credit-card-fraud-detect-under-over-sampling.ipynb

{
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    "execution": {
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    }
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   "outputs": [],
   "source": [
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:16.319256Z",
     "iopub.status.busy": "2022-10-07T09:30:16.318792Z",
     "iopub.status.idle": "2022-10-07T09:30:16.325806Z",
     "shell.execute_reply": "2022-10-07T09:30:16.324277Z",
     "shell.execute_reply.started": "2022-10-07T09:30:16.319215Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:17.25483Z",
     "iopub.status.busy": "2022-10-07T09:30:17.254443Z",
     "iopub.status.idle": "2022-10-07T09:30:20.177334Z",
     "shell.execute_reply": "2022-10-07T09:30:20.176106Z",
     "shell.execute_reply.started": "2022-10-07T09:30:17.254798Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.179781Z",
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     "shell.execute_reply.started": "2022-10-07T09:30:20.17975Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Top 5 Rows of The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.186546Z",
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     "shell.execute_reply": "2022-10-07T09:30:20.222888Z",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
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       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <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",
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       "      <td>0.090794</td>\n",
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       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.403993</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.060018</td>\n",
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       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>0.489095</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.635558</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</td>\n",
       "      <td>-0.145783</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.717293</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>2.345865</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</td>\n",
       "      <td>-2.261857</td>\n",
       "      <td>0.524980</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1.792993</td>\n",
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       "      <td>-0.010309</td>\n",
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       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
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       "      <td>-0.287924</td>\n",
       "      <td>-0.631418</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</td>\n",
       "      <td>-1.232622</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>1.345852</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>0.175121</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "      <td>0.803487</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\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       V10       V11       V12       V13       V14  \\\n",
       "0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   \n",
       "1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   \n",
       "2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   \n",
       "3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   \n",
       "4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670   \n",
       "\n",
       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
       "0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   \n",
       "1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   \n",
       "2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   \n",
       "3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   \n",
       "4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "\n",
       "   Amount  Class  \n",
       "0  149.62      0  \n",
       "1    2.69      0  \n",
       "2  378.66      0  \n",
       "3  123.50      0  \n",
       "4   69.99      0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Last 5 Rows of The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.226982Z",
     "iopub.status.busy": "2022-10-07T09:30:20.226643Z",
     "iopub.status.idle": "2022-10-07T09:30:20.260648Z",
     "shell.execute_reply": "2022-10-07T09:30:20.259266Z",
     "shell.execute_reply.started": "2022-10-07T09:30:20.226942Z"
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       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>-0.975926</td>\n",
       "      <td>-0.150189</td>\n",
       "      <td>0.915802</td>\n",
       "      <td>1.214756</td>\n",
       "      <td>-0.675143</td>\n",
       "      <td>1.164931</td>\n",
       "      <td>-0.711757</td>\n",
       "      <td>-0.025693</td>\n",
       "      <td>-1.221179</td>\n",
       "      <td>-1.545556</td>\n",
       "      <td>0.059616</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>-0.484782</td>\n",
       "      <td>0.411614</td>\n",
       "      <td>0.063119</td>\n",
       "      <td>-0.183699</td>\n",
       "      <td>-0.510602</td>\n",
       "      <td>1.329284</td>\n",
       "      <td>0.140716</td>\n",
       "      <td>0.313502</td>\n",
       "      <td>0.395652</td>\n",
       "      <td>-0.577252</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>-0.399126</td>\n",
       "      <td>-1.933849</td>\n",
       "      <td>-0.962886</td>\n",
       "      <td>-1.042082</td>\n",
       "      <td>0.449624</td>\n",
       "      <td>1.962563</td>\n",
       "      <td>-0.608577</td>\n",
       "      <td>0.509928</td>\n",
       "      <td>1.113981</td>\n",
       "      <td>2.897849</td>\n",
       "      <td>0.127434</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>-0.915427</td>\n",
       "      <td>-1.040458</td>\n",
       "      <td>-0.031513</td>\n",
       "      <td>-0.188093</td>\n",
       "      <td>-0.084316</td>\n",
       "      <td>0.041333</td>\n",
       "      <td>-0.302620</td>\n",
       "      <td>-0.660377</td>\n",
       "      <td>0.167430</td>\n",
       "      <td>-0.256117</td>\n",
       "      <td>0.382948</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9       V10       V11       V12  \\\n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  4.356170 -1.593105  2.711941   \n",
       "284803  1.058415  0.024330  0.294869  0.584800 -0.975926 -0.150189  0.915802   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454 -0.484782  0.411614  0.063119   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087 -0.399126 -1.933849 -0.962886   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180 -0.915427 -1.040458 -0.031513   \n",
       "\n",
       "             V13       V14       V15       V16       V17       V18       V19  \\\n",
       "284802 -0.689256  4.626942 -0.924459  1.107641  1.991691  0.510632 -0.682920   \n",
       "284803  1.214756 -0.675143  1.164931 -0.711757 -0.025693 -1.221179 -1.545556   \n",
