Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing, neighbors, svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Debernardi et al 2020 data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>sample_id</th>\n",
       "      <th>patient_cohort</th>\n",
       "      <th>sample_origin</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>stage</th>\n",
       "      <th>benign_sample_diagnosis</th>\n",
       "      <th>plasma_CA19_9</th>\n",
       "      <th>creatinine</th>\n",
       "      <th>LYVE1</th>\n",
       "      <th>REG1B</th>\n",
       "      <th>TFF1</th>\n",
       "      <th>REG1A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S1</td>\n",
       "      <td>Cohort1</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1.83222</td>\n",
       "      <td>0.893219</td>\n",
       "      <td>52.948840</td>\n",
       "      <td>654.282174</td>\n",
       "      <td>1262.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S10</td>\n",
       "      <td>Cohort1</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>81</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97266</td>\n",
       "      <td>2.037585</td>\n",
       "      <td>94.467030</td>\n",
       "      <td>209.488250</td>\n",
       "      <td>228.407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S100</td>\n",
       "      <td>Cohort2</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.78039</td>\n",
       "      <td>0.145589</td>\n",
       "      <td>102.366000</td>\n",
       "      <td>461.141000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S101</td>\n",
       "      <td>Cohort2</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>61</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.70122</td>\n",
       "      <td>0.002805</td>\n",
       "      <td>60.579000</td>\n",
       "      <td>142.950000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S102</td>\n",
       "      <td>Cohort2</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>62</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.21489</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>65.540000</td>\n",
       "      <td>41.088000</td>\n",
       "      <td>NaN</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>585</th>\n",
       "      <td>S549</td>\n",
       "      <td>Cohort2</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>68</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.52026</td>\n",
       "      <td>7.058209</td>\n",
       "      <td>156.241000</td>\n",
       "      <td>525.178000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>S558</td>\n",
       "      <td>Cohort2</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>71</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.85956</td>\n",
       "      <td>8.341207</td>\n",
       "      <td>16.915000</td>\n",
       "      <td>245.947000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>S560</td>\n",
       "      <td>Cohort2</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>63</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.36851</td>\n",
       "      <td>7.674707</td>\n",
       "      <td>289.701000</td>\n",
       "      <td>537.286000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>S583</td>\n",
       "      <td>Cohort2</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>75</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.33458</td>\n",
       "      <td>8.206777</td>\n",
       "      <td>205.930000</td>\n",
       "      <td>722.523000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>S590</td>\n",
       "      <td>Cohort1</td>\n",
       "      <td>BPTB</td>\n",
       "      <td>74</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1488.0</td>\n",
       "      <td>1.50423</td>\n",
       "      <td>8.200958</td>\n",
       "      <td>411.938275</td>\n",
       "      <td>2021.321078</td>\n",
       "      <td>13200.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>590 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sample_id patient_cohort sample_origin  age sex  diagnosis stage   \n",
       "0          S1        Cohort1          BPTB   33   F          1   NaN  \\\n",
       "1         S10        Cohort1          BPTB   81   F          1   NaN   \n",
       "2        S100        Cohort2          BPTB   51   M          1   NaN   \n",
       "3        S101        Cohort2          BPTB   61   M          1   NaN   \n",
       "4        S102        Cohort2          BPTB   62   M          1   NaN   \n",
       "..        ...            ...           ...  ...  ..        ...   ...   \n",
       "585      S549        Cohort2          BPTB   68   M          3    IV   \n",
       "586      S558        Cohort2          BPTB   71   F          3    IV   \n",
