Credit_Card_Transactions_Fraud_Detection.ipynb

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    "# 🔍 Credit Card Transactions Fraud Detection🛡️\n",
    "\n",
    "## **Dataset Link:**  \n",
    "Access the Kaggle dataset for Credit Card Transactions Fraud Detection Dataset [here](https://www.kaggle.com/datasets/kartik2112/fraud-detection).\n",
    "\n",
    "## **Context**\n",
    "It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.\n",
    "\n",
    "## **About the Dataset**\n",
    "This is a simulated credit card transaction dataset containing legitimate and fraud transactions from the duration 1st Jan 2019 - 31st Dec 2020. It covers credit cards of 1000 customers doing transactions with a pool of 800 merchants.\n",
    "\n",
    "## **Inspiration**\n",
    "Identify fraudulent credit card transactions.\n",
    "\n",
    "Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.\n",
    "\n",
    "## **Source of Simulation**\n",
    "This was generated using Sparkov Data Generation | Github tool created by Brandon Harris. This simulation was run for the duration - 1 Jan 2019 to 31 Dec 2020. The files were combined and converted into a standard format.\n",
    "\n",
    "Thank you for sharing your understanding of the simulator! It's fascinating how it combines pre-defined lists, profiles, and statistical distributions to generate simulated transactions. Merging transactions across different profiles to create a more realistic representation is a clever approach.\n",
    "\n",
    "Now, when you mention \"medium size,\" could you please clarify what you're referring to? Are you asking for a medium-sized dataset of simulated transactions, or is there something else you'd like me to assist with? Feel free to provide additional context, and I'll be happy to help! 😊\n",
    "\n",
    "## **Acknowledgements**\n",
    "Brandon Harris for his amazing work in creating this easy-to-use simulation tool for creating fraud transaction datasets."
   ]
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   "source": [
    "# Import libraries\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import warnings\n",
    "\n",
    "# Ignore warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08d03d3",
   "metadata": {},
   "source": [
    "## Data Loading & Framing"
   ]
  },
  {
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   "execution_count": 2,
   "id": "159fe457",
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    "execution": {
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   "source": [
    "# Load train and test datasets\n",
    "train = pd.read_csv(\"fraudTrain.csv\")\n",
    "test = pd.read_csv(\"fraudTest.csv\")"
   ]
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       "      <td>grocery_pos</td>\n",
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       "      <td>Stephanie</td>\n",
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       "      <td>43039 Riley Greens Suite 393</td>\n",
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       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
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       "      <td>1978-06-21</td>\n",
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       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
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       "      <td>Edward</td>\n",
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       "      <td>M</td>\n",
       "      <td>594 White Dale Suite 530</td>\n",
       "      <td>...</td>\n",
       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>Nature conservation officer</td>\n",
       "      <td>1962-01-19</td>\n",
       "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
       "      <td>1325376051</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>3 rows × 23 columns</p>\n",
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       "   Unnamed: 0 trans_date_trans_time            cc_num  \\\n",
       "0           0   2019-01-01 00:00:18  2703186189652095   \n",
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       "                          merchant       category     amt      first     last  \\\n",
       "0       fraud_Rippin, Kub and Mann       misc_net    4.97   Jennifer    Banks   \n",
       "1  fraud_Heller, Gutmann and Zieme    grocery_pos  107.23  Stephanie     Gill   \n",
       "2             fraud_Lind-Buckridge  entertainment  220.11     Edward  Sanchez   \n",
       "\n",
       "  gender                        street  ...      lat      long  city_pop  \\\n",
       "0      F                561 Perry Cove  ...  36.0788  -81.1781      3495   \n",
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       "2      M      594 White Dale Suite 530  ...  42.1808 -112.2620      4154   \n",
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       "                                 job         dob  \\\n",
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       "2        Nature conservation officer  1962-01-19   \n",
       "\n",
       "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0  0b242abb623afc578575680df30655b9  1325376018  36.011293  -82.048315   \n",
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "# Display first few rows\n",
    "train.head(3)"
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       "      <td>fraud_Sporer-Keebler</td>\n",
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       "      <td>Joanne</td>\n",
       "      <td>Williams</td>\n",
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       "      <td>3638 Marsh Union</td>\n",
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       "      <td>40.3207</td>\n",
       "      <td>-110.4360</td>\n",
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       "      <td>1990-01-17</td>\n",
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       "      <td>fraud_Swaniawski, Nitzsche and Welch</td>\n",
       "      <td>health_fitness</td>\n",
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       "      <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",
       "      <td>1371816893</td>\n",
       "      <td>40.495810</td>\n",
       "      <td>-74.196111</td>\n",
       "      <td>0</td>\n",
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       "   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",
       "\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",
       "\n",
       "       last gender                street  ...      lat      long  city_pop  \\\n",
       "0   Elliott      M     351 Darlene Green  ...  33.9659  -80.9355    333497   \n",
       "1  Williams      F      3638 Marsh Union  ...  40.3207 -110.4360       302   \n",
       "2     Lopez      F  9333 Valentine Point  ...  40.6729  -73.5365     34496   \n",
       "\n",
       "                      job         dob                         trans_num  \\\n",
       "0     Mechanical engineer  1968-03-19  2da90c7d74bd46a0caf3777415b3ebd3   \n",
       "1  Sales professional, IT  1990-01-17  324cc204407e99f51b0d6ca0055005e7   \n",
       "2       Librarian, public  1970-10-21  c81755dbbbea9d5c77f094348a7579be   \n",
       "\n",
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       "0  1371816865  33.986391  -81.200714         0  \n",
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   "source": [
    "# Display first few rows\n",
    "test.head(3)"
   ]
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      "Data columns (total 23 columns):\n",
      " #   Column                 Non-Null Count    Dtype  \n",
      "---  ------                 --------------    -----  \n",
      " 0   Unnamed: 0             1296675 non-null  int64  \n",
      " 1   trans_date_trans_time  1296675 non-null  object \n",
      " 2   cc_num                 1296675 non-null  int64  \n",
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      " 4   category               1296675 non-null  object \n",
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      " 7   last                   1296675 non-null  object \n",
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      " 10  city                   1296675 non-null  object \n",
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      " 13  lat                    1296675 non-null  float64\n",
      " 14  long                   1296675 non-null  float64\n",
      " 15  city_pop               1296675 non-null  int64  \n",
      " 16  job                    1296675 non-null  object \n",
      " 17  dob                    1296675 non-null  object \n",
      " 18  trans_num              1296675 non-null  object \n",
      " 19  unix_time              1296675 non-null  int64  \n",
      " 20  merch_lat              1296675 non-null  float64\n",
      " 21  merch_long             1296675 non-null  float64\n",
      " 22  is_fraud               1296675 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(12)\n",
      "memory usage: 227.5+ MB\n"
     ]
    }
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   "source": [
    "# Display information about the train dataset\n",
    "train.info()"
   ]
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       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
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       "        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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>6.483370e+05</td>\n",
       "      <td>3.743180e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.241685e+05</td>\n",
       "      <td>6.483370e+05</td>\n",
       "      <td>9.725055e+05</td>\n",
       "      <td>1.296674e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cc_num</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>4.171920e+17</td>\n",
       "      <td>1.308806e+18</td>\n",
       "      <td>6.041621e+10</td>\n",
       "      <td>1.800429e+14</td>\n",
       "      <td>3.521417e+15</td>\n",
       "      <td>4.642255e+15</td>\n",
       "      <td>4.992346e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amt</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>7.035104e+01</td>\n",
       "      <td>1.603160e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.650000e+00</td>\n",
       "      <td>4.752000e+01</td>\n",
       "      <td>8.314000e+01</td>\n",
       "      <td>2.894890e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>4.880067e+04</td>\n",
