4_Credit_card_fraud_detection_with_Machine_Learning.ipynb

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      "name": "4. Credit card fraud detection with Machine Learning.ipynb",
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      "source": [
        "##Credit Card Fraud Detection With Machine Learning in Python\r\n",
        "Using XGBoost, Random forest, KNN, Logistic regression, SVM, and Decision tree to solve classification problems\r\n",
        "\r\n",
        "\r\n",
        "Assume that you are employed to help a credit card company to detect potential fraud cases so that the customers are ensured that they won’t be charged for the items they did not purchase. You are given a dataset containing the transactions between people, the information that they are fraud or not, and you are asked to differentiate between them. This is the case we are going to deal with. Our ultimate intent is to tackle this situation by building classification models to classify and distinguish fraud transactions.\r\n",
        "Why Classification? Classification is the process of predicting discrete variables (binary, Yes/no, etc.). Given the case, it will be more optimistic to deploy a classification model rather than any others."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LgJlaekzwRi",
        "outputId": "7353c3b5-9e23-404c-94af-b7b546b090b3"
      },
      "source": [
        "# to read google drive data files\r\n",
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OBkGO_P-zesH",
        "outputId": "f67b3a99-6a7c-4966-b31a-8cc4f8006e70"
      },
      "source": [
        "# IMPORTING PACKAGES\r\n",
        "\r\n",
        "import pandas as pd # data processing\r\n",
        "import numpy as np # working with arrays\r\n",
        "import matplotlib.pyplot as plt # visualization\r\n",
        "from termcolor import colored as cl # text customization\r\n",
        "import itertools # advanced tools\r\n",
        "\r\n",
        "from sklearn.preprocessing import StandardScaler # data normalization\r\n",
        "from sklearn.model_selection import train_test_split # data split\r\n",
        "from sklearn.tree import DecisionTreeClassifier # Decision tree algorithm\r\n",
        "from sklearn.neighbors import KNeighborsClassifier # KNN algorithm\r\n",
        "from sklearn.linear_model import LogisticRegression # Logistic regression algorithm\r\n",
        "from sklearn.svm import SVC # SVM algorithm\r\n",
        "from sklearn.ensemble import RandomForestClassifier # Random forest tree algorithm\r\n",
        "from xgboost import XGBClassifier # XGBoost algorithm\r\n",
        "\r\n",
        "from sklearn.metrics import confusion_matrix # evaluation metric\r\n",
        "from sklearn.metrics import accuracy_score # evaluation metric\r\n",
        "from sklearn.metrics import f1_score # evaluation metric\r\n",
        "\r\n",
        "# IMPORTING DATA\r\n",
        "\r\n",
        "df = pd.read_csv('/content/drive/My Drive/Credit Card Data/creditcard.csv')\r\n",
        "df.drop('Time', axis = 1, inplace = True)\r\n",
        "\r\n",
        "print(df.head())\r\n",
        "\r\n",
        "# EDA\r\n",
        "\r\n",
        "# 1. Count & percentage\r\n",
        "\r\n",
        "cases = len(df)\r\n",
        "nonfraud_count = len(df[df.Class == 0])\r\n",
        "fraud_count = len(df[df.Class == 1])\r\n",
        "fraud_percentage = round(fraud_count/nonfraud_count*100, 2)\r\n",
        "\r\n",
        "print(cl('CASE COUNT', attrs = ['bold']))\r\n",
        "print(cl('--------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('Total number of cases are {}'.format(cases), attrs = ['bold']))\r\n",
        "print(cl('Number of Non-fraud cases are {}'.format(nonfraud_count), attrs = ['bold']))\r\n",
        "print(cl('Number of Non-fraud cases are {}'.format(fraud_count), attrs = ['bold']))\r\n",
        "print(cl('Percentage of fraud cases is {}'.format(fraud_percentage), attrs = ['bold']))\r\n",
        "print(cl('--------------------------------------------', attrs = ['bold']))\r\n",
        "\r\n",
        "# 2. Description\r\n",
        "\r\n",
        "nonfraud_cases = df[df.Class == 0]\r\n",
        "fraud_cases = df[df.Class == 1]\r\n",
        "\r\n",
        "print(cl('CASE AMOUNT STATISTICS', attrs = ['bold']))\r\n",
        "print(cl('--------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('NON-FRAUD CASE AMOUNT STATS', attrs = ['bold']))\r\n",
        "print(nonfraud_cases.Amount.describe())\r\n",
        "print(cl('--------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('FRAUD CASE AMOUNT STATS', attrs = ['bold']))\r\n",
        "print(fraud_cases.Amount.describe())\r\n",
