Credit-Card-Fraud.ipynb

{
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   "cell_type": "code",
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   "id": "a490c3cf-5ba2-487b-931a-c87d92da7ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split  \n",
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dab4a075-939e-4558-8260-4379992e2669",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"C:\\\\Users\\\\ayush\\\\Downloads\\\\fraudTest.csv\\\\fraudTest.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6d43673-7b0a-4261-b3a9-6f2a8e72f69c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2020-06-21 12:14:25</td>\n",
       "      <td>2291163933867244</td>\n",
       "      <td>fraud_Kirlin and Sons</td>\n",
       "      <td>personal_care</td>\n",
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       "      <td>Jeff</td>\n",
       "      <td>Elliott</td>\n",
       "      <td>M</td>\n",
       "      <td>351 Darlene Green</td>\n",
       "      <td>...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2020-06-21 12:14:33</td>\n",
       "      <td>3573030041201292</td>\n",
       "      <td>fraud_Sporer-Keebler</td>\n",
       "      <td>personal_care</td>\n",
       "      <td>29.84</td>\n",
       "      <td>Joanne</td>\n",
       "      <td>Williams</td>\n",
       "      <td>F</td>\n",
       "      <td>3638 Marsh Union</td>\n",
       "      <td>...</td>\n",
       "      <td>40.3207</td>\n",
       "      <td>-110.4360</td>\n",
       "      <td>302</td>\n",
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       "      <td>2</td>\n",
       "      <td>2020-06-21 12:14:53</td>\n",
       "      <td>3598215285024754</td>\n",
       "      <td>fraud_Swaniawski, Nitzsche and Welch</td>\n",
       "      <td>health_fitness</td>\n",
       "      <td>41.28</td>\n",
       "      <td>Ashley</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>F</td>\n",
       "      <td>9333 Valentine Point</td>\n",
       "      <td>...</td>\n",
       "      <td>40.6729</td>\n",
       "      <td>-73.5365</td>\n",
       "      <td>34496</td>\n",
       "      <td>Librarian, public</td>\n",
       "      <td>1970-10-21</td>\n",
       "      <td>c81755dbbbea9d5c77f094348a7579be</td>\n",
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       "      <td>40.495810</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-06-21 12:15:15</td>\n",
       "      <td>3591919803438423</td>\n",
       "      <td>fraud_Haley Group</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>60.05</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Williams</td>\n",
       "      <td>M</td>\n",
       "      <td>32941 Krystal Mill Apt. 552</td>\n",
       "      <td>...</td>\n",
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       "      <td>-80.8191</td>\n",
       "      <td>54767</td>\n",
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       "      <td>1987-07-25</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-06-21 12:15:17</td>\n",
       "      <td>3526826139003047</td>\n",
       "      <td>fraud_Johnston-Casper</td>\n",
       "      <td>travel</td>\n",
       "      <td>3.19</td>\n",
       "      <td>Nathan</td>\n",
       "      <td>Massey</td>\n",
       "      <td>M</td>\n",
       "      <td>5783 Evan Roads Apt. 465</td>\n",
       "      <td>...</td>\n",
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       "      <td>1126</td>\n",
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       "      <td>1955-07-06</td>\n",
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       "<p>5 rows × 23 columns</p>\n",
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      "text/plain": [
       "   Unnamed: 0 trans_date_trans_time            cc_num  \\\n",
       "0           0   2020-06-21 12:14:25  2291163933867244   \n",
       "1           1   2020-06-21 12:14:33  3573030041201292   \n",
       "2           2   2020-06-21 12:14:53  3598215285024754   \n",
       "3           3   2020-06-21 12:15:15  3591919803438423   \n",
       "4           4   2020-06-21 12:15:17  3526826139003047   \n",
       "\n",
       "                               merchant        category    amt   first  \\\n",
       "0                 fraud_Kirlin and Sons   personal_care   2.86    Jeff   \n",
       "1                  fraud_Sporer-Keebler   personal_care  29.84  Joanne   \n",
       "2  fraud_Swaniawski, Nitzsche and Welch  health_fitness  41.28  Ashley   \n",
       "3                     fraud_Haley Group        misc_pos  60.05   Brian   \n",
       "4                 fraud_Johnston-Casper          travel   3.19  Nathan   \n",
