PredictRemuneration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df  = pd.read_csv('survey_results_public.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ResponseId</th>\n",
       "      <th>MainBranch</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Country</th>\n",
       "      <th>US_State</th>\n",
       "      <th>UK_Country</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>Age1stCode</th>\n",
       "      <th>LearnCode</th>\n",
       "      <th>YearsCode</th>\n",
       "      <th>...</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Sexuality</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Accessibility</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>SurveyLength</th>\n",
       "      <th>SurveyEase</th>\n",
       "      <th>ConvertedCompYearly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>18 - 24 years</td>\n",
       "      <td>Coding Bootcamp;Other online resources (ex: vi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "      <td>62268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>I am a student who is learning to code</td>\n",
       "      <td>Student, full-time</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>11 - 17 years</td>\n",
       "      <td>Other online resources (ex: videos, blogs, etc...</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>18-24 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I am not primarily a developer, but I write co...</td>\n",
       "      <td>Student, full-time</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>11 - 17 years</td>\n",
       "      <td>Other online resources (ex: videos, blogs, etc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>18-24 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Austria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>11 - 17 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>35-44 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>I am deaf / hard of hearing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>England</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>5 - 10 years</td>\n",
       "      <td>Friend or family member</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>25-34 years old</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>None of the above</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ResponseId                                         MainBranch  \\\n",
       "0           1                     I am a developer by profession   \n",
       "1           2             I am a student who is learning to code   \n",
       "2           3  I am not primarily a developer, but I write co...   \n",
       "3           4                     I am a developer by profession   \n",
       "4           5                     I am a developer by profession   \n",
       "\n",
       "                                          Employment  \\\n",
       "0  Independent contractor, freelancer, or self-em...   \n",
       "1                                 Student, full-time   \n",
       "2                                 Student, full-time   \n",
       "3                                 Employed full-time   \n",
       "4  Independent contractor, freelancer, or self-em...   \n",
       "\n",
       "                                             Country US_State UK_Country  \\\n",
       "0                                           Slovakia      NaN        NaN   \n",
       "1                                        Netherlands      NaN        NaN   \n",
       "2                                 Russian Federation      NaN        NaN   \n",
       "3                                            Austria      NaN        NaN   \n",
       "4  United Kingdom of Great Britain and Northern I...      NaN    England   \n",
       "\n",
       "                                             EdLevel     Age1stCode  \\\n",
       "0  Secondary school (e.g. American high school, G...  18 - 24 years   \n",
       "1       Bachelor’s degree (B.A., B.S., B.Eng., etc.)  11 - 17 years   \n",
       "2       Bachelor’s degree (B.A., B.S., B.Eng., etc.)  11 - 17 years   \n",
       "3    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)  11 - 17 years   \n",
       "4    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   5 - 10 years   \n",
       "\n",
       "                                           LearnCode YearsCode  ...  \\\n",
       "0  Coding Bootcamp;Other online resources (ex: vi...       NaN  ...   \n",
       "1  Other online resources (ex: videos, blogs, etc...         7  ...   \n",
       "2  Other online resources (ex: videos, blogs, etc...       NaN  ...   \n",
       "3                                                NaN       NaN  ...   \n",
       "4                            Friend or family member        17  ...   \n",
       "\n",
       "               Age Gender Trans                Sexuality  \\\n",
       "0  25-34 years old    Man    No  Straight / Heterosexual   \n",
       "1  18-24 years old    Man    No  Straight / Heterosexual   \n",
       "2  18-24 years old    Man    No        Prefer not to say   \n",
       "3  35-44 years old    Man    No  Straight / Heterosexual   \n",
       "4  25-34 years old    Man    No                      NaN   \n",
       "\n",
       "                      Ethnicity                Accessibility  \\\n",
       "0  White or of European descent            None of the above   \n",
       "1  White or of European descent            None of the above   \n",
       "2             Prefer not to say            None of the above   \n",
       "3  White or of European descent  I am deaf / hard of hearing   \n",
       "4  White or of European descent            None of the above   \n",
       "\n",
       "        MentalHealth           SurveyLength                  SurveyEase  \\\n",
       "0  None of the above  Appropriate in length                        Easy   \n",
