pred-dis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da801030",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85109e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4920, 18)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(r'E:\\datasets\\dis-sym.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb8f3cf6",
   "metadata": {},
   "outputs": [],
   "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:]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a41edb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f80baaa",
   "metadata": {},
   "outputs": [],
   "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())\n",
    "#we have nan values\n",
    "symptoms = [i for i in symptoms if str(i) != \"nan\"]\n",
    "print(len(symptoms))\n",
    "\n",
    "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": null,
   "id": "58775270",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23d3972a",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data['Disease']=df['Disease']\n",
    "new_data=new_data.drop('list_of_Symps',axis=1)\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "292e491a",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.dtypes.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1daf3b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(new_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78dbb2e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the DataFrame to a new CSV file\n",
    "new_data.to_csv('E:\\datasets\\dis-sym-cat.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f7be237",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04b6085d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#disease detection by filtering dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "276dd5e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['itching', ' skin_rash', ' nodal_skin_eruptions',\n",
       "       ' dischromic _patches', ' 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', 'Disease'], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"E:\\datasets\\dis-sym-cat.csv\")\n",
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7817ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['itching', 'skin_rash', 'nodal_skin_eruptions',\n",
       "       'dischromic _patches', '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', 'abdominal_pain',\n",
       "       'yellowing_of_eyes', 'burning_micturition', 'spotting_ urination',\n",
       "       'passage_of_gases', 'internal_itching', 'indigestion',\n",
       "       'muscle_wasting', 'patches_in_throat', 'high_fever',\n",
       "       'extra_marital_contacts', 'fatigue', 'weight_loss', 'restlessness',\n",
       "       'lethargy', 'irregular_sugar_level',\n",
       "       'blurred_and_distorted_vision', 'obesity', 'excessive_hunger',\n",
       "       'increased_appetite', 'polyuria', 'sunken_eyes', 'dehydration',\n",
       "       'diarrhoea', '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', 'throat_irritation',\n",
       "       'redness_of_eyes', 'sinus_pressure', 'runny_nose', 'congestion',\n",
       "       'loss_of_smell', 'fast_heart_rate', 'rusty_sputum',\n",
       "       'pain_during_bowel_movements', 'pain_in_anal_region',\n",
       "       'bloody_stool', 'irritation_in_anus', 'cramps', 'bruising',\n",
       "       'swollen_legs', 'swollen_blood_vessels', 'prominent_veins_on_calf',\n",
       "       'weight_gain', 'cold_hands_and_feets', 'mood_swings',\n",
       "       'puffy_face_and_eyes', 'enlarged_thyroid', 'brittle_nails',\n",
       "       'swollen_extremeties', 'abnormal_menstruation', 'muscle_weakness',\n",
       "       'anxiety', 'slurred_speech', 'palpitations',\n",
       "       'drying_and_tingling_lips', 'knee_pain', 'hip_joint_pain',\n",
       "       'swelling_joints', 'painful_walking', 'movement_stiffness',\n",
       "       'spinning_movements', 'unsteadiness', 'pus_filled_pimples',\n",
       "       'blackheads', 'scurring', 'bladder_discomfort',\n",
       "       'foul_smell_of urine', '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', 'Disease'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing training spaces from column names\n",
    "\n",
    "new_column_names = {col: col.strip() for col in df.columns}\n",
    "df = df.rename(columns=new_column_names)\n",
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e688a5dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter symptom:itching\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  app.launch_new_instance()\n",
      "D:\\python\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "enter 'no' to stop writing symptoms:no\n",
      "for these symptoms: ['itching']\n",
      "u may have --- Chicken pox --- with probability ---9.0909 %\n",
      "u may have --- Chronic cholestasis --- with probability ---14.2857 %\n",
      "u may have --- Drug Reaction --- with probability ---20.0 %\n",
      "u may have --- Fungal infection --- with probability ---25.0 %\n",
      "u may have --- Hepatitis B --- with probability ---8.3333 %\n",
      "u may have --- Jaundice --- with probability ---12.5 %\n"
     ]
    }
   ],
   "source": [
    "filtered_df=[]\n",
    "sym=[]\n",
    "c=0\n",
    "while True:\n",
    "    x=input(\"enter symptom:\")\n",
    "    sym.append(x)\n",
    "    c+=1\n",
    "    print(c)\n",
    "    if(c==1):\n",
    "        filtered_df = df[df[x] == 1]\n",
    "    else:\n",
    "        filtered_df = filtered_df[filtered_df[x] == 1]\n",
    "    # Count the number of '1' values in each row\n",
    "    row_counts = (filtered_df == 1).sum(axis=1)\n",
    "\n",
    "    # Display the DataFrame with row counts\n",
    "    filtered_df['Row_Counts'] = row_counts\n",
    "    filtered_df['prob'] = round((c / row_counts) * 100, 4)\n",
    "    if(filtered_df.empty):\n",
    "        print(sym)\n",
    "        break\n",
    "    y=input(\"enter 'no' to stop writing symptoms:\")\n",
    "    if(y=='no'):\n",
    "        # Select the row with the highest 'prob' value for each unique 'Disease'\n",
    "        selected_rows = filtered_df.loc[filtered_df.groupby('Disease')['prob'].idxmin()]\n",
    "\n",
    "        cols_to_print = ['Disease', 'prob']\n",
    "    \n",
    "        print(\"for these symptoms:\",sym)\n",
