Salary_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 707,
   "id": "981a62f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "id": "9f0ca2d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sl = pd.read_csv('../Cyber_salaries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "id": "79147543",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>EN</td>\n",
       "      <td>FT</td>\n",
       "      <td>Information Security Officer</td>\n",
       "      <td>68000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>72762</td>\n",
       "      <td>DE</td>\n",
       "      <td>100</td>\n",
       "      <td>DE</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Security Officer</td>\n",
       "      <td>123400</td>\n",
       "      <td>USD</td>\n",
       "      <td>123400</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Security Officer</td>\n",
       "      <td>88100</td>\n",
       "      <td>USD</td>\n",
       "      <td>88100</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Security Engineer</td>\n",
       "      <td>163575</td>\n",
       "      <td>USD</td>\n",
       "      <td>163575</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Security Engineer</td>\n",
       "      <td>115800</td>\n",
       "      <td>USD</td>\n",
       "      <td>115800</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   work_year experience_level employment_type                     job_title  \\\n",
       "0       2022               EN              FT  Information Security Officer   \n",
       "1       2022               SE              FT              Security Officer   \n",
       "2       2022               SE              FT              Security Officer   \n",
       "3       2022               SE              FT             Security Engineer   \n",
       "4       2022               SE              FT             Security Engineer   \n",
       "\n",
       "   salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
       "0   68000             EUR          72762                 DE           100   \n",
       "1  123400             USD         123400                 US             0   \n",
       "2   88100             USD          88100                 US             0   \n",
       "3  163575             USD         163575                 US           100   \n",
       "4  115800             USD         115800                 US           100   \n",
       "\n",
       "  company_location company_size  \n",
       "0               DE            S  \n",
       "1               US            M  \n",
       "2               US            M  \n",
       "3               US            M  \n",
       "4               US            M  "
      ]
     },
     "execution_count": 709,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "id": "cc568061",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['Information Security Officer', 'Security Officer', 'Security Officer',\\n       'Security Engineer', 'Security Engineer',\\n       'Penetration Testing Engineer', 'Security Analyst', 'Security Analyst',\\n       'Security Engineer', 'Security Engineer',\\n       ...\\n       'Application Security Engineer', 'Information Security Specialist',\\n       'Application Security Engineer', 'Cyber Security Analyst',\\n       'Ethical Hacker', 'Cyber Security Analyst',\\n       'Information Security Manager', 'Penetration Testing Engineer',\\n       'Information Security Analyst', 'Threat Intelligence Analyst'],\\n      dtype='object', length=1349)] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/jv/yvxky56d79b3tfm780gsj5z00000gn/T/ipykernel_59459/1113774899.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"job_title\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3509\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3510\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3511\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3513\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   5794\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5796\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5798\u001b[0m         \u001b[0mkeyarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   5854\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5855\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5856\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5858\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['Information Security Officer', 'Security Officer', 'Security Officer',\\n       'Security Engineer', 'Security Engineer',\\n       'Penetration Testing Engineer', 'Security Analyst', 'Security Analyst',\\n       'Security Engineer', 'Security Engineer',\\n       ...\\n       'Application Security Engineer', 'Information Security Specialist',\\n       'Application Security Engineer', 'Cyber Security Analyst',\\n       'Ethical Hacker', 'Cyber Security Analyst',\\n       'Information Security Manager', 'Penetration Testing Engineer',\\n       'Information Security Analyst', 'Threat Intelligence Analyst'],\\n      dtype='object', length=1349)] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "sl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "id": "fe627113",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14839"
      ]
     },
     "execution_count": 669,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "id": "3f7aaf86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1349, 11)"
      ]
     },
     "execution_count": 670,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c330eae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "id": "47d757e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size: 14839\n",
      "shape: (1349, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>work_year</th>\n",
       "      <td>1349.0</td>\n",
       "      <td>2021.368421</td>\n",
       "      <td>7.112360e-01</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>1349.0</td>\n",
       "      <td>528824.761305</td>\n",
       "      <td>1.361367e+07</td>\n",
       "      <td>1740.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>160080.0</td>\n",
       "      <td>500000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary_in_usd</th>\n",
       "      <td>1349.0</td>\n",
       "      <td>121578.622683</td>\n",
       "      <td>6.897295e+04</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>152000.0</td>\n",
       "      <td>899920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remote_ratio</th>\n",
       "      <td>1349.0</td>\n",
       "      <td>72.238695</td>\n",
       "      <td>3.935533e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count           mean           std     min      25%       50%  \\\n",
       "work_year      1349.0    2021.368421  7.112360e-01  2020.0   2021.0    2022.0   \n",
       "salary         1349.0  528824.761305  1.361367e+07  1740.0  80000.0  120000.0   \n",
       "salary_in_usd  1349.0  121578.622683  6.897295e+04  2000.0  75000.0  115000.0   \n",
       "remote_ratio   1349.0      72.238695  3.935533e+01     0.0     50.0     100.0   \n",
       "\n",
       "                    75%          max  \n",
       "work_year        2022.0       2022.0  \n",
       "salary         160080.0  500000000.0  \n",
       "salary_in_usd  152000.0     899920.0  \n",
       "remote_ratio      100.0        100.0  "
      ]
     },
     "execution_count": 671,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "print(\"size:\",  sl.size)\n",
