Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfd14857",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('dataset.csv')\n",
    "a = pd.read_csv('symptom_Description.csv')\n",
    "b = pd.read_csv('symptom_precaution.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "326284a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the custom written class \n",
    "from DecisionTree import Node \n",
    "import inspect\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7664197b",
   "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>Disease</th>\n",
       "      <th>Symptom_1</th>\n",
       "      <th>Symptom_2</th>\n",
       "      <th>Symptom_3</th>\n",
       "      <th>Symptom_4</th>\n",
       "      <th>Symptom_5</th>\n",
       "      <th>Symptom_6</th>\n",
       "      <th>Symptom_7</th>\n",
       "      <th>Symptom_8</th>\n",
       "      <th>Symptom_9</th>\n",
       "      <th>Symptom_10</th>\n",
       "      <th>Symptom_11</th>\n",
       "      <th>Symptom_12</th>\n",
       "      <th>Symptom_13</th>\n",
       "      <th>Symptom_14</th>\n",
       "      <th>Symptom_15</th>\n",
       "      <th>Symptom_16</th>\n",
       "      <th>Symptom_17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Disease   Symptom_1              Symptom_2              Symptom_3  \\\n",
       "0  Fungal infection     itching              skin_rash   nodal_skin_eruptions   \n",
       "1  Fungal infection   skin_rash   nodal_skin_eruptions    dischromic _patches   \n",
       "2  Fungal infection     itching   nodal_skin_eruptions    dischromic _patches   \n",
       "3  Fungal infection     itching              skin_rash    dischromic _patches   \n",
       "4  Fungal infection     itching              skin_rash   nodal_skin_eruptions   \n",
       "\n",
       "              Symptom_4 Symptom_5 Symptom_6 Symptom_7 Symptom_8 Symptom_9  \\\n",
       "0   dischromic _patches       NaN       NaN       NaN       NaN       NaN   \n",
       "1                   NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2                   NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3                   NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4                   NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "  Symptom_10 Symptom_11 Symptom_12 Symptom_13 Symptom_14 Symptom_15  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "  Symptom_16 Symptom_17  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0cb6332b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no.of diesases\n",
    "len(df['Disease'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "590c6c4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_273035/2311229904.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"list_of_Symps\"][i] = values[1:values.index(0)]\n",
      "/home/venugoapal/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n"
     ]
    }
   ],
   "source": [
    "df[\"list_of_Symps\"] = 0\n",
    "for i in range(df.shape[0]):\n",
    "    values = df.iloc[i].values\n",
    "    values = values.tolist()\n",
    "    if 0 in values:\n",
    "        df[\"list_of_Symps\"][i] = values[1:values.index(0)]\n",
    "    else:\n",
    "        df[\"list_of_Symps\"][i] = values[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4278a3fb",
   "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>Disease</th>\n",
       "      <th>Symptom_1</th>\n",
       "      <th>Symptom_2</th>\n",
       "      <th>Symptom_3</th>\n",
       "      <th>Symptom_4</th>\n",
       "      <th>Symptom_5</th>\n",
       "      <th>Symptom_6</th>\n",
       "      <th>Symptom_7</th>\n",
       "      <th>Symptom_8</th>\n",
       "      <th>Symptom_9</th>\n",
       "      <th>Symptom_10</th>\n",
       "      <th>Symptom_11</th>\n",
       "      <th>Symptom_12</th>\n",
       "      <th>Symptom_13</th>\n",
       "      <th>Symptom_14</th>\n",
       "      <th>Symptom_15</th>\n",
       "      <th>Symptom_16</th>\n",
       "      <th>Symptom_17</th>\n",
       "      <th>list_of_Symps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[itching,  skin_rash,  nodal_skin_eruptions,  ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[ skin_rash,  nodal_skin_eruptions,  dischromi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[itching,  nodal_skin_eruptions,  dischromic _...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[itching,  skin_rash,  dischromic _patches, na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[itching,  skin_rash,  nodal_skin_eruptions, n...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Disease   Symptom_1              Symptom_2              Symptom_3  \\\n",
       "0  Fungal infection     itching              skin_rash   nodal_skin_eruptions   \n",
       "1  Fungal infection   skin_rash   nodal_skin_eruptions    dischromic _patches   \n",
       "2  Fungal infection     itching   nodal_skin_eruptions    dischromic _patches   \n",
       "3  Fungal infection     itching              skin_rash    dischromic _patches   \n",
       "4  Fungal infection     itching              skin_rash   nodal_skin_eruptions   \n",
       "\n",
       "              Symptom_4 Symptom_5 Symptom_6 Symptom_7 Symptom_8 Symptom_9  \\\n",
       "0   dischromic _patches       NaN       NaN       NaN       NaN       NaN   \n",
       "1                   NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2                   NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3                   NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4                   NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "  Symptom_10 Symptom_11 Symptom_12 Symptom_13 Symptom_14 Symptom_15  \\\n",
       "0        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "  Symptom_16 Symptom_17                                      list_of_Symps  \n",
       "0        NaN        NaN  [itching,  skin_rash,  nodal_skin_eruptions,  ...  \n",
       "1        NaN        NaN  [ skin_rash,  nodal_skin_eruptions,  dischromi...  \n",
       "2        NaN        NaN  [itching,  nodal_skin_eruptions,  dischromic _...  \n",
       "3        NaN        NaN  [itching,  skin_rash,  dischromic _patches, na...  \n",
