VB3.0_disease_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from joblib import dump,load\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "#naive Bayes approach\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "#trees approach\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Ensemble Approach\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "#visulization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_path = \"D:/Disease-Prediction-from-Symptoms/dataset/training_data.csv\"\n",
    "test_data_path = \"D:/Disease-Prediction-from-Symptoms/dataset/test_data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(train_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>continuous_sneezing</th>\n",
       "      <th>shivering</th>\n",
       "      <th>chills</th>\n",
       "      <th>joint_pain</th>\n",
       "      <th>stomach_pain</th>\n",
       "      <th>acidity</th>\n",
       "      <th>ulcers_on_tongue</th>\n",
       "      <th>...</th>\n",
       "      <th>scurring</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>prognosis</th>\n",
       "      <th>Unnamed: 133</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <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>Fungal infection</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</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",
       "      <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>Fungal infection</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>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>Fungal infection</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <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>Fungal infection</td>\n",
       "      <td>NaN</td>\n",
       "    </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>0</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>Fungal infection</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   itching  skin_rash  nodal_skin_eruptions  continuous_sneezing  shivering  \\\n",
       "0        1          1                     1                    0          0   \n",
       "1        0          1                     1                    0          0   \n",
       "2        1          0                     1                    0          0   \n",
       "3        1          1                     0                    0          0   \n",
       "4        1          1                     1                    0          0   \n",
       "\n",
       "   chills  joint_pain  stomach_pain  acidity  ulcers_on_tongue  ...  scurring  \\\n",
       "0       0           0             0        0                 0  ...         0   \n",
       "1       0           0             0        0                 0  ...         0   \n",
       "2       0           0             0        0                 0  ...         0   \n",
       "3       0           0             0        0                 0  ...         0   \n",
       "4       0           0             0        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",
       "          prognosis  Unnamed: 133  \n",
       "0  Fungal infection           NaN  \n",
       "1  Fungal infection           NaN  \n",
       "2  Fungal infection           NaN  \n",
       "3  Fungal infection           NaN  \n",
       "4  Fungal infection           NaN  \n",
       "\n",
       "[5 rows x 134 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132\n"
     ]
    }
   ],
   "source": [
    "#feature and lable separation\n",
    "features = train.columns[:-2]\n",
    "train_x = train[features]\n",
    "train_y = train['prognosis']\n",
    "\n",
    "print(len(train_x.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in train_x.columns:\n",
    "    with open('D:/Downloads/symptomes.txt', 'a') as f:\n",
    "        f.write(str(i) + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(test_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>continuous_sneezing</th>\n",
       "      <th>shivering</th>\n",
       "      <th>chills</th>\n",
       "      <th>joint_pain</th>\n",
       "      <th>stomach_pain</th>\n",
       "      <th>acidity</th>\n",
       "      <th>ulcers_on_tongue</th>\n",
       "      <th>...</th>\n",
       "      <th>blackheads</th>\n",
       "      <th>scurring</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>prognosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <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>Fungal infection</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>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>Allergy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <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>GERD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <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>Chronic cholestasis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>1</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>Drug Reaction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   itching  skin_rash  nodal_skin_eruptions  continuous_sneezing  shivering  \\\n",
       "0        1          1                     1                    0          0   \n",
       "1        0          0                     0                    1          1   \n",
       "2        0          0                     0                    0          0   \n",