    "print(\"shape:\", sl.shape)\n",
    "sl.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "id": "908d91d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1349 entries, 0 to 1348\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   work_year           1349 non-null   int64 \n",
      " 1   experience_level    1349 non-null   object\n",
      " 2   employment_type     1349 non-null   object\n",
      " 3   job_title           1349 non-null   object\n",
      " 4   salary              1349 non-null   int64 \n",
      " 5   salary_currency     1349 non-null   object\n",
      " 6   salary_in_usd       1349 non-null   int64 \n",
      " 7   employee_residence  1349 non-null   object\n",
      " 8   remote_ratio        1349 non-null   int64 \n",
      " 9   company_location    1349 non-null   object\n",
      " 10  company_size        1349 non-null   object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 116.1+ KB\n"
     ]
    }
   ],
   "source": [
    "sl.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "id": "56da8767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>data_type</th>\n",
       "      <th>total_null_count</th>\n",
       "      <th>total_unique_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>work_year</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experience_level</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_type</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_title</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary_currency</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary_in_usd</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee_residence</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remote_ratio</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_location</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_size</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   data_type  total_null_count  total_unique_count\n",
       "work_year              int64                 0                   3\n",
       "experience_level      object                 0                   4\n",
       "employment_type       object                 0                   4\n",
       "job_title             object                 0                  87\n",
       "salary                 int64                 0                 416\n",
       "salary_currency       object                 0                  21\n",
       "salary_in_usd          int64                 0                 615\n",
       "employee_residence    object                 0                  60\n",
       "remote_ratio           int64                 0                   3\n",
       "company_location      object                 0                  57\n",
       "company_size          object                 0                   3"
      ]
     },
     "execution_count": 673,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_t = pd.DataFrame(index=sl.columns)\n",
    "info_t['data_type'] = sl.dtypes\n",
    "info_t['total_null_count'] = sl.isnull().sum()\n",
    "info_t['total_unique_count'] = sl.nunique()\n",
    "info_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "id": "816aaadd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sl.drop(['salary','salary_currency'],inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 675,
   "id": "98fe771f",
   "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>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>EN</td>\n",
       "      <td>FT</td>\n",
       "      <td>Information Security Officer</td>\n",
       "      <td>72762</td>\n",
       "      <td>DE</td>\n",
       "      <td>100</td>\n",
       "      <td>DE</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   work_year experience_level employment_type                     job_title  \\\n",
       "0       2022               EN              FT  Information Security Officer   \n",
       "\n",
       "   salary_in_usd employee_residence  remote_ratio company_location  \\\n",
       "0          72762                 DE           100               DE   \n",
       "\n",
       "  company_size  \n",
       "0            S  "
      ]
     },
     "execution_count": 675,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "id": "3c2200e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>data_type</th>\n",
       "      <th>null_count</th>\n",
       "      <th>unique_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>work_year</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experience_level</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_type</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_title</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary_in_usd</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee_residence</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remote_ratio</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_location</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_size</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   data_type  null_count  unique_count\n",
       "work_year              int64           0             3\n",
       "experience_level      object           0             4\n",
       "employment_type       object           0             4\n",
       "job_title             object           0            87\n",
       "salary_in_usd          int64           0           615\n",
       "employee_residence    object           0            60\n",
       "remote_ratio           int64           0             3\n",
       "company_location      object           0            57\n",
       "company_size          object           0             3"
      ]
     },
     "execution_count": 676,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.DataFrame(index=sl.columns)\n",
    "temp['data_type'] = sl.dtypes\n",
    "temp['null_count'] = sl.isnull().sum()\n",
    "temp['unique_count'] = sl.nunique()\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "id": "1161d89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sl.drop(['employment_type'],inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "id": "bc881799",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sl.drop(['company_size'],inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "id": "1d361f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022    681\n",
      "2021    484\n",
      "2020    184\n",
      "Name: work_year, dtype: int64\n",
      "SE    622\n",
      "MI    433\n",
      "EN    218\n",
      "EX     76\n",
      "Name: experience_level, dtype: int64\n",
      "100    851\n",
      "0      251\n",
      "50     247\n",
      "Name: remote_ratio, dtype: int64\n",
      "L    798\n",
      "M    459\n",
      "S     92\n",
      "Name: company_size, dtype: int64\n",
      "FT    1326\n",
      "CT      11\n",
      "PT       9\n",
      "FL       3\n",
      "Name: employment_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(sl['work_year'].value_counts())\n",
    "print(sl['experience_level'].value_counts())\n",
    "print(sl['remote_ratio'].value_counts())\n",
    "print(sl['company_size'].value_counts())\n",
    "print(sl['employment_type'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "id": "47a4b951",
   "metadata": {},
   "outputs": [],
   "source": [