       "      <td>2.689322e+04</td>\n",
       "      <td>1.257000e+03</td>\n",
       "      <td>2.623700e+04</td>\n",
       "      <td>4.817400e+04</td>\n",
       "      <td>7.204200e+04</td>\n",
       "      <td>9.978300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>3.853762e+01</td>\n",
       "      <td>5.075808e+00</td>\n",
       "      <td>2.002710e+01</td>\n",
       "      <td>3.462050e+01</td>\n",
       "      <td>3.935430e+01</td>\n",
       "      <td>4.194040e+01</td>\n",
       "      <td>6.669330e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>-9.022634e+01</td>\n",
       "      <td>1.375908e+01</td>\n",
       "      <td>-1.656723e+02</td>\n",
       "      <td>-9.679800e+01</td>\n",
       "      <td>-8.747690e+01</td>\n",
       "      <td>-8.015800e+01</td>\n",
       "      <td>-6.795030e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_pop</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>8.882444e+04</td>\n",
       "      <td>3.019564e+05</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>7.430000e+02</td>\n",
       "      <td>2.456000e+03</td>\n",
       "      <td>2.032800e+04</td>\n",
       "      <td>2.906700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unix_time</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>1.349244e+09</td>\n",
       "      <td>1.284128e+07</td>\n",
       "      <td>1.325376e+09</td>\n",
       "      <td>1.338751e+09</td>\n",
       "      <td>1.349250e+09</td>\n",
       "      <td>1.359385e+09</td>\n",
       "      <td>1.371817e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merch_lat</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>3.853734e+01</td>\n",
       "      <td>5.109788e+00</td>\n",
       "      <td>1.902779e+01</td>\n",
       "      <td>3.473357e+01</td>\n",
       "      <td>3.936568e+01</td>\n",
       "      <td>4.195716e+01</td>\n",
       "      <td>6.751027e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merch_long</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>-9.022646e+01</td>\n",
       "      <td>1.377109e+01</td>\n",
       "      <td>-1.666712e+02</td>\n",
       "      <td>-9.689728e+01</td>\n",
       "      <td>-8.743839e+01</td>\n",
       "      <td>-8.023680e+01</td>\n",
       "      <td>-6.695090e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_fraud</th>\n",
       "      <td>1296675.0</td>\n",
       "      <td>5.788652e-03</td>\n",
       "      <td>7.586269e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count          mean           std           min           25%  \\\n",
       "Unnamed: 0  1296675.0  6.483370e+05  3.743180e+05  0.000000e+00  3.241685e+05   \n",
       "cc_num      1296675.0  4.171920e+17  1.308806e+18  6.041621e+10  1.800429e+14   \n",
       "amt         1296675.0  7.035104e+01  1.603160e+02  1.000000e+00  9.650000e+00   \n",
       "zip         1296675.0  4.880067e+04  2.689322e+04  1.257000e+03  2.623700e+04   \n",
       "lat         1296675.0  3.853762e+01  5.075808e+00  2.002710e+01  3.462050e+01   \n",
       "long        1296675.0 -9.022634e+01  1.375908e+01 -1.656723e+02 -9.679800e+01   \n",
       "city_pop    1296675.0  8.882444e+04  3.019564e+05  2.300000e+01  7.430000e+02   \n",
       "unix_time   1296675.0  1.349244e+09  1.284128e+07  1.325376e+09  1.338751e+09   \n",
       "merch_lat   1296675.0  3.853734e+01  5.109788e+00  1.902779e+01  3.473357e+01   \n",
       "merch_long  1296675.0 -9.022646e+01  1.377109e+01 -1.666712e+02 -9.689728e+01   \n",
       "is_fraud    1296675.0  5.788652e-03  7.586269e-02  0.000000e+00  0.000000e+00   \n",
       "\n",
       "                     50%           75%           max  \n",
       "Unnamed: 0  6.483370e+05  9.725055e+05  1.296674e+06  \n",
       "cc_num      3.521417e+15  4.642255e+15  4.992346e+18  \n",
       "amt         4.752000e+01  8.314000e+01  2.894890e+04  \n",
       "zip         4.817400e+04  7.204200e+04  9.978300e+04  \n",
       "lat         3.935430e+01  4.194040e+01  6.669330e+01  \n",
       "long       -8.747690e+01 -8.015800e+01 -6.795030e+01  \n",
       "city_pop    2.456000e+03  2.032800e+04  2.906700e+06  \n",
       "unix_time   1.349250e+09  1.359385e+09  1.371817e+09  \n",
       "merch_lat   3.936568e+01  4.195716e+01  6.751027e+01  \n",
       "merch_long -8.743839e+01 -8.023680e+01 -6.695090e+01  \n",
       "is_fraud    0.000000e+00  0.000000e+00  1.000000e+00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display descriptive statistics for the train dataset\n",
    "train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eff4f72e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:57:22.449648Z",
     "iopub.status.busy": "2024-05-10T10:57:22.449312Z",
     "iopub.status.idle": "2024-05-10T10:57:22.456280Z",
     "shell.execute_reply": "2024-05-10T10:57:22.455186Z",
     "shell.execute_reply.started": "2024-05-10T10:57:22.449620Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1296675, 23)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of the train dataset\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098c708b",
   "metadata": {},
   "source": [
    "## **🔍 Exploratory Data Analysis 🌐**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cbe8bd18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:57:22.459052Z",
     "iopub.status.busy": "2024-05-10T10:57:22.458689Z",
     "iopub.status.idle": "2024-05-10T10:57:24.125946Z",
     "shell.execute_reply": "2024-05-10T10:57:24.124624Z",
     "shell.execute_reply.started": "2024-05-10T10:57:22.459023Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values in the train dataset\n",
    "train.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b3080e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:57:24.127445Z",
     "iopub.status.busy": "2024-05-10T10:57:24.127105Z",
     "iopub.status.idle": "2024-05-10T10:58:06.933969Z",
     "shell.execute_reply": "2024-05-10T10:58:06.932750Z",
     "shell.execute_reply.started": "2024-05-10T10:57:24.127403Z"
    }
   },
   "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": [
    "# Plot a heatmap to visualize missing values in the train dataset\n",
    "sns.heatmap(train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1d17662",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:06.935939Z",
     "iopub.status.busy": "2024-05-10T10:58:06.935584Z",
     "iopub.status.idle": "2024-05-10T10:58:06.943775Z",
     "shell.execute_reply": "2024-05-10T10:58:06.942505Z",
     "shell.execute_reply.started": "2024-05-10T10:58:06.935909Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'trans_date_trans_time', 'cc_num', 'merchant', 'category',\n",
       "       'amt', 'first', 'last', 'gender', 'street', 'city', 'state', 'zip',\n",
       "       'lat', 'long', 'city_pop', 'job', 'dob', 'trans_num', 'unix_time',\n",
       "       'merch_lat', 'merch_long', 'is_fraud'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f1dcf08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:06.945639Z",
     "iopub.status.busy": "2024-05-10T10:58:06.945258Z",
     "iopub.status.idle": "2024-05-10T10:58:06.955035Z",
     "shell.execute_reply": "2024-05-10T10:58:06.953726Z",
     "shell.execute_reply.started": "2024-05-10T10:58:06.945599Z"
    }
   },
   "outputs": [],
   "source": [
    "def cleaning_data(clean):\n",
    "    clean.drop([\"Unnamed: 0\",'cc_num','first', 'last', 'street', 'city', 'state', 'zip', 'dob', 'trans_num',\n",
    "                'trans_date_trans_time'],axis=1, inplace=True)\n",
    "    clean.dropna()\n",
    "    return clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d87323d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:06.957068Z",
     "iopub.status.busy": "2024-05-10T10:58:06.956729Z",
     "iopub.status.idle": "2024-05-10T10:58:07.710552Z",
     "shell.execute_reply": "2024-05-10T10:58:07.709385Z",
     "shell.execute_reply.started": "2024-05-10T10:58:06.957040Z"
    }
   },
   "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>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>gender</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</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>fraud_Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>F</td>\n",
       "      <td>36.0788</td>\n",
       "      <td>-81.1781</td>\n",
       "      <td>3495</td>\n",
       "      <td>Psychologist, counselling</td>\n",
       "      <td>1325376018</td>\n",
       "      <td>36.011293</td>\n",
       "      <td>-82.048315</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>F</td>\n",
       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
       "      <td>-118.186462</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fraud_Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>M</td>\n",
       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>Nature conservation officer</td>\n",
       "      <td>1325376051</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>M</td>\n",
       "      <td>46.2306</td>\n",
       "      <td>-112.1138</td>\n",
       "      <td>1939</td>\n",
       "      <td>Patent attorney</td>\n",
       "      <td>1325376076</td>\n",
       "      <td>47.034331</td>\n",
       "      <td>-112.561071</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fraud_Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>M</td>\n",
       "      <td>38.4207</td>\n",
       "      <td>-79.4629</td>\n",
       "      <td>99</td>\n",
       "      <td>Dance movement psychotherapist</td>\n",
       "      <td>1325376186</td>\n",
       "      <td>38.674999</td>\n",
       "      <td>-78.632459</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296670</th>\n",
       "      <td>fraud_Reichel Inc</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>15.56</td>\n",
       "      <td>M</td>\n",
       "      <td>37.7175</td>\n",
       "      <td>-112.4777</td>\n",
       "      <td>258</td>\n",
       "      <td>Geoscientist</td>\n",
       "      <td>1371816728</td>\n",
       "      <td>36.841266</td>\n",
       "      <td>-111.690765</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296671</th>\n",
       "      <td>fraud_Abernathy and Sons</td>\n",
       "      <td>food_dining</td>\n",
       "      <td>51.70</td>\n",
       "      <td>M</td>\n",
       "      <td>39.2667</td>\n",
