diff --git a/report/heart_disease_predictor_report.ipynb b/report/heart_disease_predictor_report.ipynb
index 5ab044f..e7197bb 100644
--- a/report/heart_disease_predictor_report.ipynb
+++ b/report/heart_disease_predictor_report.ipynb
@@ -1352,7 +1352,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Save the processed data to be further used"
+    "## Data Validation"
    ]
   },
   {
@@ -1360,6 +1360,90 @@
    "execution_count": 24,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import pandera as pa \n",
+    "from pandera import Column, Check, DataFrameSchema\n",
+    "\n",
+    "schema = pa.DataFrameSchema(\n",
+    "    {\n",
+    "        # Numeric columns with no missing values allowed\n",
+    "        \"age\": pa.Column(int, pa.Check.between(0, 120), nullable=False),\n",
+    "        \"sex\": pa.Column(int, pa.Check.isin([0, 1]), nullable=False),\n",
+    "\n",
+    "        # Categorical columns with string types\n",
+    "        \"chest_pain_type\": pa.Column(\n",
+    "            str,\n",
+    "            pa.Check.isin([\"typical angina\", \"atypical angina\", \"non-anginal pain\", \"asymptomatic\"]),\n",
+    "            nullable=False\n",
+    "        ),\n",
+    "\n",
+    "        # Numeric columns with range checks and no missing values\n",
+    "        \"resting_blood_pressure\": pa.Column(int, pa.Check.between(50, 250), nullable=False),\n",
+    "        \"cholesterol\": pa.Column(int, pa.Check.between(100, 600), nullable=False),\n",
+    "        \"fasting_blood_sugar\": pa.Column(int, pa.Check.isin([0, 1]), nullable=False),\n",
+    "\n",
+    "        # Categorical columns\n",
+    "        \"rest_ecg\": pa.Column(\n",
+    "            str,\n",
+    "            pa.Check.isin([\"normal\", \"ST-T wave abnormality\", \"left ventricular hypertrophy\"]),\n",
+    "            nullable=False\n",
+    "        ),\n",
+    "       # Numeric columns with range checks\n",
+    "        \"max_heart_rate\": Column(int, Check.between(50, 220), nullable=False),\n",
+    "        \n",
+    "        # Categorical column with a string type\n",
+    "        \"exercise_induced_angina\": Column(\n",
+    "            str,\n",
+    "            Check.isin([\"yes\", \"no\"]),\n",
+    "            nullable=False),    \n",
+    "        \n",
+    "        # Numeric columns with missing values allowed up to 5%\n",
+    "        \"st_depression\": Column(\n",
+    "            float,\n",
+    "            Check.between(0.0, 10.0),\n",
+    "            Check(lambda s: s.isna().mean() <= 0.05, element_wise=False, \n",
+    "                  error=\"Too many null values in 'st_depression' column.\"),\n",
+    "            nullable=True),\n",
+    "        \n",
+    "        \"num_of_vessels\": Column(\n",
+    "            float,\n",
+    "            Check(lambda s: s.isna() | ((s >= 0) & (s <= 4)), element_wise=True),\n",
+    "            Check(lambda s: s.isna().mean() <= 0.05, element_wise=False, \n",
+    "                  error=\"Too many null values in 'num_of_vessels' column.\"),\n",
+    "            nullable=True),\n",
+    "        \n",
+    "        # Categorical column with missing values allowed up to 5%\n",
+    "        \"thalassemia\": Column(\n",
+    "            str,\n",
+    "            Check.isin([\"normal\", \"fixed defect\", \"reversable defect\"]),\n",
+    "            Check(lambda s: s.isna().mean() <= 0.05, element_wise=False, \n",
+    "                  error=\"Too many null values in 'thalassemia' column.\"),\n",
+    "            nullable=True), \n",
+    "     \n",
+    "        # Target column\n",
+    "        \"diagnosis\": Column(int, Check.isin([0, 1]), nullable=False),\n",
+    "    },\n",
+    "        checks=[\n",
+    "        # Check for8\n",
+    "        Check(lambda df: ~df.duplicated().any(), error=\"Duplicate rows found.\"),\n",
+    "        # Check for empty rows\n",
+    "        Check(lambda df: ~(df.isna().all(axis=1)).any(), error=\"Empty rows found.\")\n",