    "sl.drop(['employment_type'],inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "id": "0dbfd097",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='remote_ratio', ylabel='count'>"
      ]
     },
     "execution_count": 679,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,4)\n",
    "\n",
    "sns.countplot(sl['work_year'],ax=axs[0])\n",
    "sns.countplot(sl['company_size'],ax=axs[1])\n",
    "sns.countplot(sl['experience_level'],ax=axs[2])\n",
    "sns.countplot(sl['remote_ratio'],ax=axs[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "id": "28f1ae2a",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='company_size', ylabel='count'>"
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(sl['company_size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "id": "bfbbc2fd",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='experience_level', ylabel='count'>"
      ]
     },
     "execution_count": 643,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(sl['experience_level'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "id": "34bc5f4f",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='remote_ratio', ylabel='count'>"
      ]
     },
     "execution_count": 644,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(sl['remote_ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "id": "97e80511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='work_year', ylabel='salary_in_usd'>"
      ]
     },
     "execution_count": 645,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2)\n",
    "sns.boxplot(sl['company_size'],sl['salary_in_usd'],ax=axs[0])\n",
    "sns.boxplot(sl['work_year'],sl['salary_in_usd'],ax = axs[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "id": "d29df7b2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='work_year', ylabel='salary_in_usd'>"
      ]
     },
     "execution_count": 646,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(sl['work_year'],sl['salary_in_usd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "id": "a9153e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary_in_usd', ylabel='Count'>"
      ]
     },
     "execution_count": 685,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G94adfXZuLEhEREOPYUelfP4APFJwUnKmQiYnhxQNTwMAHGzqhBBC5mqIiEjtGHZUqs3lBSQJWviRrNfKXU6EwqwUSBLQ0eNDc6dH7nKIiEjlGHZUytEdDBFJ8EJSwG0ijpek12JkRnCI7WATh7KIiGhoMeyolKPbCwBIhlfmSvpXlN07lNXcJXMlRESkdgw7KtXW1duzIylzmKhoeHCvHfbsEBHRUGPYUSnF9+yEJymzZ4eIiIYW742lQkKIiDk7Z8vv88Jms/U5XlZWBqNx4Mvajz9PT1Owrl21Tfjoo49gMBxbMXam5yUiIjoVhh0V6vEG4PYFAEQn7NjrqvGGw4Eqlzl8rKF6H+4BMGvWrEGdx+WXAKThaJcf//Xnt1E6/dxBn5eIiOhUGHZUKNSrYwh4oNVFZx+b7JHW8N3Uo3EeIQQ+2vwNvH4N0kYUR+XcRERE/eGcHRUKD2GJHpkrOTlJkpCREhy6ckEvczVERKRm7NmJU263G5WVlRHHbDYbAoG08ORkJYcdABiWooe9w40eKGuHZyIiUheGnThVWVmJp9dvRJ61JHzMVrENlrFT0JY1AgCQFFB22An37Aj27BAR0dBh2IljedaSiLkuDdX7AOC4nh03lDxSyWEsIiKKBeV+EtKgCAG0x8kwVkZqMORwGIuIiIYSw47KuKGDXwhoJQkGoczdk0OGJQdDjg9a9Hj9MldDRERqxbCjMu7ekUlTsg7Kuv1nXwadBvreQNbWrcydnomIKP4x7KiMu3f+S3pSfMyDSQq4AQBtLmX3QhERUfxi2FEZtwj27KQnx8fcc6PoDTvs2SEioiHCsKMycdez0zuJut3FsENEREODYUdlQnN24iXsGAPs2SEioqHFsKMy4bATJ8NYSeFhLM7ZISKiocGwoyIBSPDEW89Ob9jp8QW4/JyIiIYEw46KeCQ9AAlajYQUg1bucgZEiwD08AHgUBYREQ0Nhh0V8UhGAEB6kg6SpPRddo5JQjDkcPk5ERENBYYdFXFLwR2J42UIKyQcdtizQ0REQ4BhR0U8obCTHGdhRwr17DDsEBFR9DHsqIj7uGGseBLq2Wlnzw4REQ0Bhh0Vccdrz054GItzdoiIKPoUH3bq6+vxwx/+EFlZWUhJScHUqVNRWVkZfl4IgRUrViA/Px/JycmYN28eqqqqZKxYPscmKMdn2OnxBeAJyFwMERGpjqLDjsPhwOzZs6HX6/Gvf/0Lu3fvxlNPPYVhw4aF2zz55JNYvXo11q5di23btiE3NxcLFixAR0eHfIXLwB8QvUvPAVOcDWNpJYHU3qXyXT5F/5EkIqI4pOhPxSeeeAIFBQV44YUXwsdGjx4d/loIgTVr1mD58uVYtGgRAOCll16CxWLBunXrcNttt8W6ZNl0un2AJEFCIG722DnesBQDujwudPkZdoiIKLoU/cny9ttvY8aMGbj66quRk5ODadOm4fnnnw8/X11djcbGRixcuDB8zGg0Yu7cuaioqJCjZNk4e1cyGeGLqz12Qsy984wYdoiIKNoU3bNz8OBBPPvss7j33nvxn//5n9i6dSvuvvtuGI1G3HTTTWhsbAQAWCyWiNdZLBbU1NSc9Lxutxtutzv8vdPpHJpfIIacPcfCTiz4fV7YbLbw9zabDYFA2qDPNywlGHY6OYxFRERRpuiwEwgEMGPGDKxcuRIAMG3aNFRVVeHZZ5/FTTfdFG53Yk+GEOKUvRurVq3Co48+OjRFy6SjJxhyjIjN8m17XTXecDhQ5TIDAGwV22AZOwVjBnm+UNjpZs8OERFFmaI/WfLy8jBx4sSIYxMmTEBtbS0AIDc3FwDCPTwhdru9T2/P8R588EG0t7eHH3V1dVGuPPY63cGwY5BidzPN7JFWWEunw1o6Hdl5o87qXMOSg8vm2bNDRETRpuhPltmzZ2Pv3r0Rx/bt24fCwkIAgNVqRW5uLsrLy8PPezwebN68GbNmzTrpeY1GI9LT0yMe8a6zt2fHEKNhrGgL9ex4hYROrj8nIqIoUvQw1j333INZs2Zh5cqVuOaaa7B161Y899xzeO655wAEh6+WLVuGlStXori4GMXFxVi5ciVSUlJwww03yFx9bIV6dmI1Zyfa9FoNUg1adHn8aOxi2CEiouhRdNiZOXMm3nrrLTz44IN47LHHYLVasWbNGixevDjc5v7774fL5cIdd9wBh8OB8847Dxs2bIDJZJKx8tjriPOeHeDY8vPGrtgNxRERkfopOuwAwJVXXokrr7zypM9LkoQVK1ZgxYoVsStKYdw+Pzz+YG9IfIcdPerbXDjKnh0iIooiRc/ZoYEJzdfRCh+0kpC5msEL7bXT2MmwQ0RE0cOwowLhlVgivu8aHpqkzGEsIiKKJoYdFTgWduL7ruGh5eccxiIiomhi2FGB8OTkeA87oV2UvQJt3fH9uxARkXIw7KhAqGdHH+fDWHqtBkmaYK/OoZZumashIiK1YNhRgU6V9OwAQKq2N+w0d8lcCRERqQXDjgqoZc4OAKTqgmGnmmGHiIiihGFHBTpUshoLAFK1waXzNS0MO0REFB2DCjtFRUVoaWnpc7ytrQ1FRUVnXRQNnMcXgMfXu6Ggmnp2OGeHiIiiZFBh59ChQ/D7++6F4na7UV9ff9ZF0cCFh7B0GmgR/0u2Q3N22LNDRETRcka3i3j77bfDX7///vswm83h7/1+PzZt2oTRo0dHrTg6vY6e4NCVyagDOmQuJgpCYaet24u2bg+GpRhkroiIiOLdGYWdq666CkDwflQ333xzxHN6vR6jR4/GU089FbXi6PRCPTtpSYq/zdmA6DRARpIER49AdXMXpo1i2CEiorNzRp+QgUDwX91WqxXbtm1Ddnb2kBRFAxdadp5mVEfYAQBLqhaOHh9qWroxbVSG3OUQEVGcG9ScnerqagYdhQitxFJT2MlNDf6x5PJzIiKKhkF/Qm7atAmbNm2C3W4P9/iE/PnPfz7rwmhgjh/GcspcS7TkpmoBcJIyERFFx6DCzqOPPorHHnsMM2bMQF5eHiRJinZdNEBdx/XsqCfs9PbscPk5ERFFwaDCzh//+Ee8+OKLuPHGG6NdD52hUM9OqkF9w1i8ZQQREUXDoObseDwezJo1K9q10BnyC6DHGxxCVMtqLADI6R3Gand5efdzIiI6a4MKO7feeivWrVsX7VroDPX4g8OHWo2EJJ167vyRpJNgSTcC4CRlIiI6e4PqDujp6cFzzz2HjRs3YsqUKdDr9RHPr169OirF0an1BIIBJ9WgVd28qcKsVBx1urn8nIiIztqgws7OnTsxdepUAMCuXbsinlPbh66ShXp2UlW07DzEmpWKrdWt7NkhIqKzNqhPyQ8//DDaddAg9ASCYUdNe+yEjM5OBcDl50REdPbUM9EjAfX4e4ex1Bh2slIAcM4OERGdvUF9Sl500UWnHK764IMPBl0QDZyae3aKhqcBAA42dUEIweFRIiIatEF9Sobm64R4vV7s2LEDu3bt6nODUBo6x+bsaGWuJPoKs1KgkYK3w2jqcCMnPUnukoiIKE4NKuw8/fTT/R5fsWIFOjs7z6ogGrjQaiw19uwk6bUoyExBTUs3vmnqYtghIqJBi+qcnR/+8Ie8L1aMCCFUPYwFAGN6h7K+aWKAJiKiwYtq2NmyZQuSkvgv8Fhw+QC/UO/ScwAYMzy4Iothh4iIzsagPiUXLVoU8b0QAg0NDfjiiy/w8MMPR6UwOrXWnuBtIow6DfRadS6qO9azwxVZREQ0eIMKO2azOeJ7jUaDcePG4bHHHsPChQujUhidmqM37Ki1VwcAxuT0hh07e3aIiGjwBvVJ+cILL0S7DjpDDlfvDUBVHHaKejcWPNLugsvjR7JBfavOiIho6J3VJ2VlZSX27NkDSZIwceJETJs2LVp10Wm0hnt21BsAMlMNGJaiR1u3F9XNXZiYny53SUREFIcGFXbsdjuuu+46fPTRRxg2bBiEEGhvb8dFF12E119/HcOHD492nXQCR48AoO6eHUmSMGZ4GiprHPimqZNhh4iIBmVQM1vvuusuOJ1OVFVVobW1FQ6HA7t27YLT6cTdd98d7RqpH+E5Owb1hh2AK7KIiOjsDeqT8r333sPGjRsxYcKE8LGJEyfiD3/4Aycox0go7KQlqT3scEUWERGdnUH17AQCAej1+j7H9Xo9AoHAWRdFp5c4PTuhe2SxZ4eIiAZnUJ+UF198MZYuXYrXXnsN+fn5AID6+nrcc889uOSSS6JaIPXlDwi0udU/Zwc4tvz8YFMXAgEBjUaC2+1GZWVln7ZlZWUwGo2xLpGIiBRuUJ+Ua9euxXe/+12MHj0aBQUFkCQJtbW1mDx5Ml599dVo10gnaOl0IyAAQCBF5cuxCzKSYdBq4PL6Ud/mQkFmCiorK/H0+o3Is5aE2zVU78M9AGbNmiVfsUREpEiDCjsFBQXYvn07ysvL8fXXX0MIgYkTJ2L+/PnRro/60ejsAQAkaYI9HWqm02pQNDwVXzd2YL+9AwWZKQCAPGsJrKXTZa6OiIjiwRnN2fnggw8wceJEOJ1OAMCCBQtw11134e6778bMmTNRWlqKTz75ZEgKpWMa24+FnURQbDEBAPYd5bwdIiI6c2cUdtasWYOf/OQnSE/vu9+J2WzGbbfdhtWrV0etOOrf0VDPjjYxJoOX9M7b2dfYIXMlREQUj84o7Hz11Vf49re/fdLnFy5c2O/EUYquo043ACBJmxg9OyW5vT07doYdIiI6c2cUdo4ePdrvkvMQnU6Hpqamsy6KTu34OTuJoKR3GOuAvROBQGL8zkREFD1nFHZGjBgBm8120ud37tyJvLy8sy6KTi08jKVJjGGsUZkpMOo06PEGUOfolrscIiKKM2cUdi6//HL88pe/RE9PT5/nXC4XHnnkEVx55ZVRK476F56gnCDDWFqNFN5ccC/n7RAR0Rk6o6XnDz30EN58802UlJRgyZIlGDduHCRJwp49e/CHP/wBfr8fy5cvH6paqVeoZyc5QcIOAIzLNWF3gxP77Z2Yxn0DiYjoDJxR2LFYLKioqMBPf/pTPPjggxAi+GErSRIuvfRSPPPMM7BYLENSKAW5PH44e3wAEmcYCwCKLb0rso52YNoomYshIqK4csabChYWFuLdd9+Fw+HAgQMHIIRAcXExMjIyhqI+OkFocrJRC+jUvZ9ghJKc4CTlvY0dwCh13yKDiIiia9CfGhkZGZg5c2Y0a6EBCM3XyUjSQEqksNO7IutgUxf8gb77PBEREZ3MoO56TvIJzdfJTEqsSzcyIxnJei08/gCOdifO8B0REZ29xPrEVIFQ2MlIsLCj0UjheTuHnX6ZqyEioniSWJ+YKhCas5OZnEBjWL3G9+6kXMOwQ0REZ4BhJ84kas8OAEzIC87VqWXYISKiM5B4n5hx7vgJyokmFHZq2hl2iIho4BLvEzPOhW4CmpBhJzcYdppdAXg5R5mIiAYo8T4x41ggIGDvSMzVWABgTtFjxLBkAEC7TytzNUREFC8S7xMzjrV2e+D1C0gSMCwp8SYoA8eGspxe/tElIqKB4SdGHAnN18lKNUKnScywMzEvuCLL6eMfXSIiGhjuux9HQiuxLOnqvROm3+eFzWbrc7ysrAxGozHcs9Pu5TAWERENDMNOHAlNTs5NTwLglbeYIWKvq8YbDgeqXObwsYbqfbgHwKxZs8Jhp8OnQSAgoEnQHi4iIho4hp04EtpQ0GJWb9gBgOyRVlhLp/f73KjMFCRpgR6/BEe3B1lp6u3lIiKi6ODEhzhytHfOTrBnJzFpNBIK0oNDWE2dbpmrISKieBBXYWfVqlWQJAnLli0LHxNCYMWKFcjPz0dycjLmzZuHqqoq+YocQqGenUQOOwBQaA52SDZ3eGSuhIiI4kHchJ1t27bhueeew5QpUyKOP/nkk1i9ejXWrl2Lbdu2ITc3FwsWLEBHR4dMlQ6d0ATlHBVPUB6I0eZgz05ozyEiIqJTiYuw09nZicWLF+P5559HRkZG+LgQAmvWrMHy5cuxaNEiTJo0CS+99BK6u7uxbt06GSseGqGwk2tO7J4dazjsuCGEkLkaIiJSurgIO3feeSeuuOIKzJ8/P+J4dXU1GhsbsXDhwvAxo9GIuXPnoqKi4qTnc7vdcDqdEQ+l6/H64egOTkpOtGGs0HL0iooKVFRUoL12DzQQcPsCaHepd6I2ERFFh+JXY73++uvYvn07tm3b1ue5xsZGAIDFYok4brFYUFNTc9Jzrlq1Co8++mh0Cx1i9t5l50adBuZkvczVxNaJy9FtFduQXJSNLiTB3uHGsBSDzBUSEZGSKbpnp66uDkuXLsWrr76KpKST92ZIUuReK0KIPseO9+CDD6K9vT38qKuri1rNQ6XxuCGsU/1uahVajm4tnY7svFFIQzD8hUIgERHRySi6Z6eyshJ2ux1lZWXhY36/Hx9//DHWrl2LvXv3Agj28OTl5YXb2O32Pr09xzMajTAa42uSb3j3ZFNiDWGdTKrkBgRwlJOUiYjoNBTds3PJJZfAZrNhx44d4ceMGTOwePFi7NixA0VFRcjNzUV5eXn4NR6PB5s3b8asWbNkrDz6jkZsKEipx/XscJIyERGdiqJ7dkwmEyZNmhRxLDU1FVlZWeHjy5Ytw8qVK1FcXIzi4mKsXLkSKSkpuOGGG+Qoecg0hjcUjK8eqaGSDA+0GgkefwBtnKRMRESnoOiwMxD3338/XC4X7rjjDjgcDpx33nnYsGEDTCaT3KVFVfhWEQm2EutkNBIwPM2IRmcP7E43OEWZiIhOJu7CzkcffRTxvSRJWLFiBVasWCFLPbESmojLsHNMTnow7Bzt6EGB3MUQEZFixV3YSVSN3FCwj+Bk7XbYnW4UDOBtcbvdqKysjDhWVlYWd5PViYjozDDsxAEhBO+L1Q9L7/wle0cPAgPIK5WVlXh6/UbkWUsAAA3V+3APoLrJ7EREFIlhJw60dXvh8QUA8L5Yx8tMNcCg1cDjD6DDN7CFhXnWElhLpw9xZUREpCSKXnpOQaFencxUA4w6rczVKIckSeFhvVYv3xciIuofw04cCN/t3MRenROFwo7Dw7BDRET9Y9iJA7zb+cnlhcIOe3aIiOgkGHbiQEM7JyefTOg96fJr4HQHZK6GiIiUiGEnDjS0BcPOiGHJMleiPEl6LTJSgneB3+/wyVwNEREpEcNOHDjS7gIA5DHs9CvPHHxfDjj8MldCRERKxLATB+rbgmEnfxiHsfoTmrfDnh0iIuoPw47CCSFwpDfscBirf6GJ2984fPD5OW+HiIgiMewonKPbix5v8AOcq7H6l5VqgE4ScPuBPQ0dcpdDREQKw7CjcKFeneEmIzcUPAlJkpBlCM7X+by6ReZqiIhIaRh2FO7YfB0OYZ1KliE4X+ezg60yV0JERErDsKNwx+brcAjrVLL0wZ6dbYdaEQgImashIiIlYdhRuFDYyTezZ+dUzPoAjFqg3eXF142ct0NERMcw7Cjckd4NBTmMdWoaCSjJ1AHgvB0iIorEsKNwnLMzcBOyesMO5+0QEdFxdHIXQKcWGsZqrt2HCufB8HGbzYZAIE2ushRpQpYeQA+2HmqFEAKSJMldEhERKQDDjoK5fX7YO9wAgL+9/ylGWYvCz9kqtsEydgrGyFWcAo3J0CJJr0Frlwf77Z0osZjkLomIiBSAYUfBjrYHg45eAxSMLoK1dHr4uYbqfXKVpVg6jYSywgz8+0ALtnzTwrBDREQAOGdH0ULzdbKTNeCIzMDMHpsNAPhkf5PMlRARkVIw7ChYaL5OVjIv00BdWDwcALDlmxZ4fLxPFhERMewoWijsZKfwMg3UxLx0ZKUa0OXxY3utQ+5yiIhIAfgpqmBH2tmzc6Y0GglzijmURUREx/BTVMHqezcUZNg5M6GhrI/3NctcCRERKQE/RRWsrrUbAJDDYawz8q3enp1dR9rR0umWuRoiIpIbP0UVyh8QOOwIhR2tzNXEl5z0JIzPNUEI4NMD7N0hIkp0DDsK1dDugtcvoNdKyEzmuvMzdWFJcChr8z7O2yEiSnQMOwpV2zuEVZCRAg032Tlj88YFw86HX9vhDwiZqyEiIjkx7ChUbUtv2MlMkbmS+DRzdCbMyXo4ur2orOESdCKiRMawo1Chnp3CLIadwdBrNbiot3dn456jMldDRERyYthRqJresDOKPTuDtmBiLgCgfPdRCMGhLCKiRMWwo1ChZeccxhq8ueOGw6DVoLq5C980dcldDhERyYRhR6E4jHX20ow6nD8mC0Cwd4eIiBITw44Ctbu8aOv2AgiuxqLBWzDRAgAo390ocyVERCQXhh0FCg1hZacZkWrUyVxNfFswIRh2vqxrQ6uLd0EnIkpEDDsKVNMSmpycLHMl8S/XnIQZhRkQAvj8iEfucoiISAYMOwpUy5VYUXXllDwAwBaGHSKihMSwo0C1rcGVQ6OyUmWuRB0un5wHSQIOOPzo9nM3aiKiRMOwo0Ds2YmunPQknGfNBAAc6eEcKCKiRMOwo0Bcdh59V07JBwDUu/QyV0JERLHGsKMwHl8AR9p6ALBnJ5oum5QLjQS0+7Ro6+bcHSKiRMI+fYWpaemCPyCQZtQhx2SUu5y44fd5YbPZIo6VlZXBaAy+h1lpRpRm62Br8mHf0U6c2zusRURE6sewozAH7J0AgDHDUyFJnEw7UPa6arzhcKDKZQYANFTvwz0AZs2aFW5zfr4hGHbsHQw7REQJhGFHYY6FnTSZK4k/2SOtsJZOP+nzM/P0+L9fCbR0etDaxaEsIqJEwTk7CvNNU2/YyWHYibY0gwbDjX4AwL6jHTJXQ0REscKwozAHesPOWIadITEiKXjPsX1HOyCEzMUQEVFMMOwoSCAg8I09uKEgw87QyDX6oJUkOLq9cPr4x5+IKBHw//YK0uDsgcvrh04jcdn5ENFrju1fxA0GiYgSA/9vryChycmFWSnY9vln4eM2mw2BAHt6oqXEYsLB5i4c6dFDcCyLiEj1GHYUJBR2MnUePL3+Y+RZSwAAtoptsIydgjFyFqci1uxUaDUSuvwaHGr3Y7bcBRER0ZDiMJaChFZi5adpkWctgbV0Oqyl05GdN0rmytTFoNPA2nuT1c94J3QiItVj2FGQUM/OiDRelqFWYgkOC352xMuhLCIileOnqoJ80xt28k1amStRv9HZqdBKAk3dAXx1uF3ucoiIaAgx7CiEo8uDlt5dffPTGHaGml6rgcXoAwD8c+cRmashIqKhxLCjEKHNBPPNSUjS8Z5YsTAiKRR2GhAIcCiLiEitGHYUYk+DEwAwLtckcyWJI8foQ5IWONLegy/rHHKXQ0REQ4RLz4eQ2+1GZWVln+NlZWUwGo0Rx6rqg2FnYn46gNZYlJfwtBJQlmvAv+s9eGdnA8oKeSd0IiI1YtgZQpWVlXh6/cbwfjkA0FC9D/cAmDVrVkTb3b09O6X5ZqCDYSdWzh+hx7/rPXjX1oCHr5gIjYZDiEREasOwM8RC++WcitcfwN7G4F24S/PTUb83FpURAEwZrocpSYejTje+qHHgXCt7d4iI1IZzdhTggL0THn8AJqMOBRm8J1as+H1efL17F6YND/41eP797aioqIDb7Za5MiIiiiZFh51Vq1Zh5syZMJlMyMnJwVVXXYW9eyO7PYQQWLFiBfLz85GcnIx58+ahqqpKpooHZ/eR4BDWhLx0DqPEkL2uGm/8+2u4O9sAAB/XurB6/cZ+51kREVH8UnTY2bx5M+6880589tlnKC8vh8/nw8KFC9HV1RVu8+STT2L16tVYu3Yttm3bhtzcXCxYsAAdHR0yVn5mqo4cPzmZYil7pBUzp06BUaeBO6CBYcQEuUsiIqIoU/Scnffeey/i+xdeeAE5OTmorKzEhRdeCCEE1qxZg+XLl2PRokUAgJdeegkWiwXr1q3DbbfdJkfZZ6zqSHAH31KGHVloNRKKhqdiT0MHGnoU/VeCiIgGIa7+z97eHgwFmZnBSaTV1dVobGzEwoULw22MRiPmzp2LioqKk4Ydt9sdMS/D6XQOYdWnJoSIXIlFsijOMWFPQweO9Oiwc6ct4rn+tgogIqL4ETdhRwiBe++9F3PmzMGkSZMAAI2NjQAAi8US0dZisaCmpuak51q1ahUeffTRoSv2DBx2uNDR44NeK2FsTprc5SSsgsxkGLQauP3AK9saUNYTDJ4n2yqAiIjih6Ln7BxvyZIl2LlzJ1577bU+z0lS5KReIUSfY8d78MEH0d7eHn7U1dVFvd6BCg1hlVhMMOji5nKojk6jQdHwVACAf3gxrKXTYS2dHrFHEhERxae46Nm566678Pbbb+Pjjz/GyJEjw8dzc3MBBHt48vLywsftdnuf3p7jGY1GxQxL7KoPDWFxvo7cxuak4evGDrQi9bSBmYiI4oeiuxKEEFiyZAnefPNNfPDBB7BarRHPW61W5Obmory8PHzM4/Fg8+bNcTPsELon0zkFw+QthFCYmQKN8MMDPY46udcOEZFaKLpn584778S6devwj3/8AyaTKTxHx2w2Izk5GZIkYdmyZVi5ciWKi4tRXFyMlStXIiUlBTfccIPM1Z+ePyCwo7YNADB9VIa8xRB0Wg2G+dvRqsvEAXsncs1JcpdERERRoOiw8+yzzwIA5s2bF3H8hRdewC233AIAuP/+++FyuXDHHXfA4XDgvPPOw4YNG2AyKf/u4XsbO9Dl8SPNqEOJRfn1JoIMvwOtukzst3dg9tgsucshIqIoUHTYEUKcto0kSVixYgVWrFgx9AVF2fba4BDW1IJh0HLnZEUwB5zQIABnjw9NHRzKIiJSA0XP2VG7UNiZXsghLKXQIoBh6AYA7Ld3ylwNERFFA8OOjLbX9IadUcPkLYQiZErB25EcsHdiAJ2LRESkcAw7MmnpdONQS7AHYRonJytKBrqg1Uhoc3nR4eNfESKieMf/k8tke+8qrOKcNJiT9fIWQxG0kkBhZgoA4AjvlUVEFPcYdmQSnq/DXh1FCt26o8HNsENEFO8YdmRS2Ttfp4yTkxWpKDsVGgno8GlxuMMvdzlERHQWGHZk4PEf20xwxmiGHSUy6rUY1TuUtfWIR+ZqiIjobLCPXgb7W33w+AOwpBuRb9KhoqIi4nmbzYZAgHdAl9vYnDQcaunG1gav3KUQEdFZYNiRQVWLDwBwQVEWtm/fjqfXb4y4u7atYhssY6dgjFwFEgBgzPA0bNpzFLVOPw42daJoOAMoEVE84jCWDHY3B3sKLhgTvB1BnrUE1tLp4Ud23ig5y6NeSXotsg3B+Tr/2tUoczVERDRYDDsx5gsA3ziCH6AXFGXLXA2dTn5SsBfunZ0NMldCRESDxbATY61eLfwCGDEsGQWZyXKXQ6eRl+SFVgL2NDixp8EpdzlERDQIDDsx1uzRAgDOL8qCJPHmn0pn0ADTc4ObPr71Zb3M1RAR0WAw7MRYiyc4Jzw0X4eU71sjDQCCYcfnD8hcDRERnSmGnRhy+/xo8wbfcoad+DHVokdGih5NHW58eqBZ7nKIiOgMMezE0GGHCwISLKkajBjG+TrxQqeR8B/n5AMA3tzOoSwionjDsBNDNb13OZ8ynDf+jDffLxsJAHi/qhHt3dxkkIgonnBTwRiqbQ2GHbOrPrxrMndLHhp+nxc2my3i2Nm815NHmDE+14SvGzvwRmUdbv1WUTTKJCKiGGDYiZG2bg/aXV5IQmD7Dhs8khEAd0seKva6arzhcKDKZQ4fO5v3WpIk3HTBaPznWza88lkNfjTbCo2Gq+mIiOIBh7FiJNSrkxboRM6IUdwtOQayR1qjujP1VdPyYUrSoaalG5v3N0WpSiIiGmoMOzESCjvpAW5MF69SDDpcXVYAAHhlS43M1RAR0UAx7MSAPyBQ1+oCAJj9DDvx7MYLCgEAH+61o6alS+ZqiIhoIBh2YqDR2QOPP4AkvQYpolvucugsWLNTMW/ccAgB/HHzN3KXQ0REA8CwEwOhHoBRmSnglNb4t+SisQCAN744jMMOhlciIqVj2ImBQ83BD0RrVqrMlVA0zBididljs+ALCDzzEXt3iIiUjmFniLn8Epo63QCAUVkpMldD0bL0khIAwBtf1KG+zSVzNUREdCoMO0PM7g5uZZSbnoQUA7c1UotzrZm4oCgLXr/A/79xn9zlEBHRKTDsDLGjbi0AYDR7dVTnvkvHAQDeqDyMr+ra5C2GiIhOimFnCPkCAk2eYG/O6GzO11ELt9uNiooKuOv3YM5IA4QAHv4fGwIBIXdpRETUD4adIbS3xQe/kJCs1yLHZJS7HIqSyspKPL1+I17fWosUdys0wo+d9U78rfKw3KUREVE/GHaG0A578O7Yo7NTIElcdK4medYSWEunY8KUaRifHrzOK/+1B0edPTJXRkREJ2LYGUJ6jQSDFMBoLjlXtaIUL6xmLdq6vbjvja84nEVEpDBcHjSErpmQDL+zCYXD0+QuhQbJ7/PCZrNFHLPZbAgEjl1T4fdiQVo9XuzMxSf7m7Fi3Ye4eGTw3xEGgyHcrqysDEYjhzOJiGKNYWeISRKg1XAIK17Z66rxhsOBKpc5fMxWsQ2WsVMw5rg2ex0OjBs3DLaOJLy6qxsb3/sIKRo/Jk4/DwDQUL0P9wCYNWtW7H8JIqIEx7BDdBrZI62wlk4Pf99Q3XdfneyRVpw7cxI6dzagurkLTfmzMVl7JOJ1REQkD87ZIYoSSZJwaakFGSl6eDUG7BcW+Dl/h4hIdgw7RFFk1GnxnSn50Ao/OpCM8j1HIQQDDxGRnBh2iKIsI9WAMZ6DkCCwt7EDnx5olrskIqKExrBDNATMASeKJDsAYHttG/Z3Gk7zCiIiGioMO0RDZLjUiTljswEAezqN+OcBbjhIRCQHhh2iIVRWmIHzrJkAgL/sduH/fnJQ5oqIiBIPww7REDu/KAslqW4AwK/+uQery/dx0jIRUQwx7BDFwLg0D34wLgkA8LtN+/HI21Vclk5EFCPcVJAoBiQJWDQuGeeMH4Nfvl2Fl7fUoKa5EzeO9SNFf2yHbd5Sgogo+hh2iGLoxgtGIyvNiHv/ugOb97fg870uXJATgEkX4C0liIiGCIexiGLs8sl5eOO2WchIktCjScYnrWnoyhiD3NElcpdGRKRK7NkhksHkkWb86sJ0PPJRC5o9OmzaY4fFmIz53f5Tvs7tdqOysrLPcQ5/ERGdHMMOkUwykjS4IMOFVtNobPmmBUfdOvz8QydqpP348RwrUo19/3pWVlbi6fUbkWc91gvE4S8iolNj2CGSkSQBMwozYc1Kxb++PIgWjw6ry/fhpYpDuG1uEa47dxTSk/QRr8mzlvBu6kREZ4BzdogUICvNiFkZLiyZnorRWSlo6fJg5btf44KVm/DLf+zCAXun3CUSEcUt9uwQxYDf54XNZos4ZrPZEAikhb+XJGDWSAOWff98vLm9Hs9/chD77Z14eUsNXt5Sg28VZ+P8TC+4HyER0Zlh2CGKAXtdNd5wOFDlMoeP2Sq2wTJ2Csac0Fan1eCamQW4esZIVHzTghcrDmHjnqP4ZH8zPgGQqk1FR10bJuSlw6Bj5ywR0ekw7BDFSPZIa8Rcm4bqfadsL0kSZo/Nxuyx2ahr7cbLWw7hL1sOocunwUf7mlBxsAWT8tMx3C+d8jxERImO/ywkigMFmSlYfsVErF1oxmRTD4Yl6+HxBbC9tg0bm1Px970udLp9cpdJRKRI7NkhUoj+5vV4PB4AgMFgAADs37MLhclpuGjSJFS3dOHzg62wd7jx9709KH98A743LgnfypOg00jh14RwLx4iSlQMO0QK0f+8no3QJpkwcfp5vd/3zvORJBRlp8GalYp/vrcBtZpcOJGCl2wu/KWyE8M79mNm6djwebgXDxElMoYdIgXpb16PNnVY+NiJ83wkSUKm34Eso0Bqyfn4vLoV3UhDQ9Y0VPlTcWHxcKQnR+7TQ0SUaBh2iFRAIwFTRg7DuFwT3tq4BUf1OfimqQuHWroxszADWYNYrs5bUyQGXmdKBAw7RCpi1GkxyncYFoMbrcPG4bDDhc+qW5GiTcX4Rg8uEAKSNLDVW7w1RWLgdaZEwLBDpEIpkhfzpo3AfnsnPtnfjE63D09t7cK/mz/DvQvG4Vxr5oDOw1tTJAZeZ1I7Lj0nUilJklBiMeHG8wsxNtUNnQb47GArrvnTFlzzxy34x456uH2nvss6EZEaqKZn55lnnsFvfvMbNDQ0oLS0FGvWrMG3vvUtucsikp1Bp8FEkwd3X5CDzzrMeOOLOmw91Iqth1qRnqTDhSXDcWHJcEzKN2NMTiqMOu2Azz3Y+R4nvu7EJfYDPc9A6unv3NGYj8K5LkT96+/vhtx/L1QRdtavX49ly5bhmWeewezZs/GnP/0Jl112GXbv3o1Ro0bJXR6RImSnaLBy/mTcdfFYrN9Wh9e31qHR2YN3djbgnZ0NAIITnTNTDRiWYkDA44KjKwVffFEHjSRBkoCermTUfd6J7ANfQCtJaHO0YF9tI1LS0iFJgAYCPR0OnL/HhZKiQiTrtUg2aJFi0CJJr4VBq4FOK2Hfnt1448MvkJNfAAnAgR1boDUkY9ykc6CTBHQS0HjozOeN9Df/ZGfFRmiSzRh/zkxoB3negf6seJ3rIgTg8wcgjvueaLBO/LuhhL8Xqgg7q1evxo9//GPceuutAIA1a9bg/fffx7PPPotVq1bJXB2RsuSZk7FsfgnuurgYO+oc+GhvEz472IK9jR1w9vjQ3OlBc6ent7UWjvae416tQ9NRL3D06HGHstAa0cSCf+zvAfbvPXUhxvH4uqX364L5AIDapuNOkzIdd5W3Ifvzj2FK0vU+9Eg1aiEE4A8I+IWAPyDg8QXQ5fGjsdmJRtNU7GtPgtcfCD6sVwGShIP24Hk1ydPx/73XBvPHH8CcrEdmqgEZqQZkpOiRkRL8rzlFD6MuGM70Og0MWg00EiBwLAhUNXuhy58AXV5xOCQYPFocavchr7kLqQYtUow6pOi10GjkuaWH1x9AS6cH9o4eNLb34KizB43OHjT0ft3Q3oOjbd3o9qZBHP3muFem4cN/tSH73x/C3PueWExJsKQbYTEnITc9CZbeR1aqQbbfj5QlEBDo8vjQ6grAVDAOKQUT4AsI6N1a7Gryoqi9B7nmJFlqi/uw4/F4UFlZiV/84hcRxxcuXIiKigqZqiJSPq1GQllhJsoKg5OVhRBo6nCjudODtm4PvrRV4aO9TcgpKEJACASEgP3wIcy0ZsFaNAa+gMCBA9/gixoHMiwjw22ajzZgTE46hmXloNvrh8vjg8vrh8vjh683mDg7u9Dm8kGjMyAgBNxuNyBpICQNAr3JwScktLgEWlwdZ/qbAT7vsW9PWH0WgIROj0Cnx4X6NtdZvIMAkAI46iO+//fmDmDzRxGtkvVapBq1SDHokGrUIa336zSjDskGLfRaDfRaCTpNsOdLpwk+tBoNAkJACIGAQO97jN7vg1/7AwLdHh+6PH50u4P/bev2oKnDDUe3FwNzYliR0OkV6GzpBlq6T/lKnUZCjsmI4elJMBl1SDFokWrUIdWohUGrhUYK/lnTaCRoJQkaCce+1gR7DKU+P59iIfRnyx8A/OGvRe/Xvf+gCASP+wICLo8f3R4/ujw+dPd+3e3xocvtR5c7+Pf8mDSgua736xRUbOmEKc+O68+VZ7Ql7sNOc3Mz/H4/LBZLxHGLxYLGxsZ+X+N2u4P/c+3V3t4OAHA6nVGtraurC7Vf74Sn59j/LBprD0BrTMPe9LR+vx9sm6E8N9uooM2hA9iKRnR1dWGgvDVV8B6yw4/g3wsJgKg5gJTkHGRnBe/D1dJcBX2dHWna9vDreuoPYIIpB6Xm0pOeu6pqLz46bEfu6OAuz1W2j6A1pmH81BnwC8AbkNB4uBZTrdnILbCixyvQ7RNw+QR6fCL4gdk7tKaRAL0EGHUSmhoOY8/hNmTn5kErCegkgQM7KqDXJ6Fk8jT4hYTGwzUos2ajwDqmN/QE0OUV6Oj9utMr0OUNfgD4AgK+AOANINx7I/U+PB43Ot0+6I+bC+T1eKHX6+CX9OjxifBrutxAV+eA3/qo0kiAySAhI0lCRpIGw4waZCZrMMwoISNZg6bag6g82IS8UYXhyNFQewjTi3KQWzgGXd4AOtwCbe4A2noCaHOL8H+dbgEPgMOuLhy2y/P7kfJIENAIP3RaDbQS4Pe6kZ2eCq3PFfXP2dD5xOnGXkWcq6+vFwBERUVFxPFf/epXYty4cf2+5pFHHhHo7ZHmgw8++OCDDz7i+1FXV3fKrBD3PTvZ2dnQarV9enHsdnuf3p6QBx98EPfee2/4+0AggNbWVmRlZQ14w7XTcTqdKCgoQF1dHdLT06NyTjo7vCbKwuuhPLwmysLrcXpCCHR0dCA/P/+U7eI+7BgMBpSVlaG8vBzf+973wsfLy8vx3e9+t9/XGI3GPkvghg0bNiT1paen8w+pwvCaKAuvh/LwmigLr8epmc3m07aJ+7ADAPfeey9uvPFGzJgxAxdccAGee+451NbW4vbbb5e7NCIiIpKZKsLOtddei5aWFjz22GNoaGjApEmT8O6776KwsFDu0oiIiEhmqgg7AHDHHXfgjjvukLuMMKPRiEceeYQ7qSoIr4my8HooD6+JsvB6RI8kBPfKJCIiIvXijUCJiIhI1Rh2iIiISNUYdoiIiEjVGHaGyDPPPAOr1YqkpCSUlZXhk08+kbskRVu1ahVmzpwJk8mEnJwcXHXVVdi7N/JGkkIIrFixAvn5+UhOTsa8efNQVVUV0cbtduOuu+5CdnY2UlNT8R//8R84fPhwRBuHw4Ebb7wRZrMZZrMZN954I9ra2iLa1NbW4jvf+Q5SU1ORnZ2Nu+++Gx6PJ6KNzWbD3LlzkZycjBEjRuCxxx47/ZblcWrVqlWQJAnLli0LH+P1iL36+nr88Ic/RFZWFlJSUjB16lRUVlaGn+c1iS2fz4eHHnoIVqsVycnJKCoqwmOPPYZAIBBuw2uiEGd3swbqz+uvvy70er14/vnnxe7du8XSpUtFamqqqKmpkbs0xbr00kvFCy+8IHbt2iV27NghrrjiCjFq1CjR2dkZbvPrX/9amEwm8fe//13YbDZx7bXXiry8POF0OsNtbr/9djFixAhRXl4utm/fLi666CJxzjnnCJ/PF27z7W9/W0yaNElUVFSIiooKMWnSJHHllVeGn/f5fGLSpEnioosuEtu3bxfl5eUiPz9fLFmyJNymvb1dWCwWcd111wmbzSb+/ve/C5PJJH77298O8TsVe1u3bhWjR48WU6ZMEUuXLg0f5/WIrdbWVlFYWChuueUW8fnnn4vq6mqxceNGceDAgXAbXpPY+tWvfiWysrLEO++8I6qrq8Ubb7wh0tLSxJo1a8JteE2UgWFnCJx77rni9ttvjzg2fvx48Ytf/EKmiuKP3W4XAMTmzZuFEEIEAgGRm5srfv3rX4fb9PT0CLPZLP74xz8KIYRoa2sTer1evP766+E29fX1QqPRiPfee08IIcTu3bsFAPHZZ5+F22zZskUAEF9//bUQQoh3331XaDQaUV9fH27z2muvCaPRKNrb24UQQjzzzDPCbDaLnp6ecJtVq1aJ/Px8EQgEov12yKajo0MUFxeL8vJyMXfu3HDY4fWIvQceeEDMmTPnpM/zmsTeFVdcIX70ox9FHFu0aJH44Q9/KITgNVESDmNFmcfjQWVlJRYuXBhxfOHChaioqJCpqvgTuhN9ZmYmAKC6uhqNjY0R76vRaMTcuXPD72tlZSW8Xm9Em/z8fEyaNCncZsuWLTCbzTjvvPPCbc4//3yYzeaINpMmTYq418qll14Kt9sdHjLYsmUL5s6dG7H/xaWXXoojR47g0KFD0XwrZHXnnXfiiiuuwPz58yOO83rE3ttvv40ZM2bg6quvRk5ODqZNm4bnn38+/DyvSezNmTMHmzZtwr59+wAAX331FT799FNcfvnlAHhNlIRhJ8qam5vh9/v73ITUYrH0uVkp9U8IgXvvvRdz5szBpEmTACD83p3qfW1sbITBYEBGRsYp2+Tk5PT5mTk5ORFtTvw5GRkZMBgMp2wT+l4t1/n111/H9u3bsWrVqj7P8XrE3sGDB/Hss8+iuLgY77//Pm6//XbcfffdePnllwHwmsjhgQcewPXXX4/x48dDr9dj2rRpWLZsGa6//noAvCZKopodlJXmxLunCyGidkd1tVuyZAl27tyJTz/9tM9zg3lfT2zTX/totBG9k/zUcJ3r6uqwdOlSbNiwAUlJSSdtx+sRO4FAADNmzMDKlSsBANOmTUNVVRWeffZZ3HTTTeF2vCaxs379erz66qtYt24dSktLsWPHDixbtgz5+fm4+eabw+14TeTHnp0oy87Ohlar7ZOS7XZ7n0RNfd111114++238eGHH2LkyJHh47m5uQD6/uvj+Pc1NzcXHo8HDofjlG2OHj3a5+c2NTVFtDnx5zgcDni93lO2sdvtAPr+Ky4eVVZWwm63o6ysDDqdDjqdDps3b8bvfvc76HS6k/5rkNdj6OTl5WHixIkRxyZMmIDa2loA/Dsih5///Of4xS9+geuuuw6TJ0/GjTfeiHvuuSfcG8prohwMO1FmMBhQVlaG8vLyiOPl5eWYNWuWTFUpnxACS5YswZtvvokPPvgAVqs14nmr1Yrc3NyI99Xj8WDz5s3h97WsrAx6vT6iTUNDA3bt2hVuc8EFF6C9vR1bt24Nt/n888/R3t4e0WbXrl1oaGgIt9mwYQOMRiPKysrCbT7++OOIZZ0bNmxAfn4+Ro8eHaV3RT6XXHIJbDYbduzYEX7MmDEDixcvxo4dO1BUVMTrEWOzZ8/usx3Dvn37wjc85t+R2Ovu7oZGE/kxqtVqw0vPeU0UJIaToRNGaOn5f//3f4vdu3eLZcuWidTUVHHo0CG5S1Osn/70p8JsNouPPvpINDQ0hB/d3d3hNr/+9a+F2WwWb775prDZbOL666/vdwnnyJEjxcaNG8X27dvFxRdf3O8SzilTpogtW7aILVu2iMmTJ/e7hPOSSy4R27dvFxs3bhQjR46MWMLZ1tYmLBaLuP7664XNZhNvvvmmSE9PV/USzuNXYwnB6xFrW7duFTqdTjz++ONi//794i9/+YtISUkRr776argNr0ls3XzzzWLEiBHhpedvvvmmyM7OFvfff3+4Da+JMjDsDJE//OEPorCwUBgMBjF9+vTwEmrqH4B+Hy+88EK4TSAQEI888ojIzc0VRqNRXHjhhcJms0Wcx+VyiSVLlojMzEyRnJwsrrzySlFbWxvRpqWlRSxevFiYTCZhMpnE4sWLhcPhiGhTU1MjrrjiCpGcnCwyMzPFkiVLIpZrCiHEzp07xbe+9S1hNBpFbm6uWLFihaqXb54Ydng9Yu9///d/xaRJk4TRaBTjx48Xzz33XMTzvCax5XQ6xdKlS8WoUaNEUlKSKCoqEsuXLxdutzvchtdEGXjXcyIiIlI1ztkhIiIiVWPYISIiIlVj2CEiIiJVY9ghIiIiVWPYISIiIlVj2CEiIiJVY9ghIiIiVWPYISIiIlVj2CGimJEkCf/zP/8jaw0rVqzA1KlTZa3hdJTwPhGpCcMOESWU++67D5s2bZK7DCKKIZ3cBRARDZTX64Verz+rc6SlpSEtLS1KFRFRPGDPDhGdkb/97W+YPHkykpOTkZWVhfnz56Orqwvbtm3DggULkJ2dDbPZjLlz52L79u2nPNcDDzyAkpISpKSkoKioCA8//DC8Xm/4+dCQ05///GcUFRXBaDTipZdeQlZWFtxud8S5vv/97+Omm246bf0nDmPdcsstuOqqq/Db3/4WeXl5yMrKwp133hlRx6n0N+Q0bNgwvPjiiwAAj8eDJUuWIC8vD0lJSRg9ejRWrVoVbrt//35ceOGFSEpKwsSJE1FeXj6gn0tEA8eeHSIasIaGBlx//fV48skn8b3vfQ8dHR345JNPIIRAR0cHbr75Zvzud78DADz11FO4/PLLsX//fphMpn7PZzKZ8OKLLyI/Px82mw0/+clPYDKZcP/994fbHDhwAH/961/x97//HVqtFsXFxVi6dCnefvttXH311QCA5uZmvPPOO3jvvfcG9Xt9+OGHyMvLw4cffogDBw7g2muvxdSpU/GTn/xkUOc73u9+9zu8/fbb+Otf/4pRo0ahrq4OdXV1AIBAIIBFixYhOzsbn332GZxOJ5YtW3bWP5OIIjHsENGANTQ0wOfzYdGiRSgsLAQATJ48