       "\n",
       "       last gender                       street  ...      lat      long  \\\n",
       "0   Elliott      M            351 Darlene Green  ...  33.9659  -80.9355   \n",
       "1  Williams      F             3638 Marsh Union  ...  40.3207 -110.4360   \n",
       "2     Lopez      F         9333 Valentine Point  ...  40.6729  -73.5365   \n",
       "3  Williams      M  32941 Krystal Mill Apt. 552  ...  28.5697  -80.8191   \n",
       "4    Massey      M     5783 Evan Roads Apt. 465  ...  44.2529  -85.0170   \n",
       "\n",
       "   city_pop                     job         dob  \\\n",
       "0    333497     Mechanical engineer  1968-03-19   \n",
       "1       302  Sales professional, IT  1990-01-17   \n",
       "2     34496       Librarian, public  1970-10-21   \n",
       "3     54767            Set designer  1987-07-25   \n",
       "4      1126      Furniture designer  1955-07-06   \n",
       "\n",
       "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0  2da90c7d74bd46a0caf3777415b3ebd3  1371816865  33.986391  -81.200714   \n",
       "1  324cc204407e99f51b0d6ca0055005e7  1371816873  39.450498 -109.960431   \n",
       "2  c81755dbbbea9d5c77f094348a7579be  1371816893  40.495810  -74.196111   \n",
       "3  2159175b9efe66dc301f149d3d5abf8c  1371816915  28.812398  -80.883061   \n",
       "4  57ff021bd3f328f8738bb535c302a31b  1371816917  44.959148  -85.884734   \n",
       "\n",
       "   is_fraud  \n",
       "0         0  \n",
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   "source": [
    "df.head()"
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   "id": "5566061c-13ff-474a-a5f7-2e62e46f2630",
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    {
     "data": {
      "text/plain": [
       "Unnamed: 0               0\n",
       "trans_date_trans_time    0\n",
       "cc_num                   0\n",
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       "first                    0\n",
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    "df.isnull().sum()"
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   "id": "72bca821-6cbd-419c-b1e7-a34a4dc07e22",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 555719 entries, 0 to 555718\n",
      "Data columns (total 23 columns):\n",
      " #   Column                 Non-Null Count   Dtype  \n",
      "---  ------                 --------------   -----  \n",
      " 0   Unnamed: 0             555719 non-null  int64  \n",
      " 1   trans_date_trans_time  555719 non-null  object \n",
      " 2   cc_num                 555719 non-null  int64  \n",
      " 3   merchant               555719 non-null  object \n",
      " 4   category               555719 non-null  object \n",
      " 5   amt                    555719 non-null  float64\n",
      " 6   first                  555719 non-null  object \n",
      " 7   last                   555719 non-null  object \n",
      " 8   gender                 555719 non-null  object \n",
      " 9   street                 555719 non-null  object \n",
      " 10  city                   555719 non-null  object \n",
      " 11  state                  555719 non-null  object \n",
      " 12  zip                    555719 non-null  int64  \n",
      " 13  lat                    555719 non-null  float64\n",
      " 14  long                   555719 non-null  float64\n",
      " 15  city_pop               555719 non-null  int64  \n",
      " 16  job                    555719 non-null  object \n",
      " 17  dob                    555719 non-null  object \n",
      " 18  trans_num              555719 non-null  object \n",
      " 19  unix_time              555719 non-null  int64  \n",
      " 20  merch_lat              555719 non-null  float64\n",
      " 21  merch_long             555719 non-null  float64\n",
      " 22  is_fraud               555719 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(12)\n",
      "memory usage: 97.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "ccfca23d-ff6c-43e7-a640-ea96e4360f08",
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       "      <td>3598215285024754</td>\n",
       "      <td>fraud_Swaniawski, Nitzsche and Welch</td>\n",
       "      <td>health_fitness</td>\n",
       "      <td>41.28</td>\n",
       "      <td>Ashley</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>F</td>\n",
       "      <td>9333 Valentine Point</td>\n",
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       "      <td>40.6729</td>\n",
       "      <td>-73.5365</td>\n",
       "      <td>34496</td>\n",
