Model_Implementation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df_encoded --> encoded dataset \\\n",
    "drug_df --> original dataset with random forest classfier predictions \\\n",
    "true_df--> sample output containing ID and Predictions  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xgboost as xgb \n",
    "import numpy as nd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_df = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>TargetID</th>\n",
       "      <th>DRUGID</th>\n",
       "      <th>DRUGTYPE</th>\n",
       "      <th>Drug_high_status</th>\n",
       "      <th>DRUGNAME</th>\n",
       "      <th>PUBCHCID</th>\n",
       "      <th>Disease_of_highest_status</th>\n",
       "      <th>Drug_Status</th>\n",
       "      <th>UNIPROID</th>\n",
       "      <th>TARGNAME</th>\n",
       "      <th>GENENAME</th>\n",
       "      <th>SYNONYMS</th>\n",
       "      <th>FUNCTION</th>\n",
       "      <th>BIOCLASS</th>\n",
       "      <th>SEQUENCE</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Accession Number</th>\n",
       "      <th>Target_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140736</td>\n",
       "      <td>T51115</td>\n",
       "      <td>D0L4YD</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solifenacin</td>\n",
       "      <td>154059</td>\n",
       "      <td>Overactive bladder</td>\n",
       "      <td>Approved</td>\n",
       "      <td>CAC1C_HUMAN</td>\n",
       "      <td>Voltage-gated calcium channel alpha Cav1.2 (CA...</td>\n",
       "      <td>CACNA1C</td>\n",
       "      <td>Voltage-gated calcium channel subunit alpha Ca...</td>\n",
       "      <td>Mediates influx of calcium ions into the cytop...</td>\n",
       "      <td>Voltage-gated ion channel</td>\n",
       "      <td>MVNENTRMYIPEENHQGSNYGSPRPAHANMNANAAAGLAPEHIPTP...</td>\n",
       "      <td>Genetic cardiac arrhythmia</td>\n",
       "      <td>Q13936</td>\n",
       "      <td>Terminated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133048</td>\n",
       "      <td>T60529</td>\n",
       "      <td>D03NMM</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Investigative</td>\n",
       "      <td>AM-643</td>\n",
       "      <td>46843035</td>\n",
       "      <td>Dermatological disease</td>\n",
       "      <td>Investigative</td>\n",
       "      <td>PGH1_HUMAN</td>\n",
       "      <td>Prostaglandin G/H synthase 1 (COX-1)</td>\n",
       "      <td>PTGS1</td>\n",
       "      <td>Prostaglandin-endoperoxide synthase 1; Prostag...</td>\n",
       "      <td>Converts arachidonate to prostaglandin H2 (PGH...</td>\n",
       "      <td>Paired donor oxygen oxidoreductase</td>\n",
       "      <td>MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGIC...</td>\n",
       "      <td>Rheumatoid arthritis</td>\n",
       "      <td>P23219</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60493</td>\n",
       "      <td>T80975</td>\n",
       "      <td>D0T2ER</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Phase 1</td>\n",
       "      <td>TAK-593</td>\n",
       "      <td>24767976</td>\n",
       "      <td>Solid tumour/cancer</td>\n",
       "      <td>Phase 1</td>\n",
       "      <td>VGFR2_HUMAN</td>\n",
       "      <td>Vascular endothelial growth factor receptor 2 ...</td>\n",
       "      <td>KDR</td>\n",
       "      <td>VEGFR2; VEGFR-2; VEGF-2 receptor; Protein-tyro...</td>\n",
       "      <td>Plays an essential role in the regulation of a...</td>\n",
       "      <td>Kinase</td>\n",
       "      <td>MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKAN...</td>\n",
       "      <td>Renal cell carcinoma</td>\n",
       "      <td>P35968</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>169176</td>\n",
       "      <td>T92072</td>\n",
       "      <td>D07ESH</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Discontinued in Phase 3</td>\n",
       "      <td>PF-1913539</td>\n",
       "      <td>176408</td>\n",
       "      <td>Alzheimer disease</td>\n",
       "      <td>Discontinued in Phase 3</td>\n",
       "      <td>AA1R_HUMAN</td>\n",
       "      <td>Adenosine A1 receptor (ADORA1)</td>\n",
       "      <td>ADORA1</td>\n",
       "      <td>Adenosine receptor A1; A(1) adenosine receptor</td>\n",
       "      <td>The activity of this receptor is mediated by G...</td>\n",
       "      <td>GPCR rhodopsin</td>\n",
       "      <td>MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...</td>\n",
       "      <td>Hyper-lipoproteinaemia</td>\n",
       "      <td>P30542</td>\n",
       "      <td>Phase 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120183</td>\n",
       "      <td>T30082</td>\n",
       "      <td>D0Q0RC</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Ethopropazine</td>\n",
       "      <td>3290</td>\n",
       "      <td>Parkinson disease</td>\n",
       "      <td>Approved</td>\n",
       "      <td>ACES_HUMAN</td>\n",
       "      <td>Acetylcholinesterase (AChE)</td>\n",
       "      <td>ACHE</td>\n",
       "      <td>YT; N-ACHE; ARACHE</td>\n",
       "      <td>Role in neuronal apoptosis. Terminates signal ...</td>\n",
       "      <td>Carboxylic ester hydrolase</td>\n",
       "      <td>MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGG...</td>\n",
       "      <td>Oesophageal/gastroduodenal disorder</td>\n",
       "      <td>P22303</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID TargetID  DRUGID              DRUGTYPE         Drug_high_status  \\\n",
       "0  140736   T51115  D0L4YD  Small molecular drug                 Approved   \n",
       "1  133048   T60529  D03NMM  Small molecular drug            Investigative   \n",
       "2   60493   T80975  D0T2ER  Small molecular drug                  Phase 1   \n",
       "3  169176   T92072  D07ESH  Small molecular drug  Discontinued in Phase 3   \n",
       "4  120183   T30082  D0Q0RC  Small molecular drug                 Approved   \n",
       "\n",
       "        DRUGNAME  PUBCHCID Disease_of_highest_status              Drug_Status  \\\n",
       "0    Solifenacin    154059        Overactive bladder                 Approved   \n",
       "1         AM-643  46843035    Dermatological disease            Investigative   \n",
       "2        TAK-593  24767976       Solid tumour/cancer                  Phase 1   \n",
       "3     PF-1913539    176408         Alzheimer disease  Discontinued in Phase 3   \n",
       "4  Ethopropazine      3290         Parkinson disease                 Approved   \n",
       "\n",
       "      UNIPROID                                           TARGNAME GENENAME  \\\n",
       "0  CAC1C_HUMAN  Voltage-gated calcium channel alpha Cav1.2 (CA...  CACNA1C   \n",
       "1   PGH1_HUMAN               Prostaglandin G/H synthase 1 (COX-1)    PTGS1   \n",
       "2  VGFR2_HUMAN  Vascular endothelial growth factor receptor 2 ...      KDR   \n",
       "3   AA1R_HUMAN                     Adenosine A1 receptor (ADORA1)   ADORA1   \n",
       "4   ACES_HUMAN                        Acetylcholinesterase (AChE)     ACHE   \n",
       "\n",
       "                                            SYNONYMS  \\\n",
       "0  Voltage-gated calcium channel subunit alpha Ca...   \n",
       "1  Prostaglandin-endoperoxide synthase 1; Prostag...   \n",
       "2  VEGFR2; VEGFR-2; VEGF-2 receptor; Protein-tyro...   \n",
       "3     Adenosine receptor A1; A(1) adenosine receptor   \n",
       "4                                 YT; N-ACHE; ARACHE   \n",
       "\n",
       "                                            FUNCTION  \\\n",
       "0  Mediates influx of calcium ions into the cytop...   \n",
       "1  Converts arachidonate to prostaglandin H2 (PGH...   \n",
       "2  Plays an essential role in the regulation of a...   \n",
       "3  The activity of this receptor is mediated by G...   \n",
       "4  Role in neuronal apoptosis. Terminates signal ...   \n",
       "\n",
       "                             BIOCLASS  \\\n",
       "0           Voltage-gated ion channel   \n",
       "1  Paired donor oxygen oxidoreductase   \n",
       "2                              Kinase   \n",
       "3                      GPCR rhodopsin   \n",
       "4          Carboxylic ester hydrolase   \n",
       "\n",
       "                                            SEQUENCE  \\\n",
       "0  MVNENTRMYIPEENHQGSNYGSPRPAHANMNANAAAGLAPEHIPTP...   \n",
       "1  MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGIC...   \n",
       "2  MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKAN...   \n",
       "3  MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...   \n",
       "4  MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGG...   \n",
       "\n",
       "                               Disease Accession Number Target_Status  \n",
       "0           Genetic cardiac arrhythmia           Q13936    Terminated  \n",
       "1                 Rheumatoid arthritis           P23219      Approved  \n",
       "2                 Renal cell carcinoma           P35968      Approved  \n",
       "3               Hyper-lipoproteinaemia           P30542       Phase 2  \n",
       "4  Oesophageal/gastroduodenal disorder           P22303      Approved  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134486 entries, 0 to 134485\n",
      "Data columns (total 19 columns):\n",
      " #   Column                     Non-Null Count   Dtype \n",
      "---  ------                     --------------   ----- \n",
      " 0   ID                         134486 non-null  int64 \n",
      " 1   TargetID                   134486 non-null  object\n",
      " 2   DRUGID                     134486 non-null  object\n",
      " 3   DRUGTYPE                   134486 non-null  object\n",
      " 4   Drug_high_status           134486 non-null  object\n",
      " 5   DRUGNAME                   134486 non-null  object\n",
      " 6   PUBCHCID                   134486 non-null  int64 \n",
      " 7   Disease_of_highest_status  134486 non-null  object\n",
      " 8   Drug_Status                134486 non-null  object\n",
      " 9   UNIPROID                   134486 non-null  object\n",
      " 10  TARGNAME                   134486 non-null  object\n",
      " 11  GENENAME                   134486 non-null  object\n",
      " 12  SYNONYMS                   134486 non-null  object\n",
      " 13  FUNCTION                   134486 non-null  object\n",
      " 14  BIOCLASS                   134486 non-null  object\n",
      " 15  SEQUENCE                   134486 non-null  object\n",
      " 16  Disease                    134486 non-null  object\n",
      " 17  Accession Number           134486 non-null  object\n",
      " 18  Target_Status              134486 non-null  object\n",
      "dtypes: int64(2), object(17)\n",
      "memory usage: 19.5+ MB\n"
     ]
    }
   ],
   "source": [
    "drug_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded=drug_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_en_1=LabelEncoder()\n",
