Salary Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "4422640b-4273-410a-92d6-5a9b376798d1",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5353f8e5",
    "execution_start": 1646128696780,
    "execution_millis": 5455,
    "deepnote_cell_type": "code"
   },
   "source": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndf = pd.read_csv('survey_results_public.csv')",
   "execution_count": 1,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "c899ff63-0cab-401e-9e9b-fe4f911d7b80",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c085b6ba",
    "execution_start": 1646128702248,
    "execution_millis": 286,
    "deepnote_cell_type": "code"
   },
   "source": "df.head()",
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 2,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 48,
       "row_count": 5,
       "columns": [
        {
         "name": "ResponseId",
         "dtype": "int64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "1",
          "max": "5",
          "histogram": [
           {
            "bin_start": 1,
            "bin_end": 1.4,
            "count": 1
           },
           {
            "bin_start": 1.4,
            "bin_end": 1.8,
            "count": 0
           },
           {
            "bin_start": 1.8,
            "bin_end": 2.2,
            "count": 1
           },
           {
            "bin_start": 2.2,
            "bin_end": 2.6,
            "count": 0
           },
           {
            "bin_start": 2.6,
            "bin_end": 3,
            "count": 0
           },
           {
            "bin_start": 3,
            "bin_end": 3.4000000000000004,
            "count": 1
           },
           {
            "bin_start": 3.4000000000000004,
            "bin_end": 3.8000000000000003,
            "count": 0
           },
           {
            "bin_start": 3.8000000000000003,
            "bin_end": 4.2,
            "count": 1
           },
           {
            "bin_start": 4.2,
            "bin_end": 4.6,
            "count": 0
           },
           {
            "bin_start": 4.6,
            "bin_end": 5,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "MainBranch",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "I am a developer by profession",
            "count": 3
           },
           {
            "name": "I am a student who is learning to code",
            "count": 1
           },
           {
            "name": "I am not primarily a developer, but I write code sometimes as part of my work",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Employment",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "Independent contractor, freelancer, or self-employed",
            "count": 2
           },
           {
            "name": "Student, full-time",
            "count": 2
           },
           {
            "name": "Employed full-time",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Country",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "Slovakia",
            "count": 1
           },
           {
            "name": "Netherlands",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "US_State",
         "dtype": "object",
         "stats": {
          "unique_count": 0,
          "nan_count": 5,
          "categories": [
           {
            "name": "Missing",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "UK_Country",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "categories": [
           {
            "name": "England",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 4
           }
          ]
         }
        },
        {
         "name": "EdLevel",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
            "count": 2
           },
           {
            "name": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
            "count": 2
           },
           {
            "name": "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Age1stCode",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "11 - 17 years",
            "count": 3
           },
           {
            "name": "18 - 24 years",
            "count": 1
           },
           {
            "name": "5 - 10 years",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "LearnCode",
         "dtype": "object",
         "stats": {
          "unique_count": 4,
          "nan_count": 1,
          "categories": [
           {
            "name": "Coding Bootcamp;Other online resources (ex: videos, blogs, etc)",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           },
           {
            "name": "Missing",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "YearsCode",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "categories": [
           {
            "name": "7",
            "count": 1
           },
           {
            "name": "17",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "YearsCodePro",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "categories": [
           {
            "name": "10",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 4
           }
          ]
         }
        },
        {
         "name": "DevType",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 2,
          "categories": [
           {
            "name": "Developer, mobile",
            "count": 1
           },
           {
            "name": "2 others",
            "count": 2
           },
           {
            "name": "Missing",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "OrgSize",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 2,
          "categories": [
           {
            "name": "20 to 99 employees",
            "count": 1
           },
           {
            "name": "2 others",
            "count": 2
           },
           {
            "name": "Missing",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "Currency",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 2,
          "categories": [
           {
            "name": "EUR European Euro",
            "count": 2
           },
           {
            "name": "GBP\tPound sterling",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "CompTotal",
         "dtype": "float64",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "min": "4800.0",
          "max": "4800.0",
          "histogram": [
           {
            "bin_start": 4799.5,
            "bin_end": 4799.6,
            "count": 0
           },
           {
            "bin_start": 4799.6,
            "bin_end": 4799.7,
            "count": 0
           },
           {
            "bin_start": 4799.7,
            "bin_end": 4799.8,
            "count": 0
           },
           {
            "bin_start": 4799.8,
            "bin_end": 4799.9,
            "count": 0
           },
           {
            "bin_start": 4799.9,
            "bin_end": 4800,
            "count": 0
           },
           {
            "bin_start": 4800,
            "bin_end": 4800.1,
            "count": 1
           },
           {
            "bin_start": 4800.1,
            "bin_end": 4800.2,
            "count": 0
           },
           {
            "bin_start": 4800.2,
            "bin_end": 4800.3,
            "count": 0
           },
           {
            "bin_start": 4800.3,
            "bin_end": 4800.4,
            "count": 0
           },
           {
            "bin_start": 4800.4,
            "bin_end": 4800.5,
            "count": 0
           }
          ]
         }
        },
        {
         "name": "CompFreq",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 3,
          "categories": [
           {
            "name": "Monthly",
            "count": 2
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "LanguageHaveWorkedWith",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "C++;HTML/CSS;JavaScript;Objective-C;PHP;Swift",
            "count": 1
           },
           {
            "name": "JavaScript;Python",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "LanguageWantToWorkWith",
         "dtype": "object",
         "stats": {
          "unique_count": 4,
          "nan_count": 1,
          "categories": [
           {
            "name": "Swift",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           },
           {
            "name": "Missing",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "DatabaseHaveWorkedWith",
         "dtype": "object",
         "stats": {
          "unique_count": 4,
          "nan_count": 1,
          "categories": [
           {
            "name": "PostgreSQL;SQLite",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           },
           {
            "name": "Missing",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "DatabaseWantToWorkWith",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 2,
          "categories": [
           {
            "name": "SQLite",
            "count": 2
           },
           {
            "name": "Cassandra;Elasticsearch;PostgreSQL;Redis",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "PlatformHaveWorkedWith",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "categories": [
           {
            "name": "Heroku",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 4
           }
          ]
         }
        },
        {
         "name": "PlatformWantToWorkWith",
         "dtype": "object",
         "stats": {
          "unique_count": 0,
          "nan_count": 5,
          "categories": [
           {
            "name": "Missing",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "WebframeHaveWorkedWith",
         "dtype": "object",
         "stats": {
          "unique_count": 4,
          "nan_count": 0,
          "categories": [
           {
            "name": "Flask",
            "count": 2
           },
           {
            "name": "Laravel;Symfony",
            "count": 1
           },
           {
            "name": "2 others",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "WebframeWantToWorkWith",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 2,
          "categories": [
           {
            "name": "Flask",
            "count": 2
           },
           {
            "name": "Angular;jQuery",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "MiscTechHaveWorkedWith",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 2,
          "categories": [
           {
            "name": "Cordova",
            "count": 1
           },
           {
            "name": "2 others",
            "count": 2
           },
           {
            "name": "Missing",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "MiscTechWantToWorkWith",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "categories": [
           {
            "name": "Keras;NumPy;Pandas;TensorFlow;Torch/PyTorch",
            "count": 1
           },
           {
            "name": "Hadoop;NumPy;Pandas",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "ToolsTechHaveWorkedWith",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "categories": [
           {
            "name": "Docker;Git;Yarn",
            "count": 1
           },
           {
            "name": "Docker;Git;Kubernetes;Yarn",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "ToolsTechWantToWorkWith",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "categories": [