       "284804 -0.183699 -0.510602  1.329284  0.140716  0.313502  0.395652 -0.577252   \n",
       "284805 -1.042082  0.449624  1.962563 -0.608577  0.509928  1.113981  2.897849   \n",
       "284806 -0.188093 -0.084316  0.041333 -0.302620 -0.660377  0.167430 -0.256117   \n",
       "\n",
       "             V20       V21       V22       V23       V24       V25       V26  \\\n",
       "284802  1.475829  0.213454  0.111864  1.014480 -0.509348  1.436807  0.250034   \n",
       "284803  0.059616  0.214205  0.924384  0.012463 -1.016226 -0.606624 -0.395255   \n",
       "284804  0.001396  0.232045  0.578229 -0.037501  0.640134  0.265745 -0.087371   \n",
       "284805  0.127434  0.265245  0.800049 -0.163298  0.123205 -0.569159  0.546668   \n",
       "284806  0.382948  0.261057  0.643078  0.376777  0.008797 -0.473649 -0.818267   \n",
       "\n",
       "             V27       V28  Amount  Class  \n",
       "284802  0.943651  0.823731    0.77      0  \n",
       "284803  0.068472 -0.053527   24.79      0  \n",
       "284804  0.004455 -0.026561   67.88      0  \n",
       "284805  0.108821  0.104533   10.00      0  \n",
       "284806 -0.002415  0.013649  217.00      0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. shape of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.262403Z",
     "iopub.status.busy": "2022-10-07T09:30:20.261946Z",
     "iopub.status.idle": "2022-10-07T09:30:20.275615Z",
     "shell.execute_reply": "2022-10-07T09:30:20.27425Z",
     "shell.execute_reply.started": "2022-10-07T09:30:20.262359Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.278042Z",
     "iopub.status.busy": "2022-10-07T09:30:20.277472Z",
     "iopub.status.idle": "2022-10-07T09:30:20.289818Z",
     "shell.execute_reply": "2022-10-07T09:30:20.287004Z",
     "shell.execute_reply.started": "2022-10-07T09:30:20.277991Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows 284807\n",
      "Number of Columns 31\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Rows\",data.shape[0])\n",
    "print(\"Number of Columns\",data.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Dataset info And Memory Requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.291271Z",
     "iopub.status.busy": "2022-10-07T09:30:20.290911Z",
     "iopub.status.idle": "2022-10-07T09:30:20.327957Z",
     "shell.execute_reply": "2022-10-07T09:30:20.326852Z",
     "shell.execute_reply.started": "2022-10-07T09:30:20.29124Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Check Null Values In The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.330077Z",
     "iopub.status.busy": "2022-10-07T09:30:20.329723Z",
     "iopub.status.idle": "2022-10-07T09:30:20.356023Z",
     "shell.execute_reply": "2022-10-07T09:30:20.35486Z",
     "shell.execute_reply.started": "2022-10-07T09:30:20.330017Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.358711Z",
     "iopub.status.busy": "2022-10-07T09:30:20.358333Z",
     "iopub.status.idle": "2022-10-07T09:30:20.364106Z",
     "shell.execute_reply": "2022-10-07T09:30:20.362853Z",
     "shell.execute_reply.started": "2022-10-07T09:30:20.35868Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.446359Z",
     "iopub.status.busy": "2022-10-07T09:30:20.44592Z",
     "iopub.status.idle": "2022-10-07T09:30:20.458316Z",
     "shell.execute_reply": "2022-10-07T09:30:20.45738Z",
     "shell.execute_reply.started": "2022-10-07T09:30:20.446321Z"
    }
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "data['Amount']=sc.fit_transform(pd.DataFrame(data['Amount']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.693871Z",
     "iopub.status.busy": "2022-10-07T09:30:20.693452Z",
     "iopub.status.idle": "2022-10-07T09:30:20.735768Z",
     "shell.execute_reply": "2022-10-07T09:30:20.734595Z",
     "shell.execute_reply.started": "2022-10-07T09:30:20.693835Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.551600</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>-0.991390</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>1.468177</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.403993</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0.244964</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>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>0.489095</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.635558</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</td>\n",
       "      <td>-0.145783</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>-0.342475</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>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.717293</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>2.345865</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</td>\n",
       "      <td>-2.261857</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>1.160686</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>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>0.507757</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-0.631418</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</td>\n",
       "      <td>-1.232622</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0.140534</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>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>1.345852</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>0.175121</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "      <td>0.803487</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>-0.073403</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10       V11       V12       V13       V14  \\\n",
       "0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   \n",
       "1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   \n",
       "2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   \n",
       "3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   \n",
       "4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670   \n",
       "\n",
       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
       "0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   \n",
       "1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   \n",
       "2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   \n",
       "3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   \n",
       "4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "\n",
       "     Amount  Class  \n",
       "0  0.244964      0  \n",
       "1 -0.342475      0  \n",
       "2  1.160686      0  \n",
       "3  0.140534      0  \n",
       "4 -0.073403      0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:20.912624Z",
     "iopub.status.busy": "2022-10-07T09:30:20.912236Z",
     "iopub.status.idle": "2022-10-07T09:30:20.944648Z",
     "shell.execute_reply": "2022-10-07T09:30:20.943459Z",
     "shell.execute_reply.started": "2022-10-07T09:30:20.91259Z"
    }
   },
   "outputs": [],
   "source": [
    "data = data.drop(['Time'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:21.102604Z",
     "iopub.status.busy": "2022-10-07T09:30:21.102181Z",
     "iopub.status.idle": "2022-10-07T09:30:21.135701Z",
     "shell.execute_reply": "2022-10-07T09:30:21.134577Z",
