Doctor Consultantcy Fees Prediction (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "052c8661",
   "metadata": {},
   "source": [
    "# Doctor Consultancy Fees Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453a7f52",
   "metadata": {},
   "source": [
    "#### In the data, we have the following columns to work with :\n",
    "\n",
    "1.Qualification: Qualification and degrees held by the doctor\n",
    "\n",
    "2.Experience: Experience of the doctor (number of years)\n",
    "\n",
    "3.Rating: Rating given by patients\n",
    "\n",
    "4.Profile: Type of the doctor\n",
    "\n",
    "5.Miscellaeous_Info: Extra information about the doctor\n",
    "\n",
    "6.Place: Area and the city where the doctor is located\n",
    "\n",
    "7.Fees: Fees charged by the doctor(Target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c34baf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#import some python modules\n",
    "import re\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8fdced69",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\",None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b3a505a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_excel('Final_Train.xlsx')\n",
    "test = pd.read_excel('Final_Test.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3225132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training_data shape (5961, 7)\n",
      "testing_data shape (1987, 6)\n"
     ]
    }
   ],
   "source": [
    "#shape of train & test set\n",
    "print(\"training_data shape\",train.shape)\n",
    "print(\"testing_data shape\",test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10b7eed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Place</th>\n",
       "      <th>Profile</th>\n",
       "      <th>Miscellaneous_Info</th>\n",
       "      <th>Fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BHMS, MD - Homeopathy</td>\n",
       "      <td>24 years experience</td>\n",
       "      <td>100%</td>\n",
       "      <td>Kakkanad, Ernakulam</td>\n",
       "      <td>Homeopath</td>\n",
       "      <td>100% 16 Feedback Kakkanad, Ernakulam</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BAMS, MD - Ayurveda Medicine</td>\n",
       "      <td>12 years experience</td>\n",
       "      <td>98%</td>\n",
       "      <td>Whitefield, Bangalore</td>\n",
       "      <td>Ayurveda</td>\n",
       "      <td>98% 76 Feedback Whitefield, Bangalore</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MBBS, MS - Otorhinolaryngology</td>\n",
       "      <td>9 years experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mathikere - BEL, Bangalore</td>\n",
       "      <td>ENT Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BSc - Zoology, BAMS</td>\n",
       "      <td>12 years experience</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bannerghatta Road, Bangalore</td>\n",
       "      <td>Ayurveda</td>\n",
       "      <td>Bannerghatta Road, Bangalore ₹250 Available on...</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BAMS</td>\n",
       "      <td>20 years experience</td>\n",
       "      <td>100%</td>\n",
       "      <td>Keelkattalai, Chennai</td>\n",
       "      <td>Ayurveda</td>\n",
       "      <td>100% 4 Feedback Keelkattalai, Chennai</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Qualification           Experience Rating  \\\n",
       "0           BHMS, MD - Homeopathy  24 years experience   100%   \n",
       "1    BAMS, MD - Ayurveda Medicine  12 years experience    98%   \n",
       "2  MBBS, MS - Otorhinolaryngology   9 years experience    NaN   \n",
       "3             BSc - Zoology, BAMS  12 years experience    NaN   \n",
       "4                            BAMS  20 years experience   100%   \n",
       "\n",
       "                          Place         Profile  \\\n",
       "0           Kakkanad, Ernakulam       Homeopath   \n",
       "1         Whitefield, Bangalore        Ayurveda   \n",
       "2    Mathikere - BEL, Bangalore  ENT Specialist   \n",
       "3  Bannerghatta Road, Bangalore        Ayurveda   \n",
       "4         Keelkattalai, Chennai        Ayurveda   \n",
       "\n",
       "                                  Miscellaneous_Info  Fees  \n",
       "0               100% 16 Feedback Kakkanad, Ernakulam   100  \n",
       "1              98% 76 Feedback Whitefield, Bangalore   350  \n",
       "2                                                NaN   300  \n",
       "3  Bannerghatta Road, Bangalore ₹250 Available on...   250  \n",
       "4              100% 4 Feedback Keelkattalai, Chennai   250  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look over data\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f803de7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Qualification          0.00\n",
       "Experience             0.00\n",
       "Rating                55.39\n",
       "Place                  0.42\n",
       "Profile                0.00\n",
       "Miscellaneous_Info    43.95\n",
       "Fees                   0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for NULL values\n",
    "round(train.isnull().sum() / len(train) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8277b505",
   "metadata": {},
   "source": [
    "## EDA "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137bd975",
   "metadata": {},
   "source": [
    "#### There will be lot's of analysis which can be assumed by observing our data as all the feature we are having is categorical even experience, except target variable(Fees) so let's begin.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c90f7a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Experience Year from Experience column And convert it into Numeric\n",
    "\n",
    "train['Experience'] = train['Experience'].str.split().str[0]\n",
    "train['Experience'] = train['Experience'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "486c58c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Place</th>\n",
       "      <th>Profile</th>\n",
       "      <th>Miscellaneous_Info</th>\n",
       "      <th>Fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BHMS, MD - Homeopathy</td>\n",
       "      <td>24</td>\n",
       "      <td>100%</td>\n",
       "      <td>Kakkanad, Ernakulam</td>\n",
       "      <td>Homeopath</td>\n",
       "      <td>100% 16 Feedback Kakkanad, Ernakulam</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BAMS, MD - Ayurveda Medicine</td>\n",
       "      <td>12</td>\n",
       "      <td>98%</td>\n",
       "      <td>Whitefield, Bangalore</td>\n",
       "      <td>Ayurveda</td>\n",
       "      <td>98% 76 Feedback Whitefield, Bangalore</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Qualification  Experience Rating                  Place  \\\n",
       "0         BHMS, MD - Homeopathy          24   100%    Kakkanad, Ernakulam   \n",
       "1  BAMS, MD - Ayurveda Medicine          12    98%  Whitefield, Bangalore   \n",
       "\n",
       "     Profile                     Miscellaneous_Info  Fees  \n",
       "0  Homeopath   100% 16 Feedback Kakkanad, Ernakulam   100  \n",
       "1   Ayurveda  98% 76 Feedback Whitefield, Bangalore   350  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d1f285",
   "metadata": {},
   "source": [
    "### 2) Moving with Place Column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1de4bb",
   "metadata": {},
   "source": [
    "* As, there are some missing values in Place column so I have replaced then with Unknown, Unknown to represent them.\n",
    "* then, we can easily extract city and locality from Place variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "17f2f407",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Place'].fillna('Unknown,Unknown', inplace=True)\n",
    "\n",
    "train['locality'] = train['Place'].str.split(\",\").str[0]\n",
    "train['city'] = train['Place'].str.split(\",\").str[1]\n",
    "\n",
    "#Now, we can drop the Place Column\n",
    "train.drop('Place',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd3bd5b",
   "metadata": {},
   "source": [
    "### 3) Rating Column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3c55c9",
