SalaryPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salary Prediction Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Collection and Pre processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing required libraries\n",
    "\n",
    "- pandas : For holding data in a data frame,\n",
    "- matplolib's pyplot function: For plotting data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sampling\n",
    "\n",
    "- Loading a external dataset surveyed by stackOverflow with its users about various info such as salary, education, location, etc. in the year 2023.\n",
    "- we use this dataset to make predictions about the salary distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Country</th>\n",
       "      <th>Employment</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>Industry</th>\n",
       "      <th>ConvertedCompYearly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18-24 years old</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25-34 years old</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Employed, full-time</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>9</td>\n",
       "      <td>Information Services, IT, Software Development...</td>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45-54 years old</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Employed, full-time</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>23</td>\n",
       "      <td>Information Services, IT, Software Development...</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25-34 years old</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Employed, full-time</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25-34 years old</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Employed, full-time;Independent contractor, fr...</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>4</td>\n",
       "      <td>Other</td>\n",
       "      <td>23456.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age                   Country  \\\n",
       "0  18-24 years old                       NaN   \n",
       "1  25-34 years old  United States of America   \n",
       "2  45-54 years old  United States of America   \n",
       "3  25-34 years old  United States of America   \n",
       "4  25-34 years old               Philippines   \n",
       "\n",
       "                                          Employment  \\\n",
       "0                                                NaN   \n",
       "1                                Employed, full-time   \n",
       "2                                Employed, full-time   \n",
       "3                                Employed, full-time   \n",
       "4  Employed, full-time;Independent contractor, fr...   \n",
       "\n",
       "                                        EdLevel YearsCodePro  \\\n",
       "0                                           NaN          NaN   \n",
       "1  Bachelor’s degree (B.A., B.S., B.Eng., etc.)            9   \n",
       "2  Bachelor’s degree (B.A., B.S., B.Eng., etc.)           23   \n",
       "3  Bachelor’s degree (B.A., B.S., B.Eng., etc.)            7   \n",
       "4  Bachelor’s degree (B.A., B.S., B.Eng., etc.)            4   \n",
       "\n",
       "                                            Industry  ConvertedCompYearly  \n",
       "0                                                NaN                  NaN  \n",
       "1  Information Services, IT, Software Development...             285000.0  \n",
       "2  Information Services, IT, Software Development...             250000.0  \n",
       "3                                                NaN             156000.0  \n",
       "4                                              Other              23456.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Datasets/survey_results_public.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Selection\n",
    "\n",
    "- We use the columns 'Country', 'EdLevel', 'YearsCodePro', 'Employment', 'ConvertedCompYearly' for training our model\n",
    "- Hence we delete the unwanted columns and use only the columns that are required for training the model.\n",
    "- we also changed 'ConvertedCompYearly' to 'Salary' for easier use later on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Education</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>9</td>\n",
       "      <td>Employed, full-time</td>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>23</td>\n",
       "      <td>Employed, full-time</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Country                                     Education  \\\n",
       "0                       NaN                                           NaN   \n",
       "1  United States of America  Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "2  United States of America  Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "\n",
       "  Experience           Employment    Salary  \n",
       "0        NaN                  NaN       NaN  \n",
       "1          9  Employed, full-time  285000.0  \n",
       "2         23  Employed, full-time  250000.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['Country', 'EdLevel', 'YearsCodePro', 'Employment', 'ConvertedCompYearly']]\n",
    "df = df.rename({\n",
    "    'EdLevel' : 'Education',\n",
    "    'ConvertedCompYearly':'Salary',\n",
    "    'YearsCodePro' : 'Experience'\n",
    "    },\n",
    "        axis=1)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data cleaning\n",
    "\n",
    "- The null values in the dataset are not useful for training the model\n",
    "- We are only using the columns were the type of employment is Employed, full-time to better suit our needs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Education</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>9</td>\n",
       "      <td>Employed, full-time</td>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>23</td>\n",
       "      <td>Employed, full-time</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>7</td>\n",
       "      <td>Employed, full-time</td>\n",
       "      <td>156000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>4</td>\n",
       "      <td>Employed, full-time;Independent contractor, fr...</td>\n",
       "      <td>23456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>21</td>\n",
       "      <td>Employed, full-time</td>\n",
       "      <td>96828.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Country  \\\n",
       "1                           United States of America   \n",
       "2                           United States of America   \n",
       "3                           United States of America   \n",
       "4                                        Philippines   \n",
       "5  United Kingdom of Great Britain and Northern I...   \n",