    "        # Iterate through the DataFrame and print selected columns\n",
    "        for _, row in selected_rows.iterrows():\n",
    "            print(\"u may have ---\",\" --- with probability ---\".join(str(row[col]) for col in cols_to_print),\"%\")\n",
    "        break\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "267901ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e440b6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting data test-train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9936bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X = df.drop(['Disease'],axis=1)\n",
    "y = df['Disease']\n",
    "\n",
    "# Train Test Split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d1cd070",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38e6fe05",
   "metadata": {},
   "outputs": [],
   "source": [
    "#RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32595b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(max_depth=6, max_features='log2', max_leaf_nodes=9,\n",
      "                       n_estimators=150)\n",
      "RandomForestClassifier(max_depth=9, max_features='log2', max_leaf_nodes=9,\n",
      "                       n_estimators=25)\n"
     ]
    }
   ],
   "source": [
    "#hyperparameter tuning by GRIDSEARCHSV\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV,\\\n",
    "RandomizedSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [25, 50, 100, 150],\n",
    "    'max_features': ['sqrt', 'log2', None],\n",
    "    'max_depth': [3, 6, 9,10],\n",
    "    'max_leaf_nodes': [3, 6, 9],\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(RandomForestClassifier(),\n",
    "                           param_grid=param_grid)\n",
    "grid_search.fit(X_train, y_train)\n",
    "print(grid_search.best_estimator_)\n",
    "\n",
    "\n",
    "#hyperparameter tuning by RandomizedSearchCV\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV,\\\n",
    "RandomizedSearchCV\n",
    "\n",
    "random_search = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                                   param_grid)\n",
    "random_search.fit(X_train, y_train)\n",
    "print(random_search.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "37bc2a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d623ae0f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      "Cross-validated accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "#rfmodel = RandomForestClassifier(max_depth=17, max_features='sqrt', max_leaf_nodes=25,n_estimators=150)\n",
    "rfmodel = RandomForestClassifier()\n",
    "\n",
    "rfmodel.fit(X_train, y_train)\n",
    "y_pred = rfmodel.predict(X_test)\n",
    "\n",
    "# accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "\n",
    "# Perform cross-validation\n",
    "cv_scores = cross_val_score(rfmodel, X, y, cv=10)\n",
    "print(cv_scores)\n",
    "mean_cv_accuracy = cv_scores.mean()\n",
    "print(\"Cross-validated accuracy:\", mean_cv_accuracy)\n",
    "\n",
    "#import joblib\n",
    "# Save the model to a joblib file\n",
    "#joblib.dump(rfmodel, 'pred-dis.joblib')\n",
    "\n",
    "import pickle\n",
    "with open('pred-dis.pkl', 'wb') as file:\n",
    "    pickle.dump(rfmodel, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7d24405a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top 5 diseases u may have with corresponding probability:\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Hypertension ': 100%\n",
      "Description not found for the predicted disease.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "meditation\n",
      "salt baths\n",
      "reduce stress\n",
      "get proper sleep\n",
      "\n",
      "\n",
      "_______________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "probabilities = rfmodel.predict_proba([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n",
    "disease_probabilities = dict(zip(rfmodel.classes_, probabilities[0]))\n",
    "\n",
    "top_n = 5  # Number of top probabilities to display\n",
    "sorted_probabilities = sorted(disease_probabilities.items(), key=lambda x: x[1], reverse=True)[:top_n]\n",
    "\n",
    "disease_df = pd.read_csv(r'E:\\datasets\\disease_description.csv')\n",
    "precaution_df = pd.read_csv(r'E:\\datasets\\disease_precaution.csv')\n",
    "\n",
    "\n",
    "count = 0\n",
    "print(\"top 5 diseases u may have with corresponding probability:\")\n",
    "print('_______________________________________________________________________________')\n",
    "for disease, probability in sorted_probabilities:\n",
    "    if probability > 0:\n",
    "        print(f\"Probability of having '{disease}': {int(probability*100)}%\")\n",
    "\n",
    "        #Filter the DataFrame to retrieve the description of the predicted disease\n",
    "        predicted_disease_description = disease_df[disease_df['Disease'] == disease]['Symptom_Description']\n",
    "        predicted_disease_precautions = precaution_df[precaution_df['Disease'] == disease]\n",
    "\n",
    "        #Print the description of the disease\n",
    "        if not predicted_disease_description.empty:\n",
    "            print(\"DESCRIPTION: \",predicted_disease_description.values[0])\n",
    "        else:\n",
    "            print(\"Description not found for the predicted disease.\")\n",
    "\n",
    "        print('\\n')\n",
    "        if not predicted_disease_precautions.empty:\n",
    "            print('PRECAUTIONS:')\n",
    "            for column in ['Symptom_precaution_0', 'Symptom_precaution_1', 'Symptom_precaution_2', 'Symptom_precaution_3']:\n",
    "                precaution = predicted_disease_precautions[column].values[0]\n",
    "                print(precaution)\n",
    "        else:\n",
    "            print(\"Precautions not found for the predicted disease.\")\n",
    "\n",
    "        print('\\n')   \n",
    "\n",
    "        if(probability*100)<50:\n",
    "            print('since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation')\n",
    "        count += 1\n",
    "        print('_______________________________________________________________________________')\n",
