Credit Card Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.6.3 |Anaconda, Inc.| (default, Oct 15 2017, 03:27:45) [MSC v.1900 64 bit (AMD64)]\n",
      "Numpy: 1.13.3\n",
      "Pandad: 0.20.3\n",
      "MatPlotLib: 2.1.0\n",
      "Seaborn: 0.8.0\n",
      "SciPy: 0.19.1\n",
      "SkLearn: 0.19.1\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import numpy\n",
    "import pandas\n",
    "import matplotlib\n",
    "import seaborn\n",
    "import scipy\n",
    "import sklearn\n",
    "\n",
    "print('Python: {}'.format(sys.version))\n",
    "print('Numpy: {}'.format(numpy.__version__))\n",
    "print('Pandad: {}'.format(pandas.__version__))\n",
    "print('MatPlotLib: {}'.format(matplotlib.__version__))\n",
    "print('Seaborn: {}'.format(seaborn.__version__))\n",
    "print('SciPy: {}'.format(scipy.__version__))\n",
    "print('SkLearn: {}'.format(sklearn.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import the necessary packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# loading the dataset from the csv file using pandas\n",
    "data = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
      "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
      "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
      "       'Class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Exploring the dataset\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 31)\n"
     ]
    }
   ],
   "source": [
    "#transactions and 31 columns\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.describe of             Time         V1         V2        V3        V4        V5  \\\n",
      "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
      "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
      "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
      "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
      "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
      "5            2.0  -0.425966   0.960523  1.141109 -0.168252  0.420987   \n",
      "6            4.0   1.229658   0.141004  0.045371  1.202613  0.191881   \n",
      "7            7.0  -0.644269   1.417964  1.074380 -0.492199  0.948934   \n",
      "8            7.0  -0.894286   0.286157 -0.113192 -0.271526  2.669599   \n",
      "9            9.0  -0.338262   1.119593  1.044367 -0.222187  0.499361   \n",
      "10          10.0   1.449044  -1.176339  0.913860 -1.375667 -1.971383   \n",
      "11          10.0   0.384978   0.616109 -0.874300 -0.094019  2.924584   \n",
      "12          10.0   1.249999  -1.221637  0.383930 -1.234899 -1.485419   \n",
      "13          11.0   1.069374   0.287722  0.828613  2.712520 -0.178398   \n",
      "14          12.0  -2.791855  -0.327771  1.641750  1.767473 -0.136588   \n",
      "15          12.0  -0.752417   0.345485  2.057323 -1.468643 -1.158394   \n",
      "16          12.0   1.103215  -0.040296  1.267332  1.289091 -0.735997   \n",
      "17          13.0  -0.436905   0.918966  0.924591 -0.727219  0.915679   \n",
      "18          14.0  -5.401258  -5.450148  1.186305  1.736239  3.049106   \n",
      "19          15.0   1.492936  -1.029346  0.454795 -1.438026 -1.555434   \n",
      "20          16.0   0.694885  -1.361819  1.029221  0.834159 -1.191209   \n",
      "21          17.0   0.962496   0.328461 -0.171479  2.109204  1.129566   \n",
      "22          18.0   1.166616   0.502120 -0.067300  2.261569  0.428804   \n",
      "23          18.0   0.247491   0.277666  1.185471 -0.092603 -1.314394   \n",
      "24          22.0  -1.946525  -0.044901 -0.405570 -1.013057  2.941968   \n",
      "25          22.0  -2.074295  -0.121482  1.322021  0.410008  0.295198   \n",
      "26          23.0   1.173285   0.353498  0.283905  1.133563 -0.172577   \n",
      "27          23.0   1.322707  -0.174041  0.434555  0.576038 -0.836758   \n",
      "28          23.0  -0.414289   0.905437  1.727453  1.473471  0.007443   \n",
