Tans_fraud.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tans fraud",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sidd1609/Chatbot/blob/master/Tans_fraud.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_ho-jbn-yjV",
        "colab_type": "code",
        "outputId": "270a5cf4-517c-4f49-c1ce-71676b533860",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "#ROC curve\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from matplotlib import pyplot\n",
        "X, y = make_classification(n_samples=1000, n_classes=2, random_state=1)\n",
        "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2)\n",
        "ns_probs = [0 for _ in range(len(testy))]\n",
        "model = LogisticRegression(solver='lbfgs')\n",
        "model.fit(trainX, trainy)\n",
        "lr_probs = model.predict_proba(testX)\n",
        "lr_probs = lr_probs[:, 1]\n",
        "ns_auc = roc_auc_score(testy, ns_probs)\n",
        "lr_auc = roc_auc_score(testy, lr_probs)\n",
        "print('No Skill: ROC AUC=%.3f' % (ns_auc))\n",
        "print('Logistic: ROC AUC=%.3f' % (lr_auc))\n",
        "ns_fpr, ns_tpr, _ = roc_curve(testy, ns_probs)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(testy, lr_probs)\n",
        "pyplot.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
        "pyplot.plot(lr_fpr, lr_tpr, marker='.', label='Logistic')\n",
        "pyplot.xlabel('False Positive Rate')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No Skill: ROC AUC=0.500\n",
            "Logistic: ROC AUC=0.903\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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fTkJ7vWxfqWDi34EOZ64SHTPb25V4zcpth3hkaSr7snK5ZWD706N0pVRw8f9A\nD2LHsgt4avkWlvy0l04tGrLo9gGc366Zt8tSSnmJBrofO5ZTwBebD3LX5Z244/JO1K2lzbSUCmYu\nrUMXkREisk1EtovIDCfP3ysiW0QkRUS+EpF27i9VARw6kcecVTswxtCheUO+n3459w7vqmGulKo8\n0EUkFHgFGAnEAdeJSFyZ3X4G4o0xvYBFwLPuLjTYGWP4cF06Q2Z9y/Nf/EpaZg6ArmBRSp3mypRL\nArDdGLMTQEQWAuOALSU7GGO+cdh/LXC9O4sMdulHc3hoySa+236EhPbhPDO+pzbTUkqdxZVAbwuk\nOzzOAPpXsP+twKfOnhCRKcAUgJiYGBdLrEDJZf8lnwfgksWSZlrHcwr56+97MDEhRptpKaWccutJ\nURG5HogHLnX2vDFmDjAHID4+vno3qgzwy/53Hckmxt5M67kJvWkXUZ82TcO8XZZSyoe5clJ0LxDt\n8DjKvq0UERkKPAKMNcbku6e8CpR32b+fKyy28a+vfuOK2atY8EMaABd1jNAwV0pVypUR+jqgs4i0\nxwrya4GJjjuISF/gDWCEMeaQ26t0JgAv+0/JOM6Di1L45cBJfte7DWP7aDMtpZTrKg10Y0yRiEwF\nPgdCgbnGmM0i8iSQbIxJBJ4DGgL/tV+huMcYM9aDdQfcZf9zv9vFX5dvoXmjurx5YzzD4lp6uySl\nlJ9xaQ7dGLMCWFFm2+MOnw91c12uCYDL/ksu0+8V1YRrLohmxsjuNAnTpYhKqarTK0W95GReIc98\n+gt1a4Xy+O/iiI8NJz5Wm2kppc6d/9+xyA9988shhs9exftJe6gVKhhTvQU/SikFOkKvUUezC3jy\nk818tGEfXVo25NVJA+gbo820lFLuoYFeg7JyC/lq6yGmDenMHZd1ok4t/QVJKeU+GugediArj482\n7OVPl3SgfWQDvptxuZ70VEp5hAa6hxhjWLgunb8t30qhzcaI81oRG9lAw1wp5TEa6B6wOzObGYs3\nsWZnJhd2COeZ8b2I1WZaSp1WWFhIRkYGeXl53i7FZ9WrV4+oqChq13Z9EOi/ge6jjbmKim1MfPNH\nsnIL+duVPbn2gmhtpqVUGRkZGTRq1IjY2Fi9XaITxhgyMzPJyMigffv2Lr/OPwPdBxtz7Th8inb2\nZlrPX20102rdRPuvKOVMXl6ehnkFRISIiAgOHz5cpdf55zILH2rMVVBk44Uvf2XEC6t4e81uAC7s\nEKFhrlQlNMwrdi7fH/8boacnQVZ66W1easy1If040xelsO3gScb1acPv+7at8RqUUqqEf43Q05Ng\n3ihInndmm4TCyGdrfLrlrczT61wAABBoSURBVO92Mf7V78nKLeStm+J58dq+hDeoU6M1KKXOnYhw\n3333nX78z3/+k5kzZ7r8+oMHDzJmzBh69+5NXFwco0aNAmDlypWMGTPmrP0TExN55plnAJg5cyb/\n/Oc/AZg8eTKLFi2qxpGc4V8j9LTVYCs8e3tuZo2VUNJMq090E65NiGHGyG40rqdLEZXyN3Xr1mXJ\nkiU89NBDREZGVvn1jz/+OMOGDWPatGkApKSkVLj/2LFjGTvWs01o/SvQYweBhICxWY8lpMamW07k\nFfL3Fb9Qr3YIf/ndeZzfLpzz22kzLaXc4Zo31py1bUyv1txwUSy5BcVMnpd01vMTzo/iD/HRHM0u\n4M//WV/quQ/+dFGl71mrVi2mTJnC7Nmzefrpp0s9l5aWxi233MKRI0do3rw58+bNO+u2mfv372f4\n8OGnH/fq1eus91i3bh1Tpkxh0aJFrF69muTkZF5++eVKaztX/jXlEp0ALXtA03Yw5kW4/FG4KdHj\n0y1fbjnIsFnf8sG6PdSpFaLNtJQKEHfccQfvvvsuWVlZpbbfeeed3HTTTaSkpDBp0iTuuusup6+9\n9dZbueyyy3j66afZt29fqed/+OEHbr/9dj7++GM6duzo0eMo4V8jdDjTAz1+ssffKvNUPk98soXE\njfvo1qoRc26Ip3d0U4+/r1LBpqIRdVid0AqfD29Qx6URuTONGzfmxhtv5KWXXiIs7MzKtDVr1rBk\nyRIAbrjhBh588MGzXnvFFVewc+dOPvvsMz799FP69u1LamoqAFu3bmXKlCl88cUXtGlTc3ce868R\neg07mVfEN9sOcc/QLiROvVjDXKkAdPfdd/PWW2+RnZ1d5deGh4czceJE3nnnHS644AJWrVoFQOvW\nralXrx4///yzu8utkAZ6GfuO5/LKN9sxxhAb2YDvZ1zOtKGdtTOiUgEqPDycq6++mrfeeuv0tgED\nBrBw4UIA3n33XQYNOvs83ddff01OTg