+    "    ]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save the processed data to be further used"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df.to_csv(\"../data/processed/processed_heart_disease_data.csv\", index=False)"
    ]
@@ -1373,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1383,7 +1467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1392,7 +1476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1442,7 +1526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1466,7 +1550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1475,7 +1559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1541,7 +1625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1576,7 +1660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -2019,12 +2103,13 @@
        "                                  'num_of_vessels', 'thalassemia'])])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "\n",
     "#splitting the features \n",
     "\n",
     "numeric_features = [\n",
@@ -2059,7 +2144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2072,7 +2157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -2484,7 +2569,7 @@
        "[207 rows x 25 columns]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2500,7 +2585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -2519,7 +2604,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2530,7 +2615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2559,7 +2644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -2590,12 +2675,12 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>fit_time</th>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0.004</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>0.005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>score_time</th>\n",
-       "      <td>0.012</td>\n",
+       "      <td>0.014</td>\n",
        "      <td>0.002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2644,8 +2729,8 @@
       ],
       "text/plain": [
        "                  mean    std\n",
-       "fit_time         0.011  0.004\n",
-       "score_time       0.012  0.002\n",
+       "fit_time         0.015  0.005\n",
+       "score_time       0.014  0.002\n",
        "test_accuracy    0.677  0.123\n",
        "train_accuracy   1.000  0.000\n",
        "test_precision   0.659  0.111\n",
@@ -2656,7 +2741,7 @@
        "train_f1         1.000  0.000"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2676,7 +2761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2689,7 +2774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -2699,7 +2784,7 @@
        "       [ 6, 31]])"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2719,7 +2804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -2778,7 +2863,7 @@
        "accuracy   0.755556  0.755556  0.755556"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2804,7 +2889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -2835,12 +2920,12 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>fit_time</th>\n",
-       "      <td>0.013</td>\n",
-       "      <td>0.004</td>\n",
+       "      <td>0.016</td>\n",
+       "      <td>0.008</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>score_time</th>\n",
-       "      <td>0.010</td>\n",
+       "      <td>0.012</td>\n",
        "      <td>0.001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2889,8 +2974,8 @@
       ],
       "text/plain": [
        "                  mean    std\n",
-       "fit_time         0.013  0.004\n",
-       "score_time       0.010  0.001\n",
+       "fit_time         0.016  0.008\n",
+       "score_time       0.012  0.001\n",
        "test_accuracy    0.841  0.068\n",
        "train_accuracy   0.890  0.011\n",
        "test_precision   0.850  0.092\n",
@@ -2901,7 +2986,7 @@
        "train_f1         0.882  0.014"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2920,7 +3005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -2945,7 +3030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -3428,7 +3513,7 @@