       "\n",
       "                                           Education Experience  \\\n",
       "1       Bachelor’s degree (B.A., B.S., B.Eng., etc.)          9   \n",
       "2       Bachelor’s degree (B.A., B.S., B.Eng., etc.)         23   \n",
       "3       Bachelor’s degree (B.A., B.S., B.Eng., etc.)          7   \n",
       "4       Bachelor’s degree (B.A., B.S., B.Eng., etc.)          4   \n",
       "5  Some college/university study without earning ...         21   \n",
       "\n",
       "                                          Employment    Salary  \n",
       "1                                Employed, full-time  285000.0  \n",
       "2                                Employed, full-time  250000.0  \n",
       "3                                Employed, full-time  156000.0  \n",
       "4  Employed, full-time;Independent contractor, fr...   23456.0  \n",
       "5                                Employed, full-time   96828.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Salary'].notnull()]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 48019 entries, 1 to 89171\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Country     48019 non-null  object \n",
      " 1   Education   48019 non-null  object \n",
      " 2   Experience  47825 non-null  object \n",
      " 3   Employment  48007 non-null  object \n",
      " 4   Salary      48019 non-null  float64\n",
      "dtypes: float64(1), object(4)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country         0\n",
       "Education       0\n",
       "Experience    194\n",
       "Employment     12\n",
       "Salary          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country       0\n",
       "Education     0\n",
       "Experience    0\n",
       "Employment    0\n",
       "Salary        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 39155 entries, 1 to 89171\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Country     39155 non-null  object \n",
      " 1   Education   39155 non-null  object \n",
      " 2   Experience  39155 non-null  object \n",
      " 3   Salary      39155 non-null  float64\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df = df[df['Employment'] == 'Employed, full-time']\n",
    "df = df.drop('Employment', axis = 1)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "United States of America                                10569\n",
       "Germany                                                  3201\n",
       "United Kingdom of Great Britain and Northern Ireland     3072\n",
       "Canada                                                   1807\n",
       "India                                                    1600\n",
       "                                                        ...  \n",
       "Botswana                                                    1\n",
       "Sierra Leone                                                1\n",
       "Guyana                                                      1\n",
       "Guinea-Bissau                                               1\n",
       "Antigua and Barbuda                                         1\n",
       "Name: count, Length: 165, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The countries where there are not enough samples could reflect a wrong prediction if used for prediction\n",
    "- So we remove the countries where there are samples less than a threshold number that seems to be sufficient for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 500\n",
    "country_counts = df['Country'].value_counts()\n",
    "countries_to_keep = country_counts[country_counts >= threshold].index\n",
    "df = df[df['Country'].isin(countries_to_keep)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Education</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>9</td>\n",
       "      <td>285000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>23</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>7</td>\n",
       "      <td>156000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Country                                     Education  \\\n",
       "1  United States of America  Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "2  United States of America  Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "3  United States of America  Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "\n",
       "  Experience    Salary  \n",
       "1          9  285000.0  \n",
       "2         23  250000.0  \n",
       "3          7  156000.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "United States of America                                10569\n",
       "Germany                                                  3201\n",
       "United Kingdom of Great Britain and Northern Ireland     3072\n",
       "Canada                                                   1807\n",
       "India                                                    1600\n",
       "France                                                   1458\n",
       "Netherlands                                              1002\n",
       "Australia                                                 985\n",
       "Brazil                                                    972\n",
       "Spain                                                     966\n",
       "Sweden                                                    882\n",
       "Italy                                                     826\n",
       "Poland                                                    740\n",
       "Switzerland                                               511\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United States of America',\n",
       "       'United Kingdom of Great Britain and Northern Ireland',\n",
       "       'Australia', 'Netherlands', 'Germany', 'Sweden', 'France', 'Spain',\n",
       "       'Brazil', 'Italy', 'Canada', 'Switzerland', 'India', 'Poland'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the outliers from the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax =  plt.subplots(1,1, figsize = (12, 8))\n",
    "df.boxplot('Salary', 'Country', ax = ax)\n",
    "plt.title('Salary ($) vs Country')\n",
    "plt.suptitle('Employee Salaries in each country')\n",
    "plt.xticks(rotation = 87)\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Salary'] <= 600000] \n",
    "df = df[df['Salary'] >= 10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax =  plt.subplots(1,1, figsize = (12, 8))\n",
    "df.boxplot('Salary', 'Country', ax = ax)\n",
    "plt.title('Salary ($) vs Country')\n",
    "plt.suptitle('Employee Salaries in each country')\n",
    "plt.xticks(rotation = 87)\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['9', '23', '7', '21', '3', 'Less than 1 year', '10', '2', '6',\n",