           {
            "name": "Git",
            "count": 1
           },
           {
            "name": "Docker;Git;Kubernetes;Yarn",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "NEWCollabToolsHaveWorkedWith",
         "dtype": "object",
         "stats": {
          "unique_count": 4,
          "nan_count": 1,
          "categories": [
           {
            "name": "PHPStorm;Xcode",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           },
           {
            "name": "Missing",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "NEWCollabToolsWantToWorkWith",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 2,
          "categories": [
           {
            "name": "Atom;Xcode",
            "count": 1
           },
           {
            "name": "2 others",
            "count": 2
           },
           {
            "name": "Missing",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "OpSys",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "MacOS",
            "count": 2
           },
           {
            "name": "Windows",
            "count": 2
           },
           {
            "name": "Linux-based",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "NEWStuck",
         "dtype": "object",
         "stats": {
          "unique_count": 4,
          "nan_count": 0,
          "categories": [
           {
            "name": "Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Google it",
            "count": 2
           },
           {
            "name": "Visit Stack Overflow;Google it",
            "count": 1
           },
           {
            "name": "2 others",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "NEWSOSites",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "Stack Overflow",
            "count": 3
           },
           {
            "name": "Stack Overflow;Stack Exchange",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "SOVisitFreq",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "Daily or almost daily",
            "count": 3
           },
           {
            "name": "Multiple times per day",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "SOAccount",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "Yes",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "SOPartFreq",
         "dtype": "object",
         "stats": {
          "unique_count": 4,
          "nan_count": 0,
          "categories": [
           {
            "name": "Daily or almost daily",
            "count": 2
           },
           {
            "name": "A few times per month or weekly",
            "count": 1
           },
           {
            "name": "2 others",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "SOComm",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "Yes, definitely",
            "count": 3
           },
           {
            "name": "Neutral",
            "count": 1
           },
           {
            "name": "Yes, somewhat",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "NEWOtherComms",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "No",
            "count": 4
           },
           {
            "name": "Yes",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Age",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "25-34 years old",
            "count": 2
           },
           {
            "name": "18-24 years old",
            "count": 2
           },
           {
            "name": "35-44 years old",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Gender",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "Man",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Trans",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "No",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Sexuality",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 1,
          "categories": [
           {
            "name": "Straight / Heterosexual",
            "count": 3
           },
           {
            "name": "Prefer not to say",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Ethnicity",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "White or of European descent",
            "count": 4
           },
           {
            "name": "Prefer not to say",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Accessibility",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "None of the above",
            "count": 4
           },
           {
            "name": "I am deaf / hard of hearing",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "MentalHealth",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 2,
          "categories": [
           {
            "name": "None of the above",
            "count": 3
           },
           {
            "name": "Missing",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "SurveyLength",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "Appropriate in length",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "SurveyEase",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "Easy",
            "count": 4
           },
           {
            "name": "Neither easy nor difficult",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "ConvertedCompYearly",
         "dtype": "float64",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "min": "62268.0",
          "max": "62268.0",
          "histogram": [
           {
            "bin_start": 62267.5,
            "bin_end": 62267.6,
            "count": 0
           },
           {
            "bin_start": 62267.6,
            "bin_end": 62267.7,
            "count": 0
           },
           {
            "bin_start": 62267.7,
            "bin_end": 62267.8,
            "count": 0
           },
           {
            "bin_start": 62267.8,
            "bin_end": 62267.9,
            "count": 0
           },
           {
            "bin_start": 62267.9,
            "bin_end": 62268,
            "count": 0
           },
           {
            "bin_start": 62268,
            "bin_end": 62268.1,
            "count": 1
           },
           {
            "bin_start": 62268.1,
            "bin_end": 62268.2,
            "count": 0
           },
           {
            "bin_start": 62268.2,
            "bin_end": 62268.3,
            "count": 0
           },
           {
            "bin_start": 62268.3,
            "bin_end": 62268.4,
            "count": 0
           },
           {
            "bin_start": 62268.4,
            "bin_end": 62268.5,
            "count": 0
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "ResponseId": 1,
         "MainBranch": "I am a developer by profession",
         "Employment": "Independent contractor, freelancer, or self-employed",
         "Country": "Slovakia",
         "US_State": "nan",
         "UK_Country": "nan",
         "EdLevel": "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)",
         "Age1stCode": "18 - 24 years",
         "LearnCode": "Coding Bootcamp;Other online resources (ex: videos, blogs, etc)",
         "YearsCode": "nan",
         "YearsCodePro": "nan",
         "DevType": "Developer, mobile",
         "OrgSize": "20 to 99 employees",
         "Currency": "EUR European Euro",
         "CompTotal": 4800,
         "CompFreq": "Monthly",
         "LanguageHaveWorkedWith": "C++;HTML/CSS;JavaScript;Objective-C;PHP;Swift",
         "LanguageWantToWorkWith": "Swift",
         "DatabaseHaveWorkedWith": "PostgreSQL;SQLite",
         "DatabaseWantToWorkWith": "SQLite",
         "PlatformHaveWorkedWith": "nan",
         "PlatformWantToWorkWith": "nan",
         "WebframeHaveWorkedWith": "Laravel;Symfony",
         "WebframeWantToWorkWith": "nan",
         "MiscTechHaveWorkedWith": "nan",
         "MiscTechWantToWorkWith": "nan",
         "ToolsTechHaveWorkedWith": "nan",
         "ToolsTechWantToWorkWith": "nan",
         "NEWCollabToolsHaveWorkedWith": "PHPStorm;Xcode",
         "NEWCollabToolsWantToWorkWith": "Atom;Xcode",
         "OpSys": "MacOS",
         "NEWStuck": "Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Google it",
         "NEWSOSites": "Stack Overflow",
         "SOVisitFreq": "Multiple times per day",
         "SOAccount": "Yes",
         "SOPartFreq": "A few times per month or weekly",
         "SOComm": "Yes, definitely",
         "NEWOtherComms": "No",
         "Age": "25-34 years old",
         "Gender": "Man",
         "Trans": "No",
         "Sexuality": "Straight / Heterosexual",
         "Ethnicity": "White or of European descent",
         "Accessibility": "None of the above",
         "MentalHealth": "None of the above",
         "SurveyLength": "Appropriate in length",
         "SurveyEase": "Easy",
         "ConvertedCompYearly": 62268,
         "_deepnote_index_column": 0
        },
        {
         "ResponseId": 2,
         "MainBranch": "I am a student who is learning to code",
         "Employment": "Student, full-time",
         "Country": "Netherlands",
         "US_State": "nan",
         "UK_Country": "nan",
         "EdLevel": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
         "Age1stCode": "11 - 17 years",
         "LearnCode": "Other online resources (ex: videos, blogs, etc);School",
         "YearsCode": "7",
         "YearsCodePro": "nan",
         "DevType": "nan",
         "OrgSize": "nan",
         "Currency": "nan",
         "CompTotal": "nan",
         "CompFreq": "nan",
         "LanguageHaveWorkedWith": "JavaScript;Python",
         "LanguageWantToWorkWith": "nan",
         "DatabaseHaveWorkedWith": "PostgreSQL",
         "DatabaseWantToWorkWith": "nan",
         "PlatformHaveWorkedWith": "nan",
         "PlatformWantToWorkWith": "nan",
         "WebframeHaveWorkedWith": "Angular;Flask;Vue.js",
         "WebframeWantToWorkWith": "nan",
         "MiscTechHaveWorkedWith": "Cordova",
         "MiscTechWantToWorkWith": "nan",
         "ToolsTechHaveWorkedWith": "Docker;Git;Yarn",
         "ToolsTechWantToWorkWith": "Git",
         "NEWCollabToolsHaveWorkedWith": "Android Studio;IntelliJ;Notepad++;PyCharm",
         "NEWCollabToolsWantToWorkWith": "nan",
         "OpSys": "Windows",
         "NEWStuck": "Visit Stack Overflow;Google it",
         "NEWSOSites": "Stack Overflow",
         "SOVisitFreq": "Daily or almost daily",
         "SOAccount": "Yes",
         "SOPartFreq": "Daily or almost daily",
         "SOComm": "Yes, definitely",
         "NEWOtherComms": "No",
         "Age": "18-24 years old",
         "Gender": "Man",
         "Trans": "No",
         "Sexuality": "Straight / Heterosexual",
         "Ethnicity": "White or of European descent",
         "Accessibility": "None of the above",
         "MentalHealth": "None of the above",
         "SurveyLength": "Appropriate in length",
         "SurveyEase": "Easy",
         "ConvertedCompYearly": "nan",
         "_deepnote_index_column": 1
        },
        {
         "ResponseId": 3,
         "MainBranch": "I am not primarily a developer, but I write code sometimes as part of my work",
         "Employment": "Student, full-time",
         "Country": "Russian Federation",
         "US_State": "nan",
         "UK_Country": "nan",
         "EdLevel": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
