DataScience_Salary_Analysis.ipynb

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   "source": [
    "## 1. Set environment"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "### Load libraries"
   ]
  },
  {
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   "id": "024f3607",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b07177b6",
   "metadata": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/kaggle/input/data-science-salaries-2023/ds_salaries.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b416a2fe",
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    "tags": []
   },
   "source": [
    "### Preliminary EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be3cf98e",
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   "outputs": [
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     "text": [
      "\n",
      "Dimensions of dataset \n",
      "\n",
      " (3755, 11)\n",
      "\n",
      "\n",
      "Summary of dataset \n",
      "\n",
      "          work_year        salary  salary_in_usd  remote_ratio\n",
      "count  3755.000000  3.755000e+03    3755.000000   3755.000000\n",
      "mean   2022.373635  1.906956e+05  137570.389880     46.271638\n",
      "std       0.691448  6.716765e+05   63055.625278     48.589050\n",
      "min    2020.000000  6.000000e+03    5132.000000      0.000000\n",
      "25%    2022.000000  1.000000e+05   95000.000000      0.000000\n",
      "50%    2022.000000  1.380000e+05  135000.000000      0.000000\n",
      "75%    2023.000000  1.800000e+05  175000.000000    100.000000\n",
      "max    2023.000000  3.040000e+07  450000.000000    100.000000\n",
      "\n",
      "\n",
      "Datatypes \n",
      "\n",
      " work_year              int64\n",
      "experience_level      object\n",
      "employment_type       object\n",
      "job_title             object\n",
      "salary                 int64\n",
      "salary_currency       object\n",
      "salary_in_usd          int64\n",
      "employee_residence    object\n",
      "remote_ratio           int64\n",
      "company_location      object\n",
      "company_size          object\n",
      "dtype: object\n",
      "\n",
      "\n",
      "Dataset glimpse \n",
      "\n"
     ]
    },
    {
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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>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": 3,
     "metadata": {},
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   ],
   "source": [
    "def get_summary(dataset):\n",
    "    print(\"\\nDimensions of dataset \\n\\n\", dataset.shape)  \n",
    "    print(\"\\n\\nSummary of dataset \\n\\n\", dataset.describe())    \n",
    "    print(\"\\n\\nDatatypes \\n\\n\", dataset.dtypes)\n",
    "    print(\"\\n\\nDataset glimpse \\n\")\n",
    "    return(dataset.head(5))\n",
    "\n",
    "get_summary(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f766828",
   "metadata": {
    "papermill": {
     "duration": 0.011751,
     "end_time": "2023-05-15T08:39:39.009036",
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     "start_time": "2023-05-15T08:39:38.997285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2. Data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb4034c8",
   "metadata": {
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     "duration": 0.011736,
     "end_time": "2023-05-15T08:39:39.032545",
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     "start_time": "2023-05-15T08:39:39.020809",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Rename categorical column values for better understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5fdba921",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['experience_level'] = df['experience_level'].replace({'SE':'Senior', 'MI':'Mid-level', 'EN':'Entry-level','EX':'Executive-level'})\n",
    "df['employment_type'] = df['employment_type'].replace({'FT':'Full-time', 'CT':'Contractual','PT':'Part-time', 'FL':'Freelancer'})\n",
    "df['remote_ratio'] = df['remote_ratio'].replace({100: 'On-site', 0:'Remote', 50:'Hybrid'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090ceeac",
   "metadata": {
    "papermill": {
     "duration": 0.01202,
     "end_time": "2023-05-15T08:39:39.097354",
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     "start_time": "2023-05-15T08:39:39.085334",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1a82924",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "duplicate_rows = df[df.duplicated()]\n",
    "duplicate_rows.shape\n",
    "\n",
    "#keep only the first row of the duplicated rows\n",
    "df = df.drop_duplicates(keep='last')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4294acc2",
   "metadata": {
    "papermill": {
     "duration": 0.014593,
     "end_time": "2023-05-15T08:39:39.182818",
     "exception": false,
     "start_time": "2023-05-15T08:39:39.168225",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Drop unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d42b6f27",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.drop(['salary_currency', 'salary'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0a325e",
   "metadata": {
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     "duration": 0.014542,
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     "start_time": "2023-05-15T08:39:39.233747",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Check for missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c78b074d",
   "metadata": {
    "_kg_hide-input": true,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_year             0\n",
       "experience_level      0\n",
       "employment_type       0\n",
       "job_title             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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51dd581e",
   "metadata": {
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     "duration": 0.011598,