       "       '14', '5', '15', '19', '13', '16', '28', '24', '25', '12', '4',\n",
       "       '20', '8', '18', '27', '17', '43', '38', '33', '29', '22', '1',\n",
       "       '11', '42', '26', '31', '30', '36', '39', '34', '40', '32', '44',\n",
       "       '37', '35', '48', 'More than 50 years', '45', '41', '50', '46',\n",
       "       '49', '47'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Experience'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experience_cleaner(value):\n",
    "    if value == \"More than 50 years\":\n",
    "        return 50\n",
    "    elif value == \"Less than 1 year\":\n",
    "        return 0.5\n",
    "    return float(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Experience'] = df['Experience'].apply(experience_cleaner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachelor’s degree (B.A., B.S., B.Eng., etc.)',\n",
       "       'Some college/university study without earning a degree',\n",
       "       'Master’s degree (M.A., M.S., M.Eng., MBA, etc.)',\n",
       "       'Professional degree (JD, MD, Ph.D, Ed.D, etc.)',\n",
       "       'Associate degree (A.A., A.S., etc.)',\n",
       "       'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)',\n",
       "       'Primary/elementary school', 'Something else'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def education_cleaner(education_level):\n",
    "    if \"Bachelor’s degree\" in education_level:\n",
    "        return 'Bachelors degree'\n",
    "    if \"Master’s degree\" in education_level:\n",
    "        return 'Masters degree'\n",
    "    if \"Professional degree\" in education_level:\n",
    "        return 'Post graduate'\n",
    "    return 'Less than Bachelors'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachelors degree', 'Less than Bachelors', 'Masters degree',\n",
       "       'Post graduate'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Education'] = df['Education'].apply(education_cleaner)\n",
    "df['Education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le_education = LabelEncoder()\n",
    "df['Education'] =  le_education.fit_transform(df['Education'])\n",
    "df['Education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 12,  0,  7,  4, 10,  3,  9,  1,  6,  2, 11,  5,  8])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le_country = LabelEncoder()\n",
    "df['Country'] =  le_country.fit_transform(df['Country'])\n",
    "df['Country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As We used a label Encoder it is better to use a algorithm that does not get confused with such encodings\n",
    "- Random Forest Regressor, DecisionTreeRegressor are good examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Salary', axis=1) # Features\n",
    "y = df['Salary'] # Target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>DecisionTreeRegressor(random_state=9)</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\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=9)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(random_state=9)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_tree_reg = DecisionTreeRegressor(random_state = 9)\n",
    "dec_tree_reg.fit(X, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dec_tree_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$53,650.26\n"
     ]
    }
   ],
   "source": [
    "error = np.sqrt(mean_squared_error(y, y_pred))\n",
    "print(\"${:,.2f}\".format(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=9)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=9)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=9)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest_Reg = RandomForestRegressor(random_state= 9)\n",
    "random_forest_Reg.fit(X, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = random_forest_Reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$53,729.31\n"
     ]
    }
   ],
   "source": [
    "error = np.sqrt(mean_squared_error(y,y_pred))\n",
    "print(\"${:,.2f}\".format(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=DecisionTreeRegressor(random_state=9),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 1, 2, 4, 6, 8, 10, 12, 14]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=DecisionTreeRegressor(random_state=9),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 1, 2, 4, 6, 8, 10, 12, 14]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=9)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=9)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=DecisionTreeRegressor(random_state=9),\n",
       "             param_grid={'max_depth': [None, 1, 2, 4, 6, 8, 10, 12, 14]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_depth = [None, 1, 2, 4, 6, 8, 10, 12, 14]\n",
    "parameters = {'max_depth' : max_depth}\n",
    "\n",
    "regressor = DecisionTreeRegressor(random_state = 9)\n",
    "gs = GridSearchCV(regressor, parameters, scoring='neg_mean_squared_error')\n",
    "gs.fit(X, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$54,973.43\n"
     ]
    }
   ],
   "source": [
    "regressor = gs.best_estimator_\n",
    "\n",
    "regressor.fit(X, y.values)\n",
    "y_pred = regressor.predict(X)\n",
    "error = np.sqrt(mean_squared_error(y, y_pred))\n",
    "print(\"${:,.2f}\".format(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Germany', 'Masters degree', '1']], dtype='<U14')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([[\"Germany\", \"Masters degree\", 1]])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#- Transform the 'Country' column using the fitted LabelEncoder\n",
    "X[:, 0] = le_country.transform(X[:, 0])\n",
    "\n",
    "#- Assuming le_education has been fitted to the 'Education' column\n",
    "#- Transform the 'Education' column\n",
    "X[:, 1] = le_education.transform(X[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sprod\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([48909.60968661])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.astype(float)\n",
    "y_pred = regressor.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'model' : regressor,\n",
    "        'le_country' : le_country,\n",
    "        'le_education' : le_education}\n",
    "with open('saved_steps.pkl', 'wb') as file:\n",
    "    pickle.dump(data, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('saved_steps.pkl', 'rb') as file:\n",
    "    data = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor_loaded = data['model']\n",
    "le_country = data['le_country']\n",
    "le_education = data['le_education']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sprod\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([48909.60968661])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor_loaded.predict(X)\n",
    "y_pred"
   ]
  }
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