diff --git a/your-code/lab_imbalance.ipynb b/your-code/lab_imbalance.ipynb
index a3a5359..d57686f 100644
--- a/your-code/lab_imbalance.ipynb
+++ b/your-code/lab_imbalance.ipynb
@@ -32,7 +32,421 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import imblearn\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>step</th>\n",
+       "      <th>type</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>nameOrig</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>nameDest</th>\n",
+       "      <th>oldbalanceDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>isFlaggedFraud</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>736176</th>\n",
+       "      <td>38</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>7959.38</td>\n",
+       "      <td>C2126805508</td>\n",
+       "      <td>26952.00</td>\n",
+       "      <td>18992.62</td>\n",
+       "      <td>M1898089637</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4402373</th>\n",
+       "      <td>321</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>26463.20</td>\n",
+       "      <td>C674711351</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>M1322840643</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1900270</th>\n",
+       "      <td>165</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>156967.14</td>\n",
+       "      <td>C95679624</td>\n",
+       "      <td>51743.00</td>\n",
+       "      <td>208710.14</td>\n",
+       "      <td>C468652831</td>\n",
+       "      <td>438548.78</td>\n",
+       "      <td>281581.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5401590</th>\n",
+       "      <td>377</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>35654.03</td>\n",
+       "      <td>C781169667</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>M1783548772</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3964088</th>\n",
+       "      <td>297</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>33506.29</td>\n",
+       "      <td>C691264809</td>\n",
+       "      <td>62007.00</td>\n",
+       "      <td>28500.71</td>\n",
+       "      <td>M632076351</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5785565</th>\n",
+       "      <td>400</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>154633.65</td>\n",
+       "      <td>C1929566219</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C1520472655</td>\n",
+       "      <td>185745.48</td>\n",
+       "      <td>340379.13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6127999</th>\n",
+       "      <td>542</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>23859.86</td>\n",
+       "      <td>C613398830</td>\n",
+       "      <td>9482.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C143665280</td>\n",
+       "      <td>12716259.38</td>\n",
+       "      <td>12740119.25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1122963</th>\n",
+       "      <td>130</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>452641.53</td>\n",
+       "      <td>C320077420</td>\n",
+       "      <td>7865861.33</td>\n",
+       "      <td>8318502.86</td>\n",
+       "      <td>C303956982</td>\n",
+       "      <td>760849.95</td>\n",
+       "      <td>308208.42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55531</th>\n",
+       "      <td>9</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>157537.88</td>\n",
+       "      <td>C2098030400</td>\n",
+       "      <td>594832.47</td>\n",
+       "      <td>437294.59</td>\n",
+       "      <td>C222581384</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>347710.24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5543544</th>\n",
+       "      <td>382</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>155881.00</td>\n",
+       "      <td>C1298557230</td>\n",
+       "      <td>144501.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C1972245755</td>\n",
+       "      <td>422651.53</td>\n",
+       "      <td>578532.53</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         step      type     amount     nameOrig  oldbalanceOrg  \\\n",
+       "736176     38   PAYMENT    7959.38  C2126805508       26952.00   \n",
+       "4402373   321   PAYMENT   26463.20   C674711351           0.00   \n",
+       "1900270   165   CASH_IN  156967.14    C95679624       51743.00   \n",
+       "5401590   377   PAYMENT   35654.03   C781169667           0.00   \n",
+       "3964088   297   PAYMENT   33506.29   C691264809       62007.00   \n",
+       "...       ...       ...        ...          ...            ...   \n",
+       "5785565   400  CASH_OUT  154633.65  C1929566219           0.00   \n",
+       "6127999   542  CASH_OUT   23859.86   C613398830        9482.00   \n",
+       "1122963   130   CASH_IN  452641.53   C320077420     7865861.33   \n",
+       "55531       9  CASH_OUT  157537.88  C2098030400      594832.47   \n",
+       "5543544   382  CASH_OUT  155881.00  C1298557230      144501.00   \n",
+       "\n",
+       "         newbalanceOrig     nameDest  oldbalanceDest  newbalanceDest  isFraud  \\\n",
+       "736176         18992.62  M1898089637            0.00            0.00        0   \n",
+       "4402373            0.00  M1322840643            0.00            0.00        0   \n",