    "        if count == top_n:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5b4da83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69a1c697",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NAIVE BAYES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "23476e2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Accuracy on test set: 1.0\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      "Cross-validated accuracy: 1.0\n",
      "Probability of class (vertigo) Paroymsal  Positional Vertigo: 3.2118605130632504e-06\n",
      "Probability of class AIDS: 6.350014241396124e-10\n",
      "Probability of class Acne: 6.350014241396124e-10\n",
      "Probability of class Alcoholic hepatitis: 2.4957294122253348e-11\n",
      "Probability of class Allergy: 6.823591792423458e-10\n",
      "Probability of class Arthritis: 1.570645662493985e-10\n",
      "Probability of class Bronchial Asthma: 6.429828668849894e-11\n",
      "Probability of class Cervical spondylosis: 9.234720731855775e-06\n",
      "Probability of class Chicken pox: 9.736334559092619e-08\n",
      "Probability of class Chronic cholestasis: 5.715220353996044e-09\n",
      "Probability of class Common Cold: 7.526360392384922e-09\n",
      "Probability of class Dengue: 1.0645267484618947e-10\n",
      "Probability of class Diabetes : 3.1765648746920517e-12\n",
      "Probability of class Dimorphic hemmorhoids(piles): 1.570645662493985e-10\n",
      "Probability of class Drug Reaction: 4.531356828990978e-08\n",
      "Probability of class Fungal infection: 1.4807194189558866e-07\n",
      "Probability of class GERD: 1.5463971770283884e-08\n",
      "Probability of class Gastroenteritis: 6.350014241396124e-10\n",
      "Probability of class Heart attack: 1.4541532612797193e-07\n",
      "Probability of class Hepatitis B: 2.471471748531011e-10\n",
      "Probability of class Hepatitis C: 6.12471255899633e-11\n",
      "Probability of class Hepatitis D: 6.118276618853396e-12\n",
      "Probability of class Hepatitis E: 6.713598128943303e-13\n",
      "Probability of class Hypertension : 99.99998693818611\n",
      "Probability of class Hyperthyroidism: 1.807210763333573e-12\n",
      "Probability of class Hypoglycemia: 2.471471748531011e-10\n",
      "Probability of class Hypothyroidism: 1.5374139715280236e-10\n",
      "Probability of class Impetigo: 1.760973423820246e-10\n",
      "Probability of class Jaundice: 2.7158540957818267e-09\n",
      "Probability of class Malaria: 2.7158540957818267e-09\n",
      "Probability of class Migraine: 1.4010853457174245e-09\n",
      "Probability of class Osteoarthristis: 5.836355339592987e-11\n",
      "Probability of class Paralysis (brain hemorrhage): 1.3779530903829654e-07\n",
      "Probability of class Peptic ulcer diseae: 6.429828668849894e-11\n",
      "Probability of class Pneumonia: 4.355377939633918e-10\n",
      "Probability of class Psoriasis: 5.836355339592987e-11\n",
      "Probability of class Tuberculosis: 4.7134437016273743e-11\n",
      "Probability of class Typhoid: 4.138512648033872e-10\n",
      "Probability of class Urinary tract infection: 6.350014241396124e-10\n",
      "Probability of class Varicose veins: 2.6023564132093367e-11\n",
      "Probability of class hepatitis A: 1.807210763333573e-12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but MultinomialNB was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "\n",
    "\n",
    "# Create a Gaussian Naive Bayes model with custom hyperparameters\n",
    "model = GaussianNB()\n",
    "\n",
    "# Create a Multinomial Naive Bayes model with custom hyperparameters\n",
    "#model = MultinomialNB(alpha=0.5,fit_prior=False)\n",
    "\n",
    "# Create a Bernoulli Naive Bayes model with custom hyperparameters\n",
    "#model = BernoulliNB(alpha=1.0, binarize=0.2)\n",
    "\n",
    "model.fit(X, y)\n",
    "y_pred=model.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# Print the accuracy\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Calculate accuracy on the test set\n",
    "accuracy = model.score(X_test, y_test)\n",
    "print(\"Accuracy on test set:\", accuracy)\n",
    "\n",
    "# Perform cross-validation\n",
    "cv_scores = cross_val_score(model, X, y, cv=10)\n",
    "print(cv_scores)\n",
    "mean_cv_accuracy = cv_scores.mean()\n",
    "print(\"Cross-validated accuracy:\", mean_cv_accuracy)\n",
    "\n",
    "# Make predictions on new data\n",
    "new_data = [[1,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
    "predictions = model.predict_proba(new_data)\n",
    "\n",
    "# Print the probabilities for each output class\n",
    "output_classes = model.classes_\n",
    "for class_idx, class_prob in enumerate(predictions[0]):\n",
    "    class_name = output_classes[class_idx]\n",
    "    print(f'Probability of class {class_name}: {class_prob*100}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "711017eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9072e26e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea27bb86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "99c8e80d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 120 candidates, totalling 600 fits\n",
      "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=1.000 total time=   2.9s\n",
      "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=1.000 total time=   2.8s\n",
      "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=1.000 total time=   2.9s\n",
      "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=1.000 total time=   2.7s\n",
      "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=1.000 total time=   2.8s\n",
      "[CV 1/5] END .....C=0.1, gamma=1, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 2/5] END .....C=0.1, gamma=1, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 3/5] END .....C=0.1, gamma=1, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 4/5] END .....C=0.1, gamma=1, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 5/5] END .....C=0.1, gamma=1, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 1/5] END .......C=0.1, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 2/5] END .......C=0.1, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 3/5] END .......C=0.1, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 4/5] END .......C=0.1, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 5/5] END .......C=0.1, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 1/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.254 total time=   4.4s\n",