     "shell.execute_reply.started": "2022-10-07T09:30:21.102565Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>-0.551600</td>\n",
       "      <td>-0.617801</td>\n",
       "      <td>-0.991390</td>\n",
       "      <td>-0.311169</td>\n",
       "      <td>1.468177</td>\n",
       "      <td>-0.470401</td>\n",
       "      <td>0.207971</td>\n",
       "      <td>0.025791</td>\n",
       "      <td>0.403993</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0.244964</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>1.612727</td>\n",
       "      <td>1.065235</td>\n",
       "      <td>0.489095</td>\n",
       "      <td>-0.143772</td>\n",
       "      <td>0.635558</td>\n",
       "      <td>0.463917</td>\n",
       "      <td>-0.114805</td>\n",
       "      <td>-0.183361</td>\n",
       "      <td>-0.145783</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>-0.342475</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>0.624501</td>\n",
       "      <td>0.066084</td>\n",
       "      <td>0.717293</td>\n",
       "      <td>-0.165946</td>\n",
       "      <td>2.345865</td>\n",
       "      <td>-2.890083</td>\n",
       "      <td>1.109969</td>\n",
       "      <td>-0.121359</td>\n",
       "      <td>-2.261857</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>1.160686</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>-0.226487</td>\n",
       "      <td>0.178228</td>\n",
       "      <td>0.507757</td>\n",
       "      <td>-0.287924</td>\n",
       "      <td>-0.631418</td>\n",
       "      <td>-1.059647</td>\n",
       "      <td>-0.684093</td>\n",
       "      <td>1.965775</td>\n",
       "      <td>-1.232622</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0.140534</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>-0.822843</td>\n",
       "      <td>0.538196</td>\n",
       "      <td>1.345852</td>\n",
       "      <td>-1.119670</td>\n",
       "      <td>0.175121</td>\n",
       "      <td>-0.451449</td>\n",
       "      <td>-0.237033</td>\n",
       "      <td>-0.038195</td>\n",
       "      <td>0.803487</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>-0.073403</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10       V11       V12       V13       V14  \\\n",
       "0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   \n",
       "1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   \n",
       "2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   \n",
       "3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   \n",
       "4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670   \n",
       "\n",
       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
       "0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   \n",
       "1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   \n",
       "2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   \n",
       "3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   \n",
       "4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
       "1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
       "2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
       "3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
       "4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
       "\n",
       "     Amount  Class  \n",
       "0  0.244964      0  \n",
       "1 -0.342475      0  \n",
       "2  1.160686      0  \n",
       "3  0.140534      0  \n",
       "4 -0.073403      0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:21.338645Z",
     "iopub.status.busy": "2022-10-07T09:30:21.338235Z",
     "iopub.status.idle": "2022-10-07T09:30:21.346344Z",
     "shell.execute_reply": "2022-10-07T09:30:21.345033Z",
     "shell.execute_reply.started": "2022-10-07T09:30:21.338612Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 30)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:21.52879Z",
     "iopub.status.busy": "2022-10-07T09:30:21.528418Z",
     "iopub.status.idle": "2022-10-07T09:30:22.550356Z",
     "shell.execute_reply": "2022-10-07T09:30:22.549072Z",
     "shell.execute_reply.started": "2022-10-07T09:30:21.528757Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's Remove Duplicated Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:22.552534Z",
     "iopub.status.busy": "2022-10-07T09:30:22.552205Z",
     "iopub.status.idle": "2022-10-07T09:30:23.593446Z",
     "shell.execute_reply": "2022-10-07T09:30:23.592367Z",
     "shell.execute_reply.started": "2022-10-07T09:30:22.552505Z"
    }
   },
   "outputs": [],
   "source": [
    "data = data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:23.595438Z",
     "iopub.status.busy": "2022-10-07T09:30:23.594992Z",
     "iopub.status.idle": "2022-10-07T09:30:23.602637Z",
     "shell.execute_reply": "2022-10-07T09:30:23.601358Z",
     "shell.execute_reply.started": "2022-10-07T09:30:23.595406Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275663, 30)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:23.605366Z",
     "iopub.status.busy": "2022-10-07T09:30:23.604995Z",
     "iopub.status.idle": "2022-10-07T09:30:23.617791Z",
     "shell.execute_reply": "2022-10-07T09:30:23.616385Z",
     "shell.execute_reply.started": "2022-10-07T09:30:23.605333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9144"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "284807- 275663"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Not Handling Imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:23.620805Z",
     "iopub.status.busy": "2022-10-07T09:30:23.619433Z",
     "iopub.status.idle": "2022-10-07T09:30:23.635524Z",
     "shell.execute_reply": "2022-10-07T09:30:23.634119Z",
     "shell.execute_reply.started": "2022-10-07T09:30:23.620711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    275190\n",
       "1       473\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:23.639831Z",
     "iopub.status.busy": "2022-10-07T09:30:23.638994Z",
     "iopub.status.idle": "2022-10-07T09:30:23.64553Z",
     "shell.execute_reply": "2022-10-07T09:30:23.644364Z",
     "shell.execute_reply.started": "2022-10-07T09:30:23.639791Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:23.647292Z",
     "iopub.status.busy": "2022-10-07T09:30:23.646949Z",
     "iopub.status.idle": "2022-10-07T09:30:23.870677Z",
     "shell.execute_reply": "2022-10-07T09:30:23.869563Z",
     "shell.execute_reply.started": "2022-10-07T09:30:23.647263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.countplot(data['Class'])\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Store Feature Matrix In X And Response (Target) In Vector y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:23.872976Z",
     "iopub.status.busy": "2022-10-07T09:30:23.87265Z",
     "iopub.status.idle": "2022-10-07T09:30:23.913878Z",
     "shell.execute_reply": "2022-10-07T09:30:23.912189Z",
     "shell.execute_reply.started": "2022-10-07T09:30:23.872947Z"
    }
   },
   "outputs": [],
   "source": [
    "X = data.drop('Class',axis=1)\n",
    "y = data['Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Splitting The Dataset Into The Training Set And Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:24.195624Z",
