Endometrial Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ea6b3f8",
   "metadata": {},
   "source": [
    "# Endometrial Cancer Prediction Dataset\n",
    "\n",
    "This dataset contains information about endometrial cancer, also known as endometrial carcinoma, which is a type of cancer that starts in the cells of the inner lining of the uterus (the endometrium). Endometrial carcinomas can be categorized into different types based on cellular characteristics observed under a microscope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9b66e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82c0c766",
   "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>Patient ID</th>\n",
       "      <th>Sample ID</th>\n",
       "      <th>Cancer Type Detailed</th>\n",
       "      <th>Overall Survival Status</th>\n",
       "      <th>Disease Free Status</th>\n",
       "      <th>Disease-specific Survival status</th>\n",
       "      <th>Mutation Count</th>\n",
       "      <th>Fraction Genome Altered</th>\n",
       "      <th>Diagnosis Age</th>\n",
       "      <th>MSI MANTIS Score</th>\n",
       "      <th>MSIsensor Score</th>\n",
       "      <th>Race Category</th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Tumor Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-2E-A9G8</td>\n",
       "      <td>TCGA-2E-A9G8-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.3311</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.3234</td>\n",
       "      <td>0.85</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_HIGH</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA-4E-A92E</td>\n",
       "      <td>TCGA-4E-A92E-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.0341</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.3396</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_LOW</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCGA-5B-A90C</td>\n",
       "      <td>TCGA-5B-A90C-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.6903</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.3344</td>\n",
       "      <td>0.55</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_HIGH</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCGA-5S-A9Q8</td>\n",
       "      <td>TCGA-5S-A9Q8-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0581</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.3199</td>\n",
       "      <td>0.09</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_LOW</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCGA-A5-A0G1</td>\n",
       "      <td>TCGA-A5-A0G1-01</td>\n",
       "      <td>Uterine Serous Carcinoma/Uterine Papillary Ser...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>10808.0</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.3108</td>\n",
       "      <td>1.74</td>\n",
       "      <td>White</td>\n",
       "      <td>UCEC_POLE</td>\n",
       "      <td>Serous Endometrial Adenocarcinoma</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>TCGA-QS-A8F1</td>\n",
       "      <td>TCGA-QS-A8F1-01</td>\n",
       "      <td>Uterine Serous Carcinoma/Uterine Papillary Ser...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1:DEAD WITH TUMOR</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.6549</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.3647</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_HIGH</td>\n",
       "      <td>Serous Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>TCGA-SJ-A6ZI</td>\n",
       "      <td>TCGA-SJ-A6ZI-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>1306.0</td>\n",
       "      <td>0.0275</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.6138</td>\n",
       "      <td>14.35</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_MSI</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>TCGA-SJ-A6ZJ</td>\n",
       "      <td>TCGA-SJ-A6ZJ-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0466</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.3382</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_LOW</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>TCGA-SL-A6J9</td>\n",
       "      <td>TCGA-SL-A6J9-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.4226</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.3503</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_HIGH</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>TCGA-SL-A6JA</td>\n",
       "      <td>TCGA-SL-A6JA-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>742.0</td>\n",
       "      <td>0.0488</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.9515</td>\n",
       "      <td>26.10</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_MSI</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>529 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Patient ID        Sample ID  \\\n",
       "0    TCGA-2E-A9G8  TCGA-2E-A9G8-01   \n",
       "1    TCGA-4E-A92E  TCGA-4E-A92E-01   \n",
       "2    TCGA-5B-A90C  TCGA-5B-A90C-01   \n",
       "3    TCGA-5S-A9Q8  TCGA-5S-A9Q8-01   \n",
       "4    TCGA-A5-A0G1  TCGA-A5-A0G1-01   \n",
       "..            ...              ...   \n",
       "524  TCGA-QS-A8F1  TCGA-QS-A8F1-01   \n",
       "525  TCGA-SJ-A6ZI  TCGA-SJ-A6ZI-01   \n",
       "526  TCGA-SJ-A6ZJ  TCGA-SJ-A6ZJ-01   \n",
       "527  TCGA-SL-A6J9  TCGA-SL-A6J9-01   \n",
       "528  TCGA-SL-A6JA  TCGA-SL-A6JA-01   \n",
       "\n",
       "                                  Cancer Type Detailed  \\\n",
       "0                       Uterine Endometrioid Carcinoma   \n",
       "1                       Uterine Endometrioid Carcinoma   \n",
       "2                       Uterine Endometrioid Carcinoma   \n",
       "3                       Uterine Endometrioid Carcinoma   \n",
       "4    Uterine Serous Carcinoma/Uterine Papillary Ser...   \n",
       "..                                                 ...   \n",
       "524  Uterine Serous Carcinoma/Uterine Papillary Ser...   \n",
       "525                     Uterine Endometrioid Carcinoma   \n",
       "526                     Uterine Endometrioid Carcinoma   \n",
       "527                     Uterine Endometrioid Carcinoma   \n",
       "528                     Uterine Endometrioid Carcinoma   \n",
       "\n",
       "    Overall Survival Status Disease Free Status  \\\n",
       "0                  0:LIVING                 NaN   \n",
       "1                  0:LIVING       0:DiseaseFree   \n",
       "2                  0:LIVING                 NaN   \n",
       "3                  0:LIVING       0:DiseaseFree   \n",
       "4                1:DECEASED       0:DiseaseFree   \n",
       "..                      ...                 ...   \n",
       "524              1:DECEASED                 NaN   \n",
       "525                0:LIVING       0:DiseaseFree   \n",
       "526                0:LIVING       0:DiseaseFree   \n",
       "527                0:LIVING                 NaN   \n",
       "528                0:LIVING       0:DiseaseFree   \n",
       "\n",
       "    Disease-specific Survival status  Mutation Count  Fraction Genome Altered  \\\n",
       "0         0:ALIVE OR DEAD TUMOR FREE            65.0                   0.3311   \n",
       "1         0:ALIVE OR DEAD TUMOR FREE           147.0                   0.0341   \n",
       "2         0:ALIVE OR DEAD TUMOR FREE            45.0                   0.6903   \n",
       "3         0:ALIVE OR DEAD TUMOR FREE            50.0                   0.0581   \n",
       "4         0:ALIVE OR DEAD TUMOR FREE         10808.0                   0.0001   \n",
       "..                               ...             ...                      ...   \n",
       "524                1:DEAD WITH TUMOR            63.0                   0.6549   \n",