+       "1900270       208710.14   C468652831       438548.78       281581.65        0   \n",
+       "5401590            0.00  M1783548772            0.00            0.00        0   \n",
+       "3964088        28500.71   M632076351            0.00            0.00        0   \n",
+       "...                 ...          ...             ...             ...      ...   \n",
+       "5785565            0.00  C1520472655       185745.48       340379.13        0   \n",
+       "6127999            0.00   C143665280     12716259.38     12740119.25        0   \n",
+       "1122963      8318502.86   C303956982       760849.95       308208.42        0   \n",
+       "55531         437294.59   C222581384            0.00       347710.24        0   \n",
+       "5543544            0.00  C1972245755       422651.53       578532.53        0   \n",
+       "\n",
+       "         isFlaggedFraud  \n",
+       "736176                0  \n",
+       "4402373               0  \n",
+       "1900270               0  \n",
+       "5401590               0  \n",
+       "3964088               0  \n",
+       "...                 ...  \n",
+       "5785565               0  \n",
+       "6127999               0  \n",
+       "1122963               0  \n",
+       "55531                 0  \n",
+       "5543544               0  \n",
+       "\n",
+       "[100000 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "financial_data = pd.read_csv(r\"C:\\Users\\dulce\\Downloads\\archive (1)\\PS_20174392719_1491204439457_log.csv\").sample(n = 100000)\n",
+    "financial_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
+      "count  100000.000000  1.000000e+05   1.000000e+05    1.000000e+05   \n",
+      "mean      243.062430  1.843948e+05   8.320349e+05    8.531603e+05   \n",
+      "std       142.303386  6.734997e+05   2.883308e+06    2.918311e+06   \n",
+      "min         1.000000  1.600000e-01   0.000000e+00    0.000000e+00   \n",
+      "25%       155.000000  1.331933e+04   0.000000e+00    0.000000e+00   \n",
+      "50%       238.000000  7.506494e+04   1.385339e+04    0.000000e+00   \n",
+      "75%       335.000000  2.095494e+05   1.067000e+05    1.445242e+05   \n",
+      "max       741.000000  7.117248e+07   3.729746e+07    3.753800e+07   \n",
+      "\n",
+      "       oldbalanceDest  newbalanceDest        isFraud  isFlaggedFraud  \n",
+      "count    1.000000e+05    1.000000e+05  100000.000000        100000.0  \n",
+      "mean     1.094580e+06    1.226624e+06       0.001250             0.0  \n",
+      "std      3.494292e+06    3.868588e+06       0.035333             0.0  \n",
+      "min      0.000000e+00    0.000000e+00       0.000000             0.0  \n",
+      "25%      0.000000e+00    0.000000e+00       0.000000             0.0  \n",
+      "50%      1.343827e+05    2.160202e+05       0.000000             0.0  \n",
+      "75%      9.284622e+05    1.102906e+06       0.000000             0.0  \n",
+      "max      2.502615e+08    3.114049e+08       1.000000             0.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(financial_data.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['step', 'type', 'amount', 'nameOrig', 'oldbalanceOrg', 'newbalanceOrig',\n",
+       "       'nameDest', 'oldbalanceDest', 'newbalanceDest', 'isFraud',\n",
+       "       'isFlaggedFraud'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "financial_data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "step                int64\n",
+       "type               object\n",
+       "amount            float64\n",
+       "nameOrig           object\n",
+       "oldbalanceOrg     float64\n",
+       "newbalanceOrig    float64\n",
+       "nameDest           object\n",
+       "oldbalanceDest    float64\n",
+       "newbalanceDest    float64\n",
+       "isFraud             int64\n",
+       "isFlaggedFraud      int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "financial_data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "step              0\n",
+       "type              0\n",
+       "amount            0\n",
+       "nameOrig          0\n",
+       "oldbalanceOrg     0\n",
+       "newbalanceOrig    0\n",
+       "nameDest          0\n",
+       "oldbalanceDest    0\n",
+       "newbalanceDest    0\n",
+       "isFraud           0\n",
+       "isFlaggedFraud    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_values = financial_data.isnull().sum()\n",
+    "missing_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\dulce\\AppData\\Local\\Temp\\ipykernel_16748\\862492489.py:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  correlation_matrix = financial_data.corr()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Understanding the Correlatio\n",
+    "\n",
+    "correlation_matrix = financial_data.corr()\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
+    "plt.title('Correlation Matrix')\n",
+    "plt.show()"
    ]
   },
   {
@@ -44,11 +458,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your response here"
+    "fraud_distribution = financial_data['isFraud'].value_counts()\n",
+    "\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.barplot(x=fraud_distribution.index, y=fraud_distribution.values)\n",
+    "plt.title('Distribution of Fraudulent Transactions')\n",