4AJ0+eZMeOHafn2Zs2bcry5ct56KGHWLlypecPwk5Tys5m\nM/xn7W6Gz17Fy19vZ3em9YPSFSxKBb777ruPI0eOnH78r3/9i3nz5tGrVy/eeecdXnzxxbNes379\neuLj4+nVqxcXXXQRt912GxdccMHp51u2bMmyZcu44447+PHHH2vkOMRbJ/ji4+NNcnJy1V84b7T1\n583L3VbLriPZzFicwo+7jjKwUwR/v7IXMRH13fb1lVKlbd26le7du3u7DJ/n7PskIuuNMfHO9ve/\nk6JuVlRs4/p//8iJvEKevaoXf4iP0kuSlVJ+KWgDffuhk8RGNKBWaAizr+lDu4j6tGxcz9tlKaXU\nOQu6OfT8omJm/e9XRrywmgX2ZloJ7cM1zJVSfi+oRug/7TnG9EUp/HboFOP7tmW8NtNSSgWQoAn0\nN1ft5G+fbqV143rMu/kCLuvawtslKaWUWwV8oNtshpAQoV+7pkzqH8P0Ed1opEsRlVIBKGDn0LNy\nC3lw0Uae+GQzAOe3C+evv++pYa6UAqBhw4bV/hr79u1jwoQJ5T5//PhxXn31VZf3r66ADPTPNx9g\n2KxvWfzTXhrUraXNtJQKBOlJsPp5608f0aZNmwp7mZcN9Mr2r66AmnI5ciqfv3y8meWb9hPXujFz\nJ19Aj7ZNvF2WUqoin86AA5sq3if/BBxMtVpnS4jVdbVu4/L3b9UTRj5T5VLKa5u7Y8cOJk2aRHZ2\nNuPGjeOFF17g1KlTpKWlMWbMGFJTU9m8eTM333wzBQUF2Gw2Fi9ezGOPPcaOHTvo06cPw4YN4447\n7ji9f3FxMdOnT+ezzz4jJCSEP/7xj9x5551VrtlRQI3QT+UVsfq3wzxwRVc+njpQw1ypQJGXdeY+\nCMZmPfaA8trmTps2jWnTprFp0yaioqKcvvb1119n2rRpbNiwgeTkZKKionjmmWfo2LEjGzZs4Lnn\nniu1/5w5c0hLS2PDhg2n36+6/H6Evvd4Lkt/yuCOyzoRG9mAHx4aQsO6fn9YSgUPV0bS6UmwYKx1\nQ/jQOnDVvz1yH4Ty2uauWbOGjz76CICJEydy//33n/Xaiy66iKeffpqMjAzGjx9P586dK3yvL7/8\nkttvv51atay8Cg+v/g1zXBqhi8gIEdkmIttFZIaT5+uKyAf2538UkdhqV1ae/BOQlY5t94+8syaN\n4bO+5ZVvdpxupqVhrlQAik6wbmZz+SM1clObczFx4kQSExMJCwtj1KhRfP311zVeQ6WBLiKhwCvA\nSCAOuE5E4srsditwzBjTCZgN/MPdhQLW/9IHNmGO76Zo3miWJi6lX7tmfHHPJcRGNvDIWyqlfER0\nAgy6z6NhXl7b3AsvvJDFixcDnH6+rJ07d9KhQwfuuusuxo0bR0pKCo0aNeLkyZNO9x82bBhvvPEG\nRUVFABw9erTa9bsyQk8AthtjdhpjCoCFwLgy+4wDFtg/XwQMEU90uNr4PgaDALUp5NkuW3j7lgSi\nw7UzolKqanJycoiKijr9MWvWrHLb5r7wwgvMmjWLXr16sX37dpo0Ofv83IcffkiPHj3o06cPqamp\n3HjjjURERDBw4EB69OjBAw88UGr/2267jZiYGHr16kXv3r157733qn1MlbbPFZEJwAhjzG32xzcA\n/Y0xUx32SbXvk2F/vMO+z5EyX2sKMAUgJibm/N27d1et2mV3Q/I8AAwg8TfDmBeq9jWUUl7nb+1z\nc3JyCAsLQ0RYuHAh77//Ph9//LHH39en2+caY+YAc8Dqh17lL9B7Ivz8LhQXIqG1rcdKKeVh69ev\nZ+rUqRhjaNq0KXPnzvV2SU65Euh7gWiHx1H2bc72yRCRWkATINMtFTqKToDJyyFtNcQO8skTI0qp\nwDNo0CA2btzo7TIq5UqgrwM6i0h7rOC+Fig7NE4EbgLWABOAr42nLs+MTtAgVyoAGGP0ZjIVOJcI\nrfSkqDGmCJgKfA5sBT40xmwWkSdFZKx9t7eACBHZDtwLnLW0USmlStSrV4/MzExty1EOYwyZmZnU\nq1e1+zT43z1FlVJ+r7CwkIyMDPLy8rxdis+qV68eUVFR1K5duqGgz5wUVUopgNq1a9O+fXtvlxFw\nAqqXi1JKBTMNdKWUChAa6EopFSC8dlJURA4DVbxU9LRI4EilewUWPebgoMccHKpzzO2MMc2dPeG1\nQK8OEUku7yxvoNJjDg56zMHBU8esUy5KKRUgNNCVUipA+Gugz/F2AV6gxxwc9JiDg0eO2S/n0JVS\nSp3NX0foSimlytBAV0qpAOHTge5TN6euIS4c870iskVEUkTkKxFp54063amyY3bY7yoRMSLi90vc\nXDlmEbna/rPeLCLVvz+Zl7nwdztGRL4RkZ/tf79HeaNOdxGRuSJyyH5HN2fPi4i8ZP9+pIhIv2q/\nqTHGJz+AUGAH0AGoA2wE4srs83/A6/bPrwU+8HbdNXDMlwH17Z//ORiO2b5fI2AVsBaI93bdNfBz\n7gz8DDSzP27h7bpr4JjnAH+2fx4HpHm77moe8yVAPyC1nOdHAZ8CAlwI/Fjd9/TlEbrv3Jy65lR6\nzMaYb4wxOfaHa7HuIOXPXPk5AzwF/AMIhH6rrhzzH4FXjDHHAIwxh2q4Rndz5ZgN0Nj+eRNgXw3W\n53bGmFXA0Qp2GQe8bSxrgaYi0ro67+nLgd4WSHd4nGHf5nQfY92IIwuIqJHqPMOVY3Z0K9b/8P6s\n0mO2/yoabYxZXpOFeZArP+cuQBcR+V5E1orIiBqrzjNcOeaZwPUikgGsAO6smdK8pqr/3iul/dD9\nlIhcD8QDl3q7Fk8SkRBgFjDZy6XUtFpY0y6DsX4LWyUiPY0xx71alWddB8w3xjwvIhcB74hID2OM\nzduF+QtfHqFX5ebUePTm1DXHlWNGRIYCjwBjjTH5NVSbp1R2zI2AHsBKEUnDmmtM9PMTo678nDOA\nRGNMoTFmF/ArVsD7K1eO+VbgQwBjzBqgHlYTq0Dl0r/3qvDlQD99c2oRqYN10jOxzD4lN6cGT9+c\numZUeswi0hd4AyvM/X1eFSo5ZmNMljEm0hgTa4yJxTpvMNYY48/3L3Tl7/ZHWKNzRCQSawpmZ00W\n6WauHPMeYAiAiHTHCvTDNVplzUoEbrSvdrkQyDLG7K/WV/T2meBKzhKPwhqZ7AAesW97EusfNFg/\n8P8C24EkoIO3a66BY/4SOAhssH8kertmTx9zmX1X4uerXFz8OQvWVNMWYBNwrbdrroFjjgO+x1oB\nswEY7u2aq3m87wP7gUKs37huBW4Hbnf4Gb9i/35scsffa730XymlAoQvT7kopZSqAg10pZQKEBro\nSikVIDTQlVIqQGigK6VUgNBAVx4jIsUissHhI7aCfWPL60pXxfdcae/ot9F+2XzXc/gat4vIjfbP\nJ4tIG4fn/i0icW6uc52I9HHhNXeLSP3qvrcKXBroypNyjTF9HD7Sauh9JxljemM1bnuuqi82xrxu\njHnb/nAy0MbhuduMMVvcUuWZOl/FtTrvBjTQVbk00FWNso/EV4vIT/aPAU72OU9Ekuyj+hQR6Wzf\nfr3D9jdEJLSSt1sFdLK/doi9z/Yme5/quvbtzzj0l/+nfdtMEblfRCZg9ct51/6eYfaRdbx9FH86\nhO0j+ZfPsc41ODRlEpHXRCRZrD7oT9i33YX1H8s3IvKNfdtwEVlj/z7+V0QaVvI+KsBpoCtPCnOY\nbllq33YIGGaM6QdcA7zk5HW3Ay8aY/pgBWqG/VLwa4CB9u3FwKRK3v93wCYRqQfMB64xxvTEanz1\nZxGJAK4EzjPG9AL+6vhiY8wiIBlrJN3HGJPr8PRi+2tLXAMsPMc6R2Bd6l/iEWNMPNALuFREehlj\nXsJqJ3uZMeYyezuAR4Gh9u9lMnBvJe+jApx2W1SelGsPNUe1gZftc8bFWD1KyloDPCIiUcASY8xv\nIjIEOB9YZ295H4b1n4Mz74pILpCG1YK1K7DLGPOr/fkFwB3Ay1j91d8SkWXAMlcPzBhzWER22ntw\n/AZ0w7ps/Y4q1lkHaAg4fp+uFpEpWP8+W2NdEp9S5rUX2rd/b3+fOljfNxXENNBVTbsHqxdNb6zf\nEM+6YYUx5j0R+REYDawQkT9h9b1YYIx5yIX3mGQcmneJSLiznYwxRSKSgNUQagIwFbi8CseyELga\n+AVYaowxYqWry3UC67Hmz/8FjBeR9sD9wAXGmGMiMh+rZ1FZAvzPGHNdFepVAU6nXFRNawLsN1aP\n6xuwbk1Wioh0AHbapxk+xpp6+AqYICIt7PuEi+v3U90GxIpIJ/vjG4Bv7XPOTYwxK7D+o+nt5LUn\nsVr4OrMU664z12GFO1Wt01jNlB4DLhSRblh37MkGskSkJTCynFrWAgNLjklEGoiIs992VBDRQFc1\n7VXgJhHZiDVNke1kn6uBVBHZgNUL/W37ypJHgS9EJAX4H9Z0RKWMMXnAzcB/RWQTYANexwrHZfav\n9x3O56DnA6+XnBQt83WPAVuBdsaYJPu2Ktdpn5t/HnjAGLMR616ivwDvYU3jlJgDfCYi3xhjDmOt\nwHnf/j5rsL6fKohpt0WllAoQOkJXSqkAoYGulFIBQgNdKaUChAa6UkoFCA10pZQKEBroSikVIDTQ\nlVIqQPw/rv7J9GOJINsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bf0S0V2ZDWVZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Classification (train and test)\n",
        "import pandas as pd\n",
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xt_PLpujNkxL",
        "colab_type": "code",
        "outputId": "9b9af1c3-e81a-4443-ebef-200b8e586c31",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-aecfba7a-69f1-4bd2-ae70-cb7da98278b5\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-aecfba7a-69f1-4bd2-ae70-cb7da98278b5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving creditcard.csv to creditcard (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWmG7sZ-XQfb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import pandas as pd\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Convolution2D, Flatten, Dropout, MaxPooling2D\n",
        "from keras.optimizers import SGD\n",
        "import tensorflow as tf\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgm99hoXX1hD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('creditcard.csv', low_memory=False)\n",
        "X = df.iloc[:,:-1]\n",
        "y = df['Amount']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmXa6eYsXeZO",
        "colab_type": "code",
        "outputId": "1f8a8676-7dcd-4f16-853a-03bfd8a45b25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "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>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0     0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62    0.0\n",
              "1     0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69    0.0\n",
              "2     1 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66    0.0\n",
              "3     1 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50    0.0\n",
              "4     2 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99    0.0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a77OFYchYg-8",
        "colab_type": "code",
        "outputId": "42b08364-aa05-4057-e75d-97df1bbb9709",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "frauds = df.loc[df['Class'] == 1]\n",
        "non_frauds = df.loc[df['Class'] == 0]\n",
        "print(\"We have\", len(frauds), \"fraud data points and\", len(non_frauds), \"regular data points.\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 216 fraud data points and 93683 regular data points.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnXr-0U2Y73E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NF_NNe0GZDdM",
        "colab_type": "code",
        "outputId": "4002d6bc-ac19-47b3-ad23-1bd32c1f47b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Size of training set: \", X_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Size of training set:  (75120, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Utj5q1hHa997",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(30, input_dim=30, activation='relu'))     # kernel_initializer='normal'\n",
        "model.add(Dense(1, activation='sigmoid'))                 # kernel_initializer='normal'\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x96MAEoybCeq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.fit(X_train.as_matrix(), y_train, epochs=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hyi77KKlc_Br",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn import datasets, linear_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARB5Nw2TdB68",