       "525       0:ALIVE OR DEAD TUMOR FREE          1306.0                   0.0275   \n",
       "526       0:ALIVE OR DEAD TUMOR FREE            56.0                   0.0466   \n",
       "527       0:ALIVE OR DEAD TUMOR FREE            48.0                   0.4226   \n",
       "528       0:ALIVE OR DEAD TUMOR FREE           742.0                   0.0488   \n",
       "\n",
       "     Diagnosis Age  MSI MANTIS Score  MSIsensor Score  \\\n",
       "0             59.0            0.3234             0.85   \n",
       "1             54.0            0.3396             0.01   \n",
       "2             69.0            0.3344             0.55   \n",
       "3             51.0            0.3199             0.09   \n",
       "4             67.0            0.3108             1.74   \n",
       "..             ...               ...              ...   \n",
       "524           85.0            0.3647             0.15   \n",
       "525           64.0            0.6138            14.35   \n",
       "526           61.0            0.3382             0.00   \n",
       "527           73.0            0.3503             0.03   \n",
       "528           77.0            0.9515            26.10   \n",
       "\n",
       "                 Race Category       Subtype  \\\n",
       "0    Black or African American  UCEC_CN_HIGH   \n",
       "1    Black or African American   UCEC_CN_LOW   \n",
       "2    Black or African American  UCEC_CN_HIGH   \n",
       "3    Black or African American   UCEC_CN_LOW   \n",
       "4                        White     UCEC_POLE   \n",
       "..                         ...           ...   \n",
       "524  Black or African American  UCEC_CN_HIGH   \n",
       "525  Black or African American      UCEC_MSI   \n",
       "526  Black or African American   UCEC_CN_LOW   \n",
       "527  Black or African American  UCEC_CN_HIGH   \n",
       "528  Black or African American      UCEC_MSI   \n",
       "\n",
       "                                  Tumor Type  \n",
       "0    Endometrioid Endometrial Adenocarcinoma  \n",
       "1    Endometrioid Endometrial Adenocarcinoma  \n",
       "2    Endometrioid Endometrial Adenocarcinoma  \n",
       "3    Endometrioid Endometrial Adenocarcinoma  \n",
       "4          Serous Endometrial Adenocarcinoma  \n",
       "..                                       ...  \n",
       "524        Serous Endometrial Adenocarcinoma  \n",
       "525  Endometrioid Endometrial Adenocarcinoma  \n",
       "526  Endometrioid Endometrial Adenocarcinoma  \n",
       "527  Endometrioid Endometrial Adenocarcinoma  \n",
       "528  Endometrioid Endometrial Adenocarcinoma  \n",
       "\n",
       "[529 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the dataset\n",
    "#Read the dataset in data varaible\n",
    "\n",
    "data = pd.read_csv(r\"Uterine Corpus Endometrial Carcinoma dataset.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9b082085",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FEATURES:\n",
      "        Patient ID        Sample ID  \\\n",
      "0    TCGA-2E-A9G8  TCGA-2E-A9G8-01   \n",
      "1    TCGA-4E-A92E  TCGA-4E-A92E-01   \n",
      "2    TCGA-5B-A90C  TCGA-5B-A90C-01   \n",
      "3    TCGA-5S-A9Q8  TCGA-5S-A9Q8-01   \n",
      "4    TCGA-A5-A0G1  TCGA-A5-A0G1-01   \n",
      "..            ...              ...   \n",
      "524  TCGA-QS-A8F1  TCGA-QS-A8F1-01   \n",
      "525  TCGA-SJ-A6ZI  TCGA-SJ-A6ZI-01   \n",
      "526  TCGA-SJ-A6ZJ  TCGA-SJ-A6ZJ-01   \n",
      "527  TCGA-SL-A6J9  TCGA-SL-A6J9-01   \n",
      "528  TCGA-SL-A6JA  TCGA-SL-A6JA-01   \n",
      "\n",
      "                                  Cancer Type Detailed  Mutation Count  \\\n",
      "0                       Uterine Endometrioid Carcinoma            65.0   \n",
      "1                       Uterine Endometrioid Carcinoma           147.0   \n",
      "2                       Uterine Endometrioid Carcinoma            45.0   \n",
      "3                       Uterine Endometrioid Carcinoma            50.0   \n",
      "4    Uterine Serous Carcinoma/Uterine Papillary Ser...         10808.0   \n",
      "..                                                 ...             ...   \n",
      "524  Uterine Serous Carcinoma/Uterine Papillary Ser...            63.0   \n",
      "525                     Uterine Endometrioid Carcinoma          1306.0   \n",
      "526                     Uterine Endometrioid Carcinoma            56.0   \n",
      "527                     Uterine Endometrioid Carcinoma            48.0   \n",
      "528                     Uterine Endometrioid Carcinoma           742.0   \n",
      "\n",
      "     Fraction Genome Altered  Diagnosis Age  MSI MANTIS Score  \\\n",
      "0                     0.3311           59.0            0.3234   \n",
      "1                     0.0341           54.0            0.3396   \n",
      "2                     0.6903           69.0            0.3344   \n",
      "3                     0.0581           51.0            0.3199   \n",
      "4                     0.0001           67.0            0.3108   \n",
      "..                       ...            ...               ...   \n",
      "524                   0.6549           85.0            0.3647   \n",
      "525                   0.0275           64.0            0.6138   \n",
      "526                   0.0466           61.0            0.3382   \n",
      "527                   0.4226           73.0            0.3503   \n",
      "528                   0.0488           77.0            0.9515   \n",
      "\n",
      "     MSIsensor Score              Race Category       Subtype  \\\n",
      "0               0.85  Black or African American  UCEC_CN_HIGH   \n",
      "1               0.01  Black or African American   UCEC_CN_LOW   \n",
      "2               0.55  Black or African American  UCEC_CN_HIGH   \n",
      "3               0.09  Black or African American   UCEC_CN_LOW   \n",
      "4               1.74                      White     UCEC_POLE   \n",
      "..               ...                        ...           ...   \n",
      "524             0.15  Black or African American  UCEC_CN_HIGH   \n",
      "525            14.35  Black or African American      UCEC_MSI   \n",
      "526             0.00  Black or African American   UCEC_CN_LOW   \n",
      "527             0.03  Black or African American  UCEC_CN_HIGH   \n",
      "528            26.10  Black or African American      UCEC_MSI   \n",
      "\n",
      "                                  Tumor Type  \n",
      "0    Endometrioid Endometrial Adenocarcinoma  \n",
      "1    Endometrioid Endometrial Adenocarcinoma  \n",
      "2    Endometrioid Endometrial Adenocarcinoma  \n",
      "3    Endometrioid Endometrial Adenocarcinoma  \n",
      "4          Serous Endometrial Adenocarcinoma  \n",
      "..                                       ...  \n",
      "524        Serous Endometrial Adenocarcinoma  \n",
      "525  Endometrioid Endometrial Adenocarcinoma  \n",
      "526  Endometrioid Endometrial Adenocarcinoma  \n",
      "527  Endometrioid Endometrial Adenocarcinoma  \n",
      "528  Endometrioid Endometrial Adenocarcinoma  \n",
      "\n",
      "[529 rows x 11 columns]\n",
      "LABELS:\n",
      "     Overall Survival Status Disease Free Status  \\\n",
      "0                  0:LIVING                 NaN   \n",
      "1                  0:LIVING       0:DiseaseFree   \n",
      "2                  0:LIVING                 NaN   \n",
      "3                  0:LIVING       0:DiseaseFree   \n",
      "4                1:DECEASED       0:DiseaseFree   \n",
      "..                      ...                 ...   \n",