    "select_cols=['TargetID','DRUGID','DRUGTYPE','Drug_high_status','DRUGNAME','PUBCHCID','Disease_of_highest_status','Drug_Status',\n",
    "             'UNIPROID','TARGNAME','GENENAME','SYNONYMS','FUNCTION','BIOCLASS','SEQUENCE','Disease','Accession Number'\n",
    "             ,'Target_Status']\n",
    "\n",
    "for ele in select_cols:\n",
    "    df_encoded[ele]=label_en_1.fit_transform(df_encoded[ele])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>TargetID</th>\n",
       "      <th>DRUGID</th>\n",
       "      <th>DRUGTYPE</th>\n",
       "      <th>Drug_high_status</th>\n",
       "      <th>DRUGNAME</th>\n",
       "      <th>PUBCHCID</th>\n",
       "      <th>Disease_of_highest_status</th>\n",
       "      <th>Drug_Status</th>\n",
       "      <th>UNIPROID</th>\n",
       "      <th>TARGNAME</th>\n",
       "      <th>GENENAME</th>\n",
       "      <th>SYNONYMS</th>\n",
       "      <th>FUNCTION</th>\n",
       "      <th>BIOCLASS</th>\n",
       "      <th>SEQUENCE</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Accession Number</th>\n",
       "      <th>Target_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140736</td>\n",
       "      <td>T51115</td>\n",
       "      <td>D0L4YD</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solifenacin</td>\n",
       "      <td>154059</td>\n",
       "      <td>Overactive bladder</td>\n",
       "      <td>Approved</td>\n",
       "      <td>CAC1C_HUMAN</td>\n",
       "      <td>Voltage-gated calcium channel alpha Cav1.2 (CA...</td>\n",
       "      <td>CACNA1C</td>\n",
       "      <td>Voltage-gated calcium channel subunit alpha Ca...</td>\n",
       "      <td>Mediates influx of calcium ions into the cytop...</td>\n",
       "      <td>Voltage-gated ion channel</td>\n",
       "      <td>MVNENTRMYIPEENHQGSNYGSPRPAHANMNANAAAGLAPEHIPTP...</td>\n",
       "      <td>Genetic cardiac arrhythmia</td>\n",
       "      <td>Q13936</td>\n",
       "      <td>Terminated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133048</td>\n",
       "      <td>T60529</td>\n",
       "      <td>D03NMM</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Investigative</td>\n",
       "      <td>AM-643</td>\n",
       "      <td>46843035</td>\n",
       "      <td>Dermatological disease</td>\n",
       "      <td>Investigative</td>\n",
       "      <td>PGH1_HUMAN</td>\n",
       "      <td>Prostaglandin G/H synthase 1 (COX-1)</td>\n",
       "      <td>PTGS1</td>\n",
       "      <td>Prostaglandin-endoperoxide synthase 1; Prostag...</td>\n",
       "      <td>Converts arachidonate to prostaglandin H2 (PGH...</td>\n",
       "      <td>Paired donor oxygen oxidoreductase</td>\n",
       "      <td>MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGIC...</td>\n",
       "      <td>Rheumatoid arthritis</td>\n",
       "      <td>P23219</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60493</td>\n",
       "      <td>T80975</td>\n",
       "      <td>D0T2ER</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Phase 1</td>\n",
       "      <td>TAK-593</td>\n",
       "      <td>24767976</td>\n",
       "      <td>Solid tumour/cancer</td>\n",
       "      <td>Phase 1</td>\n",
       "      <td>VGFR2_HUMAN</td>\n",
       "      <td>Vascular endothelial growth factor receptor 2 ...</td>\n",
       "      <td>KDR</td>\n",
       "      <td>VEGFR2; VEGFR-2; VEGF-2 receptor; Protein-tyro...</td>\n",
       "      <td>Plays an essential role in the regulation of a...</td>\n",
       "      <td>Kinase</td>\n",
       "      <td>MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKAN...</td>\n",
       "      <td>Renal cell carcinoma</td>\n",
       "      <td>P35968</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>169176</td>\n",
       "      <td>T92072</td>\n",
       "      <td>D07ESH</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Discontinued in Phase 3</td>\n",
       "      <td>PF-1913539</td>\n",
       "      <td>176408</td>\n",
       "      <td>Alzheimer disease</td>\n",
       "      <td>Discontinued in Phase 3</td>\n",
       "      <td>AA1R_HUMAN</td>\n",
       "      <td>Adenosine A1 receptor (ADORA1)</td>\n",
       "      <td>ADORA1</td>\n",
       "      <td>Adenosine receptor A1; A(1) adenosine receptor</td>\n",
       "      <td>The activity of this receptor is mediated by G...</td>\n",
       "      <td>GPCR rhodopsin</td>\n",
       "      <td>MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...</td>\n",
       "      <td>Hyper-lipoproteinaemia</td>\n",
       "      <td>P30542</td>\n",
       "      <td>Phase 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120183</td>\n",
       "      <td>T30082</td>\n",
       "      <td>D0Q0RC</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Ethopropazine</td>\n",
       "      <td>3290</td>\n",
       "      <td>Parkinson disease</td>\n",
       "      <td>Approved</td>\n",
       "      <td>ACES_HUMAN</td>\n",
       "      <td>Acetylcholinesterase (AChE)</td>\n",
       "      <td>ACHE</td>\n",
       "      <td>YT; N-ACHE; ARACHE</td>\n",
       "      <td>Role in neuronal apoptosis. Terminates signal ...</td>\n",
       "      <td>Carboxylic ester hydrolase</td>\n",
       "      <td>MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGG...</td>\n",
       "      <td>Oesophageal/gastroduodenal disorder</td>\n",
       "      <td>P22303</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID TargetID  DRUGID              DRUGTYPE         Drug_high_status  \\\n",
       "0  140736   T51115  D0L4YD  Small molecular drug                 Approved   \n",
       "1  133048   T60529  D03NMM  Small molecular drug            Investigative   \n",
       "2   60493   T80975  D0T2ER  Small molecular drug                  Phase 1   \n",
       "3  169176   T92072  D07ESH  Small molecular drug  Discontinued in Phase 3   \n",
       "4  120183   T30082  D0Q0RC  Small molecular drug                 Approved   \n",
       "\n",
       "        DRUGNAME  PUBCHCID Disease_of_highest_status              Drug_Status  \\\n",
       "0    Solifenacin    154059        Overactive bladder                 Approved   \n",
       "1         AM-643  46843035    Dermatological disease            Investigative   \n",
       "2        TAK-593  24767976       Solid tumour/cancer                  Phase 1   \n",
       "3     PF-1913539    176408         Alzheimer disease  Discontinued in Phase 3   \n",
       "4  Ethopropazine      3290         Parkinson disease                 Approved   \n",
       "\n",
       "      UNIPROID                                           TARGNAME GENENAME  \\\n",
       "0  CAC1C_HUMAN  Voltage-gated calcium channel alpha Cav1.2 (CA...  CACNA1C   \n",
       "1   PGH1_HUMAN               Prostaglandin G/H synthase 1 (COX-1)    PTGS1   \n",
       "2  VGFR2_HUMAN  Vascular endothelial growth factor receptor 2 ...      KDR   \n",
       "3   AA1R_HUMAN                     Adenosine A1 receptor (ADORA1)   ADORA1   \n",
       "4   ACES_HUMAN                        Acetylcholinesterase (AChE)     ACHE   \n",
       "\n",
       "                                            SYNONYMS  \\\n",
       "0  Voltage-gated calcium channel subunit alpha Ca...   \n",
       "1  Prostaglandin-endoperoxide synthase 1; Prostag...   \n",
       "2  VEGFR2; VEGFR-2; VEGF-2 receptor; Protein-tyro...   \n",
       "3     Adenosine receptor A1; A(1) adenosine receptor   \n",
       "4                                 YT; N-ACHE; ARACHE   \n",
       "\n",
       "                                            FUNCTION  \\\n",
       "0  Mediates influx of calcium ions into the cytop...   \n",
       "1  Converts arachidonate to prostaglandin H2 (PGH...   \n",
       "2  Plays an essential role in the regulation of a...   \n",
       "3  The activity of this receptor is mediated by G...   \n",
       "4  Role in neuronal apoptosis. Terminates signal ...   \n",
       "\n",
       "                             BIOCLASS  \\\n",
       "0           Voltage-gated ion channel   \n",
       "1  Paired donor oxygen oxidoreductase   \n",
       "2                              Kinase   \n",
       "3                      GPCR rhodopsin   \n",
       "4          Carboxylic ester hydrolase   \n",
       "\n",
       "                                            SEQUENCE  \\\n",
       "0  MVNENTRMYIPEENHQGSNYGSPRPAHANMNANAAAGLAPEHIPTP...   \n",
       "1  MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGIC...   \n",
       "2  MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKAN...   \n",
       "3  MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...   \n",
       "4  MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGG...   \n",
       "\n",
       "                               Disease Accession Number Target_Status  \n",
       "0           Genetic cardiac arrhythmia           Q13936    Terminated  \n",
       "1                 Rheumatoid arthritis           P23219      Approved  \n",
       "2                 Renal cell carcinoma           P35968      Approved  \n",
       "3               Hyper-lipoproteinaemia           P30542       Phase 2  \n",
       "4  Oesophageal/gastroduodenal disorder           P22303      Approved  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>ID</th>\n",
       "      <th>TargetID</th>\n",
       "      <th>DRUGID</th>\n",
       "      <th>DRUGTYPE</th>\n",
       "      <th>Drug_high_status</th>\n",
       "      <th>DRUGNAME</th>\n",
       "      <th>PUBCHCID</th>\n",
       "      <th>Disease_of_highest_status</th>\n",
       "      <th>Drug_Status</th>\n",
       "      <th>UNIPROID</th>\n",
       "      <th>TARGNAME</th>\n",
       "      <th>GENENAME</th>\n",
       "      <th>SYNONYMS</th>\n",
       "      <th>FUNCTION</th>\n",
       "      <th>BIOCLASS</th>\n",
       "      <th>SEQUENCE</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Accession Number</th>\n",
       "      <th>Target_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140736</td>\n",
       "      <td>332</td>\n",
       "      <td>1645</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2076</td>\n",
       "      <td>889</td>\n",
       "      <td>485</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>676</td>\n",
       "      <td>92</td>\n",
       "      <td>583</td>\n",
       "      <td>376</td>\n",
       "      <td>101</td>\n",
       "      <td>662</td>\n",
       "      <td>220</td>\n",
       "      <td>547</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133048</td>\n",
       "      <td>402</td>\n",
       "      <td>394</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>2231</td>\n",
       "      <td>177</td>\n",
       "      <td>12</td>\n",
       "      <td>516</td>\n",
       "      <td>505</td>\n",
       "      <td>554</td>\n",
       "      <td>296</td>\n",
       "      <td>145</td>\n",
       "      <td>72</td>\n",
       "      <td>600</td>\n",
       "      <td>487</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60493</td>\n",
       "      <td>547</td>\n",