        "colab_type": "code",
        "outputId": "7d6375af-9ca6-4d05-e9e5-e5d811665a4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#K-FOLD algorithm\n",
        "from sklearn.model_selection import KFold  \n",
        "kf = KFold(n_splits=2) \n",
        "kf.get_n_splits(X) \n",
        "print(kf) \n",
        "KFold(n_splits=2, random_state=None, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KFold(n_splits=2, random_state=None, shuffle=False)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KFold(n_splits=2, random_state=None, shuffle=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_4PJ5OPe8Rr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3w0qUXIfm--",
        "colab_type": "code",
        "outputId": "4386cca7-38e3-48c0-f879-ac4c3ad9d3df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "data = pd.read_csv('creditcard.csv')\n",
        "data.head()"
      ],
      "execution_count": 0,
      "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>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0     0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62    0.0\n",
              "1     0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69    0.0\n",
              "2     1 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66    0.0\n",
              "3     1 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50    0.0\n",
              "4     2 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99    0.0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-Tl0yDEfrrL",
        "colab_type": "code",
        "outputId": "0180872c-c34f-438e-9004-147f7917fbc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "source": [
        "target = pd.value_counts(data['Class'],sort=True).sort_index()\n",
        "target.plot(kind = 'bar')\n",
        "plt.title(\"Fraud counts\")\n",
        "plt.xlabel(\"class\")\n",
        "plt.ylabel(\"Frequency\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jkCStxhpDJMkuVfXjqlrn52RV1btoP05sM5F3VNUbk5wBvJrBNYxDga+0Tc5u699t/d+s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\nNZMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4tqdbg6h3dz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = data.loc[:,data.columns !='Class']\n",
        "y = data.loc[:,data.columns =='Class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHHO4WwJiDsp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "number_fraud_records = len(data[data.Class == 1])\n",
        "fraud_indices = np.array(data[data.Class == 1].index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNJaXKKMiIQy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "normal_indices = data[data.Class == 0].index\n",
        "random_normal_indices = np.random.choice(normal_indices,number_fraud_records,replace = False)\n",
        "random_normal_indices = np.array(random_normal_indices)\n",
        "under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])\n",
        "under_sample_data = data.iloc[under_sample_indices,:]\n",
        "X_undersample = under_sample_data.loc[:, under_sample_data.columns != 'Class']\n",
        "y_undersample = under_sample_data.loc[:, under_sample_data.columns == 'Class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPC6_z-TiUex",
        "colab_type": "code",
        "outputId": "7bd48e0d-4ef7-4bea-d62d-cdcb8c723f06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(\"Percentage of normal transactions: \", len(under_sample_data[under_sample_data.Class == 0])/len(under_sample_data))\n",
        "print(\"Percentage of fraud transactions: \", len(under_sample_data[under_sample_data.Class == 1])/len(under_sample_data))\n",
        "print(\"Total number of transactions in resampled data: \", len(under_sample_data))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percentage of normal transactions:  0.5\n",
            "Percentage of fraud transactions:  0.5\n",
            "Total number of transactions in resampled data:  432\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaEdMklEid0U",