      "524              1:DECEASED                 NaN   \n",
      "525                0:LIVING       0:DiseaseFree   \n",
      "526                0:LIVING       0:DiseaseFree   \n",
      "527                0:LIVING                 NaN   \n",
      "528                0:LIVING       0:DiseaseFree   \n",
      "\n",
      "    Disease-specific Survival status  \n",
      "0         0:ALIVE OR DEAD TUMOR FREE  \n",
      "1         0:ALIVE OR DEAD TUMOR FREE  \n",
      "2         0:ALIVE OR DEAD TUMOR FREE  \n",
      "3         0:ALIVE OR DEAD TUMOR FREE  \n",
      "4         0:ALIVE OR DEAD TUMOR FREE  \n",
      "..                               ...  \n",
      "524                1:DEAD WITH TUMOR  \n",
      "525       0:ALIVE OR DEAD TUMOR FREE  \n",
      "526       0:ALIVE OR DEAD TUMOR FREE  \n",
      "527       0:ALIVE OR DEAD TUMOR FREE  \n",
      "528       0:ALIVE OR DEAD TUMOR FREE  \n",
      "\n",
      "[529 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Explore features and labels\n",
    "features = data.drop(['Overall Survival Status', 'Disease Free Status', 'Disease-specific Survival status'], axis=1)\n",
    "labels = data[['Overall Survival Status', 'Disease Free Status', 'Disease-specific Survival status']]\n",
    "print(\"FEATURES:\\n\",features)\n",
    "\n",
    "print(\"LABELS:\\n\",labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d125cb6e",
   "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>Patient ID</th>\n",
       "      <th>Sample ID</th>\n",
       "      <th>Cancer Type Detailed</th>\n",
       "      <th>Overall Survival Status</th>\n",
       "      <th>Disease Free Status</th>\n",
       "      <th>Disease-specific Survival status</th>\n",
       "      <th>Mutation Count</th>\n",
       "      <th>Fraction Genome Altered</th>\n",
       "      <th>Diagnosis Age</th>\n",
       "      <th>MSI MANTIS Score</th>\n",
       "      <th>MSIsensor Score</th>\n",
       "      <th>Race Category</th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Tumor Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-2E-A9G8</td>\n",
       "      <td>TCGA-2E-A9G8-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.3311</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.3234</td>\n",
       "      <td>0.85</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_HIGH</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA-4E-A92E</td>\n",
       "      <td>TCGA-4E-A92E-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.0341</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.3396</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_LOW</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCGA-5B-A90C</td>\n",
       "      <td>TCGA-5B-A90C-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.6903</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.3344</td>\n",
       "      <td>0.55</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_HIGH</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCGA-5S-A9Q8</td>\n",
       "      <td>TCGA-5S-A9Q8-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0581</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.3199</td>\n",
       "      <td>0.09</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_LOW</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCGA-A5-A0G1</td>\n",
       "      <td>TCGA-A5-A0G1-01</td>\n",
       "      <td>Uterine Serous Carcinoma/Uterine Papillary Ser...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>10808.0</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.3108</td>\n",
       "      <td>1.74</td>\n",
       "      <td>White</td>\n",
       "      <td>UCEC_POLE</td>\n",
       "      <td>Serous Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Patient ID        Sample ID  \\\n",
       "0  TCGA-2E-A9G8  TCGA-2E-A9G8-01   \n",
       "1  TCGA-4E-A92E  TCGA-4E-A92E-01   \n",
       "2  TCGA-5B-A90C  TCGA-5B-A90C-01   \n",
       "3  TCGA-5S-A9Q8  TCGA-5S-A9Q8-01   \n",
       "4  TCGA-A5-A0G1  TCGA-A5-A0G1-01   \n",
       "\n",
       "                                Cancer Type Detailed Overall Survival Status  \\\n",
       "0                     Uterine Endometrioid Carcinoma                0:LIVING   \n",
       "1                     Uterine Endometrioid Carcinoma                0:LIVING   \n",
       "2                     Uterine Endometrioid Carcinoma                0:LIVING   \n",
       "3                     Uterine Endometrioid Carcinoma                0:LIVING   \n",
       "4  Uterine Serous Carcinoma/Uterine Papillary Ser...              1:DECEASED   \n",
       "\n",
       "  Disease Free Status Disease-specific Survival status  Mutation Count  \\\n",
       "0                 NaN       0:ALIVE OR DEAD TUMOR FREE            65.0   \n",
       "1       0:DiseaseFree       0:ALIVE OR DEAD TUMOR FREE           147.0   \n",
       "2                 NaN       0:ALIVE OR DEAD TUMOR FREE            45.0   \n",
       "3       0:DiseaseFree       0:ALIVE OR DEAD TUMOR FREE            50.0   \n",
       "4       0:DiseaseFree       0:ALIVE OR DEAD TUMOR FREE         10808.0   \n",
       "\n",
       "   Fraction Genome Altered  Diagnosis Age  MSI MANTIS Score  MSIsensor Score  \\\n",
       "0                   0.3311           59.0            0.3234             0.85   \n",
       "1                   0.0341           54.0            0.3396             0.01   \n",
       "2                   0.6903           69.0            0.3344             0.55   \n",
       "3                   0.0581           51.0            0.3199             0.09   \n",
       "4                   0.0001           67.0            0.3108             1.74   \n",
       "\n",
       "               Race Category       Subtype  \\\n",
       "0  Black or African American  UCEC_CN_HIGH   \n",
       "1  Black or African American   UCEC_CN_LOW   \n",
       "2  Black or African American  UCEC_CN_HIGH   \n",
       "3  Black or African American   UCEC_CN_LOW   \n",
       "4                      White     UCEC_POLE   \n",
       "\n",
       "                                Tumor Type  \n",
       "0  Endometrioid Endometrial Adenocarcinoma  \n",
       "1  Endometrioid Endometrial Adenocarcinoma  \n",
       "2  Endometrioid Endometrial Adenocarcinoma  \n",
       "3  Endometrioid Endometrial Adenocarcinoma  \n",
       "4        Serous Endometrial Adenocarcinoma  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the first 5 rows of the dataset\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e668681f",
   "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>Patient ID</th>\n",
       "      <th>Sample ID</th>\n",
       "      <th>Cancer Type Detailed</th>\n",
       "      <th>Overall Survival Status</th>\n",
       "      <th>Disease Free Status</th>\n",
       "      <th>Disease-specific Survival status</th>\n",
       "      <th>Mutation Count</th>\n",
       "      <th>Fraction Genome Altered</th>\n",
       "      <th>Diagnosis Age</th>\n",
       "      <th>MSI MANTIS Score</th>\n",
       "      <th>MSIsensor Score</th>\n",
       "      <th>Race Category</th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Tumor Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>TCGA-QS-A8F1</td>\n",
       "      <td>TCGA-QS-A8F1-01</td>\n",
       "      <td>Uterine Serous Carcinoma/Uterine Papillary Ser...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1:DEAD WITH TUMOR</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.6549</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.3647</td>\n",
       "      <td>0.15</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_HIGH</td>\n",
