SalaryPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8b11fd46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"survey_results_public.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0a94c801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>MainBranch</th>\n",
       "      <th>Hobbyist</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age1stCode</th>\n",
       "      <th>CompFreq</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>Country</th>\n",
       "      <th>CurrencyDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>SurveyEase</th>\n",
       "      <th>SurveyLength</th>\n",
       "      <th>Trans</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>WebframeDesireNextYear</th>\n",
       "      <th>WebframeWorkedWith</th>\n",
       "      <th>WelcomeChange</th>\n",
       "      <th>WorkWeekHrs</th>\n",
       "      <th>YearsCode</th>\n",
       "      <th>YearsCodePro</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>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "      <td>European Euro</td>\n",
       "      <td>...</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>No</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>ASP.NET Core</td>\n",
       "      <td>ASP.NET;ASP.NET Core</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>50.0</td>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Pound sterling</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Somewhat more welcome now than last year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I code primarily as a hobby</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Somewhat more welcome now than last year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</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>Yes</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Albanian lek</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Somewhat less welcome now than last year</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>I used to be a developer by profession, but no...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Easy</td>\n",
       "      <td>Too short</td>\n",
       "      <td>No</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Django;Ruby on Rails</td>\n",
       "      <td>Ruby on Rails</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent                                         MainBranch Hobbyist  \\\n",
       "0           1                     I am a developer by profession      Yes   \n",
       "1           2                     I am a developer by profession       No   \n",
       "2           3                        I code primarily as a hobby      Yes   \n",
       "3           4                     I am a developer by profession      Yes   \n",
       "4           5  I used to be a developer by profession, but no...      Yes   \n",
       "\n",
       "    Age Age1stCode CompFreq  CompTotal  ConvertedComp             Country  \\\n",
       "0   NaN         13  Monthly        NaN            NaN             Germany   \n",
       "1   NaN         19      NaN        NaN            NaN      United Kingdom   \n",
       "2   NaN         15      NaN        NaN            NaN  Russian Federation   \n",
       "3  25.0         18      NaN        NaN            NaN             Albania   \n",
       "4  31.0         16      NaN        NaN            NaN       United States   \n",
       "\n",
       "     CurrencyDesc  ...                  SurveyEase           SurveyLength  \\\n",
       "0   European Euro  ...  Neither easy nor difficult  Appropriate in length   \n",
       "1  Pound sterling  ...                         NaN                    NaN   \n",
       "2             NaN  ...  Neither easy nor difficult  Appropriate in length   \n",
       "3    Albanian lek  ...                         NaN                    NaN   \n",
       "4             NaN  ...                        Easy              Too short   \n",
       "\n",
       "  Trans                                     UndergradMajor  \\\n",
       "0    No  Computer science, computer engineering, or sof...   \n",
       "1   NaN  Computer science, computer engineering, or sof...   \n",
       "2   NaN                                                NaN   \n",
       "3    No  Computer science, computer engineering, or sof...   \n",
       "4    No  Computer science, computer engineering, or sof...   \n",
       "\n",
       "  WebframeDesireNextYear    WebframeWorkedWith  \\\n",
       "0           ASP.NET Core  ASP.NET;ASP.NET Core   \n",
       "1                    NaN                   NaN   \n",
       "2                    NaN                   NaN   \n",
       "3                    NaN                   NaN   \n",
       "4   Django;Ruby on Rails         Ruby on Rails   \n",
       "\n",
       "                              WelcomeChange WorkWeekHrs YearsCode YearsCodePro  \n",
       "0   Just as welcome now as I felt last year        50.0        36           27  \n",
       "1  Somewhat more welcome now than last year         NaN         7            4  \n",
       "2  Somewhat more welcome now than last year         NaN         4          NaN  \n",
       "3  Somewhat less welcome now than last year        40.0         7            4  \n",
       "4   Just as welcome now as I felt last year         NaN        15            8  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2e754e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>Germany</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>27</td>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>4</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>8</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country                                          EdLevel  \\\n",
       "0             Germany  Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",
       "1      United Kingdom     Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "2  Russian Federation                                              NaN   \n",
       "3             Albania  Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",
       "4       United States     Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "\n",
       "  YearsCodePro                                         Employment  Salary  \n",
       "0           27  Independent contractor, freelancer, or self-em...     NaN  \n",
       "1            4                                 Employed full-time     NaN  \n",
       "2          NaN                                                NaN     NaN  \n",
       "3            4                                                NaN     NaN  \n",