        "                 LogisticRegression(max_iter=1000, random_state=123))])"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3448,7 +3533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3457,7 +3542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3472,245 +3557,245 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_d6e4e_row0_col1 {\n",
+       "#T_0b6d4_row0_col1 {\n",
        "  background-color: #b40426;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d6e4e_row1_col1 {\n",
+       "#T_0b6d4_row1_col1 {\n",
        "  background-color: #da5a49;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d6e4e_row2_col1 {\n",
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-       "#T_d6e4e_row3_col1 {\n",
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        "</style>\n",
-       "<table id=\"T_d6e4e\">\n",
+       "<table id=\"T_0b6d4\">\n",
        "  <caption>Table 1: Logistic Regression Coefficients</caption>\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_d6e4e_level0_col0\" class=\"col_heading level0 col0\" >Feature</th>\n",
-       "      <th id=\"T_d6e4e_level0_col1\" class=\"col_heading level0 col1\" >Coefficient</th>\n",
+       "      <th id=\"T_0b6d4_level0_col0\" class=\"col_heading level0 col0\" >Feature</th>\n",
+       "      <th id=\"T_0b6d4_level0_col1\" class=\"col_heading level0 col1\" >Coefficient</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
-       "      <td id=\"T_d6e4e_row0_col0\" class=\"data row0 col0\" >chest_pain_type_asymptomatic</td>\n",
-       "      <td id=\"T_d6e4e_row0_col1\" class=\"data row0 col1\" >1.242617</td>\n",
+       "      <th id=\"T_0b6d4_level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
+       "      <td id=\"T_0b6d4_row0_col0\" class=\"data row0 col0\" >chest_pain_type_asymptomatic</td>\n",
+       "      <td id=\"T_0b6d4_row0_col1\" class=\"data row0 col1\" >1.242617</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
-       "      <td id=\"T_d6e4e_row1_col0\" class=\"data row1 col0\" >num_of_vessels_2.0</td>\n",
-       "      <td id=\"T_d6e4e_row1_col1\" class=\"data row1 col1\" >0.936761</td>\n",
+       "      <th id=\"T_0b6d4_level0_row1\" class=\"row_heading level0 row1\" >20</th>\n",
+       "      <td id=\"T_0b6d4_row1_col0\" class=\"data row1 col0\" >num_of_vessels_2.0</td>\n",
+       "      <td id=\"T_0b6d4_row1_col1\" class=\"data row1 col1\" >0.936761</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row2\" class=\"row_heading level0 row2\" >24</th>\n",
-       "      <td id=\"T_d6e4e_row2_col0\" class=\"data row2 col0\" >thalassemia_reversable defect</td>\n",
-       "      <td id=\"T_d6e4e_row2_col1\" class=\"data row2 col1\" >0.908607</td>\n",
+       "      <th id=\"T_0b6d4_level0_row2\" class=\"row_heading level0 row2\" >24</th>\n",
+       "      <td id=\"T_0b6d4_row2_col0\" class=\"data row2 col0\" >thalassemia_reversable defect</td>\n",
+       "      <td id=\"T_0b6d4_row2_col1\" class=\"data row2 col1\" >0.908607</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row3\" class=\"row_heading level0 row3\" >16</th>\n",
-       "      <td id=\"T_d6e4e_row3_col0\" class=\"data row3 col0\" >slope_flat</td>\n",
-       "      <td id=\"T_d6e4e_row3_col1\" class=\"data row3 col1\" >0.789179</td>\n",
+       "      <th id=\"T_0b6d4_level0_row3\" class=\"row_heading level0 row3\" >16</th>\n",
+       "      <td id=\"T_0b6d4_row3_col0\" class=\"data row3 col0\" >slope_flat</td>\n",
+       "      <td id=\"T_0b6d4_row3_col1\" class=\"data row3 col1\" >0.789179</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row4\" class=\"row_heading level0 row4\" >6</th>\n",
-       "      <td id=\"T_d6e4e_row4_col0\" class=\"data row4 col0\" >sex</td>\n",
-       "      <td id=\"T_d6e4e_row4_col1\" class=\"data row4 col1\" >0.467793</td>\n",
+       "      <th id=\"T_0b6d4_level0_row4\" class=\"row_heading level0 row4\" >6</th>\n",
+       "      <td id=\"T_0b6d4_row4_col0\" class=\"data row4 col0\" >sex</td>\n",
+       "      <td id=\"T_0b6d4_row4_col1\" class=\"data row4 col1\" >0.467793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row5\" class=\"row_heading level0 row5\" >1</th>\n",