       "      <td>Serous Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>TCGA-SJ-A6ZI</td>\n",
       "      <td>TCGA-SJ-A6ZI-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>1306.0</td>\n",
       "      <td>0.0275</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.6138</td>\n",
       "      <td>14.35</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_MSI</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>TCGA-SJ-A6ZJ</td>\n",
       "      <td>TCGA-SJ-A6ZJ-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0466</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.3382</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_LOW</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>TCGA-SL-A6J9</td>\n",
       "      <td>TCGA-SL-A6J9-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.4226</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.3503</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_CN_HIGH</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>TCGA-SL-A6JA</td>\n",
       "      <td>TCGA-SL-A6JA-01</td>\n",
       "      <td>Uterine Endometrioid Carcinoma</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>742.0</td>\n",
       "      <td>0.0488</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.9515</td>\n",
       "      <td>26.10</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>UCEC_MSI</td>\n",
       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Patient ID        Sample ID  \\\n",
       "524  TCGA-QS-A8F1  TCGA-QS-A8F1-01   \n",
       "525  TCGA-SJ-A6ZI  TCGA-SJ-A6ZI-01   \n",
       "526  TCGA-SJ-A6ZJ  TCGA-SJ-A6ZJ-01   \n",
       "527  TCGA-SL-A6J9  TCGA-SL-A6J9-01   \n",
       "528  TCGA-SL-A6JA  TCGA-SL-A6JA-01   \n",
       "\n",
       "                                  Cancer Type Detailed  \\\n",
       "524  Uterine Serous Carcinoma/Uterine Papillary Ser...   \n",
       "525                     Uterine Endometrioid Carcinoma   \n",
       "526                     Uterine Endometrioid Carcinoma   \n",
       "527                     Uterine Endometrioid Carcinoma   \n",
       "528                     Uterine Endometrioid Carcinoma   \n",
       "\n",
       "    Overall Survival Status Disease Free Status  \\\n",
       "524              1:DECEASED                 NaN   \n",
       "525                0:LIVING       0:DiseaseFree   \n",
       "526                0:LIVING       0:DiseaseFree   \n",
       "527                0:LIVING                 NaN   \n",
       "528                0:LIVING       0:DiseaseFree   \n",
       "\n",
       "    Disease-specific Survival status  Mutation Count  Fraction Genome Altered  \\\n",
       "524                1:DEAD WITH TUMOR            63.0                   0.6549   \n",
       "525       0:ALIVE OR DEAD TUMOR FREE          1306.0                   0.0275   \n",
       "526       0:ALIVE OR DEAD TUMOR FREE            56.0                   0.0466   \n",
       "527       0:ALIVE OR DEAD TUMOR FREE            48.0                   0.4226   \n",
       "528       0:ALIVE OR DEAD TUMOR FREE           742.0                   0.0488   \n",
       "\n",
       "     Diagnosis Age  MSI MANTIS Score  MSIsensor Score  \\\n",
       "524           85.0            0.3647             0.15   \n",
       "525           64.0            0.6138            14.35   \n",
       "526           61.0            0.3382             0.00   \n",
       "527           73.0            0.3503             0.03   \n",
       "528           77.0            0.9515            26.10   \n",
       "\n",
       "                 Race Category       Subtype  \\\n",
       "524  Black or African American  UCEC_CN_HIGH   \n",
       "525  Black or African American      UCEC_MSI   \n",
       "526  Black or African American   UCEC_CN_LOW   \n",
       "527  Black or African American  UCEC_CN_HIGH   \n",
       "528  Black or African American      UCEC_MSI   \n",
       "\n",
       "                                  Tumor Type  \n",
       "524        Serous Endometrial Adenocarcinoma  \n",
       "525  Endometrioid Endometrial Adenocarcinoma  \n",
       "526  Endometrioid Endometrial Adenocarcinoma  \n",
       "527  Endometrioid Endometrial Adenocarcinoma  \n",
       "528  Endometrioid Endometrial Adenocarcinoma  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the last 5 rows of the dataset\n",
    "\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a7689f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patient ID\n",
      "Sample ID\n",
      "Cancer Type Detailed\n",
      "Overall Survival Status\n",
      "Disease Free Status\n",
      "Disease-specific Survival status\n",
      "Mutation Count\n",
      "Fraction Genome Altered\n",
      "Diagnosis Age\n",
      "MSI MANTIS Score\n",
      "MSIsensor Score\n",
      "Race Category\n",
      "Subtype\n",
      "Tumor Type\n"
     ]
    }
   ],
   "source": [
    "# List of column names\n",
    "\n",
    "for col in list(data.columns):\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18eb88f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 529\n",
      "Number of columns: 14\n"
     ]
    }
   ],
   "source": [
    "#gives the size/shape of the dataset\n",
    "#How many columns and rows are there in the dataset.\n",
    "\n",
    "num_rows, num_columns = data.shape\n",
    "\n",
    "print(\"Number of rows:\", num_rows)\n",
    "print(\"Number of columns:\", num_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "92e36767",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the dataset: 7406\n",
      "Shape of the dataset: (529, 14)\n"
     ]
    }
   ],
   "source": [
    "# Explore size/shape of the dataset\n",
    "print(\"Size of the dataset:\", data.size)\n",
    "print(\"Shape of the dataset:\", data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "21946a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Patient ID                           object\n",
       "Sample ID                            object\n",
       "Cancer Type Detailed                 object\n",
       "Overall Survival Status              object\n",
       "Disease Free Status                  object\n",
       "Disease-specific Survival status     object\n",
       "Mutation Count                      float64\n",
       "Fraction Genome Altered             float64\n",
       "Diagnosis Age                       float64\n",
       "MSI MANTIS Score                    float64\n",
       "MSIsensor Score                     float64\n",
       "Race Category                        object\n",
       "Subtype                              object\n",
       "Tumor Type                           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the data type of dataframe pcos\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "13c408f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Convert columns to better data types if possible\n",
    "data['Diagnosis Age'] = pd.to_numeric(data['Diagnosis Age'], errors='coerce')\n",
    "data['MSI MANTIS Score'] = pd.to_numeric(data['MSI MANTIS Score'], errors='coerce')\n",
    "data['MSIsensor Score'] = pd.to_numeric(data['MSIsensor Score'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a2142912",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original_memory:  375453\n",
      "optimized_memory:  375453\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calculate memory usage differences\n",
    "original_memory = data.memory_usage(deep=True).sum()\n",
    "optimized_memory = data.memory_usage(deep=True).sum()\n",
    "print(\"original_memory: \",original_memory)\n",
    "print(\"optimized_memory: \",optimized_memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77be34a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 529 entries, 0 to 528\n",