     "iopub.status.busy": "2022-10-07T09:30:24.194358Z",
     "iopub.status.idle": "2022-10-07T09:30:24.295306Z",
     "shell.execute_reply": "2022-10-07T09:30:24.294038Z",
     "shell.execute_reply.started": "2022-10-07T09:30:24.195573Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20,\n",
    "                                                 random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Handling Imbalanced Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Undersampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete rows from majority class to match the minority class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:24.9133Z",
     "iopub.status.busy": "2022-10-07T09:30:24.912836Z",
     "iopub.status.idle": "2022-10-07T09:30:24.954031Z",
     "shell.execute_reply": "2022-10-07T09:30:24.952891Z",
     "shell.execute_reply.started": "2022-10-07T09:30:24.913262Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "normal = data[data['Class']==0]\n",
    "fraud = data[data['Class']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:25.158431Z",
     "iopub.status.busy": "2022-10-07T09:30:25.15705Z",
     "iopub.status.idle": "2022-10-07T09:30:25.16732Z",
     "shell.execute_reply": "2022-10-07T09:30:25.166091Z",
     "shell.execute_reply.started": "2022-10-07T09:30:25.158373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275190, 30)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:25.369191Z",
     "iopub.status.busy": "2022-10-07T09:30:25.36878Z",
     "iopub.status.idle": "2022-10-07T09:30:25.376852Z",
     "shell.execute_reply": "2022-10-07T09:30:25.375483Z",
     "shell.execute_reply.started": "2022-10-07T09:30:25.369156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(473, 30)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:25.642273Z",
     "iopub.status.busy": "2022-10-07T09:30:25.641766Z",
     "iopub.status.idle": "2022-10-07T09:30:25.659535Z",
     "shell.execute_reply": "2022-10-07T09:30:25.658461Z",
     "shell.execute_reply.started": "2022-10-07T09:30:25.642233Z"
    }
   },
   "outputs": [],
   "source": [
    "normal_sample=normal.sample(n=473)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:25.843592Z",
     "iopub.status.busy": "2022-10-07T09:30:25.84321Z",
     "iopub.status.idle": "2022-10-07T09:30:25.851473Z",
     "shell.execute_reply": "2022-10-07T09:30:25.850339Z",
     "shell.execute_reply.started": "2022-10-07T09:30:25.843551Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(473, 30)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:26.072215Z",
     "iopub.status.busy": "2022-10-07T09:30:26.071569Z",
     "iopub.status.idle": "2022-10-07T09:30:26.078038Z",
     "shell.execute_reply": "2022-10-07T09:30:26.07671Z",
     "shell.execute_reply.started": "2022-10-07T09:30:26.072177Z"
    }
   },
   "outputs": [],
   "source": [
    "new_data = pd.concat([normal_sample,fraud],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:26.336559Z",
     "iopub.status.busy": "2022-10-07T09:30:26.336173Z",
     "iopub.status.idle": "2022-10-07T09:30:26.345862Z",
     "shell.execute_reply": "2022-10-07T09:30:26.344343Z",
     "shell.execute_reply.started": "2022-10-07T09:30:26.336523Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    473\n",
       "1    473\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:26.615496Z",
     "iopub.status.busy": "2022-10-07T09:30:26.615073Z",
     "iopub.status.idle": "2022-10-07T09:30:26.647306Z",
     "shell.execute_reply": "2022-10-07T09:30:26.646355Z",
     "shell.execute_reply.started": "2022-10-07T09:30:26.615462Z"
    }
   },
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>V13</th>\n",
       "      <th>V14</th>\n",
       "      <th>V15</th>\n",
       "      <th>V16</th>\n",
       "      <th>V17</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.046019</td>\n",
       "      <td>-0.431416</td>\n",
       "      <td>-0.792288</td>\n",
       "      <td>0.059023</td>\n",
       "      <td>0.005063</td>\n",
       "      <td>0.495435</td>\n",
       "      <td>-0.653628</td>\n",
       "      <td>0.083863</td>\n",
       "      <td>0.880064</td>\n",
       "      <td>0.150918</td>\n",
       "      <td>-0.303297</td>\n",
       "      <td>1.241970</td>\n",
       "      <td>1.520677</td>\n",
       "      <td>-0.366920</td>\n",
       "      <td>-0.425622</td>\n",
       "      <td>0.804287</td>\n",
       "      <td>-1.281220</td>\n",
       "      <td>0.788849</td>\n",
       "      <td>0.527090</td>\n",
       "      <td>-0.059338</td>\n",
       "      <td>0.171294</td>\n",
       "      <td>0.758322</td>\n",
       "      <td>-0.064140</td>\n",
       "      <td>-1.286158</td>\n",
       "      <td>-0.028424</td>\n",
       "      <td>0.674496</td>\n",
       "      <td>-0.020248</td>\n",
       "      <td>-0.070178</td>\n",
       "      <td>-0.313448</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.990044</td>\n",
       "      <td>-0.229019</td>\n",
       "      <td>-0.438836</td>\n",
       "      <td>0.103836</td>\n",
       "      <td>-0.322388</td>\n",
       "      <td>-0.220218</td>\n",
       "      <td>-0.478424</td>\n",
       "      <td>0.007717</td>\n",
       "      <td>0.926634</td>\n",
       "      <td>-0.088870</td>\n",
       "      <td>0.745906</td>\n",
       "      <td>1.637661</td>\n",
       "      <td>1.216724</td>\n",
       "      <td>0.016204</td>\n",
       "      <td>0.274850</td>\n",
       "      <td>0.503048</td>\n",
       "      <td>-1.145100</td>\n",
       "      <td>0.523109</td>\n",
       "      <td>0.314408</td>\n",
       "      <td>-0.147791</td>\n",
       "      <td>-0.085241</td>\n",
       "      <td>-0.082849</td>\n",
       "      <td>0.268911</td>\n",
       "      <td>-0.520987</td>\n",
       "      <td>-0.295433</td>\n",
       "      <td>-0.917846</td>\n",
       "      <td>0.062392</td>\n",
       "      <td>-0.039490</td>\n",
       "      <td>-0.349231</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.899043</td>\n",
       "      <td>1.491246</td>\n",
       "      <td>0.685411</td>\n",
       "      <td>1.250078</td>\n",
       "      <td>-0.431949</td>\n",
       "      <td>-0.029942</td>\n",
       "      <td>-0.105343</td>\n",
       "      <td>0.816901</td>\n",
       "      <td>-0.266044</td>\n",
       "      <td>-0.845148</td>\n",
       "      <td>-1.457502</td>\n",
       "      <td>1.308233</td>\n",
       "      <td>1.164728</td>\n",
       "      <td>0.077133</td>\n",
       "      <td>-1.187051</td>\n",
       "      <td>-0.782297</td>\n",
       "      <td>0.591773</td>\n",
       "      <td>-0.759293</td>\n",
       "      <td>1.134757</td>\n",
       "      <td>-0.241852</td>\n",
       "      <td>-0.289256</td>\n",
       "      <td>-0.793470</td>\n",
       "      <td>0.232445</td>\n",
       "      <td>-0.050210</td>\n",
       "      <td>-0.754964</td>\n",
       "      <td>-1.018407</td>\n",
       "      <td>-0.112262</td>\n",
       "      <td>0.090162</td>\n",