      "29          23.0   1.059387  -0.175319  1.266130  1.186110 -0.786002   \n",
      "...          ...        ...        ...       ...       ...       ...   \n",
      "284777  172764.0   2.079137  -0.028723 -1.343392  0.358000 -0.045791   \n",
      "284778  172764.0  -0.764523   0.588379 -0.907599 -0.418847  0.901528   \n",
      "284779  172766.0   1.975178  -0.616244 -2.628295 -0.406246  2.327804   \n",
      "284780  172766.0  -1.727503   1.108356  2.219561  1.148583 -0.884199   \n",
      "284781  172766.0  -1.139015  -0.155510  1.894478 -1.138957  1.451777   \n",
      "284782  172767.0  -0.268061   2.540315 -1.400915  4.846661  0.639105   \n",
      "284783  172768.0  -1.796092   1.929178 -2.828417 -1.689844  2.199572   \n",
      "284784  172768.0  -0.669662   0.923769 -1.543167 -1.560729  2.833960   \n",
      "284785  172768.0   0.032887   0.545338 -1.185844 -1.729828  2.932315   \n",
      "284786  172768.0  -2.076175   2.142238 -2.522704 -1.888063  1.982785   \n",
      "284787  172769.0  -1.029719  -1.110670 -0.636179 -0.840816  2.424360   \n",
      "284788  172770.0   2.007418  -0.280235 -0.208113  0.335261 -0.715798   \n",
      "284789  172770.0  -0.446951   1.302212 -0.168583  0.981577  0.578957   \n",
      "284790  172771.0  -0.515513   0.971950 -1.014580 -0.677037  0.912430   \n",
      "284791  172774.0  -0.863506   0.874701  0.420358 -0.530365  0.356561   \n",
      "284792  172774.0  -0.724123   1.485216 -1.132218 -0.607190  0.709499   \n",
      "284793  172775.0   1.971002  -0.699067 -1.697541 -0.617643  1.718797   \n",
      "284794  172777.0  -1.266580  -0.400461  0.956221 -0.723919  1.531993   \n",
      "284795  172778.0 -12.516732  10.187818 -8.476671 -2.510473 -4.586669   \n",
      "284796  172780.0   1.884849  -0.143540 -0.999943  1.506772 -0.035300   \n",
      "284797  172782.0  -0.241923   0.712247  0.399806 -0.463406  0.244531   \n",
      "284798  172782.0   0.219529   0.881246 -0.635891  0.960928 -0.152971   \n",
      "284799  172783.0  -1.775135  -0.004235  1.189786  0.331096  1.196063   \n",
      "284800  172784.0   2.039560  -0.175233 -1.196825  0.234580 -0.008713   \n",
      "284801  172785.0   0.120316   0.931005 -0.546012 -0.745097  1.130314   \n",
      "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
      "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
      "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
      "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
      "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
      "\n",
      "              V6        V7        V8        V9  ...         V21       V22  \\\n",
      "0       0.462388  0.239599  0.098698  0.363787  ...   -0.018307  0.277838   \n",
      "1      -0.082361 -0.078803  0.085102 -0.255425  ...   -0.225775 -0.638672   \n",
      "2       1.800499  0.791461  0.247676 -1.514654  ...    0.247998  0.771679   \n",
      "3       1.247203  0.237609  0.377436 -1.387024  ...   -0.108300  0.005274   \n",
      "4       0.095921  0.592941 -0.270533  0.817739  ...   -0.009431  0.798278   \n",
      "5      -0.029728  0.476201  0.260314 -0.568671  ...   -0.208254 -0.559825   \n",
      "6       0.272708 -0.005159  0.081213  0.464960  ...   -0.167716 -0.270710   \n",
      "7       0.428118  1.120631 -3.807864  0.615375  ...    1.943465 -1.015455   \n",
      "8       3.721818  0.370145  0.851084 -0.392048  ...   -0.073425 -0.268092   \n",
      "9      -0.246761  0.651583  0.069539 -0.736727  ...   -0.246914 -0.633753   \n",
      "10     -0.629152 -1.423236  0.048456 -1.720408  ...   -0.009302  0.313894   \n",
      "11      3.317027  0.470455  0.538247 -0.558895  ...    0.049924  0.238422   \n",
      "12     -0.753230 -0.689405 -0.227487 -2.094011  ...   -0.231809 -0.483285   \n",
      "13      0.337544 -0.096717  0.115982 -0.221083  ...   -0.036876  0.074412   \n",