       "      <td>-77.5101</td>\n",
       "      <td>100</td>\n",
       "      <td>Production assistant, television</td>\n",
       "      <td>1371816739</td>\n",
       "      <td>38.906881</td>\n",
       "      <td>-78.246528</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296672</th>\n",
       "      <td>fraud_Stiedemann Ltd</td>\n",
       "      <td>food_dining</td>\n",
       "      <td>105.93</td>\n",
       "      <td>M</td>\n",
       "      <td>32.9396</td>\n",
       "      <td>-105.8189</td>\n",
       "      <td>899</td>\n",
       "      <td>Naval architect</td>\n",
       "      <td>1371816752</td>\n",
       "      <td>33.619513</td>\n",
       "      <td>-105.130529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296673</th>\n",
       "      <td>fraud_Reinger, Weissnat and Strosin</td>\n",
       "      <td>food_dining</td>\n",
       "      <td>74.90</td>\n",
       "      <td>M</td>\n",
       "      <td>43.3526</td>\n",
       "      <td>-102.5411</td>\n",
       "      <td>1126</td>\n",
       "      <td>Volunteer coordinator</td>\n",
       "      <td>1371816816</td>\n",
       "      <td>42.788940</td>\n",
       "      <td>-103.241160</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296674</th>\n",
       "      <td>fraud_Langosh, Wintheiser and Hyatt</td>\n",
       "      <td>food_dining</td>\n",
       "      <td>4.30</td>\n",
       "      <td>M</td>\n",
       "      <td>45.8433</td>\n",
       "      <td>-113.8748</td>\n",
       "      <td>218</td>\n",
       "      <td>Therapist, horticultural</td>\n",
       "      <td>1371816817</td>\n",
       "      <td>46.565983</td>\n",
       "      <td>-114.186110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1296675 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    merchant       category     amt gender  \\\n",
       "0                 fraud_Rippin, Kub and Mann       misc_net    4.97      F   \n",
       "1            fraud_Heller, Gutmann and Zieme    grocery_pos  107.23      F   \n",
       "2                       fraud_Lind-Buckridge  entertainment  220.11      M   \n",
       "3         fraud_Kutch, Hermiston and Farrell  gas_transport   45.00      M   \n",
       "4                        fraud_Keeling-Crist       misc_pos   41.96      M   \n",
       "...                                      ...            ...     ...    ...   \n",
       "1296670                    fraud_Reichel Inc  entertainment   15.56      M   \n",
       "1296671             fraud_Abernathy and Sons    food_dining   51.70      M   \n",
       "1296672                 fraud_Stiedemann Ltd    food_dining  105.93      M   \n",
       "1296673  fraud_Reinger, Weissnat and Strosin    food_dining   74.90      M   \n",
       "1296674  fraud_Langosh, Wintheiser and Hyatt    food_dining    4.30      M   \n",
       "\n",
       "             lat      long  city_pop                                job  \\\n",
       "0        36.0788  -81.1781      3495          Psychologist, counselling   \n",
       "1        48.8878 -118.2105       149  Special educational needs teacher   \n",
       "2        42.1808 -112.2620      4154        Nature conservation officer   \n",
       "3        46.2306 -112.1138      1939                    Patent attorney   \n",
       "4        38.4207  -79.4629        99     Dance movement psychotherapist   \n",
       "...          ...       ...       ...                                ...   \n",
       "1296670  37.7175 -112.4777       258                       Geoscientist   \n",
       "1296671  39.2667  -77.5101       100   Production assistant, television   \n",
       "1296672  32.9396 -105.8189       899                    Naval architect   \n",
       "1296673  43.3526 -102.5411      1126              Volunteer coordinator   \n",
       "1296674  45.8433 -113.8748       218           Therapist, horticultural   \n",
       "\n",
       "          unix_time  merch_lat  merch_long  is_fraud  \n",
       "0        1325376018  36.011293  -82.048315         0  \n",
       "1        1325376044  49.159047 -118.186462         0  \n",
       "2        1325376051  43.150704 -112.154481         0  \n",
       "3        1325376076  47.034331 -112.561071         0  \n",
       "4        1325376186  38.674999  -78.632459         0  \n",
       "...             ...        ...         ...       ...  \n",
       "1296670  1371816728  36.841266 -111.690765         0  \n",
       "1296671  1371816739  38.906881  -78.246528         0  \n",
       "1296672  1371816752  33.619513 -105.130529         0  \n",
       "1296673  1371816816  42.788940 -103.241160         0  \n",
       "1296674  1371816817  46.565983 -114.186110         0  \n",
       "\n",
       "[1296675 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaning_data(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dba3e35d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:07.712474Z",
     "iopub.status.busy": "2024-05-10T10:58:07.712043Z",
     "iopub.status.idle": "2024-05-10T10:58:08.028002Z",
     "shell.execute_reply": "2024-05-10T10:58:08.026803Z",
     "shell.execute_reply.started": "2024-05-10T10:58:07.712414Z"
    }
   },
   "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>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>gender</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</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>fraud_Kirlin and Sons</td>\n",
       "      <td>personal_care</td>\n",
       "      <td>2.86</td>\n",
       "      <td>M</td>\n",
       "      <td>33.9659</td>\n",
       "      <td>-80.9355</td>\n",
       "      <td>333497</td>\n",
       "      <td>Mechanical engineer</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>fraud_Sporer-Keebler</td>\n",
       "      <td>personal_care</td>\n",
       "      <td>29.84</td>\n",
       "      <td>F</td>\n",
       "      <td>40.3207</td>\n",
       "      <td>-110.4360</td>\n",
       "      <td>302</td>\n",
       "      <td>Sales professional, IT</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>fraud_Swaniawski, Nitzsche and Welch</td>\n",
       "      <td>health_fitness</td>\n",
       "      <td>41.28</td>\n",
       "      <td>F</td>\n",
       "      <td>40.6729</td>\n",
       "      <td>-73.5365</td>\n",
       "      <td>34496</td>\n",
       "      <td>Librarian, public</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>fraud_Haley Group</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>60.05</td>\n",
       "      <td>M</td>\n",
       "      <td>28.5697</td>\n",
       "      <td>-80.8191</td>\n",
       "      <td>54767</td>\n",
       "      <td>Set designer</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>fraud_Johnston-Casper</td>\n",
       "      <td>travel</td>\n",
       "      <td>3.19</td>\n",
       "      <td>M</td>\n",
       "      <td>44.2529</td>\n",
       "      <td>-85.0170</td>\n",
       "      <td>1126</td>\n",
       "      <td>Furniture designer</td>\n",
       "      <td>1371816917</td>\n",
       "      <td>44.959148</td>\n",
       "      <td>-85.884734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555714</th>\n",
       "      <td>fraud_Reilly and Sons</td>\n",
       "      <td>health_fitness</td>\n",
       "      <td>43.77</td>\n",
       "      <td>M</td>\n",
       "      <td>40.4931</td>\n",
       "      <td>-91.8912</td>\n",
       "      <td>519</td>\n",
       "      <td>Town planner</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>fraud_Hoppe-Parisian</td>\n",
       "      <td>kids_pets</td>\n",
       "      <td>111.84</td>\n",
       "      <td>M</td>\n",
       "      <td>29.0393</td>\n",
       "      <td>-95.4401</td>\n",
       "      <td>28739</td>\n",
       "      <td>Futures trader</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>fraud_Rau-Robel</td>\n",
       "      <td>kids_pets</td>\n",
       "      <td>86.88</td>\n",
       "      <td>F</td>\n",
       "      <td>46.1966</td>\n",
       "      <td>-118.9017</td>\n",
       "      <td>3684</td>\n",
       "      <td>Musician</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>fraud_Breitenberg LLC</td>\n",
       "      <td>travel</td>\n",
       "      <td>7.99</td>\n",
       "      <td>M</td>\n",
       "      <td>44.6255</td>\n",
       "      <td>-116.4493</td>\n",
       "      <td>129</td>\n",
       "      <td>Cartographer</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>fraud_Dare-Marvin</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>38.13</td>\n",
       "      <td>M</td>\n",
       "      <td>35.6665</td>\n",
       "      <td>-97.4798</td>\n",
       "      <td>116001</td>\n",
       "      <td>Media buyer</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 × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    merchant        category     amt gender  \\\n",
       "0                      fraud_Kirlin and Sons   personal_care    2.86      M   \n",
       "1                       fraud_Sporer-Keebler   personal_care   29.84      F   \n",
       "2       fraud_Swaniawski, Nitzsche and Welch  health_fitness   41.28      F   \n",
       "3                          fraud_Haley Group        misc_pos   60.05      M   \n",
       "4                      fraud_Johnston-Casper          travel    3.19      M   \n",
       "...                                      ...             ...     ...    ...   \n",
       "555714                 fraud_Reilly and Sons  health_fitness   43.77      M   \n",
       "555715                  fraud_Hoppe-Parisian       kids_pets  111.84      M   \n",
       "555716                       fraud_Rau-Robel       kids_pets   86.88      F   \n",
       "555717                 fraud_Breitenberg LLC          travel    7.99      M   \n",
       "555718                     fraud_Dare-Marvin   entertainment   38.13      M   \n",
       "\n",
       "            lat      long  city_pop                     job   unix_time  \\\n",
       "0       33.9659  -80.9355    333497     Mechanical engineer  1371816865   \n",
       "1       40.3207 -110.4360       302  Sales professional, IT  1371816873   \n",
       "2       40.6729  -73.5365     34496       Librarian, public  1371816893   \n",
       "3       28.5697  -80.8191     54767            Set designer  1371816915   \n",
       "4       44.2529  -85.0170      1126      Furniture designer  1371816917   \n",