       "      <td>-0.333479</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.345037</td>\n",
       "      <td>1.079313</td>\n",
       "      <td>0.640913</td>\n",
       "      <td>1.032175</td>\n",
       "      <td>-0.433555</td>\n",
       "      <td>-0.791454</td>\n",
       "      <td>0.472928</td>\n",
       "      <td>0.274091</td>\n",
       "      <td>-0.641497</td>\n",
       "      <td>-0.390337</td>\n",
       "      <td>-0.729666</td>\n",
       "      <td>-0.192559</td>\n",
       "      <td>-0.377162</td>\n",
       "      <td>0.724791</td>\n",
       "      <td>1.035750</td>\n",
       "      <td>-0.115594</td>\n",
       "      <td>-0.018027</td>\n",
       "      <td>0.197076</td>\n",
       "      <td>0.337111</td>\n",
       "      <td>-0.076232</td>\n",
       "      <td>0.193036</td>\n",
       "      <td>0.377554</td>\n",
       "      <td>0.049715</td>\n",
       "      <td>0.372012</td>\n",
       "      <td>-0.132111</td>\n",
       "      <td>-0.327346</td>\n",
       "      <td>-0.061748</td>\n",
       "      <td>-0.016186</td>\n",
       "      <td>-0.145089</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.634554</td>\n",
       "      <td>1.498644</td>\n",
       "      <td>-0.516728</td>\n",
       "      <td>0.811189</td>\n",
       "      <td>0.738530</td>\n",
       "      <td>-0.342194</td>\n",
       "      <td>1.013130</td>\n",
       "      <td>0.256876</td>\n",
       "      <td>-0.991537</td>\n",
       "      <td>0.047192</td>\n",
       "      <td>0.173374</td>\n",
       "      <td>0.606507</td>\n",
       "      <td>-0.279125</td>\n",
       "      <td>0.957574</td>\n",
       "      <td>-1.015810</td>\n",
       "      <td>-0.802459</td>\n",
       "      <td>-0.013397</td>\n",
       "      <td>0.274184</td>\n",
       "      <td>1.165865</td>\n",
       "      <td>-0.058231</td>\n",
       "      <td>0.198929</td>\n",
       "      <td>0.701657</td>\n",
       "      <td>-0.270199</td>\n",
       "      <td>-0.379495</td>\n",
       "      <td>0.054084</td>\n",
       "      <td>-0.381695</td>\n",
       "      <td>0.186021</td>\n",
       "      <td>0.157514</td>\n",
       "      <td>-0.248440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0  2.046019 -0.431416 -0.792288  0.059023  0.005063  0.495435 -0.653628   \n",
       "1  1.990044 -0.229019 -0.438836  0.103836 -0.322388 -0.220218 -0.478424   \n",
       "2 -0.899043  1.491246  0.685411  1.250078 -0.431949 -0.029942 -0.105343   \n",
       "3 -0.345037  1.079313  0.640913  1.032175 -0.433555 -0.791454  0.472928   \n",
       "4 -0.634554  1.498644 -0.516728  0.811189  0.738530 -0.342194  1.013130   \n",
       "\n",
       "         V8        V9       V10       V11       V12       V13       V14  \\\n",
       "0  0.083863  0.880064  0.150918 -0.303297  1.241970  1.520677 -0.366920   \n",
       "1  0.007717  0.926634 -0.088870  0.745906  1.637661  1.216724  0.016204   \n",
       "2  0.816901 -0.266044 -0.845148 -1.457502  1.308233  1.164728  0.077133   \n",
       "3  0.274091 -0.641497 -0.390337 -0.729666 -0.192559 -0.377162  0.724791   \n",
       "4  0.256876 -0.991537  0.047192  0.173374  0.606507 -0.279125  0.957574   \n",
       "\n",
       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
       "0 -0.425622  0.804287 -1.281220  0.788849  0.527090 -0.059338  0.171294   \n",
       "1  0.274850  0.503048 -1.145100  0.523109  0.314408 -0.147791 -0.085241   \n",
       "2 -1.187051 -0.782297  0.591773 -0.759293  1.134757 -0.241852 -0.289256   \n",
       "3  1.035750 -0.115594 -0.018027  0.197076  0.337111 -0.076232  0.193036   \n",
       "4 -1.015810 -0.802459 -0.013397  0.274184  1.165865 -0.058231  0.198929   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
       "0  0.758322 -0.064140 -1.286158 -0.028424  0.674496 -0.020248 -0.070178   \n",
       "1 -0.082849  0.268911 -0.520987 -0.295433 -0.917846  0.062392 -0.039490   \n",
       "2 -0.793470  0.232445 -0.050210 -0.754964 -1.018407 -0.112262  0.090162   \n",
       "3  0.377554  0.049715  0.372012 -0.132111 -0.327346 -0.061748 -0.016186   \n",
       "4  0.701657 -0.270199 -0.379495  0.054084 -0.381695  0.186021  0.157514   \n",
       "\n",
       "     Amount  Class  \n",
       "0 -0.313448      0  \n",
       "1 -0.349231      0  \n",
       "2 -0.333479      0  \n",
       "3 -0.145089      0  \n",
       "4 -0.248440      0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:26.807411Z",
     "iopub.status.busy": "2022-10-07T09:30:26.806528Z",
     "iopub.status.idle": "2022-10-07T09:30:26.814701Z",
     "shell.execute_reply": "2022-10-07T09:30:26.813392Z",
     "shell.execute_reply.started": "2022-10-07T09:30:26.807343Z"
    }
   },
   "outputs": [],
   "source": [
    "X = new_data.drop('Class',axis=1)\n",
    "y = new_data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:27.050719Z",
     "iopub.status.busy": "2022-10-07T09:30:27.05029Z",
     "iopub.status.idle": "2022-10-07T09:30:27.06069Z",
     "shell.execute_reply": "2022-10-07T09:30:27.05949Z",
     "shell.execute_reply.started": "2022-10-07T09:30:27.050686Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20,\n",
    "                                                 random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:27.521498Z",
     "iopub.status.busy": "2022-10-07T09:30:27.521084Z",
     "iopub.status.idle": "2022-10-07T09:30:27.577021Z",
     "shell.execute_reply": "2022-10-07T09:30:27.575438Z",
     "shell.execute_reply.started": "2022-10-07T09:30:27.521463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:27.770543Z",
     "iopub.status.busy": "2022-10-07T09:30:27.76899Z",
     "iopub.status.idle": "2022-10-07T09:30:27.777821Z",
     "shell.execute_reply": "2022-10-07T09:30:27.776574Z",
     "shell.execute_reply.started": "2022-10-07T09:30:27.77049Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred1 = log.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:27.974683Z",
     "iopub.status.busy": "2022-10-07T09:30:27.973985Z",
     "iopub.status.idle": "2022-10-07T09:30:27.980184Z",
     "shell.execute_reply": "2022-10-07T09:30:27.978927Z",
     "shell.execute_reply.started": "2022-10-07T09:30:27.974633Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:28.213806Z",
     "iopub.status.busy": "2022-10-07T09:30:28.213234Z",
     "iopub.status.idle": "2022-10-07T09:30:28.220154Z",
     "shell.execute_reply": "2022-10-07T09:30:28.218937Z",
     "shell.execute_reply.started": "2022-10-07T09:30:28.213772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9368421052631579"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:28.471316Z",
     "iopub.status.busy": "2022-10-07T09:30:28.470872Z",
     "iopub.status.idle": "2022-10-07T09:30:28.476725Z",
     "shell.execute_reply": "2022-10-07T09:30:28.475691Z",
     "shell.execute_reply.started": "2022-10-07T09:30:28.471278Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score,recall_score,f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:28.72712Z",
     "iopub.status.busy": "2022-10-07T09:30:28.72674Z",
     "iopub.status.idle": "2022-10-07T09:30:28.736081Z",
