Detecting Credit Card Fraud.ipynb

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       "  <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      "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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('creditcard.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset Link : https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud?resource=download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2160x2160 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=30,figsize=(30,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>...</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>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-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  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-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   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-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   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-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   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to do scaling in amount column as it has a kind of outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The amount ranges from 0 to 25691.16 and that too within very small range so we want to scale it using RobustScaler\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### more about scalers :\n",
    "https://www.geeksforgeeks.org/standardscaler-minmaxscaler-and-robustscaler-techniques-ml/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "new_df = df.copy()\n",
    "new_df['Amount'] = RobustScaler().fit_transform(new_df['Amount'].to_numpy().reshape(-1,1))\n",
    "#Amount column is Series-->converted to numpy array-->reshaped from 1D to 2D\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df['Amount'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284807.000000\n",
       "mean          0.927124\n",
       "std           3.495006\n",
       "min          -0.307413\n",
       "25%          -0.229162\n",
       "50%           0.000000\n",
       "75%           0.770838\n",
       "max         358.683155\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['Amount'].describe()\n",
    "#much smaller standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.000000\n",
       "1         0.000000\n",
       "2         0.000006\n",
       "3         0.000006\n",
       "4         0.000012\n",
       "            ...   \n",
       "284802    0.999965\n",
       "284803    0.999971\n",
       "284804    0.999977\n",
       "284805    0.999977\n",
       "284806    1.000000\n",
       "Name: Time, Length: 284807, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#no outliers in time\n",
    "#so simply scaling it between 0 and 1\n",
    "time = new_df['Time']\n",
    "new_df['Time'] = (time - time.min())/(time.max() - time.min())\n",
    "new_df['Time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>...</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>169876</th>\n",
       "      <td>0.693938</td>\n",
       "      <td>-0.611712</td>\n",
       "      <td>-0.769705</td>\n",
       "      <td>-0.149759</td>\n",
       "      <td>-0.224877</td>\n",
       "      <td>2.028577</td>\n",
       "      <td>-2.019887</td>\n",
       "      <td>0.292491</td>\n",
       "      <td>-0.523020</td>\n",
       "      <td>0.358468</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.075208</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.380739</td>\n",
       "      <td>0.023440</td>\n",
       "      <td>-2.220686</td>\n",
       "      <td>-0.201146</td>\n",
       "      <td>0.066501</td>\n",
       "      <td>0.221180</td>\n",
       "      <td>-0.282401</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127467</th>\n",
       "      <td>0.453377</td>\n",
       "      <td>-0.814682</td>\n",
       "      <td>1.319219</td>\n",
       "      <td>1.329415</td>\n",
       "      <td>0.027273</td>\n",
       "      <td>-0.284871</td>\n",
       "      <td>-0.653985</td>\n",
       "      <td>0.321552</td>\n",
       "      <td>0.435975</td>\n",
       "      <td>-0.704298</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.128619</td>\n",
       "      <td>-0.368565</td>\n",
       "      <td>0.090660</td>\n",
       "      <td>0.401147</td>\n",
       "      <td>-0.261034</td>\n",
       "      <td>0.080621</td>\n",
       "      <td>0.162427</td>\n",
       "      <td>0.059456</td>\n",
       "      <td>-0.279746</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137900</th>\n",
       "      <td>0.476770</td>\n",
       "      <td>-0.318193</td>\n",
       "      <td>1.118618</td>\n",
       "      <td>0.969864</td>\n",
       "      <td>-0.127052</td>\n",
       "      <td>0.569563</td>\n",
       "      <td>-0.532484</td>\n",
       "      <td>0.706252</td>\n",
       "      <td>-0.064966</td>\n",
       "      <td>-0.463271</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.305402</td>\n",
       "      <td>-0.774704</td>\n",
       "      <td>-0.123884</td>\n",
       "      <td>-0.495687</td>\n",
       "      <td>-0.018148</td>\n",
       "      <td>0.121679</td>\n",
       "      <td>0.249050</td>\n",
       "      <td>0.092516</td>\n",
       "      <td>-0.294977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21513</th>\n",
       "      <td>0.183556</td>\n",
       "      <td>-1.328271</td>\n",
       "      <td>1.018378</td>\n",
       "      <td>1.775426</td>\n",
       "      <td>-1.574193</td>\n",
       "      <td>-0.117696</td>\n",
       "      <td>-0.457733</td>\n",
       "      <td>0.681867</td>\n",
       "      <td>-0.031641</td>\n",
       "      <td>0.383872</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.220815</td>\n",
       "      <td>-0.419013</td>\n",
       "      <td>-0.239197</td>\n",
       "      <td>0.009967</td>\n",
       "      <td>0.232829</td>\n",
       "      <td>0.814177</td>\n",
       "      <td>0.098797</td>\n",
       "      <td>-0.004273</td>\n",
       "      <td>-0.084119</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134700</th>\n",
       "      <td>0.468326</td>\n",
       "      <td>1.276712</td>\n",
       "      <td>0.617120</td>\n",
       "      <td>-0.578014</td>\n",
       "      <td>0.879173</td>\n",
       "      <td>0.061706</td>\n",
       "      <td>-1.472002</td>\n",
       "      <td>0.373692</td>\n",
       "      <td>-0.287204</td>\n",
       "      <td>-0.084482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.160161</td>\n",
       "      <td>-0.430404</td>\n",
       "      <td>-0.076738</td>\n",
       "      <td>0.258708</td>\n",
       "      <td>0.552170</td>\n",
       "      <td>0.370701</td>\n",
       "      <td>-0.034255</td>\n",
       "      <td>0.041709</td>\n",
       "      <td>-0.296793</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196117</th>\n",
       "      <td>0.760244</td>\n",
       "      <td>0.077197</td>\n",
       "      <td>0.482928</td>\n",
       "      <td>-2.234233</td>\n",
       "      <td>-1.309124</td>\n",
       "      <td>2.386570</td>\n",
       "      <td>3.392581</td>\n",
       "      <td>-0.156385</td>\n",
       "      <td>1.353569</td>\n",
       "      <td>-0.047112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060865</td>\n",
       "      <td>-0.060530</td>\n",
       "      <td>0.379575</td>\n",
       "      <td>0.598853</td>\n",
       "      <td>-0.878618</td>\n",
       "      <td>0.254859</td>\n",
       "      <td>-0.088550</td>\n",
       "      <td>-0.023446</td>\n",
       "      <td>0.669322</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24533</th>\n",
       "      <td>0.192567</td>\n",
       "      <td>-0.958584</td>\n",
       "      <td>1.109086</td>\n",
       "      <td>1.558159</td>\n",
       "      <td>0.878707</td>\n",
       "      <td>1.914559</td>\n",
       "      <td>1.564757</td>\n",
       "      <td>1.300978</td>\n",
       "      <td>0.074098</td>\n",
       "      <td>-1.376977</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155271</td>\n",
       "      <td>0.657607</td>\n",
       "      <td>-0.296222</td>\n",
       "      <td>-1.053362</td>\n",
       "      <td>0.006475</td>\n",
       "      <td>-0.058981</td>\n",
       "      <td>-0.528679</td>\n",
       "      <td>-0.374450</td>\n",
