Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9d874e66-67fe-466a-9eac-66b4eb6884e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b575c08-0cfb-4554-b2e6-5d079accfe80",
   "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>language</th>\n",
       "      <th>city</th>\n",
       "      <th>level</th>\n",
       "      <th>years_of_experience</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>php</td>\n",
       "      <td>lt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>php</td>\n",
       "      <td>UK</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.net</td>\n",
       "      <td>Danija</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>php</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  language     city  level  years_of_experience  salary\n",
       "0      php       lt    NaN                   10       1\n",
       "1      NaN      NaN    1.0                    2       3\n",
       "2      php       UK    3.0                   10    3000\n",
       "3     .net   Danija    2.0                   10    2700\n",
       "4      php  Vilnius    3.0                   10    2800"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the csv file #\n",
    "df = pd.read_csv('data/salaries-2023.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2bb0705-ace9-4323-9f9a-f0ce3cae5842",
   "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>language</th>\n",
       "      <th>city</th>\n",
       "      <th>level</th>\n",
       "      <th>years_of_experience</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>php</td>\n",
       "      <td>UK</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>.net</td>\n",
       "      <td>Danija</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>php</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>php</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>java</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  language     city  level  years_of_experience  salary\n",
       "2      php       UK    3.0                   10    3000\n",
       "3     .net   Danija    2.0                   10    2700\n",
       "4      php  Vilnius    3.0                   10    2800\n",
       "5      php  Vilnius    3.0                   10    3400\n",
       "6     java  Vilnius    3.0                   10    2500"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove the 'NAN' values in our data #\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "290e27d0-974d-48d5-8b7d-d7c0f22b8ae9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(760, 5)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 760 entries, 2 to 761\n",
      "Data columns (total 5 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   language             760 non-null    object \n",
      " 1   city                 760 non-null    object \n",
      " 2   level                760 non-null    float64\n",
      " 3   years_of_experience  760 non-null    int64  \n",
      " 4   salary               760 non-null    int64  \n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 35.6+ KB\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cbf06de2-7d51-4f93-90e3-39108543c3b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            level  years_of_experience        salary\n",
      "count  760.000000           760.000000    760.000000\n",
      "mean     2.203947             5.234211   2839.484211\n",
      "std      0.718097             2.948462   2071.858144\n",
      "min      1.000000             1.000000    670.000000\n",
      "25%      2.000000             3.000000   1800.000000\n",
      "50%      2.000000             5.000000   2500.000000\n",
      "75%      3.000000             8.000000   3300.000000\n",
      "max      3.000000            10.000000  30000.000000\n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8999a9ec-91d4-4eff-95b8-1c072781a735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "language\n",
       "php           217\n",
       "js            181\n",
       ".net          105\n",
       "java           93\n",
       "python         37\n",
       "go             26\n",
       "c++            24\n",
       "node           19\n",
       "kotlin         15\n",
       "ruby           14\n",
       "swift           6\n",
       "rust            4\n",
       "flutter         4\n",
       "sql             2\n",
       "sap             2\n",
       "scala           2\n",
       "salesforce      1\n",
       "vue             1\n",
       "blockchain      1\n",
       "devops          1\n",
       "progress        1\n",
       "openedge        1\n",
       "pascal          1\n",
       "uipath          1\n",
       "unity           1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.language.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "38777d43-5e82-4dc1-98fd-3a9313286b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(596, 5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter data only top 4 languages to use #\n",
    "languages = ['php', 'js', '.net', 'java']\n",
    "df = df[df['language'].isin(languages)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e724b569-f88d-4077-a069-08b8d345687e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Vilnius                     337\n",
       "Kaunas                      176\n",
       "Klaipėda                     11\n",
       "JAV                           9\n",
       "UK                            8\n",
       "Kaune                         7\n",
       "Šiauliai                      7\n",
       "Vokietija                     6\n",
       "Danija                        4\n",
       "Klaipeda                      4\n",
       "Norvegija                     2\n",
       "Olandija                      2\n",
       "Šveicarija                    2\n",
       "Panevėžys                     2\n",