       "...         ...       ...       ...                     ...         ...   \n",
       "555714  40.4931  -91.8912       519            Town planner  1388534347   \n",
       "555715  29.0393  -95.4401     28739          Futures trader  1388534349   \n",
       "555716  46.1966 -118.9017      3684                Musician  1388534355   \n",
       "555717  44.6255 -116.4493       129            Cartographer  1388534364   \n",
       "555718  35.6665  -97.4798    116001             Media buyer  1388534374   \n",
       "\n",
       "        merch_lat  merch_long  is_fraud  \n",
       "0       33.986391  -81.200714         0  \n",
       "1       39.450498 -109.960431         0  \n",
       "2       40.495810  -74.196111         0  \n",
       "3       28.812398  -80.883061         0  \n",
       "4       44.959148  -85.884734         0  \n",
       "...           ...         ...       ...  \n",
       "555714  39.946837  -91.333331         0  \n",
       "555715  29.661049  -96.186633         0  \n",
       "555716  46.658340 -119.715054         0  \n",
       "555717  44.470525 -117.080888         0  \n",
       "555718  36.210097  -97.036372         0  \n",
       "\n",
       "[555719 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaning_data(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d02fc26b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:08.032534Z",
     "iopub.status.busy": "2024-05-10T10:58:08.032140Z",
     "iopub.status.idle": "2024-05-10T10:58:08.152800Z",
     "shell.execute_reply": "2024-05-10T10:58:08.151381Z",
     "shell.execute_reply.started": "2024-05-10T10:58:08.032502Z"
    }
   },
   "outputs": [],
   "source": [
    "# Map 'M' to 0 and 'F' to 1 in the 'gender' column of the train dataset\n",
    "train['gender'] = train['gender'].map({'M': 0, 'F': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "598a1b27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:08.154587Z",
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       "                          merchant       category     amt  gender      lat  \\\n",
       "0       fraud_Rippin, Kub and Mann       misc_net    4.97       1  36.0788   \n",
       "1  fraud_Heller, Gutmann and Zieme    grocery_pos  107.23       1  48.8878   \n",
       "2             fraud_Lind-Buckridge  entertainment  220.11       0  42.1808   \n",
       "\n",
       "       long  city_pop                                job   unix_time  \\\n",
       "0  -81.1781      3495          Psychologist, counselling  1325376018   \n",
       "1 -118.2105       149  Special educational needs teacher  1325376044   \n",
       "2 -112.2620      4154        Nature conservation officer  1325376051   \n",
       "\n",
       "   merch_lat  merch_long  is_fraud  \n",
       "0  36.011293  -82.048315         0  \n",
       "1  49.159047 -118.186462         0  \n",
       "2  43.150704 -112.154481         0  "
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1296675 entries, 0 to 1296674\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count    Dtype  \n",
      "---  ------      --------------    -----  \n",
      " 0   merchant    1296675 non-null  object \n",
      " 1   category    1296675 non-null  object \n",
      " 2   amt         1296675 non-null  float64\n",
      " 3   gender      1296675 non-null  int64  \n",
      " 4   lat         1296675 non-null  float64\n",
      " 5   long        1296675 non-null  float64\n",
      " 6   city_pop    1296675 non-null  int64  \n",
      " 7   job         1296675 non-null  object \n",
      " 8   unix_time   1296675 non-null  int64  \n",
      " 9   merch_lat   1296675 non-null  float64\n",
      " 10  merch_long  1296675 non-null  float64\n",
      " 11  is_fraud    1296675 non-null  int64  \n",
      "dtypes: float64(5), int64(4), object(3)\n",
      "memory usage: 118.7+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "09d14269",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:08.615391Z",
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   "outputs": [],
   "source": [
    "# Initialize LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# Define function to encode categorical features\n",
    "def encode(data):\n",
    "    data[\"merchant\"] = encoder.fit_transform(data[\"merchant\"])\n",
    "    data[\"category\"] = encoder.fit_transform(data[\"category\"])\n",
    "    data[\"gender\"] = encoder.fit_transform(data[\"gender\"])\n",
    "    data[\"job\"] = encoder.fit_transform(data[\"job\"])\n",
    "    return data"
   ]
  },
  {
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       "         merchant  category     amt  gender      lat      long  city_pop  job  \\\n",
       "0             514         8    4.97       1  36.0788  -81.1781      3495  370   \n",
       "1             241         4  107.23       1  48.8878 -118.2105       149  428   \n",
       "2             390         0  220.11       0  42.1808 -112.2620      4154  307   \n",
       "3             360         2   45.00       0  46.2306 -112.1138      1939  328   \n",
       "4             297         9   41.96       0  38.4207  -79.4629        99  116   \n",
       "...           ...       ...     ...     ...      ...       ...       ...  ...   \n",
       "1296670       499         0   15.56       0  37.7175 -112.4777       258  215   \n",
       "1296671         2         1   51.70       0  39.2667  -77.5101       100  360   \n",
       "1296672       599         1  105.93       0  32.9396 -105.8189       899  308   \n",
       "1296673       509         1   74.90       0  43.3526 -102.5411      1126  485   \n",
       "1296674       370         1    4.30       0  45.8433 -113.8748       218  467   \n",
       "\n",
       "          unix_time  merch_lat  merch_long  is_fraud  \n",
       "0        1325376018  36.011293  -82.048315         0  \n",
       "1        1325376044  49.159047 -118.186462         0  \n",
       "2        1325376051  43.150704 -112.154481         0  \n",
       "3        1325376076  47.034331 -112.561071         0  \n",
       "4        1325376186  38.674999  -78.632459         0  \n",
       "...             ...        ...         ...       ...  \n",
       "1296670  1371816728  36.841266 -111.690765         0  \n",
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       "1296674  1371816817  46.565983 -114.186110         0  \n",
       "\n",
       "[1296675 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode categorical features in the train dataset\n",
    "encode(train)"
   ]
  },
  {
   "cell_type": "code",
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       "      <th>3</th>\n",
       "      <td>222</td>\n",
       "      <td>9</td>\n",
       "      <td>60.05</td>\n",
       "      <td>1</td>\n",
       "      <td>28.5697</td>\n",
       "      <td>-80.8191</td>\n",
       "      <td>54767</td>\n",
       "      <td>407</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>292</td>\n",
       "      <td>13</td>\n",
       "      <td>3.19</td>\n",
       "      <td>1</td>\n",
       "      <td>44.2529</td>\n",
       "      <td>-85.0170</td>\n",
       "      <td>1126</td>\n",
       "      <td>196</td>\n",
       "      <td>1371816917</td>\n",
       "      <td>44.959148</td>\n",
       "      <td>-85.884734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555714</th>\n",
       "      <td>507</td>\n",
       "      <td>5</td>\n",
       "      <td>43.77</td>\n",
       "      <td>1</td>\n",
       "      <td>40.4931</td>\n",
       "      <td>-91.8912</td>\n",
       "      <td>519</td>\n",
       "      <td>460</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>264</td>\n",
       "      <td>7</td>\n",
       "      <td>111.84</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0393</td>\n",
       "      <td>-95.4401</td>\n",
       "      <td>28739</td>\n",
       "      <td>198</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>496</td>\n",
       "      <td>7</td>\n",
       "      <td>86.88</td>\n",
       "      <td>0</td>\n",
       "      <td>46.1966</td>\n",
       "      <td>-118.9017</td>\n",
       "      <td>3684</td>\n",
       "      <td>294</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>75</td>\n",
       "      <td>13</td>\n",
       "      <td>7.99</td>\n",
       "      <td>1</td>\n",
       "      <td>44.6255</td>\n",
       "      <td>-116.4493</td>\n",
       "      <td>129</td>\n",
       "      <td>58</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>125</td>\n",
       "      <td>0</td>\n",
       "      <td>38.13</td>\n",
       "      <td>1</td>\n",
       "      <td>35.6665</td>\n",
       "      <td>-97.4798</td>\n",
       "      <td>116001</td>\n",
       "      <td>276</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 × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        merchant  category     amt  gender      lat      long  city_pop  job  \\\n",
       "0            319        10    2.86       1  33.9659  -80.9355    333497  275   \n",
       "1            591        10   29.84       0  40.3207 -110.4360       302  392   \n",
       "2            611         5   41.28       0  40.6729  -73.5365     34496  259   \n",
       "3            222         9   60.05       1  28.5697  -80.8191     54767  407   \n",
       "4            292        13    3.19       1  44.2529  -85.0170      1126  196   \n",
       "...          ...       ...     ...     ...      ...       ...       ...  ...   \n",
       "555714       507         5   43.77       1  40.4931  -91.8912       519  460   \n",
       "555715       264         7  111.84       1  29.0393  -95.4401     28739  198   \n",
       "555716       496         7   86.88       0  46.1966 -118.9017      3684  294   \n",
       "555717        75        13    7.99       1  44.6255 -116.4493       129   58   \n",
       "555718       125         0   38.13       1  35.6665  -97.4798    116001  276   \n",
       "\n",
       "         unix_time  merch_lat  merch_long  is_fraud  \n",
       "0       1371816865  33.986391  -81.200714         0  \n",
       "1       1371816873  39.450498 -109.960431         0  \n",
       "2       1371816893  40.495810  -74.196111         0  \n",
       "3       1371816915  28.812398  -80.883061         0  \n",
       "4       1371816917  44.959148  -85.884734         0  \n",
       "...            ...        ...         ...       ...  \n",