       "      <td>2049</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>2125</td>\n",
       "      <td>2059</td>\n",
       "      <td>592</td>\n",
       "      <td>15</td>\n",
       "      <td>689</td>\n",
       "      <td>668</td>\n",
       "      <td>371</td>\n",
       "      <td>564</td>\n",
       "      <td>435</td>\n",
       "      <td>58</td>\n",
       "      <td>513</td>\n",
       "      <td>479</td>\n",
       "      <td>363</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>169176</td>\n",
       "      <td>636</td>\n",
       "      <td>760</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1621</td>\n",
       "      <td>925</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>604</td>\n",
       "      <td>45</td>\n",
       "      <td>488</td>\n",
       "      <td>248</td>\n",
       "      <td>316</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120183</td>\n",
       "      <td>210</td>\n",
       "      <td>1889</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>807</td>\n",
       "      <td>145</td>\n",
       "      <td>494</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>603</td>\n",
       "      <td>557</td>\n",
       "      <td>28</td>\n",
       "      <td>537</td>\n",
       "      <td>407</td>\n",
       "      <td>249</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  TargetID  DRUGID  DRUGTYPE  Drug_high_status  DRUGNAME  PUBCHCID  \\\n",
       "0  140736       332    1645         4                 0      2076       889   \n",
       "1  133048       402     394         4                11        76      2231   \n",
       "2   60493       547    2049         4                14      2125      2059   \n",
       "3  169176       636     760         4                 8      1621       925   \n",
       "4  120183       210    1889         4                 0       807       145   \n",
       "\n",
       "   Disease_of_highest_status  Drug_Status  UNIPROID  TARGNAME  GENENAME  \\\n",
       "0                        485            1       102       676        92   \n",
       "1                        177           12       516       505       554   \n",
       "2                        592           15       689       668       371   \n",
       "3                         41            9        16        24        17   \n",
       "4                        494            1        26        19         9   \n",
       "\n",
       "   SYNONYMS  FUNCTION  BIOCLASS  SEQUENCE  Disease  Accession Number  \\\n",
       "0       583       376       101       662      220               547   \n",
       "1       296       145        72       600      487               256   \n",
       "2       564       435        58       513      479               363   \n",
       "3         7       604        45       488      248               316   \n",
       "4       603       557        28       537      407               249   \n",
       "\n",
       "   Target_Status  \n",
       "0             31  \n",
       "1              1  \n",
       "2              1  \n",
       "3             22  \n",
       "4              1  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Drug_high_status  Drug_Status\n",
      "0                      0            1\n",
      "1                     11           12\n",
      "2                     14           15\n",
      "3                      8            9\n",
      "4                      0            1\n",
      "...                  ...          ...\n",
      "134481                17           17\n",
      "134482                16           16\n",
      "134483                 0           21\n",
      "134484                 0           15\n",
      "134485                 3            4\n",
      "\n",
      "[134486 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "corr_mat =df_encoded[['Drug_high_status','Drug_Status']]\n",
    "print(corr_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.heatmap(df_encoded.corr(numeric_only=True), annot=True, fmt='.1f', cmap='coolwarm').set(title='Correlation Heatmap')\n",
    "plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'TargetID', 'DRUGID', 'DRUGTYPE', 'Drug_high_status', 'DRUGNAME',\n",
       "       'PUBCHCID', 'Disease_of_highest_status', 'Drug_Status', 'UNIPROID',\n",
       "       'TARGNAME', 'GENENAME', 'SYNONYMS', 'FUNCTION', 'BIOCLASS', 'SEQUENCE',\n",
       "       'Disease', 'Accession Number', 'Target_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1=df_encoded[['ID','DRUGNAME','DRUGTYPE','GENENAME','Drug_high_status','Disease_of_highest_status','Drug_Status','BIOCLASS','Disease','SEQUENCE','TARGNAME']]\n",
    "Y1=df_encoded['Target_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets (e.g., 80% train, 20% test)\n",
    "X1_train, X1_test, Y1_train, Y1_test = train_test_split(X1, Y1, test_size=0.2,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-17 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-17 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-17 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-17 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-17 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-17 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-17 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-17 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-17 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-17 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1=LogisticRegression()\n",
    "model_1.fit(X1_train,Y1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "Y1_predict=model_1.predict(X1_test)\n",
    "acc_1=accuracy_score(Y1_test,Y1_predict)\n",
    "cfmat_1=confusion_matrix(Y1_test,Y1_predict)\n",
    "class_report_1=classification_report(Y1_test,Y1_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23206186333556397\n"
     ]
    }
   ],
   "source": [
    "print(acc_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    8    0 ...    0    0    0]\n",
      " [   0 4279    0 ...    0    0    0]\n",
      " [   0   26    0 ...    0    0    0]\n",
      " ...\n",
      " [   0   22    0 ...    0    0    0]\n",
      " [   0  715    0 ...    0    0    0]\n",
      " [   0  230    0 ...    0    0    0]]\n"
     ]
    }
   ],
   "source": [
    "print(cfmat_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        13\n",
      "           1       0.23      0.69      0.35      6168\n",
      "           2       0.00      0.00      0.00        32\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.00      0.00      0.00        70\n",
      "           5       0.00      0.00      0.00       475\n",
      "           6       0.00      0.00      0.00        24\n",
      "           7       0.00      0.00      0.00      1177\n",
      "           8       0.00      0.00      0.00        11\n",
      "           9       0.00      0.00      0.00         3\n",
      "          10       0.00      0.00      0.00       506\n",
      "          11       0.00      0.00      0.00         4\n",
      "          12       0.00      0.00      0.00        20\n",
      "          13       0.00      0.00      0.00        16\n",
      "          14       0.00      0.00      0.00      1866\n",
      "          15       0.00      0.00      0.00       628\n",
      "          16       0.00      0.00      0.00         3\n",
      "          17       0.00      0.00      0.00      3379\n",
      "          18       0.00      0.00      0.00      1214\n",
      "          19       0.00      0.00      0.00         4\n",
      "          20       0.00      0.00      0.00        39\n",
      "          22       0.24      0.34      0.28      5858\n",
      "          23       0.00      0.00      0.00       283\n",
      "          24       0.00      0.00      0.00        32\n",
      "          25       0.00      0.00      0.00         7\n",
      "          26       0.00      0.00      0.00      3132\n",
      "          27       0.00      0.00      0.00       102\n",
      "          28       0.00      0.00      0.00       393\n",
      "          29       0.00      0.00      0.00        50\n",
      "          30       0.00      0.00      0.00        23\n",
      "          31       0.00      0.00      0.00      1058\n",
      "          32       0.00      0.00      0.00       307\n",
      "\n",
      "    accuracy                           0.23     26898\n",
      "   macro avg       0.01      0.03      0.02     26898\n",
      "weighted avg       0.10      0.23      0.14     26898\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(class_report_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'TargetID', 'DRUGID', 'DRUGTYPE', 'Drug_high_status', 'DRUGNAME',\n",
       "       'PUBCHCID', 'Disease_of_highest_status', 'Drug_Status', 'UNIPROID',\n",
       "       'TARGNAME', 'GENENAME', 'SYNONYMS', 'FUNCTION', 'BIOCLASS', 'SEQUENCE',\n",
       "       'Disease', 'Accession Number', 'Target_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1_sample = drug_df.drop(columns=['TargetID', 'DRUGID','PUBCHCID','SYNONYMS','FUNCTION','Accession Number','UNIPROID','GENENAME','SEQUENCE', 'ID'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2=df_encoded[['Drug_high_status','DRUGNAME', 'Disease_of_highest_status','Drug_Status', 'TARGNAME', 'GENENAME', 'BIOCLASS', 'SEQUENCE','Disease']]\n",
    "Y2=df_encoded['Target_Status']\n",
    "# Dropped from the COlumns : UNIPROID,GENENAME,SEQUENCE, ID\n",
    "# CONFIRMED DROPPED COLUMNS :TARGETID, DRUGID,PUBCHID,SYNONYMS,FUNCTION,ACCESSION NUMBER "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets (e.g., 80% train, 20% test)\n",
    "X2_train, X2_test, Y2_train, Y2_test = train_test_split(X2, Y2, test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-18 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-18 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-18 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-18 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-18 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-18 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-18 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-18 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-18 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-18 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-18 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-18 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomfc_1=RandomForestClassifier()\n",
    "randomfc_1.fit(X2_train,Y2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "Y2_predict=randomfc_1.predict(X2_test)\n",
    "acc_2=accuracy_score(Y2_test,Y2_predict)\n",
    "cfmat_2=confusion_matrix(Y2_test,Y2_predict)\n",
    "class_report_2=classification_report(Y2_test,Y2_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9203658264554986\n"
     ]
    }
   ],
   "source": [
    "print(acc_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.42      0.59        19\n",
      "           1       0.91      0.95      0.93      6222\n",