       "1  None of the above  Appropriate in length                        Easy   \n",
       "2  None of the above  Appropriate in length                        Easy   \n",
       "3                NaN  Appropriate in length  Neither easy nor difficult   \n",
       "4                NaN  Appropriate in length                        Easy   \n",
       "\n",
       "  ConvertedCompYearly  \n",
       "0             62268.0  \n",
       "1                 NaN  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4                 NaN  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"Country\", \"EdLevel\", \"YearsCodePro\", \"Employment\", \"ConvertedCompYearly\"]]\n",
    "df = df.rename({\"ConvertedCompYearly\": \"Salary\"}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>62268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Student, full-time</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Student, full-time</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>10</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Country  \\\n",
       "0                                           Slovakia   \n",
       "1                                        Netherlands   \n",
       "2                                 Russian Federation   \n",
       "3                                            Austria   \n",
       "4  United Kingdom of Great Britain and Northern I...   \n",
       "\n",
       "                                             EdLevel YearsCodePro  \\\n",
       "0  Secondary school (e.g. American high school, G...          NaN   \n",
       "1       Bachelor’s degree (B.A., B.S., B.Eng., etc.)          NaN   \n",
       "2       Bachelor’s degree (B.A., B.S., B.Eng., etc.)          NaN   \n",
       "3    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)          NaN   \n",
       "4    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)           10   \n",
       "\n",
       "                                          Employment   Salary  \n",
       "0  Independent contractor, freelancer, or self-em...  62268.0  \n",
       "1                                 Student, full-time      NaN  \n",
       "2                                 Student, full-time      NaN  \n",
       "3                                 Employed full-time      NaN  \n",
       "4  Independent contractor, freelancer, or self-em...      NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>62268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>4</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>51552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>5</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>46482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>6</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>77290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>2</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>17748.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country                                            EdLevel YearsCodePro  \\\n",
       "0   Slovakia  Secondary school (e.g. American high school, G...          NaN   \n",
       "9     Sweden    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)            4   \n",
       "11     Spain       Bachelor’s degree (B.A., B.S., B.Eng., etc.)            5   \n",
       "12   Germany    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)            6   \n",
       "16    Turkey       Bachelor’s degree (B.A., B.S., B.Eng., etc.)            2   \n",
       "\n",
       "                                           Employment   Salary  \n",
       "0   Independent contractor, freelancer, or self-em...  62268.0  \n",
       "9                                  Employed full-time  51552.0  \n",
       "11                                 Employed full-time  46482.0  \n",
       "12                                 Employed full-time  77290.0  \n",
       "16                                 Employed full-time  17748.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df[\"Salary\"].notnull()]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 46844 entries, 0 to 83438\n",
      "Data columns (total 5 columns):\n",
      "Country         46844 non-null object\n",
      "EdLevel         46823 non-null object\n",
      "YearsCodePro    46391 non-null object\n",
      "Employment      46831 non-null object\n",
      "Salary          46844 non-null float64\n",
      "dtypes: float64(1), object(4)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country         0\n",
       "EdLevel         0\n",
       "YearsCodePro    0\n",
       "Employment      0\n",
       "Salary          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 40211 entries, 9 to 83438\n",
      "Data columns (total 4 columns):\n",
      "Country         40211 non-null object\n",
      "EdLevel         40211 non-null object\n",
      "YearsCodePro    40211 non-null object\n",
      "Salary          40211 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": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America                                9175\n",
       "India                                                   3385\n",
       "Germany                                                 2753\n",
       "United Kingdom of Great Britain and Northern Ireland    2604\n",
       "Canada                                                  1553\n",
       "France                                                  1396\n",
       "Brazil                                                  1340\n",
       "Spain                                                    890\n",
       "Netherlands                                              835\n",
       "Australia                                                825\n",
       "Poland                                                   775\n",
       "Italy                                                    756\n",