        "colab_type": "code",
        "outputId": "62eb0d96-8e9f-435d-9d91-4ed93d84a2f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 0)\n",
        "\n",
        "print(\"Number transactions train dataset: \", len(X_train))\n",
        "print(\"Number transactions test dataset: \", len(X_test))\n",
        "print(\"Total number of transactions: \", len(X_train)+len(X_test))\n",
        "\n",
        "X_train_undersample,X_test_undersample,y_train_undersample,y_test_undersample = train_test_split(X_undersample,y_undersample,test_size = 0.2, random_state = 0)\n",
        "\n",
        "print(\"\")\n",
        "print(\"Number of transactions train dataset: \", len(X_train_undersample))\n",
        "print(\"Number of transactions test dataset: \", len(X_test_undersample))\n",
        "print(\"Total number of transactions: \", len(X_train_undersample)+len(X_test_undersample))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number transactions train dataset:  75120\n",
            "Number transactions test dataset:  18780\n",
            "Total number of transactions:  93900\n",
            "\n",
            "Number of transactions train dataset:  345\n",
            "Number of transactions test dataset:  87\n",
            "Total number of transactions:  432\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2Lnvk8Oi1yA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression \n",
        "from sklearn.metrics import confusion_matrix,precision_recall_curve,auc,roc_auc_score,roc_curve,recall_score,classification_report \n",
        "from sklearn.model_selection import KFold, cross_val_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4sBwsGCjFUg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Kfold_scores(x_train_data,y_train_data):\n",
        "    fold = KFold(len(y_train_data),5,shuffle = False)\n",
        "    C_param_range = [0.01,0.1,1,10,100]\n",
        "    results_table = pd.DataFrame(index = range(len(C_param_range),2), columns = ['C_parameter','Mean recall score'])\n",
        "    results_table['C_parameter'] = C_param_range\n",
        "    j = 0\n",
        "    for C_param in C_param_range:\n",
        "        print('-------------------------')\n",
        "        print('C parameter: ', C_param)\n",
        "        print('-------------------------')\n",
        "        print('')\n",
        "        recall_accs = []\n",
        "        for iteration, indices in enumerate(fold,start=1):\n",
        "            lr = LogisticRegression(C = C_param, penalty = 'l1')\n",
        "\n",
        "            lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())\n",
        "            y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)\n",
        "            recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample)\n",
        "            recall_accs.append(recall_acc)\n",
        "            print('Iteration ', iteration,': recall score = ', recall_acc)\n",
        "        \n",
        "        results_table.ix[j,'Mean recall score'] = np.mean(recall_accs)\n",
        "        j += 1\n",
        "        print('')\n",
        "        print('Mean recall score ', np.mean(recall_accs))\n",
        "        print('')\n",
        "    best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']\n",
        "    print(\"Best C score:\",best_c)\n",
        "    return best_c\n",
        "    best_c = Kfold_scores(X_train_undersample,y_train_undersample)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7GihifQ7kZh6",
        "colab_type": "code",
        "outputId": "1cc2f681-3169-4ddc-f472-1d2513f73d3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "#precion and recall\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import auc\n",
        "from matplotlib import pyplot\n",
        "\n",
        "X, y = make_classification(n_samples=1000, n_classes=2, random_state=1)\n",
        "\n",
        "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2)\n",
        "\n",
