KNN on Credit Card fraud detection-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>...</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>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",
       "      <td>...</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>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <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": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zero=df[df['Class']==0]\n",
    "one=df[df['Class']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284315"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9982725143693799"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "284315/(284315+492) # accuracy not a good metric because of unbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.concat((one, zero[1000:3000])) # take 2000 genuine transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirement\n",
    "High TPR more important than high TNR<br>\n",
    "A fraudulent transaction necessary to catch than a genuine transaction<br>\n",
    "As explored earlier, transaction amount is not a good discriminating feature and will not be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=df.drop('Amount', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(random_state=0) # repeat minority class points - Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_resampled, y_resampled = ros.fit_sample(df.drop('Class', axis=1), df['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data=np.concatenate((X_resampled, np.reshape(y_resampled, newshape=(4000,1))), axis=1), columns=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zero_resampled=df[df['Class']==0]\n",
    "one_resampled=df[df['Class']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zero_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use best features observed during EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df[['V14', 'V10', 'V12', 'V4', 'V11', 'V3', 'V2', 'V9', 'V7', 'V6', 'V1', 'V5']], df['Class'], test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X_train) # fit to train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train=scaler.transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN with 10 fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neighbors=[]\n",
    "accuracy=[]\n",
    "for k in range(1, 3998, 2):\n",
    "    model=KNeighborsClassifier(n_neighbors=k,algorithm='kd_tree', weights='distance')\n",
    "    avg_accuracy=np.mean(cross_val_score(model, X_train, y_train,cv=10))\n",
    "    neighbors.append(k)\n",
    "    accuracy.append(avg_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.98562204281780108, 0.99593846741667402)"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mv6H1AkJ9htFwGUdmZvYbWq/LqNo9tGlTCgbuLjIzK6T1AgI4CJiZjUPrdRmZmdm4lOsO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\nMDOzzAHBzMwA+P+JoUm5DcB5BQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff96985a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( neighbors,accuracy, 'r.')\n",
    "plt.xlim(np.min(neighbors), np.max(neighbors))\n",
    "plt.ylim(np.min(accuracy), np.max(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "191"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "383"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbors[np.argmax(accuracy)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99593846741667402"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98562204281780108"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_test=KNeighborsClassifier(n_neighbors=383, algorithm='kd_tree', weights='distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=2881, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "C=confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[419,   7],\n",
       "       [  0, 374]])"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9816272965879265"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(374)/(381)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99125"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(419+374)/(419+374+7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "TPR of 98.16% <br>\n",
    "TNR of 100% <br>\n",
    "Accuracy 99.12% <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "KNN works extremely well in case of credit card fraud detection"
   ]
  }
 ],
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