       "4            8                                 Employed full-time     NaN  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[[\"Country\", \"EdLevel\", \"YearsCodePro\", \"Employment\", \"ConvertedComp\"]]\n",
    "df = df.rename({\"ConvertedComp\": \"Salary\"}, axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c7653efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>7</th>\n",
       "      <td>United States</td>\n",
       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
       "      <td>13</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>116000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>United Kingdom</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>32315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>United Kingdom</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>40070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>7</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>14268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>20</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>38916.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country                                            EdLevel  \\\n",
       "7    United States       Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "9   United Kingdom    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",
       "10  United Kingdom       Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",
       "11           Spain  Some college/university study without earning ...   \n",
       "12     Netherlands  Secondary school (e.g. American high school, G...   \n",
       "\n",
       "   YearsCodePro          Employment    Salary  \n",
       "7            13  Employed full-time  116000.0  \n",
       "9             4  Employed full-time   32315.0  \n",
       "10            2  Employed full-time   40070.0  \n",
       "11            7  Employed full-time   14268.0  \n",
       "12           20  Employed full-time   38916.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df[\"Salary\"].notnull()]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1c0a8af1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 34756 entries, 7 to 64154\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Country       34756 non-null  object \n",
      " 1   EdLevel       34188 non-null  object \n",
      " 2   YearsCodePro  34621 non-null  object \n",
      " 3   Employment    34717 non-null  object \n",
      " 4   Salary        34756 non-null  float64\n",
      "dtypes: float64(1), object(4)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c1c2b6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country         0\n",
       "EdLevel         0\n",
       "YearsCodePro    0\n",
       "Employment      0\n",
       "Salary          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b876948a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30019 entries, 7 to 64154\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Country       30019 non-null  object \n",
      " 1   EdLevel       30019 non-null  object \n",
      " 2   YearsCodePro  30019 non-null  object \n",
      " 3   Salary        30019 non-null  float64\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 1.1+ 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": 46,
   "id": "c551ab05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States     7569\n",
       "India             2425\n",
       "United Kingdom    2287\n",
       "Germany           1903\n",
       "Canada            1178\n",
       "                  ... \n",
       "Benin                1\n",
       "Fiji                 1\n",
       "San Marino           1\n",
       "Guinea               1\n",
       "Andorra              1\n",
       "Name: Country, Length: 154, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cb8e3d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten_categories(categories, cutoff):\n",
    "    categorical_map = {}\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": 48,
   "id": "cc09a5fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other                 8549\n",
       "United States         7569\n",
       "India                 2425\n",
       "United Kingdom        2287\n",
       "Germany               1903\n",
       "Canada                1178\n",
       "Brazil                 991\n",
       "France                 972\n",
       "Spain                  670\n",
       "Australia              659\n",
       "Netherlands            654\n",
       "Poland                 566\n",
       "Italy                  560\n",
       "Russian Federation     522\n",
       "Sweden                 514\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_map = shorten_categories(df.Country.value_counts(), 400)\n",
    "df['Country'] = df['Country'].map(country_map)\n",
    "df.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "03a7116f",
   "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$) v Country')\n",
    "plt.title('')\n",
    "plt.ylabel('Salary')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "29b6b862",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"Salary\"] <= 250000]\n",
    "df = df[df[\"Salary\"] >= 10000]\n",
    "df = df[df['Country'] != 'Other']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ca55b532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12, 7))\n",
    "df.boxplot('Salary', 'Country', ax=ax)\n",
    "plt.suptitle('Salary (US$) v Country')\n",
    "plt.title('')\n",
    "plt.ylabel('Salary')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "82cf4507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['13', '4', '2', '7', '20', '1', '3', '10', '12', '29', '6', '28',\n",
       "       '8', '23', '15', '25', '9', '11', 'Less than 1 year', '5', '21',\n",
       "       '16', '18', '14', '32', '19', '22', '38', '30', '26', '27', '17',\n",
       "       '24', '34', '35', '33', '36', '40', '39', 'More than 50 years',\n",
       "       '31', '37', '41', '45', '42', '44', '43', '50', '49'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"YearsCodePro\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4f035a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_experience(x):\n",
    "    if x ==  'More than 50 years':\n",
    "        return 50\n",
    "    if x == 'Less than 1 year':\n",
    "        return 0.5\n",
    "    return float(x)\n",
    "\n",
    "df['YearsCodePro'] = df['YearsCodePro'].apply(clean_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c40b1ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachelor’s degree (B.A., B.S., B.Eng., etc.)',\n",
       "       'Master’s degree (M.A., M.S., M.Eng., MBA, etc.)',\n",
       "       'Some college/university study without earning a degree',\n",