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       "      <td>1970-10-21</td>\n",
       "      <td>c81755dbbbea9d5c77f094348a7579be</td>\n",
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       "      <td>3591919803438423</td>\n",
       "      <td>fraud_Haley Group</td>\n",
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       "      <td>60.05</td>\n",
       "      <td>Brian</td>\n",
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       "      <td>54767</td>\n",
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       "      <td>fraud_Johnston-Casper</td>\n",
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       "      <td>3.19</td>\n",
       "      <td>Nathan</td>\n",
       "      <td>Massey</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555714</th>\n",
       "      <td>555714</td>\n",
       "      <td>2020-12-31 23:59:07</td>\n",
       "      <td>30560609640617</td>\n",
       "      <td>fraud_Reilly and Sons</td>\n",
       "      <td>health_fitness</td>\n",
       "      <td>43.77</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Olson</td>\n",
       "      <td>M</td>\n",
       "      <td>558 Michael Estates</td>\n",
       "      <td>...</td>\n",
       "      <td>40.4931</td>\n",
       "      <td>-91.8912</td>\n",
       "      <td>519</td>\n",
       "      <td>Town planner</td>\n",
       "      <td>1966-02-13</td>\n",
       "      <td>9b1f753c79894c9f4b71f04581835ada</td>\n",
       "      <td>1388534347</td>\n",
       "      <td>39.946837</td>\n",
       "      <td>-91.333331</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555715</th>\n",
       "      <td>555715</td>\n",
       "      <td>2020-12-31 23:59:09</td>\n",
       "      <td>3556613125071656</td>\n",
       "      <td>fraud_Hoppe-Parisian</td>\n",
       "      <td>kids_pets</td>\n",
       "      <td>111.84</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Vasquez</td>\n",
       "      <td>M</td>\n",
       "      <td>572 Davis Mountains</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0393</td>\n",
       "      <td>-95.4401</td>\n",
       "      <td>28739</td>\n",
       "      <td>Futures trader</td>\n",
       "      <td>1999-12-27</td>\n",
       "      <td>2090647dac2c89a1d86c514c427f5b91</td>\n",
       "      <td>1388534349</td>\n",
       "      <td>29.661049</td>\n",
       "      <td>-96.186633</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555716</th>\n",
       "      <td>555716</td>\n",
       "      <td>2020-12-31 23:59:15</td>\n",
       "      <td>6011724471098086</td>\n",
       "      <td>fraud_Rau-Robel</td>\n",
       "      <td>kids_pets</td>\n",
       "      <td>86.88</td>\n",
       "      <td>Ann</td>\n",
       "      <td>Lawson</td>\n",
       "      <td>F</td>\n",
       "      <td>144 Evans Islands Apt. 683</td>\n",
       "      <td>...</td>\n",
       "      <td>46.1966</td>\n",
       "      <td>-118.9017</td>\n",
       "      <td>3684</td>\n",
       "      <td>Musician</td>\n",
       "      <td>1981-11-29</td>\n",
       "      <td>6c5b7c8add471975aa0fec023b2e8408</td>\n",
       "      <td>1388534355</td>\n",
       "      <td>46.658340</td>\n",
       "      <td>-119.715054</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555717</th>\n",
       "      <td>555717</td>\n",
       "      <td>2020-12-31 23:59:24</td>\n",
       "      <td>4079773899158</td>\n",
       "      <td>fraud_Breitenberg LLC</td>\n",
       "      <td>travel</td>\n",
       "      <td>7.99</td>\n",
       "      <td>Eric</td>\n",
       "      <td>Preston</td>\n",
       "      <td>M</td>\n",
       "      <td>7020 Doyle Stream Apt. 951</td>\n",
       "      <td>...</td>\n",
       "      <td>44.6255</td>\n",
       "      <td>-116.4493</td>\n",
       "      <td>129</td>\n",
       "      <td>Cartographer</td>\n",
       "      <td>1965-12-15</td>\n",
       "      <td>14392d723bb7737606b2700ac791b7aa</td>\n",
       "      <td>1388534364</td>\n",
       "      <td>44.470525</td>\n",
       "      <td>-117.080888</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555718</th>\n",
       "      <td>555718</td>\n",
       "      <td>2020-12-31 23:59:34</td>\n",
       "      <td>4170689372027579</td>\n",
       "      <td>fraud_Dare-Marvin</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>38.13</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>Frey</td>\n",
       "      <td>M</td>\n",
       "      <td>830 Myers Plaza Apt. 384</td>\n",
       "      <td>...</td>\n",
       "      <td>35.6665</td>\n",
       "      <td>-97.4798</td>\n",
       "      <td>116001</td>\n",
       "      <td>Media buyer</td>\n",