       "      <td>-0.215888</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13629</th>\n",
       "      <td>0.139810</td>\n",
       "      <td>-0.992899</td>\n",
       "      <td>1.430204</td>\n",
       "      <td>1.071256</td>\n",
       "      <td>1.363127</td>\n",
       "      <td>0.116315</td>\n",
       "      <td>0.217868</td>\n",
       "      <td>0.208391</td>\n",
       "      <td>0.319128</td>\n",
       "      <td>1.483134</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.258903</td>\n",
       "      <td>-0.104189</td>\n",
       "      <td>-0.100144</td>\n",
       "      <td>-0.369103</td>\n",
       "      <td>-0.068048</td>\n",
       "      <td>-0.266731</td>\n",
       "      <td>0.080402</td>\n",
       "      <td>-0.034571</td>\n",
       "      <td>-0.293440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246673</th>\n",
       "      <td>0.887055</td>\n",
       "      <td>-1.143693</td>\n",
       "      <td>-0.250983</td>\n",
       "      <td>1.013022</td>\n",
       "      <td>-0.671080</td>\n",
       "      <td>1.363438</td>\n",
       "      <td>0.312673</td>\n",
       "      <td>0.786158</td>\n",
       "      <td>-0.089323</td>\n",
       "      <td>-0.272429</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.094134</td>\n",
       "      <td>-0.137349</td>\n",
       "      <td>-0.047086</td>\n",
       "      <td>0.058485</td>\n",
       "      <td>0.825118</td>\n",
       "      <td>0.316019</td>\n",
       "      <td>-0.377194</td>\n",
       "      <td>-0.246404</td>\n",
       "      <td>0.868441</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91842</th>\n",
       "      <td>0.368356</td>\n",
       "      <td>0.555043</td>\n",
       "      <td>-0.099484</td>\n",
       "      <td>-0.102234</td>\n",
       "      <td>-0.624145</td>\n",
       "      <td>1.484364</td>\n",
       "      <td>4.154536</td>\n",
       "      <td>-1.242699</td>\n",
       "      <td>0.286054</td>\n",
       "      <td>0.694670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.649935</td>\n",
       "      <td>-0.578303</td>\n",
       "      <td>0.057823</td>\n",
       "      <td>1.026542</td>\n",
       "      <td>0.440906</td>\n",
       "      <td>0.303285</td>\n",
       "      <td>0.146932</td>\n",
       "      <td>0.172708</td>\n",
       "      <td>-0.066513</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "169876  0.693938 -0.611712 -0.769705 -0.149759 -0.224877  2.028577 -2.019887   \n",
       "127467  0.453377 -0.814682  1.319219  1.329415  0.027273 -0.284871 -0.653985   \n",
       "137900  0.476770 -0.318193  1.118618  0.969864 -0.127052  0.569563 -0.532484   \n",
       "21513   0.183556 -1.328271  1.018378  1.775426 -1.574193 -0.117696 -0.457733   \n",
       "134700  0.468326  1.276712  0.617120 -0.578014  0.879173  0.061706 -1.472002   \n",
       "196117  0.760244  0.077197  0.482928 -2.234233 -1.309124  2.386570  3.392581   \n",
       "24533   0.192567 -0.958584  1.109086  1.558159  0.878707  1.914559  1.564757   \n",
       "13629   0.139810 -0.992899  1.430204  1.071256  1.363127  0.116315  0.217868   \n",
       "246673  0.887055 -1.143693 -0.250983  1.013022 -0.671080  1.363438  0.312673   \n",
       "91842   0.368356  0.555043 -0.099484 -0.102234 -0.624145  1.484364  4.154536   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "169876  0.292491 -0.523020  0.358468  ... -0.075208  0.045536  0.380739   \n",
       "127467  0.321552  0.435975 -0.704298  ... -0.128619 -0.368565  0.090660   \n",
       "137900  0.706252 -0.064966 -0.463271  ... -0.305402 -0.774704 -0.123884   \n",
       "21513   0.681867 -0.031641  0.383872  ... -0.220815 -0.419013 -0.239197   \n",
       "134700  0.373692 -0.287204 -0.084482  ... -0.160161 -0.430404 -0.076738   \n",
       "196117 -0.156385  1.353569 -0.047112  ...  0.060865 -0.060530  0.379575   \n",
       "24533   1.300978  0.074098 -1.376977  ...  0.155271  0.657607 -0.296222   \n",
       "13629   0.208391  0.319128  1.483134  ... -0.258903 -0.104189 -0.100144   \n",
       "246673  0.786158 -0.089323 -0.272429  ... -0.094134 -0.137349 -0.047086   \n",
       "91842  -1.242699  0.286054  0.694670  ...  0.649935 -0.578303  0.057823   \n",
       "\n",
       "             V24       V25       V26       V27       V28    Amount  Class  \n",
       "169876  0.023440 -2.220686 -0.201146  0.066501  0.221180 -0.282401      0  \n",
       "127467  0.401147 -0.261034  0.080621  0.162427  0.059456 -0.279746      0  \n",
       "137900 -0.495687 -0.018148  0.121679  0.249050  0.092516 -0.294977      0  \n",
       "21513   0.009967  0.232829  0.814177  0.098797 -0.004273 -0.084119      0  \n",
       "134700  0.258708  0.552170  0.370701 -0.034255  0.041709 -0.296793      0  \n",
       "196117  0.598853 -0.878618  0.254859 -0.088550 -0.023446  0.669322      0  \n",
       "24533  -1.053362  0.006475 -0.058981 -0.528679 -0.374450 -0.215888      0  \n",
       "13629  -0.369103 -0.068048 -0.266731  0.080402 -0.034571 -0.293440      0  \n",
       "246673  0.058485  0.825118  0.316019 -0.377194 -0.246404  0.868441      0  \n",
       "91842   1.026542  0.440906  0.303285  0.146932  0.172708 -0.066513      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shuffling the dataset\n",
    "new_df = new_df.sample(frac=1,random_state=1)\n",
    "new_df.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    239589\n",
       "1       411\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train,test,val = new_df[:240000],new_df[240000:262000],new_df[262000:]\n",
    "train['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21955\n",
       "1       45\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22771\n",
       "1       36\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#it's advised to have fraud and non-fraud counts in test and val datasets as nearly same "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((240000, 31), (22000, 31), (22807, 31))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_np,test_np,val_np = train.to_numpy(),test.to_numpy(),val.to_numpy()\n",
    "train.shape,test.shape,val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((240000, 30), (240000,), (22000, 30), (22000,), (22807, 30), (22807,))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we need all rows and just final column\n",
    "x_train,y_train = train_np[:,:-1],train_np[:,-1]\n",
    "x_test,y_test = test_np[:,:-1],test_np[:,-1]\n",
    "x_val,y_val = val_np[:,:-1],val_np[:,-1]\n",
    "x_train.shape,y_train.shape,x_test.shape,y_test.shape,x_val.shape,y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9992375"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic_model = LogisticRegression()\n",
    "logistic_model.fit(x_train,y_train)\n",
    "logistic_model.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       1.00      1.00      1.00     22771\n",
      "       Fraud       0.73      0.53      0.61        36\n",
      "\n",
      "    accuracy                           1.00     22807\n",
      "   macro avg       0.87      0.76      0.81     22807\n",
      "weighted avg       1.00      1.00      1.00     22807\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_val,logistic_model.predict(x_val),target_names=['Not Fraud','Fraud']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "precision against fraud is how many times it wasn't fraud but predicted fraud\n",
    "\n",
    "recall is how many times it was fraus but predicted not fraud\n",
    "\n",
    "so these should be as close to 1 as possible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "althoug accuracy shown is 1,but from precision and recall it is evident that accuracy is not 100%\n",
    "\n",
    "it might have been 99.999999% which is stilll bad as precision and recall scores are not very high for fraud\n",
    "\n",
    "This is due to imbalanced dataset\n",
    "\n",
    "The model will mostly predict not fraud beacuse it is seeing most transactions to be not fraud and that will even get right most of times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are not happy with our model\n",
    "#let's try a shallow NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import InputLayer,Dense,BatchNormalization\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "shallow_nn = Sequential()\n",
    "shallow_nn.add(InputLayer((x_train.shape[1])))\n",