       "555714  1388534347  39.946837  -91.333331         0  \n",
       "555715  1388534349  29.661049  -96.186633         0  \n",
       "555716  1388534355  46.658340 -119.715054         0  \n",
       "555717  1388534364  44.470525 -117.080888         0  \n",
       "555718  1388534374  36.210097  -97.036372         0  \n",
       "\n",
       "[555719 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode categorical features in the test dataset\n",
    "encode(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f5660b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:10.678224Z",
     "iopub.status.busy": "2024-05-10T10:58:10.677815Z",
     "iopub.status.idle": "2024-05-10T10:58:10.839701Z",
     "shell.execute_reply": "2024-05-10T10:58:10.838591Z",
     "shell.execute_reply.started": "2024-05-10T10:58:10.678187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4oVbbqepzUZXyf096vR5hYWGNPmykofddKSFEjdtuzP1cSq1WIzw8vMLyK1euYObMmfD394der0dQUBCGDBkCoOLfpxQJCQkICwuzfSkrVd1peSFEjV9Svb29AVT/Ja98HZVts6r9KVV4eHiFWqUeX6OiovDEE0/gvffeQ2BgIMaMGYM333yz1u9zo3RprazX5ksvvYQnnngCgwcPxqZNm7Bz507s3r0bHTt2LNPNvqqdZ7FY6lxPfdZptVoRHBxcoWVd+rN8+fIyz6/qfL+UP976qOw9v3TpEkaMGIH09HS89tpr+Pbbb7F7927btYvywxuk1D548GBcunQJH3zwATp16oT33nsPPXr0wHvvvWd7zpQpU/Duu+9i/vz5+PLLL7Fr1y7bwb62Qyoak5TXu3DhQpw/fx4vv/wy3Nzc8Oyzz6J9+/Z1agXc7PTp0wCA1q1bV/mc4OBgHD9+HNu3b8e4ceOwZ88ejB07tsrrU5Vpyh7U9fkbrS0p+y4gIEDyl9XG2s+lbj7TVspisWDUqFH49ttvsWjRImzbtg27d++2TUDQ2MfFUllZWWX6pFQmJiYGAHDq1Kl6b+9mtX1d9T2+rly5EidPnsSSJUtQVFSEBQsWoGPHjkhMTJRWMJpwRqCtW7di2LBheP/993HPPfdg9OjRGDlyJLKzs8s8r/QbY/nl5b+ZlA4aLn+a0GQyIT4+vk7rrEx0dDQyMjIwYMCACq3rkSNHomvXrjWuo7y6nHqsix07dsBgMGD79u2YN28ebrvtNowcObLeB1J/f3/MmjULmzdvxtWrV9GlSxcsXboUQMkf4E8//YTFixdj2bJlGD9+PEaNGlWh9R8UFARvb29beFSlNu9VREQELly4UCGY//rrL9vjdREdHY0nn3wSu3btwunTp2E0GrFy5co6ratU6fjbmk6darVa3HnnnVi7di0uXbqEefPm4cMPP8TFixcBNPxnqfzfU35+PlJSUsp0hvHz86vwt2Q0GpGSklJmmdz7LiYmBllZWZJbEo2xn6tz6tQpnD9/HitXrsSiRYtw1113YeTIkZV2iqzsPQcqPy6mpKTYzmSUOnfuXJV1xMfH19gRceDAgfDz88PmzZslBXVERESl2yy/P+tzbK5KTZ+7zp0745lnnsHevXuxb98+JCUl4e2335a8/iYLTZVKVeHbwJYtW5CUlFRmWXR0NABg7969tmUWiwXr1q0r87xevXohKCgIb7/9NoxGo235hg0bKuwAqeuszJQpU2CxWPDCCy9UeMxsNlf6Qa5J6fisuvxubZR+K7v5fc/JycH69evrvM6MjIwy/9br9WjdurVthpvKtgkAr7/+epl/K5VK3H333dixY0elU7eV/n5t3qvbbrsNqamp+Oyzz2zLzGYz1qxZA71ebzvtJVVhYSGKi4vLLIuOjoaXl1e9ZvT55JNP8N577+GWW27BiBEjqnxe+fdaqVSiS5cuAGDbfkN/ltatWweTyWT791tvvQWz2YyxY8falkVHR5f5Wyr9vfIHUzn3HQDccsstEELg6NGj1T6vsfZzTSr7WxFCYPXq1RWeGx0djb/++gvXr1+3LTtx4gT2799f5nm33XYbzGYz3nrrLdsyi8WCNWvWVFpDTk4OLl26VOOEBB4eHli0aBHOnj2LRYsWVdqy27RpEw4dOmSr49ChQzhw4IDt8YKCAqxbtw6RkZHo0KGD7XUBdTs2V6Wqz11ubi7MZnOZZZ07d4ZSqazVfm6ykah33HEHli9fjlmzZqF///44deoUPv744wotkI4dO6Jfv354+umnkZmZCX9/f3z66acVXqxGo8GLL76IefPmYfjw4Zg6dSri4+Oxfv36Oq+zMkOGDMG8efPw8ssv4/jx4xg9ejQ0Gg0uXLiALVu2YPXq1Zg0aVKt3otu3bpBpVJhxYoVyMnJgU6nw/DhwxEcHFyr9dRk9OjRtpbKvHnzkJ+fj3fffRfBwcEVWgVSdejQAUOHDkXPnj3h7++PI0eOYOvWrXjkkUcAlFz7KL0mZDKZ0Lx5c+zatatC6x8oOWW/a9cuDBkyBHPnzkX79u2RkpKCLVu24LfffoOvr2+t3qu5c+finXfewcyZM3H06FFERkZi69at2L9/P15//XXJnRhKnT9/HiNGjMCUKVPQoUMHqNVqfPXVV7h27RruueceSevYunUr9Ho9jEajbUag/fv3o2vXrjUOKXjggQeQmZmJ4cOHIzw8HAkJCVizZg26detmaxk09GfJaDTaXvO5c+ewdu1aDBw4sMyMMQ888ADmz5+PiRMnYtSoUThx4gR27txZ4RSfnPsOKGkdBQQE4Mcff8Tw4cOrfF5D7Oe6iImJQXR0NP7+978jKSkJ3t7e+OKLLyo9pTx79my89tprGDNmDObMmYO0tDS8/fbb6Nixo62TDgDceeedGDBgABYvXozLly+jQ4cO+PLLL6tsbf/444+2YSo1+cc//oHY2FisXLkSe/bswaRJkxAaGorU1FRs27YNhw4dwu+//w6gZF7bzZs3Y+zYsViwYAH8/f2xceNGxMfH44svvrCdqq7Psbkq0dHR8PX1xdtvvw0vLy94enqib9++OHHiBB555BFMnjwZbdu2hdlsxkcffQSVSoWJEydK30BtutpKndygsi69xcXF4sknnxRhYWHC3d1dDBgwQBw4cKDSrtSXLl0SI0eOtA2MX7Jkidi9e3el3dfXrl1rG0Tfq1evSic3qM06KxssLETJUJaePXsKd3d34eXlJTp37iyeeuopkZycbHtO6WDy8iqr59133xWtWrUSKpWqVsNPqpvcoDLbt28XXbp0EW5ubiIyMlKsWLFCfPDBBxW6eUut/cUXXxR9+vQRvr6+wt3dXcTExIh//etfZYYpJCYmivHjxwtfX1/h4+MjJk+eLJKTkysdqpCQkCCmT58ugoKChE6nE61atRIPP/xwmWFEVb1XVQ2QnzVrlggMDBRarVZ07ty5QtfzmwfIl3dzjenp6eLhhx8WMTExwtPTU/j4+Ii+ffuKzz//vNL3+malQ05Kf9zc3ER4eLi44447xAcffCCKi4sr/E75z97WrVvF6NGjRXBwsNBqtaJly5Zi3rx5IiUlpczvVfX+VPe5qGlyAz8/P6HX68V9990nMjIyyvyuxWIRixYtEoGBgcLDw0OMGTNGXLx4scI6q6utsfddqQULFojWrVtXuo7SdddnP9+suiEnnp6elf7OmTNnxMiRI4VerxeBgYHib3/7mzhx4kSlQyY2bdpkm8yhW7duYufOnZUerzIyMsS0adNskxtMmzatyskNpk6dKgYOHFir11n6ufT39xdqtVqEhYWJqVOnVphEonRyA19fX+Hm5ib69OlTYXKD0udJOTaXTm5QXmXvwddffy06dOgg1Gq17XXHxcWJ2bNni+joaOHm5ib8/f3FsGHDxI8//lir168QopF7qBARySQuLg4xMTH4/vvvqz0V7opSU1MRFRWFTz/9VFJLk0owNInIqT344IO4ePFivca7OqPFixfj559/tl2HJGkYmkRE1cjPz6/QG7W8oKAgSdPLkeNzgSnpiYjq7tVXX8WyZcuqfU58fDxvXu8i2NIkIqpGXFxcjdO0DRw4sFFuN0b2h6FJREQkEe8MTUREJBFDk4iISCKGJhERkUQMTSIiIokYmkRERBIxNImIiCRiaBIREUnE0CQiIpKIoUlERCQRQ5OIiEgihiYREZFEDE0iIiKJGJpEREQSMTSJiIgkYmgSERFJxNAkIiKSiKFJREQkEUOTiIhIIoYmERGRRAxNIiIiiRiaREREEjE0iYiIJGJoEhERScTQJCIikoihSUREJBFDk4iISCKGJhERkUQMTSIiIokYmkRERBIxNImIiCRiaBIREUnE0CQiIpKIoUlERCQRQ5OIiEgihiYREZFEDE0iIiKJGJpEREQSMTSJiIgkYmgSERFJxNAkIiKSiKFJREQkEUOTiIhIIoYmERGRRAxNIiIiidRyF0DkzAoMZqTnG5Ceb0RGvgG5xWYUmSwoNlpQZLrxY7zxY7LAbLVCiP/9vlKhgEqpgFqpgFqlgEalhN5NDW83DfQ6Nbzc1PBy09z4b8nyIC8d3DQq+V40kRNjaBLVUYHBjISMQlzJLMDljEJcySxEWq7hRkgakJFvRJHJIkttvh4ahHq7IdTHrcJ/W/p7ICLAEyqlQpbaiByZQoibv9cSUXmJWYU4k5yLc6l5iM8oQEJGIRIyCpGeb5C7tDrTqpRoGeCB6CBPtA7WIzroxk+wHnodv0sTVYWhSXSD2WLFxev5OJOci9jkXJxJzsXZ1FxkF5rkLq1Jhfu5o2u4L7qE+6BLuC86h/swSIluYGiSy8o3mHHkciYOX87E4fgsnEjMhsFslbssu6NUAK2C9OgS7oOu4b7o0dIPHZt5Q8nTu+SCGJrkMtLzDTgcn4lDN4LybEoeLFZ+/OvC202Nfq0C0D86AP1bB6JtiJfcJRE1CYYmOS2rVeDY1Wzs+SsNe86l4UxKLvhpbxyBeh1uiQ7AgOgADGobhOa+7nKXRNQoGJrkVHIKTfjlfBr2/JWGvRfSkVlglLskl9QhzBujO4ZgdIdQdGjmLXc5RA2GoUkOLy2vGN+eTMH3p1Jx9EoWT7namXA/d4zqUBKgfaL8OdSFHBpDkxxSTqEJ359OwfYTyfgjPpNB6SD8PDS4tVMoxncPR+9IPygUDFByLAxNchgFBjN2n7mG7SeSse/CdZgs/Og6shb+7hjfrTnG9whHVKCn3OUQScLQJLt35HImPj18Fd+dSkGhUZ4ZdqhxdW/piwndm+POrs3g66GVuxyiKjE0yS7lFJqw5ehVfHb4Ki6k5ctdDjURrUqJMZ1CcX/flujbKkDucogqYGiSXTl+NRubDibgm5PJKDZxogFX1i7EC/ffEoEJ3ZvDkzMSkZ1gaJLsrFaBH2JT8c7eOJy4mi13OWRnvNzUmNKrBWb2j0QLfw+5yyEXx9Ak2RSbLNhy5Cre+y0eCRmFcpdDdk6pAEa2D8FDw1qjWwtfucshF8XQpCaXWWDEhwcu46MDCcjg5ANUBwNbB+LhYa1xSzSve1LTYmhSk0nNKcZbv1zE50cSZbvPJDmXXhF+eHh4awxrFyx3KeQiGJrU6DLyDVj7yyVsOpjAu4hQo+jU3BsPD22NWzuFcsIEalQMTWo0OUUmvLs3Duv3x6OA4yupCXRs5o3FY2MwqE2Q3KWQk2JoUoMrNJqxfv9lrNsbh5wi17qBM9mHQW0CsejWGHRq7iN3KeRkGJrUYCxWgU8OXcHqH88jPZ8dfEheCgVwV9dmeHJ0Ow5VoQbD0KQGcTAuA8t2nMHZlFy5SyEqQ6tS4v5+EXh0eGv4eXKKPqofhibVS1J2EV767iy+PZkidylE1fLz0OCpW2NwT+8W7CxEdcbQpDopNlnw9q+X8PavlzjdHTmU7i198cJdnXi9k+qEoUm1tis2Fct2nEFSdpHcpRDViUqpwLR+EXhidFt4u2nkLoccCEOTJEvPN+D57bE8FUtOI8hLhyW3xWB893C5SyEHwdAkSb78MxEvfHMGWYUcQkLOZ2DrQKyY1AXNfd3lLoXsHEOTqpWcXYR/fnUKe85dl7sUokblpVPjmTvaY2rvlnKXQnaMoUmVEkJg0x9XsOL7v5BvMMtdDlGTGdYuCCsmdkGwt5vcpZAdYmhSBWl5xXjy8xPYdyFd7lKIZOHjrsGycR1xd/fmcpdCdoahSWX8dPYantp6krfsIgJwa8dQ/Gt8JwTodXKXQnaCoUkASsZdvvzdWWw8kCB3KUR2JcRbh//e0x19W/HencTQJAAXruXh0c3H8FdqntylENkllVKBx0e2wcPDWnM2IRfH0HRxmw4m4MVvz3BWHyIJBrUJxOtTu/F0rQtjaLqoYpMFT395Cl8dS5K7FCKHwtO1ro2h6YISswox76OjiE3mHUmI6kKlVOCJUW3x0NBonq51MQxNF/P7pXQ88skxZLJ3LFG93dY5FCsnd4O7ViV3KdREGJou5L19cXj5+79gsXKXEzWUjs288d6MXgjz4RR8roCh6QJ4/ZKocQV56fDOtJ7o0dJP7lKokTE0nVx6vgFzNh7BiavZcpdC5NS0aiVWTOzMO6Y4OYamE4tPL8DM9YeQkFEodylELmP+kGg8NaYdlEp2EHJGDE0n9eeVLDyw8Qg7/BDJ4PbOYVg1tRu0aqXcpVADY2g6oZ2xqXjs02OcsIBIRgNbB+KdaT3hqVPLXQo1IIamk/nwwGUs3R4LdpAlkl+XcB9smNUH/p5auUuhBsLQdCIrfvgLb/1ySe4yiOgmrYI88eHsPgj385C7FGoADE0nIITAc1/H4qODvEMJkT0K9XbDh3P6oG2Il9ylUD0xNB2c1Sqw6IuT2HI0Ue5SiKgaPu4arJ/Vm2M5HRxD04FZrAILPzuOHSeS5S6FiCTQ69TYOLs3ekb4y10K1RFD00GZLVY89ulxfHsqRe5SiKgW9Do1NszqjV6RDE5HxNB0QCaLFY9+cgw/xKbKXQoR1YGnVoWNs/swOB0QR946GItVYMFmBiaRIyswWjBz/WEcu5IldylUSwxNByKEwOIvTuL70wxMIkeXbzBjxgeHcCoxR+5SqBYYmg7kxW/PspcskRPJLTZj2gd/4K9U3hDeUTA0HcSany7g/d/i5S6DiBpYdqEJMz84jKTsIrlLIQkYmg7gwwOXsXL3ebnLIKJGkppbjBkfHEJ2IW+wYO8YmnZu27EkPL89Vu4yiKiRXUzLx5yNR1BssshdClWDoWnHfj1/HX/fcgIcFETkGo4mZOGRT47Bwjsu2C2Gpp06fy0Pj3z8J8z84yFyKT+evYZntp2SuwyqAkPTDmXkGzB7w2HkGcxyl0JEMth86CpWsR+DXWJo2hmD2YK5Hx1FYhZ70hG5stU/XcA3JzmvtL1haNqZp7aexNEEzhJCRMA/tpxEbDInP7AnDE07svrHC/j6OL9ZElGJIpMFcz88iox8g9yl0A0MTTvx7ckUvP4Tr2EQUVlJ2UV48OM/YbJY5S6FwNC0CxfT8vCPrRxaQkSVOxSfiaUcr20XGJoyKzSa8eCmP1Fo5IBmIqrax39cwcd/JMhdhstjaMpsyZencCEtX+4yiMgBLN0eixNXs+Uuw6UxNGW06WACtrHjDxFJZLIIPLr5GPKKTXKX4rIYmjI5lZiD5d+ckbsMInIwVzILsfhLzhgkF4amDHKKTHjok6Mwmtkbjohq79uTKdh86IrcZbgkhqYMFm09iauZnPGHiOpu2Y5YnL+WJ3cZLoeh2cS2Hk3ED7GpcpdBRA6u2GTFI5/8yVuJNTGGZhNKyi7CMo61IqIGcv5aPpbt4DGlKTE0m4gQAn///ATvXEJEDWrzoavY81ea3GW4DIZmE3n/t3gciMuQuwwickJLvjqFXA5DaRIMzSZw4Voe/rPznNxlEJGTSskpxoscwtYkGJqNzGSx4vHPj8PA4SVE1Ig+P5KIX89fl7sMp8fQbGTr9sbhdFKu3GUQkQt4+ouTnC2okTE0G9GVjEKs+fmC3GUQkYtIzinGS9+dlbsMp8bQbETPfH0axSaeliWiprP50FX8diFd7jKcFkOzkew4kYy9vL5ARDJ47uvTnKazkTA0G0FusQkvsCcbEckkLr0A7/8WL3cZTomh2Qhe3XkOaXkGucsgIhe25ucLSM0plrsMp8PQbGAnrmZj00HeXZ2I5FVotODFb3nGq6ExNBuQEALPbY+FVchdCRER8M3JFPx+iZ2CGhJDswFtP5GME1ez5S6DiMhm6fZYmC3sFNRQGJoNxGC2cKo8IrI756/lY8Pvl+Uuw2kwNBvIhv2XkZjFG0sTkf1Z8/NF5BRxpqCGwNBsAFkFRry556LcZRARVSqnyIR3fr0kdxlOgaHZAFb/dAG5xbxPJhHZr/X7LyMtj0NQ6ouhWU+X0wvw8R8cYkJE9q3IZMF/f+Jc2PXF0Kyn/+w6B5OFY0yIyP59dvgqEjIK5C7DoTE06+H8tTx8dypF7jKIiCQxWQRW7jovdxkOjaFZD2t+vgjBRiYROZAdJ5NxJpn3+K0rhmYdXbqej29PJstdBhFRrQgBvP4jW5t1xdCsozd+vsjp8ojIIe0+ew0X0/LkLsMhMTTr4HJ6AbafYCuTiByTEMBbv8TJXYZDYmjWwRt7LsLCZiYRObDtJ5KQnM1ZzGqLoVlLVzMLse1YktxlEBHVi8ki8O4+tjZri6FZS+//Fg8zW5lE5AQ+O3wVWQVGuctwKAzNWsgtNmHLkatyl0FE1CAKjRas5x1QaoWhWQufHrqCAqNF7jKIiBrMhwcuo4jHNckYmhJZrAIbf+ccs0TkXLILTdh2nP00pGJoSrT7TCqS2NOMiJzQhwfYIJCKoSkRW5lE5KzOpuTiUHym3GU4BIamBBeu5eFAXIbcZRARNZoPD1yWuwSHwNCUYNNBtjKJyLntir2GjHyD3GXYPYZmDQxmC7Yd55R5ROTcjBYrthxNlLsMu8fQrMHuM9eQU2SSuwwioka3+dAVCN7vsFoMzRps5TcvInIRCRmF7L9RA4ZmNdJyi7HvQrrcZRARNZmvj/FyVHUYmtX48lgS72ZCRC7lu9MpMJg5Q1BVGJrV4KlZInI1ecVm7PkrTe4y7BZDswrHrmThYlq+3GUQETW5rzlioEoMzSrwQ0NEruqnv9KQW8xRA5VhaFZCCIGdsalyl0FEJAuj2YofTvEYWBmGZiWOX81GSk6x3GUQEcmGdz6pHEOzEj+wlUlELu5gXAan1asEQ7MSu2KvyV0CEZGsrALYc+663GXYHYZmOX+l5iI+vUDuMoiIZPfjGTYgymNolvM9L34TEQEA9l24zokOymFolsNes0REJQqMFhyM482pb8bQvElydhH+Ss2TuwwiIrvBU7RlMTRv8hsnZyciKuNnTqlXBkPzJvsuMjSJiG6WlF2EM8m5cpdhNxiaNwgh8DtDk4iogn0XOPSkFEPzhjMpucgoMMpdBhGR3TnIG1PbMDRv4PVMIqLKHbmcxXsL38DQvOE3npolIqpUnsGM2OQcucuwCwxNAMUmCw7FcywSEVFVeIq2BEMTwMnEHBjMVrnLICKyW5zkoARDE8Dxq1lyl0BEZNcOX87kdU0wNAEAx65ky10CEZFdyys2c7wmGJoASm46TURE1fvzCs/KuXxopuYUIyWnWO4yiIjs3qkk9qB1+dDk9UwiImlOMzQZmryeSUQkzcW0fBSbXPv+mgxNXs8kIpLEbBU4m+LanYFcPjTZG4yISLrTLn7MdOnQTMouQr7BLHcZREQO43Sia1/XdOnQvHAtT+4SiIgcymkXn4PWxUMzX+4SiIgcyoVr+S49M5Brh2YaW5pERLVhtFiRlFUkdxmycenQPM+WJhFRrcWlu+6x06VD81Ka6+54IqK6upxeIHcJsnHZ0EzOLkIee84SEdVaPEPT9bjyTiciqo84Fz5+umxouvKFbCKi+nDlRofrhmY2Q5OIqC6Ss4tgMLvmHLQuG5rJDE0iojqxCiDRRc/WuW5o5rjmDiciaghpuQa5S5CFy4ZmSjZvPE1EVFdpea55DHXZ0GRLk4io7tjSdCEZ+QYUm6xyl0FE5LDY0nQhqbmuubOJiBpKWh5bmi4ju9AkdwlERA6Np2ddSE4RQ5OIqD54etaFMDSJiOrnOk/Pug6eniUiqp8CI2cEchlsaRIR1Y/FKlDkgsHJ0CQiojrJd8HbKzZpaM6cORMKhQL//ve/yyzftm0bFApFk9WRy9AkIqq3QiNDs9G5ublhxYoVyMrKaupN2+QWMzSJiOqLLc0mMHLkSISGhuLll1+u8jlffPEFOnbsCJ1Oh8jISKxcubJBa3DW8/BWQyEyf1yHxLdm4crKCUj96O8wpJy3PW4pyEL6t6uQ+OZ0XFk5Edc+fw6mzKQy68j86V1cXX0PEtfORH7snjKPFfz1G9K2LmuS10JE9q/QSY+l1Wny0FSpVHjppZewZs0aJCYmVnj86NGjmDJlCu655x6cOnUKS5cuxbPPPosNGzY0WA1Gi3NOoZfxwxoUXz6OwDueRNjsN+AW1R3XPn0G5rx0CCGQ9uWLMGenImjCMwibuRpq72Bc++wZWI0l460KL/6BgrO/InjKC/AbOguZP6yBpTAHAGA1FCB774fwH/2gnC+RiOwIW5pNZPz48ejWrRuef/75Co+99tprGDFiBJ599lm0bdsWM2fOxCOPPIL//Oc/DbZ9o9n5QtNqMqDw3H74DpsFtxadoPFrBt+B90HjF4a8Y9/DnJUMY/I5+I9+CLqwttAEhMN/zEMQZiMKzv4KADBlXIVbi87QhbWBZ4chUGg9YM65BgDI2rMeXt1vg9o7WM6XSUR2pNDAlmaTWbFiBTZu3IizZ8+WWX727FkMGDCgzLIBAwbgwoULsFgaZgc5ZUvTagGEFQqVpsxihVoHQ2IshMV049/a/z2mUEKh0sCQeAYAoA2KgjH1IizF+TCkXoQwG6D2a4bixFgYr12CV887m+71EDWx4qunkbZ1GRLfnI6EFXeg8PyBMo/n/PElrq65D1fX3IfcQ1+WecyQfA4pGx6DsLpWiJic8VhaA9lCc/DgwRgzZgyefvrpJt+22SKafJuNTanzgK5ZDHJ+/xTmvAwIqwX5sXtgSP4LloIsaPzDofIOQvavG2EpzoewmJBzcCsseemw5GcCANxb9YRnx6FI3fg4Mr5dhcDbH4dSo0PmzrXwH/Mw8o59h6R35yF10z9gvJ4g8ysmaljCWAxNcCv4j5pf4TFjWjxyfvsYgeOeQuCd/0D2vk0wXr9c8ntWCzJ2vgn/MQ9DoVQ1cdXysgrnO5bWRNZxmv/+97+xY8cOHDjwv2907du3x/79+8s8b//+/Wjbti1Uqob5QDrrjg6440kAQNLaGbjy6njkHd0Oz/aDASigUKkRNP6fMGUlIXH1PbiyciKKr5yEW6uegOJ/HwPfgfeh+bx30WzOm/Bo2x85B7bALbIbFEoVcg58htD7XoG+y2hkfPuaTK+SqHG4R/eC3+Bp8Gjbv8JjpoxEaIIi4R7RFe6R3aAJioQpo6RPRu4fX8CtRUfowto2dcmyszbAoVQIgZEjR2LMmDEVHlu7di18fX2xadMmKBSKSn9SU1MBAIWFhXj66acRHR0NNzc3BAUFYciQIfj666/rX+RN1A26tlrq3Lkz7rvvPvz3v/+1LXvyySfRu3dvvPDCC5g6dSoOHDiAN954A2vXrm2w7VobYk/bIY1fGEL/79+wGothNRZCrffH9a9XQOMbCgDQhbZGs1lrYDUUQFjMUHn4IOXDJ6ANbVPp+kwZV1FwZg/CZv4X+Sd3wy28E1QePvCIGYSM71fDaiiEUufRlC/RpXiqrNApXet0n71IAKBXW+CrKbms4dGsObKykqAvTAaEgCUrCUFhzaDIu4LU07vRbs5KqDSuN5RNjfp3BFIoFFi/fj06d+6Md955B/PmzQMAxMfH46mnnsJbb72F8PBwAMC5c+fg7e1d5veDg0v6WcyfPx9//PEH1qxZgw4dOiAjIwO///47MjIy6l3jzWQNTQBYvnw5PvvsM9u/e/Togc8//xzPPfccXnjhBYSFhWH58uWYOXNmg23T4qQtzVJKrRuUWjdYivNRFP8n/IbOKvu4zhMAYMpMgjH1InwH3V9hHUIIZOx8E37DH4BS6w4IK4T1xh9I6X+F613PaEoFFiXMAvDVmOGjtsBbbYa32gRvtRleKhP0KhM8lWZ4Kk3wVBjhoTTBXWGCOwxwUxihE0ZoYYROGKAVBmisBqitxVBbDVBZDFBZiqG0FENhLobCXASF2TXvWlEZBYDXlKtxd2kfgRDg7RFmrPqk5NTtGyO0mB/yDEZ+WIB/jdLCfHkOlv5igEYFrL7VDYMjZD+0Ng3lmwAi672aFi1aYPXq1XjkkUcwevRoREZGYs6cORg9ejSmTZuGX375BUBJQPr6+la6ju3bt2P16tW47bbbAACRkZHo2bNnvWsrr0n3bGXDRiIjI2EwlJ0tf+LEiZg4cWKj1aFqwtmHmlJR3FEAgNq/OcxZKcj65QNo/MOh7zwSQMk4S5WHN1TewTBdv4zMH9fBo00/uEf1qLCu/BM7oXL3hkfrvgAAXfP2yP7tExiS/kJR3FFoAlpC6aZvuhfnogxWJa4ZtLjWBDeUUCgEvNUW+KrN8NGY4a22wEtVEtJ6ZWlIl/y4K0zwUBjhrjDCDSa4wQA3GKGFAVqrARphhOZGQKutxVBZDFCai2whDXMxFFbHGq4wv5cW83v9ryPdxuNGeOkUuCVchXZv5OPw3zyRmCtwz9YixD+mh07tnMeZshruNc6YMQNfffUVZs+ejQkTJuD06dOIjY2V/PuhoaH47rvvMGHCBHh5eTVYXeW5yNehsrRq55xy12ooRPbejTDnpUPl5gWPdv3hO3g6FKqS3WzJz0TWz+/BUpANld4P+o7D4TPgngrrsRRkIefA5wi9/3/DfHTN2sG7z3ikbV0GpYcPAm9/vMleFzUNIRTIMamRY1IDRY2/PTelBb4aC7zVFviUtqDLtaL1ShM8lEZ4KExwVxjhDqOtFa2DAVphvKkVXRrQN0La8r8WdEO3otMLrVj2qwF7Z3nijyQL2gYo0SZAhTYBgMkKnM+wonOIC3QKauCOT+vWrUPHjh2xd+9efPHFFwgKCirzeOlp2lIRERG2YF23bh3uu+8+BAQEoGvXrhg4cCAmTZpUYTRGfblkaOrUzvlh9mw/CJ7tB1X5uHevcfDuNa7G9ag8/RD+4AcVlvsOuBe+A+6tV41EpYqtKqQaVEg1AIB7o25LoRDwUZvhq7bAW2OGj9oML1VJSOuVJltL2kNZ0oIGnsPpwFvRrEUk3GCEDiXhrBUlrehH3/8DD44KQ0CrIBhyUmFEMqwegVCYi2G25sEJO+hXTtmwERIcHIx58+Zh27ZtuPvuuys8vm/fvjKtSI3mf0PsBg8ejLi4OBw8eBC///47fvrpJ6xevRrLli3Ds88+22A1umZoapyzpUlElRNCgWyTBtkmTZWtaKuxCOasFAAlndteOxuAtQVRULrry0zqURR/DNlXY3Fw6Kt4M1EJsy4dyWlzEXZ4Osx56chVfIip3u/BT6jhc+NUt5fKfONatBmeKiP0ypKWdEkruqQl7WZrRRtuhLQRWmsxNMJY0oq2FEF143q00lwEhe16tIzXosuNC28IarUaanXl0RQVFVXlNU2gJEQHDRqEQYMGYdGiRXjxxRexfPlyLFq0CFqttsrfq1V9DbIWB6Nz0tOzRFR3xtQLuLZ5ie3fWT+/BwDw7DTCdjnCajIg88e3ETRuERQ3hmqpvQPhN3Ie0r9/HQqVBgG3Pw6D0gOpBiDV0DAH6uooFAK+agt81OYKrWivm69FK4zwUJngDtONa9HGG63o/53m1orS09w3TnWbSzqLlZ7qhqkYCnFTj261W6O/vvro0KEDzGYziouLGZr14aynZ4mo7txadkHEom+qfY5So0Pzv71TYblX1zHw6lpxnGFTEEKBLJMaWU10LdpdZSkJaY0ZL6u7oHvjb9ImLS0NxcVlW9YBAQHQaDQYOnQo7r33XvTq1QsBAQE4c+YMlixZgmHDhlUYplIfLhmaztoRiIiosRVZVCiyqJBi0EKpadxr0eW1a9euwrIDBw6gX79+GDNmDDZu3IglS5agsLAQzZo1wx133IHnnnuuQWtQCOHkgxYr8eCmo/j+dKrcZRARObTdjw9Gm5DGG95hj1yyyeWhdckGNhFRg/LQud6x1CVD08+j4Xt8ERG5Gj1D0zX46xu/RxsRkTPTqBTwcXe9BohrhqYHQ5OIqD78PV3zOOqaoemiO5uIqKEEeOrkLkEWDE0iIqq1ABe9zMXQJCKiWgvUs6XpMhiaRET146rHUZcMTR93DTQqV7jXHRFR4+DpWReiUCgQ5tO00z8RETmTIJ6edS0t/BmaRER1Fe7nIXcJsnDd0HTRHU5E1BAiAlzzGOq6oenvmjuciKi+tGolQr3t+16ajcVlQzPcj6dniYjqItzPHUqla3amdNnQZEuTiKhuIlz4+Om6oclrmkREdRIR4Cl3CbJx2dAM8tLBXaOSuwwiIofjymfqXDY0AaB1sF7uEoiIHA5Pz7qodqFecpdARORwXPnY6dKhGePCO56IqC683NQ8Peuq2od5y10CEZFDaR/q2sdNhiYREUnWPsy1z9C5dGj6e2oR5OWakw4TEdVFh2au3dhw6dAEeF2TiKg2XP0MncuHpqt/AIiIpFIpFWgb4toNDZcPza7hvnKXQETkEFoFesLNxSeFcfnQ7BnhJ3cJREQOoVsLX7lLkJ3Lh2aojxua+/KOJ0RENekd6S93CbJz+dAEgF6RbG0SEdWkdxRDk6EJoBdP0RIRVStQr0NUoOve3aQUQxNAzwh+eyIiqg4bFyUYmigZq6nXqeUug4jIbvHUbAmGJgClUoHuLX3lLoOIyG71Zt8PAAxNm1uiA+QugYjILnloVejAiWAAMDRthrQNkrsEIiK71K9VANQqxgXA0LTp2MwHwZy8nYiogmHt2KgoxdC8yaA2/GAQEZU3tF2w3CXYDYbmTYbw2xQRURmtg/Vo4e8hdxl2g6F5k8FtAqFUyF0FEZH9GMr+HmUwNG/i66FFF971hIjIZlgMT83ejKFZzlCeoiUiAgDodWpO0l4OQ7Oc0R1C5S6BiMgu9I8OgFbNmLgZ341yOjTzRitOSkxEhFs7sRFRHkOzErd1DpO7BCIiWenUSozuyNAsj6FZidu7MDSJyLUNbRfEG1lUgqFZifZh3ogO4ilaInJdd3RpJncJdomhWYXbeYqWiFyUh1aFke1D5C7DLjE0q3AbT9ESkYsaHhMMd61K7jLsEkOzCjGh3mgdrJe7DCKiJsdTs1VjaFZjQo/mcpdARNSkvNzUGBbDSV6qwtCsxsQe4VBxMloiciF3dWsGnZqnZqvC0KxGiLcbBrcJlLsMIqImc2+flnKXYNcYmjWY2ruF3CUQETWJLuE+6NjMR+4y7BpDswYj2ocgUK+TuwwiokbHVmbNGJo10KiUmNQzXO4yiIgaladWhXFd2Wu2JgxNCe7t0wIK9gciIic2rlszeHLavBoxNCWICPDEwNbsEEREzounZqVhaEo0Z2CU3CUQETWKTs290SXcV+4yHAJDU6Kh7YLRNoQzBBGR82GjQDqGZi3wg0VEzibU243T5tUCQ7MW7u7enMNPiMipTO8fAY2KUSAV36la0KlVmH5LhNxlEBE1CE+tCvf14TGtNhiatXR/vwi4afi2EZHju7dPS/h4aOQuw6Hw6F9L/p5aTOjByQ6IyLFpVArMGcR+GrXF0KyD+YOjoebdT4jIgd3VrTnCfNzlLsPhMDTroGWAB++1SUQOS61U4OFhreUuwyExNOvo0eFtoFGxtUlEjmdCj+aICvSUuwyHxNCsoxb+HpjUk7cNIyLHolUpsWBEG7nLcFgMzXp4dHhraDm+iYgcyNTeLRDu5yF3GQ6LR/x6aObrzptUE5HDcNMo8ehwXsusD4ZmPT08rDV0ar6NRGT/7u8bgWBvN7nLcGg82tdTqI8b7u/HGTWIyL55alV4cGi03GU4PIZmA1gwvA38OKsGEdmxOYNaIYBzZ9cbQ7MB+HhosHBkW7nLICKqVJiPGx4cwlZmQ2BoNpD7+rZEm2Deb5OI7M/isTFw16rkLsMpMDQbiFqlxDN3dJC7DCKiMnpF+OGubpzBrKEwNBvQkLZBGNYuSO4yiIgAAAoF8PydHeUuw6kwNBvYM3d04GTuRGQXJvUIR+dwH7nLcCoMzQYWHaTH9Fsi5S6DiFycXqfGU7fGyF2G02FoNoInRrdFmA8HEBORfBaMaI0gLw4xaWgMzUag16nxwl2d5C6DiFxU5+Y+mDOwldxlOCWGZiMZ2SEEt3cOk7sMInIxaqUCKyZ2gYp9KxoFQ7MRLR3XET7unCmIiJrOvCGt0KGZt9xlOC2GZiMK8tJhyW28EE9ETaNVkCfvldnIGJqNbGrvlujXyl/uMojIySkUwIqJXaBTc+afxsTQbAIvT+gCNw3faiJqPPf3jUDvSH5Bb2w8kjeBqEBP/PN2TrFHRI0j3M8di8byUlBTYGg2kWn9IjAiJljuMojIyaiUCqy+pxv0OrXcpbgEhmYTWjGpCwJ5PzsiakCPDm+NnhE8LdtUGJpNKFCvw38md5G7DCJyEr0i/PDocPaWbUoMzSY2rF0wpt8SIXcZROTgvNzUWDW1GycxaGIMTRksua09WvOG1URUDy/e3Qkt/D3kLsPlMDRl4KZRYc293TkMhYjqZEL35ryxtEx41JZJ+zBv/OvuznKXQUQOpnWwHsvv5g0h5MLQlNHEnuGY1o/XN4lIGi+dGuum9eTwEhkxNGX23J0d0KOlr9xlEJGdUyiA16Z2Q6sg9oeQE0NTZhqVEmvv68nxm0RUrUeHtcaoDiFyl+HyGJp2INTHDW/8X3eo2XWciCoxPCYYC0e2lbsMAkPTbvRrFYDFnDuSiMqJDPDAqqndoOSXarvA0LQjDwxqhXt6t5C7DCKyE3qdGu9M68Wb2dsRhqadefHuThjUJlDuMohIZmqlAm/e1wPtQr3kLoVuwtC0M2qVEmvv64EY/qEQubSXxnfGkLZBcpdB5TA07ZCXmwYfzOyNYC/2qCVyRQuGt8YUXqqxSwxNO9XM1x0fzOwNT61K7lKIqAlN6NEcT4xuJ3cZVAWGph3r1NwHa/6vO+9iQOQiBrQOwIqJvH2gPWNo2rnhMSF4aTznmSRydjGhXnj7/p7QqHhYtmfcOw5gau+WeOb29nKXQUSNpFWgJz6c0wdebhxaYu8Ymg7igUGtsGAE79BO5Gxa+Lvj47/1RbCXm9ylkAQMTQfyxKi2eGBglNxlEFEDaebjhk8e6IcwH3e5SyGJGJoO5pk7OmD6LbydGJGjC/bS4ZO/9UMLfw+5S6FaYGg6oGXjOnK6PSIHFuCpxSd/64vIQE+5S6FaYmg6IIVCgZfGd8a9fVrKXQoR1ZKvhwabHuiL1sGc9csRKYQQQu4iqO5e/OYM3vstXu4yiEiCQL0Omx7og5hQb7lLoTpiaDqBVbvPY/VPF+Qug4iqEebjho8f6ItWQXq5S6F6YGg6iXf3xuFf352VuwwiqkREgAc2zenLTj9OgKHpRD7+IwHPbjsNK/cokd2ICfXCh3P6cBymk2BoOpltx5Lw9y0nYGZyEsmud6Qf3pvRmzeRdiIMTSe078J1PLTpT+QZzHKXQuSyRrYPxhv/1wNuGt6pyJkwNJ3UudQ8zN5wGEnZRXKXQuRy5gyMwj9vaw8l71DkdBiaTiwtrxgPbDyCk4k5cpdC5BLUSgWWjuuI+/tx1i5nxdB0ckVGCx779Bh2nbkmdylETs3LTY03/68HBrcNkrsUakQMTRdgtQr867uzeJ+TIBA1inA/d6yf2RttQjjLj7NjaLqQTw9dwXPbY2E0W+Uuhchp9Gjpi3XTeyFQr5O7FGoCDE0XczIxGw9u+pMdhIgawPRbIvDM7R2gVXMab1fB0HRBWQVGLPj0GPZdSJe7FCKH5KFV4eUJnXFXt+Zyl0JNjKHpoqxWgVU/nscbey6CnwAi6aKDPPH2/T15/dJFMTRd3E9nr+Hxz44jt5gTIRDV5I4uYVgxsQs8dWq5SyGZMDQJVzIKseDTYzh+NVvuUojskkalwD9va4+ZA6LkLoVkxtAkAIDZYsWany/ijT0XYeG8tUQ2bUP0eG1KN3Rq7iN3KWQHGJpUxp9XsvD4Z8eRkFEodylEslIogNkDovCPMe04fyzZMDSpggKDGct3nMFnR67KXQqRLJr5uOHVyV3Rv3Wg3KWQnWFoUpV2xqbi6S9PIbPAKHcpRE3m7m7NsPzuTvB24+28qCKGJlUrPd+AZTvOYMeJZLlLIWpUAZ5aLLurI+7o0kzuUsiOMTRJkj3n0vDMV6c5kxA5HYUCmNKzBZ6+LQa+Hlq5yyE7x9AkyQqNZry26zzW/36ZPWzJKbQO1uOl8Z3RJ8pf7lLIQTA0qdZOJ+Vg8ZcncTopV+5SiOpEp1bikWGtMW9INOeNpVphaFKdWKwC6/fHY/VPF5DH2YTIgQxsHYgX7+6EyEBPuUshB8TQpHrJLDDitd3nsPnQVZ6yJbsWGeCBxWPb49ZOoXKXQg6MoUkN4vy1PLz47VnsPX9d7lKIyvBx12DBiDaYfksENCqeiqX6YWhSg9pzLg3/+vYsLqbly10KuTitSolpt0RgwfA28PHgmEtqGAxNanBmixWbD13Bmp8vIi3PIHc55ILGdgrF4rExiAjgdUtqWAxNajTFJgs+/uMK3vrlEtLzGZ7U+Ia1C8JjI9uiWwtfuUshJ8XQpEZXbLJg08EEvP3rJaTnc0o+angjYoKxYEQbdGVYUiNjaFKTKTJa8NHBy3jn1zhkcD5bagAj24fgsRFt0Dmct+2ipsHQpCZXaDTjkz+uYP3+y5yWj2pNqQBGdQjBo8Pb8B6X1OQYmiQbs8WKb06m4N19cYhN5uxCVD29To3JvcIxq38UWgZ4yF0OuSiGJtmFA5cysH5/PH48ew2cI4Fu1tLfAzP6R2JKr3B48XZdJDOGJtmVq5mF2Pj7ZXzxZyKyCk1yl0My6tfKH7MHRGFk+xAolQq5yyECwNAkO2UwW7D7zDV8dvgq9l9MZ+vTRfh7ajG+e3NM7d0CbUO85C6HqAKGJtm9pOwibDlyFVuOJLLjkBNSKRUY1CYQk3u2wKgOIbzrCNk1hiY5DKtVYP+ldGw5koifzl5DgdEid0lUD+1CvDCxZ3Pc3a05gr3d5C6HSBKGJjmkYpMFv5y7ju9OpTBAHUhMqBfGdgrD2M6hPP1KDomhSQ6v2GTBr+ev4/tTKfjxbBryDby/pz3pGu6DWzuF4dZOoYjiPSzJwTE0yakYzBbsv5iOX89dx94L6YhPL5C7JJejUyvRJ8ofQ9oG4dZOoQj345hKch4MTXJqVzML8ev569h7/jp+v5TBVmgjUChKrk8ObhuEga0D0SfKH24aldxlETUKhia5DLPFij+vZOO3i+k4mpCJ41eyeS20jlr6e6BXpB8GtQnEwNZBCPLSyV0SUZNgaJLLslgF/krNxZ8JWTiakIWjV7JwNZNDWsrz0KrQJdwHPVr6oXtLP3Rv6YtAPUOSXBNDk+gmaXnFOH4lG+dS8/DXtTycS81DfHoBLC4yu4KPuwZtQ/RoG+KF9mHe6NHSD+1CvaDijDxEABiaRDUymC24mJaPc6klIXruWh6uZBYiKasIBrNV7vLqxM9Dg4gAT7QL8UKbED3ahXqhbYgXQjhekqhaDE2iOhJC4Hq+AYlZRTd+Cm3/n55nQHahEZmFRhSbmi5YFYqSu4H4emgQ6u2G5r7uaObrjuZ+7mjue+PHzx0eWnWT1UTkTBiaRI2syGhBVqERmQVGZBeakFloRH6xGcUmC4rNFhhMVhjMVpgtVpitAiaLFVYBaFQKqJVKaNQKaJRKaFRKqFUKaFVKaFQK+Hho4OuuvfFfDXw9tPBx1/BUKlEjYmgSERFJxJmRiYiIJGJoEhERScTQJCIikoihSUREJBFDk4iISCKGJhERkUQMTSIiIokYmkRERBIxNImIiCRiaBIREUnE0CQiIpKIoUlERCQRQ5OIiEgihiYREZFEDE0iIiKJGJpEREQSMTSJiIgkYmgSERFJxNAkIiKSiKFJREQkEUOTiIhIIoYmERGRRAxNIiIiiRiaREREEjE0iYiIJGJoEhERScTQJCIikoihSUREJBFDk4iISCKGJhERkUQMTSIiIokYmkRERBIxNImIiCRiaBIREUnE0CQiIpKIoUlERCQRQ5OIiEgihiYREZFEDE0iIiKJGJpEREQSMTSJiIgkYmgSERFJxNAkIiKSiKFJREQkEUOTiIhIIoYmERGRRAxNIiIiiRiaREREEjE0iYiIJGJoEhERScTQJCIikuj/ATxBJt7yX2tbAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the counts of 'is_fraud' values in the train dataset\n",