       "3        1          0                     0                    0          0   \n",
       "4        1          1                     0                    0          0   \n",
       "\n",
       "   chills  joint_pain  stomach_pain  acidity  ulcers_on_tongue  ...  \\\n",
       "0       0           0             0        0                 0  ...   \n",
       "1       1           0             0        0                 0  ...   \n",
       "2       0           0             1        1                 1  ...   \n",
       "3       0           0             0        0                 0  ...   \n",
       "4       0           0             1        0                 0  ...   \n",
       "\n",
       "   blackheads  scurring  skin_peeling  silver_like_dusting  \\\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",
       "   small_dents_in_nails  inflammatory_nails  blister  red_sore_around_nose  \\\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",
       "   yellow_crust_ooze            prognosis  \n",
       "0                  0     Fungal infection  \n",
       "1                  0              Allergy  \n",
       "2                  0                 GERD  \n",
       "3                  0  Chronic cholestasis  \n",
       "4                  0        Drug Reaction  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x = test[test.columns[:-1]]\n",
    "test_y = test['prognosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = train_x.corr()\n",
    "sns.heatmap(corr, annot = False, cmap=\"YlGnBu\")\n",
    "plt.title(\"feature correlation\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x,val_x,train_y,val_y = train_test_split(train_x, train_y, test_size = 0.2, shuffle = True, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross validation score = 1.0\n",
      "accuracy on validation set = 1.0\n",
      "                                         precision    recall  f1-score   support\n",
      "\n",
      "(vertigo) Paroymsal  Positional Vertigo       1.00      1.00      1.00        18\n",
      "                                   AIDS       1.00      1.00      1.00        30\n",
      "                                   Acne       1.00      1.00      1.00        24\n",
      "                    Alcoholic hepatitis       1.00      1.00      1.00        25\n",
      "                                Allergy       1.00      1.00      1.00        24\n",
      "                              Arthritis       1.00      1.00      1.00        23\n",
      "                       Bronchial Asthma       1.00      1.00      1.00        33\n",
      "                   Cervical spondylosis       1.00      1.00      1.00        23\n",
      "                            Chicken pox       1.00      1.00      1.00        21\n",
      "                    Chronic cholestasis       1.00      1.00      1.00        15\n",
      "                            Common Cold       1.00      1.00      1.00        23\n",
      "                                 Dengue       1.00      1.00      1.00        26\n",
      "                              Diabetes        1.00      1.00      1.00        21\n",
      "           Dimorphic hemmorhoids(piles)       1.00      1.00      1.00        29\n",
      "                          Drug Reaction       1.00      1.00      1.00        24\n",
      "                       Fungal infection       1.00      1.00      1.00        19\n",
      "                                   GERD       1.00      1.00      1.00        28\n",
      "                        Gastroenteritis       1.00      1.00      1.00        25\n",
      "                           Heart attack       1.00      1.00      1.00        23\n",
      "                            Hepatitis B       1.00      1.00      1.00        27\n",
      "                            Hepatitis C       1.00      1.00      1.00        26\n",
      "                            Hepatitis D       1.00      1.00      1.00        23\n",
      "                            Hepatitis E       1.00      1.00      1.00        29\n",
      "                          Hypertension        1.00      1.00      1.00        25\n",
      "                        Hyperthyroidism       1.00      1.00      1.00        24\n",
      "                           Hypoglycemia       1.00      1.00      1.00        26\n",
      "                         Hypothyroidism       1.00      1.00      1.00        21\n",
      "                               Impetigo       1.00      1.00      1.00        24\n",
      "                               Jaundice       1.00      1.00      1.00        19\n",
      "                                Malaria       1.00      1.00      1.00        22\n",
      "                               Migraine       1.00      1.00      1.00        25\n",
      "                        Osteoarthristis       1.00      1.00      1.00        22\n",
      "           Paralysis (brain hemorrhage)       1.00      1.00      1.00        24\n",
      "                    Peptic ulcer diseae       1.00      1.00      1.00        17\n",
      "                              Pneumonia       1.00      1.00      1.00        28\n",
      "                              Psoriasis       1.00      1.00      1.00        22\n",
      "                           Tuberculosis       1.00      1.00      1.00        25\n",