   "metadata": {},
   "source": [
    "* Rating has more then 50% of missing values so first we have to deal with them, so we will fill the rating with respect to Profile column having mean rating, then convert Rating to int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "974f042d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the missing Values in Ratingcolumn with -1% to provide importance to missing value.\n",
    "train['Rating'].fillna('-1%',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8fe2368b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, Extract the actual value of Rating except '%' sign\n",
    "train['Rating'] = train['Rating'].str.slice(stop=-1).astype(int)\n",
    "\n",
    "# Alternate way to do this,\n",
    "#train['Rating'] = train['Rating'].apply(lambda x: str(x).replace('%','')).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "346c7ed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Profile</th>\n",
       "      <th>Miscellaneous_Info</th>\n",
       "      <th>Fees</th>\n",
       "      <th>locality</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BHMS, MD - Homeopathy</td>\n",
       "      <td>24</td>\n",
       "      <td>100</td>\n",
       "      <td>Homeopath</td>\n",
       "      <td>100% 16 Feedback Kakkanad, Ernakulam</td>\n",
       "      <td>100</td>\n",
       "      <td>Kakkanad</td>\n",
       "      <td>Ernakulam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BAMS, MD - Ayurveda Medicine</td>\n",
       "      <td>12</td>\n",
       "      <td>98</td>\n",
       "      <td>Ayurveda</td>\n",
       "      <td>98% 76 Feedback Whitefield, Bangalore</td>\n",
       "      <td>350</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MBBS, MS - Otorhinolaryngology</td>\n",
       "      <td>9</td>\n",
       "      <td>-1</td>\n",
       "      <td>ENT Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "      <td>Mathikere - BEL</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Qualification  Experience  Rating         Profile  \\\n",
       "0           BHMS, MD - Homeopathy          24     100       Homeopath   \n",
       "1    BAMS, MD - Ayurveda Medicine          12      98        Ayurveda   \n",
       "2  MBBS, MS - Otorhinolaryngology           9      -1  ENT Specialist   \n",
       "\n",
       "                      Miscellaneous_Info  Fees         locality        city  \n",
       "0   100% 16 Feedback Kakkanad, Ernakulam   100         Kakkanad   Ernakulam  \n",
       "1  98% 76 Feedback Whitefield, Bangalore   350       Whitefield   Bangalore  \n",
       "2                                    NaN   300  Mathikere - BEL   Bangalore  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df6cf847",
   "metadata": {},
   "source": [
    "#### Now, I grouped the rating in 10 groups (bins of size 10). Values less then 0 (Missing Values) will fall under 0 group. while, 0-9% will be class 1, 10–19% will be class 2, so on and so forth.we will use pd.cut() for this.**\n",
    "\n",
    "* Use pandas cut() when you need to segment and sort data values into bins. For example, cut could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6a5cba7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "less then 0% 3302\n",
      "0-9% 1\n",
      "10-19% 0\n",
      "20-29% 0\n",
      "30-29% 3\n",
      "And so on..\n"
     ]
    }
   ],
   "source": [
    "print(\"less then 0%\",len(train[train['Rating']<0]))\n",
    "print(\"0-9%\",len(train[(train['Rating']>0) & (train['Rating']<10)]))\n",
    "print(\"10-19%\",len(train[(train['Rating']>=10) & (train['Rating']<20)]))\n",
    "print(\"20-29%\",len(train[(train['Rating']>=20) & (train['Rating']<30)]))\n",
    "print(\"30-29%\",len(train[(train['Rating']>=30) & (train['Rating']<40)]))\n",
    "print(\"And so on..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "79f227ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [-1,0,10,20,30,40,50,60,70,80,90,100]   #bins takes 1 fewer value then labels\n",
    "labels = [i for i in range(11)]\n",
    "train['Rating'] = pd.cut(train['Rating'], bins=bins, labels=labels, include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "49fe392b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3302\n",
       "1        1\n",
       "2        0\n",
       "3        0\n",
       "4        4\n",
       "5        3\n",
       "6       19\n",
       "7       32\n",
       "8       98\n",
       "9      280\n",
       "10    2222\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Rating'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0d8dd6",
   "metadata": {},
   "source": [
    "### 4)working with Qualification Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b006406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     2628\n",
       "1     1962\n",
       "3      995\n",
       "4      257\n",
       "5       84\n",
       "6       23\n",
       "7        6\n",
       "9        3\n",
       "8        2\n",
       "10       1\n",
       "Name: Qualification_count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of qualification done by one doctor\n",
    "train['Qualification_count'] = train['Qualification'].apply(lambda x: len(x.split(\",\")))\n",
    "\n",
    "train['Qualification_count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fc5c6252",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Profile</th>\n",
       "      <th>Miscellaneous_Info</th>\n",
       "      <th>Fees</th>\n",
       "      <th>locality</th>\n",
       "      <th>city</th>\n",
       "      <th>Qualification_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>Fellowship in Oral implantlogy, Certified Adva...</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>RCT - Root Canal Treatment Cosmetic/ Aesthetic...</td>\n",
       "      <td>300</td>\n",
       "      <td>Ashok Nagar</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Qualification  Experience Rating  \\\n",
       "2035  Fellowship in Oral implantlogy, Certified Adva...          22     10   \n",
       "\n",
       "      Profile                                 Miscellaneous_Info  Fees  \\\n",
       "2035  Dentist  RCT - Root Canal Treatment Cosmetic/ Aesthetic...   300   \n",
       "\n",
       "         locality      city  Qualification_count  \n",
       "2035  Ashok Nagar   Chennai                   10  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# max 10 qual is done by 1 doctor, let's have a look \n",
    "train[train['Qualification_count'] == 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8cedcaeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1420"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Qualification'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6aab87",
   "metadata": {},
   "source": [
    "* here there are lot of Qualification but most of doctors only has 1 or 2 qualification.\n",
    "* And the data is to much messy like, 'MBA -Healthcare' and ‘MBA’ which referred to same category so here we will take the top 10 Qualification that occurs the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f982c8e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dict of each Qualification with their counts\n",
    "train['Qualification'] = train['Qualification'].str.split(\",\")\n",
    "Qualification = {}\n",
    "\n",
    "for x in train['Qualification'].values:\n",
    "    for qual in x:\n",
    "        qual = qual.strip()\n",
    "        if qual in Qualification:\n",
    "            Qualification[qual] += 1\n",
    "        else:\n",
    "            Qualification[qual] = 1\n",
    "\n",
    "#print(Qualification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "82922a56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MBBS', 'BDS', 'BAMS', 'BHMS', 'MD - Dermatology', 'MS - ENT', 'Venereology & Leprosy', 'MD - General Medicine', 'Diploma in Otorhinolaryngology (DLO)', 'MD - Homeopathy']\n"
     ]
    }
   ],
   "source": [
    "# Now, Extract Top 10 Qual from Qualification\n",
    "most_qual = sorted(Qualification.items(), key=lambda x: x[1], reverse=True)[:10]\n",
    "final_qual = []\n",
    "\n",
    "for qual in most_qual:\n",
    "    final_qual.append(qual[0])\n",
    "\n",