        "print(cl('--------------------------------------------', attrs = ['bold']))\r\n",
        "\r\n",
        "# DATA SPLIT\r\n",
        "\r\n",
        "X = df.drop('Class', axis = 1).values\r\n",
        "y = df['Class'].values\r\n",
        "\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\r\n",
        "\r\n",
        "print(cl('X_train samples : ', attrs = ['bold']), X_train[:1])\r\n",
        "print(cl('X_test samples : ', attrs = ['bold']), X_test[0:1])\r\n",
        "print(cl('y_train samples : ', attrs = ['bold']), y_train[0:10])\r\n",
        "print(cl('y_test samples : ', attrs = ['bold']), y_test[0:10])\r\n",
        "\r\n",
        "# MODELING\r\n",
        "\r\n",
        "# 1. Decision Tree\r\n",
        "\r\n",
        "tree_model = DecisionTreeClassifier(max_depth = 4, criterion = 'entropy')\r\n",
        "tree_model.fit(X_train, y_train)\r\n",
        "tree_yhat = tree_model.predict(X_test)\r\n",
        "\r\n",
        "# 2. K-Nearest Neighbors\r\n",
        "\r\n",
        "n = 5\r\n",
        "\r\n",
        "knn = KNeighborsClassifier(n_neighbors = n)\r\n",
        "knn.fit(X_train, y_train)\r\n",
        "knn_yhat = knn.predict(X_test)\r\n",
        "\r\n",
        "# 3. Logistic Regression\r\n",
        "\r\n",
        "lr = LogisticRegression()\r\n",
        "lr.fit(X_train, y_train)\r\n",
        "lr_yhat = lr.predict(X_test)\r\n",
        "\r\n",
        "# 4. SVM \r\n",
        "\r\n",
        "svm = SVC()\r\n",
        "svm.fit(X_train, y_train)\r\n",
        "svm_yhat = svm.predict(X_test)\r\n",
        "\r\n",
        "# 5. Random Forest Tree\r\n",
        "\r\n",
        "rf = RandomForestClassifier(max_depth = 4)\r\n",
        "rf.fit(X_train, y_train)\r\n",
        "rf_yhat = rf.predict(X_test)\r\n",
        "\r\n",
        "# 6. XGBoost\r\n",
        "\r\n",
        "xgb = XGBClassifier(max_depth = 4)\r\n",
        "xgb.fit(X_train, y_train)\r\n",
        "xgb_yhat = xgb.predict(X_test)\r\n",
        "\r\n",
        "# EVALUATION\r\n",
        "\r\n",
        "# 1. Accuracy score\r\n",
        "\r\n",
        "print(cl('ACCURACY SCORE', attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('Accuracy score of the Decision Tree model is {}'.format(accuracy_score(y_test, tree_yhat)), attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('Accuracy score of the KNN model is {}'.format(accuracy_score(y_test, knn_yhat)), attrs = ['bold'], color = 'green'))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('Accuracy score of the Logistic Regression model is {}'.format(accuracy_score(y_test, lr_yhat)), attrs = ['bold'], color = 'red'))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('Accuracy score of the SVM model is {}'.format(accuracy_score(y_test, svm_yhat)), attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('Accuracy score of the Random Forest Tree model is {}'.format(accuracy_score(y_test, rf_yhat)), attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('Accuracy score of the XGBoost model is {}'.format(accuracy_score(y_test, xgb_yhat)), attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "\r\n",
        "# 2. F1 score\r\n",
        "\r\n",
        "print(cl('F1 SCORE', attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('F1 score of the Decision Tree model is {}'.format(f1_score(y_test, tree_yhat)), attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('F1 score of the KNN model is {}'.format(f1_score(y_test, knn_yhat)), attrs = ['bold'], color = 'green'))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('F1 score of the Logistic Regression model is {}'.format(f1_score(y_test, lr_yhat)), attrs = ['bold'], color = 'red'))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('F1 score of the SVM model is {}'.format(f1_score(y_test, svm_yhat)), attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('F1 score of the Random Forest Tree model is {}'.format(f1_score(y_test, rf_yhat)), attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "print(cl('F1 score of the XGBoost model is {}'.format(f1_score(y_test, xgb_yhat)), attrs = ['bold']))\r\n",
        "print(cl('------------------------------------------------------------------------', attrs = ['bold']))\r\n",
        "\r\n",
        "# 3. Confusion Matrix\r\n",
        "\r\n",
        "# defining the plot function\r\n",
        "\r\n",
        "def plot_confusion_matrix(cm, classes, title, normalize = False, cmap = plt.cm.Blues):\r\n",
        "    title = 'Confusion Matrix of {}'.format(title)\r\n",
        "    if normalize:\r\n",
        "        cm = cm.astype(float) / cm.sum(axis=1)[:, np.newaxis]\r\n",
        "\r\n",