       "4        NaN        NaN  [itching,  skin_rash,  nodal_skin_eruptions, n...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7477f7a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Disease', 'Symptom_1', 'Symptom_2', 'Symptom_3', 'Symptom_4', 'Symptom_5', 'Symptom_6', 'Symptom_7', 'Symptom_8', 'Symptom_9', 'Symptom_10', 'Symptom_11', 'Symptom_12', 'Symptom_13', 'Symptom_14', 'Symptom_15', 'Symptom_16', 'Symptom_17', 'list_of_Symps']\n",
      "['itching' ' skin_rash' ' nodal_skin_eruptions' ... nan nan nan]\n"
     ]
    }
   ],
   "source": [
    "objects = (df.dtypes == 'object')\n",
    "object_cols = list(objects[objects].index)\n",
    "print(object_cols)\n",
    "column_values = df[['Symptom_1', 'Symptom_2', 'Symptom_3', 'Symptom_4',\n",
    "       'Symptom_5', 'Symptom_6', 'Symptom_7', 'Symptom_8', 'Symptom_9',\n",
    "       'Symptom_10', 'Symptom_11', 'Symptom_12', 'Symptom_13', 'Symptom_14',\n",
    "       'Symptom_15', 'Symptom_16', 'Symptom_17']].values.ravel()\n",
    "print(column_values)\n",
    "symptoms=pd.unique(column_values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6d88d798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symptoms.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bfd5a81e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['itching', ' skin_rash', ' nodal_skin_eruptions',\n",
       "       ' dischromic _patches', nan, ' continuous_sneezing', ' shivering',\n",
       "       ' chills', ' watering_from_eyes', ' stomach_pain', ' acidity',\n",
       "       ' ulcers_on_tongue', ' vomiting', ' cough', ' chest_pain',\n",
       "       ' yellowish_skin', ' nausea', ' loss_of_appetite',\n",
       "       ' abdominal_pain', ' yellowing_of_eyes', ' burning_micturition',\n",
       "       ' spotting_ urination', ' passage_of_gases', ' internal_itching',\n",
       "       ' indigestion', ' muscle_wasting', ' patches_in_throat',\n",
       "       ' high_fever', ' extra_marital_contacts', ' fatigue',\n",
       "       ' weight_loss', ' restlessness', ' lethargy',\n",
       "       ' irregular_sugar_level', ' blurred_and_distorted_vision',\n",
       "       ' obesity', ' excessive_hunger', ' increased_appetite',\n",
       "       ' polyuria', ' sunken_eyes', ' dehydration', ' diarrhoea',\n",
       "       ' breathlessness', ' family_history', ' mucoid_sputum',\n",
       "       ' headache', ' dizziness', ' loss_of_balance',\n",
       "       ' lack_of_concentration', ' stiff_neck', ' depression',\n",
       "       ' irritability', ' visual_disturbances', ' back_pain',\n",
       "       ' weakness_in_limbs', ' neck_pain', ' weakness_of_one_body_side',\n",
       "       ' altered_sensorium', ' dark_urine', ' sweating', ' muscle_pain',\n",
       "       ' mild_fever', ' swelled_lymph_nodes', ' malaise',\n",
       "       ' red_spots_over_body', ' joint_pain', ' pain_behind_the_eyes',\n",
       "       ' constipation', ' toxic_look_(typhos)', ' belly_pain',\n",
       "       ' yellow_urine', ' receiving_blood_transfusion',\n",
       "       ' receiving_unsterile_injections', ' coma', ' stomach_bleeding',\n",
       "       ' acute_liver_failure', ' swelling_of_stomach',\n",
       "       ' distention_of_abdomen', ' history_of_alcohol_consumption',\n",
       "       ' fluid_overload', ' phlegm', ' blood_in_sputum',\n",
       "       ' throat_irritation', ' redness_of_eyes', ' sinus_pressure',\n",
       "       ' runny_nose', ' congestion', ' loss_of_smell', ' fast_heart_rate',\n",
       "       ' rusty_sputum', ' pain_during_bowel_movements',\n",
       "       ' pain_in_anal_region', ' bloody_stool', ' irritation_in_anus',\n",
       "       ' cramps', ' bruising', ' swollen_legs', ' swollen_blood_vessels',\n",
       "       ' prominent_veins_on_calf', ' weight_gain',\n",
       "       ' cold_hands_and_feets', ' mood_swings', ' puffy_face_and_eyes',\n",
       "       ' enlarged_thyroid', ' brittle_nails', ' swollen_extremeties',\n",
       "       ' abnormal_menstruation', ' muscle_weakness', ' anxiety',\n",
       "       ' slurred_speech', ' palpitations', ' drying_and_tingling_lips',\n",
       "       ' knee_pain', ' hip_joint_pain', ' swelling_joints',\n",
       "       ' painful_walking', ' movement_stiffness', ' spinning_movements',\n",
       "       ' unsteadiness', ' pus_filled_pimples', ' blackheads', ' scurring',\n",
       "       ' bladder_discomfort', ' foul_smell_of urine',\n",
       "       ' continuous_feel_of_urine', ' skin_peeling',\n",
       "       ' silver_like_dusting', ' small_dents_in_nails',\n",
       "       ' inflammatory_nails', ' blister', ' red_sore_around_nose',\n",
       "       ' yellow_crust_ooze'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symptoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "134b6899",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame(columns = symptoms,index = df.index)\n",
    "#adding the disease\n",
    "new_data['list_of_Symps']=df['list_of_Symps']\n",
    "\n",
    "#Filling our data frame with \n",
    "for i in new_data:\n",
    "    new_data[i] = df.apply(lambda x:1 if i in x.list_of_Symps else 0, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac165e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                  'itching',                ' skin_rash',\n",
       "           ' nodal_skin_eruptions',      ' dischromic _patches',\n",
       "                               nan,      ' continuous_sneezing',\n",
       "                      ' shivering',                   ' chills',\n",
       "             ' watering_from_eyes',             ' stomach_pain',\n",
       "       ...\n",
       "            ' foul_smell_of urine', ' continuous_feel_of_urine',\n",
       "                   ' skin_peeling',      ' silver_like_dusting',\n",
       "           ' small_dents_in_nails',       ' inflammatory_nails',\n",
       "                        ' blister',     ' red_sore_around_nose',\n",
       "              ' yellow_crust_ooze',             'list_of_Symps'],\n",
       "      dtype='object', length=133)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c3c80add",
   "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>itching</th>\n",
       "      <th>skin_rash</th>\n",
       "      <th>nodal_skin_eruptions</th>\n",
       "      <th>dischromic _patches</th>\n",