    "exit_counts = train[\"is_fraud\"].value_counts()\n",
    "\n",
    "# Create a figure for the pie chart\n",
    "plt.figure(figsize=(9, 6))\n",
    "\n",
    "# Create subplot for the pie chart\n",
    "plt.subplot(1, 2, 1)\n",
    "\n",
    "# Plot the pie chart\n",
    "plt.pie(exit_counts, labels=[\"No\", \"YES\"], autopct=\"%0.0f%%\")\n",
    "\n",
    "# Set title for the pie chart\n",
    "plt.title(\"Fraudulent Transactions Distribution(is_fraud) Counts\")\n",
    "\n",
    "# Adjust layout to prevent overlapping\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c0ab1934",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:10.841588Z",
     "iopub.status.busy": "2024-05-10T10:58:10.841192Z",
     "iopub.status.idle": "2024-05-10T10:58:11.462642Z",
     "shell.execute_reply": "2024-05-10T10:58:11.461551Z",
     "shell.execute_reply.started": "2024-05-10T10:58:10.841557Z"
    }
   },
   "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>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>gender</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</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>merchant</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032302</td>\n",
       "      <td>-0.002633</td>\n",
       "      <td>0.000915</td>\n",
       "      <td>-0.002266</td>\n",
       "      <td>-0.000697</td>\n",
       "      <td>0.001911</td>\n",
       "      <td>0.001360</td>\n",
       "      <td>-0.000999</td>\n",
       "      <td>-0.002263</td>\n",
       "      <td>-0.000673</td>\n",
       "      <td>-0.000536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <td>0.032302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030867</td>\n",
       "      <td>0.028259</td>\n",
       "      <td>-0.008660</td>\n",
       "      <td>-0.000767</td>\n",
       "      <td>0.009386</td>\n",
       "      <td>0.003490</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>-0.008519</td>\n",
       "      <td>-0.000736</td>\n",
       "      <td>0.020205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amt</th>\n",
       "      <td>-0.002633</td>\n",
       "      <td>0.030867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001034</td>\n",
       "      <td>-0.001926</td>\n",
       "      <td>-0.000187</td>\n",
       "      <td>0.005818</td>\n",
       "      <td>-0.000628</td>\n",
       "      <td>-0.000293</td>\n",
       "      <td>-0.001873</td>\n",
       "      <td>-0.000151</td>\n",
       "      <td>0.219404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.000915</td>\n",
       "      <td>0.028259</td>\n",
       "      <td>-0.001034</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042935</td>\n",
       "      <td>-0.050404</td>\n",
       "      <td>0.028649</td>\n",
       "      <td>0.079908</td>\n",
       "      <td>0.000960</td>\n",
       "      <td>-0.042645</td>\n",
       "      <td>-0.050350</td>\n",
       "      <td>-0.007642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>-0.002266</td>\n",
       "      <td>-0.008660</td>\n",
       "      <td>-0.001926</td>\n",
       "      <td>-0.042935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015533</td>\n",
       "      <td>-0.155730</td>\n",
       "      <td>-0.027593</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>0.993592</td>\n",
       "      <td>-0.015509</td>\n",
       "      <td>0.001894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>-0.000697</td>\n",
       "      <td>-0.000767</td>\n",
       "      <td>-0.000187</td>\n",
       "      <td>-0.050404</td>\n",
       "      <td>-0.015533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.052715</td>\n",
       "      <td>-0.002729</td>\n",
       "      <td>-0.000642</td>\n",
       "      <td>-0.015452</td>\n",
       "      <td>0.999120</td>\n",
       "      <td>0.001721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_pop</th>\n",
       "      <td>0.001911</td>\n",
       "      <td>0.009386</td>\n",
       "      <td>0.005818</td>\n",
       "      <td>0.028649</td>\n",
       "      <td>-0.155730</td>\n",
       "      <td>-0.052715</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.054945</td>\n",
       "      <td>-0.001714</td>\n",
       "      <td>-0.154781</td>\n",
       "      <td>-0.052687</td>\n",
       "      <td>0.002136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>0.001360</td>\n",
       "      <td>0.003490</td>\n",
       "      <td>-0.000628</td>\n",
       "      <td>0.079908</td>\n",
       "      <td>-0.027593</td>\n",
       "      <td>-0.002729</td>\n",
       "      <td>-0.054945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>-0.027412</td>\n",
       "      <td>-0.002727</td>\n",
       "      <td>-0.000093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unix_time</th>\n",
       "      <td>-0.000999</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>-0.000293</td>\n",
       "      <td>0.000960</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>-0.000642</td>\n",
       "      <td>-0.001714</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>-0.005078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merch_lat</th>\n",
       "      <td>-0.002263</td>\n",
       "      <td>-0.008519</td>\n",
       "      <td>-0.001873</td>\n",
       "      <td>-0.042645</td>\n",
       "      <td>0.993592</td>\n",
       "      <td>-0.015452</td>\n",
       "      <td>-0.154781</td>\n",
       "      <td>-0.027412</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015431</td>\n",
       "      <td>0.001741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merch_long</th>\n",
       "      <td>-0.000673</td>\n",
       "      <td>-0.000736</td>\n",
       "      <td>-0.000151</td>\n",
       "      <td>-0.050350</td>\n",
       "      <td>-0.015509</td>\n",
       "      <td>0.999120</td>\n",
       "      <td>-0.052687</td>\n",
       "      <td>-0.002727</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>-0.015431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_fraud</th>\n",
       "      <td>-0.000536</td>\n",
       "      <td>0.020205</td>\n",
       "      <td>0.219404</td>\n",
       "      <td>-0.007642</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.002136</td>\n",
       "      <td>-0.000093</td>\n",
       "      <td>-0.005078</td>\n",
       "      <td>0.001741</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            merchant  category       amt    gender       lat      long  \\\n",
       "merchant    1.000000  0.032302 -0.002633  0.000915 -0.002266 -0.000697   \n",
       "category    0.032302  1.000000  0.030867  0.028259 -0.008660 -0.000767   \n",
       "amt        -0.002633  0.030867  1.000000 -0.001034 -0.001926 -0.000187   \n",
       "gender      0.000915  0.028259 -0.001034  1.000000 -0.042935 -0.050404   \n",
       "lat        -0.002266 -0.008660 -0.001926 -0.042935  1.000000 -0.015533   \n",
       "long       -0.000697 -0.000767 -0.000187 -0.050404 -0.015533  1.000000   \n",
       "city_pop    0.001911  0.009386  0.005818  0.028649 -0.155730 -0.052715   \n",
       "job         0.001360  0.003490 -0.000628  0.079908 -0.027593 -0.002729   \n",
       "unix_time  -0.000999  0.000182 -0.000293  0.000960  0.000632 -0.000642   \n",
       "merch_lat  -0.002263 -0.008519 -0.001873 -0.042645  0.993592 -0.015452   \n",
       "merch_long -0.000673 -0.000736 -0.000151 -0.050350 -0.015509  0.999120   \n",
       "is_fraud   -0.000536  0.020205  0.219404 -0.007642  0.001894  0.001721   \n",
       "\n",
       "            city_pop       job  unix_time  merch_lat  merch_long  is_fraud  \n",
       "merchant    0.001911  0.001360  -0.000999  -0.002263   -0.000673 -0.000536  \n",
       "category    0.009386  0.003490   0.000182  -0.008519   -0.000736  0.020205  \n",
       "amt         0.005818 -0.000628  -0.000293  -0.001873   -0.000151  0.219404  \n",
       "gender      0.028649  0.079908   0.000960  -0.042645   -0.050350 -0.007642  \n",
       "lat        -0.155730 -0.027593   0.000632   0.993592   -0.015509  0.001894  \n",
       "long       -0.052715 -0.002729  -0.000642  -0.015452    0.999120  0.001721  \n",
       "city_pop    1.000000 -0.054945  -0.001714  -0.154781   -0.052687  0.002136  \n",
       "job        -0.054945  1.000000   0.000032  -0.027412   -0.002727 -0.000093  \n",
       "unix_time  -0.001714  0.000032   1.000000   0.000561   -0.000635 -0.005078  \n",
       "merch_lat  -0.154781 -0.027412   0.000561   1.000000   -0.015431  0.001741  \n",
       "merch_long -0.052687 -0.002727  -0.000635  -0.015431    1.000000  0.001721  \n",
       "is_fraud    0.002136 -0.000093  -0.005078   0.001741    0.001721  1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the correlation matrix for the train dataset\n",
    "train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "812f557f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:11.464996Z",
     "iopub.status.busy": "2024-05-10T10:58:11.464090Z",
     "iopub.status.idle": "2024-05-10T10:58:12.817473Z",
     "shell.execute_reply": "2024-05-10T10:58:12.816364Z",
     "shell.execute_reply.started": "2024-05-10T10:58:11.464964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "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": [
    "# Plot a heatmap to visualize the correlation matrix for the train dataset\n",
    "sns.heatmap(train.corr(), annot=True, cmap='coolwarm', fmt=\".2f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cde0c53d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:12.819203Z",
     "iopub.status.busy": "2024-05-10T10:58:12.818872Z",
     "iopub.status.idle": "2024-05-10T10:58:12.869203Z",
     "shell.execute_reply": "2024-05-10T10:58:12.868218Z",
     "shell.execute_reply.started": "2024-05-10T10:58:12.819176Z"
    }
   },
   "outputs": [],
   "source": [
    "# Separate features (X) and target variable (y)\n",
    "x = train.drop(columns=[\"is_fraud\"], inplace=False)\n",
    "y = train[\"is_fraud\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f01e229",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:12.870769Z",
     "iopub.status.busy": "2024-05-10T10:58:12.870464Z",
     "iopub.status.idle": "2024-05-10T10:58:13.114797Z",