-       "      <td id=\"T_d6e4e_row5_col0\" class=\"data row5 col0\" >resting_blood_pressure</td>\n",
-       "      <td id=\"T_d6e4e_row5_col1\" class=\"data row5 col1\" >0.464303</td>\n",
+       "      <th id=\"T_0b6d4_level0_row5\" class=\"row_heading level0 row5\" >1</th>\n",
+       "      <td id=\"T_0b6d4_row5_col0\" class=\"data row5 col0\" >resting_blood_pressure</td>\n",
+       "      <td id=\"T_0b6d4_row5_col1\" class=\"data row5 col1\" >0.464303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row6\" class=\"row_heading level0 row6\" >21</th>\n",
-       "      <td id=\"T_d6e4e_row6_col0\" class=\"data row6 col0\" >num_of_vessels_3.0</td>\n",
-       "      <td id=\"T_d6e4e_row6_col1\" class=\"data row6 col1\" >0.416970</td>\n",
+       "      <th id=\"T_0b6d4_level0_row6\" class=\"row_heading level0 row6\" >21</th>\n",
+       "      <td id=\"T_0b6d4_row6_col0\" class=\"data row6 col0\" >num_of_vessels_3.0</td>\n",
+       "      <td id=\"T_0b6d4_row6_col1\" class=\"data row6 col1\" >0.416970</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row7\" class=\"row_heading level0 row7\" >5</th>\n",
-       "      <td id=\"T_d6e4e_row7_col0\" class=\"data row7 col0\" >st_depression</td>\n",
-       "      <td id=\"T_d6e4e_row7_col1\" class=\"data row7 col1\" >0.400422</td>\n",
+       "      <th id=\"T_0b6d4_level0_row7\" class=\"row_heading level0 row7\" >5</th>\n",
+       "      <td id=\"T_0b6d4_row7_col0\" class=\"data row7 col0\" >st_depression</td>\n",
+       "      <td id=\"T_0b6d4_row7_col1\" class=\"data row7 col1\" >0.400422</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row8\" class=\"row_heading level0 row8\" >12</th>\n",
-       "      <td id=\"T_d6e4e_row8_col0\" class=\"data row8 col0\" >rest_ecg_left ventricular hypertrophy</td>\n",
-       "      <td id=\"T_d6e4e_row8_col1\" class=\"data row8 col1\" >0.268959</td>\n",
+       "      <th id=\"T_0b6d4_level0_row8\" class=\"row_heading level0 row8\" >12</th>\n",
+       "      <td id=\"T_0b6d4_row8_col0\" class=\"data row8 col0\" >rest_ecg_left ventricular hypertrophy</td>\n",
+       "      <td id=\"T_0b6d4_row8_col1\" class=\"data row8 col1\" >0.268959</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row9\" class=\"row_heading level0 row9\" >14</th>\n",
-       "      <td id=\"T_d6e4e_row9_col0\" class=\"data row9 col0\" >exercise_induced_angina_yes</td>\n",
-       "      <td id=\"T_d6e4e_row9_col1\" class=\"data row9 col1\" >0.210783</td>\n",
+       "      <th id=\"T_0b6d4_level0_row9\" class=\"row_heading level0 row9\" >14</th>\n",
+       "      <td id=\"T_0b6d4_row9_col0\" class=\"data row9 col0\" >exercise_induced_angina_yes</td>\n",
+       "      <td id=\"T_0b6d4_row9_col1\" class=\"data row9 col1\" >0.210783</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row10\" class=\"row_heading level0 row10\" >8</th>\n",
-       "      <td id=\"T_d6e4e_row10_col0\" class=\"data row10 col0\" >chest_pain_type_atypical angina</td>\n",
-       "      <td id=\"T_d6e4e_row10_col1\" class=\"data row10 col1\" >0.151616</td>\n",
+       "      <th id=\"T_0b6d4_level0_row10\" class=\"row_heading level0 row10\" >8</th>\n",
+       "      <td id=\"T_0b6d4_row10_col0\" class=\"data row10 col0\" >chest_pain_type_atypical angina</td>\n",
+       "      <td id=\"T_0b6d4_row10_col1\" class=\"data row10 col1\" >0.151616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "      <td id=\"T_d6e4e_row11_col0\" class=\"data row11 col0\" >rest_ecg_ST-T wave abnormality</td>\n",
-       "      <td id=\"T_d6e4e_row11_col1\" class=\"data row11 col1\" >0.150904</td>\n",
+       "      <th id=\"T_0b6d4_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_0b6d4_row11_col0\" class=\"data row11 col0\" >rest_ecg_ST-T wave abnormality</td>\n",
+       "      <td id=\"T_0b6d4_row11_col1\" class=\"data row11 col1\" >0.150904</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row12\" class=\"row_heading level0 row12\" >3</th>\n",
-       "      <td id=\"T_d6e4e_row12_col0\" class=\"data row12 col0\" >cholesterol</td>\n",