      "           2       1.00      0.76      0.86        21\n",
      "           4       0.96      0.77      0.85        61\n",
      "           5       0.91      0.90      0.91       455\n",
      "           6       0.86      0.75      0.80        24\n",
      "           7       0.92      0.88      0.90      1147\n",
      "           8       0.78      0.88      0.82         8\n",
      "           9       1.00      1.00      1.00         1\n",
      "          10       0.91      0.88      0.89       511\n",
      "          11       1.00      1.00      1.00         2\n",
      "          12       0.94      0.85      0.89        34\n",
      "          13       1.00      1.00      1.00        14\n",
      "          14       0.91      0.91      0.91      1827\n",
      "          15       0.93      0.94      0.93       595\n",
      "          16       0.67      0.57      0.62         7\n",
      "          17       0.93      0.94      0.93      3419\n",
      "          18       0.93      0.92      0.92      1233\n",
      "          19       1.00      1.00      1.00         4\n",
      "          20       0.24      0.31      0.27        26\n",
      "          21       0.00      0.00      0.00         0\n",
      "          22       0.93      0.93      0.93      5980\n",
      "          23       0.87      0.83      0.85       296\n",
      "          24       0.97      1.00      0.98        28\n",
      "          25       0.60      1.00      0.75         3\n",
      "          26       0.92      0.91      0.91      3052\n",
      "          27       0.90      0.89      0.89       105\n",
      "          28       0.92      0.85      0.88       386\n",
      "          29       1.00      0.92      0.96        51\n",
      "          30       0.95      0.95      0.95        21\n",
      "          31       0.93      0.89      0.91      1048\n",
      "          32       0.86      0.84      0.85       298\n",
      "\n",
      "    accuracy                           0.92     26898\n",
      "   macro avg       0.86      0.83      0.84     26898\n",
      "weighted avg       0.92      0.92      0.92     26898\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(class_report_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y2_series=pd.Series(label_en_1.inverse_transform(Y2_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Method to Covert to df**\n",
    "1. Y2_series=pd.Series(label_en.inverse_transform(Y2_predict))\n",
    "2. Y2_series.index=X2_test.index\n",
    "3. true_df=drug_df[['ID','Prediction']].iloc[Y2_series.index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y2_series.index=X2_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20202                    Approved\n",
       "114185                 Terminated\n",
       "61191                   Phase 1/2\n",
       "53935                  Terminated\n",
       "5080                      Phase 3\n",
       "                   ...           \n",
       "78665     Discontinued in Phase 3\n",
       "65085     Discontinued in Phase 3\n",
       "6181                      Phase 1\n",
       "91231                    Approved\n",
       "60837                     Phase 3\n",
       "Length: 26898, dtype: object"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y2_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_df['Prediction']=pd.Series(Y2_series,index=Y2_series.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>ID</th>\n",
       "      <th>TargetID</th>\n",
       "      <th>DRUGID</th>\n",
       "      <th>DRUGTYPE</th>\n",
       "      <th>Drug_high_status</th>\n",
       "      <th>DRUGNAME</th>\n",
       "      <th>PUBCHCID</th>\n",
       "      <th>Disease_of_highest_status</th>\n",
       "      <th>Drug_Status</th>\n",
       "      <th>UNIPROID</th>\n",
       "      <th>TARGNAME</th>\n",
       "      <th>GENENAME</th>\n",
       "      <th>SYNONYMS</th>\n",
       "      <th>FUNCTION</th>\n",
       "      <th>BIOCLASS</th>\n",
       "      <th>SEQUENCE</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Accession Number</th>\n",
       "      <th>Target_Status</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>140736</td>\n",
       "      <td>T51115</td>\n",
       "      <td>D0L4YD</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solifenacin</td>\n",
       "      <td>154059</td>\n",
       "      <td>Overactive bladder</td>\n",
       "      <td>Approved</td>\n",
       "      <td>CAC1C_HUMAN</td>\n",
       "      <td>Voltage-gated calcium channel alpha Cav1.2 (CA...</td>\n",
       "      <td>CACNA1C</td>\n",
       "      <td>Voltage-gated calcium channel subunit alpha Ca...</td>\n",
       "      <td>Mediates influx of calcium ions into the cytop...</td>\n",
       "      <td>Voltage-gated ion channel</td>\n",
       "      <td>MVNENTRMYIPEENHQGSNYGSPRPAHANMNANAAAGLAPEHIPTP...</td>\n",
       "      <td>Genetic cardiac arrhythmia</td>\n",
       "      <td>Q13936</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133048</td>\n",
       "      <td>T60529</td>\n",
       "      <td>D03NMM</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Investigative</td>\n",
       "      <td>AM-643</td>\n",
       "      <td>46843035</td>\n",
       "      <td>Dermatological disease</td>\n",
       "      <td>Investigative</td>\n",
       "      <td>PGH1_HUMAN</td>\n",
       "      <td>Prostaglandin G/H synthase 1 (COX-1)</td>\n",
       "      <td>PTGS1</td>\n",
       "      <td>Prostaglandin-endoperoxide synthase 1; Prostag...</td>\n",
       "      <td>Converts arachidonate to prostaglandin H2 (PGH...</td>\n",
       "      <td>Paired donor oxygen oxidoreductase</td>\n",
       "      <td>MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGIC...</td>\n",
       "      <td>Rheumatoid arthritis</td>\n",
       "      <td>P23219</td>\n",
       "      <td>Approved</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60493</td>\n",
       "      <td>T80975</td>\n",
       "      <td>D0T2ER</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Phase 1</td>\n",
       "      <td>TAK-593</td>\n",
       "      <td>24767976</td>\n",
       "      <td>Solid tumour/cancer</td>\n",
       "      <td>Phase 1</td>\n",
       "      <td>VGFR2_HUMAN</td>\n",
       "      <td>Vascular endothelial growth factor receptor 2 ...</td>\n",
       "      <td>KDR</td>\n",
       "      <td>VEGFR2; VEGFR-2; VEGF-2 receptor; Protein-tyro...</td>\n",
       "      <td>Plays an essential role in the regulation of a...</td>\n",
       "      <td>Kinase</td>\n",
       "      <td>MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKAN...</td>\n",
       "      <td>Renal cell carcinoma</td>\n",
       "      <td>P35968</td>\n",
       "      <td>Approved</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>169176</td>\n",
       "      <td>T92072</td>\n",
       "      <td>D07ESH</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Discontinued in Phase 3</td>\n",
       "      <td>PF-1913539</td>\n",
       "      <td>176408</td>\n",
       "      <td>Alzheimer disease</td>\n",
       "      <td>Discontinued in Phase 3</td>\n",
       "      <td>AA1R_HUMAN</td>\n",
       "      <td>Adenosine A1 receptor (ADORA1)</td>\n",
       "      <td>ADORA1</td>\n",
       "      <td>Adenosine receptor A1; A(1) adenosine receptor</td>\n",
       "      <td>The activity of this receptor is mediated by G...</td>\n",
       "      <td>GPCR rhodopsin</td>\n",
       "      <td>MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...</td>\n",
       "      <td>Hyper-lipoproteinaemia</td>\n",
       "      <td>P30542</td>\n",
       "      <td>Phase 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120183</td>\n",
       "      <td>T30082</td>\n",
       "      <td>D0Q0RC</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Ethopropazine</td>\n",
       "      <td>3290</td>\n",
       "      <td>Parkinson disease</td>\n",
       "      <td>Approved</td>\n",
       "      <td>ACES_HUMAN</td>\n",
       "      <td>Acetylcholinesterase (AChE)</td>\n",
       "      <td>ACHE</td>\n",
       "      <td>YT; N-ACHE; ARACHE</td>\n",
       "      <td>Role in neuronal apoptosis. Terminates signal ...</td>\n",
       "      <td>Carboxylic ester hydrolase</td>\n",
       "      <td>MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGG...</td>\n",
       "      <td>Oesophageal/gastroduodenal disorder</td>\n",
       "      <td>P22303</td>\n",
       "      <td>Approved</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134481</th>\n",
       "      <td>147910</td>\n",
       "      <td>T59328</td>\n",
       "      <td>D06WRF</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Phase 2</td>\n",
       "      <td>KD019</td>\n",
       "      <td>10458325</td>\n",
       "      <td>Meningioma metastases</td>\n",
       "      <td>Phase 2</td>\n",
       "      <td>EGFR_HUMAN</td>\n",
       "      <td>Epidermal growth factor receptor (EGFR)</td>\n",
       "      <td>EGFR</td>\n",
       "      <td>Receptor tyrosine-protein kinase erbB-1; Proto...</td>\n",
       "      <td>Receptor tyrosine kinase binding ligands of th...</td>\n",
       "      <td>Kinase</td>\n",
       "      <td>MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFED...</td>\n",
       "      <td>Chronic pain</td>\n",
       "      <td>P00533</td>\n",
       "      <td>Phase 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134482</th>\n",
       "      <td>37688</td>\n",
       "      <td>T65236</td>\n",
       "      <td>D0WM5O</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Phase 1/2</td>\n",
       "      <td>G1</td>\n",
       "      <td>5322399</td>\n",
       "      <td>Lymphoma</td>\n",
       "      <td>Phase 1/2</td>\n",
       "      <td>GPER1_HUMAN</td>\n",
       "      <td>G-protein coupled estrogen receptor 1 (GPER1)</td>\n",
       "      <td>GPER1</td>\n",
       "      <td>Membrane estrogen receptor; Lymphocyte-derived...</td>\n",
       "      <td>Receptor for estrogen. .</td>\n",
       "      <td>GPCR rhodopsin</td>\n",
       "      <td>MDVTSQARGVGLEMYPGTAQPAAPNTTSPELNLSHPLLGTALANGT...</td>\n",
       "      <td>Lymphoma</td>\n",
       "      <td>Q99527</td>\n",
       "      <td>Phase 1/2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134483</th>\n",
       "      <td>30073</td>\n",
       "      <td>T47768</td>\n",
       "      <td>D04JHN</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Hydromorphone</td>\n",
       "      <td>5284570</td>\n",
       "      <td>Chronic pain</td>\n",
       "      <td>Phase 3</td>\n",
       "      <td>OPRM_HUMAN</td>\n",
       "      <td>Opioid receptor mu (MOP)</td>\n",
       "      <td>OPRM1</td>\n",
       "      <td>hMOP; Mu-type opioid receptor; Mu opioid recep...</td>\n",
       "      <td>Receptor for natural and synthetic opioids inc...</td>\n",
       "      <td>GPCR rhodopsin</td>\n",
       "      <td>MDSSAAPTNASNCTDALAYSSCSPAPSPGSWVNLSHLDGNLSDPCG...</td>\n",
       "      <td>Movement disorder</td>\n",
       "      <td>P35372</td>\n",