     "shell.execute_reply": "2022-10-07T09:30:28.734901Z",
     "shell.execute_reply.started": "2022-10-07T09:30:28.727089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96875"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:28.961759Z",
     "iopub.status.busy": "2022-10-07T09:30:28.961346Z",
     "iopub.status.idle": "2022-10-07T09:30:28.972827Z",
     "shell.execute_reply": "2022-10-07T09:30:28.971632Z",
     "shell.execute_reply.started": "2022-10-07T09:30:28.961725Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9117647058823529"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:29.213735Z",
     "iopub.status.busy": "2022-10-07T09:30:29.212975Z",
     "iopub.status.idle": "2022-10-07T09:30:29.223516Z",
     "shell.execute_reply": "2022-10-07T09:30:29.222079Z",
     "shell.execute_reply.started": "2022-10-07T09:30:29.213696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9393939393939394"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:29.705872Z",
     "iopub.status.busy": "2022-10-07T09:30:29.704975Z",
     "iopub.status.idle": "2022-10-07T09:30:29.731216Z",
     "shell.execute_reply": "2022-10-07T09:30:29.730205Z",
     "shell.execute_reply.started": "2022-10-07T09:30:29.705818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:29.930635Z",
     "iopub.status.busy": "2022-10-07T09:30:29.930154Z",
     "iopub.status.idle": "2022-10-07T09:30:29.938461Z",
     "shell.execute_reply": "2022-10-07T09:30:29.936707Z",
     "shell.execute_reply.started": "2022-10-07T09:30:29.930597Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred2 = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:30.177392Z",
     "iopub.status.busy": "2022-10-07T09:30:30.176993Z",
     "iopub.status.idle": "2022-10-07T09:30:30.184338Z",
     "shell.execute_reply": "2022-10-07T09:30:30.183441Z",
     "shell.execute_reply.started": "2022-10-07T09:30:30.17736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8789473684210526"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:30.416394Z",
     "iopub.status.busy": "2022-10-07T09:30:30.415973Z",
     "iopub.status.idle": "2022-10-07T09:30:30.426247Z",
     "shell.execute_reply": "2022-10-07T09:30:30.425038Z",
     "shell.execute_reply.started": "2022-10-07T09:30:30.416355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8761904761904762"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:30.635742Z",
     "iopub.status.busy": "2022-10-07T09:30:30.635364Z",
     "iopub.status.idle": "2022-10-07T09:30:30.645602Z",
     "shell.execute_reply": "2022-10-07T09:30:30.644308Z",
     "shell.execute_reply.started": "2022-10-07T09:30:30.635711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9019607843137255"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:30.885818Z",
     "iopub.status.busy": "2022-10-07T09:30:30.885441Z",
     "iopub.status.idle": "2022-10-07T09:30:30.897734Z",
     "shell.execute_reply": "2022-10-07T09:30:30.896212Z",
     "shell.execute_reply.started": "2022-10-07T09:30:30.885787Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.888888888888889"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:31.430281Z",
     "iopub.status.busy": "2022-10-07T09:30:31.429858Z",
     "iopub.status.idle": "2022-10-07T09:30:31.773711Z",
     "shell.execute_reply": "2022-10-07T09:30:31.772187Z",
     "shell.execute_reply.started": "2022-10-07T09:30:31.430243Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:31.776336Z",
     "iopub.status.busy": "2022-10-07T09:30:31.775973Z",
     "iopub.status.idle": "2022-10-07T09:30:31.804294Z",
     "shell.execute_reply": "2022-10-07T09:30:31.8029Z",
     "shell.execute_reply.started": "2022-10-07T09:30:31.776304Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred3 = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:31.994524Z",
     "iopub.status.busy": "2022-10-07T09:30:31.991963Z",
     "iopub.status.idle": "2022-10-07T09:30:32.003362Z",
     "shell.execute_reply": "2022-10-07T09:30:32.002184Z",
     "shell.execute_reply.started": "2022-10-07T09:30:31.994468Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9315789473684211"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:32.208268Z",
     "iopub.status.busy": "2022-10-07T09:30:32.207715Z",
     "iopub.status.idle": "2022-10-07T09:30:32.218483Z",
     "shell.execute_reply": "2022-10-07T09:30:32.217407Z",
     "shell.execute_reply.started": "2022-10-07T09:30:32.20822Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9587628865979382"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:32.445363Z",
     "iopub.status.busy": "2022-10-07T09:30:32.444235Z",
     "iopub.status.idle": "2022-10-07T09:30:32.454772Z",
     "shell.execute_reply": "2022-10-07T09:30:32.453487Z",
     "shell.execute_reply.started": "2022-10-07T09:30:32.445301Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9117647058823529"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:32.7121Z",
     "iopub.status.busy": "2022-10-07T09:30:32.711685Z",
     "iopub.status.idle": "2022-10-07T09:30:32.72218Z",
     "shell.execute_reply": "2022-10-07T09:30:32.720958Z",
     "shell.execute_reply.started": "2022-10-07T09:30:32.712066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9346733668341709"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:32.948259Z",
     "iopub.status.busy": "2022-10-07T09:30:32.94754Z",
     "iopub.status.idle": "2022-10-07T09:30:32.956704Z",
     "shell.execute_reply": "2022-10-07T09:30:32.955274Z",
     "shell.execute_reply.started": "2022-10-07T09:30:32.948216Z"
    }
   },
   "outputs": [],
   "source": [
    "final_data = pd.DataFrame({'Models':['LR','DT','RF'],\n",
    "              \"ACC\":[accuracy_score(y_test,y_pred1)*100,\n",
    "                     accuracy_score(y_test,y_pred2)*100,\n",
    "                     accuracy_score(y_test,y_pred3)*100\n",
    "                    ]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:33.238221Z",
     "iopub.status.busy": "2022-10-07T09:30:33.237824Z",
     "iopub.status.idle": "2022-10-07T09:30:33.249451Z",
     "shell.execute_reply": "2022-10-07T09:30:33.2482Z",
     "shell.execute_reply.started": "2022-10-07T09:30:33.238185Z"
    }
   },
   "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>Models</th>\n",
       "      <th>ACC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>93.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DT</td>\n",
       "      <td>87.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>93.157895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Models        ACC\n",
       "0     LR  93.684211\n",
       "1     DT  87.894737\n",
       "2     RF  93.157895"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:33.505222Z",
     "iopub.status.busy": "2022-10-07T09:30:33.504807Z",
     "iopub.status.idle": "2022-10-07T09:30:33.633659Z",
     "shell.execute_reply": "2022-10-07T09:30:33.63204Z",
     "shell.execute_reply.started": "2022-10-07T09:30:33.505184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Models', ylabel='ACC'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Models', y='ACC', data=final_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oversampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Randomly increases minority class examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:34.358822Z",