     "end_time": "2023-05-15T08:39:39.311644",
     "exception": false,
     "start_time": "2023-05-15T08:39:39.300046",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccf4a21a",
   "metadata": {
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     "duration": 0.011316,
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     "start_time": "2023-05-15T08:39:39.323222",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Top 10 : Job titles and associated average salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a455ac5c",
   "metadata": {
    "_kg_hide-input": true,
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    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary_in_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data Science Tech Lead</th>\n",
       "      <td>375000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud Data Architect</th>\n",
       "      <td>250000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Lead</th>\n",
       "      <td>212500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Analytics Lead</th>\n",
       "      <td>211254.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Principal Data Scientist</th>\n",
       "      <td>198171.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Director of Data Science</th>\n",
       "      <td>195140.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Principal Data Engineer</th>\n",
       "      <td>192500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine Learning Software Engineer</th>\n",
       "      <td>192420.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Applied Scientist</th>\n",
       "      <td>190342.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Principal Machine Learning Engineer</th>\n",
       "      <td>190000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     salary_in_usd\n",
       "job_title                                         \n",
       "Data Science Tech Lead                   375000.00\n",
       "Cloud Data Architect                     250000.00\n",
       "Data Lead                                212500.00\n",
       "Data Analytics Lead                      211254.50\n",
       "Principal Data Scientist                 198171.12\n",
       "Director of Data Science                 195140.73\n",
       "Principal Data Engineer                  192500.00\n",
       "Machine Learning Software Engineer       192420.00\n",
       "Applied Scientist                        190342.58\n",
       "Principal Machine Learning Engineer      190000.00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_salary =  df.groupby('job_title').agg({'salary_in_usd':'mean'}).round(2).sort_values('salary_in_usd', ascending=False).head(10)\n",
    "top_salary.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "274dce21",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:39.414515Z",
     "iopub.status.busy": "2023-05-15T08:39:39.413865Z",
     "iopub.status.idle": "2023-05-15T08:39:39.780635Z",
     "shell.execute_reply": "2023-05-15T08:39:39.779769Z"
    },
    "papermill": {
     "duration": 0.384074,
     "end_time": "2023-05-15T08:39:39.782726",
     "exception": false,
     "start_time": "2023-05-15T08:39:39.398652",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax= sns.barplot(y = top_salary.index, x ='salary_in_usd', \n",
    "            data = top_salary,           \n",
    "            palette = \"viridis\",\n",
    "            width=0.9)\n",
    "            \n",
    "plt.title('Top 10 Highest Annual Salaries and Job Titles\\n', fontsize=16, fontweight=\"bold\", loc=\"center\")\n",
    "plt.suptitle(\"\\nSalary calculated by average\\n\", fontsize = 10, color=\"gray\")\n",
    "plt.xlabel('\\nSalary (USD)', color=\"black\", fontsize=10)\n",
    "plt.ylabel('Job title', color=\"black\", fontsize=10)\n",
    "plt.xticks(fontsize=10, color=\"black\")\n",
    "plt.yticks(fontsize=10, color=\"black\")\n",
    "\n",
    "for i in ax.containers:\n",
    "    ax.bar_label(i, size=10, label_type = \"center\", color=\"white\", fontweight=\"bold\")\n",
    "\n",
    "plt.show() #not needed in Jupyter environment but this opens the plot in another window in other environments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c34561f0",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:39.814925Z",
     "iopub.status.busy": "2023-05-15T08:39:39.814529Z",
     "iopub.status.idle": "2023-05-15T08:39:40.080590Z",
     "shell.execute_reply": "2023-05-15T08:39:40.079508Z"
    },
    "papermill": {
     "duration": 0.284822,
     "end_time": "2023-05-15T08:39:40.082964",
     "exception": false,
     "start_time": "2023-05-15T08:39:39.798142",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-100000.,       0.,  100000.,  200000.,  300000.,  400000.,\n",
       "         500000.]),\n",
       " [Text(0, -100000.0, '−100000'),\n",
       "  Text(0, 0.0, '0'),\n",
       "  Text(0, 100000.0, '100000'),\n",
       "  Text(0, 200000.0, '200000'),\n",
       "  Text(0, 300000.0, '300000'),\n",
       "  Text(0, 400000.0, '400000'),\n",
       "  Text(0, 500000.0, '500000')])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "median_salary = df.groupby('experience_level')['salary_in_usd'].median().reset_index().sort_values('salary_in_usd')\n",
    "\n",
    "sns.boxplot(data=df, x='experience_level', y='salary_in_usd', \n",
    "            order=median_salary['experience_level'], \n",
    "            palette=\"viridis\")\n",
    "\n",
    "plt.title(\"Salaries according to Experience Level\\n\", fontsize=16, fontweight=\"bold\", loc=\"center\")\n",
    "plt.xlabel(\"\\nExperience Level\", color=\"black\", fontsize=10)\n",
    "plt.ylabel(\"Salary\", color=\"black\", fontsize=10)\n",
    "plt.xticks(fontsize=10, color=\"black\")\n",
    "plt.yticks(fontsize=10, color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b225b723",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:40.112873Z",
