data_description.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix, classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import SVC\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import pickle\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disease</th>\n",
       "      <th>Symptom_1</th>\n",
       "      <th>Symptom_2</th>\n",
       "      <th>Symptom_3</th>\n",
       "      <th>Symptom_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>dischromic _patches</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>itching</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>nodal_skin_eruptions</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4915</th>\n",
       "      <td>(vertigo) Paroymsal  Positional Vertigo</td>\n",
       "      <td>vomiting</td>\n",
       "      <td>headache</td>\n",
       "      <td>nausea</td>\n",
       "      <td>spinning_movements</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4916</th>\n",
       "      <td>Acne</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>pus_filled_pimples</td>\n",
       "      <td>blackheads</td>\n",
       "      <td>scurring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4917</th>\n",
       "      <td>Urinary tract infection</td>\n",
       "      <td>burning_micturition</td>\n",
       "      <td>bladder_discomfort</td>\n",
       "      <td>foul_smell_of urine</td>\n",
       "      <td>continuous_feel_of_urine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4918</th>\n",
       "      <td>Psoriasis</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>joint_pain</td>\n",
       "      <td>skin_peeling</td>\n",
       "      <td>silver_like_dusting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4919</th>\n",
       "      <td>Impetigo</td>\n",
       "      <td>skin_rash</td>\n",
       "      <td>high_fever</td>\n",
       "      <td>blister</td>\n",
       "      <td>red_sore_around_nose</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4920 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Disease             Symptom_1  \\\n",
       "0                            Fungal infection               itching   \n",
       "1                            Fungal infection             skin_rash   \n",
       "2                            Fungal infection               itching   \n",
       "3                            Fungal infection               itching   \n",
       "4                            Fungal infection               itching   \n",
       "...                                       ...                   ...   \n",
       "4915  (vertigo) Paroymsal  Positional Vertigo              vomiting   \n",
       "4916                                     Acne             skin_rash   \n",
       "4917                  Urinary tract infection   burning_micturition   \n",
       "4918                                Psoriasis             skin_rash   \n",
       "4919                                 Impetigo             skin_rash   \n",
       "\n",
       "                  Symptom_2              Symptom_3                  Symptom_4  \n",
       "0                 skin_rash   nodal_skin_eruptions        dischromic _patches  \n",
       "1      nodal_skin_eruptions    dischromic _patches                        NaN  \n",
       "2      nodal_skin_eruptions    dischromic _patches                        NaN  \n",
       "3                 skin_rash    dischromic _patches                        NaN  \n",
       "4                 skin_rash   nodal_skin_eruptions                        NaN  \n",
       "...                     ...                    ...                        ...  \n",
       "4915               headache                 nausea         spinning_movements  \n",
       "4916     pus_filled_pimples             blackheads                   scurring  \n",
       "4917     bladder_discomfort    foul_smell_of urine   continuous_feel_of_urine  \n",
       "4918             joint_pain           skin_peeling        silver_like_dusting  \n",
       "4919             high_fever                blister       red_sore_around_nose  \n",
       "\n",
       "[4920 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description= pd.read_csv('archive/description.csv')\n",
    "diets= pd.read_csv('archive/diets.csv')\n",
    "\n",
    "medications= pd.read_csv('archive/medications.csv')\n",
    "precautions= pd.read_csv('archive/precautions_df.csv', index_col= 0)\n",
    "Symptom_severity_df= pd.read_csv('archive/Symptom-severity.csv')\n",
    "\n",
    "sym_des= pd.read_csv('archive/symtoms_df.csv', index_col= 0)\n",
    "\n",
    "\n",
    "df= pd.read_csv('archive/Training.csv')\n",