        "    plt.imshow(cm, interpolation = 'nearest', cmap = cmap)\r\n",
        "    plt.title(title)\r\n",
        "    plt.colorbar()\r\n",
        "    tick_marks = np.arange(len(classes))\r\n",
        "    plt.xticks(tick_marks, classes, rotation = 45)\r\n",
        "    plt.yticks(tick_marks, classes)\r\n",
        "\r\n",
        "    fmt = '.2f' if normalize else 'd'\r\n",
        "    thresh = cm.max() / 2.\r\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\r\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\r\n",
        "                 horizontalalignment = 'center',\r\n",
        "                 color = 'white' if cm[i, j] > thresh else 'black')\r\n",
        "\r\n",
        "    plt.tight_layout()\r\n",
        "    plt.ylabel('True label')\r\n",
        "    plt.xlabel('Predicted label')\r\n",
        "\r\n",
        "# Compute confusion matrix for the models\r\n",
        "\r\n",
        "tree_matrix = confusion_matrix(y_test, tree_yhat, labels = [0, 1]) # Decision Tree\r\n",
        "knn_matrix = confusion_matrix(y_test, knn_yhat, labels = [0, 1]) # K-Nearest Neighbors\r\n",
        "lr_matrix = confusion_matrix(y_test, lr_yhat, labels = [0, 1]) # Logistic Regression\r\n",
        "svm_matrix = confusion_matrix(y_test, svm_yhat, labels = [0, 1]) # Support Vector Machine\r\n",
        "rf_matrix = confusion_matrix(y_test, rf_yhat, labels = [0, 1]) # Random Forest Tree\r\n",
        "xgb_matrix = confusion_matrix(y_test, xgb_yhat, labels = [0, 1]) # XGBoost\r\n",
        "\r\n",
        "# Plot the confusion matrix\r\n",
        "\r\n",
        "plt.rcParams['figure.figsize'] = (6, 6)\r\n",
        "\r\n",
        "# 1. Decision tree\r\n",
        "\r\n",
        "tree_cm_plot = plot_confusion_matrix(tree_matrix, \r\n",
        "                                classes = ['Non-Default(0)','Default(1)'], \r\n",
        "                                normalize = False, title = 'Decision Tree')\r\n",
        "plt.savefig('tree_cm_plot.png')\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# 2. K-Nearest Neighbors\r\n",
        "\r\n",
        "knn_cm_plot = plot_confusion_matrix(knn_matrix, \r\n",
        "                                classes = ['Non-Default(0)','Default(1)'], \r\n",
        "                                normalize = False, title = 'KNN')\r\n",
        "plt.savefig('knn_cm_plot.png')\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# 3. Logistic regression\r\n",
        "\r\n",
        "lr_cm_plot = plot_confusion_matrix(lr_matrix, \r\n",
        "                                classes = ['Non-Default(0)','Default(1)'], \r\n",
        "                                normalize = False, title = 'Logistic Regression')\r\n",
        "plt.savefig('lr_cm_plot.png')\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# 4. Support Vector Machine\r\n",
        "\r\n",
        "svm_cm_plot = plot_confusion_matrix(svm_matrix, \r\n",
        "                                classes = ['Non-Default(0)','Default(1)'], \r\n",
        "                                normalize = False, title = 'SVM')\r\n",
        "plt.savefig('svm_cm_plot.png')\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# 5. Random forest tree\r\n",
        "\r\n",
        "rf_cm_plot = plot_confusion_matrix(rf_matrix, \r\n",
        "                                classes = ['Non-Default(0)','Default(1)'], \r\n",
        "                                normalize = False, title = 'Random Forest Tree')\r\n",
        "plt.savefig('rf_cm_plot.png')\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# 6. XGBoost\r\n",
        "\r\n",
        "xgb_cm_plot = plot_confusion_matrix(xgb_matrix, \r\n",
        "                                classes = ['Non-Default(0)','Default(1)'], \r\n",
        "                                normalize = False, title = 'XGBoost')\r\n",
        "plt.savefig('xgb_cm_plot.png')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         V1        V2        V3        V4  ...       V27       V28  Amount  Class\n",
            "0 -1.359807 -0.072781  2.536347  1.378155  ...  0.133558 -0.021053  149.62      0\n",
            "1  1.191857  0.266151  0.166480  0.448154  ... -0.008983  0.014724    2.69      0\n",
            "2 -1.358354 -1.340163  1.773209  0.379780  ... -0.055353 -0.059752  378.66      0\n",
            "3 -0.966272 -0.185226  1.792993 -0.863291  ...  0.062723  0.061458  123.50      0\n",
            "4 -1.158233  0.877737  1.548718  0.403034  ...  0.219422  0.215153   69.99      0\n",
            "\n",
            "[5 rows x 30 columns]\n",
            "\u001b[1mCASE COUNT\u001b[0m\n",
            "\u001b[1m--------------------------------------------\u001b[0m\n",