         "Age1stCode": "11 - 17 years",
         "LearnCode": "Other online resources (ex: videos, blogs, etc);Online Forum",
         "YearsCode": "nan",
         "YearsCodePro": "nan",
         "DevType": "nan",
         "OrgSize": "nan",
         "Currency": "nan",
         "CompTotal": "nan",
         "CompFreq": "nan",
         "LanguageHaveWorkedWith": "Assembly;C;Python;R;Rust",
         "LanguageWantToWorkWith": "Julia;Python;Rust",
         "DatabaseHaveWorkedWith": "SQLite",
         "DatabaseWantToWorkWith": "SQLite",
         "PlatformHaveWorkedWith": "Heroku",
         "PlatformWantToWorkWith": "nan",
         "WebframeHaveWorkedWith": "Flask",
         "WebframeWantToWorkWith": "Flask",
         "MiscTechHaveWorkedWith": "NumPy;Pandas;TensorFlow;Torch/PyTorch",
         "MiscTechWantToWorkWith": "Keras;NumPy;Pandas;TensorFlow;Torch/PyTorch",
         "ToolsTechHaveWorkedWith": "nan",
         "ToolsTechWantToWorkWith": "nan",
         "NEWCollabToolsHaveWorkedWith": "IPython/Jupyter;PyCharm;RStudio;Sublime Text;Visual Studio Code",
         "NEWCollabToolsWantToWorkWith": "IPython/Jupyter;RStudio;Sublime Text;Visual Studio Code",
         "OpSys": "MacOS",
         "NEWStuck": "Visit Stack Overflow;Google it;Watch help / tutorial videos;Do other work and come back later",
         "NEWSOSites": "Stack Overflow;Stack Exchange",
         "SOVisitFreq": "Multiple times per day",
         "SOAccount": "Yes",
         "SOPartFreq": "Multiple times per day",
         "SOComm": "Yes, definitely",
         "NEWOtherComms": "Yes",
         "Age": "18-24 years old",
         "Gender": "Man",
         "Trans": "No",
         "Sexuality": "Prefer not to say",
         "Ethnicity": "Prefer not to say",
         "Accessibility": "None of the above",
         "MentalHealth": "None of the above",
         "SurveyLength": "Appropriate in length",
         "SurveyEase": "Easy",
         "ConvertedCompYearly": "nan",
         "_deepnote_index_column": 2
        },
        {
         "ResponseId": 4,
         "MainBranch": "I am a developer by profession",
         "Employment": "Employed full-time",
         "Country": "Austria",
         "US_State": "nan",
         "UK_Country": "nan",
         "EdLevel": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
         "Age1stCode": "11 - 17 years",
         "LearnCode": "nan",
         "YearsCode": "nan",
         "YearsCodePro": "nan",
         "DevType": "Developer, front-end",
         "OrgSize": "100 to 499 employees",
         "Currency": "EUR European Euro",
         "CompTotal": "nan",
         "CompFreq": "Monthly",
         "LanguageHaveWorkedWith": "JavaScript;TypeScript",
         "LanguageWantToWorkWith": "JavaScript;TypeScript",
         "DatabaseHaveWorkedWith": "nan",
         "DatabaseWantToWorkWith": "nan",
         "PlatformHaveWorkedWith": "nan",
         "PlatformWantToWorkWith": "nan",
         "WebframeHaveWorkedWith": "Angular;jQuery",
         "WebframeWantToWorkWith": "Angular;jQuery",
         "MiscTechHaveWorkedWith": "nan",
         "MiscTechWantToWorkWith": "nan",
         "ToolsTechHaveWorkedWith": "nan",
         "ToolsTechWantToWorkWith": "nan",
         "NEWCollabToolsHaveWorkedWith": "nan",
         "NEWCollabToolsWantToWorkWith": "nan",
         "OpSys": "Windows",
         "NEWStuck": "Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Google it",
         "NEWSOSites": "Stack Overflow",
         "SOVisitFreq": "Daily or almost daily",
         "SOAccount": "Yes",
         "SOPartFreq": "Daily or almost daily",
         "SOComm": "Neutral",
         "NEWOtherComms": "No",
         "Age": "35-44 years old",
         "Gender": "Man",
         "Trans": "No",
         "Sexuality": "Straight / Heterosexual",
         "Ethnicity": "White or of European descent",
         "Accessibility": "I am deaf / hard of hearing",
         "MentalHealth": "nan",
         "SurveyLength": "Appropriate in length",
         "SurveyEase": "Neither easy nor difficult",
         "ConvertedCompYearly": "nan",
         "_deepnote_index_column": 3
        },
        {
         "ResponseId": 5,
         "MainBranch": "I am a developer by profession",
         "Employment": "Independent contractor, freelancer, or self-employed",
         "Country": "United Kingdom of Great Britain and Northern Ireland",
         "US_State": "nan",
         "UK_Country": "England",
         "EdLevel": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
         "Age1stCode": "5 - 10 years",
         "LearnCode": "Friend or family member",
         "YearsCode": "17",
         "YearsCodePro": "10",
         "DevType": "Developer, desktop or enterprise applications;Developer, full-stack;Engineer, data;Other (please sp…",
         "OrgSize": "Just me - I am a freelancer, sole proprietor, etc.",
         "Currency": "GBP\tPound sterling",
         "CompTotal": "nan",
         "CompFreq": "nan",
         "LanguageHaveWorkedWith": "Bash/Shell;HTML/CSS;Python;SQL",
         "LanguageWantToWorkWith": "Bash/Shell;HTML/CSS;Python;SQL",
         "DatabaseHaveWorkedWith": "Elasticsearch;PostgreSQL;Redis",
         "DatabaseWantToWorkWith": "Cassandra;Elasticsearch;PostgreSQL;Redis",
         "PlatformHaveWorkedWith": "nan",
         "PlatformWantToWorkWith": "nan",
         "WebframeHaveWorkedWith": "Flask",
         "WebframeWantToWorkWith": "Flask",
         "MiscTechHaveWorkedWith": "Apache Spark;Hadoop;NumPy;Pandas",
         "MiscTechWantToWorkWith": "Hadoop;NumPy;Pandas",
         "ToolsTechHaveWorkedWith": "Docker;Git;Kubernetes;Yarn",
         "ToolsTechWantToWorkWith": "Docker;Git;Kubernetes;Yarn",
         "NEWCollabToolsHaveWorkedWith": "Atom;IPython/Jupyter;Notepad++;PyCharm;Vim",
         "NEWCollabToolsWantToWorkWith": "Atom;IPython/Jupyter;Notepad++;PyCharm;Vim;Visual Studio Code",
         "OpSys": "Linux-based",
         "NEWStuck": "Visit Stack Overflow;Go for a walk or other physical activity;Google it;Watch help / tutorial video…",
         "NEWSOSites": "Stack Overflow;Stack Exchange",
         "SOVisitFreq": "Daily or almost daily",
         "SOAccount": "Yes",
         "SOPartFreq": "A few times per week",
         "SOComm": "Yes, somewhat",
         "NEWOtherComms": "No",
         "Age": "25-34 years old",
         "Gender": "Man",
         "Trans": "No",
         "Sexuality": "nan",
         "Ethnicity": "White or of European descent",
         "Accessibility": "None of the above",
         "MentalHealth": "nan",
         "SurveyLength": "Appropriate in length",
         "SurveyEase": "Easy",
         "ConvertedCompYearly": "nan",
         "_deepnote_index_column": 4
        }
       ]
      },
      "text/plain": "   ResponseId                                         MainBranch  \\\n0           1                     I am a developer by profession   \n1           2             I am a student who is learning to code   \n2           3  I am not primarily a developer, but I write co...   \n3           4                     I am a developer by profession   \n4           5                     I am a developer by profession   \n\n                                          Employment  \\\n0  Independent contractor, freelancer, or self-em...   \n1                                 Student, full-time   \n2                                 Student, full-time   \n3                                 Employed full-time   \n4  Independent contractor, freelancer, or self-em...   \n\n                                             Country US_State UK_Country  \\\n0                                           Slovakia      NaN        NaN   \n1                                        Netherlands      NaN        NaN   \n2                                 Russian Federation      NaN        NaN   \n3                                            Austria      NaN        NaN   \n4  United Kingdom of Great Britain and Northern I...      NaN    England   \n\n                                             EdLevel     Age1stCode  \\\n0  Secondary school (e.g. American high school, G...  18 - 24 years   \n1       Bachelor’s degree (B.A., B.S., B.Eng., etc.)  11 - 17 years   \n2       Bachelor’s degree (B.A., B.S., B.Eng., etc.)  11 - 17 years   \n3    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)  11 - 17 years   \n4    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   5 - 10 years   \n\n                                           LearnCode YearsCode  ...  \\\n0  Coding Bootcamp;Other online resources (ex: vi...       NaN  ...   \n1  Other online resources (ex: videos, blogs, etc...         7  ...   \n2  Other online resources (ex: videos, blogs, etc...       NaN  ...   \n3                                                NaN       NaN  ...   \n4                            Friend or family member        17  ...   \n\n               Age Gender Trans                Sexuality  \\\n0  25-34 years old    Man    No  Straight / Heterosexual   \n1  18-24 years old    Man    No  Straight / Heterosexual   \n2  18-24 years old    Man    No        Prefer not to say   \n3  35-44 years old    Man    No  Straight / Heterosexual   \n4  25-34 years old    Man    No                      NaN   \n\n                      Ethnicity                Accessibility  \\\n0  White or of European descent            None of the above   \n1  White or of European descent            None of the above   \n2             Prefer not to say            None of the above   \n3  White or of European descent  I am deaf / hard of hearing   \n4  White or of European descent            None of the above   \n\n        MentalHealth           SurveyLength                  SurveyEase  \\\n0  None of the above  Appropriate in length                        Easy   \n1  None of the above  Appropriate in length                        Easy   \n2  None of the above  Appropriate in length                        Easy   \n3                NaN  Appropriate in length  Neither easy nor difficult   \n4                NaN  Appropriate in length                        Easy   \n\n  ConvertedCompYearly  \n0             62268.0  \n1                 NaN  \n2                 NaN  \n3                 NaN  \n4                 NaN  \n\n[5 rows x 48 columns]",
