DS Salary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee9be6ba",
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   "outputs": [
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Principal Data Scientist</td>\n",
       "      <td>80000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>85847</td>\n",
       "      <td>ES</td>\n",
       "      <td>100</td>\n",
       "      <td>ES</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>MI</td>\n",
       "      <td>CT</td>\n",
       "      <td>ML Engineer</td>\n",
       "      <td>30000</td>\n",
       "      <td>USD</td>\n",
       "      <td>30000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>MI</td>\n",
       "      <td>CT</td>\n",
       "      <td>ML Engineer</td>\n",
       "      <td>25500</td>\n",
       "      <td>USD</td>\n",
       "      <td>25500</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>175000</td>\n",
       "      <td>USD</td>\n",
       "      <td>175000</td>\n",
       "      <td>CA</td>\n",
       "      <td>100</td>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>120000</td>\n",
       "      <td>USD</td>\n",
       "      <td>120000</td>\n",
       "      <td>CA</td>\n",
       "      <td>100</td>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   work_year experience_level employment_type                 job_title  \\\n",
       "0       2023               SE              FT  Principal Data Scientist   \n",
       "1       2023               MI              CT               ML Engineer   \n",
       "2       2023               MI              CT               ML Engineer   \n",
       "3       2023               SE              FT            Data Scientist   \n",
       "4       2023               SE              FT            Data Scientist   \n",
       "\n",
       "   salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
       "0   80000             EUR          85847                 ES           100   \n",
       "1   30000             USD          30000                 US           100   \n",
       "2   25500             USD          25500                 US           100   \n",
       "3  175000             USD         175000                 CA           100   \n",
       "4  120000             USD         120000                 CA           100   \n",
       "\n",
       "  company_location company_size  \n",
       "0               ES            L  \n",
       "1               US            S  \n",
       "2               US            S  \n",
       "3               CA            M  \n",
       "4               CA            M  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"ds_salaries0.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106ea1ab",
   "metadata": {},
   "source": [
    "#  Understand the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a267f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_year             0\n",
       "experience_level      0\n",
       "employment_type       0\n",
       "job_title             0\n",
       "salary                0\n",
       "salary_currency       0\n",
       "salary_in_usd         0\n",
       "employee_residence    0\n",
       "remote_ratio          0\n",
       "company_location      0\n",
       "company_size          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84978965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3755 entries, 0 to 3754\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   work_year           3755 non-null   int64 \n",
      " 1   experience_level    3755 non-null   object\n",
      " 2   employment_type     3755 non-null   object\n",
      " 3   job_title           3755 non-null   object\n",
      " 4   salary              3755 non-null   int64 \n",
      " 5   salary_currency     3755 non-null   object\n",
      " 6   salary_in_usd       3755 non-null   int64 \n",
      " 7   employee_residence  3755 non-null   object\n",
      " 8   remote_ratio        3755 non-null   int64 \n",
      " 9   company_location    3755 non-null   object\n",
      " 10  company_size        3755 non-null   object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 322.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d0abda1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_year\n",
       "2023    1785\n",
       "2022    1664\n",
       "2021     230\n",
       "2020      76\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['work_year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a464906b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experience_level\n",
       "SE    2516\n",
       "MI     805\n",
       "EN     320\n",
       "EX     114\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['experience_level'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "61d98dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job_title\n",
       "Data Engineer                          1040\n",
       "Data Scientist                          840\n",
       "Data Analyst                            612\n",
       "Machine Learning Engineer               289\n",
       "Analytics Engineer                      103\n",
       "                                       ... \n",
       "Principal Machine Learning Engineer       1\n",
       "Azure Data Engineer                       1\n",
       "Manager Data Management                   1\n",
       "Marketing Data Engineer                   1\n",
       "Finance Data Analyst                      1\n",
       "Name: count, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['job_title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dfdede19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "remote_ratio\n",
       "0      1923\n",
       "100    1643\n",
       "50      189\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['remote_ratio'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "656dca75",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Principal Data Scientist</td>\n",
       "      <td>85847</td>\n",
       "      <td>ES</td>\n",
       "      <td>100</td>\n",
       "      <td>ES</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>MI</td>\n",
       "      <td>CT</td>\n",
       "      <td>ML Engineer</td>\n",
       "      <td>30000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   work_year experience_level employment_type                 job_title  \\\n",
       "0       2023               SE              FT  Principal Data Scientist   \n",