       "      <td>Investigative</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134484</th>\n",
       "      <td>37855</td>\n",
       "      <td>T40276</td>\n",
       "      <td>D0O0LS</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Entrectinib</td>\n",
       "      <td>25141092</td>\n",
       "      <td>Mammary analogue secretory carcinoma</td>\n",
       "      <td>Phase 1</td>\n",
       "      <td>KPCB_HUMAN</td>\n",
       "      <td>Protein kinase C beta (PRKCB)</td>\n",
       "      <td>PRKCB</td>\n",
       "      <td>Protein kinase C beta type; PRKCB1; PKCB; PKC-...</td>\n",
       "      <td>Plays a key role in B-cell activation by regul...</td>\n",
       "      <td>Kinase</td>\n",
       "      <td>MADPAAGPPPSEGEESTVRFARKGALRQKNVHEVKNHKFTARFFKQ...</td>\n",
       "      <td>Malignant haematopoietic neoplasm</td>\n",
       "      <td>P05771</td>\n",
       "      <td>Phase 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134485</th>\n",
       "      <td>3288</td>\n",
       "      <td>T68251</td>\n",
       "      <td>D00UCC</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Discontinued in Phase 1</td>\n",
       "      <td>ABT-518</td>\n",
       "      <td>9827497</td>\n",
       "      <td>Solid tumour/cancer</td>\n",
       "      <td>Discontinued in Phase 1</td>\n",
       "      <td>MMP2_HUMAN</td>\n",
       "      <td>Matrix metalloproteinase-2 (MMP-2)</td>\n",
       "      <td>MMP2</td>\n",
       "      <td>TBE-1; Matrix metalloproteinase 2; CLG4A; 72 k...</td>\n",
       "      <td>As well as degrading extracellular matrix prot...</td>\n",
       "      <td>Peptidase</td>\n",
       "      <td>MEALMARGALTGPLRALCLLGCLLSHAAAAPSPIIKFPGDVAPKTD...</td>\n",
       "      <td>Multiple sclerosis</td>\n",
       "      <td>P08253</td>\n",
       "      <td>Terminated</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134486 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID TargetID  DRUGID              DRUGTYPE  \\\n",
       "0       140736   T51115  D0L4YD  Small molecular drug   \n",
       "1       133048   T60529  D03NMM  Small molecular drug   \n",
       "2        60493   T80975  D0T2ER  Small molecular drug   \n",
       "3       169176   T92072  D07ESH  Small molecular drug   \n",
       "4       120183   T30082  D0Q0RC  Small molecular drug   \n",
       "...        ...      ...     ...                   ...   \n",
       "134481  147910   T59328  D06WRF  Small molecular drug   \n",
       "134482   37688   T65236  D0WM5O  Small molecular drug   \n",
       "134483   30073   T47768  D04JHN  Small molecular drug   \n",
       "134484   37855   T40276  D0O0LS  Small molecular drug   \n",
       "134485    3288   T68251  D00UCC  Small molecular drug   \n",
       "\n",
       "               Drug_high_status       DRUGNAME  PUBCHCID  \\\n",
       "0                      Approved    Solifenacin    154059   \n",
       "1                 Investigative         AM-643  46843035   \n",
       "2                       Phase 1        TAK-593  24767976   \n",
       "3       Discontinued in Phase 3     PF-1913539    176408   \n",
       "4                      Approved  Ethopropazine      3290   \n",
       "...                         ...            ...       ...   \n",
       "134481                  Phase 2          KD019  10458325   \n",
       "134482                Phase 1/2             G1   5322399   \n",
       "134483                 Approved  Hydromorphone   5284570   \n",
       "134484                 Approved    Entrectinib  25141092   \n",
       "134485  Discontinued in Phase 1        ABT-518   9827497   \n",
       "\n",
       "                   Disease_of_highest_status              Drug_Status  \\\n",
       "0                         Overactive bladder                 Approved   \n",
       "1                     Dermatological disease            Investigative   \n",
       "2                        Solid tumour/cancer                  Phase 1   \n",
       "3                          Alzheimer disease  Discontinued in Phase 3   \n",
       "4                          Parkinson disease                 Approved   \n",
       "...                                      ...                      ...   \n",
       "134481                 Meningioma metastases                  Phase 2   \n",
       "134482                              Lymphoma                Phase 1/2   \n",
       "134483                          Chronic pain                  Phase 3   \n",
       "134484  Mammary analogue secretory carcinoma                  Phase 1   \n",
       "134485                   Solid tumour/cancer  Discontinued in Phase 1   \n",
       "\n",
       "           UNIPROID                                           TARGNAME  \\\n",
       "0       CAC1C_HUMAN  Voltage-gated calcium channel alpha Cav1.2 (CA...   \n",
       "1        PGH1_HUMAN               Prostaglandin G/H synthase 1 (COX-1)   \n",
       "2       VGFR2_HUMAN  Vascular endothelial growth factor receptor 2 ...   \n",
       "3        AA1R_HUMAN                     Adenosine A1 receptor (ADORA1)   \n",
       "4        ACES_HUMAN                        Acetylcholinesterase (AChE)   \n",
       "...             ...                                                ...   \n",
       "134481   EGFR_HUMAN            Epidermal growth factor receptor (EGFR)   \n",
       "134482  GPER1_HUMAN      G-protein coupled estrogen receptor 1 (GPER1)   \n",
       "134483   OPRM_HUMAN                           Opioid receptor mu (MOP)   \n",
       "134484   KPCB_HUMAN                      Protein kinase C beta (PRKCB)   \n",
       "134485   MMP2_HUMAN                 Matrix metalloproteinase-2 (MMP-2)   \n",
       "\n",
       "       GENENAME                                           SYNONYMS  \\\n",
       "0       CACNA1C  Voltage-gated calcium channel subunit alpha Ca...   \n",
       "1         PTGS1  Prostaglandin-endoperoxide synthase 1; Prostag...   \n",
       "2           KDR  VEGFR2; VEGFR-2; VEGF-2 receptor; Protein-tyro...   \n",
       "3        ADORA1     Adenosine receptor A1; A(1) adenosine receptor   \n",
       "4          ACHE                                 YT; N-ACHE; ARACHE   \n",
       "...         ...                                                ...   \n",
       "134481     EGFR  Receptor tyrosine-protein kinase erbB-1; Proto...   \n",
       "134482    GPER1  Membrane estrogen receptor; Lymphocyte-derived...   \n",
       "134483    OPRM1  hMOP; Mu-type opioid receptor; Mu opioid recep...   \n",
       "134484    PRKCB  Protein kinase C beta type; PRKCB1; PKCB; PKC-...   \n",
       "134485     MMP2  TBE-1; Matrix metalloproteinase 2; CLG4A; 72 k...   \n",
       "\n",
       "                                                 FUNCTION  \\\n",
       "0       Mediates influx of calcium ions into the cytop...   \n",
       "1       Converts arachidonate to prostaglandin H2 (PGH...   \n",
       "2       Plays an essential role in the regulation of a...   \n",
       "3       The activity of this receptor is mediated by G...   \n",
       "4       Role in neuronal apoptosis. Terminates signal ...   \n",
       "...                                                   ...   \n",
       "134481  Receptor tyrosine kinase binding ligands of th...   \n",
       "134482                           Receptor for estrogen. .   \n",
       "134483  Receptor for natural and synthetic opioids inc...   \n",
       "134484  Plays a key role in B-cell activation by regul...   \n",
       "134485  As well as degrading extracellular matrix prot...   \n",
       "\n",
       "                                  BIOCLASS  \\\n",
       "0                Voltage-gated ion channel   \n",
       "1       Paired donor oxygen oxidoreductase   \n",
       "2                                   Kinase   \n",
       "3                           GPCR rhodopsin   \n",
       "4               Carboxylic ester hydrolase   \n",
       "...                                    ...   \n",
       "134481                              Kinase   \n",
       "134482                      GPCR rhodopsin   \n",
       "134483                      GPCR rhodopsin   \n",
       "134484                              Kinase   \n",
       "134485                           Peptidase   \n",
       "\n",
       "                                                 SEQUENCE  \\\n",
       "0       MVNENTRMYIPEENHQGSNYGSPRPAHANMNANAAAGLAPEHIPTP...   \n",
       "1       MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGIC...   \n",
       "2       MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKAN...   \n",
       "3       MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...   \n",
       "4       MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGG...   \n",
       "...                                                   ...   \n",
       "134481  MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFED...   \n",
       "134482  MDVTSQARGVGLEMYPGTAQPAAPNTTSPELNLSHPLLGTALANGT...   \n",
       "134483  MDSSAAPTNASNCTDALAYSSCSPAPSPGSWVNLSHLDGNLSDPCG...   \n",
       "134484  MADPAAGPPPSEGEESTVRFARKGALRQKNVHEVKNHKFTARFFKQ...   \n",
       "134485  MEALMARGALTGPLRALCLLGCLLSHAAAAPSPIIKFPGDVAPKTD...   \n",
       "\n",
       "                                    Disease Accession Number  Target_Status  \\\n",
       "0                Genetic cardiac arrhythmia           Q13936     Terminated   \n",
       "1                      Rheumatoid arthritis           P23219       Approved   \n",
       "2                      Renal cell carcinoma           P35968       Approved   \n",
       "3                    Hyper-lipoproteinaemia           P30542        Phase 2   \n",
       "4       Oesophageal/gastroduodenal disorder           P22303       Approved   \n",
       "...                                     ...              ...            ...   \n",
       "134481                         Chronic pain           P00533        Phase 2   \n",
       "134482                             Lymphoma           Q99527      Phase 1/2   \n",
       "134483                    Movement disorder           P35372  Investigative   \n",
       "134484    Malignant haematopoietic neoplasm           P05771        Phase 3   \n",
       "134485                   Multiple sclerosis           P08253     Terminated   \n",
       "\n",
       "       Prediction  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "...           ...  \n",
       "134481        NaN  \n",
       "134482        NaN  \n",
       "134483        NaN  \n",
       "134484        NaN  \n",
       "134485        NaN  \n",
       "\n",
       "[134486 rows x 20 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_df =drug_df[['ID','Prediction']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_df=drug_df[['ID','Prediction']].iloc[Y2_series.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>ID</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20202</th>\n",
       "      <td>99048</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114185</th>\n",