     "iopub.status.busy": "2023-05-15T08:39:40.112489Z",
     "iopub.status.idle": "2023-05-15T08:39:40.123186Z",
     "shell.execute_reply": "2023-05-15T08:39:40.122163Z"
    },
    "papermill": {
     "duration": 0.028698,
     "end_time": "2023-05-15T08:39:40.125625",
     "exception": false,
     "start_time": "2023-05-15T08:39:40.096927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2023    1156\n",
       "2022    1125\n",
       "2021     228\n",
       "2020      75\n",
       "Name: work_year, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.work_year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a2b2383",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:40.155248Z",
     "iopub.status.busy": "2023-05-15T08:39:40.154867Z",
     "iopub.status.idle": "2023-05-15T08:39:40.508712Z",
     "shell.execute_reply": "2023-05-15T08:39:40.507720Z"
    },
    "papermill": {
     "duration": 0.371885,
     "end_time": "2023-05-15T08:39:40.511663",
     "exception": false,
     "start_time": "2023-05-15T08:39:40.139778",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist(df['salary_in_usd'], bins=20, alpha = 0.8, color=\"cadetblue\")\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax = sns.histplot(df['salary_in_usd'], alpha=0.8, color = \"teal\", bins=20,\n",
    "             kde = True)\n",
    "\n",
    "plt.xticks(range(0,500000,50000));\n",
    "plt.title(\"Salary Distribution\\n\", fontsize=16, fontweight = \"bold\")\n",
    "plt.xlabel('\\nSalary (USD)', color=\"black\",fontsize=10)\n",
    "plt.ylabel('Count', color = \"black\", fontsize=10)\n",
    "ax.lines[0].set_color('orangered') #change color of kde line\n",
    "#ax.lines[0].set_dashes((5,2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7594d49",
   "metadata": {
    "papermill": {
     "duration": 0.013716,
     "end_time": "2023-05-15T08:39:40.539721",
     "exception": false,
     "start_time": "2023-05-15T08:39:40.526005",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Does job type influence salary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13948120",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:40.569678Z",
     "iopub.status.busy": "2023-05-15T08:39:40.569299Z",
     "iopub.status.idle": "2023-05-15T08:39:40.582282Z",
     "shell.execute_reply": "2023-05-15T08:39:40.581095Z"
    },
    "papermill": {
     "duration": 0.030367,
     "end_time": "2023-05-15T08:39:40.584196",
     "exception": false,
     "start_time": "2023-05-15T08:39:40.553829",
     "status": "completed"
    },
    "tags": []
   },
   "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>salary_in_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full-time</th>\n",
       "      <td>134435.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contractual</th>\n",
       "      <td>113447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freelancer</th>\n",
       "      <td>51808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Part-time</th>\n",
       "      <td>39534.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 salary_in_usd\n",
       "employment_type               \n",
       "Full-time             134435.0\n",
       "Contractual           113447.0\n",
       "Freelancer             51808.0\n",
       "Part-time              39534.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_type = df.groupby('employment_type').agg({'salary_in_usd':'mean'}).round(0).sort_values('salary_in_usd', ascending=False)\n",
    "\n",
    "job_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a02aa3b5",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:40.613342Z",
     "iopub.status.busy": "2023-05-15T08:39:40.613006Z",
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.scatter(job_type, x=\"salary_in_usd\", y=None,\n",
    "                 size=\"salary_in_usd\", color=job_type.index,\n",
    "                 size_max=60)\n",
    "\n",
    "fig.update_layout(yaxis=dict(title=''), xaxis=dict(title='\\nAvg. Annual Salary'), title={'text':'Job Type and Average Salary', 'font':{'size':22}})\n",
    "fig.update_xaxes(range=[5000, 150000], dtick=10000)\n",
    "fig.update_layout(width=800, height=400)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de550c0d",
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     "start_time": "2023-05-15T08:39:42.963197",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Salary range according to company size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c0a67c59",
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    "_kg_hide-input": true,
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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "ax = sns.catplot(data=df, x='salary_in_usd',  col='company_size', kind='boxen', color=\"teal\")\n",
    "\n",
    "ax.set_xlabels(\"\\nSalary\", fontsize =16)\n",
    "plt.xticks(range(0,500000,100000));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b5ed0c00",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:43.781945Z",
     "iopub.status.busy": "2023-05-15T08:39:43.781576Z",
     "iopub.status.idle": "2023-05-15T08:39:43.794445Z",
     "shell.execute_reply": "2023-05-15T08:39:43.793330Z"
    },
    "papermill": {
     "duration": 0.031013,
     "end_time": "2023-05-15T08:39:43.796438",
     "exception": false,
     "start_time": "2023-05-15T08:39:43.765425",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salary according to company size \n",
      "\n"
     ]
    },
    {
     "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>salary_in_usd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>113202.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>141474.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>78364.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              salary_in_usd\n",
       "company_size               \n",
       "L                 113202.24\n",
       "M                 141474.51\n",