      "[CV 2/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.281 total time=   4.4s\n",
      "[CV 3/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.316 total time=   4.4s\n",
      "[CV 4/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.291 total time=   4.4s\n",
      "[CV 5/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.271 total time=   4.5s\n",
      "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=1.000 total time=   2.2s\n",
      "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=1.000 total time=   2.3s\n",
      "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=1.000 total time=   2.5s\n",
      "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=1.000 total time=   2.3s\n",
      "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=1.000 total time=   2.5s\n",
      "[CV 1/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 2/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=1.000 total time=   0.7s\n",
      "[CV 3/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 4/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 5/5] END ...C=0.1, gamma=0.1, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 1/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.780 total time=   2.7s\n",
      "[CV 2/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.779 total time=   2.5s\n",
      "[CV 3/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.775 total time=   2.7s\n",
      "[CV 4/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.767 total time=   2.6s\n",
      "[CV 5/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.784 total time=   2.5s\n",
      "[CV 1/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.997 total time=   3.9s\n",
      "[CV 2/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.997 total time=   3.9s\n",
      "[CV 3/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=1.000 total time=   3.4s\n",
      "[CV 4/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.996 total time=   3.4s\n",
      "[CV 5/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.996 total time=   3.3s\n",
      "[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.690 total time=   6.5s\n",
      "[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.706 total time=   6.9s\n",
      "[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.690 total time=   6.8s\n",
      "[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.676 total time=   6.3s\n",
      "[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.684 total time=   6.4s\n",
      "[CV 1/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 2/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 3/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 4/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 5/5] END ..C=0.1, gamma=0.01, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 1/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.027 total time=   3.9s\n",
      "[CV 2/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.027 total time=   3.9s\n",
      "[CV 3/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.027 total time=   4.0s\n",
      "[CV 4/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.027 total time=   4.2s\n",
      "[CV 5/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.027 total time=   4.3s\n",
      "[CV 1/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.093 total time=   8.4s\n",
      "[CV 2/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.093 total time=   7.8s\n",
      "[CV 3/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.090 total time=   7.9s\n",
      "[CV 4/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.094 total time=   8.0s\n",
      "[CV 5/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.089 total time=   8.3s\n",
      "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.027 total time=   7.1s\n",
      "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.027 total time=   6.8s\n",
      "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.027 total time=   6.7s\n",
      "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.027 total time=   7.0s\n",
      "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.027 total time=   6.4s\n",
      "[CV 1/5] END .C=0.1, gamma=0.001, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 2/5] END .C=0.1, gamma=0.001, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 3/5] END .C=0.1, gamma=0.001, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 4/5] END .C=0.1, gamma=0.001, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 5/5] END .C=0.1, gamma=0.001, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 1/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.027 total time=   3.9s\n",
      "[CV 2/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.027 total time=   3.9s\n",
      "[CV 3/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.027 total time=   4.0s\n",
      "[CV 4/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.027 total time=   4.0s\n",
      "[CV 5/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.027 total time=   4.0s\n",
      "[CV 1/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.027 total time=   7.7s\n",
      "[CV 2/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.027 total time=   7.7s\n",
      "[CV 3/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.027 total time=   7.8s\n",
      "[CV 4/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.027 total time=   8.2s\n",
      "[CV 5/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.027 total time=   8.1s\n",
      "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.027 total time=   6.5s\n",
      "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.027 total time=   6.5s\n",
      "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.027 total time=   7.1s\n",
      "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.027 total time=   6.6s\n",
      "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.027 total time=   6.4s\n",
      "[CV 1/5] END C=0.1, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 2/5] END C=0.1, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 3/5] END C=0.1, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 4/5] END C=0.1, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.6s\n",