+    "plt.xlabel('isFraud')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xticks([0, 1], ['Non-Fraudulent (0)', 'Fraudulent (1)'])\n",
+    "plt.show()"
    ]
   },
   {
@@ -60,11 +493,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "financial_data_cleaned = financial_data.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "financial_data_cleaned['timestamp'] = pd.to_datetime(financial_data_cleaned['step'] * 3600, unit='s')\n",
+    "\n",
+    "financial_data_cleaned['time_since_last_transaction'] = financial_data_cleaned.groupby('nameOrig')['timestamp'].diff().dt.total_seconds().fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "column_name_mapping = {\n",
+    "    'step': 'Time_step',\n",
+    "    'type': 'Transaction_type',\n",
+    "    'amount': 'Transaction_amount',\n",
+    "    'nameOrig': 'Sender_account_name',\n",
+    "    'oldbalanceOrg': 'Sender_starting_balance',\n",
+    "    'newbalanceOrig': 'Sender_ending_balance',\n",
+    "    'nameDest': 'Destinatary_account_name',\n",
+    "    'oldbalanceDest': 'Destinatary_starting_balance',\n",
+    "    'newbalanceDest': 'Destinatary_ending_balance',\n",
+    "    'isFraud': 'Fraudulent_transaction',\n",
+    "    'isFlaggedFraud': 'Flagged_as_fraud'\n",
+    "}\n",
+    "\n",
+    "financial_data_cleaned.rename(columns=column_name_mapping, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "financial_data_cleaned.to_csv('cleaned_financial_data.csv', index = False)"
    ]
   },
   {
@@ -76,11 +552,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 1.00\n",
+      "Confusion Matrix:\n",
+      "[[19944    33]\n",
+      " [    0    23]]\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00     19977\n",
+      "           1       0.41      1.00      0.58        23\n",
+      "\n",
+      "    accuracy                           1.00     20000\n",
+      "   macro avg       0.71      1.00      0.79     20000\n",
+      "weighted avg       1.00      1.00      1.00     20000\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "X = financial_data_cleaned[['Transaction_amount', 'Sender_starting_balance', 'Sender_ending_balance']]\n",
+    "y = financial_data_cleaned['Fraudulent_transaction']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
+    "\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f'Accuracy: {accuracy:.2f}')\n",
+    "\n",
+    "confusion = confusion_matrix(y_test, y_pred)\n",
+    "report = classification_report(y_test, y_pred)\n",
+    "\n",
+    "print('Confusion Matrix:')\n",
+    "print(confusion)\n",
+    "print('\\nClassification Report:')\n",
+    "print(report)"
    ]
   },
   {
@@ -92,11 +609,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 1.00\n",
+      "Confusion Matrix:\n",
+      "[[19981     0]\n",
+      " [   17     2]]\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00     19981\n",
+      "           1       1.00      0.11      0.19        19\n",
+      "\n",
+      "    accuracy                           1.00     20000\n",
+      "   macro avg       1.00      0.55      0.60     20000\n",
+      "weighted avg       1.00      1.00      1.00     20000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "X = financial_data_cleaned[['Transaction_amount', 'Sender_starting_balance', 'Sender_ending_balance']]\n",
+    "y = financial_data_cleaned['Fraudulent_transaction']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
+    "\n",
+    "model = SVC()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f'Accuracy: {accuracy:.2f}')\n",
+    "\n",
+    "confusion = confusion_matrix(y_test, y_pred)\n",
+    "report = classification_report(y_test, y_pred)\n",
+    "\n",
+    "print('Confusion Matrix:')\n",
+    "print(confusion)\n",
+    "print('\\nClassification Report:')\n",
+    "print(report)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 1.00\n",
+      "Confusion Matrix:\n",
+      "[[19976     2]\n",
+      " [   14     8]]\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00     19978\n",
+      "           1       0.80      0.36      0.50        22\n",
+      "\n",
+      "    accuracy                           1.00     20000\n",
+      "   macro avg       0.90      0.68      0.75     20000\n",
+      "weighted avg       1.00      1.00      1.00     20000\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "X = financial_data_cleaned[['Transaction_amount', 'Sender_starting_balance', 'Sender_ending_balance']]\n",
+    "y = financial_data_cleaned['Fraudulent_transaction']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
+    "\n",
+    "model = RandomForestClassifier()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f'Accuracy: {accuracy:.2f}')\n",
+    "\n",
+    "confusion = confusion_matrix(y_test, y_pred)\n",
+    "report = classification_report(y_test, y_pred)\n",
+    "\n",
+    "print('Confusion Matrix:')\n",
+    "print(confusion)\n",
+    "print('\\nClassification Report:')\n",
+    "print(report)"
    ]
   },
   {
@@ -108,11 +720,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Seems like the Random Forest classifier performs better with a higher F1-score, precision, \n",
+    "# and recall compared to the SVM. "
    ]
   },
   {
@@ -139,7 +752,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,