       "S                  78364.28"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Salary according to company size \\n\")\n",
    "round(df.groupby('company_size').agg({'salary_in_usd':'mean'}),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5cf878fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:43.828166Z",
     "iopub.status.busy": "2023-05-15T08:39:43.827639Z",
     "iopub.status.idle": "2023-05-15T08:39:43.833314Z",
     "shell.execute_reply": "2023-05-15T08:39:43.831794Z"
    },
    "papermill": {
     "duration": 0.023736,
     "end_time": "2023-05-15T08:39:43.835237",
     "exception": false,
     "start_time": "2023-05-15T08:39:43.811501",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum salary: $ 5132 \n",
      "Average salary: $ 133409.28 \n",
      "Maximum salary: $ 450000\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum salary: $\", df.salary_in_usd.min(),\"\\nAverage salary: $\", round(df.salary_in_usd.mean(),2), \"\\nMaximum salary: $\", df.salary_in_usd.max()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28fd475",
   "metadata": {
    "papermill": {
     "duration": 0.014854,
     "end_time": "2023-05-15T08:39:43.865267",
     "exception": false,
     "start_time": "2023-05-15T08:39:43.850413",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Which work mode is more prevalent: on-site, remote, or hybrid?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f18f051",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:43.897831Z",
     "iopub.status.busy": "2023-05-15T08:39:43.897482Z",
     "iopub.status.idle": "2023-05-15T08:39:44.015267Z",
     "shell.execute_reply": "2023-05-15T08:39:44.014557Z"
    },
    "papermill": {
     "duration": 0.136421,
     "end_time": "2023-05-15T08:39:44.017169",
     "exception": false,
     "start_time": "2023-05-15T08:39:43.880748",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Work Mode\\n')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,6))\n",
    "colors = sns.color_palette('Set2')[0:3]\n",
    "\n",
    "values = df.groupby('remote_ratio').size()\n",
    "labels = values.index\n",
    "#round(values/values.sum()*100, 2)\n",
    "\n",
    "plt.pie(values, labels=labels, autopct='%1.2f%%', \n",
    "             startangle=60, textprops={'fontsize': 11, 'fontweight':\"bold\"},\n",
    "             colors = colors)\n",
    "plt.title(\"Work Mode\\n\", fontsize=16, fontweight=\"bold\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c51485e",
   "metadata": {
    "papermill": {
     "duration": 0.021445,
     "end_time": "2023-05-15T08:39:44.060677",
     "exception": false,
     "start_time": "2023-05-15T08:39:44.039232",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Country mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f110d0b0",
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:39:44.104080Z",
     "iopub.status.busy": "2023-05-15T08:39:44.103493Z",
     "iopub.status.idle": "2023-05-15T08:40:09.326676Z",
     "shell.execute_reply": "2023-05-15T08:40:09.325301Z"
    },
    "papermill": {
     "duration": 25.247473,
     "end_time": "2023-05-15T08:40:09.329141",
     "exception": false,
     "start_time": "2023-05-15T08:39:44.081668",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pycountry\r\n",
      "  Downloading pycountry-22.3.5.tar.gz (10.1 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.1/10.1 MB\u001b[0m \u001b[31m80.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \bdone\r\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from pycountry) (59.8.0)\r\n",
      "Building wheels for collected packages: pycountry\r\n",
      "  Building wheel for pycountry (pyproject.toml) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \bdone\r\n",
      "\u001b[?25h  Created wheel for pycountry: filename=pycountry-22.3.5-py2.py3-none-any.whl size=10681832 sha256=5967f49c7c37a3c2042dbdcd4bd5ce30a56781c3a299ef243b5e97a7a64c837a\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/03/57/cc/290c5252ec97a6d78d36479a3c5e5ecc76318afcb241ad9dbe\r\n",
      "Successfully built pycountry\r\n",
      "Installing collected packages: pycountry\r\n",
      "Successfully installed pycountry-22.3.5\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install pycountry\n",
    "\n",
    "import pycountry\n",
    "\n",
    "country_map = {}\n",
    "\n",
    "for country in pycountry.countries:\n",
    "    country_map[country.alpha_2] = country.name\n",
    "\n",
    "df['company_location'] = df['company_location'].replace(country_map)\n",
    "df['employee_residence'] = df['employee_residence'].replace(country_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "277a9df1",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:09.367692Z",
     "iopub.status.busy": "2023-05-15T08:40:09.367313Z",
     "iopub.status.idle": "2023-05-15T08:40:20.215327Z",
     "shell.execute_reply": "2023-05-15T08:40:20.214165Z"
    },
    "papermill": {
     "duration": 10.870284,
     "end_time": "2023-05-15T08:40:20.217762",
     "exception": false,
     "start_time": "2023-05-15T08:40:09.347478",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: geopandas in /opt/conda/lib/python3.10/site-packages (0.12.2)\r\n",
      "Requirement already satisfied: shapely>=1.7 in /opt/conda/lib/python3.10/site-packages (from geopandas) (1.8.5.post1)\r\n",
      "Requirement already satisfied: pandas>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from geopandas) (1.5.3)\r\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from geopandas) (21.3)\r\n",
      "Requirement already satisfied: fiona>=1.8 in /opt/conda/lib/python3.10/site-packages (from geopandas) (1.8.22)\r\n",
      "Requirement already satisfied: pyproj>=2.6.1.post1 in /opt/conda/lib/python3.10/site-packages (from geopandas) (3.5.0)\r\n",
      "Requirement already satisfied: munch in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8->geopandas) (2.5.0)\r\n",