      "[CV 5/5] END C=0.1, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 1/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.027 total time=   3.9s\n",
      "[CV 2/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.027 total time=   4.0s\n",
      "[CV 3/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.027 total time=   3.9s\n",
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      "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=1.000 total time=   0.9s\n",
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      "[CV 1/5] END .....C=100, gamma=1, kernel=linear;, score=1.000 total time=   0.5s\n",
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      "[CV 1/5] END .......C=100, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 2/5] END .......C=100, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 3/5] END .......C=100, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 4/5] END .......C=100, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 5/5] END .......C=100, gamma=1, kernel=poly;, score=1.000 total time=   0.6s\n",
      "[CV 1/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.845 total time=   1.0s\n",
      "[CV 2/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.841 total time=   1.0s\n",
      "[CV 3/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.816 total time=   1.0s\n",
      "[CV 4/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.877 total time=   1.0s\n",
      "[CV 5/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.839 total time=   1.0s\n",
      "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.8s\n",
      "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.8s\n",
      "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.8s\n",
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      "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=1.000 total time=   0.8s\n",
      "[CV 1/5] END ...C=100, gamma=0.1, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 2/5] END ...C=100, gamma=0.1, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 3/5] END ...C=100, gamma=0.1, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 4/5] END ...C=100, gamma=0.1, kernel=linear;, score=1.000 total time=   0.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 5/5] END ...C=100, gamma=0.1, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 1/5] END .....C=100, gamma=0.1, kernel=poly;, score=1.000 total time=   0.6s\n",
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      "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.8s\n",
      "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.8s\n",
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      "[CV 1/5] END ..C=100, gamma=0.01, kernel=linear;, score=1.000 total time=   0.5s\n",
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      "[CV 5/5] END ..C=100, gamma=0.01, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 1/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.780 total time=   2.3s\n",
      "[CV 2/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.779 total time=   2.3s\n",
      "[CV 3/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.775 total time=   2.3s\n",
      "[CV 4/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.767 total time=   2.2s\n",
      "[CV 5/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.784 total time=   2.3s\n",
      "[CV 1/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
      "[CV 2/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
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      "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.8s\n",
      "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.8s\n",
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      "[CV 1/5] END .C=100, gamma=0.001, kernel=linear;, score=1.000 total time=   0.5s\n",
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      "[CV 1/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.027 total time=   3.9s\n",
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      "[CV 5/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.027 total time=   3.9s\n",
      "[CV 1/5] END C=100, gamma=0.001, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
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      "[CV 3/5] END C=100, gamma=0.001, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
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      "[CV 5/5] END C=100, gamma=0.001, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
      "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   1.6s\n",
      "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   1.7s\n",
      "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   1.6s\n",
      "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   1.8s\n",
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      "[CV 1/5] END C=100, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 2/5] END C=100, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 3/5] END C=100, gamma=0.0001, kernel=linear;, score=1.000 total time=   0.5s\n",
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      "[CV 1/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.027 total time=   4.1s\n",
      "[CV 2/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.027 total time=   3.9s\n",
      "[CV 3/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.027 total time=   4.0s\n",
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      "[CV 5/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.027 total time=   4.0s\n",
      "[CV 1/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=1.000 total time=   2.8s\n",
      "[CV 2/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=1.000 total time=   2.7s\n",
      "[CV 3/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=1.000 total time=   2.8s\n",
      "[CV 4/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=1.000 total time=   2.7s\n",
      "[CV 5/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=1.000 total time=   2.7s\n",
      "[CV 1/5] END .....C=100, gamma=auto, kernel=rbf;, score=1.000 total time=   0.7s\n",
      "[CV 2/5] END .....C=100, gamma=auto, kernel=rbf;, score=1.000 total time=   0.8s\n",
      "[CV 3/5] END .....C=100, gamma=auto, kernel=rbf;, score=1.000 total time=   0.8s\n",