       "      <td>1993-05-10</td>\n",
       "      <td>1765bb45b3aa3224b4cdcb6e7a96cee3</td>\n",
       "      <td>1388534374</td>\n",
       "      <td>36.210097</td>\n",
       "      <td>-97.036372</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>555719 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0 trans_date_trans_time            cc_num  \\\n",
       "0                0   2020-06-21 12:14:25  2291163933867244   \n",
       "1                1   2020-06-21 12:14:33  3573030041201292   \n",
       "2                2   2020-06-21 12:14:53  3598215285024754   \n",
       "3                3   2020-06-21 12:15:15  3591919803438423   \n",
       "4                4   2020-06-21 12:15:17  3526826139003047   \n",
       "...            ...                   ...               ...   \n",
       "555714      555714   2020-12-31 23:59:07    30560609640617   \n",
       "555715      555715   2020-12-31 23:59:09  3556613125071656   \n",
       "555716      555716   2020-12-31 23:59:15  6011724471098086   \n",
       "555717      555717   2020-12-31 23:59:24     4079773899158   \n",
       "555718      555718   2020-12-31 23:59:34  4170689372027579   \n",
       "\n",
       "                                    merchant        category     amt    first  \\\n",
       "0                      fraud_Kirlin and Sons   personal_care    2.86     Jeff   \n",
       "1                       fraud_Sporer-Keebler   personal_care   29.84   Joanne   \n",
       "2       fraud_Swaniawski, Nitzsche and Welch  health_fitness   41.28   Ashley   \n",
       "3                          fraud_Haley Group        misc_pos   60.05    Brian   \n",
       "4                      fraud_Johnston-Casper          travel    3.19   Nathan   \n",
       "...                                      ...             ...     ...      ...   \n",
       "555714                 fraud_Reilly and Sons  health_fitness   43.77  Michael   \n",
       "555715                  fraud_Hoppe-Parisian       kids_pets  111.84     Jose   \n",
       "555716                       fraud_Rau-Robel       kids_pets   86.88      Ann   \n",
       "555717                 fraud_Breitenberg LLC          travel    7.99     Eric   \n",
       "555718                     fraud_Dare-Marvin   entertainment   38.13   Samuel   \n",
       "\n",
       "            last gender                       street  ...      lat      long  \\\n",
       "0        Elliott      M            351 Darlene Green  ...  33.9659  -80.9355   \n",
       "1       Williams      F             3638 Marsh Union  ...  40.3207 -110.4360   \n",
       "2          Lopez      F         9333 Valentine Point  ...  40.6729  -73.5365   \n",
       "3       Williams      M  32941 Krystal Mill Apt. 552  ...  28.5697  -80.8191   \n",
       "4         Massey      M     5783 Evan Roads Apt. 465  ...  44.2529  -85.0170   \n",
       "...          ...    ...                          ...  ...      ...       ...   \n",
       "555714     Olson      M          558 Michael Estates  ...  40.4931  -91.8912   \n",
       "555715   Vasquez      M          572 Davis Mountains  ...  29.0393  -95.4401   \n",
       "555716    Lawson      F   144 Evans Islands Apt. 683  ...  46.1966 -118.9017   \n",
       "555717   Preston      M   7020 Doyle Stream Apt. 951  ...  44.6255 -116.4493   \n",
       "555718      Frey      M     830 Myers Plaza Apt. 384  ...  35.6665  -97.4798   \n",
       "\n",
       "        city_pop                     job         dob  \\\n",
       "0         333497     Mechanical engineer  1968-03-19   \n",
       "1            302  Sales professional, IT  1990-01-17   \n",
       "2          34496       Librarian, public  1970-10-21   \n",
       "3          54767            Set designer  1987-07-25   \n",
       "4           1126      Furniture designer  1955-07-06   \n",
       "...          ...                     ...         ...   \n",
       "555714       519            Town planner  1966-02-13   \n",
       "555715     28739          Futures trader  1999-12-27   \n",
       "555716      3684                Musician  1981-11-29   \n",
       "555717       129            Cartographer  1965-12-15   \n",
       "555718    116001             Media buyer  1993-05-10   \n",
       "\n",
       "                               trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0       2da90c7d74bd46a0caf3777415b3ebd3  1371816865  33.986391  -81.200714   \n",
       "1       324cc204407e99f51b0d6ca0055005e7  1371816873  39.450498 -109.960431   \n",
       "2       c81755dbbbea9d5c77f094348a7579be  1371816893  40.495810  -74.196111   \n",