      "14      0.807596 -0.422911 -1.907107  0.755713  ...    1.151663  0.222182   \n",
      "15     -0.077850 -0.608581  0.003603 -0.436167  ...    0.499625  1.353650   \n",
      "16      0.288069 -0.586057  0.189380  0.782333  ...   -0.024612  0.196002   \n",
      "17     -0.127867  0.707642  0.087962 -0.665271  ...   -0.194796 -0.672638   \n",
      "18     -1.763406 -1.559738  0.160842  1.233090  ...   -0.503600  0.984460   \n",
      "19     -0.720961 -1.080664 -0.053127 -1.978682  ...   -0.177650 -0.175074   \n",
      "20      1.309109 -0.878586  0.445290 -0.446196  ...   -0.295583 -0.571955   \n",
      "21      1.696038  0.107712  0.521502 -1.191311  ...    0.143997  0.402492   \n",
      "22      0.089474  0.241147  0.138082 -0.989162  ...    0.018702 -0.061972   \n",
      "23     -0.150116 -0.946365 -1.617935  1.544071  ...    1.650180  0.200454   \n",
      "24      2.955053 -0.063063  0.855546  0.049967  ...   -0.579526 -0.799229   \n",
      "25     -0.959537  0.543985 -0.104627  0.475664  ...   -0.403639 -0.227404   \n",
      "26     -0.916054  0.369025 -0.327260 -0.246651  ...    0.067003  0.227812   \n",
      "27     -0.831083 -0.264905 -0.220982 -1.071425  ...   -0.284376 -0.323357   \n",
      "28     -0.200331  0.740228 -0.029247 -0.593392  ...    0.077237  0.457331   \n",
      "29      0.578435 -0.767084  0.401046  0.699500  ...    0.013676  0.213734   \n",
      "...          ...       ...       ...       ...  ...         ...       ...   \n",
      "284777 -1.345452  0.227476 -0.378355  0.665911  ...    0.235758  0.829758   \n",
      "284778 -0.760802  0.758545  0.414698 -0.730854  ...    0.003530 -0.431876   \n",
      "284779  3.664740 -0.533297  0.842937  1.128798  ...    0.086043  0.543613   \n",
      "284780  0.793083 -0.527298  0.866429  0.853819  ...   -0.094708  0.236818   \n",
      "284781  0.093598  0.191353  0.092211 -0.062621  ...   -0.191027 -0.631658   \n",
      "284782  0.186479 -0.045911  0.936448 -2.419986  ...   -0.263889 -0.857904   \n",
      "284783  3.123732 -0.270714  1.657495  0.465804  ...    0.271170  1.145750   \n",
      "284784  3.240843  0.181576  1.282746 -0.893890  ...    0.183856  0.202670   \n",
      "284785  3.401529  0.337434  0.925377 -0.165663  ...   -0.266113 -0.716336   \n",
      "284786  3.732950 -1.217430 -0.536644  0.272867  ...    2.016666 -1.588269   \n",
      "284787 -2.956733  0.283610 -0.332656 -0.247488  ...    0.353722  0.488487   \n",
      "284788 -0.751373 -0.458972 -0.140140  0.959971  ...   -0.208260 -0.430347   \n",
      "284789 -0.605641  1.253430 -1.042610 -0.417116  ...    0.851800  0.305268   \n",
      "284790 -0.316187  0.396137  0.532364 -0.224606  ...   -0.280302 -0.849919   \n",
      "284791 -1.046238  0.757051  0.230473 -0.506856  ...   -0.108846 -0.480820   \n",
      "284792 -0.482638  0.548393  0.343003 -0.226323  ...    0.414621  1.307511   \n",
      "284793  3.911336 -1.259306  1.056209  1.315006  ...    0.188758  0.694418   \n",
      "284794 -1.788600  0.314741  0.004704  0.013857  ...   -0.157831 -0.883365   \n",
      "284795 -1.394465 -3.632516  5.498583  4.893089  ...   -0.944759 -1.565026   \n",
      "284796 -0.613638  0.190241 -0.249058  0.666458  ...    0.144008  0.634646   \n",
      "284797 -1.343668  0.929369 -0.206210  0.106234  ...   -0.228876 -0.514376   \n",
      "284798 -1.014307  0.427126  0.121340 -0.285670  ...    0.099936  0.337120   \n",
      "284799  5.519980 -1.518185  2.080825  1.159498  ...    0.103302  0.654850   \n",
      "284800 -0.726571  0.017050 -0.118228  0.435402  ...   -0.268048 -0.717211   \n",
      "284801 -0.235973  0.812722  0.115093 -0.204064  ...   -0.314205 -0.808520   \n",
      "284802 -2.606837 -4.918215  7.305334  1.914428  ...    0.213454  0.111864   \n",
      "284803  1.058415  0.024330  0.294869  0.584800  ...    0.214205  0.924384   \n",