            "\u001b[1mTotal number of cases are 284807\u001b[0m\n",
            "\u001b[1mNumber of Non-fraud cases are 284315\u001b[0m\n",
            "\u001b[1mNumber of Non-fraud cases are 492\u001b[0m\n",
            "\u001b[1mPercentage of fraud cases is 0.17\u001b[0m\n",
            "\u001b[1m--------------------------------------------\u001b[0m\n",
            "\u001b[1mCASE AMOUNT STATISTICS\u001b[0m\n",
            "\u001b[1m--------------------------------------------\u001b[0m\n",
            "\u001b[1mNON-FRAUD CASE AMOUNT STATS\u001b[0m\n",
            "count    284315.000000\n",
            "mean         88.291022\n",
            "std         250.105092\n",
            "min           0.000000\n",
            "25%           5.650000\n",
            "50%          22.000000\n",
            "75%          77.050000\n",
            "max       25691.160000\n",
            "Name: Amount, dtype: float64\n",
            "\u001b[1m--------------------------------------------\u001b[0m\n",
            "\u001b[1mFRAUD CASE AMOUNT STATS\u001b[0m\n",
            "count     492.000000\n",
            "mean      122.211321\n",
            "std       256.683288\n",
            "min         0.000000\n",
            "25%         1.000000\n",
            "50%         9.250000\n",
            "75%       105.890000\n",
            "max      2125.870000\n",
            "Name: Amount, dtype: float64\n",
            "\u001b[1m--------------------------------------------\u001b[0m\n",
            "\u001b[1mX_train samples : \u001b[0m [[-1.11504743e+00  1.03558276e+00  8.00712441e-01 -1.06039825e+00\n",
            "   3.26211690e-02  8.53422160e-01 -6.14243480e-01 -3.23116112e+00\n",
            "   1.53994798e+00 -8.16908791e-01 -1.30559201e+00  1.08177199e-01\n",
            "  -8.59609580e-01 -7.19342108e-02  9.06655628e-01 -1.72092961e+00\n",
            "   7.97853221e-01 -6.75939779e-03  1.95677806e+00 -6.44895565e-01\n",
            "   3.02038533e+00 -5.39617976e-01  3.31564886e-02 -7.74945766e-01\n",
            "   1.05867812e-01 -4.30853482e-01  2.29736936e-01 -7.05913036e-02\n",
            "   1.29500000e+01]]\n",
            "\u001b[1mX_test samples : \u001b[0m [[-3.23333572e-01  1.05745525e+00 -4.83411518e-02 -6.07204308e-01\n",
            "   1.25982115e+00 -9.17607168e-02  1.15910150e+00 -1.24334606e-01\n",
            "  -1.74639536e-01 -1.64440065e+00 -1.11886302e+00  2.02647310e-01\n",
            "   1.14596495e+00 -1.80235956e+00 -2.47177932e-01 -6.09453515e-02\n",
            "   8.46605738e-01  3.79454387e-01  8.47262245e-01  1.86409421e-01\n",
            "  -2.07098267e-01 -4.33890272e-01 -2.61613283e-01 -4.66506063e-02\n",
            "   2.11512300e-01  8.29721214e-03  1.08494430e-01  1.61139167e-01\n",
            "   4.00000000e+01]]\n",
            "\u001b[1my_train samples : \u001b[0m [0 0 0 0 0 0 0 0 0 0]\n",
            "\u001b[1my_test samples : \u001b[0m [0 0 0 0 0 0 0 0 0 0]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mACCURACY SCORE\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1mAccuracy score of the Decision Tree model is 0.9993679997191109\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1m\u001b[32mAccuracy score of the KNN model is 0.9993328885923949\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1m\u001b[31mAccuracy score of the Logistic Regression model is 0.9990344440153085\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1mAccuracy score of the SVM model is 0.998735999438222\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1mAccuracy score of the Random Forest Tree model is 0.9992802219023208\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1mAccuracy score of the XGBoost model is 0.9994733330992591\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1mF1 SCORE\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1mF1 score of the Decision Tree model is 0.8105263157894738\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1m\u001b[32mF1 score of the KNN model is 0.7865168539325842\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1m\u001b[31mF1 score of the Logistic Regression model is 0.6666666666666666\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1mF1 score of the SVM model is 0.5\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1mF1 score of the Random Forest Tree model is 0.768361581920904\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n",
            "\u001b[1mF1 score of the XGBoost model is 0.8421052631578948\u001b[0m\n",
            "\u001b[1m------------------------------------------------------------------------\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}