      "Data columns (total 14 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   Patient ID                        529 non-null    object \n",
      " 1   Sample ID                         529 non-null    object \n",
      " 2   Cancer Type Detailed              529 non-null    object \n",
      " 3   Overall Survival Status           529 non-null    object \n",
      " 4   Disease Free Status               414 non-null    object \n",
      " 5   Disease-specific Survival status  527 non-null    object \n",
      " 6   Mutation Count                    515 non-null    float64\n",
      " 7   Fraction Genome Altered           519 non-null    float64\n",
      " 8   Diagnosis Age                     526 non-null    float64\n",
      " 9   MSI MANTIS Score                  526 non-null    float64\n",
      " 10  MSIsensor Score                   528 non-null    float64\n",
      " 11  Race Category                     497 non-null    object \n",
      " 12  Subtype                           507 non-null    object \n",
      " 13  Tumor Type                        529 non-null    object \n",
      "dtypes: float64(5), object(9)\n",
      "memory usage: 58.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#Information about the dataset\n",
    "#This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage. \n",
    "\n",
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bfb8dc6f",
   "metadata": {
    "scrolled": true
   },
   "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>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>Mutation Count</th>\n",
       "      <td>515.0</td>\n",
       "      <td>1046.423301</td>\n",
       "      <td>2734.365494</td>\n",
       "      <td>7.000</td>\n",
       "      <td>47.00000</td>\n",
       "      <td>76.0000</td>\n",
       "      <td>563.500000</td>\n",
       "      <td>25696.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraction Genome Altered</th>\n",
       "      <td>519.0</td>\n",
       "      <td>0.185959</td>\n",
       "      <td>0.226088</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00360</td>\n",
       "      <td>0.0678</td>\n",
       "      <td>0.314450</td>\n",
       "      <td>0.9487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diagnosis Age</th>\n",
       "      <td>526.0</td>\n",
       "      <td>63.766160</td>\n",
       "      <td>11.060030</td>\n",
       "      <td>31.000</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>64.0000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>90.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSI MANTIS Score</th>\n",
       "      <td>526.0</td>\n",
       "      <td>0.438948</td>\n",
       "      <td>0.239666</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0.28395</td>\n",
       "      <td>0.3113</td>\n",
       "      <td>0.543225</td>\n",
       "      <td>1.3049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSIsensor Score</th>\n",
       "      <td>528.0</td>\n",
       "      <td>6.395890</td>\n",
       "      <td>10.175853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.11000</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>10.117500</td>\n",
       "      <td>40.4300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count         mean          std     min       25%  \\\n",
       "Mutation Count           515.0  1046.423301  2734.365494   7.000  47.00000   \n",
       "Fraction Genome Altered  519.0     0.185959     0.226088   0.000   0.00360   \n",
       "Diagnosis Age            526.0    63.766160    11.060030  31.000  57.00000   \n",
       "MSI MANTIS Score         526.0     0.438948     0.239666   0.237   0.28395   \n",
       "MSIsensor Score          528.0     6.395890    10.175853   0.000   0.11000   \n",
       "\n",
       "                             50%         75%         max  \n",
       "Mutation Count           76.0000  563.500000  25696.0000  \n",
       "Fraction Genome Altered   0.0678    0.314450      0.9487  \n",
       "Diagnosis Age            64.0000   71.000000     90.0000  \n",
       "MSI MANTIS Score          0.3113    0.543225      1.3049  \n",
       "MSIsensor Score           0.4350   10.117500     40.4300  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#If we would like to get a statistical summary of each column, such as count, column mean value, column standard deviation, etc.\n",
    "#We use the describe method:\n",
    "\n",
    "data.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1402eaab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistical facts of the dataset:\n",
      "       Mutation Count  Fraction Genome Altered  Diagnosis Age  \\\n",
      "count      515.000000               519.000000      526.00000   \n",
      "mean      1046.423301                 0.185959       63.76616   \n",
      "std       2734.365494                 0.226088       11.06003   \n",
      "min          7.000000                 0.000000       31.00000   \n",
      "25%         47.000000                 0.003600       57.00000   \n",
      "50%         76.000000                 0.067800       64.00000   \n",
      "75%        563.500000                 0.314450       71.00000   \n",
      "max      25696.000000                 0.948700       90.00000   \n",
      "\n",
      "       MSI MANTIS Score  MSIsensor Score  \n",
      "count        526.000000       528.000000  \n",
      "mean           0.438948         6.395890  \n",
      "std            0.239666        10.175853  \n",
      "min            0.237000         0.000000  \n",
      "25%            0.283950         0.110000  \n",
      "50%            0.311300         0.435000  \n",
      "75%            0.543225        10.117500  \n",
      "max            1.304900        40.430000  \n"
     ]
    }
   ],
   "source": [
    "# Explore statistical facts\n",
    "print(\"Statistical facts of the dataset:\")\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b931888d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Patient ID                            0\n",
       "Sample ID                             0\n",
       "Cancer Type Detailed                  0\n",
       "Overall Survival Status               0\n",
       "Disease Free Status                 115\n",
       "Disease-specific Survival status      2\n",
       "Mutation Count                       14\n",
       "Fraction Genome Altered              10\n",
       "Diagnosis Age                         3\n",
       "MSI MANTIS Score                      3\n",
       "MSIsensor Score                       1\n",
       "Race Category                        32\n",
       "Subtype                              22\n",
       "Tumor Type                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the null values present in dataset \n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0822b004",
   "metadata": {},
   "source": [
    "# matplotlib.pyplot, seaborn, and scipy.stats libraries are used for data visualization and statistical analysis. These are commonly used libraries in Python for such tasks.\n",
    "\n",
    "matplotlib.pyplot is a plotting library that provides a MATLAB-like interface for creating visualizations in Python.\n",
    "\n",
    "seaborn is a statistical data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n",
    "\n",
    "scipy.stats contains a large number of probability distributions and statistical functions. It's useful for statistical analysis and hypothesis testing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "63b47d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8477629",
   "metadata": {},
   "source": [
    "##  Univariate Analysis\n",