      "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>ResponseId</th>\n      <th>MainBranch</th>\n      <th>Employment</th>\n      <th>Country</th>\n      <th>US_State</th>\n      <th>UK_Country</th>\n      <th>EdLevel</th>\n      <th>Age1stCode</th>\n      <th>LearnCode</th>\n      <th>YearsCode</th>\n      <th>...</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Trans</th>\n      <th>Sexuality</th>\n      <th>Ethnicity</th>\n      <th>Accessibility</th>\n      <th>MentalHealth</th>\n      <th>SurveyLength</th>\n      <th>SurveyEase</th>\n      <th>ConvertedCompYearly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>I am a developer by profession</td>\n      <td>Independent contractor, freelancer, or self-em...</td>\n      <td>Slovakia</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Secondary school (e.g. American high school, G...</td>\n      <td>18 - 24 years</td>\n      <td>Coding Bootcamp;Other online resources (ex: vi...</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>25-34 years old</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>Straight / Heterosexual</td>\n      <td>White or of European descent</td>\n      <td>None of the above</td>\n      <td>None of the above</td>\n      <td>Appropriate in length</td>\n      <td>Easy</td>\n      <td>62268.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>I am a student who is learning to code</td>\n      <td>Student, full-time</td>\n      <td>Netherlands</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n      <td>11 - 17 years</td>\n      <td>Other online resources (ex: videos, blogs, etc...</td>\n      <td>7</td>\n      <td>...</td>\n      <td>18-24 years old</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>Straight / Heterosexual</td>\n      <td>White or of European descent</td>\n      <td>None of the above</td>\n      <td>None of the above</td>\n      <td>Appropriate in length</td>\n      <td>Easy</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>I am not primarily a developer, but I write co...</td>\n      <td>Student, full-time</td>\n      <td>Russian Federation</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n      <td>11 - 17 years</td>\n      <td>Other online resources (ex: videos, blogs, etc...</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>18-24 years old</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>Prefer not to say</td>\n      <td>Prefer not to say</td>\n      <td>None of the above</td>\n      <td>None of the above</td>\n      <td>Appropriate in length</td>\n      <td>Easy</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>I am a developer by profession</td>\n      <td>Employed full-time</td>\n      <td>Austria</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n      <td>11 - 17 years</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>35-44 years old</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>Straight / Heterosexual</td>\n      <td>White or of European descent</td>\n      <td>I am deaf / hard of hearing</td>\n      <td>NaN</td>\n      <td>Appropriate in length</td>\n      <td>Neither easy nor difficult</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>I am a developer by profession</td>\n      <td>Independent contractor, freelancer, or self-em...</td>\n      <td>United Kingdom of Great Britain and Northern I...</td>\n      <td>NaN</td>\n      <td>England</td>\n      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n      <td>5 - 10 years</td>\n      <td>Friend or family member</td>\n      <td>17</td>\n      <td>...</td>\n      <td>25-34 years old</td>\n      <td>Man</td>\n      <td>No</td>\n      <td>NaN</td>\n      <td>White or of European descent</td>\n      <td>None of the above</td>\n      <td>NaN</td>\n      <td>Appropriate in length</td>\n      <td>Easy</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 48 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "04e6c280-a4dd-4174-b7a3-4dca8e8a8ef4",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ca083ec6",
    "execution_start": 1646128702545,
    "execution_millis": 64,
    "deepnote_cell_type": "code"
   },
   "source": "# df = df[[\"Country\", \"EdLevel\", \"YearsCodePro\", \"Employment\", \"ConvertedCompYearly\"]]\n# df = df.rename({'ConvertedCompYearly': 'Salary'}, axis = 1)\n# df.head()\n\ndf = df.drop(columns=['ResponseId','MainBranch','US_State','UK_Country','Age1stCode','LearnCode','YearsCode','DevType','OrgSize','Currency','CompTotal','CompFreq','LanguageHaveWorkedWith','LanguageWantToWorkWith','DatabaseHaveWorkedWith','DatabaseWantToWorkWith','PlatformHaveWorkedWith','PlatformWantToWorkWith','WebframeHaveWorkedWith','WebframeWantToWorkWith','MiscTechHaveWorkedWith','MiscTechWantToWorkWith','ToolsTechHaveWorkedWith','ToolsTechWantToWorkWith','NEWCollabToolsHaveWorkedWith','NEWCollabToolsWantToWorkWith','OpSys','NEWStuck','NEWSOSites','SOVisitFreq','SOAccount','SOPartFreq','SOComm','NEWOtherComms','Age','Gender','Trans','Sexuality','Ethnicity','Accessibility','MentalHealth','SurveyLength','SurveyEase'], axis=1)\n\ndf.head()\n",
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 3,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 5,
       "row_count": 5,
       "columns": [
        {
         "name": "Employment",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "Independent contractor, freelancer, or self-employed",
            "count": 2
           },
           {
            "name": "Student, full-time",
            "count": 2
           },
           {
            "name": "Employed full-time",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Country",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "Slovakia",
            "count": 1
           },
           {
            "name": "Netherlands",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "EdLevel",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
            "count": 2
           },
           {
            "name": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
            "count": 2
           },
           {
            "name": "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "YearsCodePro",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "categories": [
           {
            "name": "10",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 4
           }
          ]
         }
        },
        {
         "name": "ConvertedCompYearly",
         "dtype": "float64",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "min": "62268.0",
          "max": "62268.0",
          "histogram": [
           {
            "bin_start": 62267.5,
            "bin_end": 62267.6,
            "count": 0
           },
           {
            "bin_start": 62267.6,
            "bin_end": 62267.7,
            "count": 0
           },
           {
            "bin_start": 62267.7,
            "bin_end": 62267.8,
            "count": 0
           },
           {
            "bin_start": 62267.8,
            "bin_end": 62267.9,
            "count": 0
           },
           {
            "bin_start": 62267.9,
            "bin_end": 62268,
            "count": 0
           },
           {
            "bin_start": 62268,
            "bin_end": 62268.1,
            "count": 1
           },
           {
            "bin_start": 62268.1,
            "bin_end": 62268.2,
            "count": 0
           },
           {
            "bin_start": 62268.2,
            "bin_end": 62268.3,
            "count": 0
           },
           {
            "bin_start": 62268.3,
            "bin_end": 62268.4,
            "count": 0
           },
           {
            "bin_start": 62268.4,
            "bin_end": 62268.5,
            "count": 0
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Employment": "Independent contractor, freelancer, or self-employed",
         "Country": "Slovakia",
         "EdLevel": "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)",
         "YearsCodePro": "nan",
         "ConvertedCompYearly": 62268,
         "_deepnote_index_column": 0
        },
        {
         "Employment": "Student, full-time",
         "Country": "Netherlands",
         "EdLevel": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
         "YearsCodePro": "nan",
         "ConvertedCompYearly": "nan",
         "_deepnote_index_column": 1
        },
        {
         "Employment": "Student, full-time",
         "Country": "Russian Federation",
         "EdLevel": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
         "YearsCodePro": "nan",
         "ConvertedCompYearly": "nan",
         "_deepnote_index_column": 2
        },
        {
         "Employment": "Employed full-time",
         "Country": "Austria",
         "EdLevel": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
         "YearsCodePro": "nan",
         "ConvertedCompYearly": "nan",
         "_deepnote_index_column": 3
        },
        {
         "Employment": "Independent contractor, freelancer, or self-employed",
         "Country": "United Kingdom of Great Britain and Northern Ireland",
         "EdLevel": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
         "YearsCodePro": "10",
         "ConvertedCompYearly": "nan",
         "_deepnote_index_column": 4
        }
       ]
      },
      "text/plain": "                                          Employment  \\\n0  Independent contractor, freelancer, or self-em...   \n1                                 Student, full-time   \n2                                 Student, full-time   \n3                                 Employed full-time   \n4  Independent contractor, freelancer, or self-em...   \n\n                                             Country  \\\n0                                           Slovakia   \n1                                        Netherlands   \n2                                 Russian Federation   \n3                                            Austria   \n4  United Kingdom of Great Britain and Northern I...   \n\n                                             EdLevel YearsCodePro  \\\n0  Secondary school (e.g. American high school, G...          NaN   \n1       Bachelor’s degree (B.A., B.S., B.Eng., etc.)          NaN   \n2       Bachelor’s degree (B.A., B.S., B.Eng., etc.)          NaN   \n3    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)          NaN   \n4    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)           10   \n\n   ConvertedCompYearly  \n0              62268.0  \n1                  NaN  \n2                  NaN  \n3                  NaN  \n4                  NaN  ",
      "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>Employment</th>\n      <th>Country</th>\n      <th>EdLevel</th>\n      <th>YearsCodePro</th>\n      <th>ConvertedCompYearly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Independent contractor, freelancer, or self-em...</td>\n      <td>Slovakia</td>\n      <td>Secondary school (e.g. American high school, G...</td>\n      <td>NaN</td>\n      <td>62268.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Student, full-time</td>\n      <td>Netherlands</td>\n      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Student, full-time</td>\n      <td>Russian Federation</td>\n      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Employed full-time</td>\n      <td>Austria</td>\n      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Independent contractor, freelancer, or self-em...</td>\n      <td>United Kingdom of Great Britain and Northern I...</td>\n      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n      <td>10</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "7defc1ee-7f3f-4448-9af2-b5fa256c9f8c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e5c60b60",
    "execution_start": 1646128702623,
    "execution_millis": 31,
    "deepnote_cell_type": "code"
   },
   "source": "df = df.rename({'ConvertedCompYearly': 'Salary'}, axis=1)\ndf.head()",
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 4,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 5,
       "row_count": 5,
       "columns": [
        {
         "name": "Employment",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "Independent contractor, freelancer, or self-employed",
            "count": 2
           },
           {
            "name": "Student, full-time",
            "count": 2
           },
           {