    "shallow_nn.add(Dense(2,'relu'))\n",
    "shallow_nn.add(BatchNormalization())\n",
    "shallow_nn.add(Dense(1,'sigmoid'))\n",
    "\n",
    "#we want to save that model only with highest val accuracy and lowest val loss\n",
    "checkpoint = ModelCheckpoint('shallow_nn',save_best_only=True)\n",
    "shallow_nn.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 2)                 62        \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 2)                8         \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 3         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 73\n",
      "Trainable params: 69\n",
      "Non-trainable params: 4\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "shallow_nn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "7486/7500 [============================>.] - ETA: 0s - loss: 0.0490 - accuracy: 0.9867INFO:tensorflow:Assets written to: shallow_nn\\assets\n",
      "7500/7500 [==============================] - 19s 2ms/step - loss: 0.0489 - accuracy: 0.9868 - val_loss: 0.0108 - val_accuracy: 0.9990\n",
      "Epoch 2/5\n",
      "7484/7500 [============================>.] - ETA: 0s - loss: 0.0036 - accuracy: 0.9993INFO:tensorflow:Assets written to: shallow_nn\\assets\n",
      "7500/7500 [==============================] - 17s 2ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.0095 - val_accuracy: 0.9992\n",
      "Epoch 3/5\n",
      "7500/7500 [==============================] - 14s 2ms/step - loss: 0.0034 - accuracy: 0.9994 - val_loss: 0.0111 - val_accuracy: 0.9990\n",
      "Epoch 4/5\n",
      "7500/7500 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9994INFO:tensorflow:Assets written to: shallow_nn\\assets\n",
      "7500/7500 [==============================] - 15s 2ms/step - loss: 0.0033 - accuracy: 0.9994 - val_loss: 0.0078 - val_accuracy: 0.9993\n",
      "Epoch 5/5\n",
      "7500/7500 [==============================] - 13s 2ms/step - loss: 0.0032 - accuracy: 0.9994 - val_loss: 0.0106 - val_accuracy: 0.9991\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c9485955e0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shallow_nn.fit(x_train,y_train,validation_data=(x_val,y_val),epochs = 5,callbacks=checkpoint)\n",
    "#callbacks is used to check at each epoch if the model should be saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.5723705e-04, 1.5723705e-04, 1.5723705e-04, ..., 1.8800016e-05,\n",
       "       1.7383695e-04, 4.4533610e-04], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shallow_nn.predict(x_train).flatten() #it gives probability of each class\n",
    "#it is low for most bcz most are not fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting prob to some class\n",
    "(shallow_nn.predict(x_train).flatten() > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def neural_net_predictions(model,x):\n",
    "    return (shallow_nn.predict(x).flatten() > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural_net_predictions(shallow_nn,x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       1.00      1.00      1.00     22771\n",
      "       Fraud       0.68      0.78      0.73        36\n",
      "\n",
      "    accuracy                           1.00     22807\n",
      "   macro avg       0.84      0.89      0.86     22807\n",
      "weighted avg       1.00      1.00      1.00     22807\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val,neural_net_predictions(shallow_nn,x_val),target_names=['Not Fraud','Fraud']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### comparison of the models\n",
    "\n",
    "|         Models tried                             |       P    |     R    |   f-1   |\n",
    "|--------------------------------------------------|------------|----------|---------|\n",
    "|shallow_nn on val                                 |     0.68   |  0.78    | 0.73    |\n",
    "|                                                  |            |          |         |\n",
    "|GradientBoostingClassifier on val                 |     0.67   |  0.67    | 0.67    |\n",
    "|                                                  |            |          |         |\n",
    "|SVM on val(1000)                                  |     0.66   |   0.64   |  0.65   |\n",
    "|                                                  |            |          |         |\n",
    "|Logistic on val                                   |     0.73   |   0.53   |  0.61   |\n",
    "|                                                  |            |          |         |\n",
    "|Random forest on val                              |     0.81   |   0.47   |  0.60   |\n",
    "|                                                  |            |          |         |\n",
    "|SVM on val(5000)                                  |     0.18   |   0.83   |  0.29   |\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying out Random Forest Classifier now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       1.00      1.00      1.00     22771\n",
      "       Fraud       0.81      0.47      0.60        36\n",
      "\n",
      "    accuracy                           1.00     22807\n",
      "   macro avg       0.90      0.74      0.80     22807\n",
      "weighted avg       1.00      1.00      1.00     22807\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(max_depth=2,n_jobs=-1)\n",
    "rf.fit(x_train,y_train)\n",
    "\n",
    "print(classification_report(y_val,rf.predict(x_val),target_names=['Not Fraud','Fraud']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying GradientBoostingClassifier now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       1.00      1.00      1.00     22771\n",
      "       Fraud       0.67      0.67      0.67        36\n",
      "\n",
      "    accuracy                           1.00     22807\n",
      "   macro avg       0.83      0.83      0.83     22807\n",
      "weighted avg       1.00      1.00      1.00     22807\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbc = GradientBoostingClassifier(n_estimators=50,learning_rate=1.0,max_depth=1,random_state=0)\n",
    "gbc.fit(x_train,y_train)\n",
    "\n",
    "print(classification_report(y_val,gbc.predict(x_val),target_names=['Not Fraud','Fraud']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "trying SVM classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       1.00      0.99      1.00     22771\n",
      "       Fraud       0.18      0.83      0.29        36\n",
      "\n",
      "    accuracy                           0.99     22807\n",
      "   macro avg       0.59      0.91      0.64     22807\n",
      "weighted avg       1.00      0.99      1.00     22807\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\sklearn\\svm\\_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "svc = LinearSVC(class_weight='balanced',max_iter = 5000)    #this means we are telling that there are not many fraud ex as compared to not fraud so place more weights when we encounter fraud data\n",
    "\n",
    "svc.fit(x_train,y_train)\n",
    "\n",
    "print(classification_report(y_val,svc.predict(x_val),target_names=['Not Fraud','Fraud']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now change the dataset to make it balanced by upsampling(increasing fraud data) or downsampling(dec not fraud data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    284315\n",
       " Name: Class, dtype: int64,\n",
       " 1    492\n",
       " Name: Class, dtype: int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_frauds = new_df.query('Class == 0')\n",
    "frauds = new_df.query('Class == 1')\n",
    "not_frauds['Class'].value_counts(),frauds['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    492\n",
       "0    492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_df = pd.concat([frauds,not_frauds.sample(len(frauds),random_state=1)])\n",
    "balanced_df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\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>...</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>18372</th>\n",
       "      <td>0.170309</td>\n",
       "      <td>-1.762593</td>\n",
       "      <td>0.256143</td>\n",
       "      <td>1.683125</td>\n",
       "      <td>-1.279233</td>\n",
       "      <td>-1.902762</td>\n",
       "      <td>1.004210</td>\n",
       "      <td>-1.009748</td>\n",
       "      <td>-2.432546</td>\n",