       "Mažeikiai                     2\n",
       "kaunas                        2\n",
       "Birštonas                     1\n",
       "Jonava                        1\n",
       "Prancūzija                    1\n",
       "Pietų Afrikos Respublika      1\n",
       "KAUNAS                        1\n",
       "Baltarusija                   1\n",
       "Marijampole                   1\n",
       "Siauliai                      1\n",
       "Suomija                       1\n",
       "Panevezys                     1\n",
       "Alytus                        1\n",
       "Tauragė                       1\n",
       "Klaipėdoje                    1\n",
       "Skandinavija                  1\n",
       "Rumsiskes                     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter cities #\n",
    "df.city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7d470dd9-b518-4982-8914-acf7e9bee328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Vilnius                     337\n",
       "Kaunas                      176\n",
       "Klaipėda                     11\n",
       "JAV                           9\n",
       "UK                            8\n",
       "Kaune                         7\n",
       "Šiauliai                      7\n",
       "Vokietija                     6\n",
       "Danija                        4\n",
       "Klaipeda                      4\n",
       "Norvegija                     2\n",
       "Olandija                      2\n",
       "Šveicarija                    2\n",
       "Panevėžys                     2\n",
       "Mažeikiai                     2\n",
       "kaunas                        2\n",
       "Birštonas                     1\n",
       "Jonava                        1\n",
       "Prancūzija                    1\n",
       "Pietų Afrikos Respublika      1\n",
       "KAUNAS                        1\n",
       "Baltarusija                   1\n",
       "Marijampole                   1\n",
       "Siauliai                      1\n",
       "Suomija                       1\n",
       "Panevezys                     1\n",
       "Alytus                        1\n",
       "Tauragė                       1\n",
       "Klaipėdoje                    1\n",
       "Skandinavija                  1\n",
       "Rumsiskes                     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vilnius_names = ['Vilniuj', 'Vilniua', 'VILNIUJE', 'VILNIUS', 'vilnius', 'Vilniuje']\n",
    "condition = df['city'].isin(vilnius_names)\n",
    "df.loc[condition, 'city'] = 'Vilnius'\n",
    "\n",
    "kaunas_names = ['KAUNAS', 'kaunas', 'Kaune']\n",
    "condifiton = df['city'].isin(kaunas_names)\n",
    "df.loc[condition, 'city'] = 'Kaunas'\n",
    "\n",
    "df.city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "95669b5d-3f37-477c-8e59-8ea33d710139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(513, 5)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop all cities and left with the top 2 cities #\n",
    "cities = ['Vilnius', 'Kaunas']\n",
    "df = df[df['city'].isin(cities)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d76eb0bc-a26e-47ac-896b-b8443ee53aad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>language</th>\n",
       "      <th>city</th>\n",
       "      <th>level</th>\n",
       "      <th>years_of_experience</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>js</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>js</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>.net</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>php</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>php</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>js</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>php</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>php</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>js</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>js</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>php</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>java</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>php</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>php</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>java</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>php</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>php</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>java</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>js</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>4660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>java</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>php</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>java</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>.net</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>.net</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>php</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>js</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>java</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>.net</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>js</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>js</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    language     city  level  years_of_experience  salary\n",
       "150       js  Vilnius    3.0                    6   14000\n",
       "704       js  Vilnius    3.0                    7    8400\n",
       "609     .net  Vilnius    3.0                   10    8000\n",
       "143      php   Kaunas    3.0                   10    7500\n",
       "735      php  Vilnius    3.0                    9    7000\n",
       "29        js  Vilnius    3.0                    9    6000\n",
       "306      php   Kaunas    3.0                    9    5500\n",
       "339      php  Vilnius    3.0                   10    5500\n",