            "name": "Employed full-time",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Country",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "Slovakia",
            "count": 1
           },
           {
            "name": "Netherlands",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "EdLevel",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
            "count": 2
           },
           {
            "name": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
            "count": 2
           },
           {
            "name": "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "YearsCodePro",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "categories": [
           {
            "name": "10",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 4
           }
          ]
         }
        },
        {
         "name": "Salary",
         "dtype": "float64",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "min": "62268.0",
          "max": "62268.0",
          "histogram": [
           {
            "bin_start": 62267.5,
            "bin_end": 62267.6,
            "count": 0
           },
           {
            "bin_start": 62267.6,
            "bin_end": 62267.7,
            "count": 0
           },
           {
            "bin_start": 62267.7,
            "bin_end": 62267.8,
            "count": 0
           },
           {
            "bin_start": 62267.8,
            "bin_end": 62267.9,
            "count": 0
           },
           {
            "bin_start": 62267.9,
            "bin_end": 62268,
            "count": 0
           },
           {
            "bin_start": 62268,
            "bin_end": 62268.1,
            "count": 1
           },
           {
            "bin_start": 62268.1,
            "bin_end": 62268.2,
            "count": 0
           },
           {
            "bin_start": 62268.2,
            "bin_end": 62268.3,
            "count": 0
           },
           {
            "bin_start": 62268.3,
            "bin_end": 62268.4,
            "count": 0
           },
           {
            "bin_start": 62268.4,
            "bin_end": 62268.5,
            "count": 0
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Employment": "Independent contractor, freelancer, or self-employed",
         "Country": "Slovakia",
         "EdLevel": "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)",
         "YearsCodePro": "nan",
         "Salary": 62268,
         "_deepnote_index_column": 0
        },
        {
         "Employment": "Student, full-time",
         "Country": "Netherlands",
         "EdLevel": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
         "YearsCodePro": "nan",
         "Salary": "nan",
         "_deepnote_index_column": 1
        },
        {
         "Employment": "Student, full-time",
         "Country": "Russian Federation",
         "EdLevel": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
         "YearsCodePro": "nan",
         "Salary": "nan",
         "_deepnote_index_column": 2
        },
        {
         "Employment": "Employed full-time",
         "Country": "Austria",
         "EdLevel": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
         "YearsCodePro": "nan",
         "Salary": "nan",
         "_deepnote_index_column": 3
        },
        {
         "Employment": "Independent contractor, freelancer, or self-employed",
         "Country": "United Kingdom of Great Britain and Northern Ireland",
         "EdLevel": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
         "YearsCodePro": "10",
         "Salary": "nan",
         "_deepnote_index_column": 4
        }
       ]
      },
      "text/plain": "                                          Employment  \\\n0  Independent contractor, freelancer, or self-em...   \n1                                 Student, full-time   \n2                                 Student, full-time   \n3                                 Employed full-time   \n4  Independent contractor, freelancer, or self-em...   \n\n                                             Country  \\\n0                                           Slovakia   \n1                                        Netherlands   \n2                                 Russian Federation   \n3                                            Austria   \n4  United Kingdom of Great Britain and Northern I...   \n\n                                             EdLevel YearsCodePro   Salary  \n0  Secondary school (e.g. American high school, G...          NaN  62268.0  \n1       Bachelor’s degree (B.A., B.S., B.Eng., etc.)          NaN      NaN  \n2       Bachelor’s degree (B.A., B.S., B.Eng., etc.)          NaN      NaN  \n3    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)          NaN      NaN  \n4    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)           10      NaN  ",
      "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>Employment</th>\n      <th>Country</th>\n      <th>EdLevel</th>\n      <th>YearsCodePro</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Independent contractor, freelancer, or self-em...</td>\n      <td>Slovakia</td>\n      <td>Secondary school (e.g. American high school, G...</td>\n      <td>NaN</td>\n      <td>62268.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Student, full-time</td>\n      <td>Netherlands</td>\n      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Student, full-time</td>\n      <td>Russian Federation</td>\n      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Employed full-time</td>\n      <td>Austria</td>\n      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Independent contractor, freelancer, or self-em...</td>\n      <td>United Kingdom of Great Britain and Northern I...</td>\n      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n      <td>10</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "9f0ce72c-defa-4620-a526-5f428309c4d6",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b2905c",
    "execution_start": 1646128702707,
    "execution_millis": 48,
    "deepnote_cell_type": "code"
   },
   "source": "df = df[df['Salary'].notnull()]\ndf.head()",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 5,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 5,
       "row_count": 5,
       "columns": [
        {
         "name": "Employment",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "Employed full-time",
            "count": 4
           },
           {
            "name": "Independent contractor, freelancer, or self-employed",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Country",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "Slovakia",
            "count": 1
           },
           {
            "name": "Sweden",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "EdLevel",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
            "count": 2
           },
           {
            "name": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
            "count": 2
           },
           {
            "name": "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "YearsCodePro",
         "dtype": "object",
         "stats": {
          "unique_count": 4,
          "nan_count": 1,
          "categories": [
           {
            "name": "4",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           },
           {
            "name": "Missing",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Salary",
         "dtype": "float64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "17748.0",
          "max": "77290.0",
          "histogram": [
           {
            "bin_start": 17748,
            "bin_end": 23702.2,
            "count": 1
           },
           {
            "bin_start": 23702.2,
            "bin_end": 29656.4,
            "count": 0
           },
           {
            "bin_start": 29656.4,
            "bin_end": 35610.6,
            "count": 0
           },
           {
            "bin_start": 35610.6,
            "bin_end": 41564.8,
            "count": 0
           },
           {
            "bin_start": 41564.8,
            "bin_end": 47519,
            "count": 1
           },
           {
            "bin_start": 47519,
            "bin_end": 53473.2,
            "count": 1
           },
           {
            "bin_start": 53473.2,
            "bin_end": 59427.4,
            "count": 0
           },
           {
            "bin_start": 59427.4,
            "bin_end": 65381.6,
            "count": 1
           },
           {
            "bin_start": 65381.6,
            "bin_end": 71335.79999999999,
            "count": 0
           },
           {
            "bin_start": 71335.79999999999,
            "bin_end": 77290,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Employment": "Independent contractor, freelancer, or self-employed",
         "Country": "Slovakia",
         "EdLevel": "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)",
         "YearsCodePro": "nan",
         "Salary": 62268,
         "_deepnote_index_column": 0
        },
        {
         "Employment": "Employed full-time",
         "Country": "Sweden",
         "EdLevel": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
         "YearsCodePro": "4",
         "Salary": 51552,
         "_deepnote_index_column": 9
        },
        {
         "Employment": "Employed full-time",
         "Country": "Spain",
         "EdLevel": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
         "YearsCodePro": "5",
         "Salary": 46482,
         "_deepnote_index_column": 11
        },
        {
         "Employment": "Employed full-time",
         "Country": "Germany",
         "EdLevel": "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
         "YearsCodePro": "6",
         "Salary": 77290,
         "_deepnote_index_column": 12
        },
        {
         "Employment": "Employed full-time",
         "Country": "Turkey",
         "EdLevel": "Bachelor’s degree (B.A., B.S., B.Eng., etc.)",
         "YearsCodePro": "2",
         "Salary": 17748,
         "_deepnote_index_column": 16
        }
       ]
      },
      "text/plain": "                                           Employment   Country  \\\n0   Independent contractor, freelancer, or self-em...  Slovakia   \n9                                  Employed full-time    Sweden   \n11                                 Employed full-time     Spain   \n12                                 Employed full-time   Germany   \n16                                 Employed full-time    Turkey   \n\n                                              EdLevel YearsCodePro   Salary  \n0   Secondary school (e.g. American high school, G...          NaN  62268.0  \n9     Master’s degree (M.A., M.S., M.Eng., MBA, etc.)            4  51552.0  \n11       Bachelor’s degree (B.A., B.S., B.Eng., etc.)            5  46482.0  \n12    Master’s degree (M.A., M.S., M.Eng., MBA, etc.)            6  77290.0  \n16       Bachelor’s degree (B.A., B.S., B.Eng., etc.)            2  17748.0  ",