       "      <td>0.458860</td>\n",
       "      <td>...</td>\n",
       "      <td>2.493579</td>\n",
       "      <td>0.320829</td>\n",
       "      <td>-0.535481</td>\n",
       "      <td>0.499401</td>\n",
       "      <td>-0.915196</td>\n",
       "      <td>-0.423434</td>\n",
       "      <td>0.107049</td>\n",
       "      <td>0.175922</td>\n",
       "      <td>2.906449</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96341</th>\n",
       "      <td>0.380388</td>\n",
       "      <td>1.227614</td>\n",
       "      <td>-0.668974</td>\n",
       "      <td>-0.271785</td>\n",
       "      <td>-0.589440</td>\n",
       "      <td>-0.604795</td>\n",
       "      <td>-0.350285</td>\n",
       "      <td>-0.486365</td>\n",
       "      <td>-0.010809</td>\n",
       "      <td>-0.794944</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.026055</td>\n",
       "      <td>-0.295255</td>\n",
       "      <td>-0.180459</td>\n",
       "      <td>-0.436539</td>\n",
       "      <td>0.494649</td>\n",
       "      <td>-0.283738</td>\n",
       "      <td>-0.001128</td>\n",
       "      <td>0.035075</td>\n",
       "      <td>1.062111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248296</th>\n",
       "      <td>0.890522</td>\n",
       "      <td>-0.613696</td>\n",
       "      <td>3.698772</td>\n",
       "      <td>-5.534941</td>\n",
       "      <td>5.620486</td>\n",
       "      <td>1.649263</td>\n",
       "      <td>-2.335145</td>\n",
       "      <td>-0.907188</td>\n",
       "      <td>0.706362</td>\n",
       "      <td>-3.747646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319261</td>\n",
       "      <td>-0.471379</td>\n",
       "      <td>-0.075890</td>\n",
       "      <td>-0.667909</td>\n",
       "      <td>-0.642848</td>\n",
       "      <td>0.070600</td>\n",
       "      <td>0.488410</td>\n",
       "      <td>0.292345</td>\n",
       "      <td>-0.307413</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264328</th>\n",
       "      <td>0.933932</td>\n",
       "      <td>-0.011624</td>\n",
       "      <td>0.640413</td>\n",
       "      <td>0.868046</td>\n",
       "      <td>-0.505279</td>\n",
       "      <td>0.261938</td>\n",
       "      <td>0.223098</td>\n",
       "      <td>0.239049</td>\n",
       "      <td>0.150877</td>\n",
       "      <td>0.225142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069401</td>\n",
       "      <td>0.268024</td>\n",
       "      <td>0.261459</td>\n",
       "      <td>0.683742</td>\n",
       "      <td>-1.567901</td>\n",
       "      <td>-0.816674</td>\n",
       "      <td>0.185781</td>\n",
       "      <td>0.283021</td>\n",
       "      <td>-0.272619</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208904</th>\n",
       "      <td>0.794730</td>\n",
       "      <td>-0.679341</td>\n",
       "      <td>1.217389</td>\n",
       "      <td>-0.316778</td>\n",
       "      <td>-1.086725</td>\n",
       "      <td>0.855349</td>\n",
       "      <td>-0.980760</td>\n",
       "      <td>0.970589</td>\n",
       "      <td>0.133116</td>\n",
       "      <td>-0.357671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.083048</td>\n",
       "      <td>-0.137032</td>\n",
       "      <td>-0.238920</td>\n",
       "      <td>-0.617244</td>\n",
       "      <td>0.039020</td>\n",
       "      <td>-0.081848</td>\n",
       "      <td>0.234633</td>\n",
       "      <td>0.128382</td>\n",
       "      <td>-0.307273</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114886</th>\n",
       "      <td>0.426299</td>\n",
       "      <td>-0.949913</td>\n",
       "      <td>-1.111152</td>\n",
       "      <td>2.845267</td>\n",
       "      <td>-1.100618</td>\n",
       "      <td>-1.404191</td>\n",
       "      <td>0.770405</td>\n",
       "      <td>-0.697717</td>\n",
       "      <td>0.361678</td>\n",
       "      <td>0.320231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.436793</td>\n",
       "      <td>1.346398</td>\n",
       "      <td>-0.053597</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>0.323704</td>\n",
       "      <td>-0.022874</td>\n",
       "      <td>0.121526</td>\n",
       "      <td>0.129964</td>\n",
       "      <td>1.749459</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243699</th>\n",
       "      <td>0.879879</td>\n",
       "      <td>-4.320609</td>\n",
       "      <td>3.199939</td>\n",
       "      <td>-5.799736</td>\n",
       "      <td>6.502330</td>\n",
       "      <td>0.378479</td>\n",
       "      <td>-1.948246</td>\n",
       "      <td>-2.167860</td>\n",
       "      <td>-0.728207</td>\n",
       "      <td>-1.977238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.476660</td>\n",
       "      <td>0.434278</td>\n",
       "      <td>-0.136940</td>\n",
       "      <td>-0.620072</td>\n",
       "      <td>0.642531</td>\n",
       "      <td>0.280717</td>\n",
       "      <td>-2.649107</td>\n",
       "      <td>0.533641</td>\n",
       "      <td>-0.293440</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276670</th>\n",
       "      <td>0.967846</td>\n",
       "      <td>2.123476</td>\n",
       "      <td>0.076734</td>\n",
       "      <td>-2.469936</td>\n",
       "      <td>-0.307455</td>\n",
       "      <td>1.102028</td>\n",
       "      <td>-0.706358</td>\n",
       "      <td>0.705575</td>\n",
       "      <td>-0.381531</td>\n",
       "      <td>-0.129247</td>\n",
       "      <td>...</td>\n",
       "      <td>0.166730</td>\n",
       "      <td>0.566593</td>\n",
       "      <td>-0.115255</td>\n",
       "      <td>0.168280</td>\n",
       "      <td>0.531314</td>\n",
       "      <td>0.222732</td>\n",
       "      <td>-0.093293</td>\n",
       "      <td>-0.084963</td>\n",
       "      <td>-0.099211</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209204</th>\n",
       "      <td>0.795482</td>\n",
       "      <td>2.237049</td>\n",
       "      <td>-0.453816</td>\n",
       "      <td>-2.081849</td>\n",
       "      <td>-0.805387</td>\n",
       "      <td>0.061937</td>\n",
       "      <td>-1.013786</td>\n",
       "      <td>-0.214840</td>\n",
       "      <td>-0.290600</td>\n",
       "      <td>-0.452635</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.070291</td>\n",
       "      <td>-0.212899</td>\n",
       "      <td>0.173485</td>\n",
       "      <td>0.365161</td>\n",
       "      <td>0.032570</td>\n",
       "      <td>-0.294383</td>\n",
       "      <td>-0.033910</td>\n",
       "      <td>-0.032633</td>\n",
       "      <td>-0.097813</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2782</th>\n",
       "      <td>0.013438</td>\n",
       "      <td>-0.353417</td>\n",
       "      <td>-3.421649</td>\n",
       "      <td>-0.253020</td>\n",
       "      <td>-1.052406</td>\n",
       "      <td>-2.055726</td>\n",
       "      <td>-0.205536</td>\n",
       "      <td>0.446410</td>\n",
       "      <td>-0.260456</td>\n",
       "      <td>2.740303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.528372</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>-1.010238</td>\n",
       "      <td>0.190236</td>\n",
       "      <td>0.610824</td>\n",
       "      <td>-0.655040</td>\n",
       "      <td>-0.034321</td>\n",
       "      <td>0.172945</td>\n",
       "      <td>11.571997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "18372   0.170309 -1.762593  0.256143  1.683125 -1.279233 -1.902762  1.004210   \n",
       "96341   0.380388  1.227614 -0.668974 -0.271785 -0.589440 -0.604795 -0.350285   \n",
       "248296  0.890522 -0.613696  3.698772 -5.534941  5.620486  1.649263 -2.335145   \n",
       "264328  0.933932 -0.011624  0.640413  0.868046 -0.505279  0.261938  0.223098   \n",
       "208904  0.794730 -0.679341  1.217389 -0.316778 -1.086725  0.855349 -0.980760   \n",
       "114886  0.426299 -0.949913 -1.111152  2.845267 -1.100618 -1.404191  0.770405   \n",
       "243699  0.879879 -4.320609  3.199939 -5.799736  6.502330  0.378479 -1.948246   \n",
       "276670  0.967846  2.123476  0.076734 -2.469936 -0.307455  1.102028 -0.706358   \n",
       "209204  0.795482  2.237049 -0.453816 -2.081849 -0.805387  0.061937 -1.013786   \n",
       "2782    0.013438 -0.353417 -3.421649 -0.253020 -1.052406 -2.055726 -0.205536   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "18372  -1.009748 -2.432546  0.458860  ...  2.493579  0.320829 -0.535481   \n",
