
            "    accuracy                           0.98     85443\n",
            "   macro avg       0.54      0.93      0.56     85443\n",
            "weighted avg       1.00      0.98      0.99     85443\n",
            "\n",
            "SMOTE Oversampling: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      0.98      0.99     85279\n",
            "       Fraud       0.07      0.87      0.12       164\n",
            "\n",
            "    accuracy                           0.98     85443\n",
            "   macro avg       0.53      0.92      0.56     85443\n",
            "weighted avg       1.00      0.98      0.99     85443\n",
            "\n",
            "ADASYN Oversampling: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      0.91      0.95     85279\n",
            "       Fraud       0.02      0.94      0.04       164\n",
            "\n",
            "    accuracy                           0.91     85443\n",
            "   macro avg       0.51      0.92      0.50     85443\n",
            "weighted avg       1.00      0.91      0.95     85443\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtNoPe9j85F7"
      },
      "source": [
        "## Combining both Oversampling & Undersampling\n",
        "\n",
        "Aside from doing oversampling & undersampling seperately, we could also combine these 2 techniques to get better sampling results. Let's introduce 2 ways:\n",
        "- SMOTE & Tomek Links\n",
        "- SMOTE & ENN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Mg6cg54_o58"
      },
      "source": [
        "### SMOTE & Tomek Links \n",
        "\n",
        "We could use SMOTETomek() to do it.\n",
        "\n",
        "- **sampling_strategy**: It can be \"minority\" to resample only the minority class, \"not minority\" to resample all classes but the minority class, \"not majority\" to resample all classes but the majority class, and \"all\" to resample all classes. If we leave it empty, it will be set default, which stands for \"not majority\". We could also use the a dictionary to set keys corresponding to the targeted classes, and the values corresponding to the desired number of samples for each targeted class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_sFhwdI85gz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01d53aba-291d-43bf-9626-ad9f02a7c412"
      },
      "source": [
        "# import the SMOTETomek.\n",
        "from imblearn.combine import SMOTETomek\n",
        "\n",
        "# create the  object, and use the automatic strategy by setting sampling_strategy ='auto'\n",
        "smotemek = SMOTETomek(sampling_strategy='auto')\n",
        "\n",
        "# fit the object to our training data.\n",
        "x_smt, y_smt = smotemek.fit_sample(X_train, y_train)\n",
        "\n",
        "# See how data ia being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_smt))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({0: 199036, 1: 199036})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fh76TRBlA0EO"
      },
      "source": [
        "### SMOTE & ENN (Edited Nearest Neighbor)\n",
        "\n",
        "We could use SMOTEENN() to resample our data. Note that **sampling_strategy** works the same way as SMOTETomek()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oU-2m4j285jZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9dfd1bc7-d71f-4c44-f7c5-3d99d0e49ab7"
      },
      "source": [
        "# import the SMOTETomek.\n",
        "from imblearn.combine import SMOTEENN\n",
        "\n",
        "# create the  object with the automatic sampling strategy.\n",
        "smoenn = SMOTEENN(sampling_strategy='auto')\n",
        "\n",
        "# fit the object to our training data.\n",
        "x_smoenn, y_smoenn = smoenn.fit_sample(X_train, y_train)\n",
        "\n",
        "# See how data ia being balanced\n",
        "print('Resampled dataset shape %s' % Counter(y_smoenn))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({1: 199036, 0: 198753})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmmYAtZX8_tv"
      },
      "source": [
        "## Test each method with both undersampling & oversampling techniques"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6W5BPFCv0NY",
        "outputId": "1a43ed3a-a9f4-4e60-82aa-e5a9f73d3d48"
      },
      "source": [
        "OverUndersamplingCombined= ['SMOTE & Tomek Links', 'SMOTE & ENN']\n",
        "\n",
        "# X data from all methods\n",
        "X_combined_data = [x_smt, x_smoenn]\n",
        "\n",
        "# y data from all methods\n",
        "y_combined_data = [y_smt, y_smoenn]\n",
        "\n",
        "# test all data using the logistic function we defined:\n",
        "for x_train_set, y_train_set, method in zip(X_combined_data, y_combined_data, OverUndersamplingCombined):\n",
        "  logistic_regression(x_train_set, y_train_set, method)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SMOTE & Tomek Links: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      0.98      0.99     85279\n",
            "       Fraud       0.07      0.87      0.12       164\n",
            "\n",
            "    accuracy                           0.98     85443\n",
            "   macro avg       0.53      0.92      0.56     85443\n",
            "weighted avg       1.00      0.98      0.99     85443\n",
            "\n",
            "SMOTE & ENN: \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      0.98      0.99     85279\n",
            "       Fraud       0.06      0.87      0.12       164\n",
            "\n",
            "    accuracy                           0.98     85443\n",
            "   macro avg       0.53      0.92      0.55     85443\n",
            "weighted avg       1.00      0.98      0.99     85443\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDXl-QvcCorW"