      "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>Employment</th>\n      <th>Country</th>\n      <th>EdLevel</th>\n      <th>YearsCodePro</th>\n      <th>Salary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Independent contractor, freelancer, or self-em...</td>\n      <td>Slovakia</td>\n      <td>Secondary school (e.g. American high school, G...</td>\n      <td>NaN</td>\n      <td>62268.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Employed full-time</td>\n      <td>Sweden</td>\n      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n      <td>4</td>\n      <td>51552.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Employed full-time</td>\n      <td>Spain</td>\n      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n      <td>5</td>\n      <td>46482.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Employed full-time</td>\n      <td>Germany</td>\n      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n      <td>6</td>\n      <td>77290.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Employed full-time</td>\n      <td>Turkey</td>\n      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n      <td>2</td>\n      <td>17748.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "f45c706a-cfa1-496c-bd13-2817a8f7c63a",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "de1e323c",
    "execution_start": 1646128702753,
    "execution_millis": 2,
    "deepnote_cell_type": "code"
   },
   "source": "df.info()",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 46844 entries, 0 to 83438\nData columns (total 5 columns):\n #   Column        Non-Null Count  Dtype  \n---  ------        --------------  -----  \n 0   Employment    46831 non-null  object \n 1   Country       46844 non-null  object \n 2   EdLevel       46823 non-null  object \n 3   YearsCodePro  46391 non-null  object \n 4   Salary        46844 non-null  float64\ndtypes: float64(1), object(4)\nmemory usage: 2.1+ MB\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "576c5345-967e-459d-9f84-12a6a511e02b",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6a8370ed",
    "execution_start": 1646128702759,
    "execution_millis": 485,
    "deepnote_cell_type": "code"
   },
   "source": "df =df.dropna()\ndf.isnull().sum()",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 7,
     "data": {
      "text/plain": "Employment      0\nCountry         0\nEdLevel         0\nYearsCodePro    0\nSalary          0\ndtype: int64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "60e5d09e-976f-475f-98e8-0977ac785680",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e92b78fd",
    "execution_start": 1646128702825,
    "execution_millis": 434,
    "deepnote_cell_type": "code"
   },
   "source": "# df = df[df['Employment'] == 'Employment full-time']\n# df = df.drop('Employment', axis=1)\n# df.info()\n\ndf = df[df[\"Employment\"] == \"Employed full-time\"]\ndf = df.drop(\"Employment\", axis=1)\ndf.info()",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 40211 entries, 9 to 83438\nData columns (total 4 columns):\n #   Column        Non-Null Count  Dtype  \n---  ------        --------------  -----  \n 0   Country       40211 non-null  object \n 1   EdLevel       40211 non-null  object \n 2   YearsCodePro  40211 non-null  object \n 3   Salary        40211 non-null  float64\ndtypes: float64(1), object(3)\nmemory usage: 1.5+ MB\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "bbe895a2-bdb6-4580-bc67-045caaa66301",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "3074e117",
    "execution_start": 1646128702904,
    "execution_millis": 356,
    "deepnote_cell_type": "code"
   },
   "source": "# df.head()\ndf['Country'].value_counts()\n",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 9,
     "data": {
      "text/plain": "United States of America                                9175\nIndia                                                   3385\nGermany                                                 2753\nUnited Kingdom of Great Britain and Northern Ireland    2604\nCanada                                                  1553\n                                                        ... \nLesotho                                                    1\nSomalia                                                    1\nSan Marino                                                 1\nBurundi                                                    1\nSaint Vincent and the Grenadines                           1\nName: Country, Length: 166, dtype: int64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "16ee7fe1-2bd7-4c74-a76c-0f025c7a7d97",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "89d44107",
    "execution_start": 1646128702930,
    "execution_millis": 16,
    "deepnote_cell_type": "code"
   },
   "source": "def shorten_categories(categories, cutoff):\n    categorical_map = {}\n    for i in range(len(categories)):\n        if categories.values[i] >= cutoff:\n            categorical_map[categories.index[i]] = categories.index[i]\n        else:\n            categorical_map[categories.index[i]] = 'Other'\n    return categorical_map",
   "execution_count": 10,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "24bed886-5028-4a7f-a616-85972e80eb04",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c78c970",
    "execution_start": 1646128702956,
    "execution_millis": 306,
    "deepnote_cell_type": "code"
   },
   "source": "country_map = shorten_categories(df.Country.value_counts(), 400)\ndf['Country'] = df['Country'].map(country_map)\ndf.Country.value_counts()",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "text/plain": "Other                                                   10725\nUnited States of America                                 9175\nIndia                                                    3385\nGermany                                                  2753\nUnited Kingdom of Great Britain and Northern Ireland     2604\nCanada                                                   1553\nFrance                                                   1396\nBrazil                                                   1340\nSpain                                                     890\nNetherlands                                               835\nAustralia                                                 825\nPoland                                                    775\nItaly                                                     756\nRussian Federation                                        741\nSweden                                                    735\nTurkey                                                    458\nSwitzerland                                               438\nIsrael                                                    425\nNorway                                                    402\nName: Country, dtype: int64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "62b3c838-4163-40a5-aaab-f1d175591d73",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "577d7efa",
    "execution_start": 1646128703032,
    "execution_millis": 1226,
    "deepnote_cell_type": "code"
   },
   "source": "fig, ax = plt.subplots(1,1, figsize=(12, 7))\ndf.boxplot('Salary', 'Country', ax=ax)\nplt.title('Salary (US$) v Country')\nplt.ylabel('Salary')\nplt.xlabel('Country')\nplt.xticks(rotation=90)\nplt.show()\nplt.tight_layout()",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x504 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 742,
       "height": 721
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "cfec438c-16bb-45d9-83ea-1aea5aa461c0",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "bbada626",
    "execution_start": 1646128704256,
    "execution_millis": 1,
    "deepnote_cell_type": "code"
   },
   "source": "df = df[df[\"Salary\"] <= 250000]\ndf = df[df[\"Salary\"] >= 10000]\ndf = df[df['Country'] != 'Other']",
   "execution_count": 13,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "df0077ac-a625-4979-b648-06a37837677d",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7fed372a",
    "execution_start": 1646128704299,
    "execution_millis": 1017,
    "deepnote_cell_type": "code"
   },
   "source": "fig, ax = plt.subplots(1,1, figsize=(12, 7))\ndf.boxplot('Salary', 'Country', ax=ax)\nplt.suptitle('Salary (US$) v Country')\nplt.title('')\nplt.ylabel('Salary')\nplt.xticks(rotation=90)\nplt.show()",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x504 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 764,
       "height": 721
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "a6ce21ac-535f-457f-a86b-d3e0f44eb816",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a151dbd3",
    "execution_start": 1646128705351,
    "execution_millis": 426,
    "deepnote_cell_type": "code"
   },
   "source": "df[\"YearsCodePro\"].unique()",
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 15,
     "data": {
      "text/plain": "array(['4', '5', '6', '2', '18', '21', '16', '10', 'Less than 1 year',\n       '3', '15', '12', '8', '17', '1', '14', '26', '34', '11', '25',\n       '24', '22', '13', '9', '7', '39', '20', '27', '23', '30', '19',\n       '35', '33', '37', '43', '40', '29', '38', '31', '42', '28', '46',\n       '32', '36', '47', '45', '44', '41', 'More than 50 years', '50',\n       '49', '48'], dtype=object)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "a8c16c9b-0c70-47f9-a39f-3159fdcddc8a",
    "deepnote_variable_name": "input_1",
    "deepnote_variable_value": "Next, I want to clean the 'YearsCodePro'. From the values we can see 'Less than 1 year', we can change that to 0.5 and those with 'More than 50 years', we can put them as integer 50years",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a52c715",
    "execution_start": 1646128705396,
    "execution_millis": 1441155,
    "deepnote_cell_type": "input-text"
   },
   "source": "input_1 = 'Next, I want to clean the \\'YearsCodePro\\'. From the values we can see \\'Less than 1 year\\', we can change that to 0.5 and those with \\'More than 50 years\\', we can put them as integer 50years'",
   "execution_count": 16,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "a333077c-59c5-4da9-bc2d-dbf98dc3adc4",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6eb92057",
    "execution_start": 1646128705396,
    "execution_millis": 1,
    "deepnote_cell_type": "code"
   },
   "source": "def cleanexperience(x):\n    if x == 'More than 50 years':\n        return 50\n    else:\n        x = 'Less than 1 year'\n        return 0.5\n        return float(x)\n\ndf['YearsCodePro'] = df['YearsCodePro'].apply(cleanexperience)",
   "execution_count": 17,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "acbb3ad2-c194-4bd7-9f5e-44077b53a5cc",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "254cdadb",
    "execution_start": 1646128705397,
    "execution_millis": 437,
    "deepnote_cell_type": "code"
   },
   "source": "df[\"EdLevel\"].unique()",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "text/plain": "array(['Master’s degree (M.A., M.S., M.Eng., MBA, etc.)',\n       'Bachelor’s degree (B.A., B.S., B.Eng., etc.)',\n       'Professional degree (JD, MD, etc.)',\n       'Some college/university study without earning a degree',\n       'Other doctoral degree (Ph.D., Ed.D., etc.)', 'Something else',\n       'Primary/elementary school', 'Associate degree (A.A., A.S., etc.)',\n       'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)'],\n      dtype=object)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "f3277288-e73b-4ce2-8fc3-dd995c6b603b",