    "Univariate analysis refers to the statistical examination of a single variable. The term \"univariate\" comes from combining \"uni,\" meaning one, and \"variate,\" meaning variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "64a2c8f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Age at Diagnosis\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data['Diagnosis Age'], bins=30, kde=True)\n",
    "plt.title('Distribution of Age at Diagnosis')\n",
    "plt.xlabel('Age at Diagnosis')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e70b710",
   "metadata": {},
   "source": [
    "## Univariate Analysis: Age at Diagnosis\n",
    "- The distribution of age at diagnosis is approximately normal but slightly right-skewed.\n",
    "- The majority of patients are diagnosed between the ages of 50 and 70.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "32945f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Mutation Count\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data['Mutation Count'], bins=50, kde=True)\n",
    "plt.title('Distribution of Mutation Count')\n",
    "plt.xlabel('Mutation Count')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7062c6",
   "metadata": {},
   "source": [
    "## Univariate Analysis: Mutation Count\n",
    "- The mutation count distribution is heavily right-skewed, with a few patients having very high mutation counts.\n",
    "- Most patients have mutation counts below 2,500.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "677c238e",
   "metadata": {},
   "source": [
    "## Multivariate Analysis\n",
    "Multivariate analysis is a set of statistical techniques used to analyze data that involves multiple variables simultaneously. It allows researchers to understand relationships between variables, patterns within the data, and the structure of complex datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e273bfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Matrix\n",
    "# Select only numeric columns for the correlation matrix\n",
    "numeric_data = data.select_dtypes(include=[np.number])\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "correlation_matrix = numeric_data.corr()\n",
    "\n",
    "# Plotting the correlation matrix\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22213c6",
   "metadata": {},
   "source": [
    "## Multivariate Analysis: Correlation Matrix\n",
    "- There is a strong positive correlation between MSI MANTIS Score and Mutation Count.\n",
    "- The Fraction Genome Altered shows a moderate positive correlation with Mutation Count.\n",
    "- Diagnosis Age shows little to no correlation with Mutation Count or MSI scores.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c815da4",
   "metadata": {},
   "source": [
    "## Checking Distribution (Skewness)\n",
    "Checking the distribution of a dataset, particularly skewness, involves assessing how much the data deviates from a symmetrical, bell-shaped (normal) distribution. Skewness is a measure of the asymmetry of the distribution of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb9b3e70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diagnosis Age             -0.201695\n",
      "Mutation Count             4.009429\n",
      "Fraction Genome Altered    1.240640\n",
      "MSI MANTIS Score           1.423920\n",
      "MSIsensor Score            1.468257\n",
      "dtype: float64\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Skewness\n",
    "print(data[['Diagnosis Age', 'Mutation Count', 'Fraction Genome Altered', 'MSI MANTIS Score', 'MSIsensor Score']].skew())\n",
    "\n",
    "# Plotting the distributions\n",
    "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
    "sns.histplot(data['Diagnosis Age'], bins=30, kde=True, ax=axes[0, 0])\n",
    "sns.histplot(data['Mutation Count'], bins=30, kde=True, ax=axes[0, 1])\n",
    "sns.histplot(data['Fraction Genome Altered'], bins=30, kde=True, ax=axes[0, 2])\n",
    "sns.histplot(data['MSI MANTIS Score'], bins=30, kde=True, ax=axes[1, 0])\n",
    "sns.histplot(data['MSIsensor Score'], bins=30, kde=True, ax=axes[1, 1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655c4718",
   "metadata": {},
   "source": [
    "## Skewness of Columns\n",
    "- **Diagnosis Age**: Slightly right-skewed.\n",
    "- **Mutation Count**: Heavily right-skewed.\n",
    "- **Fraction Genome Altered**: Right-skewed.\n",
    "- **MSI MANTIS Score**: Right-skewed.\n",
    "- **MSIsensor Score**: Right-skewed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1440a656",
   "metadata": {},
   "source": [
    "## Detecting Outliers\n",
    "Detecting outliers involves identifying observations in a dataset that significantly deviate from the rest of the data. Outliers can occur due to various reasons such as data entry errors, measurement errors, or genuine anomalies in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c02bae5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots to detect outliers\n",
    "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
    "sns.boxplot(data['Diagnosis Age'], ax=axes[0, 0])\n",
    "sns.boxplot(data['Mutation Count'], ax=axes[0, 1])\n",
    "sns.boxplot(data['Fraction Genome Altered'], ax=axes[0, 2])\n",
    "sns.boxplot(data['MSI MANTIS Score'], ax=axes[1, 0])\n",
    "sns.boxplot(data['MSIsensor Score'], ax=axes[1, 1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e40b0a4",
   "metadata": {},
   "source": [
    "## Outliers Detection\n",
    "- **Diagnosis Age**: Few outliers at the higher age spectrum.\n",
    "- **Mutation Count**: Significant number of outliers with high mutation counts.\n",
    "- **Fraction Genome Altered**: Presence of outliers with higher altered fractions.\n",
    "- **MSI MANTIS Score**: Outliers present with higher MSI scores.\n",
    "- **MSIsensor Score**: Similar to MSI MANTIS, outliers with high scores are observed.\n",
    "\n",
    "Outliers indicate extreme values in mutation counts and MSI scores, which may need special attention during analysis.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6baeaf",
   "metadata": {},
   "source": [
    "## Class Label Imbalance\n",
    "Class label imbalance, also known as class imbalance, occurs when one class (or label) in a classification problem has significantly fewer observations than the other classes. This imbalance can skew the predictive model's performance and bias it towards the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1d64856d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q2NhYNW/e3GG9cuXK6cEHH1R8fLxOnz6tESNG5LmF9L333lOHDh0UHR2tfv36qXLlyjp16pT279+vHTt26J///Oc1+9exY0fFx8erdu3aatCggRISEvTmm2+W6O9TREdHq0qVKurUqZNq166tS5cuKTExUVOmTJGXl5fDDz317t1bY8aM0SuvvKKWLVtq3759mjFjhnx9fYu0zcIcv4J0795dw4cPV/fu3ZWZmZnnW3O7du3k6uqq7t27a+TIkbpw4YJmz56t1NTUIvUxV1pamqpVq6ZHH31Ubdu2VUhIiM6ePav169fr7bffVp06dfTQQw9Jkry9vRUaGqovvvhCbdq0kZ+fnwICAuy3Qxbmb1mjRg15eHho/vz5qlOnjry8vBQcHKzg4GD17t1b7733nnr16qWnnnpKJ0+e1OTJk+Xj4+PQxlNPPSUPDw+1aNFClSpVUkpKiiZNmiRfX1/7bbdX8/vvv2vLli0yxujMmTPas2ePPvnkE+3cuVPPPfecnnrqqaser1atWqlHjx6qXbu2vL29tW3bNq1YscJ+nCSpfv36WrJkiWbPnq2IiAiVKVNGkZGRRfnTOFiyZImcnZ3Vrl077d27V2PGjFHDhg3VtWtXe53Cvn7r1asnSXr//ffl7e0td3d3hYWF5Xs6pF27doqOjtaoUaOUnp6uFi1aaNeuXRo7dqwaNWqk3r17F3ufcIOV2uWq+J+yefNm88gjj5jAwEDj7OxsKlasaB566CGzadOmAtdZuXKl/a6Fn3/+Od86O3fuNF27djUVK1Y0Li4uJigoyLRu3dq8++679jq5d3Rs27Ytz/qpqammf//+pmLFisbT09Pcfffd5vvvv89zt8L13KWyaNEi06NHDxMeHm68vLyMi4uLqVq1qundu7fZt2+fQ93MzEwzcuRIExISYjw8PEzLli1NYmJigXep5LdPhT1+V96lcrkePXoYSaZFixb5Lv/Xv/5lGjZsaNzd3U3lypXNCy+8YL755ps8dx4U5i6VzMxM89Zbb5kOHTqYqlWrGjc3N+Pu7m7q1KljRo4caU6ePOlQf/Xq1aZRo0bGzc3NSLIfl8L+LY0x5tNPPzW1a9c2Li4uee6smDNnjqlTp45xd3c3devWNYsWLcqzH3PmzDGtWrUygYGBxtXV1QQHB5uuXbuaXbt2XXVfjTH2v4kkU6ZMGePj42Pq169vnn76abN58+Y89a987