       "      <th>NaN</th>\n",
       "      <th>continuous_sneezing</th>\n",
       "      <th>shivering</th>\n",
       "      <th>chills</th>\n",
       "      <th>watering_from_eyes</th>\n",
       "      <th>stomach_pain</th>\n",
       "      <th>...</th>\n",
       "      <th>foul_smell_of urine</th>\n",
       "      <th>continuous_feel_of_urine</th>\n",
       "      <th>skin_peeling</th>\n",
       "      <th>silver_like_dusting</th>\n",
       "      <th>small_dents_in_nails</th>\n",
       "      <th>inflammatory_nails</th>\n",
       "      <th>blister</th>\n",
       "      <th>red_sore_around_nose</th>\n",
       "      <th>yellow_crust_ooze</th>\n",
       "      <th>list_of_Symps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   itching   skin_rash   nodal_skin_eruptions   dischromic _patches  NaN  \\\n",
       "0        1           1                      1                     1    1   \n",
       "1        0           1                      1                     1    1   \n",
       "2        1           0                      1                     1    1   \n",
       "3        1           1                      0                     1    1   \n",
       "4        1           1                      1                     0    1   \n",
       "\n",
       "    continuous_sneezing   shivering   chills   watering_from_eyes  \\\n",
       "0                     0           0        0                    0   \n",
       "1                     0           0        0                    0   \n",
       "2                     0           0        0                    0   \n",
       "3                     0           0        0                    0   \n",
       "4                     0           0        0                    0   \n",
       "\n",
       "    stomach_pain  ...   foul_smell_of urine   continuous_feel_of_urine  \\\n",
       "0              0  ...                     0                          0   \n",
       "1              0  ...                     0                          0   \n",
       "2              0  ...                     0                          0   \n",
       "3              0  ...                     0                          0   \n",
       "4              0  ...                     0                          0   \n",
       "\n",
       "    skin_peeling   silver_like_dusting   small_dents_in_nails  \\\n",
       "0              0                     0                      0   \n",
       "1              0                     0                      0   \n",
       "2              0                     0                      0   \n",
       "3              0                     0                      0   \n",
       "4              0                     0                      0   \n",
       "\n",
       "    inflammatory_nails   blister   red_sore_around_nose   yellow_crust_ooze  \\\n",
       "0                    0         0                      0                   0   \n",
       "1                    0         0                      0                   0   \n",
       "2                    0         0                      0                   0   \n",
       "3                    0         0                      0                   0   \n",
       "4                    0         0                      0                   0   \n",
       "\n",
       "   list_of_Symps  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2311b45c",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data['Disease']=df['Disease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cef384c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "new_data=new_data.drop('list_of_Symps',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b57d81d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "itching                  0\n",
       " skin_rash               0\n",
       " nodal_skin_eruptions    0\n",
       " dischromic _patches     0\n",
       "NaN                      0\n",
       "                        ..\n",
       " inflammatory_nails      0\n",
       " blister                 0\n",
       " red_sore_around_nose    0\n",
       " yellow_crust_ooze       0\n",
       "Disease                  0\n",
       "Length: 133, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "554ffe43",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_data.drop('Disease',axis=1)\n",
    "Y = new_data['Disease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f7220e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node():\n",
    "    def __init__(self, feature_index=None, threshold=None, left=None, right=None, info_gain=None, value=None):\n",
    "        ''' constructor ''' \n",
    "        \n",
    "        # for decision node\n",
    "        self.feature_index = feature_index\n",
    "        self.threshold = threshold\n",
    "        self.left = left\n",
    "        self.right = right\n",
    "        self.info_gain = info_gain\n",
    "        \n",
    "        # for leaf node\n",
    "        self.value = value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "90cecd4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecisionTreeClassifier():\n",
    "    def __init__(self, min_samples_split=2, max_depth=2):\n",
    "        ''' constructor '''\n",
    "        \n",
    "        # initialize the root of the tree \n",
    "        self.root = None\n",
    "        \n",
    "        # stopping conditions\n",
    "        self.min_samples_split = min_samples_split\n",
    "        self.max_depth = max_depth\n",
    "        \n",
    "    def build_tree(self, dataset, curr_depth=0):\n",
    "        ''' recursive function to build the tree ''' \n",
    "        \n",
    "        X, Y = dataset[:,:-1], dataset[:,-1]\n",
    "        num_samples, num_features = np.shape(X)\n",
    "        \n",
    "        # split until stopping conditions are met\n",
    "        if num_samples>=self.min_samples_split and curr_depth<=self.max_depth:\n",
    "            # find the best split\n",
    "            best_split = self.get_best_split(dataset, num_samples, num_features)\n",
    "            # check if information gain is positive\n",
    "            if best_split[\"info_gain\"]>0:\n",
    "                # recur left\n",