      "284804  3.031260 -0.296827  0.708417  0.432454  ...    0.232045  0.578229   \n",
      "284805  0.623708 -0.686180  0.679145  0.392087  ...    0.265245  0.800049   \n",
      "284806 -0.649617  1.577006 -0.414650  0.486180  ...    0.261057  0.643078   \n",
      "\n",
      "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
      "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
      "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
      "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
      "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
      "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
      "5      -0.026398 -0.371427 -0.232794  0.105915  0.253844  0.081080    3.67   \n",
      "6      -0.154104 -0.780055  0.750137 -0.257237  0.034507  0.005168    4.99   \n",
      "7       0.057504 -0.649709 -0.415267 -0.051634 -1.206921 -1.085339   40.80   \n",
      "8      -0.204233  1.011592  0.373205 -0.384157  0.011747  0.142404   93.20   \n",
      "9      -0.120794 -0.385050 -0.069733  0.094199  0.246219  0.083076    3.68   \n",
      "10      0.027740  0.500512  0.251367 -0.129478  0.042850  0.016253    7.80   \n",
      "11      0.009130  0.996710 -0.767315 -0.492208  0.042472 -0.054337    9.99   \n",
      "12      0.084668  0.392831  0.161135 -0.354990  0.026416  0.042422  121.50   \n",
      "13     -0.071407  0.104744  0.548265  0.104094  0.021491  0.021293   27.50   \n",
      "14      1.020586  0.028317 -0.232746 -0.235557 -0.164778 -0.030154   58.80   \n",
      "15     -0.256573 -0.065084 -0.039124 -0.087086 -0.180998  0.129394   15.99   \n",
      "16      0.013802  0.103758  0.364298 -0.382261  0.092809  0.037051   12.99   \n",
      "17     -0.156858 -0.888386 -0.342413 -0.049027  0.079692  0.131024    0.89   \n",
      "18      2.458589  0.042119 -0.481631 -0.621272  0.392053  0.949594   46.80   \n",
      "19      0.040002  0.295814  0.332931 -0.220385  0.022298  0.007602    5.00   \n",
      "20     -0.050881 -0.304215  0.072001 -0.422234  0.086553  0.063499  231.71   \n",
      "21     -0.048508 -1.371866  0.390814  0.199964  0.016371 -0.014605   34.09   \n",
      "22     -0.103855 -0.370415  0.603200  0.108556 -0.040521 -0.011418    2.28   \n",
      "23     -0.185353  0.423073  0.820591 -0.227632  0.336634  0.250475   22.75   \n",
      "24      0.870300  0.983421  0.321201  0.149650  0.707519  0.014600    0.89   \n",
      "25      0.742435  0.398535  0.249212  0.274404  0.359969  0.243232   26.43   \n",
      "26     -0.150487  0.435045  0.724825 -0.337082  0.016368  0.030041   41.88   \n",
      "27     -0.037710  0.347151  0.559639 -0.280158  0.042335  0.028822   16.00   \n",
      "28     -0.038500  0.642522 -0.183891 -0.277464  0.182687  0.152665   33.00   \n",
      "29      0.014462  0.002951  0.294638 -0.395070  0.081461  0.024220   12.99   \n",
      "...          ...       ...       ...       ...       ...       ...     ...   \n",
      "284777 -0.002063  0.001344  0.262183 -0.105327 -0.022363 -0.060283    1.00   \n",
      "284778  0.141759  0.587119 -0.200998  0.267337 -0.152951 -0.065285   80.00   \n",
      "284779 -0.032129  0.768379  0.477688 -0.031833  0.014151 -0.066542   25.00   \n",
      "284780 -0.204280  1.158185  0.627801 -0.399981  0.510818  0.233265   30.00   \n",
      "284781 -0.147249  0.212931  0.354257 -0.241068 -0.161717 -0.149188   13.00   \n",
      "284782  0.235172 -0.681794 -0.668894  0.044657 -0.066751 -0.072447   12.82   \n",
      "284783  0.084783  0.721269 -0.529906 -0.240117  0.129126 -0.080620   11.46   \n",
      "284784 -0.373023  0.651122  1.073823  0.844590 -0.286676 -0.187719   40.00   \n",
      "284785  0.108519  0.688519 -0.460220  0.161939  0.265368  0.090245    1.79   \n",
      "284786  0.588482  0.632444 -0.201064  0.199251  0.438657  0.172923    8.95   \n",
      "284787  0.293632  0.107812 -0.935586  1.138216  0.025271  0.255347    9.99   \n",