-       "      <td id=\"T_d6e4e_row12_col1\" class=\"data row12 col1\" >0.141675</td>\n",
+       "      <th id=\"T_0b6d4_level0_row12\" class=\"row_heading level0 row12\" >3</th>\n",
+       "      <td id=\"T_0b6d4_row12_col0\" class=\"data row12 col0\" >cholesterol</td>\n",
+       "      <td id=\"T_0b6d4_row12_col1\" class=\"data row12 col1\" >0.141675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row13\" class=\"row_heading level0 row13\" >19</th>\n",
-       "      <td id=\"T_d6e4e_row13_col0\" class=\"data row13 col0\" >num_of_vessels_1.0</td>\n",
-       "      <td id=\"T_d6e4e_row13_col1\" class=\"data row13 col1\" >0.131598</td>\n",
+       "      <th id=\"T_0b6d4_level0_row13\" class=\"row_heading level0 row13\" >19</th>\n",
+       "      <td id=\"T_0b6d4_row13_col0\" class=\"data row13 col0\" >num_of_vessels_1.0</td>\n",
+       "      <td id=\"T_0b6d4_row13_col1\" class=\"data row13 col1\" >0.131598</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row14\" class=\"row_heading level0 row14\" >0</th>\n",
-       "      <td id=\"T_d6e4e_row14_col0\" class=\"data row14 col0\" >age</td>\n",
-       "      <td id=\"T_d6e4e_row14_col1\" class=\"data row14 col1\" >-0.089145</td>\n",
+       "      <th id=\"T_0b6d4_level0_row14\" class=\"row_heading level0 row14\" >0</th>\n",
+       "      <td id=\"T_0b6d4_row14_col0\" class=\"data row14 col0\" >age</td>\n",
+       "      <td id=\"T_0b6d4_row14_col1\" class=\"data row14 col1\" >-0.089145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row15\" class=\"row_heading level0 row15\" >2</th>\n",
-       "      <td id=\"T_d6e4e_row15_col0\" class=\"data row15 col0\" >fasting_blood_sugar</td>\n",
-       "      <td id=\"T_d6e4e_row15_col1\" class=\"data row15 col1\" >-0.226329</td>\n",
+       "      <th id=\"T_0b6d4_level0_row15\" class=\"row_heading level0 row15\" >2</th>\n",
+       "      <td id=\"T_0b6d4_row15_col0\" class=\"data row15 col0\" >fasting_blood_sugar</td>\n",
+       "      <td id=\"T_0b6d4_row15_col1\" class=\"data row15 col1\" >-0.226329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
-       "      <td id=\"T_d6e4e_row16_col0\" class=\"data row16 col0\" >slope_upsloping</td>\n",
-       "      <td id=\"T_d6e4e_row16_col1\" class=\"data row16 col1\" >-0.373995</td>\n",
+       "      <th id=\"T_0b6d4_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
+       "      <td id=\"T_0b6d4_row16_col0\" class=\"data row16 col0\" >slope_upsloping</td>\n",
+       "      <td id=\"T_0b6d4_row16_col1\" class=\"data row16 col1\" >-0.373995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row17\" class=\"row_heading level0 row17\" >15</th>\n",
-       "      <td id=\"T_d6e4e_row17_col0\" class=\"data row17 col0\" >slope_downsloping</td>\n",
-       "      <td id=\"T_d6e4e_row17_col1\" class=\"data row17 col1\" >-0.385061</td>\n",
+       "      <th id=\"T_0b6d4_level0_row17\" class=\"row_heading level0 row17\" >15</th>\n",
+       "      <td id=\"T_0b6d4_row17_col0\" class=\"data row17 col0\" >slope_downsloping</td>\n",
+       "      <td id=\"T_0b6d4_row17_col1\" class=\"data row17 col1\" >-0.385061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row18\" class=\"row_heading level0 row18\" >13</th>\n",
-       "      <td id=\"T_d6e4e_row18_col0\" class=\"data row18 col0\" >rest_ecg_normal</td>\n",
-       "      <td id=\"T_d6e4e_row18_col1\" class=\"data row18 col1\" >-0.389740</td>\n",
+       "      <th id=\"T_0b6d4_level0_row18\" class=\"row_heading level0 row18\" >13</th>\n",
+       "      <td id=\"T_0b6d4_row18_col0\" class=\"data row18 col0\" >rest_ecg_normal</td>\n",
+       "      <td id=\"T_0b6d4_row18_col1\" class=\"data row18 col1\" >-0.389740</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row19\" class=\"row_heading level0 row19\" >22</th>\n",
-       "      <td id=\"T_d6e4e_row19_col0\" class=\"data row19 col0\" >thalassemia_fixed defect</td>\n",
-       "      <td id=\"T_d6e4e_row19_col1\" class=\"data row19 col1\" >-0.411370</td>\n",
+       "      <th id=\"T_0b6d4_level0_row19\" class=\"row_heading level0 row19\" >22</th>\n",