V24cMEMHDjQNGjQwPj4+BgPDw9Tq1YtM3bsWHPu3Dn7eqdOnTKPPPKIKVeunLHZbPa/dW57b7755jW3Zcz/f50kJCSYTp06GS8vL+Pt7W26d+9uv7MsV2Ffv8YYExsba8LCwoyTk5PDNvN7zWRkZJhRo0aZ0NBQ4+LiYipVqmSeeeYZk5qa6lAvNDTU3H///Xn2q6C7j3Bj2YwpxCXRAAAA14FrOAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALMcPf0m6dOmSjh07Jm9v7yL/3C8AAP/LzP/9gF1wcPBVf2CQwCHp2LFjCgkJKe1uAADwl3XkyJGr/kozgUOyP3PjyJEjeX7CGAAAFCw9PV0hISHXfH4VgUP//2FUPj4+BA4AAIrhWpckcNEoAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACzHs1RugIgXPintLgCWS3izT2l3AcBNjBEOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJYjcAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAliNwAAAAyxE4AACA5W6awDFp0iTZbDbFxMTYy4wxGjdunIKDg+Xh4aGoqCjt3bvXYb3MzEwNHTpUAQEBKlu2rDp37qyjR4/e4N4DAICruSkCx7Zt2/T++++rQYMGDuWTJ0/W1KlTNWPGDG3btk1BQUFq166dzpw5Y68TExOjpUuXauHChdq4caPOnj2rjh07Kicn50bvBgAAKECpB46zZ8+qZ8+e+uCDD1S+fHl7uTFGsbGxevnll/XQQw+pXr16mjNnjs6fP68FCxZIktLS0vTRRx9pypQpatu2rRo1aqR58+Zp9+7dWr16dWntEgAAuEKpB47Bgwfr/vvvV9u2bR3Kk5KSlJKSovbt29vL3Nzc1LJlS23atEmSlJCQoIsXLzrUCQ4OVr169ex18pOZman09HSHCQAAWMe5NDe+cOFC7dixQ9u2bcuzLCUlRZIUGBjoUB4YGKhDhw7Z67i6ujqMjOTWyV0/P5MmTdL48eOvt/sAAKCQSm2E48iRIxo2bJjmzZsnd3f3AuvZbDaHeWNMnrIrXavO6NGjlZaWZp+OHDlStM4DAIAiKbXAkZCQoOPHjysiIkLOzs5ydnbWhg0b9M4778jZ2dk+snHlSMXx48fty4KCgpSVlaXU1NQC6+THzc1NPj4+DhMAALBOqQWONm3aaPfu3UpMTLRPkZGR6tmzpxITE1W9enUFBQVp1apV9nWysrK0YcMGNW/eXJIUEREhFxcXhzrJycnas2ePvQ4AACh9pXYNh7e3t+rVq+dQVrZsWfn7+9vLY2JiNHHiRIWHhys8PFwTJ06Up6enevToIUny9fVV//799fzzz8vf319+fn4aMWKE6tevn+ciVAAAUHpK9aLRaxk5cqQyMjI0aNAgpaamqmnTplq5cqW8vb3tdaZNmyZnZ2d17dpVGRkZatOmjeLj4+Xk5FSKPQcAAJezGWNMaXeitKWnp8vX11dpaWmWXM8R8cInJd4mcLNJeLNPaXcBQCko7Gdoqf8OBwAAuPUROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAliNwAAAAyxE4AACA5QgcAADAcgQOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJYjcAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAliNwAAAAyxE4AACA5QgcAADAcgQOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJYjcAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAlivVwDF79mw1aNBAPj4+8vHxUbNmzfTNN9/YlxtjNG7cOAUHB8vDw0NRUVHau3evQxuZmZkaOnSoAgICVLZsWXXu3FlHjx690bsCAACuolQDR5UqVfTGG29o+/bt2r59u1q3bq0uXbrYQ8XkyZM1depUzZgxQ9u2bVNQUJDatWunM2fO2NuIiYnR0qVLtXDhQm3cuFFnz55Vx44dlZOTU1q7BQAArmAzxpjS7sTl/Pz89Oabb+qJJ55QcHCwYmJiNGrUKEl/jmYEBgbqH//4hwYMGKC0tDRVqFBBc+fOVbdu3SRJx44dU0hIiL7++mtFR0cXapvp6eny9fVVWlqafHx8SnyfIl74pMTbBG42CW/2Ke0uACgFhf0MvWmu4cjJydHChQt17tw5NWvWTElJSUpJSVH79u3tddzc3NSyZUtt2rRJkpSQkKCLFy861AkODla9evXsdfKTmZmp9PR0hwkAAFin1APH7t275eXlJTc3Nw0cOFBLly5V3bp1lZKSIkkKDAx0qB8YGGhflpKSIldXV5UvX77AOvmZNGmSfH197VNISEgJ7xUAALhcqQeOWrVqKTExUVu2bNEzzzyjvn37at++ffblNpvNob4xJk/Zla5VZ/To0UpLS7NPR44cub6dAAAAV1XqgcPV1VU1a9ZUZGSkJk2apIYNG+rtt99WUFCQJOUZqTh+/Lh91CMoKEhZWVlKTU0tsE5+3Nzc7HfG5E4AAMA6pR44rmSMUWZmpsLCwhQUFKRVq1bZl2VlZWnDhg1q3ry5JCkiIkIuLi4OdZKTk7Vnzx57HQAAUPqcS3PjL730kjp06KCQkBCdOXNGCxcu1Pr167VixQrZbDbFxMRo4sSJCg8PV3h4uCZOnChPT0/16NFDkuTr66v+/fvr+eefl7+/v/z8/DRixAjVr19fbdu2Lc1dAwAAlynVwPH777+rd+/eSk5Olq+vrxo0aKAVK1aoXbt2kqSRI0cqIyNDgwYNUmpqqpo2baqVK1fK29vb3sa0adPk7Oysrl27KiMjQ23atFF8fLycnJxKa7cAAMAVbrrf4SgN/A4HcP34HQ7gf9Nf7nc4AADArYvAAQAALEfgAAAAliNwAAAAyxE4AACA5QgcAADAcgQOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJYjcAAAAMsVK3BUr15dJ0+ezFN++vRpVa9e/bo7BQAAbi3FChwHDx5UTk5OnvLMzEz99ttv190pAABwa3EuSuUvv/zS/u9vv/1Wvr6+9vmcnBytWbNG1apVK7HOAQCAW0ORAscDDzwgSbLZbOrbt6/DMhcXF1WrVk1Tpkwpsc4BAIBbQ5ECx6VLlyRJYWFh2rZtmwICAizpFAAAuLUUKXDkSkpKKul+AACAW1ixAockrVmzRmvWrNHx48ftIx+5Pv744+vuGAAAuHUUK3CMHz9eEyZMUGRkpCpVqiSbzVbS/QIAALeQYgWOd999V/Hx8erdu3dJ9wcAANyCivU7HFlZWWrevHlJ9wUAANyiihU4nnzySS1YsKCk+wIAAG5RxTqlcuHCBb3//vtavXq1GjRoIBcXF4flU6dOLZHOAQCAW0OxAseuXbt0xx13SJL27NnjsIwLSAEAwJWKFTjWrVtX0v0AAAC3MB5PDwAALFesEY5WrVpd9dTJ2rVri90hAABw6ylW4Mi9fiPXxYsXlZiYqD179uR5qBsAAECxAse0adPyLR83bpzOnj17XR0CAAC3nhK9hqNXr148RwUAAORRooFj8+bNcnd3L8kmAQDALaBYp1Qeeughh3ljjJKTk7V9+3aNGTOmRDoGAABuHcUKHL6+vg7zZcqUUa1atTRhwgS1b9++RDoGAABuHcUKHHFxcSXdDwAAcAsrVuDIlZCQoP3798tms6lu3bpq1KhRSfULAADcQooVOI4fP67HHntM69evV7ly5WSMUVpamlq1aqWFCxeqQoUKJd1PAADwF1asu1SGDh2q9PR07d27V6dOnVJqaqr27Nmj9PR0PfvssyXdRwAA8BdXrBGOFStWaPXq1apTp469rG7dupo5cyYXjQIAgDyKNcJx6dIlubi45Cl3cXHRpUuXrrtTAADg1lKswNG6dWsNGzZMx44ds5f99ttveu6559SmTZsS6xwAALg1FCtwzJgxQ2fOnFG1atVUo0YN1axZU2FhYTpz5oymT59e0n0EAAB/ccW6hiMkJEQ7duzQqlWrdODAARljVLduXbVt27ak+wcAAG4BRRrhWLt2rerWrav09HRJUrt27TR06FA9++yzatKkiW6//XZ9//33lnQUAAD8dRUpcMTGxuqpp56Sj49PnmW+vr4aMGCApk6dWmKdAwAAt4YiBY6dO3fqb3/7W4HL27dvr4SEhOvuFAAAuLUUKXD8/vvv+d4Om8vZ2Vl//PHHdXcKAADcWooUOCpXrqzdu3cXuHzXrl2qVKlSodubNGmSmjRpIm9vb1WsWFEPPPCAfvrpJ4c6xhiNGzdOwcHB8vDwUFRUlPbu3etQJzMzU0OHDlVAQIDKli2rzp076+jRo0XZNQAAYKEiBY777rtPr7zyii5cuJBnWUZGhsaOHauOHTsWur0NGzZo8ODB2rJli1atWqXs7Gy1b99e586ds9eZPHmypk6dqhkzZmjbtm0KCgpSu3btdObMGXudmJgYLV26VAsXLtTGjRt19uxZdezYUTk5OUXZPQAAYBGbMcYUtvLvv/+uxo0by8nJSUOGDFGtWrVks9m0f/9+zZw5Uzk5OdqxY4cCAwOL1Zk//vhDFStW1IYNG3TvvffKGKPg4GDFxMRo1KhRkv4czQgMDNQ//vEPDRgwQGlpaapQoYLmzp2rbt26SZKOHTumkJAQff3114qOjr7mdtPT0+Xr66u0tLR8L4i9XhEvfFLibQI3m4Q3+5R2FwCUgsJ+hhbpdzgCAwO1adMmPfPMMxo9erRys4rNZlN0dLRmzZpV7LAhSWlpaZIkPz8/SVJSUpJSUlIcns/i5uamli1batOmTRowYIASEhJ08eJFhzrBwcGqV6+eNm3alG/gyMzMVGZmpn0+9zZfAABgjSL/8FdoaKi+/vprpaam6tdff5UxRuHh4Spfvvx1dcQYo+HDh+vuu+9WvXr1JEkpKSmSlCfEBAYG6tChQ/Y6rq6uebYfGBhoX/9KkyZN0vjx46+rvwAAoPCK9UujklS+fHk1adKkxDoyZMgQ7dq1Sxs3bsyzzGazOcwbY/KUXelqdUaPHq3hw4fb59PT0xUSElKMXgMAgMIo1rNUStrQoUP15Zdfat26dapSpYq9PCgoSJLyjFQcP37cPuoRFBSkrKwspaamFljnSm5ubvLx8XGYAACAdUo1cBhjNGTIEC1ZskRr165VWFiYw/KwsDAFBQVp1apV9rKsrCxt2LBBzZs3lyRFRETIxcXFoU5ycrL27NljrwMAAEpXsU+plITBgwdrwYIF+uKLL+Tt7W0fyfD19ZWHh4dsNptiYmI0ceJEhYeHKzw8XBMnTpSnp6d69Ohhr9u/f389//zz8vf3l5+fn0aMGKH69evzMDkAAG4SpRo4Zs+eLUmKiopyKI+Li1O/fv0kSSNHjlRGRoYGDRqk1NRUNW3aVCtXrpS3t7e9/rRp0+Ts7KyuXbsqIyNDbdq0UXx8vJycnG7UrgAAgKso0u9w3Kr4HQ7g+vE7HMD/psJ+ht4UF40CAIBbG4EDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJYjcAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAliNwAAAAyxE4AACA5QgcAADAcgQOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJYjcAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAliNwAAAAyxE4AACA5QgcAADAcgQOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMBypRo4vvvuO3Xq1EnBwcGy2WxatmyZw3JjjMaNG6fg4GB5eHgoKipKe/fudaiTmZmpoUOHKiAgQGXLllXnzp119OjRG7gXAADgWko1cJw7d04NGzbUjBkz8l0+efJkTZ06VTNmzNC2bdsUFBSkdu3a6cyZM/Y6MTExWrp0qRYuXKiNGzfq7Nmz6tixo3Jycm7UbgAAgGtwLs2Nd+jQQR06dMh3mTFGsbGxevnll/XQQw9JkubMmaPAwEAtWLBAAwYMUFpamj766CPNnTtXbdu2lSTNmzdPISEhWr16taKjo2/YvgAAgILdtNdwJCUlKSUlRe3bt7eXubm5qWXLltq0aZMkKSEhQRcvXnSoExwcrHr16tnr5CczM1Pp6ekOEwAAsM5NGzhSUlIkSYGBgQ7lgYGB9mUpKSlydXVV+fLlC6yTn0mTJsnX19c+hYSElHDvAQDA5W7awJHLZrM5zBtj8pRd6Vp1Ro8erbS0NPt05MiREukrAADI300bOIKCgiQpz0jF8ePH7aMeQUFBysrKUmpqaoF18uPm5iYfHx+HCQAAWOemDRxhYWEKCgrSqlWr7GVZWVnasGGDmjdvLkmKiIiQi4uLQ53k5GTt2bPHXgcAAJS+Ur1L5ezZs/r111/t80lJSUpMTJSfn5+qVq2qmJgYTZw4UeHh4QoPD9fEiRPl6empHj16SJJ8fX3Vv39/Pf/88/L395efn59GjBih+vXr2+9aAQAApa9UA8f27dvVqlUr+/zw4cMlSX379lV8fLxGjhypjIwMDRo0SKmpqWratKlWrlwpb29v+zrTpk2Ts7OzunbtqoyMDLVp00bx8fFycnK64fsDAADyZzPGmNLuRGlLT0+Xr6+v0tLSLLmeI+KFT0q8TeBmk/Bmn9LuAoBSUNjP0Jv2Gg4AAHDrIHAAAADLleo1HABwMzg8oX5pdwGwXNVXdpfq9hnhAAAAliNwAAAAyxE4AACA5QgcAADAcgQOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJYjcAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAliNwAAAAyxE4AACA5QgcAADAcgQOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACWI3AAAADLETgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJYjcAAAAMsROAAAgOUIHAAAwHIEDgAAYDkCBwAAsByBAwAAWI7AAQAALEfgAAAAliNwAAAAyxE4AACA5QgcAADAcgQOAABguVsmcMyaNUthYWFyd3dXRESEvv/++9LuEgAA+D+3ROBYtGiRYmJi9PLLL+vHH3/UPffcow4dOujw4cOl3TUAAKBbJHBMnTpV/fv315NPPqk6deooNjZWISEhmj17dml3DQAASHIu7Q5cr6ysLCUkJOjFF190KG/fvr02bdqU7zqZmZnKzMy0z6elpUmS0tPTLeljTmaGJe0CNxOr3j83wpkLOaXdBcByVr1Hc9s1xly13l8+cJw4cUI5OTkKDAx0KA8MDFRKSkq+60yaNEnjx4/PUx4SEmJJH4H/Bb7TB5Z2FwBczSRfS5s/c+aMfH0L3sZfPnDkstlsDvPGmDxluUaPHq3hw4fb5