        "model = LogisticRegression(solver='lbfgs')\n",
        "model.fit(trainX, trainy)\n",
        "\n",
        "lr_probs = model.predict_proba(testX)\n",
        "\n",
        "lr_probs = lr_probs[:, 1]\n",
        "\n",
        "yhat = model.predict(testX)\n",
        "lr_precision, lr_recall, _ = precision_recall_curve(testy, lr_probs)\n",
        "lr_f1, lr_auc = f1_score(testy, yhat), auc(lr_recall, lr_precision)\n",
        "\n",
        "print('Logistic: f1=%.3f auc=%.3f' % (lr_f1, lr_auc))\n",
        "\n",
        "no_skill = len(testy[testy==1]) / len(testy)\n",
        "pyplot.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')\n",
        "pyplot.plot(lr_recall, lr_precision, marker='.', label='Logistic')\n",
        "\n",
        "pyplot.xlabel('Recall')\n",
        "pyplot.ylabel('Precision')\n",
        "\n",
        "pyplot.legend()\n",
        "\n",
        "pyplot.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic: f1=0.841 auc=0.898\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/1cR9b/w29LdVIBARyRWjpyfuq18Z+sAyBQIRkVxRUg79j0rcH/LAMgUCEZFc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\nKXAKBCIiBe7/A+dJWh96uoADAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIrqAgFlk3pl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#EDA analysis\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGFSR9uRmlZ-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "b885b544-7866-4827-e880-a0a1c9555f0b"
      },
      "source": [
        "df = pd.read_csv(\"creditcard.csv\")\n",
        "df"
      ],
      "execution_count": 76,
      "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>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93895</th>\n",
              "      <td>64635</td>\n",
              "      <td>-0.703660</td>\n",
              "      <td>0.030938</td>\n",
              "      <td>2.187296</td>\n",
              "      <td>-0.561172</td>\n",
              "      <td>0.437259</td>\n",
              "      <td>0.885211</td>\n",
              "      <td>0.046137</td>\n",
              "      <td>0.321082</td>\n",
              "      <td>0.434080</td>\n",
              "      <td>-0.559013</td>\n",
              "      <td>0.125464</td>\n",
              "      <td>0.204269</td>\n",
              "      <td>-0.724880</td>\n",
              "      <td>-0.434710</td>\n",
              "      <td>-0.745491</td>\n",
              "      <td>0.419558</td>\n",
              "      <td>-0.783273</td>\n",
              "      <td>0.257913</td>\n",
              "      <td>-0.328792</td>\n",
              "      <td>0.001839</td>\n",
              "      <td>-0.002984</td>\n",
              "      <td>0.162524</td>\n",
              "      <td>-0.171664</td>\n",
              "      <td>-0.831475</td>\n",
              "      <td>-0.278265</td>\n",
              "      <td>0.176700</td>\n",
              "      <td>-0.067569</td>\n",
              "      <td>-0.112620</td>\n",
              "      <td>23.42</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93896</th>\n",
              "      <td>64637</td>\n",
              "      <td>1.277595</td>\n",
              "      <td>0.454794</td>\n",
              "      <td>-0.125866</td>\n",
              "      <td>1.113676</td>\n",
              "      <td>-0.023589</td>\n",
              "      <td>-1.210690</td>\n",
              "      <td>0.505676</td>\n",
              "      <td>-0.317333</td>\n",
              "      <td>-0.176815</td>\n",
              "      <td>0.065553</td>\n",
              "      <td>-0.475950</td>\n",
              "      <td>-0.141739</td>\n",
              "      <td>-0.677255</td>\n",
              "      <td>0.697800</td>\n",
              "      <td>0.755743</td>\n",
              "      <td>-0.185718</td>\n",
              "      <td>-0.273878</td>\n",
              "      <td>-0.288082</td>\n",
              "      <td>-0.248515</td>\n",
              "      <td>-0.199564</td>\n",
              "      <td>-0.005112</td>\n",
              "      <td>0.011904</td>\n",