+       "      <td id=\"T_0b6d4_row19_col0\" class=\"data row19 col0\" >thalassemia_fixed defect</td>\n",
+       "      <td id=\"T_0b6d4_row19_col1\" class=\"data row19 col1\" >-0.411370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row20\" class=\"row_heading level0 row20\" >23</th>\n",
-       "      <td id=\"T_d6e4e_row20_col0\" class=\"data row20 col0\" >thalassemia_normal</td>\n",
-       "      <td id=\"T_d6e4e_row20_col1\" class=\"data row20 col1\" >-0.467113</td>\n",
+       "      <th id=\"T_0b6d4_level0_row20\" class=\"row_heading level0 row20\" >23</th>\n",
+       "      <td id=\"T_0b6d4_row20_col0\" class=\"data row20 col0\" >thalassemia_normal</td>\n",
+       "      <td id=\"T_0b6d4_row20_col1\" class=\"data row20 col1\" >-0.467113</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row21\" class=\"row_heading level0 row21\" >4</th>\n",
-       "      <td id=\"T_d6e4e_row21_col0\" class=\"data row21 col0\" >max_heart_rate</td>\n",
-       "      <td id=\"T_d6e4e_row21_col1\" class=\"data row21 col1\" >-0.648489</td>\n",
+       "      <th id=\"T_0b6d4_level0_row21\" class=\"row_heading level0 row21\" >4</th>\n",
+       "      <td id=\"T_0b6d4_row21_col0\" class=\"data row21 col0\" >max_heart_rate</td>\n",
+       "      <td id=\"T_0b6d4_row21_col1\" class=\"data row21 col1\" >-0.648489</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row22\" class=\"row_heading level0 row22\" >9</th>\n",
-       "      <td id=\"T_d6e4e_row22_col0\" class=\"data row22 col0\" >chest_pain_type_non-anginal pain</td>\n",
-       "      <td id=\"T_d6e4e_row22_col1\" class=\"data row22 col1\" >-0.670571</td>\n",
+       "      <th id=\"T_0b6d4_level0_row22\" class=\"row_heading level0 row22\" >9</th>\n",
+       "      <td id=\"T_0b6d4_row22_col0\" class=\"data row22 col0\" >chest_pain_type_non-anginal pain</td>\n",
+       "      <td id=\"T_0b6d4_row22_col1\" class=\"data row22 col1\" >-0.670571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row23\" class=\"row_heading level0 row23\" >10</th>\n",
-       "      <td id=\"T_d6e4e_row23_col0\" class=\"data row23 col0\" >chest_pain_type_typical angina</td>\n",
-       "      <td id=\"T_d6e4e_row23_col1\" class=\"data row23 col1\" >-0.693539</td>\n",
+       "      <th id=\"T_0b6d4_level0_row23\" class=\"row_heading level0 row23\" >10</th>\n",
+       "      <td id=\"T_0b6d4_row23_col0\" class=\"data row23 col0\" >chest_pain_type_typical angina</td>\n",
+       "      <td id=\"T_0b6d4_row23_col1\" class=\"data row23 col1\" >-0.693539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d6e4e_level0_row24\" class=\"row_heading level0 row24\" >18</th>\n",
-       "      <td id=\"T_d6e4e_row24_col0\" class=\"data row24 col0\" >num_of_vessels_0.0</td>\n",
-       "      <td id=\"T_d6e4e_row24_col1\" class=\"data row24 col1\" >-1.455206</td>\n",
+       "      <th id=\"T_0b6d4_level0_row24\" class=\"row_heading level0 row24\" >18</th>\n",
+       "      <td id=\"T_0b6d4_row24_col0\" class=\"data row24 col0\" >num_of_vessels_0.0</td>\n",
+       "      <td id=\"T_0b6d4_row24_col1\" class=\"data row24 col1\" >-1.455206</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x155eded20>"
+       "<pandas.io.formats.style.Styler at 0x145f2acd0>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3730,7 +3815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -3758,7 +3843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3769,7 +3854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -3779,7 +3864,7 @@
        "       [ 8, 29]])"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3799,7 +3884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -3858,7 +3943,7 @@
        "accuracy   0.822222  0.822222  0.822222"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3938,9 +4023,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:heart_disease_522]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-heart_disease_522-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3952,7 +4037,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,