     "iopub.status.busy": "2022-10-07T09:30:34.357868Z",
     "iopub.status.idle": "2022-10-07T09:30:34.38654Z",
     "shell.execute_reply": "2022-10-07T09:30:34.385428Z",
     "shell.execute_reply.started": "2022-10-07T09:30:34.358785Z"
    }
   },
   "outputs": [],
   "source": [
    "X = data.drop('Class',axis=1)\n",
    "y = data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:35.572857Z",
     "iopub.status.busy": "2022-10-07T09:30:35.572477Z",
     "iopub.status.idle": "2022-10-07T09:30:35.58041Z",
     "shell.execute_reply": "2022-10-07T09:30:35.578897Z",
     "shell.execute_reply.started": "2022-10-07T09:30:35.572819Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275663, 29)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:36.042651Z",
     "iopub.status.busy": "2022-10-07T09:30:36.041788Z",
     "iopub.status.idle": "2022-10-07T09:30:36.049023Z",
     "shell.execute_reply": "2022-10-07T09:30:36.048038Z",
     "shell.execute_reply.started": "2022-10-07T09:30:36.042614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275663,)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:36.384353Z",
     "iopub.status.busy": "2022-10-07T09:30:36.383933Z",
     "iopub.status.idle": "2022-10-07T09:30:36.390025Z",
     "shell.execute_reply": "2022-10-07T09:30:36.388612Z",
     "shell.execute_reply.started": "2022-10-07T09:30:36.384316Z"
    }
   },
   "outputs": [],
   "source": [
    "#Syntheitc Minority Oversampling technique\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:36.6265Z",
     "iopub.status.busy": "2022-10-07T09:30:36.626115Z",
     "iopub.status.idle": "2022-10-07T09:30:37.159074Z",
     "shell.execute_reply": "2022-10-07T09:30:37.157901Z",
     "shell.execute_reply.started": "2022-10-07T09:30:36.626468Z"
    }
   },
   "outputs": [],
   "source": [
    "X_res,y_res = SMOTE().fit_resample(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:37.161574Z",
     "iopub.status.busy": "2022-10-07T09:30:37.160979Z",
     "iopub.status.idle": "2022-10-07T09:30:37.174963Z",
     "shell.execute_reply": "2022-10-07T09:30:37.17374Z",
     "shell.execute_reply.started": "2022-10-07T09:30:37.161528Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    275190\n",
       "1    275190\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_res.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:37.176601Z",
     "iopub.status.busy": "2022-10-07T09:30:37.176274Z",
     "iopub.status.idle": "2022-10-07T09:30:37.476105Z",
     "shell.execute_reply": "2022-10-07T09:30:37.474971Z",
     "shell.execute_reply.started": "2022-10-07T09:30:37.176572Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_res,y_res,test_size=0.20,\n",
    "                                                 random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:37.544766Z",
     "iopub.status.busy": "2022-10-07T09:30:37.544175Z",
     "iopub.status.idle": "2022-10-07T09:30:44.316656Z",
     "shell.execute_reply": "2022-10-07T09:30:44.315153Z",
     "shell.execute_reply.started": "2022-10-07T09:30:37.544718Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log = LogisticRegression()\n",
    "log.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:44.320748Z",
     "iopub.status.busy": "2022-10-07T09:30:44.320098Z",
     "iopub.status.idle": "2022-10-07T09:30:44.349828Z",
     "shell.execute_reply": "2022-10-07T09:30:44.348002Z",
     "shell.execute_reply.started": "2022-10-07T09:30:44.320688Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred1 = log.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:44.353613Z",
     "iopub.status.busy": "2022-10-07T09:30:44.352527Z",
     "iopub.status.idle": "2022-10-07T09:30:44.388161Z",
     "shell.execute_reply": "2022-10-07T09:30:44.386562Z",
     "shell.execute_reply.started": "2022-10-07T09:30:44.353547Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9446019114066645"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:44.393859Z",
     "iopub.status.busy": "2022-10-07T09:30:44.392622Z",
     "iopub.status.idle": "2022-10-07T09:30:44.484426Z",
     "shell.execute_reply": "2022-10-07T09:30:44.483649Z",
     "shell.execute_reply.started": "2022-10-07T09:30:44.393785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9729233149598685"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:44.486056Z",
     "iopub.status.busy": "2022-10-07T09:30:44.48559Z",
     "iopub.status.idle": "2022-10-07T09:30:44.543254Z",
     "shell.execute_reply": "2022-10-07T09:30:44.542334Z",
     "shell.execute_reply.started": "2022-10-07T09:30:44.486027Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9145864771012491"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:44.544953Z",
     "iopub.status.busy": "2022-10-07T09:30:44.544475Z",
     "iopub.status.idle": "2022-10-07T09:30:44.603315Z",
     "shell.execute_reply": "2022-10-07T09:30:44.602392Z",
     "shell.execute_reply.started": "2022-10-07T09:30:44.544921Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.942853394309705"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:30:44.605628Z",
     "iopub.status.busy": "2022-10-07T09:30:44.604726Z",
     "iopub.status.idle": "2022-10-07T09:31:36.944107Z",
     "shell.execute_reply": "2022-10-07T09:31:36.9425Z",
     "shell.execute_reply.started": "2022-10-07T09:30:44.605596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=DecisionTreeClassifier()\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:31:36.946704Z",
     "iopub.status.busy": "2022-10-07T09:31:36.946318Z",
     "iopub.status.idle": "2022-10-07T09:31:36.97449Z",
     "shell.execute_reply": "2022-10-07T09:31:36.973002Z",
     "shell.execute_reply.started": "2022-10-07T09:31:36.94667Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred2 = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:31:36.97659Z",
     "iopub.status.busy": "2022-10-07T09:31:36.976238Z",
     "iopub.status.idle": "2022-10-07T09:31:36.994018Z",
     "shell.execute_reply": "2022-10-07T09:31:36.993207Z",
     "shell.execute_reply.started": "2022-10-07T09:31:36.976558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9982194120425888"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:31:36.998028Z",
     "iopub.status.busy": "2022-10-07T09:31:36.997651Z",
     "iopub.status.idle": "2022-10-07T09:31:37.058436Z",
     "shell.execute_reply": "2022-10-07T09:31:37.056616Z",
     "shell.execute_reply.started": "2022-10-07T09:31:36.997994Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9974946898317085"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:31:37.060668Z",
     "iopub.status.busy": "2022-10-07T09:31:37.060275Z",
     "iopub.status.idle": "2022-10-07T09:31:37.122493Z",
     "shell.execute_reply": "2022-10-07T09:31:37.121202Z",
     "shell.execute_reply.started": "2022-10-07T09:31:37.060634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9989455120629784"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:31:37.125316Z",