GQBw8cUXR7T905/+hIyMDGzevBlXXnllv+d76KGHwl+PHj0aP/vZz7B+/fqIsOPxePDKK69g+PDh4WM33HADXnjhhXDY+ctf/oKRI0di3rx5g/q9MjIysHbtWmi1WowfPx5XXHEFNm3aFJWwU1tbi+LiYsyZMweSJIXfNwDYuHEj9uzZg0OHDmHkyJEAgJUrV+Kyyy47659LRMdwGIuIBuycc87BJZdcgsmTJ+Pqq6/G888/D4fDAQCw2+24/fbbUVJSArPZDLPZjM7OTtTW1p70fH/7298wZ84c5ObmIi0tDQ8//HCf9oWFhRFBBwB+8pOfYMOGDaivrwcAvPDCC7jlllsgSdKgfq/S0lJotdrw93l5ebDb7YM614luueUW7NixA+PGjcPdd9+NDRs2hJ/bs2cPRo0aFQ46AHDBBRdE5ecS0TEMO0Q0YFqtFuXl5fjXv/6FiRMn4ve//z3GjRuH6upq3HLLLaisrMSaNWtQUVGBHTt2ICsrCx6Pp99zffbZZ7juuutw2WWX4Z133sGXX36J5cuX92mfmpra57XTpk3DOeecg5dffhnbt2+HzWbDLbfcMujf68RJz5IkIRAIDOi1kiRBCBFx7Pj5PtOnT0d1dTX+67/+Cy6XC9dccw1+8IMfAECf14XOR0TRxWEsIjojkiRh9uzZmD17Nn75y1+isLAQb731Fj755BM888wzuPzyywEAdXV1aG5uPul5/v3vf6OwsBDLly8PH6upqRlwHbfeeiuefvpp1NfXY/78+SgoKBj8L3UWhg8fjoaGhvD3+/fvR3d3d0Sb9PR0XHvttbj22mvxgx/8AN/+9rfR2tqKiRMnora2FkeOHEF+fj4AYMuWLTGtnygRMOwQ0YB9/vnn2LRpExYuXIicnBx8/vnnaGpqwoQJEzB27Fi88sormDFjBpxOJ37+858jOTn5pOcaO3Ysamtr8frrr2PmzJn45z//ibfeemvAtSxevBj33Xcfnn/+ebz88svR+PUG5eKLL8batWtx/vnnIxAI4IEHHojoKXr66aeRl5eHqVOnQqPR4I033kBubi6GDRuG+fPnY9y4cbjpppvw1FNPwel0RoQ/IooODmMR0YClp6fj448/xuWXX46SkhI89NBDeOqpp3DZZZfhz3/+MxwOB6ZNm4Ybb7wRd999N3Jyck56ru9+97u45557sGTJEkydOhUVFRV4+OGHz6iW73//+0hLS8NVV10Vhd9ucJ566ikUFBTgwgsvxA033ID77rsPKSkp4efT0tLwxBNPYMaMGZg5cyYOHTqEd999FxqNBhqNBm+99RbcbjfOPfdc3HrrrXj88cdl+12I1EoS/Q0aExHFgQULFmDChAnh5e5ERP1h2CGiuNPa2ooNGzZg8eLF2L17N8aNGyd3SUSkYJyzQ0RxZ/r06XA4HHjiiSf6BJ3S0tKTTnT+05/+hMWLFw/453zyySen3POms7NzwOciIvmwZ4eIVKWmpuakt3qwWCwn3c25Py6XK7yXT3/Gjh17xvURUewx7BAREZGqcTUWERERqRrDDhEREakaww4RERGpGsMOERERqRrDDhEREakaww4RERGpGsMOERERqRrDDhEREana/wPYWooVOmtzywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(sl['salary_in_usd'],kde = True, bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "id": "17c9f15d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='job_title'>"
      ]
     },
     "execution_count": 656,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,3))\n",
    "sl1=pd.DataFrame(sl['job_title'].value_counts().head(5))\n",
    "sns.barplot(sl1.index,sl1['job_title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "id": "712d5bab",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='company_location'>"
      ]
     },
     "execution_count": 706,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,3))\n",
    "sl2=pd.DataFrame(sl['company_location'].value_counts().head(5))\n",
    "sns.barplot(sl2.index,sl2['company_location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "f6f7d2fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,3))\n",
    "sl1=pd.DataFrame(sl.groupby('company_location')[\"salary_in_usd\"].sum().sort_values(ascending=False).head(5))\n",
    "sns.barplot(sl1.index,sl1['salary_in_usd']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "id": "f7eaa410",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,3))\n",
    "sl1=pd.DataFrame(sl.groupby('job_title')[\"salary_in_usd\"].sum().sort_values(ascending=False).head(10))\n",
    "sns.barplot(sl1.index,sl1['salary_in_usd']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "50956d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary_in_usd'>"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(sl['salary_in_usd'])\n",
    "#outliers datalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "5d6e2b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "out=[]\n",
    "def outlier(x):\n",
    "    threshold = 3\n",
    "    mean = np.mean(x)\n",
    "    std = np.std(x)\n",
    "    for i in x:\n",
    "        z = (i-mean)/std\n",
    "        if np.abs(z)>threshold:\n",
    "            out.append(i)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "345ac3de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[360000, 410000, 899920, 500000, 360000, 600000, 375000, 600000, 450000, 400000, 456621]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = outlier(sl['salary_in_usd'])\n",
    "print(lst)\n",
    "len(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "6a5197e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old Shape:  (1349, 8)\n",
      "Current Shape (1311, 8)\n"
     ]
    }
   ],
   "source": [
    "Q1 = np.percentile(sl[\"salary_in_usd\"], 25, interpolation = \"midpoint\")\n",
    "Q3 = np.percentile(sl['salary_in_usd'], 75, interpolation = \"midpoint\")\n",
    "IQR = Q3-Q1\n",
    "print('Old Shape: ',sl.shape)\n",
    "# lower line\n",
    "upper =np.where(sl[\"salary_in_usd\"] >= (Q3+1.5*IQR))\n",
    "# upper line\n",
    "lower = np.where(sl['salary_in_usd'] <= (Q1-1.5*IQR))\n",
    "sl.drop(upper[0],inplace=True)\n",
    "sl.drop(lower[0],inplace=True)\n",
    "print('Current Shape', sl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "e8861a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# upper bound\n",
    "upper =np.where(sl[\"salary_in_usd\"] >= (Q3+1.5*IQR))\n",
    "# lower bound\n",
    "lower = np.where(sl['salary_in_usd'] <= (Q1-1.5*IQR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "91b34faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Shape (1311, 8)\n"
     ]
    }
   ],
   "source": [
    "sl.drop(upper[0],inplace=True)\n",
    "sl.drop(lower[0],inplace=True)\n",
    "print('New Shape', sl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "a23e85eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary_in_usd'>"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(sl[\"salary_in_usd\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "dcc14e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_year             0\n",
       "experience_level      0\n",
       "job_title             0\n",
       "salary_in_usd         0\n",
       "employee_residence    0\n",
       "remote_ratio          0\n",
       "company_location      0\n",
       "company_size          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "id": "76cce6de",
   "metadata": {
    "collapsed": true
   },
   "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>data_type</th>\n",
       "      <th>null_count</th>\n",
       "      <th>unique_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>work_year</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experience_level</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_title</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary_in_usd</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee_residence</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remote_ratio</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_location</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_size</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   data_type  null_count  unique_count\n",
       "work_year              int64           0             3\n",
       "experience_level      object           0             4\n",
       "job_title             object           0            87\n",
       "salary_in_usd          int64           0           615\n",
       "employee_residence    object           0            60\n",
       "remote_ratio           int64           0             3\n",
       "company_location      object           0            57\n",
       "company_size          object           0             3"
      ]
     },
     "execution_count": 682,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.DataFrame(index=sl.columns)\n",
    "temp['data_type'] = sl.dtypes\n",
    "temp['null_count'] = sl.isnull().sum()\n",