    "                left_subtree = self.build_tree(best_split[\"dataset_left\"], curr_depth+1)\n",
    "                # recur right\n",
    "                right_subtree = self.build_tree(best_split[\"dataset_right\"], curr_depth+1)\n",
    "                # return decision node\n",
    "                return Node(best_split[\"feature_index\"], best_split[\"threshold\"], \n",
    "                            left_subtree, right_subtree, best_split[\"info_gain\"])\n",
    "        \n",
    "        # compute leaf node\n",
    "        leaf_value = self.calculate_leaf_value(Y)\n",
    "        # return leaf node\n",
    "        return Node(value=leaf_value)\n",
    "    \n",
    "    def get_best_split(self, dataset, num_samples, num_features):\n",
    "        ''' function to find the best split '''\n",
    "        \n",
    "        # dictionary to store the best split\n",
    "        best_split = {}\n",
    "        max_info_gain = -float(\"inf\")\n",
    "        \n",
    "        # loop over all the features\n",
    "        for feature_index in range(num_features):\n",
    "            feature_values = dataset[:, feature_index]\n",
    "            possible_thresholds = np.unique(feature_values)\n",
    "            # loop over all the feature values present in the data\n",
    "            for threshold in possible_thresholds:\n",
    "                # get current split\n",
    "                dataset_left, dataset_right = self.split(dataset, feature_index, threshold)\n",
    "                # check if childs are not null\n",
    "                if len(dataset_left)>0 and len(dataset_right)>0:\n",
    "                    y, left_y, right_y = dataset[:, -1], dataset_left[:, -1], dataset_right[:, -1]\n",
    "                    # compute information gain\n",
    "                    curr_info_gain = self.information_gain(y, left_y, right_y, \"gini\")\n",
    "                    # update the best split if needed\n",
    "                    if curr_info_gain>max_info_gain:\n",
    "                        best_split[\"feature_index\"] = feature_index\n",
    "                        best_split[\"threshold\"] = threshold\n",
    "                        best_split[\"dataset_left\"] = dataset_left\n",
    "                        best_split[\"dataset_right\"] = dataset_right\n",
    "                        best_split[\"info_gain\"] = curr_info_gain\n",
    "                        max_info_gain = curr_info_gain\n",
    "                        \n",
    "        # return best split\n",
    "        return best_split\n",
    "    \n",
    "    def split(self, dataset, feature_index, threshold):\n",
    "        ''' function to split the data '''\n",
    "        \n",
    "        dataset_left = np.array([row for row in dataset if row[feature_index]<=threshold])\n",
    "        dataset_right = np.array([row for row in dataset if row[feature_index]>threshold])\n",
    "        return dataset_left, dataset_right\n",
    "    \n",
    "    def information_gain(self, parent, l_child, r_child, mode=\"entropy\"):\n",
    "        ''' function to compute information gain '''\n",
    "        \n",
    "        weight_l = len(l_child) / len(parent)\n",
    "        weight_r = len(r_child) / len(parent)\n",
    "        if mode==\"gini\":\n",
    "            gain = self.gini_index(parent) - (weight_l*self.gini_index(l_child) + weight_r*self.gini_index(r_child))\n",
    "        else:\n",
    "            gain = self.entropy(parent) - (weight_l*self.entropy(l_child) + weight_r*self.entropy(r_child))\n",
    "        return gain\n",
    "    \n",
    "    def entropy(self, y):\n",
    "        ''' function to compute entropy '''\n",
    "        \n",
    "        class_labels = np.unique(y)\n",
    "        entropy = 0\n",
    "        for cls in class_labels:\n",
    "            p_cls = len(y[y == cls]) / len(y)\n",
    "            entropy += -p_cls * np.log2(p_cls)\n",
    "        return entropy\n",
    "    \n",
    "    def gini_index(self, y):\n",
    "        ''' function to compute gini index '''\n",
    "        \n",
    "        class_labels = np.unique(y)\n",
    "        gini = 0\n",
    "        for cls in class_labels:\n",
    "            p_cls = len(y[y == cls]) / len(y)\n",
    "            gini += p_cls**2\n",
    "        return 1 - gini\n",
    "        \n",
    "    def calculate_leaf_value(self, Y):\n",
    "        ''' function to compute leaf node '''\n",
    "        \n",
    "        Y = list(Y)\n",
    "        return max(Y, key=Y.count)\n",
    "    \n",
    "    def print_tree(self, tree=None, indent=\" \"):\n",
    "        ''' function to print the tree '''\n",
    "        \n",
    "        if not tree:\n",
    "            tree = self.root\n",
    "\n",
    "        if tree.value is not None:\n",
    "            print(tree.value)\n",
    "\n",
    "        else:\n",
    "            print(\"X_\"+str(tree.feature_index), \"<=\", tree.threshold, \"?\", tree.info_gain)\n",
    "            print(\"%sleft:\" % (indent), end=\"\")\n",
    "            self.print_tree(tree.left, indent + indent)\n",
    "            print(\"%sright:\" % (indent), end=\"\")\n",
    "            self.print_tree(tree.right, indent + indent)\n",
    "    \n",
    "    def fit(self, X, Y):\n",
    "        ''' function to train the tree '''\n",
    "        \n",
    "        dataset = np.concatenate((X, Y), axis=1)\n",
    "        self.root = self.build_tree(dataset)\n",
    "    \n",
    "    def predict(self, X):\n",
    "        ''' function to predict new dataset '''\n",
    "        \n",
    "        preditions = [self.make_prediction(x, self.root) for x in X]\n",
    "        return preditions\n",
    "    \n",
    "    def make_prediction(self, x, tree):\n",
    "        ''' function to predict a single data point '''\n",
    "        \n",
    "        if tree.value!=None: return tree.value\n",
    "        feature_val = x[tree.feature_index]\n",
    "        if feature_val<=tree.threshold:\n",
    "            return self.make_prediction(x, tree.left)\n",
    "        else:\n",
    "            return self.make_prediction(x, tree.right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dea2fb1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "Y= label_encoder.fit_transform(Y)\n",