    "deepnote_variable_name": "input_2",
    "deepnote_variable_value": "Next, I would clean the Education level. We do not need the certificates (M.A,M.Eng,  B.A) and those not up to Bachelor's we assign a new variable or name.  ",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a7926e67",
    "execution_start": 1646128705440,
    "execution_millis": 1,
    "deepnote_cell_type": "input-text"
   },
   "source": "input_2 = 'Next, I would clean the Education level. We do not need the certificates (M.A,M.Eng,  B.A) and those not up to Bachelor\\'s we assign a new variable or name.  '",
   "execution_count": 19,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "901b6077-fc28-4034-8712-5cfe497f6955",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "41fb0ef7",
    "execution_start": 1646128705455,
    "execution_millis": 21,
    "deepnote_cell_type": "code"
   },
   "source": "def clean_education(x):\n    if 'Bachelor’s degree' in x:\n        return 'Bachelor’s degree'\n    if 'Master’s degree' in x:\n        return 'Master’s degree'\n    if 'Professional degree' in x or 'Other doctoral' in x:\n        return 'Post grad'\n    return 'Less than a Bachelors'\n\ndf['EdLevel'] = df['EdLevel'].apply(clean_education)",
   "execution_count": 20,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "9af8a8dc-7d4a-440a-9425-2a8d136b3185",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e608f2cb",
    "execution_start": 1646128705493,
    "execution_millis": 343,
    "deepnote_cell_type": "code"
   },
   "source": "df[\"EdLevel\"].unique",
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 21,
     "data": {
      "text/plain": "<bound method Series.unique of 9              Master’s degree\n11           Bachelor’s degree\n12             Master’s degree\n16           Bachelor’s degree\n17           Bachelor’s degree\n                 ...          \n83430          Master’s degree\n83432        Bachelor’s degree\n83434        Bachelor’s degree\n83436    Less than a Bachelors\n83438                Post grad\nName: EdLevel, Length: 26230, dtype: object>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "211a772a-e70e-4f3b-b2ca-d8779f6a8351",
    "deepnote_variable_name": "input_5",
    "deepnote_variable_value": "Next, I would be transforming the string values in Country, Edlevel, to numerical values using LabelEncoder",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b6def70b",
    "execution_start": 1646128705532,
    "execution_millis": 9,
    "deepnote_cell_type": "input-text"
   },
   "source": "input_5 = 'Next, I would be transforming the string values in Country, Edlevel, to numerical values using LabelEncoder'",
   "execution_count": 22,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "aac2020f-4b69-47d9-91bf-415b7e11f507",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "4829ceb3",
    "execution_start": 1646128705554,
    "execution_millis": 360,
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.preprocessing import LabelEncoder\nle_education = LabelEncoder()\ndf['EdLevel'] = le_education.fit_transform(df['EdLevel'])\ndf[\"EdLevel\"].unique()\n#le.classes_",
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "text/plain": "array([2, 0, 3, 1])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "9b914f7b-1adb-4e33-b1cb-c0f7f18d7781",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "96046d43",
    "execution_start": 1646128705940,
    "execution_millis": 13,
    "deepnote_cell_type": "code"
   },
   "source": "le_country = LabelEncoder()\ndf['Country'] = le_country.fit_transform(df['Country'])\ndf[\"Country\"].unique()",
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 24,
     "data": {
      "text/plain": "array([13, 12,  4, 15,  2,  3, 14, 16, 11,  6, 17,  1,  7,  8, 10,  0,  5,\n        9])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "923e2282-990a-4b7f-8ca4-5bdd3e6a15ed",
    "deepnote_variable_name": "input_4",
    "deepnote_variable_value": "Since we are predicting Salary, we would drop the column 'Salary' and every other variable would be assigned to X, for training.",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f026cfc8",
    "execution_start": 1646128705961,
    "execution_millis": 8,
    "deepnote_cell_type": "input-text"
   },
   "source": "input_4 = 'Since we are predicting Salary, we would drop the column \\'Salary\\' and every other variable would be assigned to X, for training.'",
   "execution_count": 25,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "99faa2c9-f450-42f8-aa84-af9a1b0727fc",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6b3e2e6b",
    "execution_start": 1646128706016,
    "execution_millis": 21,
    "deepnote_cell_type": "code"
   },
   "source": "X = df.drop(\"Salary\", axis=1)\ny = df[\"Salary\"]\n# We would using the features X to train the data. ",
   "execution_count": 26,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "773a71d2-2179-4cd4-9797-cdde870d47f9",
    "deepnote_variable_name": "input_5",
    "deepnote_variable_value": "Since we are predicting Salaries, which is a continuous number. We would be using Linear Regression for prediction.",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6da6f613",
    "execution_start": 1646128706055,
    "execution_millis": 1441288,
    "deepnote_cell_type": "input-text"
   },
   "source": "input_5 = 'Since we are predicting Salaries, which is a continuous number. We would be using Linear Regression for prediction.'",
   "execution_count": 27,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "7b87aa32-47f7-4b9d-84b8-10bd7979d039",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a6f80cff",
    "execution_start": 1646128706074,
    "execution_millis": 285,
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.linear_model import LinearRegression\nlinear_reg = LinearRegression()\nlinear_reg.fit(X, y.values)",
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 28,
     "data": {
      "text/plain": "LinearRegression()"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "8752e3d8-c1d5-43db-9d15-dba9c96edd6f",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "41ac9b44",
    "execution_start": 1646128706383,
    "execution_millis": 26,
    "deepnote_cell_type": "code"
   },
   "source": "y_pred = linear_reg.predict(X)",
   "execution_count": 29,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "e9876551-5928-46d7-9264-88b80dbf8d43",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "577e14e7",
    "execution_start": 1646128706431,
    "execution_millis": 15,
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.metrics import mean_squared_error, mean_absolute_error\nimport numpy as np\nerror= np.sqrt(mean_squared_error(y, y_pred))",
   "execution_count": 30,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "1052fce0-1bfe-4085-a605-cba636bd8250",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e9306b17",
    "execution_start": 1646128706462,
    "execution_millis": 21,
    "deepnote_cell_type": "code"
   },
   "source": "error",
   "execution_count": 31,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 31,
     "data": {
      "text/plain": "44536.09844287163"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "7637423d-1c06-4ee6-bc2b-70a89d100c49",
    "deepnote_variable_name": "input_6",
    "deepnote_variable_value": "From the result of the error, using Linear Regression this is a really high rate of error. I would be using a different model to predict.",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fd30136a",
    "execution_start": 1646128706516,
    "execution_millis": 1441259,
    "deepnote_cell_type": "input-text"
   },
   "source": "input_6 = 'From the result of the error, using Linear Regression this is a really high rate of error. I would be using a different model to predict.'",
   "execution_count": 32,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "421a1f7e-da3f-41b5-91b0-f73685afad6e",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "16b0920",
    "execution_start": 1646128706541,
    "execution_millis": 128,
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.tree import DecisionTreeRegressor\ndec_reg = DecisionTreeRegressor(random_state=0)\ndec_reg.fit(X, y.values)",
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 33,
     "data": {
      "text/plain": "DecisionTreeRegressor(random_state=0)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "7bb770dd-535d-4246-a2c5-ce21b8315130",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "304064a7",
    "execution_start": 1646128706682,
    "execution_millis": 6,
    "deepnote_cell_type": "code"
   },
   "source": "y_pred_dec = dec_reg.predict(X)",
   "execution_count": 34,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "e0b41846-83af-4552-ae2a-fe791e974ec5",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c149926b",
    "execution_start": 1646128706717,
    "execution_millis": 1441083,
    "deepnote_cell_type": "code"
   },
   "source": "dec_error = np.sqrt(mean_squared_error(y,y_pred_dec))\nprint('${:,.02f}'.format(dec_error))",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "text": "$35,906.21\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "37018826-a453-45f9-9170-f29f71aae7eb",
    "deepnote_variable_name": "input_7",
    "deepnote_variable_value": "Comparing the error with that of Linear Regression model, this is a lot better but still on the high side. I would be trying another model this time Random Forest Regression",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b382e2f2",
    "execution_start": 1646128706745,
    "execution_millis": 1441075,
    "deepnote_cell_type": "input-text"
   },
   "source": "input_7 = 'Comparing the error with that of Linear Regression model, this is a lot better but still on the high side. I would be trying another model this time Random Forest Regression'",
   "execution_count": 36,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "f0a68c9f-7921-4d96-8022-58e61018e8b4",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7e7d3632",
    "execution_start": 1646128706764,
    "execution_millis": 971,
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.ensemble import RandomForestRegressor\nrandom_reg = RandomForestRegressor(random_state=0)\nrandom_reg.fit(X, y.values)",
   "execution_count": 37,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 37,
     "data": {
      "text/plain": "RandomForestRegressor(random_state=0)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "f3b2c904-2caf-4704-8ee2-6d2a83145ec2",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "151e7b91",
    "execution_start": 1646128707745,
    "execution_millis": 238,
    "deepnote_cell_type": "code"
   },
   "source": "y_pred_random = random_reg.predict(X)",
   "execution_count": 38,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "8154c999-2f04-487d-8b4a-2fdc99740575",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d32ec58c",
    "execution_start": 1646128707984,
    "execution_millis": 287,
    "deepnote_cell_type": "code"
   },
   "source": "error_ran = np.sqrt(mean_squared_error(y, y_pred_random))\nprint('${:,.02f}'.format(error_ran))",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "text": "$35,907.32\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "1ce3f734-a600-44d4-a4f5-6aeaceb9964f",