       "96341  -0.486365 -0.010809 -0.794944  ... -0.026055 -0.295255 -0.180459   \n",
       "248296 -0.907188  0.706362 -3.747646  ...  0.319261 -0.471379 -0.075890   \n",
       "264328  0.239049  0.150877  0.225142  ...  0.069401  0.268024  0.261459   \n",
       "208904  0.970589  0.133116 -0.357671  ... -0.083048 -0.137032 -0.238920   \n",
       "114886 -0.697717  0.361678  0.320231  ...  0.436793  1.346398 -0.053597   \n",
       "243699 -2.167860 -0.728207 -1.977238  ...  0.476660  0.434278 -0.136940   \n",
       "276670  0.705575 -0.381531 -0.129247  ...  0.166730  0.566593 -0.115255   \n",
       "209204 -0.214840 -0.290600 -0.452635  ... -0.070291 -0.212899  0.173485   \n",
       "2782    0.446410 -0.260456  2.740303  ...  0.528372  0.213120 -1.010238   \n",
       "\n",
       "             V24       V25       V26       V27       V28     Amount  Class  \n",
       "18372   0.499401 -0.915196 -0.423434  0.107049  0.175922   2.906449      0  \n",
       "96341  -0.436539  0.494649 -0.283738 -0.001128  0.035075   1.062111      1  \n",
       "248296 -0.667909 -0.642848  0.070600  0.488410  0.292345  -0.307413      1  \n",
       "264328  0.683742 -1.567901 -0.816674  0.185781  0.283021  -0.272619      0  \n",
       "208904 -0.617244  0.039020 -0.081848  0.234633  0.128382  -0.307273      0  \n",
       "114886 -0.017646  0.323704 -0.022874  0.121526  0.129964   1.749459      0  \n",
       "243699 -0.620072  0.642531  0.280717 -2.649107  0.533641  -0.293440      1  \n",
       "276670  0.168280  0.531314  0.222732 -0.093293 -0.084963  -0.099211      0  \n",
       "209204  0.365161  0.032570 -0.294383 -0.033910 -0.032633  -0.097813      0  \n",
       "2782    0.190236  0.610824 -0.655040 -0.034321  0.172945  11.571997      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_df = balanced_df.sample(frac=1,random_state=1)\n",
    "balanced_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((700, 30), (700,), (142, 30), (142,), (142, 30), (142,))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_df_np =balanced_df.to_numpy()\n",
    "\n",
    "x_train_b,y_train_b = balanced_df_np[:700,:-1],balanced_df_np[:700,-1].astype(int)\n",
    "x_test_b,y_test_b = balanced_df_np[700:842,:-1],balanced_df_np[700:842,-1].astype(int)\n",
    "x_val_b,y_val_b = balanced_df_np[842:,:-1],balanced_df_np[842:,-1].astype(int)\n",
    "\n",
    "x_train_b.shape,y_train_b.shape,x_test_b.shape ,y_test_b.shape,x_val_b.shape,y_val_b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1    353\n",
       " 0    347\n",
       " dtype: int64,\n",
       " 0    73\n",
       " 1    69\n",
       " dtype: int64,\n",
       " 0    72\n",
       " 1    70\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_train_b).value_counts(),pd.Series(y_test_b).value_counts(),pd.Series(y_val_b).value_counts(),"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying Logistic Regression on balanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9542857142857143"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model_b = LogisticRegression()\n",
    "logistic_model_b.fit(x_train_b,y_train_b)\n",
    "logistic_model_b.score(x_train_b,y_train_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       0.96      0.93      0.94        72\n",
      "       Fraud       0.93      0.96      0.94        70\n",
      "\n",
      "    accuracy                           0.94       142\n",
      "   macro avg       0.94      0.94      0.94       142\n",
      "weighted avg       0.94      0.94      0.94       142\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val_b,logistic_model_b.predict(x_val_b),target_names=['Not Fraud','Fraud']))\n",
    "\n",
    "#pretty good performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### comparison of the models on balanced dataset\n",
    "\n",
    "|         Models tried                             |       P    |     R    |   f-1   |\n",
    "|--------------------------------------------------|------------|----------|---------|\n",
    "|shallow_nn on val                                 |            |          |         |\n",
    "|Not Fraud                                         |      0.89  |   1.00   |  0.94   |\n",
    "|Fraud                                             |      1.00  |   0.87   |  0.93   |    \n",
    "|                                                  |            |          |         |\n",
    "|GradientBoostingClassifier on val                 |            |          |         |\n",
    "|Not Fraud                                         |      0.94  |   0.92   |  0.93   |\n",
    "|Fraud                                             |      0.92  |   0.94   |  0.93   |  \n",
    "|                                                  |            |          |         |\n",
    "|SVM on val(1000)                                  |            |          |         |\n",
    "|Not Fraud                                         |      0.96  |   0.93   |  0.94   |\n",
    "|Fraud                                             |      0.93  |   0.96   |  0.94   |  \n",
    "|                                                  |            |          |         |\n",
    "|Logistic on val                                   |            |          |         |\n",
    "|Fraud                                             |     0.96   |   0.93   |  0.94   |\n",
    "|Not fraud                                         |     0.93   |   0.96   |  0.94   |\n",
    "|                                                  |            |          |         |\n",
    "|Random forest on val                              |            |          |         |\n",
    "|Not Fraud                                         |      0.99  |   0.92   |  0.95   |\n",
    "|Fraud                                             |      0.98  |   0.91   |  0.95   |  \n",
    "|                                                  |            |          |         |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "21/22 [===========================>..] - ETA: 0s - loss: 0.6877 - accuracy: 0.5670 INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 4s 105ms/step - loss: 0.6873 - accuracy: 0.5700 - val_loss: 0.6551 - val_accuracy: 0.7676\n",
      "Epoch 2/40\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.6705 - accuracy: 0.7672INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 94ms/step - loss: 0.6698 - accuracy: 0.7757 - val_loss: 0.6445 - val_accuracy: 0.8028\n",
      "Epoch 3/40\n",
      "21/22 [===========================>..] - ETA: 0s - loss: 0.6578 - accuracy: 0.8140INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 101ms/step - loss: 0.6572 - accuracy: 0.8186 - val_loss: 0.6350 - val_accuracy: 0.8380\n",
      "Epoch 4/40\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.6429 - accuracy: 0.8717INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 4s 181ms/step - loss: 0.6430 - accuracy: 0.8614 - val_loss: 0.6234 - val_accuracy: 0.8803\n",
      "Epoch 5/40\n",
      "17/22 [======================>.......] - ETA: 0s - loss: 0.6300 - accuracy: 0.8676INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 4s 178ms/step - loss: 0.6289 - accuracy: 0.8657 - val_loss: 0.6114 - val_accuracy: 0.8732\n",
      "Epoch 6/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.6122 - accuracy: 0.8767INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 4s 170ms/step - loss: 0.6120 - accuracy: 0.8714 - val_loss: 0.5972 - val_accuracy: 0.8732\n",
      "Epoch 7/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.5992 - accuracy: 0.8733INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 3s 143ms/step - loss: 0.5956 - accuracy: 0.8829 - val_loss: 0.5809 - val_accuracy: 0.8803\n",
      "Epoch 8/40\n",
      "15/22 [===================>..........] - ETA: 0s - loss: 0.5814 - accuracy: 0.8813INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 4s 181ms/step - loss: 0.5758 - accuracy: 0.8771 - val_loss: 0.5649 - val_accuracy: 0.8803\n",
      "Epoch 9/40\n",
      "16/22 [====================>.........] - ETA: 0s - loss: 0.5613 - accuracy: 0.8906INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 7s 342ms/step - loss: 0.5590 - accuracy: 0.8886 - val_loss: 0.5458 - val_accuracy: 0.8732\n",