y9duqRTp07J39+/wHXw15Genq6QkBAdOXJEPj4+pd0dAFfgPXprMcbozJkzCg4Ovmq9v3zgCAgIkJOTU57RjOPHj+cZ9cjl5uYmNzc3h7Jy5cpZ1UWUEh8fH/4zA25ivEdvHVcb2cj1l79o1NXVVREREVq1apVD+apVq9S8efNS6hUAALjcX36EQ5KGDx+u3r17KzIyUs2aNdP777+vw4cPa+BAzikDAHAzuCUCR7du3XTy5ElNmDBBycnJqlevnr7++muFhoaWdtdQCtzc3DR27Ng8p80A3Bx4j/5vsplr3ccCAABwnf7y13AAAICbH4EDAABYjsABAAAsR+AAAACWI3Dghpk1a5bCwsLk7u6uiIgIff/99wXWPXjwoGw2mxITE/NdHh8fb/+xtilTpsjX11fnz5/PU+/ChQsqV66cpk6dKkmqVq2aYmNj7curVasmm82mLVu2OKwXExOjqKgoh7L09HSNGTNGt99+uzw8POTv768mTZpo8uTJSk1NvfYBACz23XffqVOnTgoODpbNZtOyZcuuWj/3fZY7eXt76/bbb9fgwYP1yy+/ONSNj493qJs7ubu7O9RLSUnR0KFDVb16dbm5uSkkJESdOnXSmjVr7HVy33dXTm+88UaePrZv315OTk553qPSnz/wOGDAAFWtWlVubm4KCgpSdHS0Nm/eXOhtFeUY4PoQOHBDLFq0SDExMXr55Zf1448/6p577lGHDh10+PDh6267T58+ysjI0OLFi/MsW7x4sc6fP6/evXsXuL67u7tGjRp11W2cOnVKd911l+Li4jRixAht3bpV//73vzV27FglJiZqwYIF170fwPU6d+6cGjZsqBkzZhRpvdWrVys5OVk7d+7UxIkTtX//fjVs2NAhJEh//jJocnKyw3To0CH78oMHDyoiIkJr167V5MmTtXv3bq1YsUKtWrXS4MGDHdrK/RmDy6ehQ4c61Dl8+LA2b96sIUOG6KOPPsrT74cfflg7d+7UnDlz9PPPP+vLL79UVFSUTp06VeRtFfYY4DoY4Aa48847zcCBAx3KateubV588cV86yclJRlJ5scff8x3eVxcnPH19bXPP/TQQyYqKipPvdatW5uHH37YPh8aGmqmTZvmMD9s2DDj6upqli9fbi8fNmyYadmypX1+wIABpmzZsubo0aP59ufSpUv5lgOlRZJZunTpVesU9D7LyckxUVFRJjQ01GRnZxtj8r7n8tOhQwdTuXJlc/bs2TzLUlNT7f++8n1YkHHjxpnHHnvM7N+/33h7ezu0m5qaaiSZ9evXX7WNa22rKMcA14cRDlguKytLCQkJat++vUN5+/bttWnTJknSuHHjVK1atWJvo3///tqwYYOSkpLsZQcPHtS6devUv3//q65brVo1DRw4UKNHj9alS5fyLL906ZIWLVqkXr16qXLlyvm2wUP/8FdQ2PdZmTJlNGzYMB06dEgJCQmFavvUqVNasWKFBg8erLJly+ZZXtTnVRljFBcXp169eql27dq67bbb9Nlnn9mXe3l5ycvLS8uWLVNmZmaR2i6M4hwDXB2BA5Y7ceKEcnJy8jxMLzAw0P7QvYCAANWoUaPY24iOjlZwcLDi4+PtZXFxcQoODs4TdPLz97//XUlJSZo/f36eZX/88YdOnz6tWrVqOZRHRETY/9Pr3r17sfsO3ChFeZ/Vrl1b0p/BPVdaWpr9NZ875b6/fv31Vxlj7Otdy6hRo/K0tX79evvy1atX6/z584qOjpYk9erVy+G0irOzs+Lj4zVnzhyVK1dOLVq00EsvvaRdu3YVeVtFOQYoPgIHbpgrRwGMMfayIUOGXNe5UicnJ/Xt21fx8fG6dOmSjDGaM2eO+vXrJycnp2uuX6FCBY0YMUKvvPKKsrKyCtX/pUuXKjExUdHR0crIyCh234EbpSjvM/N/P0J9+eve29tbiYmJDlNcXFyB9a/mhRdeyNNW06ZN7cs/+ugjdevWTc7Ofz6Bo3v37tq6dat++ukne52HH35Yx44d05dffqno6GitX79ejRs3dvjiUZhtFeUYoPgIHLBcQECAnJyc7KMZuY4fP55n1ON6PPHEEzpy5IjWrl2rNWvW6PDhw3r88ccLvf7w4cOVkZGhWbNmOZRXqFBB5cqV04EDBxzKq1atqpo1a8rb27tE+g/cTPbv3y9JCgsLs5eVKVNGNWvWdJhyTzOGh4fLZrPZ17uWgICAPG15eHhI+vP0zLJlyzRr1iw5OzvL2dlZlStXVnZ2tj7++GOHdtzd3dWuXTu98sor2rRpk/r166exY8cWeltFPQYoPgIHLOfq6qqIiAitWrXKoXzVqlVq3rx5iW2nRo0aatmypeLi4vTxxx8rKiqqSKdpvLy8NGbMGL3++utKT0+3l5cpU0Zdu3bVvHnz9Ntvv5VYf4Gb1aVLl/TOO+8oLCxMjRo1KtQ6fn5+io6O1syZM3Xu3Lk8y0+fPl3o7c+fP19VqlTRzp07HUYlYmNjNWfOHGVnZxe4bt26dfPdflEV5xjg6m6Jp8Xi5jd8+HD17t1bkZGRatasmd5//30dPnxYAwcOlCTNmDFDS5cuzTPce/nwaa66desWuJ3+/fvrqaeekiR9+OGHRe7n008/rWnTpunTTz91GHKdOHGi1q9fr6ZNm2rChAmKjIxU2bJltWvXLm3evFn16tUr8raAknb27Fn9+uuv9vmkpCQlJibKz89PVatWLfB9dvLkSaWkpOj8+fPas2ePYmNj9cMPP2j58uUOpySNMXlGKiWpYsWKKlOmjGbNmqXmzZvrzjvv1IQJE9SgQQNlZ2dr1apVmj17tsPox5kzZ/K05enpKR8fH3300Ud65JFH8ryvQkNDNWrUKC1fvlx33323Hn30UT3xxBNq0KCBvL29tX37dk2ePFldunRxWO9q2yrqMcB1KL0bZPC/ZubMmSY0NNS4urqaxo0bmw0bNtiXjR071oSGhtrnc29Vy29KSkoq8Ba98+fPG19fX+Pr62vOnz+fZ3l+t8VeecvcggULjCSH22KNMeb06dNm9OjRpnbt2sbNzc14eHiYBg0amDFjxpiTJ08W55AAJWrdunX5vmf69u1rjLn2+8zT09PUqVPHDBo0yPzyyy8ObcfFxRX4nkxOTrbXO3bsmBk8eLD9vV65cmXTuXNns27dOnud0NDQfNsZMGCA2b59u5Fkfvjhh3z3sVOnTqZTp07mwoUL5sUXXzSNGzc2vr6+xtPT09SqVcv8/e9/d3jvX21bRT0GuD48nh4AAFiOazgAAIDlCBwAAMByBA4AAGA5AgcAALAcgQMAAFiOwAEAACxH4AAAAJYjcAAAAMsROADkERUVpZiYGPt8tWrVFBsbW2r9uZqDBw/KZrMpMTGxxNqMj49XuXLlSqw9AAQO4IY7cuSI+vfvr+DgYLm6uio0NFTDhg3TyZMnS7trxXbu3DmNGjVK1atXl7u7uypUqKCoqCh99dVXlm87JCREycnJN/x5NuvWrVOrVq3k5+cnT09PhYeHq2/fvvYHixU3tKxfv142m61IDzsD/goIHMAN9N///leRkZH6+eef9emnn+rXX3/Vu+++qzVr1qhZs2Y6deqUpdu/ePGiJe0OHDhQy5Yt04wZM3TgwAGtWLFCDz/88HWHqML018nJSUFBQXJ2vnHPoty7d686dOigJk2a6LvvvtPu3bs1ffp0ubi46NKlSzesH8BfSmk/zAX4X/K3v/3NVKlSJc+D5ZKTk42np6cZOHCgMcaYF1980TRt2jTP+vXr1zevvPKKff7jjz+2P0yuVq1aZubMmfZluQ+lWrRokWnZsqVxc3MzH3/8sTlx4oR57LHHTOXKlY2Hh4epV6+eWbBggcN2WrZsaYYNG2afz+8hd5fz9fU18fHxV913SWbp0qV51ouLiyuwv7Gxscbd3d188803DustXrzYeHp6mjNnztjX+/HHH01OTo6pXLmymT17tkP9hIQEI8n85z//McYYM2XKFFOvXj3j6elpqlSpYp555hlz5swZe/2CHg6Ya9q0aaZatWoFLs/vIWpjx441xhgzd+5cExERYby8vExgYKDp3r27+f333x2OweVT7oPX8vsbNGzY0N6uMX8+nC0kJMS4urqaSpUqmaFDhxbYR+BGY4QDuEFOnTqlb7/9VoMGDZKHh4fDsqCgIPXs2VOLFi2SMUY9e/bU1q1b9Z///MdeZ+/evdq9e7d69uwpSfrggw/08ssv6/XXX9f+/fs1ceJEjRkzRnPmzHFoe9SoUXr22We1f/9+RUdH68KFC4qIiNBXX32lPXv26Omnn1bv3r21devWYu9bUFCQvv76a505c6bYbeTX30cffVT333+/5s+f71BnwYIF6tKli7y8vBzKy5Qpo8ceeyzf+s2aNVP16tXt9d555x3t2bNHc+bM0dq1azVy5MhC9zEoKEjJycn67rvv8l3evHlzxcbGysfHR8nJyUpOTtaIESMkSVlZWXr11Ve1c+dOLVu2TElJSerXr5+kP08PLV68WJL0008/KTk5WW+//Xah+vT5559r2rRpeu+99/TLL79o2bJlql+/fqH3CbBcaSce4H/Fli1b8v2Wn2vq1KlGkv3bboMGDcyECRPsy0ePHm2aNGlinw8JCckzMvHqq6+aZs2aGWP+/7fl2NjYa/btvvvuM88//7x9vqgjHBs2bDBVqlQxLi4uJjIy0sTExJiNGzc61Mlv3/Mb4biyv0uWLDFeXl7m3Llzxhhj0tLSjLu7u1m+fLnDej/++KMxxpgdO3YYm81mDh48aIwx9lGPy0d/rvTZZ58Zf39/+/y1Rjiys7NNv379jCQTFBRkHnjgATN9+nSTlpZW6DZy/fDDD0aSfYQld3QkNTXVod61RjimTJlibrvtNpOVlXXNbQKlgREO4CZhjJEk2Ww2SVLPnj3t39SNMfr000/toxt//PGH/eJTLy8v+/Taa685jIpIUmRkpMN8Tk6OXn/9dTVo0ED+/v7y8vLSypUrdfjw4WL3/d5779V///tfrVmzRg8//LD27t2re+65R6+++mqR27qyv/fff7+cnZ315ZdfSpIWL14sb29vtW/fPt/1GzVqpNq1a+vTTz+VJG3YsEHHjx9X165d7XXWrVundu3aqXLlyvL29lafPn108uRJnTt3rlB9dHJyUlxcnI4eParJkycrODhYr7/+um6//XYlJydfdd0ff/xRXbp0UWhoqLy9vRUVFSVJ13X8JenRRx9VRkaGqlevrqeeekpLly61X8AK3AwIHMANUrNmTdlsNu3bty/f5QcOHFD58uUVEBAgSerRo4d+/vln7dixQ5s2bdKRI0f02GOPSZL9wsQPPvhAiYmJ9mnPnj3asmWLQ7tly5Z1mJ8yZYqmTZumkSNHau3atUpMTFR0dLSysrKua/9cXFx0zz336MUXX9TKlSs1YcIEvfrqq/Z2bTabPVTlyu+i0Cv76+rqqkceeUQLFiyQ9OfpkW7dul31ItGePXs61I+OjrYf10OHDum+++5TvXr1tHjxYiUkJGjmzJkF9udqKleurN69e2vmzJnat2+fLly4oHfffbfA+ufOnVP79u3l5eWlefPmadu2bVq6dKkkXfP4lylT5qrHLyQkRD/99JNmzpwpDw8PDRo0SPfee69lFwoDRUXgAG4Qf39/tWvXTrNmzVJGRobDspSUFM2fP1/dunWzj3BUqVJF9957r+bPn6/58+erbdu2CgwMlCQFBgaqcuXK+u9//6uaNWs6TGFhYVftx/fff68uXbqoV69eatiwoapXr65ffvmlxPe3bt26ys7O1oULFyRJFSpUcPj2/8svv+j8+fOFaqtnz55asWKF9u7dq3Xr1tlHegrSo0cP7d69WwkJCfr8888d6m/fvl3Z2dmaMmWK7rrrLt122206duxYMfbQUfny5VWpUiX7KImrq6tycnIc6hw4cEAnTpzQG2+8oXvuuUe1a9fW8ePHHeq4urpKUp51rzx+6enpSkpKcqjj4eGhzp0765133tH69eu1efNm7d69+7r3DSgJN+4+MgCaMWOGmjdvrujoaL322msKCwvT3r179cILL6hy5cp6/fXXHer37NlT48aNU1ZWlqZNm+awbNy4cXr22Wfl4+OjDh06KDMzU9u3b1dqaqqGDx9eYB9q1qypxYsXa9OmTSpfvrymTp2qlJQU1alTp9j7FRUVpe7duysyMlL+/v7at2+fXnrpJbVq1Uo+Pj6SpNatW2vGjBm66667dOnSJY0aNUouLi6Far9ly5YKDAxUz549Va1aNd11111XrR8WFqbmzZurf//+ys7OVpcuXezLatSooezsbE2fPl2dOnXSv//976uOSuTnvffeU2Jioh588EHVqFFDFy5c0CeffKK9e/dq+vTpkv78sbSzZ89qzZo1atiwoTw9PVW1alW5urpq+vTpGjhwoPbs2ZPntFNoaKhsNpu++uor3XffffLw8JCXl5dat26t+Ph4derUSeXLl9eYMWPk5ORkXy8+Pl45OTlq2rSpPD09NXfuXHl4eCg0NLRI+wZYpnQvIQH+9xw8eND069fPBAUFGRcXFxMSEmKGDh1qTpw4kaduamqqcXNzs98CeqX58+ebO+64w7i6upry5cube++91yxZssQYk/diylwnT540Xbp0MV5eXqZixYrm73//u+nTp4/p0qWLvU5RLxqdOHGiadasmfHz8zPu7u6mevXq5tlnn3XYp99++820b9/elC1b1oSHh5uvv/4634tGr+xvrhdeeMFIcrgt+GrrzZw500gyffr0ydPW1KlTTaVKlYyHh4eJjo42n3zyicOFmte64HPHjh2mV69eJiwszLi5uRl/f39z7733mi+//NKh3sCBA42/v7/DbbELFiww1apVM25ubqZZs2bmyy+/zNP/CRMmmKCgIGOz2ey3xaalpZmuXbsaHx8fExISYuLj4x0uGl26dKlp2rSp8fHxMWXLljV33XWXWb16dYH7ANxoNmOuOCkIAABQwriGAwAAWI7AAQAALEfgAAAAliNwAAAAyxE4AACA5QgcAADAcgQOAABgOQIHAACwHIEDAABYjsABAAAsR+AAAACW+38PxwqkmpanxwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Overall Survival Status\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='Overall Survival Status', data=data)\n",
    "plt.title('Overall Survival Status Distribution')\n",
    "plt.xlabel('Overall Survival Status')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b8bfd5",
   "metadata": {},
   "source": [
    "## Class Label Imbalance: Overall Survival Status\n",
    "- There is a class imbalance in the overall survival status with a higher number of living patients (84%) compared to deceased (16%).\n"
   ]
  }
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