      "Requirement already satisfied: certifi in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8->geopandas) (2022.12.7)\r\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8->geopandas) (59.8.0)\r\n",
      "Requirement already satisfied: attrs>=17 in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8->geopandas) (22.2.0)\r\n",
      "Requirement already satisfied: click>=4.0 in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8->geopandas) (8.1.3)\r\n",
      "Requirement already satisfied: cligj>=0.5 in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8->geopandas) (0.7.2)\r\n",
      "Requirement already satisfied: click-plugins>=1.0 in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8->geopandas) (1.1.1)\r\n",
      "Requirement already satisfied: six>=1.7 in /opt/conda/lib/python3.10/site-packages (from fiona>=1.8->geopandas) (1.16.0)\r\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.0.0->geopandas) (2023.3)\r\n",
      "Requirement already satisfied: numpy>=1.21.0 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.0.0->geopandas) (1.23.5)\r\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.0.0->geopandas) (2.8.2)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->geopandas) (3.0.9)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!pip install geopandas\n",
    "import geopandas as gpd\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "merged = world.merge(df, left_on = 'name', right_on ='company_location')\n",
    "\n",
    "merged.plot(column='remote_ratio', cmap='Paired', linewidth=0.5, ax=ax, edgecolor='black', legend = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "013ea32a",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:20.260785Z",
     "iopub.status.busy": "2023-05-15T08:40:20.259503Z",
     "iopub.status.idle": "2023-05-15T08:40:20.292645Z",
     "shell.execute_reply": "2023-05-15T08:40:20.291522Z"
    },
    "papermill": {
     "duration": 0.057281,
     "end_time": "2023-05-15T08:40:20.294787",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.237506",
     "status": "completed"
    },
    "tags": []
   },
   "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>remote_ratio</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>On-site</th>\n",
       "      <th>Remote</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>39</td>\n",
       "      <td>917</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>17</td>\n",
       "      <td>55</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latvia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "remote_ratio      Hybrid  On-site  Remote\n",
       "company_location                         \n",
       "United States         39      917     973\n",
       "United Kingdom        17       55      83\n",
       "Canada                12       44      27\n",
       "Spain                  5       21      18\n",
       "Germany               14       25      16\n",
       "India                 20       29       8\n",
       "Portugal               3        4       7\n",
       "Ireland                0        3       4\n",
       "Latvia                 0        0       4\n",
       "Netherlands            3        6       4"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot = df.groupby(['company_location', 'remote_ratio']).size().reset_index(name=\"count\")\n",
    "df_pivot = df_pivot.pivot_table(index='company_location', columns='remote_ratio', values='count', fill_value=0)\n",
    "df_pivot.sort_values('Remote', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6ec74864",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:20.337720Z",
     "iopub.status.busy": "2023-05-15T08:40:20.337053Z",
     "iopub.status.idle": "2023-05-15T08:40:20.345429Z",
     "shell.execute_reply": "2023-05-15T08:40:20.343531Z"
    },
    "papermill": {
     "duration": 0.032583,
     "end_time": "2023-05-15T08:40:20.347990",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.315407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 72 company locations in the dataset. 74.65 % companies are located in the United States.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "us_job_prcnt = round(df.company_location[df.company_location=='United States'].count()/df.company_location.count() *100,2)\n",
    "\n",
    "print(\"There are 72 company locations in the dataset.\", us_job_prcnt, \"% companies are located in the United States.\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351f949d",
   "metadata": {
    "papermill": {
     "duration": 0.019478,
     "end_time": "2023-05-15T08:40:20.387412",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.367934",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 4. Linear regression - Predict success factors for higher salary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe6067f",
   "metadata": {
    "papermill": {
     "duration": 0.019388,
     "end_time": "2023-05-15T08:40:20.426711",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.407323",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Prepare data for regression\n",
    "\n",
    "- Remove outliers from the dataset using IQR method.\n",
    "\n",
    "- One of the key assumptions of linear regression is that the outcome variable is normally distributed. If the outcome variable is not normally distributed, it can lead to biased or inefficient estimates of the regression coefficients and inflated standard errors. To make non-normal values normal, transform the outcome variable (eg. log transformation, Box-Cox).\n",
    "\n",
    "- Scale the outcome variable.\n",
    "\n",
    "- Split data to create training, validation, and test sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "458ff2a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:20.468081Z",
     "iopub.status.busy": "2023-05-15T08:40:20.467448Z",
     "iopub.status.idle": "2023-05-15T08:40:20.475887Z",
     "shell.execute_reply": "2023-05-15T08:40:20.474876Z"
