creditcardfraud2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b54ba09-008d-439f-81fb-f10348b43764",
   "metadata": {},
   "source": [
    "# CREDIT CARD FRAUD 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "267542a9-015a-4ad2-91f2-c9268503e09e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1a210ac-9e2c-4d83-b730-1e3b9dc3b6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</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>...</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>...</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>...</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",
       "<p>284807 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284807 rows x 31 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r'C:\\Users\\GLORY COMPUTERS\\Documents\\datasets\\creditcard.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8a45a8b-db92-4d1e-9cca-590d8edb45ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c423b4c-2b38-4788-97aa-72deaae97546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    284315\n",
       "1       492\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff585a19-8271-41e7-9dc4-0e31f9ac3a1e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c0de66f-e2ac-4d97-a590-344447ffd20b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e9396010-e3c4-4c77-b38c-d64b9ef5b61c",
   "metadata": {},
   "source": [
    "**Data Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4cbe1e18-5497-4346-86de-0ae0aedd2854",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scalar = StandardScaler()\n",
    "X = df.drop('Class', axis=1)\n",
    "y = df.Class\n",
    "X = scalar.fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d5ea3dc-77e2-4a91-96f2-eb8eedaea325",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ba6f2e-6a48-4025-a6b3-3f56b847521f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "92eb5cda-6806-4171-851c-bd17ba1013bc",
   "metadata": {},
   "source": [
    "**Modeling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30cce206-ffcc-457b-8f2b-d79cba42a873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model_svc = SVC()\n",
    "model_svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd8f76ef-7058-434e-9c3f-be653040e142",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "564b36c0-dfa8-49f7-a917-fbc25b863478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9996752178015756"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40e59957-33f6-4af4-ba2a-404d931956dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc65d0bb-ee6e-479f-bce4-6c7e6a827a8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999385555282469"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e435208-7050-4ca1-97d8-f027d97efc80",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17f1e43a-ac91-4b2d-955a-7923b36a0b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = model_svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dadd605-f9ea-4cc5-8111-f0948f1c19e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c92e620-4dac-4ce3-881f-15d63385f62b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b98f1180-b76b-42b8-befc-a85c10465d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "**Implementing Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7cc499e6-e39c-4df0-ac07-813fcdd15da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted fraud</th>\n",
       "      <th>predicted normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>is Fraud</th>\n",
       "      <td>55</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is Normal</th>\n",
       "      <td>3</td>\n",
       "      <td>56872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           predicted fraud  predicted normal\n",
       "is Fraud                55                32\n",
       "is Normal                3             56872"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report , confusion_matrix\n",
    "import numpy as np\n",
    "cm = np.array(confusion_matrix(y_test, y_predict, labels=[1,0]))\n",
    "confusion = pd.DataFrame(cm, index=['is Fraud', 'is Normal'],columns=['predicted fraud','predicted normal'])\n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "844d415f-cb25-411e-8825-83c00925b513",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba173ec8-9c95-4bbb-9887-9e87433aff1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(confusion, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73070a55-65a0-41c2-878a-cedf5b3a1637",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a4cf159-d228-4e81-b740-809a95cb88ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56875\n",
      "           1       0.95      0.63      0.76        87\n",
      "\n",
      "    accuracy                           1.00     56962\n",
      "   macro avg       0.97      0.82      0.88     56962\n",
      "weighted avg       1.00      1.00      1.00     56962\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predict))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}