    "print(final_qual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "36d2c173",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode extracted top 10 qualification into train dataset as new column.\n",
    "for qual in final_qual:\n",
    "    train[qual] = 0\n",
    "    \n",
    "for x,y in zip(train['Qualification'].values, np.array([i for i in range(len(train))])):\n",
    "    for c in x:\n",
    "        c = c.strip()\n",
    "        if c in final_qual:\n",
    "            train[c][y] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "16eaf0a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the column which are note required now,\n",
    "train.drop(['Qualification','Qualification_count'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "44880562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experience</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Profile</th>\n",
       "      <th>Miscellaneous_Info</th>\n",
       "      <th>Fees</th>\n",
       "      <th>locality</th>\n",
       "      <th>city</th>\n",
       "      <th>MBBS</th>\n",
       "      <th>BDS</th>\n",
       "      <th>BAMS</th>\n",
       "      <th>BHMS</th>\n",
       "      <th>MD - Dermatology</th>\n",
       "      <th>MS - ENT</th>\n",
       "      <th>Venereology &amp; Leprosy</th>\n",
       "      <th>MD - General Medicine</th>\n",
       "      <th>Diploma in Otorhinolaryngology (DLO)</th>\n",
       "      <th>MD - Homeopathy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>Homeopath</td>\n",
       "      <td>100% 16 Feedback Kakkanad, Ernakulam</td>\n",
       "      <td>100</td>\n",
       "      <td>Kakkanad</td>\n",
       "      <td>Ernakulam</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Ayurveda</td>\n",
       "      <td>98% 76 Feedback Whitefield, Bangalore</td>\n",
       "      <td>350</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>ENT Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "      <td>Mathikere - BEL</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Ayurveda</td>\n",
       "      <td>Bannerghatta Road, Bangalore ₹250 Available on...</td>\n",
       "      <td>250</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>Ayurveda</td>\n",
       "      <td>100% 4 Feedback Keelkattalai, Chennai</td>\n",
       "      <td>250</td>\n",
       "      <td>Keelkattalai</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Experience Rating         Profile  \\\n",
       "0          24     10       Homeopath   \n",
       "1          12     10        Ayurveda   \n",
       "2           9      0  ENT Specialist   \n",
       "3          12      0        Ayurveda   \n",
       "4          20     10        Ayurveda   \n",
       "\n",
       "                                  Miscellaneous_Info  Fees           locality  \\\n",
       "0               100% 16 Feedback Kakkanad, Ernakulam   100           Kakkanad   \n",
       "1              98% 76 Feedback Whitefield, Bangalore   350         Whitefield   \n",
       "2                                                NaN   300    Mathikere - BEL   \n",
       "3  Bannerghatta Road, Bangalore ₹250 Available on...   250  Bannerghatta Road   \n",
       "4              100% 4 Feedback Keelkattalai, Chennai   250       Keelkattalai   \n",
       "\n",
       "         city  MBBS  BDS  BAMS  BHMS  MD - Dermatology  MS - ENT  \\\n",
       "0   Ernakulam     0    0     0     1                 0         0   \n",
       "1   Bangalore     0    0     1     0                 0         0   \n",
       "2   Bangalore     1    0     0     0                 0         0   \n",
       "3   Bangalore     0    0     1     0                 0         0   \n",
       "4     Chennai     0    0     1     0                 0         0   \n",
       "\n",
       "   Venereology & Leprosy  MD - General Medicine  \\\n",
       "0                      0                      0   \n",
       "1                      0                      0   \n",
       "2                      0                      0   \n",
       "3                      0                      0   \n",
       "4                      0                      0   \n",
       "\n",
       "   Diploma in Otorhinolaryngology (DLO)  MD - Homeopathy  \n",
       "0                                     0                1  \n",
       "1                                     0                0  \n",
       "2                                     0                0  \n",
       "3                                     0                0  \n",
       "4                                     0                0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2371f827",
   "metadata": {},
   "source": [
    "* Now, we can see that we are having top 10 Qual columns in our training set which are perfectly one-hot encoded. where there is 2 Qual from top 10 in that row two 1 are there, and it's correct."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c3be9d",
   "metadata": {},
   "source": [
    "### 5) City Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1e5a998f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Ernakulam', ' Bangalore', ' Chennai', ' Delhi', ' Hyderabad',\n",
       "       ' Coimbatore', ' Mumbai', ' Thiruvananthapuram', 'Unknown',\n",
       "       ' Sector 5', nan], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ba48b08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bangalore             1258\n",
       "Mumbai                1219\n",
       "Delhi                 1184\n",
       "Hyderabad              951\n",
       "Chennai                855\n",
       "Coimbatore             228\n",
       "Ernakulam              153\n",
       "Thiruvananthapuram      86\n",
       "Unknown                 25\n",
       "Sector5                  1\n",
       "nan                      1\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove the extra Spaces from the city\n",
    "train['city'] = train['city'].apply(lambda x: re.sub(' +','',str(x)))\n",
    "\n",
    "train['city'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "18300dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experience</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Profile</th>\n",
       "      <th>Miscellaneous_Info</th>\n",
       "      <th>Fees</th>\n",
       "      <th>locality</th>\n",
       "      <th>city</th>\n",
       "      <th>MBBS</th>\n",
       "      <th>BDS</th>\n",
       "      <th>BAMS</th>\n",
       "      <th>BHMS</th>\n",
       "      <th>MD - Dermatology</th>\n",
       "      <th>MS - ENT</th>\n",
       "      <th>Venereology &amp; Leprosy</th>\n",
       "      <th>MD - General Medicine</th>\n",
       "      <th>Diploma in Otorhinolaryngology (DLO)</th>\n",
       "      <th>MD - Homeopathy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2169</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250</td>\n",
       "      <td>Dwarka</td>\n",
       "      <td>Sector5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3980</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250</td>\n",
       "      <td>e</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Experience Rating  Profile Miscellaneous_Info  Fees locality     city  \\\n",
       "2169          11      0  Dentist                NaN   250   Dwarka  Sector5   \n",
       "3980          23      0  Dentist                NaN   250        e      nan   \n",
       "\n",
       "      MBBS  BDS  BAMS  BHMS  MD - Dermatology  MS - ENT  \\\n",
       "2169     0    1     0     0                 0         0   \n",
       "3980     0    1     0     0                 0         0   \n",
       "\n",
       "      Venereology & Leprosy  MD - General Medicine  \\\n",
       "2169                      0                      0   \n",
       "3980                      0                      0   \n",
       "\n",
       "      Diploma in Otorhinolaryngology (DLO)  MD - Homeopathy  \n",
       "2169                                     0                0  \n",
       "3980                                     0                0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are some mis-entry in city column, let's find it out\n",