      "Epoch 10/40\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.5408 - accuracy: 0.8849INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 4s 174ms/step - loss: 0.5394 - accuracy: 0.8843 - val_loss: 0.5305 - val_accuracy: 0.8873\n",
      "Epoch 11/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.5210 - accuracy: 0.9045INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 3s 163ms/step - loss: 0.5212 - accuracy: 0.8943 - val_loss: 0.5120 - val_accuracy: 0.8803\n",
      "Epoch 12/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.5047 - accuracy: 0.8924INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 3s 151ms/step - loss: 0.5027 - accuracy: 0.8914 - val_loss: 0.4935 - val_accuracy: 0.8803\n",
      "Epoch 13/40\n",
      "21/22 [===========================>..] - ETA: 0s - loss: 0.4843 - accuracy: 0.9018INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 4s 185ms/step - loss: 0.4826 - accuracy: 0.9043 - val_loss: 0.4776 - val_accuracy: 0.8873\n",
      "Epoch 14/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.4623 - accuracy: 0.8976INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 116ms/step - loss: 0.4652 - accuracy: 0.8943 - val_loss: 0.4603 - val_accuracy: 0.8803\n",
      "Epoch 15/40\n",
      "17/22 [======================>.......] - ETA: 0s - loss: 0.4517 - accuracy: 0.8860INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 81ms/step - loss: 0.4497 - accuracy: 0.8871 - val_loss: 0.4411 - val_accuracy: 0.8873\n",
      "Epoch 16/40\n",
      "21/22 [===========================>..] - ETA: 0s - loss: 0.4330 - accuracy: 0.8914INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 50ms/step - loss: 0.4332 - accuracy: 0.8929 - val_loss: 0.4261 - val_accuracy: 0.8873\n",
      "Epoch 17/40\n",
      "14/22 [==================>...........] - ETA: 0s - loss: 0.4092 - accuracy: 0.9129INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.4149 - accuracy: 0.9057 - val_loss: 0.4134 - val_accuracy: 0.8944\n",
      "Epoch 18/40\n",
      "16/22 [====================>.........] - ETA: 0s - loss: 0.4069 - accuracy: 0.9023INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 46ms/step - loss: 0.3977 - accuracy: 0.9086 - val_loss: 0.3996 - val_accuracy: 0.8944\n",
      "Epoch 19/40\n",
      "14/22 [==================>...........] - ETA: 0s - loss: 0.3954 - accuracy: 0.8839INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 78ms/step - loss: 0.3858 - accuracy: 0.9000 - val_loss: 0.3851 - val_accuracy: 0.8944\n",
      "Epoch 20/40\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.3705 - accuracy: 0.9143INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 44ms/step - loss: 0.3705 - accuracy: 0.9143 - val_loss: 0.3756 - val_accuracy: 0.8873\n",
      "Epoch 21/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.3597 - accuracy: 0.9219INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 79ms/step - loss: 0.3566 - accuracy: 0.9214 - val_loss: 0.3611 - val_accuracy: 0.8873\n",
      "Epoch 22/40\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.3384 - accuracy: 0.9109INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 44ms/step - loss: 0.3393 - accuracy: 0.9129 - val_loss: 0.3448 - val_accuracy: 0.9014\n",
      "Epoch 23/40\n",
      "13/22 [================>.............] - ETA: 0s - loss: 0.3357 - accuracy: 0.9135INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 86ms/step - loss: 0.3338 - accuracy: 0.9071 - val_loss: 0.3358 - val_accuracy: 0.9085\n",
      "Epoch 24/40\n",
      "15/22 [===================>..........] - ETA: 0s - loss: 0.3078 - accuracy: 0.9271INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 44ms/step - loss: 0.3101 - accuracy: 0.9229 - val_loss: 0.3200 - val_accuracy: 0.9085\n",
      "Epoch 25/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.2987 - accuracy: 0.9253INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 50ms/step - loss: 0.3027 - accuracy: 0.9186 - val_loss: 0.3059 - val_accuracy: 0.9155\n",
      "Epoch 26/40\n",
      " 1/22 [>.............................] - ETA: 0s - loss: 0.2791 - accuracy: 0.9062INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 52ms/step - loss: 0.2880 - accuracy: 0.9171 - val_loss: 0.2955 - val_accuracy: 0.9155\n",
      "Epoch 27/40\n",
      "17/22 [======================>.......] - ETA: 0s - loss: 0.2871 - accuracy: 0.9118INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 84ms/step - loss: 0.2811 - accuracy: 0.9186 - val_loss: 0.2855 - val_accuracy: 0.9155\n",
      "Epoch 28/40\n",
      "15/22 [===================>..........] - ETA: 0s - loss: 0.2706 - accuracy: 0.9187INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 49ms/step - loss: 0.2645 - accuracy: 0.9257 - val_loss: 0.2748 - val_accuracy: 0.9155\n",
      "Epoch 29/40\n",
      "14/22 [==================>...........] - ETA: 0s - loss: 0.2613 - accuracy: 0.9420INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 57ms/step - loss: 0.2602 - accuracy: 0.9329 - val_loss: 0.2643 - val_accuracy: 0.9225\n",
      "Epoch 30/40\n",
      "17/22 [======================>.......] - ETA: 0s - loss: 0.2488 - accuracy: 0.9301INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.2439 - accuracy: 0.9357 - val_loss: 0.2560 - val_accuracy: 0.9296\n",
      "Epoch 31/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.2513 - accuracy: 0.9306INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 101ms/step - loss: 0.2478 - accuracy: 0.9314 - val_loss: 0.2497 - val_accuracy: 0.9296\n",
      "Epoch 32/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.2273 - accuracy: 0.9375INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 82ms/step - loss: 0.2313 - accuracy: 0.9343 - val_loss: 0.2401 - val_accuracy: 0.9296\n",
      "Epoch 33/40\n",
      "15/22 [===================>..........] - ETA: 0s - loss: 0.2200 - accuracy: 0.9417INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.2228 - accuracy: 0.9429 - val_loss: 0.2317 - val_accuracy: 0.9366\n",
      "Epoch 34/40\n",
      "16/22 [====================>.........] - ETA: 0s - loss: 0.2339 - accuracy: 0.9434INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 77ms/step - loss: 0.2284 - accuracy: 0.9357 - val_loss: 0.2206 - val_accuracy: 0.9366\n",
      "Epoch 35/40\n",
      " 1/22 [>.............................] - ETA: 0s - loss: 0.2493 - accuracy: 0.9062INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 40ms/step - loss: 0.2177 - accuracy: 0.9429 - val_loss: 0.2174 - val_accuracy: 0.9366\n",
      "Epoch 36/40\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.2153 - accuracy: 0.9375INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 47ms/step - loss: 0.2104 - accuracy: 0.9414 - val_loss: 0.2120 - val_accuracy: 0.9366\n",
      "Epoch 37/40\n",
      " 1/22 [>.............................] - ETA: 0s - loss: 0.1475 - accuracy: 1.0000INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 45ms/step - loss: 0.1925 - accuracy: 0.9457 - val_loss: 0.2064 - val_accuracy: 0.9366\n",
      "Epoch 38/40\n",
      "21/22 [===========================>..] - ETA: 0s - loss: 0.1923 - accuracy: 0.9464INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 79ms/step - loss: 0.1962 - accuracy: 0.9457 - val_loss: 0.2033 - val_accuracy: 0.9366\n",
      "Epoch 39/40\n",
      "14/22 [==================>...........] - ETA: 0s - loss: 0.1927 - accuracy: 0.9464INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 49ms/step - loss: 0.1948 - accuracy: 0.9443 - val_loss: 0.1983 - val_accuracy: 0.9437\n",
      "Epoch 40/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.1858 - accuracy: 0.9462INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 61ms/step - loss: 0.1806 - accuracy: 0.9500 - val_loss: 0.1954 - val_accuracy: 0.9437\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c94836b700>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shallow_nn_b = Sequential()\n",
    "shallow_nn_b.add(InputLayer((x_train.shape[1])))\n",
    "shallow_nn_b.add(Dense(2,'relu'))\n",
    "shallow_nn_b.add(BatchNormalization())\n",
    "shallow_nn_b.add(Dense(1,'sigmoid'))\n",
    "\n",
    "#we want to save that model only with highest val accuracy and lowest val loss\n",
    "checkpoint = ModelCheckpoint('shallow_nn_b',save_best_only=True)\n",
    "shallow_nn_b.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "\n",
    "#epochs increased due to small dataset now\n",