    "Y = pd.DataFrame(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af2d3307",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f1bd6786",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.2, random_state=41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "55e0a900",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'value'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_273035/2722386595.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mclassifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_samples_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_273035/3608933039.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_273035/3608933039.py\u001b[0m in \u001b[0;36mbuild_tree\u001b[0;34m(self, dataset, curr_depth)\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"info_gain\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m                 \u001b[0;31m# recur left\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m                 \u001b[0mleft_subtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dataset_left\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_depth\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m                 \u001b[0;31m# recur right\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m                 \u001b[0mright_subtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dataset_right\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_depth\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_273035/3608933039.py\u001b[0m in \u001b[0;36mbuild_tree\u001b[0;34m(self, dataset, curr_depth)\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"info_gain\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m                 \u001b[0;31m# recur left\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m                 \u001b[0mleft_subtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dataset_left\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_depth\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m                 \u001b[0;31m# recur right\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m                 \u001b[0mright_subtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dataset_right\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_depth\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_273035/3608933039.py\u001b[0m in \u001b[0;36mbuild_tree\u001b[0;34m(self, dataset, curr_depth)\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"info_gain\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m                 \u001b[0;31m# recur left\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m                 \u001b[0mleft_subtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dataset_left\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_depth\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m                 \u001b[0;31m# recur right\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m                 \u001b[0mright_subtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dataset_right\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_depth\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_273035/3608933039.py\u001b[0m in \u001b[0;36mbuild_tree\u001b[0;34m(self, dataset, curr_depth)\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"info_gain\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m                 \u001b[0;31m# recur left\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m                 \u001b[0mleft_subtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dataset_left\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_depth\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m                 \u001b[0;31m# recur right\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m                 \u001b[0mright_subtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_split\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"dataset_right\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_depth\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_273035/3608933039.py\u001b[0m in \u001b[0;36mbuild_tree\u001b[0;34m(self, dataset, curr_depth)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mleaf_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_leaf_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0;31m# return leaf node\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mNode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleaf_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_best_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'value'"
     ]
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier(min_samples_split=3, max_depth=3)\n",
    "classifier.fit(X_train,Y_train)\n",
    "classifier.print_tree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2527dec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dfcb93b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cde4f19f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "32690676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 15, 15, ..., 38, 35, 27])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7937c1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([X,Y],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a476107c",
   "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>itching</th>\n",
       "      <th>skin_rash</th>\n",
       "      <th>nodal_skin_eruptions</th>\n",
       "      <th>dischromic _patches</th>\n",
       "      <th>NaN</th>\n",
       "      <th>continuous_sneezing</th>\n",
       "      <th>shivering</th>\n",
       "      <th>chills</th>\n",
       "      <th>watering_from_eyes</th>\n",
       "      <th>stomach_pain</th>\n",
       "      <th>...</th>\n",
       "      <th>foul_smell_of urine</th>\n",
       "      <th>continuous_feel_of_urine</th>\n",
       "      <th>skin_peeling</th>\n",
       "      <th>silver_like_dusting</th>\n",
       "      <th>small_dents_in_nails</th>\n",
       "      <th>inflammatory_nails</th>\n",
       "      <th>blister</th>\n",
       "      <th>red_sore_around_nose</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4920 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      itching   skin_rash   nodal_skin_eruptions   dischromic _patches  NaN  \\\n",