    "workout=pd.read_csv('archive/workout_df.csv').iloc[:, 2:]\n",
    "\n",
    "sym_des\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precautions Dataframe\n",
      " Disease         0\n",
      "Precaution_1    0\n",
      "Precaution_2    0\n",
      "Precaution_3    0\n",
      "Precaution_4    0\n",
      "dtype: int64\n",
      "\n",
      "Symptons Dataframe\n",
      " Disease      0\n",
      "Symptom_1    0\n",
      "Symptom_2    0\n",
      "Symptom_3    0\n",
      "Symptom_4    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Eliminating all NaN from the dataset\n",
    "\n",
    "sym_des.dropna(inplace= True)\n",
    "precautions.dropna(inplace= True)\n",
    "\n",
    "print(\"precautions Dataframe\\n\", precautions.isna().sum())\n",
    "print(\"\\nSymptons Dataframe\\n\", sym_des.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DataFrames\n",
    "df_sym_des = pd.DataFrame(sym_des)\n",
    "df_df = pd.DataFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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       "      <th>Symptom_3</th>\n",
       "      <th>Symptom_4</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>4920</td>\n",
       "      <td>4920</td>\n",
       "      <td>4920</td>\n",
       "      <td>4920</td>\n",
       "      <td>4572</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>41</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
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       "      <td>50</td>\n",
       "    </tr>\n",
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       "      <th>top</th>\n",
       "      <td>Fungal infection</td>\n",
       "      <td>vomiting</td>\n",
       "      <td>vomiting</td>\n",
       "      <td>fatigue</td>\n",
       "      <td>high_fever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>120</td>\n",
       "      <td>822</td>\n",
       "      <td>870</td>\n",
       "      <td>726</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "                 Disease  Symptom_1  Symptom_2 Symptom_3    Symptom_4\n",
       "count               4920       4920       4920      4920         4572\n",
       "unique                41         34         48        54           50\n",
       "top     Fungal infection   vomiting   vomiting   fatigue   high_fever\n",
       "freq                 120        822        870       726          378"
      ]
     },
     "execution_count": 45,
     "metadata": {},
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   ],
   "source": [
    "df_sym_des.describe()\n",
    "\n",
    "#min/max\n",
    "#count and count uniques \n",
    "#average \n",
    "#outliers\n",
    "#Description \n",
    "#Mean/Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_frequent_disease = df_sym_des['Disease'].value_counts()\n",
    "most_frequent_symptom1 = df_sym_des['Symptom_1'].value_counts()\n",
    "most_frequent_symptom2 = df_sym_des['Symptom_2'].value_counts()\n",
    "most_frequent_symptom3 = df_sym_des['Symptom_3'].value_counts()\n",
    "most_frequent_symptom4 = df_sym_des['Symptom_4'].value_counts()"
   ]
  },
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   "execution_count": 73,
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   "source": [
    "# Most Frequent Diseases\n",
    "df_mfd = pd.DataFrame(most_frequent_disease)\n",
    "df_mfd = df_mfd.reset_index()\n",
    "\n",
    "\n",
    "fig = px.treemap(df_mfd, path=[px.Constant(\"all\"), 'Disease'], values= 'count')\n",
    "\n",
    "fig.update_traces(root_color=\"lightgray\")\n",
    "fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Most Frequent Symptom_1\n",
    "df_mfs1 = pd.DataFrame(most_frequent_symptom1)\n",
    "df_mfs1 = df_mfs1.reset_index()\n",
    "\n",
    "fig = px.treemap(df_mfs1, path=[px.Constant(\"all\"), 'Symptom_1'], values= 'count')\n",
    "\n",
    "fig.update_traces(root_color=\"lightgray\")\n",
    "fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
    "fig.show()"
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  },
  {
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   "execution_count": 75,
   "metadata": {},
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   "source": [
    "# Most Frequent Symptom_2\n",
    "df_mfs2 = pd.DataFrame(most_frequent_symptom2)\n",
    "df_mfs2 = df_mfs2.reset_index()\n",
    "\n",
    "fig = px.treemap(df_mfs2, path=[px.Constant(\"all\"), 'Symptom_2'], values= 'count')\n",
    "\n",
    "fig.update_traces(root_color=\"lightgray\")\n",
    "fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
    "fig.show()"
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  },
  {
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   "execution_count": 76,