       "Russian Federation                                       741\n",
       "Sweden                                                   735\n",
       "Turkey                                                   458\n",
       "Switzerland                                              438\n",
       "Israel                                                   425\n",
       "Norway                                                   402\n",
       "Mexico                                                   382\n",
       "Ukraine                                                  381\n",
       "Iran, Islamic Republic of...                             348\n",
       "Denmark                                                  347\n",
       "Belgium                                                  346\n",
       "Finland                                                  337\n",
       "Austria                                                  324\n",
       "Argentina                                                309\n",
       "Pakistan                                                 308\n",
       "South Africa                                             308\n",
       "Czech Republic                                           298\n",
       "Portugal                                                 283\n",
       "                                                        ... \n",
       "Congo, Republic of the...                                  4\n",
       "Belize                                                     3\n",
       "Isle of Man                                                3\n",
       "Tajikistan                                                 3\n",
       "Yemen                                                      3\n",
       "Malawi                                                     3\n",
       "Botswana                                                   2\n",
       "Guyana                                                     2\n",
       "Benin                                                      2\n",
       "Andorra                                                    2\n",
       "Suriname                                                   2\n",
       "Haiti                                                      2\n",
       "Niger                                                      2\n",
       "Bhutan                                                     2\n",
       "Saint Lucia                                                1\n",
       "Djibouti                                                   1\n",
       "Burundi                                                    1\n",
       "San Marino                                                 1\n",
       "Lao People's Democratic Republic                           1\n",
       "Monaco                                                     1\n",
       "Lesotho                                                    1\n",
       "Saint Vincent and the Grenadines                           1\n",
       "Gambia                                                     1\n",
       "Libyan Arab Jamahiriya                                     1\n",
       "Dominica                                                   1\n",
       "Mauritania                                                 1\n",
       "Cape Verde                                                 1\n",
       "Guinea                                                     1\n",
       "Saint Kitts and Nevis                                      1\n",
       "Somalia                                                    1\n",
       "Name: Country, Length: 166, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Country\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def short_categories(categories, cutoff):\n",
    "    categorical_map = {}\n",
    "    \n",
    "    for i in range(len(categories)):\n",
    "        if categories.values[i] >= cutoff:\n",
    "            categorical_map[categories.index[i]] = categories.index[i]\n",
    "        else:\n",
    "            categorical_map[categories.index[i]] = \"Other\"\n",
    "    return categorical_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_map = short_categories(df.Country.value_counts(), 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other                                                   10725\n",
       "United States of America                                 9175\n",
       "India                                                    3385\n",
       "Germany                                                  2753\n",
       "United Kingdom of Great Britain and Northern Ireland     2604\n",
       "Canada                                                   1553\n",
       "France                                                   1396\n",
       "Brazil                                                   1340\n",
       "Spain                                                     890\n",
       "Netherlands                                               835\n",
       "Australia                                                 825\n",
       "Poland                                                    775\n",
       "Italy                                                     756\n",
       "Russian Federation                                        741\n",
       "Sweden                                                    735\n",
       "Turkey                                                    458\n",
       "Switzerland                                               438\n",
       "Israel                                                    425\n",
       "Norway                                                    402\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'] = df['Country'].map(country_map)\n",
    "df.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12, 7))\n",
    "df.boxplot('Salary', 'Country', ax=ax)\n",