       "      <td>140616</td>\n",
       "      <td>Terminated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61191</th>\n",
       "      <td>145620</td>\n",
       "      <td>Phase 1/2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>36053</td>\n",
       "      <td>Terminated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5080</th>\n",
       "      <td>165075</td>\n",
       "      <td>Phase 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78665</th>\n",
       "      <td>24049</td>\n",
       "      <td>Discontinued in Phase 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65085</th>\n",
       "      <td>8083</td>\n",
       "      <td>Discontinued in Phase 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181</th>\n",
       "      <td>143913</td>\n",
       "      <td>Phase 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91231</th>\n",
       "      <td>46706</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60837</th>\n",
       "      <td>84357</td>\n",
       "      <td>Phase 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26898 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID               Prediction\n",
       "20202    99048                 Approved\n",
       "114185  140616               Terminated\n",
       "61191   145620                Phase 1/2\n",
       "53935    36053               Terminated\n",
       "5080    165075                  Phase 3\n",
       "...        ...                      ...\n",
       "78665    24049  Discontinued in Phase 3\n",
       "65085     8083  Discontinued in Phase 3\n",
       "6181    143913                  Phase 1\n",
       "91231    46706                 Approved\n",
       "60837    84357                  Phase 3\n",
       "\n",
       "[26898 rows x 2 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_df.to_csv(\"final.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing  GridsearchCV and XG Boost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3 = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "X3=df_encoded[['Drug_high_status','DRUGNAME', 'Disease_of_highest_status','Drug_Status', 'TARGNAME', 'GENENAME', 'BIOCLASS', 'SEQUENCE','Disease']]\n",
    "Y3=df_encoded['Target_Status']\n",
    "\n",
    "# TANMAY PARAMETER DROPPED : -['TargetID','DRUGID','PUBCHCID','Accession Number','UNIPROID','SYNONYMS','FUNCTION','DRUGTYPE'],axis=1\n",
    "\n",
    "X3_train, X3_test, Y3_train, Y3_test = train_test_split(X3, Y3, test_size=0.2,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the parameter Dictionary \n",
    "param_grid = {\n",
    "    'learning_rate': [0.01, 0.1, 0.2],  # Step size shrinkage\n",
    "    'n_estimators': [100, 200, 300],     # Number of trees (boosting rounds)\n",
    "    'max_depth': [3, 5, 7],              # Maximum depth of trees\n",
    "    'subsample': [0.7, 0.8, 0.9],        # Subsampling ratio of the training set\n",
    "    'colsample_bytree': [0.7, 0.8, 0.9], # Subsampling ratio of features per tree\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the grid-search-CV\n",
    "grid_search = GridSearchCV(estimator=model_3,param_grid=param_grid,scoring='accuracy',cv=5,verbose=1,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the Best Parameters\n",
    "grid_search.fit(X3_train, Y3_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3.fit(X3_train, Y3_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y3_predict=model_3.predict(X3_test)\n",
    "acc_3=accuracy_score(Y3_test,Y3_predict)\n",
    "cfmat_3=confusion_matrix(Y3_test,Y3_predict)\n",
    "class_report_3=classification_report(Y3_test,Y3_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(acc_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(class_report_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the Test Data Seperately using Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df=pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>ID</th>\n",
       "      <th>TargetID</th>\n",
       "      <th>DRUGID</th>\n",
       "      <th>DRUGTYPE</th>\n",
       "      <th>Drug_high_status</th>\n",
       "      <th>DRUGNAME</th>\n",
       "      <th>PUBCHCID</th>\n",
       "      <th>Disease_of_highest_status</th>\n",
       "      <th>Drug_Status</th>\n",
       "      <th>UNIPROID</th>\n",
       "      <th>TARGNAME</th>\n",
       "      <th>GENENAME</th>\n",
       "      <th>SYNONYMS</th>\n",
       "      <th>FUNCTION</th>\n",
       "      <th>BIOCLASS</th>\n",
       "      <th>SEQUENCE</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Accession Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1076</td>\n",
       "      <td>T51487</td>\n",
       "      <td>D0HO2T</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Phase 2</td>\n",
       "      <td>AZD7325</td>\n",
       "      <td>23581869</td>\n",
       "      <td>Anxiety disorder</td>\n",
       "      <td>Phase 2</td>\n",
       "      <td>GBRA1_HUMAN</td>\n",
       "      <td>GABA(A) receptor alpha-1 (GABRA1)</td>\n",
       "      <td>GABRA1</td>\n",
       "      <td>Gamma-aminobutyric acid receptor subunit alpha...</td>\n",
       "      <td>Ligand-gated chloride channel which is a compo...</td>\n",
       "      <td>Ligand-gated ion channel</td>\n",
       "      <td>MRKSPGLSDCLWAWILLLSTLTGRSYGQPSLQDELKDNTTVFTRIL...</td>\n",
       "      <td>Malaria</td>\n",
       "      <td>P14867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>190816</td>\n",
       "      <td>T74312</td>\n",
       "      <td>D0Q4EQ</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Investigative</td>\n",
       "      <td>JNJ-38158471</td>\n",
       "      <td>16742063</td>\n",
       "      <td>Angiogenesis disorder</td>\n",
       "      <td>Investigative</td>\n",
       "      <td>FLT3_HUMAN</td>\n",
       "      <td>Fms-like tyrosine kinase 3 (FLT-3)</td>\n",
       "      <td>FLT3</td>\n",
       "      <td>Stem cell tyrosine kinase 1; STK1; STK-1; Rece...</td>\n",
       "      <td>Tyrosine-protein kinase that acts as cell-surf...</td>\n",
       "      <td>Kinase</td>\n",
       "      <td>MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSS...</td>\n",
       "      <td>Malignant haematopoietic neoplasm</td>\n",
       "      <td>P36888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180551</td>\n",
       "      <td>T40474</td>\n",
       "      <td>D0R0MW</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Sunitinib</td>\n",
       "      <td>5329102</td>\n",
       "      <td>Gastrointestinal stromal tumour</td>\n",
       "      <td>Approved</td>\n",
       "      <td>MET_HUMAN</td>\n",
       "      <td>Proto-oncogene c-Met (MET)</td>\n",
       "      <td>MET</td>\n",
       "      <td>Tyrosine-protein kinase Met; Scatter factor re...</td>\n",
       "      <td>Regulates many physiological processes includi...</td>\n",
       "      <td>Kinase</td>\n",
       "      <td>MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNF...</td>\n",
       "      <td>Liver cancer</td>\n",
       "      <td>Q86W50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51630</td>\n",
       "      <td>T67162</td>\n",
       "      <td>D0T2XU</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Promethazine</td>\n",
       "      <td>4927</td>\n",
       "      <td>Nausea</td>\n",
       "      <td>Approved</td>\n",
       "      <td>DRD2_HUMAN</td>\n",
       "      <td>Dopamine D2 receptor (D2R)</td>\n",
       "      <td>DRD2</td>\n",
       "      <td>Dopamine receptor 2; D(2) dopamine receptor</td>\n",
       "      <td>Dopamine receptor whose activity is mediated b...</td>\n",
       "      <td>GPCR rhodopsin</td>\n",
       "      <td>MDPLNLSWYDDDLERQNWSRPFNGSDGKADRPHYNYYATLLTLLIA...</td>\n",
       "      <td>Depression</td>\n",
       "      <td>P14416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50566</td>\n",
       "      <td>T67162</td>\n",
       "      <td>D0H7KF</td>\n",
       "      <td>Small molecular drug</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Quetiapine</td>\n",
       "      <td>5002</td>\n",
       "      <td>Alcohol dependence</td>\n",
       "      <td>Phase 3</td>\n",
       "      <td>DRD2_HUMAN</td>\n",
       "      <td>Dopamine D2 receptor (D2R)</td>\n",
       "      <td>DRD2</td>\n",
       "      <td>Dopamine receptor 2; D(2) dopamine receptor</td>\n",
       "      <td>Dopamine receptor whose activity is mediated b...</td>\n",
       "      <td>GPCR rhodopsin</td>\n",
       "      <td>MDPLNLSWYDDDLERQNWSRPFNGSDGKADRPHYNYYATLLTLLIA...</td>\n",
       "      <td>Lymphoma</td>\n",
       "      <td>P14416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID TargetID  DRUGID              DRUGTYPE Drug_high_status  \\\n",
       "0    1076   T51487  D0HO2T  Small molecular drug          Phase 2   \n",
       "1  190816   T74312  D0Q4EQ  Small molecular drug    Investigative   \n",
       "2  180551   T40474  D0R0MW  Small molecular drug         Approved   \n",
       "3   51630   T67162  D0T2XU  Small molecular drug         Approved   \n",
       "4   50566   T67162  D0H7KF  Small molecular drug         Approved   \n",
       "\n",
       "       DRUGNAME  PUBCHCID        Disease_of_highest_status    Drug_Status  \\\n",
       "0       AZD7325  23581869                 Anxiety disorder        Phase 2   \n",
       "1  JNJ-38158471  16742063            Angiogenesis disorder  Investigative   \n",
       "2     Sunitinib   5329102  Gastrointestinal stromal tumour       Approved   \n",
       "3  Promethazine      4927                           Nausea       Approved   \n",
       "4    Quetiapine      5002               Alcohol dependence        Phase 3   \n",
       "\n",
       "      UNIPROID                            TARGNAME GENENAME  \\\n",
       "0  GBRA1_HUMAN   GABA(A) receptor alpha-1 (GABRA1)   GABRA1   \n",
       "1   FLT3_HUMAN  Fms-like tyrosine kinase 3 (FLT-3)     FLT3   \n",
       "2    MET_HUMAN          Proto-oncogene c-Met (MET)      MET   \n",
       "3   DRD2_HUMAN          Dopamine D2 receptor (D2R)     DRD2   \n",
       "4   DRD2_HUMAN          Dopamine D2 receptor (D2R)     DRD2   \n",
       "\n",
       "                                            SYNONYMS  \\\n",
       "0  Gamma-aminobutyric acid receptor subunit alpha...   \n",
       "1  Stem cell tyrosine kinase 1; STK1; STK-1; Rece...   \n",
       "2  Tyrosine-protein kinase Met; Scatter factor re...   \n",
       "3        Dopamine receptor 2; D(2) dopamine receptor   \n",
       "4        Dopamine receptor 2; D(2) dopamine receptor   \n",
       "\n",
       "                                            FUNCTION  \\\n",
       "0  Ligand-gated chloride channel which is a compo...   \n",
       "1  Tyrosine-protein kinase that acts as cell-surf...   \n",
       "2  Regulates many physiological processes includi...   \n",
       "3  Dopamine receptor whose activity is mediated b...   \n",
       "4  Dopamine receptor whose activity is mediated b...   \n",
       "\n",
       "                   BIOCLASS  \\\n",