       "0           1           1                      1                     1    1   \n",
       "1           0           1                      1                     1    1   \n",
       "2           1           0                      1                     1    1   \n",
       "3           1           1                      0                     1    1   \n",
       "4           1           1                      1                     0    1   \n",
       "...       ...         ...                    ...                   ...  ...   \n",
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       "4918        0           1                      0                     0    1   \n",
       "4919        0           1                      0                     0    1   \n",
       "\n",
       "       continuous_sneezing   shivering   chills   watering_from_eyes  \\\n",
       "0                        0           0        0                    0   \n",
       "1                        0           0        0                    0   \n",
       "2                        0           0        0                    0   \n",
       "3                        0           0        0                    0   \n",
       "4                        0           0        0                    0   \n",
       "...                    ...         ...      ...                  ...   \n",
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       "4917                     0           0        0                    0   \n",
       "4918                     0           0        0                    0   \n",
       "4919                     0           0        0                    0   \n",
       "\n",
       "       stomach_pain  ...   foul_smell_of urine   continuous_feel_of_urine  \\\n",
       "0                 0  ...                     0                          0   \n",
       "1                 0  ...                     0                          0   \n",
       "2                 0  ...                     0                          0   \n",
       "3                 0  ...                     0                          0   \n",
       "4                 0  ...                     0                          0   \n",
       "...             ...  ...                   ...                        ...   \n",
       "4915              0  ...                     0                          0   \n",
       "4916              0  ...                     0                          0   \n",
       "4917              0  ...                     1                          1   \n",
       "4918              0  ...                     0                          0   \n",
       "4919              0  ...                     0                          0   \n",
       "\n",
       "       skin_peeling   silver_like_dusting   small_dents_in_nails  \\\n",
       "0                 0                     0                      0   \n",
       "1                 0                     0                      0   \n",
       "2                 0                     0                      0   \n",
       "3                 0                     0                      0   \n",
       "4                 0                     0                      0   \n",
       "...             ...                   ...                    ...   \n",
       "4915              0                     0                      0   \n",
       "4916              0                     0                      0   \n",
       "4917              0                     0                      0   \n",
       "4918              1                     1                      1   \n",
       "4919              0                     0                      0   \n",
       "\n",
       "       inflammatory_nails   blister   red_sore_around_nose  \\\n",
       "0                       0         0                      0   \n",
       "1                       0         0                      0   \n",
       "2                       0         0                      0   \n",
       "3                       0         0                      0   \n",
       "4                       0         0                      0   \n",
       "...                   ...       ...                    ...   \n",
       "4915                    0         0                      0   \n",
       "4916                    0         0                      0   \n",
       "4917                    0         0                      0   \n",
       "4918                    1         0                      0   \n",
       "4919                    0         1                      1   \n",
       "\n",
       "       yellow_crust_ooze   0  \n",
       "0                      0  15  \n",
       "1                      0  15  \n",
       "2                      0  15  \n",
       "3                      0  15  \n",
       "4                      0  15  \n",
       "...                  ...  ..  \n",
       "4915                   0   0  \n",
       "4916                   0   2  \n",
       "4917                   0  38  \n",
       "4918                   0  35  \n",
       "4919                   1  27  \n",
       "\n",
       "[4920 rows x 133 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f731c34b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>4916</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4917</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4918</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4919</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4920 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      itching   skin_rash   nodal_skin_eruptions   dischromic _patches  NaN  \\\n",
       "0           1           1                      1                     1    1   \n",
       "1           0           1                      1                     1    1   \n",
       "2           1           0                      1                     1    1   \n",
       "3           1           1                      0                     1    1   \n",
       "4           1           1                      1                     0    1   \n",
       "...       ...         ...                    ...                   ...  ...   \n",