      "[CV 4/5] END .....C=100, gamma=auto, kernel=rbf;, score=1.000 total time=   0.7s\n",
      "[CV 5/5] END .....C=100, gamma=auto, kernel=rbf;, score=1.000 total time=   0.8s\n",
      "[CV 1/5] END ..C=100, gamma=auto, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 2/5] END ..C=100, gamma=auto, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 3/5] END ..C=100, gamma=auto, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 4/5] END ..C=100, gamma=auto, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 5/5] END ..C=100, gamma=auto, kernel=linear;, score=1.000 total time=   0.5s\n",
      "[CV 1/5] END ....C=100, gamma=auto, kernel=poly;, score=0.599 total time=   2.9s\n",
      "[CV 2/5] END ....C=100, gamma=auto, kernel=poly;, score=0.572 total time=   2.8s\n",
      "[CV 3/5] END ....C=100, gamma=auto, kernel=poly;, score=0.531 total time=   2.9s\n",
      "[CV 4/5] END ....C=100, gamma=auto, kernel=poly;, score=0.530 total time=   2.9s\n",
      "[CV 5/5] END ....C=100, gamma=auto, kernel=poly;, score=0.573 total time=   2.8s\n",
      "[CV 1/5] END .C=100, gamma=auto, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
      "[CV 2/5] END .C=100, gamma=auto, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
      "[CV 3/5] END .C=100, gamma=auto, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
      "[CV 4/5] END .C=100, gamma=auto, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
      "[CV 5/5] END .C=100, gamma=auto, kernel=sigmoid;, score=1.000 total time=   0.9s\n",
      "{'C': 0.1, 'gamma': 1, 'kernel': 'rbf'}\n",
      "SVC(C=0.1, gamma=1, probability=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "  \n",
    "# defining parameter range\n",
    "para m_grid = {'C': [0.1, 1, 10,20, 100], \n",
    "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001,'auto'],\n",
    "              'kernel': ['rbf','linear','poly','sigmoid']} \n",
    "  \n",
    "grid = GridSearchCV(svm.SVC(probability=True), param_grid, refit = True, verbose = 3)\n",
    "  \n",
    "# fitting the model for grid search\n",
    "grid.fit(X_train, y_train)\n",
    "# print best parameter after tuning\n",
    "print(grid.best_params_)\n",
    "  \n",
    "# print how our model looks after hyper-parameter tuning\n",
    "print(grid.best_estimator_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "737294c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Hypertension '], dtype=object)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_predictions = grid.predict([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n",
    "grid_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "461fffaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top 5 diseases u may have with corresponding probability:\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Hypertension ': 13%\n",
      "Description not found for the predicted disease.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "meditation\n",
      "salt baths\n",
      "reduce stress\n",
      "get proper sleep\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Jaundice': 3%\n",
      "DESCRIPTION:  Yellow staining of the skin and sclerae (the whites of the eyes) by abnormally high blood levels of the bile pigment bilirubin. The yellowing extends to other tissues and body fluids. Jaundice was once called the Morbus regius\" (the regal disease) in the belief that only the touch of a king could cure it\"\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "drink plenty of water\n",
      "consume milk thistle\n",
      "eat fruits and high fibre food\n",
      "medication\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Tuberculosis': 3%\n",
      "DESCRIPTION:  Tuberculosis (TB) is an infectious disease usually caused by Mycobacterium tuberculosis (MTB) bacteria. Tuberculosis generally affects the lungs, but can also affect other parts of the body. Most infections show no symptoms, in which case it is known as latent tuberculosis.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "cover mouth\n",
      "consult doctor\n",
      "medication\n",
      "rest\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Hepatitis B': 3%\n",
      "DESCRIPTION:  Hepatitis B is an infection of your liver. It can cause scarring of the organ, liver failure, and cancer. It can be fatal if it isn't treated. It's spread when people come in contact with the blood, open sores, or body fluids of someone who has the hepatitis B virus.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "consult nearest hospital\n",
      "vaccination\n",
      "eat healthy\n",
      "medication\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Hepatitis E': 3%\n",
      "DESCRIPTION:  A rare form of liver inflammation caused by infection with the hepatitis E virus (HEV). It is transmitted via food or drinks handled by an infected person or through infected water supplies in areas where the faecal matter may get into the water. Hepatitis E does not cause chronic liver disease.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "stop alcohol consumption\n",
      "rest\n",
      "consult doctor\n",
      "medication\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Hypoglycemia': 3%\n",
      "DESCRIPTION:  Hypoglycemia is a condition in which your blood sugar (glucose) level is lower than normal. Glucose is your body's main energy source. Hypoglycemia is often related to diabetes treatment. But other drugs and a variety of conditions — many rare — can cause low blood sugar in people who don't have diabetes.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "lie down on side\n",
      "check in pulse\n",
      "drink sugary drinks\n",
      "consult doctor\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Pneumonia': 3%\n",
      "DESCRIPTION:  Pneumonia is an infection in one or both lungs. Bacteria, viruses, and fungi cause it. The infection causes inflammation in the air sacs in your lungs, which are called alveoli. The alveoli fill with fluid or pus, making it difficult to breathe.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "consult doctor\n",
      "medication\n",
      "rest\n",
      "follow up\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Common Cold': 3%\n",