    "shallow_nn_b.fit(x_train_b,y_train_b,validation_data=(x_val_b,y_val_b),epochs = 40,callbacks=checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      " 1/22 [>.............................] - ETA: 0s - loss: 0.1218 - accuracy: 0.9688INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 82ms/step - loss: 0.1740 - accuracy: 0.9486 - val_loss: 0.1921 - val_accuracy: 0.9437\n",
      "Epoch 2/40\n",
      "15/22 [===================>..........] - ETA: 0s - loss: 0.1771 - accuracy: 0.9438INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 79ms/step - loss: 0.1887 - accuracy: 0.9400 - val_loss: 0.1904 - val_accuracy: 0.9366\n",
      "Epoch 3/40\n",
      "12/22 [===============>..............] - ETA: 0s - loss: 0.1665 - accuracy: 0.9531INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 54ms/step - loss: 0.1785 - accuracy: 0.9443 - val_loss: 0.1855 - val_accuracy: 0.9437\n",
      "Epoch 4/40\n",
      "10/22 [============>.................] - ETA: 0s - loss: 0.1828 - accuracy: 0.9438INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 60ms/step - loss: 0.1793 - accuracy: 0.9443 - val_loss: 0.1837 - val_accuracy: 0.9437\n",
      "Epoch 5/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1696 - accuracy: 0.9443 - val_loss: 0.1846 - val_accuracy: 0.9437\n",
      "Epoch 6/40\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1686 - accuracy: 0.9429INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 51ms/step - loss: 0.1686 - accuracy: 0.9429 - val_loss: 0.1817 - val_accuracy: 0.9437\n",
      "Epoch 7/40\n",
      "17/22 [======================>.......] - ETA: 0s - loss: 0.1621 - accuracy: 0.9504INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 91ms/step - loss: 0.1633 - accuracy: 0.9486 - val_loss: 0.1796 - val_accuracy: 0.9437\n",
      "Epoch 8/40\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.1775 - accuracy: 0.9386INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 100ms/step - loss: 0.1775 - accuracy: 0.9386 - val_loss: 0.1790 - val_accuracy: 0.9437\n",
      "Epoch 9/40\n",
      "16/22 [====================>.........] - ETA: 0s - loss: 0.1688 - accuracy: 0.9395INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 46ms/step - loss: 0.1574 - accuracy: 0.9457 - val_loss: 0.1773 - val_accuracy: 0.9437\n",
      "Epoch 10/40\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.1522 - accuracy: 0.9457INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 81ms/step - loss: 0.1626 - accuracy: 0.9400 - val_loss: 0.1768 - val_accuracy: 0.9366\n",
      "Epoch 11/40\n",
      "17/22 [======================>.......] - ETA: 0s - loss: 0.1641 - accuracy: 0.9467INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 86ms/step - loss: 0.1550 - accuracy: 0.9471 - val_loss: 0.1741 - val_accuracy: 0.9437\n",
      "Epoch 12/40\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1558 - accuracy: 0.9500 - val_loss: 0.1749 - val_accuracy: 0.9366\n",
      "Epoch 13/40\n",
      "17/22 [======================>.......] - ETA: 0s - loss: 0.1484 - accuracy: 0.9540INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.1509 - accuracy: 0.9543 - val_loss: 0.1735 - val_accuracy: 0.9437\n",
      "Epoch 14/40\n",
      "16/22 [====================>.........] - ETA: 0s - loss: 0.1438 - accuracy: 0.9512INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 58ms/step - loss: 0.1510 - accuracy: 0.9486 - val_loss: 0.1735 - val_accuracy: 0.9366\n",
      "Epoch 15/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1658 - accuracy: 0.9457 - val_loss: 0.1747 - val_accuracy: 0.9366\n",
      "Epoch 16/40\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.1437 - accuracy: 0.9514INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 1s 53ms/step - loss: 0.1508 - accuracy: 0.9486 - val_loss: 0.1726 - val_accuracy: 0.9366\n",
      "Epoch 17/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1520 - accuracy: 0.9514 - val_loss: 0.1733 - val_accuracy: 0.9366\n",
      "Epoch 18/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1558 - accuracy: 0.9529 - val_loss: 0.1728 - val_accuracy: 0.9366\n",
      "Epoch 19/40\n",
      "14/22 [==================>...........] - ETA: 0s - loss: 0.1508 - accuracy: 0.9487INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 95ms/step - loss: 0.1410 - accuracy: 0.9543 - val_loss: 0.1714 - val_accuracy: 0.9437\n",
      "Epoch 20/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1519 - accuracy: 0.9486 - val_loss: 0.1746 - val_accuracy: 0.9366\n",
      "Epoch 21/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1460 - accuracy: 0.9514 - val_loss: 0.1747 - val_accuracy: 0.9366\n",
      "Epoch 22/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1405 - accuracy: 0.9514 - val_loss: 0.1729 - val_accuracy: 0.9437\n",
      "Epoch 23/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1573 - accuracy: 0.9414 - val_loss: 0.1730 - val_accuracy: 0.9366\n",
      "Epoch 24/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1396 - accuracy: 0.9529 - val_loss: 0.1749 - val_accuracy: 0.9366\n",
      "Epoch 25/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1477 - accuracy: 0.9543 - val_loss: 0.1736 - val_accuracy: 0.9366\n",
      "Epoch 26/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1510 - accuracy: 0.9429 - val_loss: 0.1717 - val_accuracy: 0.9437\n",
      "Epoch 27/40\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.1441 - accuracy: 0.9500INFO:tensorflow:Assets written to: shallow_nn_b\\assets\n",
      "22/22 [==============================] - 2s 73ms/step - loss: 0.1388 - accuracy: 0.9529 - val_loss: 0.1712 - val_accuracy: 0.9437\n",
      "Epoch 28/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1452 - accuracy: 0.9514 - val_loss: 0.1729 - val_accuracy: 0.9437\n",
      "Epoch 29/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1373 - accuracy: 0.9586 - val_loss: 0.1735 - val_accuracy: 0.9366\n",
      "Epoch 30/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1492 - accuracy: 0.9457 - val_loss: 0.1742 - val_accuracy: 0.9366\n",
      "Epoch 31/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1472 - accuracy: 0.9500 - val_loss: 0.1728 - val_accuracy: 0.9366\n",
      "Epoch 32/40\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1437 - accuracy: 0.9529 - val_loss: 0.1725 - val_accuracy: 0.9366\n",
      "Epoch 33/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1363 - accuracy: 0.9500 - val_loss: 0.1725 - val_accuracy: 0.9366\n",
      "Epoch 34/40\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1386 - accuracy: 0.9500 - val_loss: 0.1737 - val_accuracy: 0.9366\n",
      "Epoch 35/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1370 - accuracy: 0.9514 - val_loss: 0.1721 - val_accuracy: 0.9437\n",
      "Epoch 36/40\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1353 - accuracy: 0.9500 - val_loss: 0.1776 - val_accuracy: 0.9366\n",
      "Epoch 37/40\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1378 - accuracy: 0.9471 - val_loss: 0.1743 - val_accuracy: 0.9366\n",
      "Epoch 38/40\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1337 - accuracy: 0.9514 - val_loss: 0.1783 - val_accuracy: 0.9366\n",
      "Epoch 39/40\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1363 - accuracy: 0.9557 - val_loss: 0.1793 - val_accuracy: 0.9366\n",
      "Epoch 40/40\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1362 - accuracy: 0.9514 - val_loss: 0.1760 - val_accuracy: 0.9366\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c950670be0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#another 40 epochs as we might not have converged\n",
    "shallow_nn_b.fit(x_train_b,y_train_b,validation_data=(x_val_b,y_val_b),epochs = 40,callbacks=checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1325 - accuracy: 0.9543 - val_loss: 0.1768 - val_accuracy: 0.9366\n",
      "Epoch 2/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1332 - accuracy: 0.9543 - val_loss: 0.1756 - val_accuracy: 0.9366\n",
      "Epoch 3/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1352 - accuracy: 0.9486 - val_loss: 0.1757 - val_accuracy: 0.9366\n",
      "Epoch 4/40\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1426 - accuracy: 0.9429 - val_loss: 0.1762 - val_accuracy: 0.9366\n",
      "Epoch 5/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1366 - accuracy: 0.9514 - val_loss: 0.1743 - val_accuracy: 0.9437\n",
      "Epoch 6/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1389 - accuracy: 0.9514 - val_loss: 0.1780 - val_accuracy: 0.9366\n",