       "587      S560        Cohort2          BPTB   63   M          3    IV   \n",
       "588      S583        Cohort2          BPTB   75   F          3    IV   \n",
       "589      S590        Cohort1          BPTB   74   M          3    IV   \n",
       "\n",
       "    benign_sample_diagnosis  plasma_CA19_9  creatinine     LYVE1       REG1B   \n",
       "0                       NaN           11.7     1.83222  0.893219   52.948840  \\\n",
       "1                       NaN            NaN     0.97266  2.037585   94.467030   \n",
       "2                       NaN            7.0     0.78039  0.145589  102.366000   \n",
       "3                       NaN            8.0     0.70122  0.002805   60.579000   \n",
       "4                       NaN            9.0     0.21489  0.000860   65.540000   \n",
       "..                      ...            ...         ...       ...         ...   \n",
       "585                     NaN            NaN     0.52026  7.058209  156.241000   \n",
       "586                     NaN            NaN     0.85956  8.341207   16.915000   \n",
       "587                     NaN            NaN     1.36851  7.674707  289.701000   \n",
       "588                     NaN            NaN     1.33458  8.206777  205.930000   \n",
       "589                     NaN         1488.0     1.50423  8.200958  411.938275   \n",
       "\n",
       "            TFF1      REG1A  \n",
       "0     654.282174   1262.000  \n",
       "1     209.488250    228.407  \n",
       "2     461.141000        NaN  \n",
       "3     142.950000        NaN  \n",
       "4      41.088000        NaN  \n",
       "..           ...        ...  \n",
       "585   525.178000        NaN  \n",
       "586   245.947000        NaN  \n",
       "587   537.286000        NaN  \n",
       "588   722.523000        NaN  \n",
       "589  2021.321078  13200.000  \n",
       "\n",
       "[590 rows x 14 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 590 entries, 0 to 589\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   sample_id                590 non-null    object \n",
      " 1   patient_cohort           590 non-null    object \n",
      " 2   sample_origin            590 non-null    object \n",
      " 3   age                      590 non-null    int64  \n",
      " 4   sex                      590 non-null    object \n",
      " 5   diagnosis                590 non-null    int64  \n",
      " 6   stage                    199 non-null    object \n",
      " 7   benign_sample_diagnosis  208 non-null    object \n",
      " 8   plasma_CA19_9            350 non-null    float64\n",
      " 9   creatinine               590 non-null    float64\n",
      " 10  LYVE1                    590 non-null    float64\n",
      " 11  REG1B                    590 non-null    float64\n",
      " 12  TFF1                     590 non-null    float64\n",
      " 13  REG1A                    306 non-null    float64\n",
      "dtypes: float64(6), int64(2), object(6)\n",
      "memory usage: 64.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sample_id                    0\n",
       "patient_cohort               0\n",
       "sample_origin                0\n",
       "age                          0\n",
       "sex                          0\n",
       "diagnosis                    0\n",
       "stage                      391\n",
       "benign_sample_diagnosis    382\n",
       "plasma_CA19_9              240\n",
       "creatinine                   0\n",
       "LYVE1                        0\n",
       "REG1B                        0\n",
       "TFF1                         0\n",
       "REG1A                      284\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cohort1', 'Cohort2'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"patient_cohort\"].unique()\n",
    "# Cohort 1 = previously used samples\n",
    "# Cohort 2 = newly added samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"patient_cohort\"] = label_encoder.fit_transform(df[\"patient_cohort\"])\n",
    "df[\"patient_cohort\"] = df[\"patient_cohort\"].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BPTB', 'LIV', 'ESP', 'UCL'], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"sample_origin\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"sample_origin\"] = label_encoder.fit_transform(df[\"sample_origin\"])\n",
    "df[\"sample_origin\"] = df[\"sample_origin\"].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"age\"] = label_encoder.fit_transform(df[\"age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"sex\"] = label_encoder.fit_transform(df[\"sex\"])\n",
    "df[\"sex\"] = df[\"sex\"].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"diagnosis\"].unique()\n",