    },
    "papermill": {
     "duration": 0.031977,
     "end_time": "2023-05-15T08:40:20.478273",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.446296",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2584, 6)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create copy of dataset\n",
    "\n",
    "df_copy = df\n",
    "\n",
    "#drop column that will not be considered a feature for LR model\n",
    "df_copy = df_copy.drop(['work_year','employee_residence','remote_ratio'], axis=1)\n",
    "df_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e95824b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:20.520922Z",
     "iopub.status.busy": "2023-05-15T08:40:20.520537Z",
     "iopub.status.idle": "2023-05-15T08:40:20.532666Z",
     "shell.execute_reply": "2023-05-15T08:40:20.531593Z"
    },
    "papermill": {
     "duration": 0.035595,
     "end_time": "2023-05-15T08:40:20.534610",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.499015",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2555, 6)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove outliers\n",
    "Q1 = df_copy['salary_in_usd'].quantile(0.25)\n",
    "Q3 = df_copy['salary_in_usd'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "df_copy = df_copy[~((df_copy['salary_in_usd']< (Q1 - 1.5 * IQR)) | (df_copy['salary_in_usd'] > (Q3 + 1.5 * IQR)))]\n",
    "df_copy.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1964eee",
   "metadata": {
    "papermill": {
     "duration": 0.020285,
     "end_time": "2023-05-15T08:40:20.575929",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.555644",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Split data into train (80%) and test (20%) sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f35ceaf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:20.619141Z",
     "iopub.status.busy": "2023-05-15T08:40:20.618482Z",
     "iopub.status.idle": "2023-05-15T08:40:20.630391Z",
     "shell.execute_reply": "2023-05-15T08:40:20.628970Z"
    },
    "papermill": {
     "duration": 0.036343,
     "end_time": "2023-05-15T08:40:20.632714",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.596371",
     "status": "completed"
    },
    "tags": []
   },
   "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>salary_in_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   salary_in_usd\n",
       "0          85847\n",
       "1          30000\n",
       "2          25500\n",
       "3         175000\n",
       "4         120000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_vars = ['experience_level','job_title' ,'company_location','company_size','employment_type']\n",
    "numeric_vars = ['salary_in_usd']\n",
    "\n",
    "X = df_copy[categorical_vars]\n",
    "#X = df_copy[categorical_vars].copy().reset_index(drop=True)\n",
    "\n",
    "X.head()\n",
    "\n",
    "y = df_copy[numeric_vars]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7c51d9c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:20.677456Z",
     "iopub.status.busy": "2023-05-15T08:40:20.677104Z",
     "iopub.status.idle": "2023-05-15T08:40:20.961465Z",
     "shell.execute_reply": "2023-05-15T08:40:20.959766Z"
    },
    "papermill": {
     "duration": 0.309402,
     "end_time": "2023-05-15T08:40:20.964060",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.654658",
     "status": "completed"
    },
    "tags": []
   },
   "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=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1df104",
   "metadata": {
    "papermill": {
     "duration": 0.021096,
     "end_time": "2023-05-15T08:40:21.006465",
     "exception": false,
     "start_time": "2023-05-15T08:40:20.985369",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Before the features are fed into the linear regression model, the categorical variables need to encoded into a binary version. This is done by preprocessing and one such method is One Hot Encoder. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72ceb2b",
   "metadata": {
    "papermill": {
     "duration": 0.02166,
     "end_time": "2023-05-15T08:40:21.049544",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.027884",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1f8f357",
   "metadata": {
    "papermill": {
     "duration": 0.020074,
     "end_time": "2023-05-15T08:40:21.090406",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.070332",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Numerical column transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "37f33d85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.136216Z",
     "iopub.status.busy": "2023-05-15T08:40:21.135409Z",
     "iopub.status.idle": "2023-05-15T08:40:21.140549Z",
     "shell.execute_reply": "2023-05-15T08:40:21.139425Z"
    },
    "papermill": {
     "duration": 0.03036,
     "end_time": "2023-05-15T08:40:21.142570",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.112210",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Transform any non-normal distributions of salary to a normal distribution\n",
    "\n",
    "df_copy['salary_in_usd'] = np.log1p(df_copy['salary_in_usd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bd147f59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.186324Z",
     "iopub.status.busy": "2023-05-15T08:40:21.185783Z",
     "iopub.status.idle": "2023-05-15T08:40:21.194429Z",
     "shell.execute_reply": "2023-05-15T08:40:21.192941Z"
    },
    "papermill": {
     "duration": 0.034011,
     "end_time": "2023-05-15T08:40:21.197098",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.163087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "df_copy['salary_in_usd'] = scaler.fit_transform(df_copy[['salary_in_usd']]) #double [] used because this function needs a 2-d array and salary column by itself is a 1-d array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9610e9f",
   "metadata": {
    "papermill": {