    "train[(train['city'] == 'Sector5') | (train['city'] == 'nan')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81178e5f",
   "metadata": {},
   "source": [
    "* whereever the locality is Dwarka the city is Delhi, and this Sector5 is area of that so it's misclassified here. which we have to correct.\n",
    "* And where city is nan there we have to impute this to unknown in locality and city both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "47f0b6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['city'] = np.where(train['city'] == \"Sector5\", \"Delhi\", train['city'])\n",
    "\n",
    "train['locality'][3980] = \"Unknown\"\n",
    "train['city'][3980] = \"Unknown\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a621c507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>locality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ernakulam</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thiruvananthapuram</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city  locality\n",
       "0           Bangalore       153\n",
       "1             Chennai       124\n",
       "2          Coimbatore        48\n",
       "3               Delhi       200\n",
       "4           Ernakulam        52\n",
       "5           Hyderabad       135\n",
       "6              Mumbai       115\n",
       "7  Thiruvananthapuram        48\n",
       "8             Unknown         1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see for each city how many locality are there\n",
    "train.groupby('city')['locality'].nunique().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac3e5cd",
   "metadata": {},
   "source": [
    "### Data Visualization & Categorical Encoding\n",
    "#### Before encoding a categorical variable, look at relationship of data with respect to target column To have better understand the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4cbe7f62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0SZIGYGyBnmTdJMcn+VmSU5P8W9++SZKjk/ymf9945DYHJDkjyelJdh1XbZIkDc04W+jXAA+vqnsBOwC7JXkA8BrgmKraDjimXyfJ9sBewN2A3YBDkqwxxvokSRqMsQV6NVf2q2v1rwJ2Bw7r2w8DntAv7w4cXlXXVNVZwBnATuOqT5KkIRnrMfQkayQ5GbgYOLqqfgzcuqouBOjfN+u7bwmcN3LzRX2bJGk1WX/tW7D+Ohux/tq3mHQpWs3GOm2tqq4HdkiyEfD5JHdfzu5Z2l38n52S/YD9ALbZZpvVUaYkzRs7b/vESZegMZmRUe5VdRlwLO3Y+EVJtgDo3y/uuy0Cth652VbABUu5rw9U1Y5VteOCBQvGWbYkSXPGOEe5L+gtc5KsB+wC/Ao4Ctin77YP8MV++ShgryTrJLk9sB1w/LjqkyRpSMbZ5b4FcFgfqX4z4Miq+nKSHwJHJnkOcC6wJ0BVnZrkSOA04Dpg/95lL0mSVmBsgV5VPwfuvZTtvwcesYzbHAgcOK6aJEkaKleKkyRpAAx0SZIGwECXJGkABn361Em4Ye31/+a7JEkzwUBfza7a7lGTLkGSNA/Z5S5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gCMLdCTbJ3k20l+meTUJC/t29+Y5PwkJ/evx4zc5oAkZyQ5Pcmu46pNkqShWXOM930d8IqqOinJhsCJSY7uP3tnVb1tdOck2wN7AXcDbgP8b5I7VdX1Y6xRkqRBGFsLvaourKqT+uUrgF8CWy7nJrsDh1fVNVV1FnAGsNO46pMkaUhm5Bh6ktsB9wZ+3De9OMnPk3woycZ925bAeSM3W8TyPwBIkqRu7IGeZAPgs8DLqupy4L+BbYEdgAuBt0/tupSb11Lub78kJyQ54ZJLLhlP0Rq7hQsXsvfee7Nw4cJJlyJJgzDWQE+yFi3MP1FVnwOoqouq6vqqugE4lL92qy8Cth65+VbABUveZ1V9oKp2rKodFyxYMM7yNUaLFy/m/PPPZ/HixZMuRZIGYZyj3AN8EPhlVb1jZPsWI7vtAfyiXz4K2CvJOkluD2wHHD+u+iRJGpJxjnLfGXgWcEqSk/u21wJPS7IDrTv9bOD5AFV1apIjgdNoI+T3d4S7pOlauHAhixcvZvPNN+eggw6adDnSjBtboFfVcSz9uPhXl3ObA4EDx1WTpOGaOowjzVeuFCdJ0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA3AOOeha4449033mPHHvO7STYA1ue7Sc2b88bf511Nm9PEkaSYY6JJWu+885KEz/phXr7kGJFy9aNGMPv5Dv/udGXssaXnscpckaQAMdEmSBsBAlyRpAAx0SZIGwECXJGkADHRJkgbAQJckaQAMdEmSBsCFZSQNwkZVf/Ndmm8MdEmD8Mzrb5h0CdJEGeiaiE3XvQG4rn+XJK0qA10T8cp7XjbpEiRpUBwUJ0nSABjokiQNgIEuSdIAGOiSJA3ASgd6ko2T3HMcxUiSpJtmWoGe5Ngkt0iyCfAz4MNJ3jHe0iRJ0nRNt4V+y6q6HHgi8OGqui+wy/jKkiRJK2O6gb5mki2ApwBfHmM9kiTpJphuoL8J+Abw26r6SZI7AL8ZX1mSJGllTGuluKr6NPDpketnAk8aV1GSJGnlTHdQ3J2SHJPkF/36PZO8frylSZKk6Zpul/uhwAHAXwCq6ufAXuMqSpIkrZzpBvrNq+r4JbZdt7qLkSRJN810A/13SbYFCiDJk4ELx1aVJElaKdM9fer+wAeAuyQ5HzgLeObYqpIkSStluqPczwR2SbI+cLOqumK8ZUmSpJUx3VHut07yQeAzVXVFku2TPGfMtUmSpGma7jH0j9AWlrlNv/5r4GVjqEeSJN0E0w30TavqSOAGgKq6Drh+bFVJkqSVMt1AvyrJrfjrKPcHAH8cW1WSJGmlTHeU+8uBo4Btk3wfWAA8eWxVSZKklbLcQE+yTVWdW1UnJXkocGcgwOlV9ZcZqVCSJK3QirrcvzBy+YiqOrWqfmGYS5I0u6wo0DNy+Q4rc8dJtk7y7SS/THJqkpf27ZskOTrJb/r3jUduc0CSM5KcnmTXlXk8SZLmsxUFei3j8nRcB7yiqu4KPADYP8n2wGuAY6pqO+CYfp3+s72AuwG7AYckWWMlH1OSpHlpRYF+rySXJ7kCuGe/fHmSK5JcvrwbVtWFVXVSv3wF8EtgS2B34LC+22HAE/rl3YHDq+qaqjoLOAPY6Sb9VpIkzTPLHRRXVaulhZzkdsC9gR8Dt66qC/v9X5hks77blsCPRm62qG9b8r72A/YD2GabbVZHeZIkzXnTnYd+kyXZAPgs8LKqWl6rPkvZ9n+6+avqA1W1Y1XtuGDBgtVVpiRJc9pYAz3JWrQw/0RVfa5vvijJFv3nWwAX9+2LgK1Hbr4VcME465MkaSjGFuhJAnwQ+GVVvWPkR0cB+/TL+wBfHNm+V5J1ktwe2A44flz1SZI0JNNdKe6m2Bl4FnBKkpP7ttcCbwWO7GdrOxfYE6CqTk1yJHAabYT8/lXlevGSJE3D2AK9qo5j6cfFAR6xjNscCBw4rpokSRqqsQ+KkyRJ42egS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNABjC/QkH0pycZJfjGx7Y5Lzk5zcvx4z8rMDkpyR5PQku46rLkmShmicLfSPALstZfs7q2qH/vVVgCTbA3sBd+u3OSTJGmOsTZKkQRlboFfVd4FLp7n77sDhVXVNVZ0FnAHsNK7aJEkamkkcQ39xkp/3LvmN+7YtgfNG9lnUt/0fSfZLckKSEy655JJx1ypJ0pww04H+38C2wA7AhcDb+/YsZd9a2h1U1Qeqaseq2nHBggVjKVKSpLlmRgO9qi6qquur6gbgUP7arb4I2Hpk162AC2ayNkmS5rIZDfQkW4xc3QOYGgF/FLBXknWS3B7YDjh+JmuTJGkuW3Ncd5zkU8DDgE2TLALeADwsyQ607vSzgecDVNWpSY4ETgOuA/avquvHVZskSUMztkCvqqctZfMHl7P/gcCB46pHkqQhc6U4SZIGwECXJGkADHRJkgbAQJckaQAMdEmSBsBAlyRpAAx0SZIGwECXJGkADHRJkgbAQJckaQAMdEmSBsBAlyRpAAx0SZIGwECXJGkADHRJkgbAQJckaQAMdEmSBsBAlyRpAAx0SZIGwECXJGkADHRJkgbAQJckaQAMdEmSBsBAlyRpAAx0SZIGwECXJGkADHRJkgbAQJckaQAMdEmSBsBAlyRpAAx0SZIGwECXJGkADHRJkgbAQJckaQAMdEmSBsBAlyRpAAx0SZIGwECXJGkADHRJkgbAQJckaQAMdEmSBsBAlyRpAMYW6Ek+lOTiJL8Y2bZJkqOT/KZ/33jkZwckOSPJ6Ul2HVddkiQN0Thb6B8Bdlti22uAY6pqO+CYfp0k2wN7AXfrtzkkyRpjrE2SpEEZW6BX1XeBS5fYvDtwWL98GPCEke2HV9U1VXUWcAaw07hqkyRpaGb6GPqtq+pCgP59s759S+C8kf0W9W2SJGkaZsuguCxlWy11x2S/JCckOeGSSy4Zc1mSJM0NMx3oFyXZAqB/v7hvXwRsPbLfVsAFS7uDqvpAVe1YVTsuWLBgrMVKkjRXzHSgHwXs0y/vA3xxZPteSdZJcntgO+D4Ga5NkqQ5a81x3XGSTwEPAzZNsgh4A/BW4MgkzwHOBfYEqKpTkxwJnAZcB+xfVdePqzZJkoZmbIFeVU9bxo8esYz9DwQOHFc9kiQN2WwZFCdJklaBgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS5J0gAY6JIkDYCBLknSABjokiQNgIEuSdIArDmJB01yNnAFcD1wXVXtmGQT4AjgdsDZwFOq6g+TqE+SpLlmki30v6+qHapqx379NcAxVbUdcEy/LkmSpmE2dbnvDhzWLx8GPGFypUiSNLdMKtAL+GaSE5Ps17fduqouBOjfN5tQbZIkzTkTOYYO7FxVFyTZDDg6ya+me8P+AWA/gG222WZc9UmSNKdMpIVeVRf07xcDnwd2Ai5KsgVA/37xMm77garasap2XLBgwUyVLEnSrDbjgZ5k/SQbTl0GHgX8AjgK2Kfvtg/wxZmuTZKkuWoSXe63Bj6fZOrxP1lVX0/yE+DIJM8BzgX2nEBtkiTNSTMe6FV1JnCvpWz/PfCIma5HkqQhmE3T1iRJ0k1koEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQABrokSQNgoEuSNAAGuiRJA2CgS5I0AAa6JEkDYKBLkjQAsy7Qk+yW5PQkZyR5zaTrkSRpLphVgZ5kDeC/gEcD2wNPS7L9ZKuSJGn2m1WBDuwEnFFVZ1bVtcDhwO4TrkmSpFlvtgX6lsB5I9cX9W2SJGk5UlWTruFGSfYEdq2q5/brzwJ2qqqXjOyzH7Bfv3pn4PQZL3TFNgV+N+ki5gCfp+nxeZo+n6vp8Xmavtn4XN22qhYsuXHNSVSyHIuArUeubwVcMLpDVX0A+MBMFrWykpxQVTtOuo7Zzudpenyeps/nanp8nqZvLj1Xs63L/SfAdklun2RtYC/gqAnXJEnSrDerWuhVdV2SFwPfANYAPlRVp064LEmSZr1ZFegAVfVV4KuTrmMVzepDArOIz9P0+DxNn8/V9Pg8Td+cea5m1aA4SZJ008y2Y+iSJOkmMNCleShJJl2DpNXLQJfmkSTrJ7lVVVWSOydZa9I1SVo9DPRZZsmWky0prWZ3AQ5J8gLg7fztug+DleTWSe7cL++ZZJNJ1zRkvm8t37ien1k3yn2+SrJmVV3XW07bAmtX1S/79ZtV1Q2TrnESkqQ/BzsD2wGnVdXxk65rrqqqE5NcAbwDeEFVnTn12pt0bWO2EfCRJKcB9wCOm2w5wzX1P9svPwP4C7B+VX14spXNDiPvaY8C1gG+trr+/2yhzwJJNgf2TrJh/yMfA7wrydcAquqGJPPubzXywn808EFgXeArSZ6XZOMJlzenTLUIkqwP/BQ4FHhRkrtPvZkMsVU19TtV1enAl4CnA4dW1YUebhiPkTB/MfAC4I/AwUmeNNHCZon+nvYY4D3AVavzw/S8C4lZ6s60s8r9I7AP8MSq2hVYJ8k3YH6FepItkqzRX/i3BQ4AHk9bt/9y4JnAPkk2nGSdc8XIB6MnAP8NHF5VLwU+T2u1LkhyO+AFQwr1JVqK9wJOBJ4NLEzypKr6S//ZOhMsc5D6IY0HAbsAdwOOBb7gc33jh+pXAS+sqm8leVCSvZPcc1Xv2y73CeothOuq6jtJNgCeBmwGXA9QVQ9P8r9JjquqB82jbve3AwuS7FZV5yTZB9gYOAi4I/AYWhhdkeSjU2/MWroe5o8E3gC8uKp+3197bweuBn4EXAO8bCoAh2AkzF8JPJz2BnpOkqtpPWBX0s4X8eAkz6mq6ydY7pw2+uGpuwH4M/D/aM/xU6rq+t67dmJV/WQihU5AkvWAe1TV8UnuCmwA/Bh4YpL9gWuBBcBtgJ+vymPNixbfbNTXqv874L5JdgXWBz4M/AF4YG8xUVW7AJVkp0nVOlNGukefDhTwwT5+4Cxgc+DC/qZxLu2wxE8N86VLslWSN49sug+tdX5JkqcDXwBe1Lc9C3h6VX1zxgsdg9FWYJInA08EntTD/LbA12m9PC+jfYh+p2F+0y3RE3LXPibjMuBM4MXAs6vqT/11tx+weHLVTsTGwO5JPgV8CDgH+CbwS+Bd/f3uEGDnJOutSi+ZLfTJCa01/lzg7sDTqup7SdagvcFWkm9W1ZlV9eBJFjpTRt4U7kd7M3gy8I0+ruBE4JoknwXuBLykqk6aWLGz3++AI5NsU1XnAr8Bngo8H/gY8EPa4LBNquoHkytz9eotoNckeXbv0bqBNmbgyUm2Ax5B65XYF3gGrYfs8knVO9ctEeb7Ay8Hjk9yNK11fgvgO0l+COwI7F1V502s4AmoqguSXAi8FDiyqi4GvpXk2H4o9SHAfwCvqqqrV+WxXPp1gpLcHvgy7djwe4Dv9j/ww2mtp2/RWu1/HlJX6PL040ifpYXPxcBHgD/Rwv02tDfk31bVsRMqcVbrr6kXVNWr+/UvANdW1VOSbAmsVVVnJ7kLcDjtg+QvJ1fx6pdkM9qH5N/0