      },
      "source": [
        "## Get all precision & recall score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LK64s7WbLpAP"
      },
      "source": [
        "Let's get all precison scores and recall scores in one place. First, let's write a function to return both scores per method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIGWBhA4Cv1G"
      },
      "source": [
        "def get_precision_recall(X_train,y_train):\n",
        "  # create the model\n",
        "  lr = LogisticRegression()\n",
        "\n",
        "  # fit the data\n",
        "  lr.fit(X_train, y_train)\n",
        "\n",
        "  # make predictions\n",
        "  lr_pred = lr.predict(X_test)\n",
        "\n",
        "  # get both precision score & recall score\n",
        "  precision = \"{0:.0%}\".format(precision_score(y_test, lr_pred))\n",
        "  recall = \"{0:.0%}\".format(recall_score(y_test, lr_pred))\n",
        "\n",
        "  return precision, recall\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HeH_88CFEWFe"
      },
      "source": [
        "# Wrap all methods' name into a single list\n",
        "all_methods = ['Without resampling', 'Random Undersampling', 'NearMiss Undersampling', 'TomekLinks Undersampling', 'Cluster Centroids Undersampling','ENN Undersampling',\n",
        "               'Random Oversampling', 'SMOTE Oversampling', 'ADASYN Oversampling', 'SMOTE & TomekLinks', 'SMOTE & ENN']\n",
        "\n",
        "\n",
        "# Define the X data and y data\n",
        "X_datas = [X_train, x_under, x_under_near, x_tomek, x_cluster, x_enn, x_over, x_smote, x_adasyn, x_smt, x_smoenn]\n",
        "y_datas = [y_train, y_under, y_under_near, y_tomek, y_cluster, y_enn, y_over, y_smote, y_adasyn, y_smt, y_smoenn]\n",
        "\n",
        "\n",
        "precision_scores = []\n",
        "recall_scores = []\n",
        "\n",
        "for x_train_set, y_train_set in zip(X_datas, y_datas):\n",
        "  precision_scores.append(get_precision_recall(x_train_set, y_train_set)[0])\n",
        "  recall_scores.append(get_precision_recall(x_train_set, y_train_set)[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "G5GpPcPUECFG",
        "outputId": "346673aa-98f5-4281-d72e-18670552339e"
      },
      "source": [
        "methods = {'Without Resampling': all_methods[0],\n",
        "           'UnderSampling': all_methods[1:6],\n",
        "           'OverSampling': all_methods[6:9],\n",
        "           'Oversampling & Undersampling': all_methods[9:]}\n",
        "\n",
        "\n",
        "data_tuples = list(zip(all_methods,precision_scores,recall_scores))\n",
        "\n",
        "df_com = pd.DataFrame(data_tuples, columns = ['Methods', 'Precisions', 'Recalls'])\n",
        "df_com"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Methods</th>\n",
              "      <th>Precisions</th>\n",
              "      <th>Recalls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Without resampling</td>\n",
              "      <td>87%</td>\n",
              "      <td>57%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Undersampling</td>\n",
              "      <td>3%</td>\n",
              "      <td>88%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NearMiss Undersampling</td>\n",
              "      <td>0%</td>\n",
              "      <td>96%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TomekLinks Undersampling</td>\n",
              "      <td>87%</td>\n",
              "      <td>58%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cluster Centroids Undersampling</td>\n",
              "      <td>4%</td>\n",
              "      <td>88%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ENN Undersampling</td>\n",
              "      <td>87%</td>\n",
              "      <td>59%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Random Oversampling</td>\n",
              "      <td>7%</td>\n",
              "      <td>88%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SMOTE Oversampling</td>\n",
              "      <td>7%</td>\n",
              "      <td>87%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ADASYN Oversampling</td>\n",
              "      <td>2%</td>\n",
              "      <td>94%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>SMOTE &amp; TomekLinks</td>\n",
              "      <td>7%</td>\n",
              "      <td>87%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>SMOTE &amp; ENN</td>\n",
              "      <td>6%</td>\n",
              "      <td>87%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            Methods Precisions Recalls\n",
              "0                Without resampling        87%     57%\n",
              "1              Random Undersampling         3%     88%\n",
              "2            NearMiss Undersampling         0%     96%\n",
              "3          TomekLinks Undersampling        87%     58%\n",
              "4   Cluster Centroids Undersampling         4%     88%\n",
              "5                 ENN Undersampling        87%     59%\n",
              "6               Random Oversampling         7%     88%\n",
              "7                SMOTE Oversampling         7%     87%\n",
              "8               ADASYN Oversampling         2%     94%\n",
              "9                SMOTE & TomekLinks         7%     87%\n",
              "10                      SMOTE & ENN         6%     87%"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7m0lVM7Edmbt"
      },
      "source": [
        "## Use Penalize Algorithms\n",
        "\n",