      "Epoch 7/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1323 - accuracy: 0.9529 - val_loss: 0.1767 - val_accuracy: 0.9366\n",
      "Epoch 8/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1296 - accuracy: 0.9500 - val_loss: 0.1792 - val_accuracy: 0.9366\n",
      "Epoch 9/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1339 - accuracy: 0.9571 - val_loss: 0.1788 - val_accuracy: 0.9366\n",
      "Epoch 10/40\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.1361 - accuracy: 0.9471 - val_loss: 0.1800 - val_accuracy: 0.9366\n",
      "Epoch 11/40\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1370 - accuracy: 0.9500 - val_loss: 0.1781 - val_accuracy: 0.9437\n",
      "Epoch 12/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1327 - accuracy: 0.9557 - val_loss: 0.1788 - val_accuracy: 0.9366\n",
      "Epoch 13/40\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1342 - accuracy: 0.9543 - val_loss: 0.1807 - val_accuracy: 0.9366\n",
      "Epoch 14/40\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1286 - accuracy: 0.9471 - val_loss: 0.1800 - val_accuracy: 0.9366\n",
      "Epoch 15/40\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1354 - accuracy: 0.9457 - val_loss: 0.1805 - val_accuracy: 0.9366\n",
      "Epoch 16/40\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1406 - accuracy: 0.9429 - val_loss: 0.1808 - val_accuracy: 0.9366\n",
      "Epoch 17/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1252 - accuracy: 0.9529 - val_loss: 0.1844 - val_accuracy: 0.9366\n",
      "Epoch 18/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1319 - accuracy: 0.9571 - val_loss: 0.1827 - val_accuracy: 0.9366\n",
      "Epoch 19/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1398 - accuracy: 0.9514 - val_loss: 0.1839 - val_accuracy: 0.9366\n",
      "Epoch 20/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1446 - accuracy: 0.9457 - val_loss: 0.1808 - val_accuracy: 0.9366\n",
      "Epoch 21/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1355 - accuracy: 0.9457 - val_loss: 0.1813 - val_accuracy: 0.9366\n",
      "Epoch 22/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1346 - accuracy: 0.9514 - val_loss: 0.1826 - val_accuracy: 0.9366\n",
      "Epoch 23/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1288 - accuracy: 0.9543 - val_loss: 0.1829 - val_accuracy: 0.9366\n",
      "Epoch 24/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1252 - accuracy: 0.9543 - val_loss: 0.1844 - val_accuracy: 0.9366\n",
      "Epoch 25/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1555 - accuracy: 0.9443 - val_loss: 0.1856 - val_accuracy: 0.9366\n",
      "Epoch 26/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1458 - accuracy: 0.9486 - val_loss: 0.1847 - val_accuracy: 0.9366\n",
      "Epoch 27/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1294 - accuracy: 0.9514 - val_loss: 0.1850 - val_accuracy: 0.9366\n",
      "Epoch 28/40\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1395 - accuracy: 0.9414 - val_loss: 0.1861 - val_accuracy: 0.9366\n",
      "Epoch 29/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1369 - accuracy: 0.9514 - val_loss: 0.1840 - val_accuracy: 0.9366\n",
      "Epoch 30/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1259 - accuracy: 0.9514 - val_loss: 0.1851 - val_accuracy: 0.9366\n",
      "Epoch 31/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1322 - accuracy: 0.9571 - val_loss: 0.1866 - val_accuracy: 0.9366\n",
      "Epoch 32/40\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1280 - accuracy: 0.9543 - val_loss: 0.1853 - val_accuracy: 0.9366\n",
      "Epoch 33/40\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1281 - accuracy: 0.9529 - val_loss: 0.1848 - val_accuracy: 0.9366\n",
      "Epoch 34/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1335 - accuracy: 0.9514 - val_loss: 0.1837 - val_accuracy: 0.9366\n",
      "Epoch 35/40\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1371 - accuracy: 0.9486 - val_loss: 0.1860 - val_accuracy: 0.9366\n",
      "Epoch 36/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1307 - accuracy: 0.9500 - val_loss: 0.1839 - val_accuracy: 0.9366\n",
      "Epoch 37/40\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1232 - accuracy: 0.9543 - val_loss: 0.1847 - val_accuracy: 0.9366\n",
      "Epoch 38/40\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1491 - accuracy: 0.9471 - val_loss: 0.1839 - val_accuracy: 0.9366\n",
      "Epoch 39/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1304 - accuracy: 0.9500 - val_loss: 0.1847 - val_accuracy: 0.9366\n",
      "Epoch 40/40\n",
      "22/22 [==============================] - 0s 5ms/step - loss: 0.1320 - accuracy: 0.9529 - val_loss: 0.1873 - val_accuracy: 0.9366\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c94a9a09a0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#another 40 epochs confirming if we have converged\n",
    "\n",
    "shallow_nn_b.fit(x_train_b,y_train_b,validation_data=(x_val_b,y_val_b),epochs = 40,callbacks=checkpoint)\n",
    "#no further model saved we are already converged\n",
    "\n",
    "#we can even see a bit of overfitting as train_accuracy is more than val_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       0.89      1.00      0.94        72\n",
      "       Fraud       1.00      0.87      0.93        70\n",
      "\n",
      "    accuracy                           0.94       142\n",
      "   macro avg       0.94      0.94      0.94       142\n",
      "weighted avg       0.94      0.94      0.94       142\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_val_b,neural_net_predictions(shallow_nn_b,x_val_b),target_names=['Not Fraud','Fraud']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       0.92      0.99      0.95        72\n",
      "       Fraud       0.98      0.91      0.95        70\n",
      "\n",
      "    accuracy                           0.95       142\n",
      "   macro avg       0.95      0.95      0.95       142\n",
      "weighted avg       0.95      0.95      0.95       142\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(max_depth=2,n_jobs=-1)\n",
    "rf.fit(x_train_b,y_train_b)\n",
    "\n",
    "print(classification_report(y_val_b,rf.predict(x_val_b),target_names=['Not Fraud','Fraud']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       0.94      0.92      0.93        72\n",
      "       Fraud       0.92      0.94      0.93        70\n",
      "\n",
      "    accuracy                           0.93       142\n",
      "   macro avg       0.93      0.93      0.93       142\n",
      "weighted avg       0.93      0.93      0.93       142\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier(n_estimators=100,learning_rate=1.0,max_depth=2,random_state=0)\n",
    "gbc.fit(x_train_b,y_train_b)\n",
    "\n",
    "print(classification_report(y_val_b,gbc.predict(x_val_b),target_names=['Not Fraud','Fraud']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       0.96      0.93      0.94        72\n",
      "       Fraud       0.93      0.96      0.94        70\n",
      "\n",
      "    accuracy                           0.94       142\n",
      "   macro avg       0.94      0.94      0.94       142\n",
      "weighted avg       0.94      0.94      0.94       142\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\sklearn\\svm\\_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "svc_b = LinearSVC(class_weight='balanced')\n",
    "\n",
    "svc_b.fit(x_train_b,y_train_b)\n",
    "\n",
    "print(classification_report(y_val_b,svc_b.predict(x_val_b),target_names=['Not Fraud','Fraud']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Not Fraud       0.92      0.95      0.93        73\n",
      "       Fraud       0.94      0.91      0.93        69\n",
      "\n",
      "    accuracy                           0.93       142\n",
      "   macro avg       0.93      0.93      0.93       142\n",
      "weighted avg       0.93      0.93      0.93       142\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#finally testing SVM bcz I feel it's best in detecting frauds and not letting them go\n",
    "print(classification_report(y_test_b,svc_b.predict(x_test_b),target_names=['Not Fraud','Fraud']))\n",
    "\n",
    "#not much diff in preision,recall and f1-scores"
   ]
  }
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