TS8G7kcLmLNpx9E/42yJ1aePz9gNeDPwUODewNlV9Z4k9++7La6qcyZU4owbGbuyGa0H9uHAzrTn5U19nx1oHyyPraqvrPJjzpOcmDVG/sgbVNWVSRYA/0DrEv1OVX2mt9KfCpxaVT+baMEzrLewXg88a2rMQNpUo3OBx9o1unxJbkELrY9V1Uv7wMGpD4XP7Ps8jjYeYWFVfWlixa5GSW5eVX/ql9eiBcvDaa+ZxUnWqqq/9C74NwCPq6qzJ1fx3Jbk72m9G99Lm6VzHHBS/+B4M+AJwINpH8r/u3fBzxv9UOENfTT7wcDDgPNpgf4iWq/RN2jP02Gr67XoMfQZNBLmjwaOSPKvtOPohwG/pg3OeTvtvPA/mA9hPnW8KMmafVzBxcAWwH1Hdns7bQ769jNf4dzRZwZcDtwe+Ick/1VVV9BmTqyX5ON916uB/arqS0MY1Z5kXeD5fbTw84BDqupVtJD5YpLb9DB/PPCvtF6JsydY8hAEODfJ5lW1GNgf+Lskz6uqG6rqc8DxtAFg8yZn+gj2qVlJ96et97BXP8ywAe3Q4XtoDbijgONX52vRFvoM66ON30b7B9gH2Al4b1Ud2t9wdgH+t6qOmmCZM6r/3nvS5pm/lTbN5Z9oA7YA9gAOqKpTJlPh3NFD/foktwROAr5RVS9KcnPaoYxLq+oZk61y9etvnt+itYIe1I9TkuRdtA+He9FGE69ZVRdOqs65LiOLXCW5Ne1w4b5V9YXean8H7QPVoX2fDfuHysHrvWP/DrypzyTZhTZu4EfADsDzaB8yD6KNEbpDVf12tdZgoM+cPn3h2bQ3ntvS/vj/jzba9iNT/wR93yWngQxSfxN4C+0N9z+B21TVg5M8hRbsd6Z1H6/y8aWhGun5eTBtnMEfq+rrPdRPpK1E9ZLeerhLVZ040YJXk5Hf+2a0FuP/0MZY7FdVXx/Z773AXYFHecjmpltiANw9quqUtMVi3gK8ovf4PIzW4/iGqvrIxIqdgP7/tSGtYXJn4BRao+TmwMeBU4F/pq0DMZ5DXVXl1xi/6B+aRq6vQZtz+HXgXn3bl2if4m635P5D/6J9wHkYrRX+fdqnVoCbTz1fS3se/frr66l/fzTwK9oMiatpc66hjTJeDLx/0rWu5t87I5fvDdyyX74/cBbw1H794f1/7laTrnkoX8ArgK8Bt+3Xn0RrcT62X3/w1P/xfPwCngN8D9ixX1+/f78jrdfs/uN6bKetjVnVjcfMH0VbAvFTVXV6kuuB65Ls2Le/vObRcb0k9wVOo803/3faIhR7VdV5fcTsI5K8um+n+n+EmiS3r6qzqnWvbwX8C22Azda0ea6vTLJRVb0lyZ1ox+wGYYmW4oto04EuSfJR2hLB+wIfTvIgWtjsXG0tA62itDUzngw8pqr+AFBVn+3vZ59M8tQa6R2ZT5I8gHZI64NJbgDelOS9VfXVPjhuqjv+x+OqYd4MVphpI4O97kobiHMmsDZtTuY2tBb6O2ldMZ+pqt8s674G6nW0ww0f7dcXARf1405vAb5SVX+q+bM63sp6Qdp8fapqEW3qy/rAW6vqLrT55gcmeWFVXV5Vxw5hABz8zXoFu9OWF70nbezFfYD9q+o7tJkjJwEPNsxvuiT3SLJwZNPNaaPZ/5Bk3SQ36+M2vgA8BThjIoVOyMj7/PbAa4DPJ7lTtRPRfJo2WHNXWu/j86qNNRjb/6Et9DHpLfMHAm+iHR9/P0CSxcBngAfSBilRbeGBQR8zX8rv9xLaYgq3pq3TfiTwfloL8xXz9VP+dFXVq5NsneRnVXWvqjqrv6mc3nf5A201uFNGbjOY11fa3N59gS2r6hrgy71VtHsPoA+WZ/daHf4AfCxtLfxf0BYs2irJOlX1Z4AkT+v/35+cZKGT0N/nH0trtH0UuAI4PMkzq+rDaVMoX0Fb+vakqduMqx4HxY1Rb4l/DTi9qp44MgL5Y7Q5wPNqtG3vAr0cOK9/wn8f7XSo70lbrrOAW1TV7yZa6Cw2MhBsvaq6OslXaMeHH5Bka1pL9Vra7In9qur7Q/iwuEQ3+5pVdV3voTgQ+ElVva7/bA/amIw3TnUJa+WlLTX9pP7BcW1aT+LiqvqnJB+gnY72S8AtaYc8HltVv55YwRPUZ1L8sKqO6M/VP9HWEXlaVZ2Rv67WOP5a5vj/+awy8mZ7F1rvxxnAerR1x79KO763GXAEsFtV/Wpixc6w/gbxZtq5kX9De05+ReuleFr1KWlDCJ9xGXl9PQ7YFXh1VV2V5NPAVlX1d2nLmz6EthrVMRMteAySPJ82uOgSWk/XZrSV4M6sqn/t+6xfVVdNrsq5L8m2wCeAo6vqX5LcGXg17XwKr0s7NepWtB62g2pgqw1OV+8+/y/aaowv69vuCrwPuA54zkyOjTLQV7O0OdWvBy4CrqSdFexo2qpAoQX7D6rqGxMrcoYluRttMYW9gHVoU4jeSQvzp/Sfvc8gX7G0dZ8Ppp017Xsj248Atq+qe4xsG9SHoyT70kYQP4e2+NLraW+cO9AGBR5fVf8xtN97Ji3RE/IPtDUzPlJVb+0fFl9Pm+v/uv7hcu2qunaCJc+okQ/V96E11n7Xv74OfL6q3twHx+1JG9Py7ao6Yqbq8xj6apRkY9rAiOfRps7cizbv8HzgcbTu93Wnwnw+vPH03opXAWdU1SV986K0OdO70ZZ0PWfoz8Nq9GDgE9WW3FwLoKr+UlVPTfKZJA+qquP69sE8p2kL49yDtmzmA2inn3xftRXgTqEt53oxDOv3nmkjYf7PtEVRvg08Lcktq+qAJP9B62n7T2AhrcdtXhgJ88fSxv+cTFvV8se0GSZfS3J32vKuj6F1u285kzUa6KvXWv37BVV1eZKf0loSD+nHMvcAfpDk0qr6z6G/8STZlDaF6grgjknuV/08yP345qeSHFFtmcTBf7hZTabOnQzt/fe6fjhjUVU9eYJ1rVa9NXgr2qjqk6vq0iTnAO+mdW8+qu/3Olp3+6cmV+2wpJ1f4qnAntWmkd4BOCzJAdWmQb6GdsKkefHhKclGtPUefp9kA9qYgZdW1XeTbEE7gdRZtDUQtgauAralhfyeM1mr09ZWwciUha37QJ2LaavAHZy25OGVtK73bZOsW+1MQw+gTWcYtP5CP5g2//6VtNHXT0o7u9CNqk9Lmw9vDCtr5PX1oCS7pK3C9RFg7979vFbv3vskraUwCL0FdATtmO0BwM97y+eXtG7MdydZO+1EK3vSWkq6iaZeZyOupx3/nWqgnA18DnhVkldX1W/ny4DeHuALgecmWdDf06+lrR1Cfx4OAe5ZVVf3gYHrAnvTTjB1+jLueiwM9FXQu18eQ3tDfVOS/6aF9SLgu2kningtcERV/bmPcj+vqs6cYNljs8Qbww20cQNPoq3W9VraPPxn9+NPWoGR19chtMFfXwTuQjt882LaspLvBl5Ww1nOdTfa8fB/rqo9quqRwKG0E1mcSTtRz1P69RfR3jTn5YCs1WGJY+b3T5uZMzX259NJbtU/dP+ONoPiyMlVO/N6gP+cNvhvr7Rlhr8DfLT3ZEBbiXDbtKW9oX0AellN4ORadrmvgt5qeAuwO+0N9g7AWVX1yiT70aZhPX9qtHENfB3pHkAPAX5aVRcl+RpwDfBM2nKk/0p7vv40wTLnjLSTPbyIFuD3ps0KOLt3gz6c9oF8o2rnN5/zhyySbEIbNPr4qvpO79X6c1X9W9ophb9CW0Tm67RW0HVVdekES57zRsL8xbRBq8fQzme+Oy3EftD/jx8LPHImR2xPWvqJaKrq8CTX0k6c9ULaqPa1gB8n+SBtueWXVZtGmv4+f+VEap7j7wETMTI44n60lap+Shsk8vSq+m2SewM/H3qAL6m30N8M/CNwx6q6one9v4Z2xqs30c4k5+pv05A2p/X/0VqmT6KtNHV62olrzqyqEyZa4Bj07va3Ag/rxyzXqbZwDEm+Q1sieRC9EZM0Ojo9bQGsf6eF9puBbatq9/6zv6c1/M6qqnmzCtzIe/yWwMV98OVDaR96Tq2q9yZ5FG3Wzh+mBqJOmi30lTDSClqTNrrzQtoSm+sCO1TVZWlLlz6PdnrUebNAStq81ddV1T/2luWJSe5TVRcm+QnthX+JYb5sS7yJXFJV1ya5lDbFb5uqWtQHwL2W9qFpcKrqK2krvh2fZMdqCxCtVVV/AS6j9fhoFaSdW+IxSd7ZD/9dBXyT9r51N1rrfOrwx/dqHs7p7/+Hu9FGs5+W5Fa0Neyvp436fzlwWFX9fpJ1LslAXwn9j/xI4OlJvkmbX/5W2iIfT0hyAe1ct2+oebLa2ciHnDX61fWrav+01aROSvJO2uGI51TVTyda7Cw2Eua70s4pfVIPtlfRzpj2uSRfpy2T+6/Vl5Ecoqr6Wu8CPmEk1PcGNqdPTdNN048B7wY8HfhNf02dQzvr4RpVtV3f7x9pwf6jSdU6SWknNHobrWF2Mi3Yf0hbgXEj2tkNNwBmVaDb5b4S0hZIOYw2CO7RtGN6J9KOp7ycNqf6q1X15SEc05yOJJv10f2kne3qhqrat19/Ce38wD+tqq9Nrsq5Ie3Me0+nrcF+Ie3Y3INpc1ofTTvz3GVV9YP58PrqLcmDaIMCn0VbyvYXk61q7ktbafAg2glDTqMN5L0jbYzLcbTlmZ8KPLv6Co5Dl7amQ3qv2Lq0Qaj/UlXPG9nnY8BxVfX+JJtX1eJJ1bssBvoKjLSctqUtdbhtVX2oHz9/Ie0f4hOj0ziG/GabtgTkvYHv0gZ+fAg4njZFbQPaNKNDlxx5POTn5Kbqr6nb0BamWJPWUjqzqu7fB4HdQDthzbdrns6zTlut7HPAvavq1EnXM1elnWL3iqr6Y79+IG3Q2xq0KaWfp73enk87VPjF+TJ7oIf5zrTnYiPaYYfP0j7ovLbameRIO+nPtVX1rokUOg12ua9AD/NH0FrlJwM7JPlSVf0k7RzAC4F1k7yjqga92EIf9LYf7eQDR9DmY76Xdjahm9O6o0JbivNv3gyG+pzcVGnrPX+aFtgnVtWfenf7t5O8pKoO7vv9nrZYxbzUe7s2mvrf0spLW/vhJOCzSY6pqvfR1svYjDYl65W0QZcfqapXTqzQCekD3tamHd66M/CCqjo1ySuAjye5PXAp7RTFL59gqSvkPPQV6G+8ewGPq6pdaWcd+mzvaj6JNgr5qPnwhtND+Ru0FvkbaCt53Z827/JTtDEF6wD/nLa6lJaiD3r7OHBgVR088kHwJNqUobckeX8fYPko2pvuvDUf/rfG7FrgY7R1IF6S5GXA39MGV24IvJHW67ZXD7Z5ozdSoB0fhzZjab0kG/fDhI+nLdp0b+A1NctPeGSX+zL0wSPr0BbuuA9tINJX+8/+E3gk8OiqumhyVU5Gki/QWpX/nrZi2XtpvRefoh3n3Q74QlXNywE1K5K24tuLqy/V2qe/7EIbxf0Z2voFP6ENSNq3qhann3p3IgVrzuvjMx5MWyvjZ7Tj5O8F3lPthDZ3pY3PmBcrwMHfHE5dUFWXpC0M83Da9L2fVtWhaecQ2LT66U9n+6FDW+hLGPnEtlZVXU1bfvL7wM59UBxV9WrgWNo/x7zRP+RAm6u6dpJ70brr/ok2j3UBbUDX5rR/Ci3dOcA1SV6Z5HPAc2krwG1Cay2dRWup34vWFTr4RYk0HknWBKi2ZsGPaKed3Z62gM8DgcP7z385n8Icbjyc+mjg80n+HXhuVX2FdkKaHZK8DzgF2Hj0NpOpdnpsoY9YYurQC2nrsP+Y1j36Ltpgkc/WBJb0m02SbEY7V/KDaCskvb9vv0W1k9J8mdY95YjkpeijaJ9MC+11aK+tk2mDCj8GvKqqft3nnB9OW5Tnstn+ZqLZIcn2wP2q6rB+/cbenf6a2oM2AOyQmkcrvy2p95QdTJtBsQ9t+vEXq51V7v60wxI/m0szdAx0/s96xn8HfIA2hePKfvkQWvfU+4DzgLdWW+N33upvDO8B9qi2eEzgxk+9UwuBiBvPHLZbVR2cvpzkMva7FzC1lOTp1c5Cd+NKadKK9Bb502iHBL9ZVR/v20dDfUfaAK8/0cbCXD/fPiz2ke17AL+gzV46kLai5euBH1fVa0b2ndXd7KPmfZd72gL7r05yy77pVrS55J+vqqNpI7f3pp0O7420E63M6zDvfgqcCjy4h1SNvOivm2Bds0qf5vdJ/nqu7v8T5kk26HODPwa8qU8Xmnour52pWjX3VdV1tPUxvg48pC/IQ1Vd36dCTnW/fwx4V1VdN1fCalVNNTr6h5u/VNWRtLVDnksb03IMrcH2d72XA5j93eyjnLbWjl3eAXh5krfTTiLy91M/7IMljqGdBMMu5K5P9Xg/sOaSITWX/gHGqYf5V4D/rqoj+ra/+bTfxyXchbZ4zGtriUWJfC41HUu8Zi5N8lVag+1hSaiqj/ZQX7OH+GBXGlyW3nv4ONoZH4+nre/w4yTrALfpvbO3oB1L/81Ei72JDPQ2UORPtDOCvaCqDkpyQpITaMdVtqCNQP7kBGuclarq+EnXMFv1T/gfoX1AXJBka2DRkgHdu9VPAn7dxx/Mme49zQ5LHDL8B9rAtyuq6uO9UfqwJDdU1cd7C35eSjtR1AuBLwPrA69N8hraaYj/mTYo9a1zNcxhnh5Dn1oooP66atKatHmIlwPHVNWbk/wbcFvaoh7v6KMfpRVKsgFtjf8fV9XHknyc9tp6S1WdN9nqNFR9fvlTaau+PQr4z6o6OsnTaeuyf76qDp9giROTdqrrBwAbVtU7007VuxdtINzrquoXSTbpvRtz9kP1fA30XWjzfTfu3TBfoJ2i8lO0tbQXA++stq7vLWw5abr6mIyDaOH9677t5rTBlX+ktQAMda2yJVrm96cN7NoVeAstvIr2Ovx62il3v1fzbGoa3Dia/X3A+bSptU+oqjOTbEQ7Kc2utIGEc34mybwMdLjx1ICHAL8BflRVb+jbHwHsSZui9kbayUY85aeWa2TK49a0OfkAL6y2lsHUVLX/oS0h+bbqC1VIqyrtnOVr0AapPpJ2at3H0AL+cbQBX1+fXIWTk+QewNuBl1TV6Un+h7Y89X9U1Vk91G8xlP/HeTvKvb/Anwc8ghbcU6Mgv0VbY/sTffCIYa7pmBqPsoi2GNE1wP/01aeoqj/TRtNuSTt2t85EqtSg9IVRXgYc31vfmwNvr7Zc7gW097JfTa7CyenTQG8DTK2SB+09/3ra8sp3qKrLhhLmMI8DHaBPU3g88Oskm9ZfHVPz5ExDWnVp69Z/JcmzgAdUWw74TbQpMB/urfOpUH8G8L5ybrlWUZI9aMeBP1FVl/fN6wOvSvKvtPUM/qvm4eIx/RDEEbTTEC8Enphkj/7+vh9tieoNJlnjOMz7Ue5V9dW0s6admuQuVfWHSdekOef+wMOAdYGrkyymrcX+TeCJwDuSvKyqru2hfvKkCtXctZRxPNcC9wAuTPLlqvpTVb0hySW0sx8+vaoWTaTYCUpyF9o4gpdU1c+TnE+bbbJvX/TqyKrad6JFjsm8PYa+pCSPBa6qqmMnXYvmht4yv39VfSrJi4FHA/9Be5PdjLYS1Zm09djfD7xorg+60WQsMQDuEbSBuxfTDuG8mzb16svloldTrfP/AX5VVXv2bRvTRvrvRZuOfMkQD6ca6EtwNLumoy8acwRt1PrhfdvraWH+tqr6SdoZrDajzXE9uGb5qRc1+yV5CS2UjqLNyHko/ZwKtHNOHN57geaNkQGptwXW6CPY70s7d/mZVfUvfb9NgHWr6oJJ1jtOBrq0kvqiMYcAH6uqD6atC32PqjqpH7vcibZG9k9HWwF+WNSqGBmxPdUTdDfauRSu7z2ML6R1s1++nLsZpCS7087vfh5wFW3q6Aa05+SSqnrVBMubMfN6UJy0snp4H0VbieuDfX3sr9NaSlTVm2iLFB0E3Ddd/5lhrmmbet1MfaedI+FntB6fewFP7WH+pL7w1VPmU5iPPD93oZ3CeRfgG7Tn5mzgBNqhrq2T3GlCZc6oeT8oTloZ1dawfxptVPv+tHNKn1xV7xzZ58Ak1/TLhrhW2hK9ORsBf6BNP9sJ2Laqtur77QvsneTYqvr9JGqdaSPPTWiL59wAHAPsSzsM8cSquirJfarqh0lOq74q6NDZ5S7dBGmnoDyaNvDm70a2PwDYoareN7HiNKctMQBuf+ApwPeAj9Kmpb0BuIIW8HsAz66qUyZU