     "shell.execute_reply": "2024-05-10T10:58:13.113453Z",
     "shell.execute_reply.started": "2024-05-10T10:58:12.870744Z"
    }
   },
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05303093",
   "metadata": {},
   "source": [
    "## **Model Prediction**\n",
    "Now it is time to start building the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c5192bee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:13.116715Z",
     "iopub.status.busy": "2024-05-10T10:58:13.116321Z",
     "iopub.status.idle": "2024-05-10T10:58:13.122324Z",
     "shell.execute_reply": "2024-05-10T10:58:13.121017Z",
     "shell.execute_reply.started": "2024-05-10T10:58:13.116683Z"
    }
   },
   "outputs": [],
   "source": [
    "# Initialize the models\n",
    "model1 = LogisticRegression()\n",
    "\n",
    "# Use simplified RandomForestClassifier if default training time is long\n",
    "# model2 = RandomForestClassifier(n_estimators=100, max_depth=10)\n",
    "\n",
    "model2 = RandomForestClassifier()\n",
    "model3 = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fccb06be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:13.123777Z",
     "iopub.status.busy": "2024-05-10T10:58:13.123467Z",
     "iopub.status.idle": "2024-05-10T10:58:13.132441Z",
     "shell.execute_reply": "2024-05-10T10:58:13.131507Z",
     "shell.execute_reply.started": "2024-05-10T10:58:13.123751Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define a list of model names\n",
    "columns = ['LogisticRegression', 'RandomForestClassifier', 'DecisionTreeClassifier']\n",
    "\n",
    "# Initialize an empty list to store accuracies\n",
    "accuracies = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3cd226c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:13.134071Z",
     "iopub.status.busy": "2024-05-10T10:58:13.133790Z",
     "iopub.status.idle": "2024-05-10T10:58:13.145859Z",
     "shell.execute_reply": "2024-05-10T10:58:13.144791Z",
     "shell.execute_reply.started": "2024-05-10T10:58:13.134047Z"
    }
   },
   "outputs": [],
   "source": [
    "def cal(model):\n",
    "    # Fit the model on the training data\n",
    "    model.fit(x_train, y_train)\n",
    "    \n",
    "    # Make predictions on the test data\n",
    "    predictions = model.predict(x_test)\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = accuracy_score(y_test, predictions)\n",
    "    \n",
    "    \n",
    "    # Append accuracy to the list\n",
    "    accuracies.append(accuracy)\n",
    "    \n",
    "    \n",
    "    # Print model evaluation on test data\n",
    "    print(\"-----Model Evaluation on Test Data-----\")\n",
    "    print(model)\n",
    "    print()\n",
    "    print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f6e81d0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:13.147703Z",
     "iopub.status.busy": "2024-05-10T10:58:13.147290Z",
     "iopub.status.idle": "2024-05-10T10:58:16.187815Z",
     "shell.execute_reply": "2024-05-10T10:58:16.186625Z",
     "shell.execute_reply.started": "2024-05-10T10:58:13.147668Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----Model Evaluation on Test Data-----\n",
      "LogisticRegression()\n",
      "\n",
      "0.9941388551487458\n"
     ]
    }
   ],
   "source": [
    "# LogisticRegression\n",
    "cal(model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b699990c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T10:58:16.189721Z",
     "iopub.status.busy": "2024-05-10T10:58:16.189288Z",
     "iopub.status.idle": "2024-05-10T11:07:24.424817Z",
     "shell.execute_reply": "2024-05-10T11:07:24.423578Z",
     "shell.execute_reply.started": "2024-05-10T10:58:16.189685Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----Model Evaluation on Test Data-----\n",
      "RandomForestClassifier()\n",
      "\n",
      "0.9974087570131297\n"
     ]
    }
   ],
   "source": [
    "# RandomForestClassifier\n",
    "cal(model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dc15b7ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T11:07:24.426598Z",
     "iopub.status.busy": "2024-05-10T11:07:24.426234Z",
     "iopub.status.idle": "2024-05-10T11:07:44.236580Z",
     "shell.execute_reply": "2024-05-10T11:07:44.235377Z",
     "shell.execute_reply.started": "2024-05-10T11:07:24.426569Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----Model Evaluation on Test Data-----\n",
      "DecisionTreeClassifier()\n",
      "\n",
      "0.9958817745387241\n"
     ]
    }
   ],
   "source": [
    "# DecisionTreeClassifier\n",
    "cal(model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "24ddb942",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T11:07:44.238206Z",
     "iopub.status.busy": "2024-05-10T11:07:44.237870Z",
     "iopub.status.idle": "2024-05-10T11:07:44.244695Z",
     "shell.execute_reply": "2024-05-10T11:07:44.243696Z",
     "shell.execute_reply.started": "2024-05-10T11:07:44.238178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9941388551487458, 0.9974087570131297, 0.9958817745387241]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the accuracies obtained from each model\n",
    "accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "09329348",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T11:07:44.252155Z",
     "iopub.status.busy": "2024-05-10T11:07:44.251830Z",
     "iopub.status.idle": "2024-05-10T11:07:44.258190Z",
     "shell.execute_reply": "2024-05-10T11:07:44.257167Z",
     "shell.execute_reply.started": "2024-05-10T11:07:44.252127Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a DataFrame to store the algorithm names and their corresponding accuracies\n",
    "FinalResult = pd.DataFrame({'Algorithm': columns, 'Accuracy': accuracies})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "988e6464",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T11:07:44.259819Z",
     "iopub.status.busy": "2024-05-10T11:07:44.259406Z",
     "iopub.status.idle": "2024-05-10T11:07:44.275157Z",
     "shell.execute_reply": "2024-05-10T11:07:44.274142Z",
     "shell.execute_reply.started": "2024-05-10T11:07:44.259783Z"
    }
   },
   "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.994139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.997409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.995882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Algorithm  Accuracy\n",
       "0      LogisticRegression  0.994139\n",
       "1  RandomForestClassifier  0.997409\n",
       "2  DecisionTreeClassifier  0.995882"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FinalResult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "60b49f82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T11:07:44.276727Z",
     "iopub.status.busy": "2024-05-10T11:07:44.276381Z",
     "iopub.status.idle": "2024-05-10T11:07:44.496556Z",
     "shell.execute_reply": "2024-05-10T11:07:44.495405Z",
     "shell.execute_reply.started": "2024-05-10T11:07:44.276700Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and axis\n",
    "fig, ax = plt.subplots(figsize=(20, 5))\n",
    "\n",
    "# Plot the accuracies for each algorithm\n",
    "plt.plot(FinalResult.Algorithm, accuracies, label=\"Accuracy\")\n",
    "\n",
    "# Add legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "afb69f03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-10T11:07:44.498209Z",
     "iopub.status.busy": "2024-05-10T11:07:44.497886Z",
     "iopub.status.idle": "2024-05-10T11:07:44.516951Z",
     "shell.execute_reply": "2024-05-10T11:07:44.516023Z",
     "shell.execute_reply.started": "2024-05-10T11:07:44.498182Z"
    }
   },
   "outputs": [],
   "source": [
    "# Separate features (X_test) and target variable (y_test) for the test dataset\n",
    "x_test = test.drop(columns=[\"is_fraud\"], inplace=False)\n",
    "y_test = test[\"is_fraud\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "da9d6c4c",
   "metadata": {
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       "array([0, 0, 0, ..., 0, 0, 0])"
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     "execution_count": 37,
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   ],
   "source": [
    "# Predict using the Random Forest Classifier model\n",
    "y_pred = model2.predict(x_test)\n",
    "\n",
    "# Display the predicted values\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5d6ac48c",
   "metadata": {
    "execution": {
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    {
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       "0.9974267570480765"
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     "execution_count": 38,
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     "output_type": "execute_result"
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   ],
   "source": [
    "# Calculate the accuracy of the Random Forest Classifier model\n",
    "accuracy = accuracy_score(test['is_fraud'], y_pred)\n",
    "\n",
    "# Display the accuracy\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07d1f98",
   "metadata": {},
   "source": [
    "## **Observations:**\n",
    "\n",
    "- The **Random Forest Classifier** 🌲 achieved the **highest accuracy of 99.74%**, followed by the **Decision Tree Classifier** 🌳 at **99.60%**, and the **Logistic Regression model** 📈 at **99.41%**.\n",
    "\n",
    "- The **Random Forest Classifier** performed exceptionally well in predicting the fraud cases, with an **accuracy of 99.74%** on the test data.\n",
    "\n",
    "- 🔍 The **Decision Tree Classifier** also demonstrated impressive performance, with an **accuracy of 99.60%**, closely following the Random Forest Classifier.\n",
    "\n",
    "- While the **Logistic Regression model** had a slightly lower **accuracy of 99.41%**, it still achieved a remarkable result 🎯 in detecting fraudulent transactions.\n",
    "\n",
    "- 👍 Overall, all three models exhibited **outstanding accuracy** in classifying fraudulent and non-fraudulent transactions, with the **Random Forest Classifier emerging as the top performer**. ⭐\n",
    "\n",
    "- 💡 It is worth noting that while these models achieved **high accuracy**, further improvements could potentially be made by exploring additional features, tuning hyperparameters, or employing more advanced techniques such as ensemble methods or deep learning algorithms. 🚀"
   ]
  }
 ],
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