       "0  Ligand-gated ion channel   \n",
       "1                    Kinase   \n",
       "2                    Kinase   \n",
       "3            GPCR rhodopsin   \n",
       "4            GPCR rhodopsin   \n",
       "\n",
       "                                            SEQUENCE  \\\n",
       "0  MRKSPGLSDCLWAWILLLSTLTGRSYGQPSLQDELKDNTTVFTRIL...   \n",
       "1  MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSS...   \n",
       "2  MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNF...   \n",
       "3  MDPLNLSWYDDDLERQNWSRPFNGSDGKADRPHYNYYATLLTLLIA...   \n",
       "4  MDPLNLSWYDDDLERQNWSRPFNGSDGKADRPHYNYYATLLTLLIA...   \n",
       "\n",
       "                             Disease Accession Number  \n",
       "0                            Malaria           P14867  \n",
       "1  Malignant haematopoietic neoplasm           P36888  \n",
       "2                       Liver cancer           Q86W50  \n",
       "3                         Depression           P14416  \n",
       "4                           Lymphoma           P14416  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_test=test_df.copy()\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding the Test Data \n",
    "label_en_2=LabelEncoder()\n",
    "select_cols_2=pd.Series(sample_test.columns)\n",
    "for ele in select_cols_2:\n",
    "    sample_test[ele]=label_en_2.fit_transform(sample_test[ele])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DRUGTYPE</th>\n",
       "      <th>Drug_high_status</th>\n",
       "      <th>DRUGNAME</th>\n",
       "      <th>PUBCHCID</th>\n",
       "      <th>Disease_of_highest_status</th>\n",
       "      <th>Drug_Status</th>\n",
       "      <th>UNIPROID</th>\n",
       "      <th>TARGNAME</th>\n",
       "      <th>GENENAME</th>\n",
       "      <th>SYNONYMS</th>\n",
       "      <th>FUNCTION</th>\n",
       "      <th>BIOCLASS</th>\n",
       "      <th>SEQUENCE</th>\n",
       "      <th>Disease</th>\n",
       "      <th>Accession Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>336</td>\n",
       "      <td>309</td>\n",
       "      <td>1421</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>146</td>\n",
       "      <td>1958</td>\n",
       "      <td>54</td>\n",
       "      <td>17</td>\n",
       "      <td>248</td>\n",
       "      <td>232</td>\n",
       "      <td>246</td>\n",
       "      <td>86</td>\n",
       "      <td>314</td>\n",
       "      <td>60</td>\n",
       "      <td>491</td>\n",
       "      <td>323</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57253</td>\n",
       "      <td>462</td>\n",
       "      <td>1827</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>1075</td>\n",
       "      <td>1943</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>238</td>\n",
       "      <td>221</td>\n",
       "      <td>238</td>\n",
       "      <td>420</td>\n",
       "      <td>621</td>\n",
       "      <td>57</td>\n",
       "      <td>428</td>\n",
       "      <td>326</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54134</td>\n",
       "      <td>248</td>\n",
       "      <td>1865</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2039</td>\n",
       "      <td>1255</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>368</td>\n",
       "      <td>487</td>\n",
       "      <td>381</td>\n",
       "      <td>501</td>\n",
       "      <td>484</td>\n",
       "      <td>57</td>\n",
       "      <td>332</td>\n",
       "      <td>310</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15300</td>\n",
       "      <td>417</td>\n",
       "      <td>1976</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1718</td>\n",
       "      <td>315</td>\n",
       "      <td>436</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>180</td>\n",
       "      <td>192</td>\n",
       "      <td>66</td>\n",
       "      <td>161</td>\n",
       "      <td>44</td>\n",
       "      <td>159</td>\n",
       "      <td>145</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14984</td>\n",
       "      <td>417</td>\n",
       "      <td>1406</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1742</td>\n",
       "      <td>325</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>193</td>\n",
       "      <td>180</td>\n",
       "      <td>192</td>\n",
       "      <td>66</td>\n",
       "      <td>161</td>\n",
       "      <td>44</td>\n",
       "      <td>159</td>\n",
       "      <td>320</td>\n",
       "      <td>184</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57632</th>\n",
       "      <td>9885</td>\n",
       "      <td>273</td>\n",
       "      <td>730</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>248</td>\n",
       "      <td>60</td>\n",
       "      <td>483</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>371</td>\n",
       "      <td>133</td>\n",
       "      <td>149</td>\n",
       "      <td>579</td>\n",
       "      <td>44</td>\n",
       "      <td>411</td>\n",
       "      <td>1</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57633</th>\n",
       "      <td>53161</td>\n",
       "      <td>133</td>\n",
       "      <td>1242</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>631</td>\n",
       "      <td>1129</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>335</td>\n",
       "      <td>148</td>\n",
       "      <td>298</td>\n",
       "      <td>426</td>\n",
       "      <td>57</td>\n",
       "      <td>297</td>\n",
       "      <td>148</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57634</th>\n",
       "      <td>172</td>\n",
       "      <td>360</td>\n",
       "      <td>1425</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>400</td>\n",
       "      <td>1735</td>\n",
       "      <td>372</td>\n",
       "      <td>21</td>\n",
       "      <td>478</td>\n",
       "      <td>449</td>\n",
       "      <td>457</td>\n",
       "      <td>264</td>\n",
       "      <td>402</td>\n",
       "      <td>57</td>\n",
       "      <td>495</td>\n",
       "      <td>10</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57635</th>\n",
       "      <td>4617</td>\n",
       "      <td>618</td>\n",
       "      <td>1202</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>1563</td>\n",
       "      <td>2112</td>\n",
       "      <td>578</td>\n",
       "      <td>17</td>\n",
       "      <td>489</td>\n",
       "      <td>420</td>\n",
       "      <td>466</td>\n",
       "      <td>627</td>\n",
       "      <td>385</td>\n",
       "      <td>57</td>\n",
       "      <td>211</td>\n",
       "      <td>54</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57636</th>\n",
       "      <td>23916</td>\n",
       "      <td>184</td>\n",
       "      <td>1406</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1742</td>\n",
       "      <td>325</td>\n",
       "      <td>561</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>370</td>\n",
       "      <td>132</td>\n",
       "      <td>148</td>\n",
       "      <td>417</td>\n",
       "      <td>44</td>\n",
       "      <td>422</td>\n",
       "      <td>211</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57637 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  TargetID  DRUGID  DRUGTYPE  Drug_high_status  DRUGNAME  \\\n",
       "0        336       309    1421         3                17       146   \n",
       "1      57253       462    1827         3                11      1075   \n",
       "2      54134       248    1865         3                 0      2039   \n",
       "3      15300       417    1976         3                 0      1718   \n",
       "4      14984       417    1406         3                 0      1742   \n",
       "...      ...       ...     ...       ...               ...       ...   \n",
       "57632   9885       273     730         3                14       248   \n",
       "57633  53161       133    1242         3                 0       631   \n",
       "57634    172       360    1425         3                22       400   \n",
       "57635   4617       618    1202         3                17      1563   \n",
       "57636  23916       184    1406         3                 0      1742   \n",
       "\n",
       "       PUBCHCID  Disease_of_highest_status  Drug_Status  UNIPROID  TARGNAME  \\\n",
       "0          1958                         54           17       248       232   \n",
       "1          1943                         50           12       238       221   \n",
       "2          1255                        255            1       368       487   \n",
       "3           315                        436            1       193       180   \n",
       "4           325                         30           21       193       180   \n",
       "...         ...                        ...          ...       ...       ...   \n",
       "57632        60                        483           15        28       371   \n",
       "57633      1129                        123            1       173       335   \n",
       "57634      1735                        372           21       478       449   \n",
       "57635      2112                        578           17       489       420   \n",
       "57636       325                        561            1        27       370   \n",
       "\n",
       "       GENENAME  SYNONYMS  FUNCTION  BIOCLASS  SEQUENCE  Disease  \\\n",
       "0           246        86       314        60       491      323   \n",
       "1           238       420       621        57       428      326   \n",
       "2           381       501       484        57       332      310   \n",
       "3           192        66       161        44       159      145   \n",
       "4           192        66       161        44       159      320   \n",
       "...         ...       ...       ...       ...       ...      ...   \n",
       "57632       133       149       579        44       411        1   \n",
       "57633       148       298       426        57       297      148   \n",
       "57634       457       264       402        57       495       10   \n",
       "57635       466       627       385        57       211       54   \n",
       "57636       132       148       417        44       422      211   \n",
       "\n",
       "       Accession Number  \n",
       "0                   190  \n",
       "1                   343  \n",
       "2                   555  \n",
       "3                   184  \n",
       "4                   184  \n",
       "...                 ...  \n",
       "57632               117  \n",
       "57633               107  \n",
       "57634               139  \n",
       "57635               401  \n",
       "57636               162  \n",
       "\n",
       "[57637 rows x 18 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_encoded[['Drug_high_status','DRUGNAME', 'Disease_of_highest_status','Drug_Status', 'TARGNAME', 'GENENAME', 'BIOCLASS', 'SEQUENCE','Disease']]\n",