    "plt.suptitle('Salary (US$) v Country')\n",
    "plt.title('')\n",
    "plt.ylabel('Salary')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"Salary\"] <= 250000]\n",
    "df = df[df[\"Salary\"] >= 10000]\n",
    "df = df[df['Country'] != 'Other']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12, 7))\n",
    "df.boxplot('Salary', 'Country', ax=ax)\n",
    "plt.suptitle('Salary ($) v Country')\n",
    "plt.title('')\n",
    "plt.ylabel('Salary')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4', '5', '6', '2', '18', '21', '16', '10', 'Less than 1 year',\n",
       "       '3', '15', '12', '8', '17', '1', '14', '26', '34', '11', '25',\n",
       "       '24', '22', '13', '9', '7', '39', '20', '27', '23', '30', '19',\n",
       "       '35', '33', '37', '43', '40', '29', '38', '31', '42', '28', '46',\n",
       "       '32', '36', '47', '45', '44', '41', 'More than 50 years', '50',\n",
       "       '49', '48'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.YearsCodePro.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experience(i):\n",
    "    if i == 'More than 50 years':\n",
    "        return 50\n",
    "    elif i == 'Less than 1 year':\n",
    "        return 0.5\n",
    "    return float(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['YearsCodePro'] = df['YearsCodePro'].apply(experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4. ,  5. ,  6. ,  2. , 18. , 21. , 16. , 10. ,  0.5,  3. , 15. ,\n",
       "       12. ,  8. , 17. ,  1. , 14. , 26. , 34. , 11. , 25. , 24. , 22. ,\n",
       "       13. ,  9. ,  7. , 39. , 20. , 27. , 23. , 30. , 19. , 35. , 33. ,\n",
       "       37. , 43. , 40. , 29. , 38. , 31. , 42. , 28. , 46. , 32. , 36. ,\n",
       "       47. , 45. , 44. , 41. , 50. , 49. , 48. ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.YearsCodePro.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Master’s degree (M.A., M.S., M.Eng., MBA, etc.)',\n",
       "       'Bachelor’s degree (B.A., B.S., B.Eng., etc.)',\n",
       "       'Professional degree (JD, MD, etc.)',\n",
       "       'Some college/university study without earning a degree',\n",
       "       'Other doctoral degree (Ph.D., Ed.D., etc.)', 'Something else',\n",
       "       'Primary/elementary school', 'Associate degree (A.A., A.S., etc.)',\n",
       "       'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.EdLevel.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def education(x):\n",
    "    if 'Bachelor’s degree' in x:\n",
    "        return 'Bachelor’s degree'\n",
    "    if 'Master’s degree' in x:\n",
    "        return 'Master’s degree'\n",
    "    if 'Professional degree' in x or 'Other doctoral' in x:\n",
    "        return 'Post grad'\n",
    "    return 'Less than a Bachelors'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EdLevel'] = df['EdLevel'].apply(education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Master’s degree', 'Bachelor’s degree', 'Post grad',\n",
       "       'Less than a Bachelors'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.EdLevel.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lbl_education = LabelEncoder()\n",
    "df['EdLevel'] = lbl_education.fit_transform(df['EdLevel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 3, 1], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.EdLevel.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl_country = LabelEncoder()\n",
    "df['Country'] = lbl_country.fit_transform(df['Country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 12,  4, 15,  2,  3, 14, 16, 11,  6, 17,  1,  7,  8, 10,  0,  5,\n",
       "        9], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Salary', axis=1)\n",
    "y = df['Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear_reg = LinearRegression()\n",
    "linear_reg.fit(X, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = linear_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import numpy as np\n",
    "error = np.sqrt(mean_squared_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41838.096750079996"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=0, splitter='best')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dec_tree_reg = DecisionTreeRegressor(random_state = 0)\n",
    "dec_tree_reg.fit(X, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dec_tree_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = np.sqrt(mean_squared_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31267.17519371074"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nahin\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "random_forest_reg = RandomForestRegressor(random_state=0)\n",
    "random_forest_reg.fit(X, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = random_forest_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = np.sqrt(mean_squared_error(y, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31422.11369091736"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nahin\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "       estimator=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=0, splitter='best'),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'max_depth': [None, 2, 4, 6, 8, 10, 12]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "max_depth = [None, 2,4,6,8,10,12]\n",
    "parameters = {\"max_depth\": max_depth}\n",
    "\n",
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "gs = GridSearchCV(regressor, parameters, scoring='neg_mean_squared_error')\n",