      "DESCRIPTION:  The common cold is a viral infection of your nose and throat (upper respiratory tract). It's usually harmless, although it might not feel that way. Many types of viruses can cause a common cold.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "drink vitamin c rich drinks\n",
      "take vapour\n",
      "avoid cold food\n",
      "keep fever in check\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Typhoid': 2%\n",
      "DESCRIPTION:  An acute illness characterized by fever caused by infection with the bacterium Salmonella typhi. Typhoid fever has an insidious onset, with fever, headache, constipation, malaise, chills, and muscle pain. Diarrhoea is uncommon, and vomiting is not usually severe.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "eat high calorie vegitables\n",
      "antiboitic therapy\n",
      "consult doctor\n",
      "medication\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Diabetes ': 2%\n",
      "Description not found for the predicted disease.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "have balanced diet\n",
      "exercise\n",
      "consult doctor\n",
      "follow up\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "probabilities = grid.predict_proba([[1,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n",
    "disease_probabilities = dict(zip(grid.classes_, probabilities[0]))\n",
    "\n",
    "top_n = 10  # Number of top probabilities to display\n",
    "sorted_probabilities = sorted(disease_probabilities.items(), key=lambda x: x[1], reverse=True)[:top_n]\n",
    "\n",
    "disease_df = pd.read_csv(r'E:\\datasets\\disease_description.csv')\n",
    "precaution_df = pd.read_csv(r'E:\\datasets\\disease_precaution.csv')\n",
    "\n",
    "\n",
    "count = 0\n",
    "print(\"top 5 diseases u may have with corresponding probability:\")\n",
    "print('_______________________________________________________________________________')\n",
    "for disease, probability in sorted_probabilities:\n",
    "    if probability > 0:\n",
    "        print(f\"Probability of having '{disease}': {int(probability*100)}%\")\n",
    "\n",
    "        #Filter the DataFrame to retrieve the description of the predicted disease\n",
    "        predicted_disease_description = disease_df[disease_df['Disease'] == disease]['Symptom_Description']\n",
    "        predicted_disease_precautions = precaution_df[precaution_df['Disease'] == disease]\n",
    "\n",
    "        #Print the description of the disease\n",
    "        if not predicted_disease_description.empty:\n",
    "            print(\"DESCRIPTION: \",predicted_disease_description.values[0])\n",
    "        else:\n",
    "            print(\"Description not found for the predicted disease.\")\n",
    "\n",
    "        print('\\n')\n",
    "        if not predicted_disease_precautions.empty:\n",
    "            print('PRECAUTIONS:')\n",
    "            for column in ['Symptom_precaution_0', 'Symptom_precaution_1', 'Symptom_precaution_2', 'Symptom_precaution_3']:\n",
    "                precaution = predicted_disease_precautions[column].values[0]\n",
    "                print(precaution)\n",
    "        else:\n",
    "            print(\"Precautions not found for the predicted disease.\")\n",
    "\n",
    "        print('\\n')   \n",
    "\n",
    "        if(probability*100)<50:\n",
    "            print('since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation')\n",
    "        count += 1\n",
    "        print('_______________________________________________________________________________')\n",
    "        if count == top_n:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c8dcb565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Accuracy on test set: 1.0\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      "Cross-validated accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Create SVM classifier with probability estimates\n",
    "svm = SVC(probability=True)\n",
    "\n",
    "# Fit the classifier to the data\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "y_pred=svm.predict(X_test)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# Print the accuracy\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Calculate accuracy on the test set\n",
    "accuracy = model.score(X_test, y_test)\n",
    "print(\"Accuracy on test set:\", accuracy)\n",
    "\n",
    "# Perform cross-validation\n",
    "cv_scores = cross_val_score(svm,X_train, y_train, cv=10)\n",
    "print(cv_scores)\n",
    "mean_cv_accuracy = cv_scores.mean()\n",
    "print(\"Cross-validated accuracy:\", mean_cv_accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6dbb400a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top 5 diseases u may have with corresponding probability:\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Hypertension ': 67%\n",
      "Description not found for the predicted disease.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "meditation\n",
      "salt baths\n",
      "reduce stress\n",
      "get proper sleep\n",
      "\n",
      "\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Cervical spondylosis': 2%\n",
      "DESCRIPTION:  Cervical spondylosis is a general term for age-related wear and tear affecting the spinal disks in your neck. As the disks dehydrate and shrink, signs of osteoarthritis develop, including bony projections along the edges of bones (bone spurs).\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "use a heating pad or cold pack\n",
      "exercise\n",
      "take an otc pain reliever\n",
      "consult a doctor\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Fungal infection': 1%\n",
      "DESCRIPTION:  In humans, fungal infections occur when an invading fungus takes over an area of the body and is too much for the immune system to handle. Fungi can live in the air, soil, water, and plants. Some fungi live naturally in the human body. Like many microbes, there are helpful fungi and harmful fungi.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "bath twice\n",
      "use Detol or neem in bathing water\n",
      "keep the infected area dry\n",
      "use clean cloths\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Heart attack': 1%\n",
      "DESCRIPTION:  The death of heart muscle due to the loss of blood supply. The loss of blood supply is usually caused by a complete blockage of a coronary artery, one of the arteries that supply blood to the heart muscle.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "call an ambulance\n",