    "Y_train = df_encoded['Target_Status']\n",
    "X_test=sample_test[['Drug_high_status','DRUGNAME', 'Disease_of_highest_status','Drug_Status', 'TARGNAME', 'GENENAME', 'BIOCLASS', 'SEQUENCE','Disease']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-19 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-19 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-19 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-19 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-19 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-19 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-19 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-19 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-19 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-19 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-19 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-19 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomfc_2=RandomForestClassifier()\n",
    "randomfc_2.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predict=randomfc_2.predict(X_test)\n",
    "Y_series=pd.Series(label_en_1.inverse_transform(Y_predict))\n",
    "Y_series.index=X_test.index\n",
    "test_df['Prediction']=pd.Series(Y_series,Y_series.index)\n",
    "submission1_df=test_df[['ID','Prediction']].iloc[Y_series.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>ID</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1076</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>190816</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180551</td>\n",
       "      <td>Phase 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51630</td>\n",
       "      <td>Phase 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50566</td>\n",
       "      <td>Phase 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID Prediction\n",
       "0    1076   Approved\n",
       "1  190816   Approved\n",
       "2  180551    Phase 1\n",
       "3   51630    Phase 2\n",
       "4   50566    Phase 1"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission1_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission1_df.to_csv(\"submissionlocal_df.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_encoded[['Drug_high_status','DRUGNAME', 'Disease_of_highest_status','Drug_Status', 'TARGNAME', 'SEQUENCE','BIOCLASS','Disease','UNIPROID']]\n",
    "Y_train = df_encoded['Target_Status']\n",
    "X_test=sample_test[['Drug_high_status','DRUGNAME', 'Disease_of_highest_status','Drug_Status', 'TARGNAME', 'BIOCLASS','Disease','SEQUENCE','UNIPROID']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-26 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-26 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-26 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-26 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-26 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-26 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-26 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-26 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-26 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-26 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-26 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-26 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-26 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-26\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective='multi:softprob', ...)"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "feature_names mismatch: ['Drug_high_status', 'DRUGNAME', 'Disease_of_highest_status', 'Drug_Status', 'TARGNAME', 'SEQUENCE', 'BIOCLASS', 'Disease', 'UNIPROID'] ['Drug_high_status', 'DRUGNAME', 'Disease_of_highest_status', 'Drug_Status', 'TARGNAME', 'BIOCLASS', 'Disease', 'SEQUENCE', 'UNIPROID']",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[279], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m Y_predict\u001b[38;5;241m=\u001b[39m\u001b[43mmodel_3\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\sklearn.py:1565\u001b[0m, in \u001b[0;36mXGBClassifier.predict\u001b[1;34m(self, X, output_margin, validate_features, base_margin, iteration_range)\u001b[0m\n\u001b[0;32m   1556\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\n\u001b[0;32m   1557\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1558\u001b[0m     X: ArrayLike,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1562\u001b[0m     iteration_range: Optional[IterationRange] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1563\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike:\n\u001b[0;32m   1564\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(verbosity\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbosity):\n\u001b[1;32m-> 1565\u001b[0m         class_probs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1566\u001b[0m \u001b[43m            \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1567\u001b[0m \u001b[43m            \u001b[49m\u001b[43moutput_margin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_margin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1568\u001b[0m \u001b[43m            \u001b[49m\u001b[43mvalidate_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1569\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbase_margin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_margin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1570\u001b[0m \u001b[43m            \u001b[49m\u001b[43miteration_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43miteration_range\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1571\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1572\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m output_margin:\n\u001b[0;32m   1573\u001b[0m             \u001b[38;5;66;03m# If output_margin is active, simply return the scores\u001b[39;00m\n\u001b[0;32m   1574\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m class_probs\n",
      "File \u001b[1;32mc:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\sklearn.py:1186\u001b[0m, in \u001b[0;36mXGBModel.predict\u001b[1;34m(self, X, output_margin, validate_features, base_margin, iteration_range)\u001b[0m\n\u001b[0;32m   1184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_can_use_inplace_predict():\n\u001b[0;32m   1185\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1186\u001b[0m         predts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_booster\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minplace_predict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1187\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1188\u001b[0m \u001b[43m            \u001b[49m\u001b[43miteration_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43miteration_range\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1189\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpredict_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmargin\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moutput_margin\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1190\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmissing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmissing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1191\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbase_margin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_margin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1192\u001b[0m \u001b[43m            \u001b[49m\u001b[43mvalidate_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1193\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1194\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m _is_cupy_alike(predts):\n\u001b[0;32m   1195\u001b[0m             \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcupy\u001b[39;00m  \u001b[38;5;66;03m# pylint: disable=import-error\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:2510\u001b[0m, in \u001b[0;36mBooster.inplace_predict\u001b[1;34m(self, data, iteration_range, predict_type, missing, validate_features, base_margin, strict_shape)\u001b[0m\n\u001b[0;32m   2508\u001b[0m     data, fns, _ \u001b[38;5;241m=\u001b[39m _transform_pandas_df(data, enable_categorical)\n\u001b[0;32m   2509\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m validate_features:\n\u001b[1;32m-> 2510\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_features\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2511\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_list(data) \u001b[38;5;129;01mor\u001b[39;00m _is_tuple(data):\n\u001b[0;32m   2512\u001b[0m     data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(data)\n",
      "File \u001b[1;32mc:\\Users\\Sian\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:3075\u001b[0m, in \u001b[0;36mBooster._validate_features\u001b[1;34m(self, feature_names)\u001b[0m\n\u001b[0;32m   3069\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m my_missing:\n\u001b[0;32m   3070\u001b[0m     msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   3071\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mtraining data did not have the following fields: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   3072\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mstr\u001b[39m(s) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m my_missing)\n\u001b[0;32m   3073\u001b[0m     )\n\u001b[1;32m-> 3075\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_names, feature_names))\n",
      "\u001b[1;31mValueError\u001b[0m: feature_names mismatch: ['Drug_high_status', 'DRUGNAME', 'Disease_of_highest_status', 'Drug_Status', 'TARGNAME', 'SEQUENCE', 'BIOCLASS', 'Disease', 'UNIPROID'] ['Drug_high_status', 'DRUGNAME', 'Disease_of_highest_status', 'Drug_Status', 'TARGNAME', 'BIOCLASS', 'Disease', 'SEQUENCE', 'UNIPROID']"
     ]
    }
   ],
   "source": [
    "Y_predict=model_3.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_series=pd.Series(label_en_1.inverse_transform(Y_predict))\n",
    "Y_series.index=X_test.index\n",
    "test_df['Prediction']=pd.Series(Y_series,Y_series.index)\n",
    "submission1_df=test_df[['ID','Prediction']].iloc[Y_series.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1076</td>\n",
       "      <td>Approved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>190816</td>\n",
       "      <td>Phase 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180551</td>\n",
       "      <td>Phase 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51630</td>\n",
       "      <td>Phase 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50566</td>\n",
       "      <td>Phase 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID Prediction\n",
       "0    1076   Approved\n",
       "1  190816    Phase 2\n",
       "2  180551    Phase 2\n",
       "3   51630    Phase 2\n",
       "4   50566    Phase 2"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission1_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission1_df.to_csv(\"second_submit.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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