      "284788  0.416765  0.064819 -0.608337  0.268436 -0.028069 -0.041367    3.99   \n",
      "284789 -0.148093 -0.038712  0.010209 -0.362666  0.503092  0.229921   60.50   \n",
      "284790  0.300245  0.000607 -0.376379  0.128660 -0.015205 -0.021486    9.81   \n",
      "284791 -0.074513 -0.003988 -0.113149  0.280378 -0.077310  0.023079   20.32   \n",
      "284792 -0.059545  0.242669 -0.665424 -0.269869 -0.170579 -0.030692    3.99   \n",
      "284793  0.163002  0.726365 -0.058282 -0.191813  0.061858 -0.043716    4.99   \n",
      "284794  0.088485 -0.076790 -0.095833  0.132720 -0.028468  0.126494    0.89   \n",
      "284795  0.890675 -1.253276  1.786717  0.320763  2.090712  1.232864    9.87   \n",
      "284796 -0.042114 -0.053206  0.316403 -0.461441  0.018265 -0.041068   60.00   \n",
      "284797  0.279598  0.371441 -0.559238  0.113144  0.131507  0.081265    5.49   \n",
      "284798  0.251791  0.057688 -1.508368  0.144023  0.181205  0.215243   24.05   \n",
      "284799 -0.348929  0.745323  0.704545 -0.127579  0.454379  0.130308   79.99   \n",
      "284800  0.297930 -0.359769 -0.315610  0.201114 -0.080826 -0.075071    2.68   \n",
      "284801  0.050343  0.102800 -0.435870  0.124079  0.217940  0.068803    2.69   \n",
      "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
      "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
      "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
      "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
      "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
      "\n",
      "        Class  \n",
      "0           0  \n",
      "1           0  \n",
      "2           0  \n",
      "3           0  \n",
      "4           0  \n",
      "5           0  \n",
      "6           0  \n",
      "7           0  \n",
      "8           0  \n",
      "9           0  \n",
      "10          0  \n",
      "11          0  \n",
      "12          0  \n",
      "13          0  \n",
      "14          0  \n",
      "15          0  \n",
      "16          0  \n",
      "17          0  \n",
      "18          0  \n",
      "19          0  \n",
      "20          0  \n",
      "21          0  \n",
      "22          0  \n",
      "23          0  \n",
      "24          0  \n",
      "25          0  \n",
      "26          0  \n",
      "27          0  \n",
      "28          0  \n",
      "29          0  \n",
      "...       ...  \n",
      "284777      0  \n",
      "284778      0  \n",
      "284779      0  \n",
      "284780      0  \n",
      "284781      0  \n",
      "284782      0  \n",
      "284783      0  \n",
      "284784      0  \n",
      "284785      0  \n",
      "284786      0  \n",
      "284787      0  \n",
      "284788      0  \n",
      "284789      0  \n",
      "284790      0  \n",
      "284791      0  \n",
      "284792      0  \n",
      "284793      0  \n",
      "284794      0  \n",
      "284795      0  \n",
      "284796      0  \n",
      "284797      0  \n",
      "284798      0  \n",
      "284799      0  \n",
      "284800      0  \n",
      "284801      0  \n",
      "284802      0  \n",
      "284803      0  \n",
      "284804      0  \n",
      "284805      0  \n",
      "284806      0  \n",
      "\n",
      "[284807 rows x 31 columns]>\n"
     ]
    }
   ],
   "source": [
    "print(data.describe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28481, 31)\n"
     ]
    }
   ],
   "source": [
    "data = data.sample(frac = 0.1, random_state = 1)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iBdl3pZHxwuHSZl2s49PBtNNY83RV5uXKK/uUG902dUa781vLKm/fpfjCBjih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\nSROvqv4e+HKSH2lFpwCfZ0h9ZFT7IR2Mq1tJUFUvr6qjq+q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4ruf+scBdY2qLNC72A612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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19a4ea0b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot histogram of each parameter\n",