   "metadata": {},
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   "source": [
    "# Most Frequent Symptom_3\n",
    "df_mfs3 = pd.DataFrame(most_frequent_symptom3)\n",
    "df_mfs3 = df_mfs3.reset_index()\n",
    "\n",
    "fig = px.treemap(df_mfs3, path=[px.Constant(\"all\"), 'Symptom_3'], values= 'count')\n",
    "\n",
    "fig.update_traces(root_color=\"lightgray\")\n",
    "fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Most Frequent Symptom_4\n",
    "df_mfs4 = pd.DataFrame(most_frequent_symptom4)\n",
    "df_mfs4 = df_mfs4.reset_index()\n",
    "\n",
    "fig = px.treemap(df_mfs4, path=[px.Constant(\"all\"), 'Symptom_4'], values= 'count')\n",
    "\n",
    "fig.update_traces(root_color=\"lightgray\")\n",
    "fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symptoms</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>itching</td>\n",
       "      <td>4920.0</td>\n",
       "      <td>0.137805</td>\n",
       "      <td>0.344730</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>continuous_sneezing</td>\n",
       "      <td>4920.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shivering</td>\n",
       "      <td>4920.0</td>\n",
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       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>small_dents_in_nails</td>\n",
       "      <td>4920.0</td>\n",
       "      <td>0.023171</td>\n",
       "      <td>0.150461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>inflammatory_nails</td>\n",
       "      <td>4920.0</td>\n",
       "      <td>0.023171</td>\n",
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       "    <tr>\n",
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       "      <td>0.150461</td>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
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       "      <td>4920.0</td>\n",
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      "text/plain": [
       "                 Symptoms   count      mean       std  min  25%  50%  75%  max\n",
       "0                 itching  4920.0  0.137805  0.344730  0.0  0.0  0.0  0.0  1.0\n",
       "1               skin_rash  4920.0  0.159756  0.366417  0.0  0.0  0.0  0.0  1.0\n",
       "2    nodal_skin_eruptions  4920.0  0.021951  0.146539  0.0  0.0  0.0  0.0  1.0\n",
       "3     continuous_sneezing  4920.0  0.045122  0.207593  0.0  0.0  0.0  0.0  1.0\n",
       "4               shivering  4920.0  0.021951  0.146539  0.0  0.0  0.0  0.0  1.0\n",
       "..                    ...     ...       ...       ...  ...  ...  ...  ...  ...\n",
       "127  small_dents_in_nails  4920.0  0.023171  0.150461  0.0  0.0  0.0  0.0  1.0\n",
       "128    inflammatory_nails  4920.0  0.023171  0.150461  0.0  0.0  0.0  0.0  1.0\n",
       "129               blister  4920.0  0.023171  0.150461  0.0  0.0  0.0  0.0  1.0\n",
       "130  red_sore_around_nose  4920.0  0.023171  0.150461  0.0  0.0  0.0  0.0  1.0\n",
       "131     yellow_crust_ooze  4920.0  0.023171  0.150461  0.0  0.0  0.0  0.0  1.0\n",
       "\n",
       "[132 rows x 9 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = df_df.describe().transpose()\n",
    "stats.reset_index(inplace= True)\n",
    "stats.columns = ['Symptoms', 'count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max']\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
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          "all/polyuria",
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          "geo": {
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           "showlakes": true,
           "showland": true,
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          "hoverlabel": {
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          "hovermode": "closest",
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           "bgcolor": "#E5ECF6",
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          "title": {
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          "xaxis": {
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           "ticks": "",
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     },
     "metadata": {},
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   ],
   "source": [
    "# sys_des. How often does each disease occur and often each often symptons occur.\n",
    "# df optional\n",
    "\n",
    "fig = px.treemap(stats, path=[px.Constant(\"all\"), 'Symptoms'], values= 'mean')\n",
    "\n",
    "fig.update_traces(root_color=\"lightgray\")\n",
    "fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n",
    "fig.show()"
   ]
  }
 ],
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