       "4915        0           0                      0                     0    1   \n",
       "4916        0           1                      0                     0    1   \n",
       "4917        0           0                      0                     0    1   \n",
       "4918        0           1                      0                     0    1   \n",
       "4919        0           1                      0                     0    1   \n",
       "\n",
       "       continuous_sneezing   shivering   chills   watering_from_eyes  \\\n",
       "0                        0           0        0                    0   \n",
       "1                        0           0        0                    0   \n",
       "2                        0           0        0                    0   \n",
       "3                        0           0        0                    0   \n",
       "4                        0           0        0                    0   \n",
       "...                    ...         ...      ...                  ...   \n",
       "4915                     0           0        0                    0   \n",
       "4916                     0           0        0                    0   \n",
       "4917                     0           0        0                    0   \n",
       "4918                     0           0        0                    0   \n",
       "4919                     0           0        0                    0   \n",
       "\n",
       "       stomach_pain  ...   bladder_discomfort   foul_smell_of urine  \\\n",
       "0                 0  ...                    0                     0   \n",
       "1                 0  ...                    0                     0   \n",
       "2                 0  ...                    0                     0   \n",
       "3                 0  ...                    0                     0   \n",
       "4                 0  ...                    0                     0   \n",
       "...             ...  ...                  ...                   ...   \n",
       "4915              0  ...                    0                     0   \n",
       "4916              0  ...                    0                     0   \n",
       "4917              0  ...                    1                     1   \n",
       "4918              0  ...                    0                     0   \n",
       "4919              0  ...                    0                     0   \n",
       "\n",
       "       continuous_feel_of_urine   skin_peeling   silver_like_dusting  \\\n",
       "0                             0              0                     0   \n",
       "1                             0              0                     0   \n",
       "2                             0              0                     0   \n",
       "3                             0              0                     0   \n",
       "4                             0              0                     0   \n",
       "...                         ...            ...                   ...   \n",
       "4915                          0              0                     0   \n",
       "4916                          0              0                     0   \n",
       "4917                          1              0                     0   \n",
       "4918                          0              1                     1   \n",
       "4919                          0              0                     0   \n",
       "\n",
       "       small_dents_in_nails   inflammatory_nails   blister  \\\n",
       "0                         0                    0         0   \n",
       "1                         0                    0         0   \n",
       "2                         0                    0         0   \n",
       "3                         0                    0         0   \n",
       "4                         0                    0         0   \n",
       "...                     ...                  ...       ...   \n",
       "4915                      0                    0         0   \n",
       "4916                      0                    0         0   \n",
       "4917                      0                    0         0   \n",
       "4918                      1                    1         0   \n",
       "4919                      0                    0         1   \n",
       "\n",
       "       red_sore_around_nose   yellow_crust_ooze  \n",
       "0                         0                   0  \n",
       "1                         0                   0  \n",
       "2                         0                   0  \n",
       "3                         0                   0  \n",
       "4                         0                   0  \n",
       "...                     ...                 ...  \n",
       "4915                      0                   0  \n",
       "4916                      0                   0  \n",
       "4917                      0                   0  \n",
       "4918                      0                   0  \n",
       "4919                      1                   1  \n",
       "\n",
       "[4920 rows x 132 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d726a56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "aa1cf786",
   "metadata": {},
   "outputs": [],
   "source": [
    "root = Node(Y,X,**hp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d784c7e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "root.grow_tree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "caf5ac42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root\n",
      "   | GINI impurity of the node: 0.5\n",
      "   | Class distribution in the node: {15: 120, 4: 120, 16: 120, 9: 120, 14: 120, 33: 120, 1: 120, 12: 120, 17: 120, 6: 120, 23: 120, 30: 120, 7: 120, 32: 120, 28: 120, 29: 120, 8: 120, 11: 120, 37: 120, 40: 120, 19: 120, 20: 120, 21: 120, 22: 120, 3: 120, 36: 120, 10: 120, 34: 120, 13: 120, 18: 120, 39: 120, 26: 120, 24: 120, 25: 120, 31: 120, 5: 120, 0: 120, 2: 120, 38: 120, 35: 120, 27: 120}\n",
      "   | Predicted class: 27\n",
      "|-------- Split rule:  vomiting <= 0.5\n",
      "           | GINI impurity of the node: 0.09\n",