7ozqLe1nAbekLaZzEG0FxvcBd6Qdcvh1Xw30bcDuVXXOpOqdaXa5SzdBtVNQPgy4S5LnASR5IHAocMYES9McNxLme9DWZH8DbTT7PrRAfzZwCm2U+zPmUZjfGfgcbbXF39Ja5j8EtqEt5X0BsHv/EPRu4PXzKczBLnfpJquqnyV5JPDVJDvQRri/pqr+d7KVaa7rA+D+nTY74tgkZwAvoS3lem1VHTTRAmdYH4j6Cdopho8a2X4R8CXgvrSzzO0IbEWbg/6t+TYQ1S53aRUluR9tyeBnVdUXJlyO5qAlg6ef5Of1wENorfDTkmzet/2eNl3y6slUO/OSPAj4blXdrF9fr/56noR3ARvTDj0Mbm75yjDQpdUgyQZVdeV8axFo1S1xzPzRwIa0lQavoS0V/EDaKT5PS7IZQFVdPKl6J6U/N+8Fdqqq3ydZt6r+nOSpwOOr6hkTLnHiPIYurR5XTboAzU0jYf4S4F9o88uPAe5A62b+IXBwX5r64vkY5gBV9TXgxcDxaecun1pA5xrgsiRrjUzxm5cMdGk1mHpTtnWu6eprGExd3o7Wvb4z7bzdZ1XVcX1Vs48BXwXm/bKuI6F+Atw4UO6twJeq6i/z/f/PLndJmmH9mPCdaKsJ/jTJzYFX9m0LgMf0RWOeTVvI6NL5Hlajevf7Z4GzgFdV1VcnXNKsYAtdkmZQkt2Ag2krv92yb14X2BTYFtivh/nTaeuR39ww/1u9pf442tQ0w7yzhS5JMyTJQ4EP0kau/3hk+1a0U3y+r39fA7h73+8Xk6h1rnAg6l8Z6JI0Q5K8jDbU4t0j294G/CPwItoZ/HYCNqd1x587iTo1N7mwjCSN2UgrclvaUqVT2x9N62p/HPAp4Mqq+vJkqtRc5zF0SRqzkS7hLwD3T3Kffv1/acfMvw98gDYHXbpJDHRJmjk/Ar4P7JVkpz7V6tp+Br9HAz9e/s2lZfMYuiTNoCRbAs8BHg78lDYI7snAE6rqtEnWprnNQJekGZZkPeA+wCNpC8kcW1W/mWxVmusMdEmSBsBj6JIkDYCBLknSABjokiQNgIEuSdIAGOiSJA2AgS7NY0muT3Jykl8k+XQ/jefK3P5TSX6e5J+TvCnJLn37sUl2HE/VkpbGtdyl+e3qqtoBIMkngBcA75j6YZI1qur6pd0wyebAA6vqtjNRqKTls4Uuacr3gDsmeViSbyf5JHBKknWTfDjJKUl+muTv+/7fBDbrLfwHJ/lIkicveadJHpXkh0lO6r0AG8zkLyXNFwa6JJKsSVtL/JS+aSfgdVW1PbA/QFXdA3gacFiSdYHHA7+tqh2q6nvLuN9NgdcDu1TVfYATgJeP9ZeR5im73KX5bb0kJ/fL3wM+CDwQOL6qzurbHwQcDFBVv0pyDnAn4PJp3P8DgO2B7ycBWBv44WqrXtKNDHRpfrvxGPqUHrxXjW5ahfsPcHRVPW0V7kPSNNjlLmlFvgs8AyDJnYBtgNOnedsfATsnuWO//c37fUhazQx0SStyCLBGklOAI4B9q+qa6dywqi4B9gU+leTntIC/y7gKleYzz7YmSdIA2EKXJGkADHRJkgbAQJckaQAMdEmSBsBAlyRpAAx0SZIGwECXJGkADHRJkgbg/wMwT2YN0GTmQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.barplot(x='Profile',y='Fees',data=train)\n",
    "plt.title(\"Doctor Consultant Fees wrt Profile\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8041a115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(x='city',y='Fees',data=train)\n",
    "plt.title(\"Doctor Fees rate in different city\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "876bb59b",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "* As Delhi, Banglore, Hyderabad, Mumbai and Chennai are in list of Tier 1 city so, as usual the doctor consultation fees in these metripolean cities are very high as compared to Tier 2 and Tier 3 city.\n",
    "* Doctor Fees with having profile as ENT Specialist and Dermatologist is very high then Homeopath and General medicine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4b79a7b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode Profile and city\n",
    "train = pd.get_dummies(train, columns=['city','Profile'], prefix=['city','Profile'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e236e6d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experience</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Miscellaneous_Info</th>\n",
       "      <th>Fees</th>\n",
       "      <th>locality</th>\n",
       "      <th>MBBS</th>\n",
       "      <th>BDS</th>\n",
       "      <th>BAMS</th>\n",
       "      <th>BHMS</th>\n",
       "      <th>MD - Dermatology</th>\n",
       "      <th>MS - ENT</th>\n",
       "      <th>Venereology &amp; Leprosy</th>\n",
       "      <th>MD - General Medicine</th>\n",
       "      <th>Diploma in Otorhinolaryngology (DLO)</th>\n",
       "      <th>MD - Homeopathy</th>\n",
       "      <th>city_Bangalore</th>\n",
       "      <th>city_Chennai</th>\n",
       "      <th>city_Coimbatore</th>\n",
       "      <th>city_Delhi</th>\n",
       "      <th>city_Ernakulam</th>\n",
       "      <th>city_Hyderabad</th>\n",
       "      <th>city_Mumbai</th>\n",
       "      <th>city_Thiruvananthapuram</th>\n",
       "      <th>city_Unknown</th>\n",
       "      <th>Profile_Ayurveda</th>\n",
       "      <th>Profile_Dentist</th>\n",
       "      <th>Profile_Dermatologists</th>\n",
       "      <th>Profile_ENT Specialist</th>\n",
       "      <th>Profile_General Medicine</th>\n",
       "      <th>Profile_Homeopath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>100% 16 Feedback Kakkanad, Ernakulam</td>\n",
       "      <td>100</td>\n",
       "      <td>Kakkanad</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>98% 76 Feedback Whitefield, Bangalore</td>\n",
       "      <td>350</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "      <td>Mathikere - BEL</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Experience Rating                     Miscellaneous_Info  Fees  \\\n",
       "0          24     10   100% 16 Feedback Kakkanad, Ernakulam   100   \n",
       "1          12     10  98% 76 Feedback Whitefield, Bangalore   350   \n",
       "2           9      0                                    NaN   300   \n",
       "\n",
       "          locality  MBBS  BDS  BAMS  BHMS  MD - Dermatology  MS - ENT  \\\n",
       "0         Kakkanad     0    0     0     1                 0         0   \n",
       "1       Whitefield     0    0     1     0                 0         0   \n",
       "2  Mathikere - BEL     1    0     0     0                 0         0   \n",
       "\n",
       "   Venereology & Leprosy  MD - General Medicine  \\\n",
       "0                      0                      0   \n",
       "1                      0                      0   \n",
       "2                      0                      0   \n",
       "\n",
       "   Diploma in Otorhinolaryngology (DLO)  MD - Homeopathy  city_Bangalore  \\\n",
       "0                                     0                1               0   \n",
       "1                                     0                0               1   \n",
       "2                                     0                0               1   \n",
       "\n",
       "   city_Chennai  city_Coimbatore  city_Delhi  city_Ernakulam  city_Hyderabad  \\\n",
       "0             0                0           0               1               0   \n",
       "1             0                0           0               0               0   \n",
       "2             0                0           0               0               0   \n",
       "\n",
       "   city_Mumbai  city_Thiruvananthapuram  city_Unknown  Profile_Ayurveda  \\\n",
       "0            0                        0             0                 0   \n",
       "1            0                        0             0                 1   \n",
       "2            0                        0             0                 0   \n",
       "\n",
       "   Profile_Dentist  Profile_Dermatologists  Profile_ENT Specialist  \\\n",
       "0                0                       0                       0   \n",
       "1                0                       0                       0   \n",
       "2                0                       0                       1   \n",
       "\n",
       "   Profile_General Medicine  Profile_Homeopath  \n",
       "0                         0                  1  \n",
       "1                         0                  0  \n",
       "2                         0                  0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d7f707b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "13115fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#change the name of some columns to use it in correct way.\n",
    "train.rename(columns={'MD - Dermatology':'MD_Dermatology','MS - ENT':'MS_ENT','Venereology & Leprosy':'Venereology_Leprosy',\n",
    "                      'MD - General Medicine':'MD_General_Medicine','Diploma in Otorhinolaryngology (DLO)':'Diploma_in_Otorhinolaryngology',\n",
    "                      'MD - Homeopathy':'MD_Homeopathy','Profile_ENT Specialist':'Profile_ENT_Specialist',\n",
    "                      'Profile_General Medicine':'Profile_General_Medicine'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff81103f",
   "metadata": {},
   "source": [
    "#### Miscellaneous_Info & locality\n",
    "\n",
    "* Miscellaneous_Info is to much messy column and it consist the data which we already have like doctor address(city & locality) and Rating\n",
    "* locality and city are approximate have same relationship so better to move with only city\n",