       "1       2023               MI              CT               ML Engineer   \n",
       "\n",
       "   salary_in_usd employee_residence  remote_ratio company_location  \\\n",
       "0          85847                 ES           100               ES   \n",
       "1          30000                 US           100               US   \n",
       "\n",
       "  company_size  \n",
       "0            L  \n",
       "1            S  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['salary', 'salary_currency'])\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909ed484",
   "metadata": {},
   "source": [
    "## Turn Categorical values into numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d87c074e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding the string columns to numeric\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "df['experience_level'] = le.fit_transform(df['experience_level'])\n",
    "df['employment_type'] = le.fit_transform(df['employment_type'])\n",
    "df['job_title'] = le.fit_transform(df['job_title'])\n",
    "df['employee_residence'] = le.fit_transform(df['employee_residence'])\n",
    "df['company_location'] = le.fit_transform(df['company_location'])\n",
    "df['company_size'] = le.fit_transform(df['company_size'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e44c08b5",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>85847</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>30000</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>25500</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>175000</td>\n",
       "      <td>11</td>\n",
       "      <td>100</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>120000</td>\n",
       "      <td>11</td>\n",
       "      <td>100</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   work_year  experience_level  employment_type  job_title  salary_in_usd  \\\n",
       "0       2023                 3                2         84          85847   \n",
       "1       2023                 2                0         66          30000   \n",
       "2       2023                 2                0         66          25500   \n",
       "3       2023                 3                2         47         175000   \n",
       "4       2023                 3                2         47         120000   \n",
       "\n",
       "   employee_residence  remote_ratio  company_location  company_size  \n",
       "0                  26           100                25             0  \n",
       "1                  75           100                70             2  \n",
       "2                  75           100                70             2  \n",
       "3                  11           100                12             1  \n",
       "4                  11           100                12             1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "79b972b4",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3755.000000</td>\n",
       "      <td>3755.000000</td>\n",
       "      <td>3755.000000</td>\n",
       "      <td>3755.000000</td>\n",
       "      <td>3755.000000</td>\n",
       "      <td>3755.000000</td>\n",
       "      <td>3755.000000</td>\n",
       "      <td>3755.000000</td>\n",
       "      <td>3755.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2022.373635</td>\n",
       "      <td>2.469241</td>\n",
       "      <td>1.996538</td>\n",
       "      <td>39.661784</td>\n",
       "      <td>137570.389880</td>\n",
       "      <td>66.149401</td>\n",
       "      <td>46.271638</td>\n",
       "      <td>62.117177</td>\n",
       "      <td>0.918509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.691448</td>\n",
       "      <td>0.906261</td>\n",
       "      <td>0.133550</td>\n",
       "      <td>18.536695</td>\n",
       "      <td>63055.625278</td>\n",
       "      <td>19.235212</td>\n",
       "      <td>48.589050</td>\n",
       "      <td>17.495432</td>\n",
       "      <td>0.392071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5132.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>95000.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>135000.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2023.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>175000.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2023.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>450000.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         work_year  experience_level  employment_type    job_title  \\\n",
       "count  3755.000000       3755.000000      3755.000000  3755.000000   \n",
       "mean   2022.373635          2.469241         1.996538    39.661784   \n",
       "std       0.691448          0.906261         0.133550    18.536695   \n",
       "min    2020.000000          0.000000         0.000000     0.000000   \n",
       "25%    2022.000000          2.000000         2.000000    27.000000   \n",
       "50%    2022.000000          3.000000         2.000000    33.000000   \n",
       "75%    2023.000000          3.000000         2.000000    47.000000   \n",
       "max    2023.000000          3.000000         3.000000    92.000000   \n",
       "\n",
       "       salary_in_usd  employee_residence  remote_ratio  company_location  \\\n",
       "count    3755.000000         3755.000000   3755.000000       3755.000000   \n",
       "mean   137570.389880           66.149401     46.271638         62.117177   \n",
       "std     63055.625278           19.235212     48.589050         17.495432   \n",
       "min      5132.000000            0.000000      0.000000          0.000000   \n",
       "25%     95000.000000           75.000000      0.000000         70.000000   \n",
       "50%    135000.000000           75.000000      0.000000         70.000000   \n",
       "75%    175000.000000           75.000000    100.000000         70.000000   \n",
       "max    450000.000000           77.000000    100.000000         71.000000   \n",
       "\n",
       "       company_size  \n",
       "count   3755.000000  \n",
       "mean       0.918509  \n",
       "std        0.392071  \n",
       "min        0.000000  \n",
       "25%        1.000000  \n",
       "50%        1.000000  \n",
       "75%        1.000000  \n",
       "max        2.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ff7bf4",
   "metadata": {},
   "source": [