       "738       js  Vilnius    3.0                   10    5300\n",
       "462       js  Vilnius    3.0                   10    5261\n",
       "195      php  Vilnius    3.0                   10    5000\n",
       "56      java   Kaunas    3.0                   10    5000\n",
       "185      php   Kaunas    3.0                   10    5000\n",
       "241      php  Vilnius    3.0                   10    5000\n",
       "385     java  Vilnius    3.0                   10    5000\n",
       "235      php   Kaunas    3.0                   10    5000\n",
       "522      php   Kaunas    3.0                    9    5000\n",
       "165     java  Vilnius    3.0                    5    5000\n",
       "606       js  Vilnius    3.0                    9    4660\n",
       "577     java  Vilnius    3.0                    9    4500\n",
       "528      php  Vilnius    3.0                    7    4500\n",
       "115     java   Kaunas    3.0                   10    4500\n",
       "299     .net   Kaunas    3.0                    7    4500\n",
       "607     .net  Vilnius    3.0                   10    4500\n",
       "341      php   Kaunas    3.0                   10    4400\n",
       "356       js  Vilnius    3.0                   10    4400\n",
       "418     java  Vilnius    3.0                   10    4300\n",
       "387     .net  Vilnius    3.0                   10    4300\n",
       "116       js  Vilnius    3.0                   10    4232\n",
       "114       js  Vilnius    3.0                    6    4200"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by highest salary #\n",
    "df_sorted = df.sort_values(by='salary', ascending=False)\n",
    "df_sorted.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "24263ce9-d191-41c1-adca-c2b68a3041a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.iloc[:, -2:-1]\n",
    "y = df.iloc[:, -1].values\n",
    "\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "352b9111-35fd-4cea-8c2a-083acad7cc21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(508, 5)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['salary'] <= 6000]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "97c80c82-195a-45ed-a325-ca5a91429d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.iloc[:, -2]\n",
    "y = df.iloc[:, -1].values\n",
    "\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1b8d2b9c-da4b-402a-8acf-755fea33aaa4",
   "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>level</th>\n",
       "      <th>years_of_experience</th>\n",
       "      <th>salary</th>\n",
       "      <th>lang_.net</th>\n",
       "      <th>lang_java</th>\n",
       "      <th>lang_js</th>\n",
       "      <th>lang_php</th>\n",
       "      <th>city_Kaunas</th>\n",
       "      <th>city_Vilnius</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2800</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3400</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2100</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1181</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1800</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1800</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2700</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2250</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1400</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2100</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2800</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2600</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>6000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2800</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2500</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2070</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    level  years_of_experience  salary  lang_.net  lang_java  lang_js  \\\n",
       "4     3.0                   10    2800      False      False    False   \n",
       "5     3.0                   10    3400      False      False    False   \n",
       "6     3.0                   10    2500      False       True    False   \n",
       "7     2.0                    4    2100      False      False    False   \n",
       "9     1.0                    1    1181       True      False    False   \n",
       "10    3.0                    8    3500      False      False    False   \n",
       "11    2.0                    2    1800       True      False    False   \n",
       "12    1.0                    5    1800      False      False    False   \n",
       "15    2.0                    5    2700      False      False    False   \n",
       "17    2.0                   10    2000      False      False     True   \n",
       "18    2.0                    7    2250       True      False    False   \n",
       "19    1.0                    1    1400      False      False     True   \n",
       "20    2.0                    6    2100      False      False    False   \n",
       "21    3.0                    6    2800       True      False    False   \n",
       "24    2.0                    3    2600      False       True    False   \n",
       "26    3.0                    6    3000      False      False    False   \n",
       "29    3.0                    9    6000      False      False     True   \n",
       "30    3.0                    9    2800       True      False    False   \n",
       "32    2.0                    3    2500      False      False    False   \n",
       "34    2.0                    3    2070      False      False     True   \n",
       "\n",
       "    lang_php  city_Kaunas  city_Vilnius  \n",
       "4       True        False          True  \n",
       "5       True        False          True  \n",
       "6      False        False          True  \n",
       "7       True         True         False  \n",
       "9      False         True         False  \n",