     "duration": 0.020206,
     "end_time": "2023-05-15T08:40:21.237780",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.217574",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Categorical column transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e91be4d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.280494Z",
     "iopub.status.busy": "2023-05-15T08:40:21.280143Z",
     "iopub.status.idle": "2023-05-15T08:40:21.294963Z",
     "shell.execute_reply": "2023-05-15T08:40:21.293596Z"
    },
    "papermill": {
     "duration": 0.039247,
     "end_time": "2023-05-15T08:40:21.297439",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.258192",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder\n",
    "\n",
    "cat_cols = df_copy.select_dtypes(include=['object']).columns\n",
    "num_cols = df_copy.select_dtypes(include=['float64']).columns\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[(\"hotencoder\", OneHotEncoder(handle_unknown=\"ignore\"))])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "669362e1",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.341501Z",
     "iopub.status.busy": "2023-05-15T08:40:21.340561Z",
     "iopub.status.idle": "2023-05-15T08:40:21.349380Z",
     "shell.execute_reply": "2023-05-15T08:40:21.347631Z"
    },
    "papermill": {
     "duration": 0.034122,
     "end_time": "2023-05-15T08:40:21.352060",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.317938",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['salary_in_usd'], dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.salary_in_usd.dtype\n",
    "cat_cols\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8bb19e",
   "metadata": {
    "papermill": {
     "duration": 0.020252,
     "end_time": "2023-05-15T08:40:21.392639",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.372387",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Combine num and cat cols into a pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e7254b43",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.436199Z",
     "iopub.status.busy": "2023-05-15T08:40:21.435830Z",
     "iopub.status.idle": "2023-05-15T08:40:21.454620Z",
     "shell.execute_reply": "2023-05-15T08:40:21.452429Z"
    },
    "papermill": {
     "duration": 0.042779,
     "end_time": "2023-05-15T08:40:21.457256",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.414477",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#preprocessor pipeline\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "#preprocessor = ColumnTransformer(transformers=[\n",
    "     #   ('cat', categorical_transformer, cat_cols),('num', 'passthrough', num_cols)])\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[ (\"cat\", categorical_transformer, cat_cols) ])\n",
    "                                              "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d6a1fc",
   "metadata": {
    "papermill": {
     "duration": 0.020536,
     "end_time": "2023-05-15T08:40:21.500074",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.479538",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Build LR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "787d3ad4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.543356Z",
     "iopub.status.busy": "2023-05-15T08:40:21.542992Z",
     "iopub.status.idle": "2023-05-15T08:40:21.649752Z",
     "shell.execute_reply": "2023-05-15T08:40:21.647839Z"
    },
    "papermill": {
     "duration": 0.131363,
     "end_time": "2023-05-15T08:40:21.652190",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.520827",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#call the LR model\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "355497ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.696422Z",
     "iopub.status.busy": "2023-05-15T08:40:21.696038Z",
     "iopub.status.idle": "2023-05-15T08:40:21.700805Z",
     "shell.execute_reply": "2023-05-15T08:40:21.699885Z"
    },
    "papermill": {
     "duration": 0.030844,
     "end_time": "2023-05-15T08:40:21.703648",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.672804",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#create the pipeline\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "lr_train = Pipeline(steps = [(\"preprocessor\", preprocessor),\n",
    "                                (\"lr\", lr)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ce2a8c27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.746801Z",
     "iopub.status.busy": "2023-05-15T08:40:21.746458Z",
     "iopub.status.idle": "2023-05-15T08:40:21.798696Z",
     "shell.execute_reply": "2023-05-15T08:40:21.797578Z"
    },
    "papermill": {
     "duration": 0.077139,
     "end_time": "2023-05-15T08:40:21.801374",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.724235",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# fit the model with data \n",
    "\n",
    "reg_model = lr_train.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ffb1e945",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.843768Z",
     "iopub.status.busy": "2023-05-15T08:40:21.843424Z",
     "iopub.status.idle": "2023-05-15T08:40:21.849247Z",
     "shell.execute_reply": "2023-05-15T08:40:21.848187Z"
    },
    "papermill": {
     "duration": 0.029569,
     "end_time": "2023-05-15T08:40:21.851696",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.822127",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept:  [49092.59391996]\n"
     ]
    }
   ],
   "source": [
    "print(\"Intercept: \", reg_model.named_steps['lr'].intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b807ef0f",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.895885Z",
     "iopub.status.busy": "2023-05-15T08:40:21.895504Z",
     "iopub.status.idle": "2023-05-15T08:40:21.900292Z",
     "shell.execute_reply": "2023-05-15T08:40:21.898917Z"
    },
    "papermill": {
     "duration": 0.029266,