       "3       2159175b9efe66dc301f149d3d5abf8c  1371816915  28.812398  -80.883061   \n",
       "4       57ff021bd3f328f8738bb535c302a31b  1371816917  44.959148  -85.884734   \n",
       "...                                  ...         ...        ...         ...   \n",
       "555714  9b1f753c79894c9f4b71f04581835ada  1388534347  39.946837  -91.333331   \n",
       "555715  2090647dac2c89a1d86c514c427f5b91  1388534349  29.661049  -96.186633   \n",
       "555716  6c5b7c8add471975aa0fec023b2e8408  1388534355  46.658340 -119.715054   \n",
       "555717  14392d723bb7737606b2700ac791b7aa  1388534364  44.470525 -117.080888   \n",
       "555718  1765bb45b3aa3224b4cdcb6e7a96cee3  1388534374  36.210097  -97.036372   \n",
       "\n",
       "        is_fraud  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  \n",
       "...          ...  \n",
       "555714         0  \n",
       "555715         0  \n",
       "555716         0  \n",
       "555717         0  \n",
       "555718         0  \n",
       "\n",
       "[555719 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop_duplicates()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "82c36567-eecf-4229-bfd9-28c44b5243b8",
   "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>amt</th>\n",
       "      <th>trans_num</th>\n",
       "      <th>unix_time</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.86</td>\n",
       "      <td>2da90c7d74bd46a0caf3777415b3ebd3</td>\n",
       "      <td>1371816865</td>\n",
       "      <td>33.986391</td>\n",
       "      <td>-81.200714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.84</td>\n",
       "      <td>324cc204407e99f51b0d6ca0055005e7</td>\n",
       "      <td>1371816873</td>\n",
       "      <td>39.450498</td>\n",
       "      <td>-109.960431</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.28</td>\n",
       "      <td>c81755dbbbea9d5c77f094348a7579be</td>\n",
       "      <td>1371816893</td>\n",
       "      <td>40.495810</td>\n",
       "      <td>-74.196111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.05</td>\n",
       "      <td>2159175b9efe66dc301f149d3d5abf8c</td>\n",
       "      <td>1371816915</td>\n",
       "      <td>28.812398</td>\n",
       "      <td>-80.883061</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.19</td>\n",
       "      <td>57ff021bd3f328f8738bb535c302a31b</td>\n",
       "      <td>1371816917</td>\n",
       "      <td>44.959148</td>\n",
       "      <td>-85.884734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     amt                         trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0   2.86  2da90c7d74bd46a0caf3777415b3ebd3  1371816865  33.986391  -81.200714   \n",
       "1  29.84  324cc204407e99f51b0d6ca0055005e7  1371816873  39.450498 -109.960431   \n",
       "2  41.28  c81755dbbbea9d5c77f094348a7579be  1371816893  40.495810  -74.196111   \n",
       "3  60.05  2159175b9efe66dc301f149d3d5abf8c  1371816915  28.812398  -80.883061   \n",
       "4   3.19  57ff021bd3f328f8738bb535c302a31b  1371816917  44.959148  -85.884734   \n",
       "\n",
       "   is_fraud  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_drop = [\"trans_date_trans_time\", \"merchant\", \"dob\", \"job\", \"city_pop\", \"long\", \"lat\",\"state\",\"zip\", \"gender\", \"street\", \"Unnamed: 0\", \"category\", \"city\", \"cc_num\", \"first\", \"last\"]\n",
    "df.drop(columns=[col for col in columns_to_drop if col in df.columns], inplace=True)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22da18ed-9fa6-4c03-bcc3-34a551038ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_fraud\n",
       "0    553574\n",
       "1      2145\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['is_fraud'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da9cd2bd-545c-4bdf-899a-58c748668929",
   "metadata": {},
   "outputs": [],
   "source": [
    "l=df[df['is_fraud']==0]\n",
    "f=df[df['is_fraud']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8cfd8b2f-7f05-4326-8713-253fde47ed90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(553574, 6)\n",
      "(2145, 6)\n"
     ]
    }
   ],
   "source": [
    "print(l.shape)\n",
    "print(f.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a048f9c3-1cc8-4029-9924-11211cc43e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    553574.000000\n",
       "mean         67.614408\n",
       "std         152.471931\n",
       "min           1.000000\n",
       "25%           9.600000\n",
       "50%          47.150000\n",
       "75%          82.620000\n",
       "max       22768.110000\n",