    "# 1 = control (no pancreatic disease)\n",
    "# 2 = benign hepatobiliary disease (119 of which are chronic pancreatitis)\n",
    "# 3 = pancreatic ductal adenocarcinoma (pancreatic cancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"diagnosis\"] = label_encoder.fit_transform(df[\"diagnosis\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"stage\"] = df[\"stage\"].fillna('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', 'I', 'IA', 'IB', 'II', 'IIA', 'IIB', 'III', 'IV'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"stage\"].unique()\n",
    "# Stage of those with pancreatic cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"stage\"] = label_encoder.fit_transform(df[\"stage\"])\n",
    "df[\"stage\"] = df[\"stage\"].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"benign_sample_diagnosis\"] = df[\"benign_sample_diagnosis\"].fillna(\"Null\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Null', 'Abdominal Pain ',\n",
       "       'Biliary Stricture (Secondary to Stent) ', 'Cholecystitis',\n",
       "       'Cholecystitis ', 'Cholecystitis (Chronic) ',\n",
       "       'Cholecystitis (Chronic) Cholelithiasis',\n",
       "       'Cholecystitis (Chronic) Cholesterolsis', 'Choledochal Cyst ',\n",
       "       'Choledocholiathiasis', 'Choledocholiathiasis ',\n",
       "       'Cholelithiasis with adenomyomatous hyperplasia ',\n",
       "       'Duodenal Stricture', 'Duodenitis', 'Gallbladder polyps ',\n",
       "       'Gallbladder Porcelain', 'Gallstones', 'Gallstones ',\n",
       "       'Gallstones - Incidental ', 'Gastritis ', 'Gastritis and Reflux ',\n",
       "       'Ill defined lesion in uncinate process',\n",
       "       'Ischaemic Common Bile Duct Stricture',\n",
       "       'Pancreatitis (Pseudocyst)', 'Pancreatitis', 'Pancreatitis ',\n",
       "       'Pancreatitis (Abscess)', 'Pancreatitis (Acute)',\n",
       "       'Pancreatitis (Alcohol-Chronic-Pseuodcyst)',\n",
       "       'Pancreatitis (Alcohol-Chronic)', 'Pancreatitis (Alcohol)',\n",
       "       'Pancreatitis (Autoimmune) ', 'Pancreatitis (Chronic-Pseudocyst) ',\n",
       "       'Pancreatitis (Chronic) ',\n",
       "       'Pancreatitis (Chronic) (Later became PDAC)',\n",
       "       'Pancreatitis (Chronic) Choledocholithiasis',\n",
       "       'Pancreatitis (Gallstone-Alcohol-Pseudocyst)',\n",
       "       'Pancreatitis (Gallstone-Pseudocyst)', 'Pancreatitis (Gallstone) ',\n",
       "       'Pancreatitis (Hereditary-Chronic) ',\n",
       "       'Pancreatitis (Hypertriglyceridemia)', 'Pancreatitis (Idiopathic)',\n",
       "       'Pancreatitis (Idiopathic) ',\n",
       "       'Pancreato-jejunostomy Anastomoses Stricture',\n",
       "       'Premalignant lesions-Adenoma-NOS',\n",
       "       'Premalignant lesions-Mucinous cystadenocarcinoma-noninvasive',\n",
       "       'Premalignant lesions-Mucinous cystadenoma-NOS',\n",
       "       'Premalignant lesions-Tubular adenoma-NOS',\n",
       "       'Premalignant lesions-Tubulovillous adenoma-NOS',\n",
       "       'Premalignant lesions-Villous adenoma-NOS',\n",
       "       'Serous cystadenoma - NOS', 'Serous microcystic adenoma',\n",
       "       'Simple benign liver cyst'], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"benign_sample_diagnosis\"].unique()\n",
    "# Diagnosis of those with benign, con=cancerous diagnosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"benign_sample_diagnosis\"] = label_encoder.fit_transform(df[\"benign_sample_diagnosis\"])\n",
    "df[\"benign_sample_diagnosis\"] = df[\"benign_sample_diagnosis\"].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"plasma_CA19_9\"] = df[\"plasma_CA19_9\"].fillna(df[\"plasma_CA19_9\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"REG1A\"] = df[\"REG1A\"].fillna(df[\"REG1A\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 590 entries, 0 to 589\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   sample_id                590 non-null    object \n",
      " 1   patient_cohort           590 non-null    int64  \n",
      " 2   sample_origin            590 non-null    int64  \n",
      " 3   age                      590 non-null    int64  \n",
      " 4   sex                      590 non-null    int64  \n",
      " 5   diagnosis                590 non-null    int64  \n",
      " 6   stage                    590 non-null    int64  \n",
      " 7   benign_sample_diagnosis  590 non-null    int64  \n",
      " 8   plasma_CA19_9            590 non-null    float64\n",
      " 9   creatinine               590 non-null    float64\n",
      " 10  LYVE1                    590 non-null    float64\n",
      " 11  REG1B                    590 non-null    float64\n",
      " 12  TFF1                     590 non-null    float64\n",