    "* so drop the Miscellaneous_Info and locality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "996a8b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Miscellaneous_Info','locality'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5abea6db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experience</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Fees</th>\n",
       "      <th>MBBS</th>\n",
       "      <th>BDS</th>\n",
       "      <th>BAMS</th>\n",
       "      <th>BHMS</th>\n",
       "      <th>MD_Dermatology</th>\n",
       "      <th>MS_ENT</th>\n",
       "      <th>Venereology_Leprosy</th>\n",
       "      <th>MD_General_Medicine</th>\n",
       "      <th>Diploma_in_Otorhinolaryngology</th>\n",
       "      <th>MD_Homeopathy</th>\n",
       "      <th>city_Bangalore</th>\n",
       "      <th>city_Chennai</th>\n",
       "      <th>city_Coimbatore</th>\n",
       "      <th>city_Delhi</th>\n",
       "      <th>city_Ernakulam</th>\n",
       "      <th>city_Hyderabad</th>\n",
       "      <th>city_Mumbai</th>\n",
       "      <th>city_Thiruvananthapuram</th>\n",
       "      <th>city_Unknown</th>\n",
       "      <th>Profile_Ayurveda</th>\n",
       "      <th>Profile_Dentist</th>\n",
       "      <th>Profile_Dermatologists</th>\n",
       "      <th>Profile_ENT_Specialist</th>\n",
       "      <th>Profile_General_Medicine</th>\n",
       "      <th>Profile_Homeopath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Experience Rating  Fees  MBBS  BDS  BAMS  BHMS  MD_Dermatology  MS_ENT  \\\n",
       "0          24     10   100     0    0     0     1               0       0   \n",
       "1          12     10   350     0    0     1     0               0       0   \n",
       "\n",
       "   Venereology_Leprosy  MD_General_Medicine  Diploma_in_Otorhinolaryngology  \\\n",
       "0                    0                    0                               0   \n",
       "1                    0                    0                               0   \n",
       "\n",
       "   MD_Homeopathy  city_Bangalore  city_Chennai  city_Coimbatore  city_Delhi  \\\n",
       "0              1               0             0                0           0   \n",
       "1              0               1             0                0           0   \n",
       "\n",
       "   city_Ernakulam  city_Hyderabad  city_Mumbai  city_Thiruvananthapuram  \\\n",
       "0               1               0            0                        0   \n",
       "1               0               0            0                        0   \n",
       "\n",
       "   city_Unknown  Profile_Ayurveda  Profile_Dentist  Profile_Dermatologists  \\\n",
       "0             0                 0                0                       0   \n",
       "1             0                 1                0                       0   \n",
       "\n",
       "   Profile_ENT_Specialist  Profile_General_Medicine  Profile_Homeopath  \n",
       "0                       0                         0                  1  \n",
       "1                       0                         0                  0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9daa6090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Doctor Fees wrt to Experience\n",
    "plt.figure(figsize=(10,8))\n",
    "ax = sns.lineplot(x='Experience',y='Fees',data=train)\n",
    "ax.set_title(\"Doctor Fees wrt Experience\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f384686d",
   "metadata": {},
   "source": [
    "* most of us assumed that there is some association between the experience of the doctor and the fees they charged. Indeed there is, but it might not what we expect it to be. The average Fees increases from 25 years and is at peak near about 30 to 35. And Average Fees Decreases as there in further increase in Experience."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5caeed2",
   "metadata": {},
   "source": [
    "#### OBSERVATIONS\n",
    "\n",
    "* the Rating is interesting column, we have grouped the rating in 10 bins, eg: bin5 will be rating of 40-49%.\n",
    "* As, we can see that High rating does not relate to high fees charged(infact low fees can be reason of high rating) and where fees are charged so high, there rating is between 30-60%.\n",
    "* The median of experience in bin 4 & 5 is 27 and 31 years respectively. whereas in highest rating of 10th bin the median experience is 14 years."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd1eb879",
   "metadata": {},
   "source": [
    "### TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "48401466",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EXPERIENCE COLUMN\n",
    "test['Experience'] = test['Experience'].str.split().str[0]\n",
    "test['Experience'] = test['Experience'].astype(int)\n",
    "\n",
    "#Place column (Extract city and locality in diff column)\n",
    "test['Place'].fillna('Unknown,Unknown',inplace=True)\n",
    "test['locality'] = test['Place'].str.split(\",\").str[0]\n",
    "test['city'] = test['Place'].str.split(\",\").str[1]\n",
    "#drop place column\n",
    "test.drop('Place',axis=1,inplace=True)\n",
    "\n",
    "#Rating column\n",
    "test['Rating'].fillna('-1%',inplace=True)\n",
    "test['Rating'] = test['Rating'].str.slice(stop=-1).astype(int)\n",
    "\n",
    "# group rating in 10 groups, missing_value to group 0\n",
    "bins = [-99,0,10,20,30,40,50,60,70,80,90,100]\n",
    "labels = [int(i) for i in range(11)]\n",
    "test['Rating'] = pd.cut(test['Rating'], bins=bins, labels=labels, include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "abbe6084",
   "metadata": {},
   "outputs": [],
   "source": [
    "# QUALIFICATION COLUMN\n",
    "# dict of qualification with counts\n",
    "# HERE the top 10 Qual of train and test col should be same so I am imputing the train col to test col\n",
    "data = pd.read_excel('Final_Train.xlsx')\n",
    "data['Qualification'] = data['Qualification'].str.split(\",\")\n",
    "Qualification = {}\n",
    "for x in data['Qualification']:\n",
    "    for each in x:\n",
    "        each = each.strip()\n",
    "        if each in Qualification:\n",
    "            Qualification[each] += 1\n",
    "        else:\n",
    "            Qualification[each] = 1\n",
    "            \n",
    "# finding out the top 10 Qualification\n",
    "most_qual = sorted(Qualification.items(), key=lambda x:x[1], reverse=True)[:10]\n",
    "final_qual = []\n",
    "for qual in most_qual:\n",
    "    final_qual.append(qual[0])\n",
    "\n",
    "#encode the top 10 qualification in test dataset\n",
    "for title in final_qual:\n",
    "    test[title] = 0\n",
    "\n",
    "for x,y in zip(test['Qualification'].values, np.array([i for i in range(len(test))])):\n",
    "    for c in x:\n",
    "        c = c.strip()\n",
    "        if c in final_qual:\n",
    "            test[c][y] = 1\n",
    "            \n",
    "#drop Qualification after getting top 10 qual.\n",
    "test.drop('Qualification',axis=1,inplace=True)\n",
    "\n",
    "#city column\n",
    "test['city'] = test['city'].apply(lambda x: re.sub(' +','',x))\n",
    "\n",
    "#encode city and profile\n",
    "test = pd.get_dummies(test, columns=['city','Profile'], prefix=['city','Profile'])\n",
    "\n",
    "#drop Miscellaneous_Info and locality\n",
    "test.drop(['Miscellaneous_Info','locality'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f9d65f2",
   "metadata": {},
   "source": [
    "#### Based on the hackathon site(MachineHack), submission are evaluated on Root-Mean-Squared-Log-Error (RMSLE) error, more specifically, 1-RMSLE. so, I will use metrics as RMSLE\n",
    "\n",
    "*  RMSLE: techniques to find out the difference between the predicted values by model and the actual values. Only we take the log of predicted values and actual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "404d3e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train.drop('Fees',axis=1)    #Independent Variables\n",
    "y = train['Fees']                # Dependent Variables(Target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "54a78424",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train-test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "88470215",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# to use RMSLE we will create our own scorer\n",