              "      <td>-0.136580</td>\n",
              "      <td>0.388051</td>\n",
              "      <td>0.830026</td>\n",
              "      <td>-0.294142</td>\n",
              "      <td>-0.012218</td>\n",
              "      <td>0.009414</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93897</th>\n",
              "      <td>64637</td>\n",
              "      <td>1.109161</td>\n",
              "      <td>0.000825</td>\n",
              "      <td>1.361188</td>\n",
              "      <td>1.420410</td>\n",
              "      <td>-0.918673</td>\n",
              "      <td>0.013521</td>\n",
              "      <td>-0.636121</td>\n",
              "      <td>0.186146</td>\n",
              "      <td>0.694237</td>\n",
              "      <td>-0.121446</td>\n",
              "      <td>-0.202007</td>\n",
              "      <td>0.513733</td>\n",
              "      <td>-0.004144</td>\n",
              "      <td>-0.210777</td>\n",
              "      <td>0.811023</td>\n",
              "      <td>-0.104027</td>\n",
              "      <td>-0.022651</td>\n",
              "      <td>-0.353560</td>\n",
              "      <td>-0.965559</td>\n",
              "      <td>-0.187478</td>\n",
              "      <td>0.169790</td>\n",
              "      <td>0.708270</td>\n",
              "      <td>-0.014479</td>\n",
              "      <td>0.415553</td>\n",
              "      <td>0.390344</td>\n",
              "      <td>-0.232143</td>\n",
              "      <td>0.085535</td>\n",
              "      <td>0.034939</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93898</th>\n",
              "      <td>64637</td>\n",
              "      <td>-0.780229</td>\n",
              "      <td>1.196878</td>\n",
              "      <td>0.946185</td>\n",
              "      <td>-0.149437</td>\n",
              "      <td>0.001161</td>\n",
              "      <td>-0.179450</td>\n",
              "      <td>0.294276</td>\n",
              "      <td>0.533023</td>\n",
              "      <td>-0.523446</td>\n",
              "      <td>-0.586802</td>\n",
              "      <td>-0.787776</td>\n",
              "      <td>-0.021733</td>\n",
              "      <td>0.068299</td>\n",
              "      <td>0.476465</td>\n",
              "      <td>1.108417</td>\n",
              "      <td>0.180271</td>\n",
              "      <td>-0.123154</td>\n",
              "      <td>-0.584072</td>\n",
              "      <td>-0.163838</td>\n",
              "      <td>-0.006594</td>\n",
              "      <td>-0.184478</td>\n",
              "      <td>-0.591717</td>\n",
              "      <td>0.031935</td>\n",
              "      <td>-0.450369</td>\n",
              "      <td>-0.174179</td>\n",
              "      <td>0.134214</td>\n",
              "      <td>0.150856</td>\n",
              "      <td>0.050276</td>\n",
              "      <td>16.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93899</th>\n",
              "      <td>64637</td>\n",
              "      <td>-0.329882</td>\n",
              "      <td>0.948924</td>\n",
              "      <td>0.930639</td>\n",
              "      <td>-0.143105</td>\n",
              "      <td>0.422714</td>\n",
              "      <td>-0.142072</td>\n",
              "      <td>0.496805</td>\n",
              "      <td>0.208572</td>\n",
              "      <td>-0.091974</td>\n",
              "      <td>-0.502930</td>\n",
              "      <td>-0.927389</td>\n",
              "      <td>-1.133127</td>\n",
              "      <td>-1.468384</td>\n",
              "      <td>-0.107826</td>\n",
              "      <td>1.384312</td>\n",
              "      <td>0.380434</td>\n",
              "      <td>0.120170</td>\n",
              "      <td>-0.144314</td>\n",
              "      <td>-0.164193</td>\n",
              "      <td>-0.008370</td>\n",
              "      <td>-0.304748</td>\n",
              "      <td>-0.846746</td>\n",
              "      <td>-0.060360</td>\n",
              "      <td>-0.749705</td>\n",