    "## assign values for x and y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aa5dee0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2023,    3,    2,   84,   26,  100,   25,    0]], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.drop('salary_in_usd', axis=1).values\n",
    "y = df['salary_in_usd'].values\n",
    "\n",
    "x[0:1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "400dee11",
   "metadata": {},
   "source": [
    "# Normalize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ed559700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.90599446,  0.58573566,  0.02592668,  2.39223443, -2.08756459,\n",
       "         1.10591825, -2.12181783, -2.34302199]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normalizing the data\n",
    "from sklearn import preprocessing\n",
    "\n",
    "x = preprocessing.StandardScaler().fit(x).transform(x)\n",
    "\n",
    "x[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a730310c",
   "metadata": {},
   "source": [
    "## See Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e053271",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between work_year and target variable: 0.22829002243287855\n",
      "Correlation between experience_level and target variable: 0.32717275721326394\n",
      "Correlation between employment_type and target variable: -0.010328990568078917\n",
      "Correlation between job_title and target variable: 0.12087494588537902\n",
      "Correlation between salary_in_usd and target variable: 1.0\n",
      "Correlation between employee_residence and target variable: 0.41403852153660453\n",
      "Correlation between remote_ratio and target variable: -0.06417098519057554\n",
      "Correlation between company_location and target variable: 0.4051833800569487\n",
      "Correlation between company_size and target variable: -0.0003723590888333255\n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation matrix between x_standardized and y\n",
    "correlation_matrix = np.corrcoef(df.T, y)\n",
    "\n",
    "# Extract the correlation between each feature and the target variable\n",
    "feature_target_correlation = correlation_matrix[:-1, -1]\n",
    "\n",
    "# Display the correlation values\n",
    "for feature, correlation in zip(df.columns, feature_target_correlation):\n",
    "    print(f'Correlation between {feature} and target variable: {correlation}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df3a5675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming df is your DataFrame with normalized features and target\n",
    "sns.set(style=\"white\")\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Include the correlation of features with the target variable\n",
    "correlation_matrix = df.corr()\n",
    "target_correlation = correlation_matrix['salary_in_usd'].sort_values(ascending=False)\n",
    "\n",
    "# Create a heatmap\n",
    "sns.heatmap(target_correlation.to_frame(), annot=True, cmap=\"coolwarm\", fmt=\".2f\", linewidths=.5)\n",
    "\n",
    "plt.title(\"Correlation Heatmap with Target Variable\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c704668a",
   "metadata": {},
   "source": [
    "# We can see that company_size, employment_type and remote_ratio does not have strong correlation with salary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de33a24",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7711426a",
   "metadata": {},
   "source": [
    "## Split the data, train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4cb5e64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39bf8730",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e26e3219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "#fitting the model\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cec2183a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 2325555392.4379745\n",
      "R-squared: 0.31\n"
     ]
    }
   ],
   "source": [
    "# Making predictions on the test set\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "# Evaluating the model using Mean Squared Error\n",
    "lr_mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "lr_r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error: \" + str(lr_mse))\n",
    "print(\"R-squared: {:.2f}\".format(lr_r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8158f664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 3019551872.2168975\n",
      "R-squared: 0.25\n"
     ]
    }
   ],
   "source": [
    "#Lets check the r2 and mse for training data\n",
    "\n",
    "train_predict = model.predict(x_train)\n",
    "train_mse = mean_squared_error(y_train, train_predict)\n",
    "\n",
    "train_r2 = r2_score(y_train, train_predict)\n",
    "\n",
    "print(\"Mean Squared Error: \" + str(train_mse))\n",
    "print(\"R-squared: {:.2f}\".format(train_r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef26af01",
   "metadata": {},
   "source": [
    "## Random Forest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0abf224b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1932702342.9248548\n",
      "R-squared: 0.42969808132908194\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "\n",
    "# Initialize the Random Forest model\n",
    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "rf_model.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = rf_model.predict(x_test)\n",
    "\n",
    "# Evaluate the model\n",
    "rf_mse = mean_squared_error(y_test, y_pred)\n",
    "rf_r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f'Mean Squared Error: {rf_mse}')\n",
    "print(f'R-squared: {rf_r2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "795195fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad10a2ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c75af46",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0db12277",
   "metadata": {},
   "source": [
    "## REPORT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2d83dfea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Metric  Linear Regression      Random Forest\n",
      "0    MSE  2325555392.437974  1932702342.924855\n",
      "1     R2               0.31               0.43\n"
     ]
    }
   ],
   "source": [
    "Report = pd.DataFrame({\n",
    "    'Metric': ['MSE', 'R2'],\n",
    "    'Linear Regression': [lr_mse, '{:.2f}'.format(lr_r2)],\n",
    "    'Random Forest': [rf_mse, '{:.2f}'.format(rf_r2)],\n",
    "})\n",
    "\n",
    "print(Report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b121801f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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