      "           | Class distribution in the node: {15: 120, 4: 120, 16: 12, 9: 6, 14: 120, 33: 6, 1: 120, 12: 120, 17: 12, 6: 120, 23: 120, 30: 120, 7: 120, 32: 12, 28: 6, 29: 6, 8: 120, 11: 6, 37: 6, 40: 6, 19: 120, 20: 120, 21: 6, 22: 6, 3: 6, 36: 6, 10: 120, 34: 120, 13: 120, 18: 12, 39: 120, 26: 120, 24: 120, 25: 6, 31: 120, 5: 120, 0: 6, 2: 120, 38: 120, 35: 120, 27: 120}\n",
      "           | Predicted class: 27\n",
      "|---------------- Split rule:  nausea <= 0.5\n",
      "                   | GINI impurity of the node: 0.0\n",
      "                   | Class distribution in the node: {15: 120, 4: 120, 16: 12, 14: 120, 33: 6, 1: 120, 12: 120, 17: 12, 6: 120, 23: 120, 30: 120, 7: 120, 32: 12, 28: 6, 8: 120, 19: 120, 20: 6, 3: 6, 36: 6, 10: 120, 34: 120, 13: 120, 18: 12, 39: 120, 26: 120, 24: 120, 31: 120, 5: 120, 2: 120, 38: 120, 35: 120, 27: 120}\n",
      "                   | Predicted class: 27\n",
      "|---------------- Split rule:  nausea > 0.5\n",
      "                   | GINI impurity of the node: 0.0\n",
      "                   | Class distribution in the node: {9: 6, 29: 6, 11: 6, 37: 6, 40: 6, 20: 114, 21: 6, 22: 6, 25: 6, 0: 6}\n",
      "                   | Predicted class: 20\n",
      "|-------- Split rule:  vomiting > 0.5\n",
      "           | GINI impurity of the node: 0.0\n",
      "           | Class distribution in the node: {16: 108, 9: 114, 33: 114, 17: 108, 32: 108, 28: 114, 29: 114, 11: 114, 37: 114, 40: 114, 21: 114, 22: 114, 3: 114, 36: 114, 18: 108, 25: 114, 0: 114}\n",
      "           | Predicted class: 0\n"
     ]
    }
   ],
   "source": [
    "root.print_tree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9aa04fd0",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_272230/3099637022.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_obs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/assign/DecisionTree.py\u001b[0m in \u001b[0;36mpredict_obs\u001b[0;34m(self, values)\u001b[0m\n\u001b[1;32m    287\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_feature\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mbest_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    290\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m                     \u001b[0mcur_node\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcur_node\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m   1443\u001b[0m             \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m             \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "root.predict_obs(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2957758c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dc17938c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spliting the data vset\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5a0d62d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e9295af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier(criterion = 'entropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d5891b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a48c1de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting labels on the test set.\n",
    "y_pred =  clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fde6fbc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Heart attack', 'hepatitis A', 'Tuberculosis', 'Hypoglycemia',\n",
       "       'Peptic ulcer diseae'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d55e5adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1525           Heart attack\n",
       "1013            hepatitis A\n",
       "1892           Tuberculosis\n",
       "1972           Hypoglycemia\n",
       "873     Peptic ulcer diseae\n",
       "Name: Disease, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a1adc839",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score on train data:  1.0\n",
      "Accuracy Score on test data:  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy Score on train data: ', accuracy_score(y_true=y_train, y_pred=clf.predict(X_train)))\n",
    "print('Accuracy Score on test data: ', accuracy_score(y_true=y_test, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ab9f78d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('disease_prediction.pkl','wb') as f:\n",
    "    pickle.dump(clf,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cf222e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9aba22e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(1994.464833541643, 2661.375, 'X[29] <= 0.5\\nentropy = 5.356\\nsamples = 3936\\nvalue = [102, 98, 89, 96, 97, 97, 92, 93, 99, 99, 86, 102\\n98, 93, 93, 96, 93, 94, 88, 101, 103, 92, 98\\n100, 97, 107, 97, 95, 94, 95, 91, 97, 90, 96, 97\\n102, 96, 95, 100, 92, 96]'),\n",
       " Text(1472.8258152482672, 2548.125, 'X[12] <= 0.5\\nentropy = 4.786\\nsamples = 2377\\nvalue = [102, 98, 89, 96, 97, 97, 7, 93, 5, 99, 5, 6, 4\\n93, 93, 96, 93, 94, 88, 5, 6, 4, 4, 100, 5, 5\\n12, 95, 5, 95, 91, 97, 90, 96, 4, 102, 6, 0\\n100, 4, 96]'),\n",
       " Text(1161.2055448244516, 2434.875, 'X[1] <= 0.5\\nentropy = 4.264\\nsamples = 1467\\nvalue = [6, 98, 89, 6, 97, 97, 7, 93, 5, 4, 5, 0, 4\\n93, 93, 96, 7, 10, 12, 5, 6, 0, 0, 100, 5, 0\\n12, 95, 0, 5, 91, 97, 10, 4, 4, 102, 0, 0, 100\\n4, 5]'),\n",
       " Text(1013.4791500965799, 2321.625, 'X[45] <= 0.5\\nentropy = 3.985\\nsamples = 1022\\nvalue = [6, 98, 5, 6, 97, 97, 7, 93, 0, 4, 5, 0, 4\\n93, 9, 11, 7, 10, 12, 5, 6, 0, 0, 100, 5, 0\\n12, 4, 0, 5, 91, 97, 10, 4, 4, 6, 0, 0, 100\\n4, 5]'),\n",
       " Text(900.6237927508237, 2208.375, 'X[115] <= 0.5\\nentropy = 3.748\\nsamples = 819\\nvalue = [0, 98, 5, 6, 97, 97, 7, 93, 0, 4, 0, 0, 4\\n93, 9, 11, 7, 10, 12, 5, 6, 0, 0, 10, 5, 0\\n12, 4, 0, 0, 4, 97, 0, 4, 4, 6, 0, 0, 100, 4\\n5]'),\n",
       " Text(796.6446994659698, 2095.125, 'X[46] <= 0.5\\nentropy = 3.636\\nsamples = 634\\nvalue = [0, 98, 5, 6, 97, 5, 7, 93, 0, 4, 0, 0, 4, 93\\n9, 11, 7, 10, 12, 5, 6, 0, 0, 10, 5, 0, 12\\n4, 0, 0, 4, 4, 0, 4, 4, 6, 0, 0, 100, 4, 5]'),\n",
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      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 3600x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(50,50))\n",
    "plot_tree(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdf14c5f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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