              "      <td>-0.194958</td>\n",
              "      <td>0.152296</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93900 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0          0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62    0.0\n",
              "1          0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69    0.0\n",
              "2          1 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66    0.0\n",
              "3          1 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50    0.0\n",
              "4          2 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99    0.0\n",
              "...      ...       ...       ...       ...  ...       ...       ...     ...    ...\n",
              "93895  64635 -0.703660  0.030938  2.187296  ... -0.067569 -0.112620   23.42    0.0\n",
              "93896  64637  1.277595  0.454794 -0.125866  ... -0.012218  0.009414    1.00    0.0\n",
              "93897  64637  1.109161  0.000825  1.361188  ...  0.085535  0.034939    1.00    0.0\n",
              "93898  64637 -0.780229  1.196878  0.946185  ...  0.150856  0.050276   16.00    0.0\n",
              "93899  64637 -0.329882  0.948924  0.930639  ...       NaN       NaN     NaN    NaN\n",
              "\n",
              "[93900 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_COrWRX9mq7s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=3)\n",
        "\n",
        "features = list(df)\n",
        "\n",
        "pca_result = pca.fit_transform(df[features].values)\n",
        "\n",
        "pca_df = pd.DataFrame()\n",
        "pca_df['Time'] = df['Time']\n",
        "pca_df['pca-one'] = pca_result[:,0]\n",
        "pca_df['pca-two'] = pca_result[:,1] \n",
        "\n",
        "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))\n",
        "from ggplot import *\n",
        "chart = ggplot( pca_df, aes(x='pca-one', y='pca-two', color='species') ) \\\n",
        "        + geom_point(size=75,alpha=0.8) \\\n",
        "        + ggtitle(\"First and Second Principal Components colored by species\")\n",
        "chart"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nU2TMlnypDA8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# Start t-SNE algorithm\n",
        "time_start = time.time()\n",
        "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=400)\n",
        "tsne_results = tsne.fit_transform(df[features].values)\n",
        "\n",
        "print('t-SNE done! Time elapsed: {} seconds'.format(time.time()-time_start))\n",
        "df_tsne = pd.DataFrame()\n",
        "df_tsne['species'] = df['species']\n",
        "df_tsne['x-tsne'] = tsne_results[:,0]\n",
        "df_tsne['y-tsne'] = tsne_results[:,1]\n",
        "\n",
        "# Create chart and display it\n",
        "chart = ggplot( df_tsne, aes(x='x-tsne', y='y-tsne', color='species') ) \\\n",
        "        + geom_point(size=70,alpha=0.1) \\\n",
        "        + ggtitle(\"tSNE dimensions colored by species\")\n",
        "chart"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aC2gOoDhetQG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Confusion Matrix \n",
        "#Package problem\n",
        "from pandas_ml import ConfusionMatrix\n",
        "y_right = np.array(y_test)\n",
        "confusion_matrix = ConfusionMatrix(y_right, y_predicted)\n",
        "print(\"Confusion matrix:\\n%s\" % confusion_matrix)\n",
        "confusion_matrix.plot(normalized=True)\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OX_ELQyA_rsR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.metrics import accuracy_score \n",
        "from sklearn.metrics import classification_report \n",
        "results = confusion_matrix(y_right, y_predicted) \n",
        "print ('Confusion Matrix :')\n",
        "print(results) \n",
        "print ('Accuracy Score :',accuracy_score(y_right, y_predicted)) \n",
        "print ('Report : ')\n",
        "print (classification_report(y_actual, y_predicted))"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}