    "data.hist(figsize = (20,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0017234102419808666\n",
      "Fraud Cases: 49\n",
      "Valid Cases: 28432\n"
     ]
    }
   ],
   "source": [
    "#calculate number of frad cases\n",
    "Fraud = data[data['Class'] == 1]\n",
    "Valid = data[data['Class'] == 0]\n",
    "\n",
    "outlier_fraction = len(Fraud) / float(len(Valid))\n",
    "print(outlier_fraction)\n",
    "\n",
    "print('Fraud Cases: {}'.format(len(Fraud)))\n",
    "print('Valid Cases: {}'.format(len(Valid)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qWJKtwBbgrqqqJC9dWKCqbpv/PckB5qa7kyRJWr1qG9anVZIJ4D8ALwcOAx9J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\n81EVD3Z4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19a4f61dac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix \n",
    "corrmat = data.corr()\n",
    "fig = plt.figure(figsize = (12,9))\n",
    "\n",
    "sns.heatmap(corrmat, vmax = 0.8, square = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28481, 30)\n",
      "(28481,)\n"
     ]
    }
   ],
   "source": [
    "#Get all the colums from the DataFrame\n",
    "columns = data.columns.tolist()\n",
    "\n",
    "#Filter the colums to remove data we do not want\n",
    "columns = [c for c in columns if c not in [\"Class\"]]\n",
    "\n",
    "#Store the variable we'll be predicting on\n",
    "target = \"Class\"\n",
    "\n",
    "X = data[columns]\n",
    "Y = data[target]\n",
    "\n",
    "#Print the shapes of X and Y\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "\n",
    "#define a random state\n",
    "state = 1\n",
    "\n",
    "#define the outlier detection methods\n",
    "classifiers = {\n",
    "    \"Isolation Forest\": IsolationForest(max_samples=len(X),\n",
    "                                       contamination = outlier_fraction,\n",
    "                                       random_state = state),\n",
    "    \"Local Outlier Factor\": LocalOutlierFactor(\n",
    "    n_neighbors = 20,\n",
    "    contamination = outlier_fraction)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isolation Forest: 71\n",
      "0.997507110003\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00     28432\n",
      "          1       0.28      0.29      0.28        49\n",
      "\n",
      "avg / total       1.00      1.00      1.00     28481\n",
      "\n",
      "Local Outlier Factor: 97\n",
      "0.996594220709\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00     28432\n",
      "          1       0.02      0.02      0.02        49\n",
      "\n",
      "avg / total       1.00      1.00      1.00     28481\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#fitting the model\n",
    "n_outliers = len(Fraud)\n",
    "\n",
    "for i, (clf_name, clf) in enumerate(classifiers.items()):\n",
    "    \n",
    "    #fit the data and tag outliers\n",
    "    if clf_name == \"Local Outlier Factor\":\n",
    "        y_pred = clf.fit_predict(X)\n",
    "        scores_pred = clf.negative_outlier_factor_\n",
    "    else:\n",
    "        clf.fit(X)\n",
    "        scores_pred = clf.decision_function(X)\n",
    "        y_pred = clf.predict(X)\n",
    "            \n",
    "    #Reshape the prediction values to 0 for valid and 1 for fraud\n",
    "    \n",
    "    y_pred[y_pred == 1] = 0\n",
    "    y_pred[y_pred == -1] = 1 # fradulant transactions\n",
    "    \n",
    "    #calculate number of errors\n",
    "    n_errors = (y_pred != Y).sum()\n",
    "    \n",
    "    #Run classification matrics\n",
    "    print('{}: {}'.format(clf_name, n_errors))\n",
    "    print(accuracy_score(Y, y_pred))\n",
    "    print(classification_report(Y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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