      "                                Typhoid       1.00      1.00      1.00        19\n",
      "                Urinary tract infection       1.00      1.00      1.00        26\n",
      "                         Varicose veins       1.00      1.00      1.00        22\n",
      "                            hepatitis A       1.00      1.00      1.00        34\n",
      "\n",
      "                            avg / total       1.00      1.00      1.00       984\n",
      "\n",
      "cross validation score = 1.0\n",
      "accuracy on validation set = 1.0\n",
      "                                         precision    recall  f1-score   support\n",
      "\n",
      "(vertigo) Paroymsal  Positional Vertigo       1.00      1.00      1.00        18\n",
      "                                   AIDS       1.00      1.00      1.00        30\n",
      "                                   Acne       1.00      1.00      1.00        24\n",
      "                    Alcoholic hepatitis       1.00      1.00      1.00        25\n",
      "                                Allergy       1.00      1.00      1.00        24\n",
      "                              Arthritis       1.00      1.00      1.00        23\n",
      "                       Bronchial Asthma       1.00      1.00      1.00        33\n",
      "                   Cervical spondylosis       1.00      1.00      1.00        23\n",
      "                            Chicken pox       1.00      1.00      1.00        21\n",
      "                    Chronic cholestasis       1.00      1.00      1.00        15\n",
      "                            Common Cold       1.00      1.00      1.00        23\n",
      "                                 Dengue       1.00      1.00      1.00        26\n",
      "                              Diabetes        1.00      1.00      1.00        21\n",
      "           Dimorphic hemmorhoids(piles)       1.00      1.00      1.00        29\n",
      "                          Drug Reaction       1.00      1.00      1.00        24\n",
      "                       Fungal infection       1.00      1.00      1.00        19\n",
      "                                   GERD       1.00      1.00      1.00        28\n",
      "                        Gastroenteritis       1.00      1.00      1.00        25\n",
      "                           Heart attack       1.00      1.00      1.00        23\n",
      "                            Hepatitis B       1.00      1.00      1.00        27\n",
      "                            Hepatitis C       1.00      1.00      1.00        26\n",
      "                            Hepatitis D       1.00      1.00      1.00        23\n",
      "                            Hepatitis E       1.00      1.00      1.00        29\n",
      "                          Hypertension        1.00      1.00      1.00        25\n",
      "                        Hyperthyroidism       1.00      1.00      1.00        24\n",
      "                           Hypoglycemia       1.00      1.00      1.00        26\n",
      "                         Hypothyroidism       1.00      1.00      1.00        21\n",
      "                               Impetigo       1.00      1.00      1.00        24\n",
      "                               Jaundice       1.00      1.00      1.00        19\n",
      "                                Malaria       1.00      1.00      1.00        22\n",
      "                               Migraine       1.00      1.00      1.00        25\n",
      "                        Osteoarthristis       1.00      1.00      1.00        22\n",
      "           Paralysis (brain hemorrhage)       1.00      1.00      1.00        24\n",
      "                    Peptic ulcer diseae       1.00      1.00      1.00        17\n",
      "                              Pneumonia       1.00      1.00      1.00        28\n",
      "                              Psoriasis       1.00      1.00      1.00        22\n",
      "                           Tuberculosis       1.00      1.00      1.00        25\n",
      "                                Typhoid       1.00      1.00      1.00        19\n",
      "                Urinary tract infection       1.00      1.00      1.00        26\n",
      "                         Varicose veins       1.00      1.00      1.00        22\n",
      "                            hepatitis A       1.00      1.00      1.00        34\n",
      "\n",
      "                            avg / total       1.00      1.00      1.00       984\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#decision tree classifier training\n",
    "type = ['gini', 'entropy']\n",
    "for t in type:\n",
    "    clf = DecisionTreeClassifier(criterion = t)\n",
    "    clf.fit(train_x,train_y)\n",
    "    \n",
    "    score = clf.score(val_x, val_y)\n",
    "    \n",
    "    print(f\"cross validation score = {score}\")\n",
    "    y_pred = clf.predict(val_x)\n",
    "    acc = accuracy_score(val_y,y_pred)\n",
    "    \n",
    "    print(f\"accuracy on validation set = {acc}\")\n",
    "    conf_mat = confusion_matrix(val_y, y_pred)\n",
    "    \n",
    "    cls = classification_report(val_y, y_pred)\n",
    "    \n",
    "    print(cls)\n",
    "    \n",
    "    dump(cls,\"D:/Downloads/\" + str(t) + \".joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}