{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Card Fraud Detection\n",
    "> In this project, it will show anomaly detection with Unsupervised Learning. With data of card transations, it can detect whether credit card fraud is occured or not. The data is from kaggle. \n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Machine_Learning]\n",
    "- image: images/ad_heatmap.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (8, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]\n",
      "Numpy: 1.18.1\n",
      "Matplotlib: 3.1.3\n",
      "Seaborn: 0.10.0\n",
      "Pandas: 1.0.1\n",
      "Scikit-learn: 0.22.1\n"
     ]
    }
   ],
   "source": [
    "print('Python: {}'.format(sys.version))\n",
    "print('Numpy: {}'.format(np.__version__))\n",
    "print('Matplotlib: {}'.format(mpl.__version__))\n",
    "print('Seaborn: {}'.format(sns.__version__))\n",
    "print('Pandas: {}'.format(pd.__version__))\n",
    "print('Scikit-learn: {}'.format(sklearn.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Load\n",
    "More data information is in [here](https://www.kaggle.com/mlg-ulb/creditcardfraud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>...</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>0.0</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>...</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</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</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>...</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</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</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>...</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</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>...</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</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>...</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</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "data = pd.read_csv('./dataset/creditcard.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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       "      <th>V8</th>\n",
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       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
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       "      <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>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
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       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.919560e-15</td>\n",
       "      <td>5.688174e-16</td>\n",
       "      <td>-8.769071e-15</td>\n",
       "      <td>2.782312e-15</td>\n",
       "      <td>-1.552563e-15</td>\n",
       "      <td>2.010663e-15</td>\n",
       "      <td>-1.694249e-15</td>\n",
       "      <td>-1.927028e-16</td>\n",
       "      <td>-3.137024e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.537294e-16</td>\n",
       "      <td>7.959909e-16</td>\n",
       "      <td>5.367590e-16</td>\n",
       "      <td>4.458112e-15</td>\n",
       "      <td>1.453003e-15</td>\n",
       "      <td>1.699104e-15</td>\n",
       "      <td>-3.660161e-16</td>\n",
       "      <td>-1.206049e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "(284807, 31)\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram of each parameter\n",
    "data.hist(figsize=(20, 20));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0017304750013189597\n",
      "Fraud Cases: 492\n",
      "Valid Cases: 284315\n"
     ]
    }
   ],
   "source": [
    "# Determine number of fraud cases in dataset\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)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through the EDA of dataset, Data imbalance is occurred, cause fraud cases are so small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix\n",
    "corrmat = data.corr()\n",
    "\n",
    "fit = plt.figure(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=0.8, square=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all the columns from the DataFrame\n",
    "columns = data.columns.tolist()\n",
    "\n",
    "# Filter the columns to remove data we don't want\n",
    "columns = [c for c in columns if c not in ['Class']]\n",
    "\n",
    "# Store the variable we want to predict\n",
    "target='Class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 30)\n",
      "(284807,)\n"
     ]
    }
   ],
   "source": [
    "X = data[columns]\n",
    "y = data[target]\n",
    "\n",
    "# Print the shape\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import LocalOutlierFactor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From `sklearn`:\n",
    "\n",
    "[`LocalOutlierFactor`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) is Unsupervised Outlier Detection using Local Outlier Factor (LOF)\n",
    "The anomaly score of each sample is called Local Outlier Factor. It measures the local deviation of density of a given sample with respect to its neighbors. It is local in that the anomaly score depends on how isolated the object is with respect to the surrounding neighborhood. More precisely, locality is given by k-nearest neighbors, whose distance is used to estimate the local density. By comparing the local density of a sample to the local densities of its neighbors, one can identify samples that have a substantially lower density than their neighbors. These are considered outliers.\n",
    "\n",
    "And [`IsolationForest`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html) ‘isolates’ observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.\n",
    "\n",
    "Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node.\n",
    "\n",
    "This path length, averaged over a forest of such random trees, is a measure of normality and our decision function.\n",
    "\n",
    "Random partitioning produces noticeably shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 1\n",
    "\n",
    "# Define the outlier detection methods\n",
    "classifiers = {\n",
    "    'Isolation Forest':IsolationForest(max_samples=len(X), contamination=outlier_fraction,\n",
    "                                       random_state=random_state),\n",
    "    'Local Outlier Factor':LocalOutlierFactor(n_neighbors=20, contamination=outlier_fraction)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isolation Forest: 645\n",
      "0.997735308472053\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    284315\n",
      "           1       0.34      0.35      0.35       492\n",
      "\n",
      "    accuracy                           1.00    284807\n",
      "   macro avg       0.67      0.67      0.67    284807\n",
      "weighted avg       1.00      1.00      1.00    284807\n",
      "\n",
      "Local Outlier Factor: 935\n",
      "0.9967170750718908\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    284315\n",
      "           1       0.05      0.05      0.05       492\n",
      "\n",
      "    accuracy                           1.00    284807\n",
      "   macro avg       0.52      0.52      0.52    284807\n",
      "weighted avg       1.00      1.00      1.00    284807\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_outliers = len(fraud)\n",
    "\n",
    "for i, (clf_name, clf) in enumerate(classifiers.items()):\n",
    "    # fit the model 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",
    "        y_pred = clf.predict(X)\n",
    "        scores_pred = clf.decision_function(X)\n",
    "    \n",
    "    # Reshape the prediction values to 0 for valid, 1 for fraud\n",
    "    # For inlier\n",
    "    y_pred[y_pred == 1] = 0\n",
    "    # For outlier\n",
    "    y_pred[y_pred == -1] = 1\n",
    "    \n",
    "    n_errors = (y_pred != y).sum()\n",
    "    \n",
    "    # Run classification metrics\n",
    "    print('{}: {}'.format(clf_name, n_errors))\n",
    "    print(accuracy_score(y, y_pred))\n",
    "    print(classification_report(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that\n",
    " - Accuracy:\n",
    "    $$ \\dfrac{tp + tn}{tp + tn + fp + fn} $$\n",
    "    \n",
    "    - Precision (Positive Predictive Value):\n",
    "    $$ \\dfrac{tp}{tp + fp}$$\n",
    "    \n",
    "    - Recall (Sensitivity, hit rate, True Positive Rate):\n",
    "    $$ \\dfrac{tp}{tp + fn}$$\n",
    "    \n",
    "    - F1 score: Harmonic mean of precision and recall\n",
    "    $$ 2 \\cdot \\dfrac{\\text{precision} \\cdot \\text{recall}}{\\text{precision} + \\text{recall}} $$\n",
    "    \n",
    "Due to the data imbalance, accuracy of both model is very high. But comparing the precision/recall score, we found that Isolation Forest is better model than Local Outlier fraction."
   ]
  }
 ],
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