    "from sklearn.metrics import make_scorer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d931a606",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate RMSLE (SCORE func).\n",
    "def score(y_pred,y):\n",
    "    y_pred = np.log(y_pred)\n",
    "    y = np.log(y)\n",
    "    return 1 - ((np.sum((y_pred - y)**2)) / len(y))**1/2\n",
    "\n",
    "# SCORING func for Cross Validation (RandomizedSearchCV)\n",
    "scorer = make_scorer(score_func = score, greater_is_better=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "69b4d426",
   "metadata": {},
   "outputs": [],
   "source": [
    "# object of algo we have imported\n",
    "knn_reg = KNeighborsRegressor()\n",
    "svm_reg = SVR(gamma='scale')\n",
    "dt_reg = DecisionTreeRegressor()\n",
    "rf_reg = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7b2a3c43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsRegressor() 0.783328807236496\n",
      "SVR() 0.7753298683985804\n",
      "DecisionTreeRegressor() 0.6738485098339748\n",
      "RandomForestRegressor() 0.7631308713684252\n"
     ]
    }
   ],
   "source": [
    "for reg in (knn_reg, svm_reg, dt_reg, rf_reg):\n",
    "    reg.fit(x_train,y_train)\n",
    "    \n",
    "    y_pred = reg.predict(x_test)\n",
    "    \n",
    "    print(reg, score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "45a09ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0cf5c924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.783508382713717\n"
     ]
    }
   ],
   "source": [
    "voting_reg = VotingRegressor([('knn',knn_reg), ('svm',svm_reg), ('dt',dt_reg), ('rf',rf_reg)])\n",
    "\n",
    "voting_reg.fit(x_train,y_train)\n",
    "\n",
    "pred = voting_reg.predict(x_test)\n",
    "\n",
    "print(\"score:\",score(pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d906f1a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "295.82\n"
     ]
    }
   ],
   "source": [
    "# check with custom input.\n",
    "fees = svm_reg.predict([[10,10,1,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0]])\n",
    "print(round(fees[0],2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b472a6",
   "metadata": {},
   "source": [
    "#### There is less parameter in Knn and after HyperParameter tuning the accuracy was 78.3. And after doing HyperParameter tuning of SVM the accuracy was 79.2. Thus, I move with RandomForest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ad35f2",
   "metadata": {},
   "source": [
    "### HyperParameter Tuning for RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f1d6f559",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "69530b74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameter tuning of RandomForest\n",
    "\n",
    "#no. of tress random_forest\n",
    "n_estimators = [int(x) for x in np.linspace(start=150,stop=1000,num=6)]\n",
    "#max levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(start=6,stop=30,num=5)]\n",
    "#min no. of splitting required to split a node\n",
    "min_samples_split = [2,7,10]\n",
    "#min no. of sample required at each leaf node\n",
    "min_samples_leaf = [2,5,10]\n",
    "#max_features\n",
    "max_features=['auto','sqrt']\n",
    "\n",
    "# generate a dictionary of all the Hyper Parameters\n",
    "random_grid = {'n_estimators':n_estimators,\n",
    "               'max_depth':max_depth,\n",
    "               'min_samples_split':min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'max_features':max_features,\n",
    "              }\n",
    "\n",
    "#print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bd203baf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [6, 12, 18, 24, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 7, 10],\n",
       "                                        &#x27;n_estimators&#x27;: [150, 320, 490, 660,\n",
       "                                                         830, 1000]},\n",
       "                   random_state=42, scoring=make_scorer(score), verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [6, 12, 18, 24, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 7, 10],\n",
       "                                        &#x27;n_estimators&#x27;: [150, 320, 490, 660,\n",
       "                                                         830, 1000]},\n",
       "                   random_state=42, scoring=make_scorer(score), verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [6, 12, 18, 24, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [2, 5, 10],\n",
       "                                        'min_samples_split': [2, 7, 10],\n",
       "                                        'n_estimators': [150, 320, 490, 660,\n",
       "                                                         830, 1000]},\n",
       "                   random_state=42, scoring=make_scorer(score), verbose=1)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# increase the n_iter for more accuracy.\n",
    "rf_random = RandomizedSearchCV(rf_reg, param_distributions=random_grid, scoring=scorer, cv=5, n_iter=20, random_state=42, verbose=1, n_jobs=-1)\n",
    "\n",
    "rf_random.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f996fd87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 660,\n",
       " 'min_samples_split': 7,\n",
       " 'min_samples_leaf': 5,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 18}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0fff0fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=24, max_features=&#x27;sqrt&#x27;, min_samples_leaf=5,\n",
       "                      min_samples_split=10, n_estimators=490)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=24, max_features=&#x27;sqrt&#x27;, min_samples_leaf=5,\n",
       "                      min_samples_split=10, n_estimators=490)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=24, max_features='sqrt', min_samples_leaf=5,\n",
       "                      min_samples_split=10, n_estimators=490)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_reg = RandomForestRegressor(n_estimators= 490, min_samples_split= 10, min_samples_leaf= 5, max_features= 'sqrt', max_depth= 24)\n",
    "\n",
    "rf_reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3f300ca7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.7943379271756539\n"
     ]
    }
   ],
   "source": [
    "mypredi = rf_reg.predict(x_test)\n",
    "print(\"score:\",score(mypredi,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "35fdd1b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "369.45\n"
     ]
    }
   ],
   "source": [
    "# check with custom input.\n",
    "fees = rf_reg.predict([[10,10,1,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0]])\n",
    "print(round(fees[0],2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fbe82ba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.7631308713684252\n"
     ]
    }
   ],
   "source": [
    "y_pred = reg.predict(x_test)\n",
    "\n",
    "print(\"score:\",score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5282c08d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "428.23\n"
     ]
    }
   ],
   "source": [
    "# check with custom input.\n",
    "fees = reg.predict([[10,10,1,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0]])\n",
    "print(round(fees[0],2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b3a781",
   "metadata": {},
   "source": [
    "### Save The Model for further use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2eab96e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "aa462e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "file=open(\"doctorrf_fees.pkl\",'wb')\n",
    "#dump the model to that file\n",
    "pickle.dump(rf_reg,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "127cf015",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=pickle.load(open('doctorrf_fees.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc382b9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d31c09db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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