     "end_time": "2023-05-15T08:40:21.902377",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.873111",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#print(\"Coefficients: \", reg_model.named_steps['lr'].coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4285b9a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:21.945688Z",
     "iopub.status.busy": "2023-05-15T08:40:21.944800Z",
     "iopub.status.idle": "2023-05-15T08:40:21.956549Z",
     "shell.execute_reply": "2023-05-15T08:40:21.955241Z"
    },
    "papermill": {
     "duration": 0.036143,
     "end_time": "2023-05-15T08:40:21.959121",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.922978",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_pred = reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "54d77b44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:22.002460Z",
     "iopub.status.busy": "2023-05-15T08:40:22.002111Z",
     "iopub.status.idle": "2023-05-15T08:40:22.008411Z",
     "shell.execute_reply": "2023-05-15T08:40:22.006936Z"
    },
    "papermill": {
     "duration": 0.030449,
     "end_time": "2023-05-15T08:40:22.010565",
     "exception": false,
     "start_time": "2023-05-15T08:40:21.980116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted values:  [[162290.41]\n",
      " [133589.86]\n",
      " [124919.5 ]\n",
      " [193196.02]\n",
      " [161097.35]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted values: \", X_pred[0:5].round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "531a1024",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:22.054873Z",
     "iopub.status.busy": "2023-05-15T08:40:22.054516Z",
     "iopub.status.idle": "2023-05-15T08:40:22.063333Z",
     "shell.execute_reply": "2023-05-15T08:40:22.061937Z"
    },
    "papermill": {
     "duration": 0.033345,
     "end_time": "2023-05-15T08:40:22.065644",
     "exception": false,
     "start_time": "2023-05-15T08:40:22.032299",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3401    140000\n",
       "3505     82744\n",
       "2839     60000\n",
       "1931    164000\n",
       "1102    140000\n",
       "Name: salary_in_usd, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.salary_in_usd[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6fddbd01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:22.111123Z",
     "iopub.status.busy": "2023-05-15T08:40:22.110722Z",
     "iopub.status.idle": "2023-05-15T08:40:22.118767Z",
     "shell.execute_reply": "2023-05-15T08:40:22.117628Z"
    },
    "papermill": {
     "duration": 0.03468,
     "end_time": "2023-05-15T08:40:22.121734",
     "exception": false,
     "start_time": "2023-05-15T08:40:22.087054",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " The mean absolute error is:  37370.32035271366 \n",
      " The r-squared score is  0.4310625943769979\n"
     ]
    }
   ],
   "source": [
    "# calculate error\n",
    "from sklearn.metrics import mean_absolute_error, r2_score\n",
    "\n",
    "mea = mean_absolute_error(y_test,X_pred)\n",
    "r2_score = r2_score(y_test, X_pred)\n",
    "\n",
    "print(\"\\n The mean absolute error is: \", mea, \"\\n The r-squared score is \", r2_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29329567",
   "metadata": {
    "papermill": {
     "duration": 0.021069,
     "end_time": "2023-05-15T08:40:22.165177",
     "exception": false,
     "start_time": "2023-05-15T08:40:22.144108",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d06ebe",
   "metadata": {
    "papermill": {
     "duration": 0.020599,
     "end_time": "2023-05-15T08:40:22.206692",
     "exception": false,
     "start_time": "2023-05-15T08:40:22.186093",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> - Linear regression analysis revealed that job title, experience level, employment type, company size, and company location are robust predictors of salary. \n",
    "\n",
    "> - Interestingly, work mode and employee's residence location do not appear to have a strong impact on salary.\n",
    "\n",
    "> - The linear regression model is expected to provide more accurate results with a lower mean error absolute value with a larger dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "360dc03e",
   "metadata": {
    "papermill": {
     "duration": 0.02087,
     "end_time": "2023-05-15T08:40:22.249655",
     "exception": false,
     "start_time": "2023-05-15T08:40:22.228785",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "016bd3f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:40:22.295722Z",
     "iopub.status.busy": "2023-05-15T08:40:22.295388Z",
     "iopub.status.idle": "2023-05-15T08:40:25.889904Z",
     "shell.execute_reply": "2023-05-15T08:40:25.888877Z"
    },
    "papermill": {
     "duration": 3.619051,
     "end_time": "2023-05-15T08:40:25.891572",
     "exception": false,
     "start_time": "2023-05-15T08:40:22.272521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1200 with 0 Axes>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!pip install wordcloud\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "text = df['job_title'].values\n",
    "text = ' '.join(text)\n",
    "\n",
    "\n",
    "wordcloud = WordCloud(width = 3000, height = 2000, collocations=False, colormap= 'viridis', \n",
    "                      random_state=1, max_words=50, background_color=\"black\").generate(str(text))\n",
    "\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n",
    "plt.tight_layout(pad = 0) \n",
    "plt.figure(figsize=[16,12])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "323a32a9",
   "metadata": {
    "papermill": {
     "duration": 0.023633,
     "end_time": "2023-05-15T08:40:25.937983",
     "exception": false,
     "start_time": "2023-05-15T08:40:25.914350",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 59.375466,
   "end_time": "2023-05-15T08:40:27.185547",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-05-15T08:39:27.810081",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}