    "temp['unique_count'] = sl.nunique()\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "id": "f15952eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_year              int64\n",
       "experience_level      object\n",
       "job_title             object\n",
       "salary_in_usd          int64\n",
       "employee_residence    object\n",
       "remote_ratio           int64\n",
       "company_location      object\n",
       "company_size          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 681,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "id": "e911331d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for label, content in sl.items():\n",
    "    if pd.api.types.is_string_dtype(content):\n",
    "        sl[label] = content.astype('category').cat.as_ordered()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "id": "e05f4ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_year                int64\n",
       "experience_level      category\n",
       "job_title             category\n",
       "salary_in_usd            int64\n",
       "employee_residence    category\n",
       "remote_ratio             int64\n",
       "company_location      category\n",
       "company_size          category\n",
       "dtype: object"
      ]
     },
     "execution_count": 684,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "20a98db0",
   "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>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>208</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>400</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>274</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>483</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>372</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>306</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>46</td>\n",
       "      <td>252</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>58</td>\n",
       "      <td>408</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>134</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>181</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1311 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      work_year  experience_level  job_title  salary_in_usd  \\\n",
       "0             3                 1         47            208   \n",
       "1             3                 4         71            400   \n",
       "2             3                 4         71            274   \n",
       "3             3                 4         68            483   \n",
       "4             3                 4         68            372   \n",
       "...         ...               ...        ...            ...   \n",
       "1344          1                 3         14            306   \n",
       "1345          2                 4         46            252   \n",
       "1346          2                 4         58            408   \n",
       "1347          2                 3         40            134   \n",
       "1348          2                 3         82            181   \n",
       "\n",
       "      employee_residence  remote_ratio  company_location  company_size  \n",
       "0                     18             3                18             3  \n",
       "1                     58             1                55             2  \n",
       "2                     58             1                55             2  \n",
       "3                     58             3                55             2  \n",
       "4                     58             3                55             2  \n",
       "...                  ...           ...               ...           ...  \n",
       "1344                   5             2                 6             2  \n",
       "1345                  26             2                26             1  \n",
       "1346                  58             3                55             1  \n",
       "1347                  26             3                26             1  \n",
       "1348                  58             1                55             1  \n",
       "\n",
       "[1311 rows x 8 columns]"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for label,content in sl.items():\n",
    "    sl[label] = pd.Categorical(content).codes+1\n",
    "sl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "0913115b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "586\n"
     ]
    }
   ],
   "source": [
    "print(sl[\"salary_in_usd\"].min())\n",
    "print(sl[\"salary_in_usd\"].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "bc92fde7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "586"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl[\"salary_in_usd\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "b841da63",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "612df72d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sl.drop('salary_in_usd', axis = 1)\n",
    "y = sl['salary_in_usd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "1e75ee37",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "530ffb6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error as mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "741089de",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "id": "809f2626",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "8e8e4ce4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error for Linear Regression Model is 96.54084211505389\n"
     ]
    }
   ],
   "source": [
    "train_predict = lr.predict(X_train)\n",
    "k = mean_absolute_error(train_predict, y_train)\n",
    "print('Training Mean Absolute Error for Linear Regression Model is',k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "300bc1b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Mean Absoulte Error for Linear Regression Model is 98.2597820826245\n"
     ]
    }
   ],
   "source": [
    "test_predict = lr.predict(X_test)\n",
    "k = mean_absolute_error(test_predict, y_test)\n",
    "print('Testing Mean Absoulte Error for Linear Regression Model is', k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "33d76ebf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error for Linear Regression Model is 124.5333771529128\n"
     ]
    }
   ],
   "source": [
    "lr_pred = lr.predict(X_test)\n",
    "print(\"Mean Squared Error for Linear Regression Model is\", np.sqrt(mse(y_test,lr_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "5a3c219b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_jobs=-1)"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfr = RandomForestRegressor(n_jobs=-1)\n",
    "rfr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "id": "4f95329e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error for Random Forest Regression is: 42.03876327551028\n"
     ]
    }
   ],
   "source": [
    "train_predict = rfr.predict(X_train)\n",
    "k = mae(train_predict, y_train)\n",
    "print('Training Mean Absolute Error for Random Forest Regression is:',k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "93352e9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Mean Absolute Error for Random Forest Regression is:  85.014923148833\n"
     ]
    }
   ],
   "source": [
    "test_predict = rfr.predict(X_test)\n",
    "k = mae(test_predict, y_test)\n",
    "print('Testing Mean Absolute Error for Random Forest Regression is: ',k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "1cd8960a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error for Random Forest Regression is 109.13021038090048\n"