       "Name: amt, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l['amt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6aca1abd-1e86-43d0-90d9-6437cf03c4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2145.000000\n",
       "mean      528.356494\n",
       "std       392.747594\n",
       "min         1.780000\n",
       "25%       214.510000\n",
       "50%       371.940000\n",
       "75%       907.770000\n",
       "max      1320.920000\n",
       "Name: amt, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f['amt'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a20114d-a301-47ad-890d-7731bf0ca495",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(columns=['is_fraud'])\n",
    "y=df['is_fraud']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "011825db-a1ff-4ea4-b030-7cc6091b113e",
   "metadata": {},
   "outputs": [],
   "source": [
    "non_numeric_columns = X_train.select_dtypes(exclude=['number']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "268ec6b4-06cd-4764-9faf-6fec3e724b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in non_numeric_columns:\n",
    "    freq_enc = X_train[col].value_counts().to_dict()\n",
    "    X_train[col] = X_train[col].map(freq_enc)\n",
    "    X_test[col] = X_test[col].map(freq_enc).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d6a445f-4da1-480e-9c32-3778af19d14a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import joblib\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e875fbf5-e3d7-4a32-8233-f20216233899",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc =StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b220767-5180-4202-be19-aed1aa4c6d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0c11d760-a89e-40ac-bc31-b0811a256dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['credit_card_fraud_model.pkl']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model,'credit_card_fraud_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "da7e30cd-a9b9-40f4-a9aa-17c909892ffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6c2bc59c-699f-4d0b-9047-e302c26fbed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8a33d860-7936-4b68-9b9e-64f17a2db654",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fcfb9a10-0fb3-4062-abdb-0514c16666f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'C':[0.1, 1,10,100],\n",
    "    'solver':['lbfgs','liblinear']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6174834c-f7f3-403c-994f-6b80de1da976",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(LogisticRegression(), param_grid, cv=5, scoring='f1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5ef6ebfa-1e88-496f-ae17-092b67e7a3e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;]},\n",
       "             scoring=&#x27;f1&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;]},\n",
       "             scoring=&#x27;f1&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.1)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.1)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100],\n",
       "                         'solver': ['lbfgs', 'liblinear']},\n",
       "             scoring='f1')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "01a562f5-6bac-4b8d-9356-8a579783d1c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'C': 0.1, 'solver': 'lbfgs'}\n",
      "Best Estimator: LogisticRegression(C=0.1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters:\", grid_search.best_params_)\n",
    "print(\"Best Estimator:\", grid_search.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0ec21d74-9879-469b-a1f7-d0833e01f6ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9959062117613187\n"
     ]
    }
   ],
   "source": [
    "y_pred = grid_search.best_estimator_.predict(X_test)\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1122c360-c7b7-433a-a925-fd3654720d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[110689     26]\n",
      " [   429      0]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3920adff-9e6b-48a3-bcd4-245fca2fb606",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    110715\n",
      "           1       0.00      0.00      0.00       429\n",
      "\n",
      "    accuracy                           1.00    111144\n",
      "   macro avg       0.50      0.50      0.50    111144\n",
      "weighted avg       0.99      1.00      0.99    111144\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50883e2a-ab26-4084-a5d8-f53fd1eb8330",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93f0961e-9669-4872-a2e8-b551d6d93c3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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