       "       'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)',\n",
       "       'Associate degree (A.A., A.S., etc.)',\n",
       "       'Professional degree (JD, MD, etc.)',\n",
       "       'Other doctoral degree (Ph.D., Ed.D., etc.)',\n",
       "       'I never completed any formal education',\n",
       "       'Primary/elementary school'], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"EdLevel\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a24a1436",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_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'\n",
    "\n",
    "df['EdLevel'] = df['EdLevel'].apply(clean_education)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9ce8792f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachelor’s degree', 'Master’s degree', 'Less than a Bachelors',\n",
       "       'Post grad'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"EdLevel\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "896e4b84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 3])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le_education = LabelEncoder()\n",
    "df['EdLevel'] = le_education.fit_transform(df['EdLevel'])\n",
    "df[\"EdLevel\"].unique()\n",
    "#le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3ee6c3b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 12, 10,  7,  4,  2,  6,  1,  3,  5, 11,  8,  0,  9])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le_country = LabelEncoder()\n",
    "df['Country'] = le_country.fit_transform(df['Country'])\n",
    "df[\"Country\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "69a25de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Salary\", axis=1)\n",
    "y = df[\"Salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8e1e3581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "id": "b8e531e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = linear_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5eb0fd40",
   "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": 63,
   "id": "8de6b173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39274.75368318509"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2e633868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=0)"
      ]
     },
     "execution_count": 64,
     "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": 65,
   "id": "9953f8bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dec_tree_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "806839af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$29,414.94\n"
     ]
    }
   ],
   "source": [
    "error = np.sqrt(mean_squared_error(y, y_pred))\n",
    "print(\"${:,.02f}\".format(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "62d12912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 69,
     "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": 70,
   "id": "9a58e86e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = random_forest_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b9c9f8a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$29,487.31\n"
     ]
    }
   ],
   "source": [
    "error = np.sqrt(mean_squared_error(y, y_pred))\n",
    "print(\"${:,.02f}\".format(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "36db7870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=DecisionTreeRegressor(random_state=0),\n",
       "             param_grid={'max_depth': [None, 2, 4, 6, 8, 10, 12]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 72,
     "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": 73,
   "id": "11fddae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$30,428.51\n"
     ]
    }
   ],
   "source": [
    "regressor = gs.best_estimator_\n",
    "\n",
    "regressor.fit(X, y.values)\n",
    "y_pred = regressor.predict(X)\n",
    "error = np.sqrt(mean_squared_error(y, y_pred))\n",
    "print(\"${:,.02f}\".format(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d1c7b5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>YearsCodePro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64113</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64116</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64122</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64127</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64129</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18491 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  EdLevel  YearsCodePro\n",
       "7           13        0          13.0\n",
       "9           12        2           4.0\n",
       "10          12        0           2.0\n",
       "11          10        1           7.0\n",
       "12           7        1          20.0\n",
       "...        ...      ...           ...\n",
       "64113       13        1          15.0\n",
       "64116       13        0           6.0\n",
       "64122       13        1           4.0\n",
       "64127       13        3          12.0\n",
       "64129       13        2           4.0\n",
       "\n",
       "[18491 rows x 3 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c947101a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['United States', 'Master’s degree', '15']], dtype='<U15')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# country, edlevel, yearscode\n",
    "X = np.array([[\"United States\", 'Master’s degree', 15 ]])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "914062f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13.,  2., 15.]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:, 0] = le_country.transform(X[:,0])\n",
    "X[:, 1] = le_education.transform(X[:,1])\n",
    "X = X.astype(float)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "258d1fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([139427.26315789])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a1203e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6fc64774",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"model\": regressor, \"le_country\": le_country, \"le_education\": le_education}\n",
    "with open('saved_steps.pkl', 'wb') as file:\n",
    "    pickle.dump(data, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6aefe42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('saved_steps.pkl', 'rb') as file:\n",
    "    data = pickle.load(file)\n",
    "\n",
    "regressor_loaded = data[\"model\"]\n",
    "le_country = data[\"le_country\"]\n",
    "le_education = data[\"le_education\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "eeb1983b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([139427.26315789])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor_loaded.predict(X)\n",
    "y_pred"
   ]
  }
 ],
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