    "gs.fit(X, y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32049.209067047657"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>YearsCodePro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83347</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83354</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>83363</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>83365</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83372</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83374</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83376</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83377</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83378</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83380</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>83382</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>83385</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>83387</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>83389</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>83390</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83392</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>83399</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83401</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83404</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83409</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83411</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83416</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83420</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83424</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83428</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83430</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83432</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83434</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83436</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83438</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26230 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  EdLevel  YearsCodePro\n",
       "9           13        2           4.0\n",
       "11          12        0           5.0\n",
       "12           4        2           6.0\n",
       "16          15        0           2.0\n",
       "17           2        0           6.0\n",
       "23           3        2           2.0\n",
       "24           4        2          18.0\n",
       "26          14        2           5.0\n",
       "27          16        0          21.0\n",
       "29          11        2          16.0\n",
       "31           6        0           2.0\n",
       "33          15        0           2.0\n",
       "35           4        2          10.0\n",
       "36          17        0           0.5\n",
       "41           3        2           3.0\n",
       "45           1        0          15.0\n",
       "55          16        2          12.0\n",
       "57           7        2          16.0\n",
       "58          11        3           3.0\n",
       "62          17        0           5.0\n",
       "67           2        1          10.0\n",
       "68          12        2          10.0\n",
       "69          16        0          18.0\n",
       "70           8        2           2.0\n",
       "75          17        0          15.0\n",
       "76          10        0           8.0\n",
       "77           2        3           8.0\n",
       "81          13        0           3.0\n",
       "86           2        1          17.0\n",
       "87          17        3           1.0\n",
       "...        ...      ...           ...\n",
       "83347        1        3           0.5\n",
       "83354       17        2           3.0\n",
       "83363       17        2           4.0\n",
       "83365       17        0           4.0\n",
       "83372        0        2          17.0\n",
       "83374       17        0           6.0\n",
       "83376       17        0           6.0\n",
       "83377       17        0          25.0\n",
       "83378       17        2           3.0\n",
       "83380       16        1          17.0\n",
       "83382        0        2           4.0\n",
       "83385        2        0           2.0\n",
       "83387        2        1          25.0\n",
       "83389       17        0           5.0\n",
       "83390        1        0          18.0\n",
       "83392        1        0           5.0\n",
       "83399        5        0           6.0\n",
       "83401       17        0           2.0\n",
       "83404       17        0           2.0\n",
       "83409        8        3           6.0\n",
       "83411        5        0           5.0\n",
       "83416       17        0          10.0\n",
       "83420        3        2           4.0\n",
       "83424        4        0          12.0\n",
       "83428        1        2           7.0\n",
       "83430       17        2          21.0\n",
       "83432        2        0           0.5\n",
       "83434       17        0           5.0\n",
       "83436       17        1           4.0\n",
       "83438        1        3           4.0\n",
       "\n",
       "[26230 rows x 3 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['United States of America', 'Master’s degree', '15']],\n",
       "      dtype='<U24')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([[\"United States of America\", 'Master’s degree', 15 ]])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17.,  2., 15.]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:, 0] = lbl_country.transform(X[:,0])\n",
    "X[:, 1] = lbl_education.transform(X[:,1])\n",
    "X = X.astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([148942.20512821])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "data = {\"model\": regressor, \"lbl_country\": lbl_country, \"lbl_education\": lbl_education}\n",
    "with open('saved_steps.pkl', 'wb') as file:\n",
    "    pickle.dump(data, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('saved_steps.pkl', 'rb') as file:\n",
    "    data = pickle.load(file)\n",
    "\n",
    "regressor_loaded = data[\"model\"]\n",
    "lbl_country = data[\"lbl_country\"]\n",
    "lbl_education = data[\"lbl_education\"]"
   ]
  }
 ],
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