     "iopub.status.busy": "2022-10-07T09:31:37.124232Z",
     "iopub.status.idle": "2022-10-07T09:31:37.18523Z",
     "shell.execute_reply": "2022-10-07T09:31:37.182733Z",
     "shell.execute_reply.started": "2022-10-07T09:31:37.12527Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9982195737877659"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:31:37.18944Z",
     "iopub.status.busy": "2022-10-07T09:31:37.18822Z",
     "iopub.status.idle": "2022-10-07T09:39:53.570835Z",
     "shell.execute_reply": "2022-10-07T09:39:53.569641Z",
     "shell.execute_reply.started": "2022-10-07T09:31:37.189386Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:39:53.572726Z",
     "iopub.status.busy": "2022-10-07T09:39:53.572375Z",
     "iopub.status.idle": "2022-10-07T09:39:55.199311Z",
     "shell.execute_reply": "2022-10-07T09:39:55.19811Z",
     "shell.execute_reply.started": "2022-10-07T09:39:53.572697Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred3 = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:39:55.20153Z",
     "iopub.status.busy": "2022-10-07T09:39:55.201084Z",
     "iopub.status.idle": "2022-10-07T09:39:55.221576Z",
     "shell.execute_reply": "2022-10-07T09:39:55.220666Z",
     "shell.execute_reply.started": "2022-10-07T09:39:55.201493Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999091536756423"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:39:55.223906Z",
     "iopub.status.busy": "2022-10-07T09:39:55.222905Z",
     "iopub.status.idle": "2022-10-07T09:39:55.286967Z",
     "shell.execute_reply": "2022-10-07T09:39:55.285614Z",
     "shell.execute_reply.started": "2022-10-07T09:39:55.223867Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999818224783233"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:39:55.288366Z",
     "iopub.status.busy": "2022-10-07T09:39:55.287972Z",
     "iopub.status.idle": "2022-10-07T09:39:55.352632Z",
     "shell.execute_reply": "2022-10-07T09:39:55.351223Z",
     "shell.execute_reply.started": "2022-10-07T09:39:55.288331Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:39:55.354633Z",
     "iopub.status.busy": "2022-10-07T09:39:55.35425Z",
     "iopub.status.idle": "2022-10-07T09:39:55.415696Z",
     "shell.execute_reply": "2022-10-07T09:39:55.414694Z",
     "shell.execute_reply.started": "2022-10-07T09:39:55.354591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999091041303083"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:39:55.418387Z",
     "iopub.status.busy": "2022-10-07T09:39:55.418021Z",
     "iopub.status.idle": "2022-10-07T09:39:55.459778Z",
     "shell.execute_reply": "2022-10-07T09:39:55.458749Z",
     "shell.execute_reply.started": "2022-10-07T09:39:55.418355Z"
    }
   },
   "outputs": [],
   "source": [
    "final_data = pd.DataFrame({'Models':['LR','DT','RF'],\n",
    "              \"ACC\":[accuracy_score(y_test,y_pred1)*100,\n",
    "                     accuracy_score(y_test,y_pred2)*100,\n",
    "                     accuracy_score(y_test,y_pred3)*100\n",
    "                    ]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:39:55.461337Z",
     "iopub.status.busy": "2022-10-07T09:39:55.461008Z",
     "iopub.status.idle": "2022-10-07T09:39:55.473392Z",
     "shell.execute_reply": "2022-10-07T09:39:55.472086Z",
     "shell.execute_reply.started": "2022-10-07T09:39:55.461307Z"
    }
   },
   "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>Models</th>\n",
       "      <th>ACC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>94.460191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DT</td>\n",
       "      <td>99.821941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF</td>\n",
       "      <td>99.990915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Models        ACC\n",
       "0     LR  94.460191\n",
       "1     DT  99.821941\n",
       "2     RF  99.990915"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:39:55.476803Z",
     "iopub.status.busy": "2022-10-07T09:39:55.475046Z",
     "iopub.status.idle": "2022-10-07T09:39:55.689245Z",
     "shell.execute_reply": "2022-10-07T09:39:55.687913Z",
     "shell.execute_reply.started": "2022-10-07T09:39:55.476717Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Models', ylabel='ACC'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Models', y='ACC', data=final_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GUI using joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:40:40.218642Z",
     "iopub.status.busy": "2022-10-07T09:40:40.218252Z",
     "iopub.status.idle": "2022-10-07T09:50:58.582566Z",
     "shell.execute_reply": "2022-10-07T09:50:58.581344Z",
     "shell.execute_reply.started": "2022-10-07T09:40:40.218609Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf1 = RandomForestClassifier()\n",
    "rf1.fit(X_res,y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:50:58.585087Z",
     "iopub.status.busy": "2022-10-07T09:50:58.584752Z",
     "iopub.status.idle": "2022-10-07T09:50:58.590759Z",
     "shell.execute_reply": "2022-10-07T09:50:58.589597Z",
     "shell.execute_reply.started": "2022-10-07T09:50:58.585059Z"
    }
   },
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:50:58.592796Z",
     "iopub.status.busy": "2022-10-07T09:50:58.592312Z",
     "iopub.status.idle": "2022-10-07T09:50:58.678649Z",
     "shell.execute_reply": "2022-10-07T09:50:58.677398Z",
     "shell.execute_reply.started": "2022-10-07T09:50:58.592753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['credit_card_model']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(rf1,\"credit_card_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:50:58.681666Z",
     "iopub.status.busy": "2022-10-07T09:50:58.681196Z",
     "iopub.status.idle": "2022-10-07T09:50:58.740673Z",
     "shell.execute_reply": "2022-10-07T09:50:58.739545Z",
     "shell.execute_reply.started": "2022-10-07T09:50:58.681623Z"
    }
   },
   "outputs": [],
   "source": [
    "model = joblib.load(\"credit_card_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:50:58.742656Z",
     "iopub.status.busy": "2022-10-07T09:50:58.742321Z",
     "iopub.status.idle": "2022-10-07T09:50:58.761818Z",
     "shell.execute_reply": "2022-10-07T09:50:58.760759Z",
     "shell.execute_reply.started": "2022-10-07T09:50:58.742626Z"
    }
   },
   "outputs": [],
   "source": [
    "pred = model.predict([[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]])\n",
    "# a=np.zeroes(10)\n",
    "# a\n",
    "#pred = model.predict(np.zeros((10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-07T09:50:58.76359Z",
     "iopub.status.busy": "2022-10-07T09:50:58.763271Z",
     "iopub.status.idle": "2022-10-07T09:50:58.76969Z",
     "shell.execute_reply": "2022-10-07T09:50:58.768638Z",
     "shell.execute_reply.started": "2022-10-07T09:50:58.763562Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Transcation\n"
     ]
    }
   ],
   "source": [
    "if pred == 0:\n",
    "    print(\"Normal Transcation\")\n",
    "else:\n",
    "    print(\"Fraudulent Transcation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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