      "chew aspirin\n",
      "keep calm\n",
      "nan\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n",
      "Probability of having 'Paralysis (brain hemorrhage)': 1%\n",
      "Description not found for the predicted disease.\n",
      "\n",
      "\n",
      "PRECAUTIONS:\n",
      "massage\n",
      "eat healthy\n",
      "exercise\n",
      "consult doctor\n",
      "\n",
      "\n",
      "since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation\n",
      "_______________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "probabilities = svm.predict_proba([[1,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n",
    "disease_probabilities = dict(zip(svm.classes_, probabilities[0]))\n",
    "\n",
    "top_n = 5  # Number of top probabilities to display\n",
    "sorted_probabilities = sorted(disease_probabilities.items(), key=lambda x: x[1], reverse=True)[:top_n]\n",
    "\n",
    "disease_df = pd.read_csv(r'E:\\datasets\\disease_description.csv')\n",
    "precaution_df = pd.read_csv(r'E:\\datasets\\disease_precaution.csv')\n",
    "\n",
    "\n",
    "count = 0\n",
    "print(\"top 5 diseases u may have with corresponding probability:\")\n",
    "print('_______________________________________________________________________________')\n",
    "for disease, probability in sorted_probabilities:\n",
    "    if probability > 0:\n",
    "        print(f\"Probability of having '{disease}': {int(probability*100)}%\")\n",
    "\n",
    "        #Filter the DataFrame to retrieve the description of the predicted disease\n",
    "        predicted_disease_description = disease_df[disease_df['Disease'] == disease]['Symptom_Description']\n",
    "        predicted_disease_precautions = precaution_df[precaution_df['Disease'] == disease]\n",
    "\n",
    "        #Print the description of the disease\n",
    "        if not predicted_disease_description.empty:\n",
    "            print(\"DESCRIPTION: \",predicted_disease_description.values[0])\n",
    "        else:\n",
    "            print(\"Description not found for the predicted disease.\")\n",
    "\n",
    "        print('\\n')\n",
    "        if not predicted_disease_precautions.empty:\n",
    "            print('PRECAUTIONS:')\n",
    "            for column in ['Symptom_precaution_0', 'Symptom_precaution_1', 'Symptom_precaution_2', 'Symptom_precaution_3']:\n",
    "                precaution = predicted_disease_precautions[column].values[0]\n",
    "                print(precaution)\n",
    "        else:\n",
    "            print(\"Precautions not found for the predicted disease.\")\n",
    "\n",
    "        print('\\n')   \n",
    "\n",
    "        if(probability*100)<50:\n",
    "            print('since the probability if LESS THAN 50---WE RECOMMEND TO CONSULT A DOCTER---for a better conformation')\n",
    "        count += 1\n",
    "        print('_______________________________________________________________________________')\n",
    "        if count == top_n:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b75d401",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5097e989",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "425f3961",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ff4a73f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#functions to get array that is used in predict function in any model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1cd2643",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "all_symptoms = ['itching', 'skin_rash', 'nodal_skin_eruptions',\n",
    "       'dischromic _patches', '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', 'abdominal_pain',\n",
    "       'yellowing_of_eyes', 'burning_micturition', 'spotting_ urination',\n",
    "       'passage_of_gases', 'internal_itching', 'indigestion',\n",
    "       'muscle_wasting', 'patches_in_throat', 'high_fever',\n",
    "       'extra_marital_contacts', 'fatigue', 'weight_loss', 'restlessness',\n",
    "       'lethargy', 'irregular_sugar_level',\n",
    "       'blurred_and_distorted_vision', 'obesity', 'excessive_hunger',\n",
    "       'increased_appetite', 'polyuria', 'sunken_eyes', 'dehydration',\n",
    "       'diarrhoea', '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', 'throat_irritation',\n",
    "       'redness_of_eyes', 'sinus_pressure', 'runny_nose', 'congestion',\n",
    "       'loss_of_smell', 'fast_heart_rate', 'rusty_sputum',\n",
    "       'pain_during_bowel_movements', 'pain_in_anal_region',\n",
    "       'bloody_stool', 'irritation_in_anus', 'cramps', 'bruising',\n",
    "       'swollen_legs', 'swollen_blood_vessels', 'prominent_veins_on_calf',\n",
    "       'weight_gain', 'cold_hands_and_feets', 'mood_swings',\n",
    "       'puffy_face_and_eyes', 'enlarged_thyroid', 'brittle_nails',\n",
    "       'swollen_extremeties', 'abnormal_menstruation', 'muscle_weakness',\n",
    "       'anxiety', 'slurred_speech', 'palpitations',\n",
    "       'drying_and_tingling_lips', 'knee_pain', 'hip_joint_pain',\n",
    "       'swelling_joints', 'painful_walking', 'movement_stiffness',\n",
    "       'spinning_movements', 'unsteadiness', 'pus_filled_pimples',\n",
    "       'blackheads', 'scurring', 'bladder_discomfort',\n",
    "       'foul_smell_of urine', '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']\n",
    "\n",
    "\n",
    "\n",
    "def create_symptom_mapping(symptoms_list, symptom_names):\n",
    "    symptom_mapping = [1 if symptom in symptoms_list else 0 for symptom in symptom_names]\n",
    "    return symptom_mapping\n",
    "\n",
    "symptoms = ['coma']\n",
    "mapping = create_symptom_mapping(symptoms, all_symptoms)\n",
    "print(mapping)\n",
    "print(len(mapping))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e9f1da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_symptoms_from_mapping(symptom_mapping,all_symptoms):\n",
    "    c=0\n",
    "    for i in symptom_mapping:\n",
    "        if(i==1):\n",
    "            print(all_symptoms[c])\n",
    "        c+=1\n",
    "a=[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "get_symptoms_from_mapping(a,all_symptoms)"
   ]
  }
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