        "We could also try to do cost-sensitive training, which is penalized learning algorithms. One of these is **Penalized Support Vector Machine**. As we do so, we increase the cost of classification mistakes on the minority class.\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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3dAjqr3Rzfp"
      },
      "source": [
        "*-----**Warning**-----*\n",
        "\n",
        "- When you train the model, you should consider implementing a series of strict steps to get a good result, which are similar to steps being demonstrated in original fraud detection notebook . Here we mainly focus on dealing with the imbalanced data, that's why we simply apply the algorithm to complete it.\n",
        "\n",
        "- Whether you will get a good results by using penalized algorithms often **depends on your data**. We are introducing you the method here, but you should be careful deciding if you are going to use them if you have a heavily imbalanced data, as sometimes it could worsen your results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HaFgUVGZWgnl"
      },
      "source": [
        "Let's first try to use a standard SVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtZfwSdETX_r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfc5b61a-7c02-4333-81e9-f3f48784d9d2"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "\n",
        "# Create the model\n",
        "svc = SVC()\n",
        "\n",
        "# Fit the model\n",
        "svc.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "svc_pred = svc.predict(X_test)\n",
        "\n",
        "# get the accuracy score\n",
        "print(accuracy_score(y_test, svc_pred))\n",
        "\n",
        "# get classification reports\n",
        "labels = ['No Fraud', 'Fraud']\n",
        "print(classification_report(y_test, svc_pred, target_names=labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9993328885923949\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      1.00      1.00     85279\n",
            "       Fraud       0.95      0.69      0.80       164\n",
            "\n",
            "    accuracy                           1.00     85443\n",
            "   macro avg       0.97      0.84      0.90     85443\n",
            "weighted avg       1.00      1.00      1.00     85443\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6mbsu-XWmYk"
      },
      "source": [
        "This time, we use Penalized Support Vector Machine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2vUiQ43dfyP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94d75369-8b84-4bdb-ac2d-6b2ad4ab4788"
      },
      "source": [
        "# Create the svm model, this time, we set class_weight = 'balanced' to penalize the minority\n",
        "svc = SVC(class_weight='balanced')\n",
        "\n",
        "\n",
        "# fit the model\n",
        "svc.fit(X_train, y_train)\n",
        "\n",
        "# make predictions\n",
        "svc_pred = svc.predict(X_test)\n",
        "\n",
        "# get the accuracy score\n",
        "print(accuracy_score(y_test, svc_pred))\n",
        "\n",
        "labels = ['No Fraud', 'Fraud']\n",
        "print(classification_report(y_test, svc_pred, target_names=labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9966176281263532\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      1.00      1.00     85279\n",
            "       Fraud       0.33      0.74      0.46       164\n",
            "\n",
            "    accuracy                           1.00     85443\n",
            "   macro avg       0.67      0.87      0.73     85443\n",
            "weighted avg       1.00      1.00      1.00     85443\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JaUhTm0xRo4Z"
      },
      "source": [
        "Also, Logistic regression has options for us to penalize the minority class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVPGHpN_lRh3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fb61af1-373d-4dad-a6b7-e3ae0973c549"
      },
      "source": [
        "# Create the penalized logistic regression model\n",
        "lr = LogisticRegression(class_weight='balanced')\n",
        "\n",
        "\n",
        "# fit the data\n",
        "lr.fit(X_train, y_train)\n",
        "\n",
        "#make predictions\n",
        "lr_pred = lr.predict(X_test)\n",
        "\n",
        "# get classification reports\n",
        "labels = ['No Fraud', 'Fraud']\n",
        "print(classification_report(y_test, lr_pred, target_names=labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    No Fraud       1.00      0.98      0.99     85279\n",
            "       Fraud       0.07      0.88      0.13       164\n",
            "\n",
            "    accuracy                           0.98     85443\n",
            "   macro avg       0.54      0.93      0.56     85443\n",
            "weighted avg       1.00      0.98      0.99     85443\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gy_J5kqhzfSr"
      },
      "source": [
        "Compare this to our original logistic regression model before, did you notice something?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6NxdwSLEdQPK"
      },
      "source": [
        "## Change the performance metrics\n",
        "\n",
        "Also, changing the performance metrics is also helpful sometimes. Usually, the accuracy is misleading, which is trhe not ideal metric to use when dealing with imbalanced datasets. Other metrics have been desgined for such a case, including\n",
        "- Confusion Matrix\n",
        "- Precison & Recall\n",
        "- F1 Score\n",
        "- ROC Curves\n",
        "\n",
        "Please refer to Fraud Detection notebook, where each of them has been implemented in detail."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lf_0BNC5pIIS"
      },
      "source": [
        "# Good job!"
      ]
    }
  ]
}