       "10      True        False          True  \n",
       "11     False         True         False  \n",
       "12      True         True         False  \n",
       "15      True        False          True  \n",
       "17     False         True         False  \n",
       "18     False        False          True  \n",
       "19     False         True         False  \n",
       "20      True        False          True  \n",
       "21     False        False          True  \n",
       "24     False        False          True  \n",
       "26      True        False          True  \n",
       "29     False        False          True  \n",
       "30     False         True         False  \n",
       "32      True         True         False  \n",
       "34     False        False          True  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot = pd.get_dummies(df['language'], prefix='lang')\n",
    "df = df.join(one_hot)\n",
    "df = df.drop('language', axis=1)\n",
    "\n",
    "one_hot = pd.get_dummies(df['city'], prefix='city')\n",
    "df = df.join(one_hot)\n",
    "df = df.drop('city', axis=1)\n",
    "\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0b51e018-59df-47b5-9a3a-513944187ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ff3c920d-1b2f-4ddf-8390-d36ca12e7875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3. 10.]\n",
      " [ 3. 10.]\n",
      " [ 3. 10.]\n",
      " [ 2.  4.]\n",
      " [ 1.  1.]\n",
      " [ 3.  8.]\n",
      " [ 2.  2.]\n",
      " [ 1.  5.]\n",
      " [ 2.  5.]\n",
      " [ 2. 10.]]\n"
     ]
    }
   ],
   "source": [
    "# take only years and level for x #\n",
    "# take only salary for y #\n",
    "\n",
    "x = df.iloc[:,0:2].values\n",
    "y = df.iloc[:,2].values\n",
    "print(x[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c00b3dc6-c442-4069-a2f1-278a124bab73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2800 3400 2500 2100 1181 3500 1800 1800 2700 2000]\n"
     ]
    }
   ],
   "source": [
    "print(y[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e0501994-2ba5-443f-83c2-1a1ad36da015",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a78a0433-067c-42cb-b6cf-8543529a80cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(355, 2)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5364d932-859f-4806-b21e-a75192e0c3f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "caab4af0-90f5-47f4-a7a0-2a6cec2a38a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1ea97cfb-e607-4478-86c1-0c6624f1a790",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2147.17274853 3353.02488055 3539.8686334  1221.58624578 1315.0081222\n",
      " 3166.1811277  3166.1811277  3539.8686334  2885.91549843 3446.44675698\n",
      " 2240.59462495 3072.75925128 2147.17274853 3446.44675698 1315.0081222\n",
      " 2053.75087211 3539.8686334  2053.75087211 2147.17274853 2334.01650138\n",
      " 1221.58624578 2240.59462495 2334.01650138 3446.44675698 2147.17274853\n",
      " 3446.44675698 3539.8686334  2334.01650138 2240.59462495 2147.17274853\n",
      " 3539.8686334  2240.59462495 3539.8686334  1315.0081222  2979.33737486\n",
      " 3259.60300413 2240.59462495 3072.75925128 1221.58624578 3446.44675698\n",
      " 2334.01650138 2334.01650138 1221.58624578 2240.59462495 2240.59462495\n",
      " 3539.8686334  1315.0081222  2801.12588349 2240.59462495 2147.17274853\n",
      " 3353.02488055 3259.60300413 3539.8686334  1221.58624578 2240.59462495\n",
      " 2427.4383778  1315.0081222  3259.60300413 2147.17274853 3353.02488055\n",
      " 3446.44675698 2427.4383778  1221.58624578 3539.8686334  3166.1811277\n",
      " 2427.4383778  2147.17274853 2520.86025422 2147.17274853 2053.75087211\n",
      " 1221.58624578 3259.60300413 2427.4383778  2334.01650138 2427.4383778\n",
      " 2147.17274853 2240.59462495 3166.1811277  3539.8686334  2147.17274853\n",
      " 2427.4383778  3539.8686334  3539.8686334  1221.58624578 3166.1811277\n",
      " 3353.02488055 3259.60300413 1501.85187505 3539.8686334  1221.58624578\n",
      " 2147.17274853 2147.17274853 2334.01650138 3446.44675698 2053.75087211\n",
      " 2979.33737486 2614.28213065 3166.1811277  3259.60300413 2334.01650138\n",
      " 3353.02488055 2240.59462495 2053.75087211 3539.8686334  3539.8686334\n",
      " 2053.75087211 3166.1811277  3259.60300413 3446.44675698 2240.59462495\n",
      " 2147.17274853 1315.0081222  1595.27375147 3539.8686334  2334.01650138\n",
      " 1315.0081222  1221.58624578 2240.59462495 1221.58624578 2801.12588349\n",
      " 2334.01650138 2240.59462495 2427.4383778  2334.01650138 1221.58624578\n",
      " 2801.12588349 2240.59462495 3166.1811277  3259.60300413 2334.01650138\n",
      " 2053.75087211 3259.60300413 3259.60300413 2147.17274853 3539.8686334\n",
      " 1315.0081222  2240.59462495 3259.60300413 3539.8686334  2053.75087211\n",
      " 2427.4383778  1315.0081222  3353.02488055 2240.59462495 1315.0081222\n",
      " 2979.33737486 2427.4383778  1221.58624578 2240.59462495 2147.17274853\n",
      " 2427.4383778  3353.02488055 2427.4383778 ]\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "29e00abc-42b1-47b7-ad8f-76f68467229b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3726.71238625 1221.58624578]\n"
     ]
    }
   ],
   "source": [
    "salaries = model.predict([[3, 12], [1, 1]])\n",
    "print(salaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9efc8506-42d2-4331-bdab-73e2dde01154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 0.6772413073003669 (67.724%)\n"
     ]
    }
   ],
   "source": [
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"R2 Score : {r2} ({r2:.3%})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "904a9f62-3e61-4f9d-bae5-af6d5c9251ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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