      " 13  REG1A                    590 non-null    float64\n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 64.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop(\"sample_id\", axis=1)\n",
    "X = X.drop(\"diagnosis\", axis=1)\n",
    "X = X.drop(\"stage\", axis=1)\n",
    "X = X.drop(\"benign_sample_diagnosis\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>590 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_cohort  sample_origin  age  sex  plasma_CA19_9  creatinine   \n",
       "0                 0              0    6    0      11.700000     1.83222  \\\n",
       "1                 0              0   54    0     654.002944     0.97266   \n",
       "2                 1              0   24    1       7.000000     0.78039   \n",
       "3                 1              0   34    1       8.000000     0.70122   \n",
       "4                 1              0   35    1       9.000000     0.21489   \n",
       "..              ...            ...  ...  ...            ...         ...   \n",
       "585               1              0   41    1     654.002944     0.52026   \n",
       "586               1              0   44    0     654.002944     0.85956   \n",
       "587               1              0   36    1     654.002944     1.36851   \n",
       "588               1              0   48    0     654.002944     1.33458   \n",
       "589               0              0   47    1    1488.000000     1.50423   \n",
       "\n",
       "        LYVE1       REG1B         TFF1         REG1A  \n",
       "0    0.893219   52.948840   654.282174   1262.000000  \n",
       "1    2.037585   94.467030   209.488250    228.407000  \n",
       "2    0.145589  102.366000   461.141000    735.281222  \n",
       "3    0.002805   60.579000   142.950000    735.281222  \n",
       "4    0.000860   65.540000    41.088000    735.281222  \n",
       "..        ...         ...          ...           ...  \n",
       "585  7.058209  156.241000   525.178000    735.281222  \n",
       "586  8.341207   16.915000   245.947000    735.281222  \n",
       "587  7.674707  289.701000   537.286000    735.281222  \n",
       "588  8.206777  205.930000   722.523000    735.281222  \n",
       "589  8.200958  411.938275  2021.321078  13200.000000  \n",
       "\n",
       "[590 rows x 10 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"diagnosis\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "585    2\n",
       "586    2\n",
       "587    2\n",
       "588    2\n",
       "589    2\n",
       "Name: diagnosis, Length: 590, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = label_encoder.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor = X.corr()\n",
    "\n",
    "plt.figure()\n",
    "sns.heatmap(cor, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2901)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.94594594594594"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "rf_clf = RandomForestClassifier(n_estimators=100).fit(X_train, y_train)\n",
    "rf_clf.score(X_test, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient Boosting Classifier\n",
    "gbc_clf = GradientBoostingClassifier().fit(X_train, y_train)\n",
    "gbc_clf.score(X_test, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.37837837837838"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes Classifier\n",
    "gbc_clf = GaussianNB().fit(X_train, y_train)\n",
    "gbc_clf.score(X_test, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.08108108108109"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K Nearest Neighbours Classifier\n",
    "knn_clf = neighbors.KNeighborsClassifier().fit(X_train, y_train)\n",
    "knn_clf.score(X_test, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.668918918918919"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machine Classifier\n",
    "svm_clf = svm.SVC(kernel=\"linear\").fit(X_train, y_train)\n",
    "svm_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.56756756756756"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XBoost Classifier\n",
    "xgb_clf = XGBClassifier(objective='binary:logistic', booster='gblinear').fit(X_train, y_train)\n",
    "xgb_clf.score(X_test, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.21621621621621"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression Classifier\n",
    "lrc_clf = LogisticRegression(max_iter=6000).fit(X_train, y_train)\n",
    "lrc_clf.score(X_test, y_test) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58.78378378378378"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree Classifier\n",
    "dtc_clf = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "dtc_clf.score(X_test, y_test) * 100"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}