     ]
    }
   ],
   "source": [
    "rfr_pred = rfr.predict(X_test)\n",
    "print(\"Mean Squared Error for Random Forest Regression is\", np.sqrt(mse(y_test,rfr_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f630e8bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "knr_pred = knr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "b1f8e9d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "692d4c6f",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr = KNeighborsRegressor()\n",
    "knr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "6435e36b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error for K Neighbors Regression is: 77.24350797266513\n"
     ]
    }
   ],
   "source": [
    "train_predict = knr.predict(X_train)\n",
    "k = mae(train_predict, y_train)\n",
    "print('Training Mean Absolute Error for K Neighbors Regression is:',k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "id": "273b902f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Mean Absolute Error for K Neighbors Regression is:  90.75196304849884\n"
     ]
    }
   ],
   "source": [
    "test_predict = knr.predict(X_test)\n",
    "k = mae(test_predict, y_test)\n",
    "print('Testing Mean Absolute Error for K Neighbors Regression is: ',k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "10968ce0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error for K Neigbors Regression Model is 117.01300217917594\n"
     ]
    }
   ],
   "source": [
    "knr_pred = knr.predict(X_test)\n",
    "print(\"Mean Squared Error for K Neigbors Regression Model is\",np.sqrt(mse(y_test,knr_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "1fdaf7c2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 516,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dtr = DecisionTreeRegressor()\n",
    "dtr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "id": "9f58068c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Mean Absolute Error for Decision Tree Regression is:  24.00161392644308\n"
     ]
    }
   ],
   "source": [
    "train_predict = dtr.predict(X_train)\n",
    "k = mae(train_predict, y_train)\n",
    "print('Training Mean Absolute Error for Decision Tree Regression is: ',k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "id": "67f9cdd3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Mean Absolute Error for Decision Tree Reggression is:  99.77537333080058\n"
     ]
    }
   ],
   "source": [
    "test_predict = dtr.predict(X_test)\n",
    "k = mae(test_predict, y_test)\n",
    "print('Testing Mean Absolute Error for Decision Tree Reggression is: ',k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "id": "00d83a64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error for Decision Tree Regression Model is 133.43739110544476\n"
     ]
    }
   ],
   "source": [
    "dtr_pred = dtr.predict(X_test)\n",
    "print(\"Mean Squared Error for Decision Tree Regression Model is\",np.sqrt(mse(y_test,dtr_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "350e18d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr_pred = rfr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "84a7c328",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred\n",
    "lr_pred = lr.predict(X_test)\n",
    "dtr_pred = dtr.predict(X_test)\n",
    "knr_pred = knr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "99f91f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error as mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "d8816b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109.13021038090048"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse(y_test,rfr_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "e583d293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124.5333771529128"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse(y_test,lr_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "d868bc5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134.07877816344916"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse(y_test,dtr_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "a0d766bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117.01300217917594"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse(y_test,knr_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "caebddd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr.n_neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "id": "7f02a907",
   "metadata": {},
   "outputs": [],
   "source": [
    "#tuning-hiperparametre ayarlaması"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "id": "165176d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr_params = {'max_depth': list(range(1,10)),\n",
    "            'max_features': [3,5,10,15],\n",
    "            'n_estimators' : [100, 200, 500, 1000, 2000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "id": "78a9f869",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr_cv_model = GridSearchCV(rfr, \n",
    "                           rf_params, \n",
    "                           cv = 10, \n",
    "                            n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "id": "8cc0f84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "4d07ef54",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestRegressor(n_jobs=-1), n_jobs=-1,\n",
       "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       "                         'max_features': [3, 5, 10, 15],\n",
       "                         'n_estimators': [100, 200, 500, 1000, 2000]})"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr_cv_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "fb4abcde",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9, 'max_features': 3, 'n_estimators': 500}"
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "id": "b5c3bb23",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr_tuned = RandomForestRegressor(max_depth=9,\n",
    "                                 max_features=3, \n",
    "                                 n_estimators = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "ac31ccda",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=9, max_features=3, n_estimators=500)"
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr_tuned.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "id": "9714f4ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning mean squared Error for Random Forest Regression Model is 101.31464736022149\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf_tuned.predict(X_test)\n",
    "print(\"Tuning mean squared Error for Random Forest Regression Model is\", np.sqrt(mse(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "id": "4053d9a0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_ranks = pd.Series(rf_tuned.feature_importances_*100,\n",
    "                        index = X_train.columns,name = 'Importance').sort_values(ascending=True,inplace=False) \n",
    "ax = model_ranks. plot(kind = \"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 692,
   "id": "49dc25ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "    from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "id": "b74e9ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5684172524675366"
      ]
     },
     "execution_count": 700,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1955884b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}