    "deepnote_variable_name": "input_8",
    "deepnote_variable_value": "It is very similar to Decision Tree.",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "68ae4df",
    "execution_start": 1646128708016,
    "execution_millis": 9,
    "deepnote_cell_type": "input-text"
   },
   "source": "input_8 = 'It is very similar to Decision Tree.'",
   "execution_count": 40,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "4a6803ec-3cba-4f22-aa07-6492fb2f4322",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8ecf397e",
    "execution_start": 1646128708036,
    "execution_millis": 721,
    "deepnote_cell_type": "code"
   },
   "source": "\nfrom sklearn.model_selection import GridSearchCV\n\nmax_depth = [None, 2,4,6,8,10,12]\nparameters = {\"max_depth\": max_depth}\n\nregressor = DecisionTreeRegressor(random_state=0)\ngs = GridSearchCV(regressor, parameters, scoring='neg_mean_squared_error')\ngs.fit(X, y.values)",
   "execution_count": 41,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 41,
     "data": {
      "text/plain": "GridSearchCV(estimator=DecisionTreeRegressor(random_state=0),\n             param_grid={'max_depth': [None, 2, 4, 6, 8, 10, 12]},\n             scoring='neg_mean_squared_error')"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": "input_9 = 'Using the GridSearch Cross Validation, this would help us choose the best parameter to give us the best possible prediction.'",
   "metadata": {
    "cell_id": "0b72e5fc-992b-436f-a9fa-78f715f286dd",
    "deepnote_variable_name": "input_9",
    "deepnote_variable_value": "Using the GridSearch Cross Validation, this would help us choose the best parameter to give us the best possible prediction.",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9878648e",
    "execution_start": 1646128708767,
    "execution_millis": 13,
    "deepnote_cell_type": "input-text"
   },
   "outputs": [],
   "execution_count": 42
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "8799e313-cea0-4438-b99f-7a8fd0bc9787",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8856088",
    "execution_start": 1646128708788,
    "execution_millis": 37,
    "deepnote_cell_type": "code"
   },
   "source": "regressor = gs.best_estimator_\nregressor.fit(X, y.values)\nreg_y_pred = regressor.predict(X)\nreg_error = np.sqrt(mean_squared_error(y, reg_y_pred))\nprint('${:,.02f}'.format(reg_error))",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stdout",
     "text": "$35,907.50\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": "input_10 = 'This is high compared to others and this is because the Decision Tree Regression is still picking based on randomness even after we have assigned random state as 0'",
   "metadata": {
    "cell_id": "f593f444-46b1-4bb5-926e-ed91aadfdf07",
    "deepnote_variable_name": "input_10",
    "deepnote_variable_value": "This is high compared to others and this is because the Decision Tree Regression is still picking based on randomness even after we have assigned random state as 0",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "cb5ac88",
    "execution_start": 1646128708839,
    "execution_millis": 583225,
    "deepnote_cell_type": "input-text"
   },
   "outputs": [],
   "execution_count": 44
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "b8485a67-03f8-4404-bef0-92c428bde396",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b2696ff6",
    "execution_start": 1646128708851,
    "execution_millis": 87,
    "deepnote_cell_type": "code"
   },
   "source": "X",
   "execution_count": 45,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 45,
     "data": {
      "application/vnd.deepnote.dataframe.v3+json": {
       "column_count": 3,
       "row_count": 26230,
       "columns": [
        {
         "name": "Country",
         "dtype": "int64",
         "stats": {
          "unique_count": 18,
          "nan_count": 0,
          "min": "0",
          "max": "17",
          "histogram": [
           {
            "bin_start": 0,
            "bin_end": 1.7,
            "count": 1908
           },
           {
            "bin_start": 1.7,
            "bin_end": 3.4,
            "count": 2796
           },
           {
            "bin_start": 3.4,
            "bin_end": 5.1,
            "count": 4817
           },
           {
            "bin_start": 5.1,
            "bin_end": 6.8,
            "count": 414
           },
           {
            "bin_start": 6.8,
            "bin_end": 8.5,
            "count": 1498
           },
           {
            "bin_start": 8.5,
            "bin_end": 10.2,
            "count": 1126
           },
           {
            "bin_start": 10.2,
            "bin_end": 11.9,
            "count": 680
           },
           {
            "bin_start": 11.9,
            "bin_end": 13.6,
            "count": 1544
           },
           {
            "bin_start": 13.6,
            "bin_end": 15.299999999999999,
            "count": 799
           },
           {
            "bin_start": 15.299999999999999,
            "bin_end": 17,
            "count": 10648
           }
          ]
         }
        },
        {
         "name": "EdLevel",
         "dtype": "int64",
         "stats": {
          "unique_count": 4,
          "nan_count": 0,
          "min": "0",
          "max": "3",
          "histogram": [
           {
            "bin_start": 0,
            "bin_end": 0.3,
            "count": 12635
           },
           {
            "bin_start": 0.3,
            "bin_end": 0.6,
            "count": 0
           },
           {
            "bin_start": 0.6,
            "bin_end": 0.8999999999999999,
            "count": 0
           },
           {
            "bin_start": 0.8999999999999999,
            "bin_end": 1.2,
            "count": 5164
           },
           {
            "bin_start": 1.2,
            "bin_end": 1.5,
            "count": 0
           },
           {
            "bin_start": 1.5,
            "bin_end": 1.7999999999999998,
            "count": 0
           },
           {
            "bin_start": 1.7999999999999998,
            "bin_end": 2.1,
            "count": 6923
           },
           {
            "bin_start": 2.1,
            "bin_end": 2.4,
            "count": 0
           },
           {
            "bin_start": 2.4,
            "bin_end": 2.6999999999999997,
            "count": 0
           },
           {
            "bin_start": 2.6999999999999997,
            "bin_end": 3,
            "count": 1508
           }
          ]
         }
        },
        {
         "name": "YearsCodePro",
         "dtype": "float64",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "min": "0.5",
          "max": "50.0",
          "histogram": [
           {
            "bin_start": 0.5,
            "bin_end": 5.45,
            "count": 26226
           },
           {
            "bin_start": 5.45,
            "bin_end": 10.4,
            "count": 0
           },
           {
            "bin_start": 10.4,
            "bin_end": 15.350000000000001,
            "count": 0
           },
           {
            "bin_start": 15.350000000000001,
            "bin_end": 20.3,
            "count": 0
           },
           {
            "bin_start": 20.3,
            "bin_end": 25.25,
            "count": 0
           },
           {
            "bin_start": 25.25,
            "bin_end": 30.200000000000003,
            "count": 0
           },
           {
            "bin_start": 30.200000000000003,
            "bin_end": 35.15,
            "count": 0
           },
           {
            "bin_start": 35.15,
            "bin_end": 40.1,
            "count": 0
           },
           {
            "bin_start": 40.1,
            "bin_end": 45.050000000000004,
            "count": 0
           },
           {
            "bin_start": 45.050000000000004,
            "bin_end": 50,
            "count": 4
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows": [
        {
         "Country": 13,
         "EdLevel": 2,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 9
        },
        {
         "Country": 12,
         "EdLevel": 0,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 11
        },
        {
         "Country": 4,
         "EdLevel": 2,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 12
        },
        {
         "Country": 15,
         "EdLevel": 0,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 16
        },
        {
         "Country": 2,
         "EdLevel": 0,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 17
        },
        {
         "Country": 3,
         "EdLevel": 2,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 23
        },
        {
         "Country": 4,
         "EdLevel": 2,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 24
        },
        {
         "Country": 14,
         "EdLevel": 2,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 26
        },
        {
         "Country": 16,
         "EdLevel": 0,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 27
        },
        {
         "Country": 11,
         "EdLevel": 2,
         "YearsCodePro": 0.5,
         "_deepnote_index_column": 29
        }
       ]
      },
      "text/plain": "       Country  EdLevel  YearsCodePro\n9           13        2           0.5\n11          12        0           0.5\n12           4        2           0.5\n16          15        0           0.5\n17           2        0           0.5\n...        ...      ...           ...\n83430       17        2           0.5\n83432        2        0           0.5\n83434       17        0           0.5\n83436       17        1           0.5\n83438        1        3           0.5\n\n[26230 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Country</th>\n      <th>EdLevel</th>\n      <th>YearsCodePro</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>9</th>\n      <td>13</td>\n      <td>2</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>0</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>4</td>\n      <td>2</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>15</td>\n      <td>0</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>83430</th>\n      <td>17</td>\n      <td>2</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>83432</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>83434</th>\n      <td>17</td>\n      <td>0</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>83436</th>\n      <td>17</td>\n      <td>1</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>83438</th>\n      <td>1</td>\n      <td>3</td>\n      <td>0.5</td>\n    </tr>\n  </tbody>\n</table>\n<p>26230 rows × 3 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": "# country, edlevel, yearscode\nX = np.array([[\"United States of America\", 'Master’s degree', 15 ]])\nX\n",
   "metadata": {
    "cell_id": "52d1a885-69a8-41f8-b513-5e767db32aed",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "4dfaac47",
    "execution_start": 1646129095937,
    "execution_millis": 19,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 57,
     "data": {
      "text/plain": "array([['United States of America', 'Master’s degree', '15']],\n      dtype='<U24')"
     },
     "metadata": {}
    }
   ],
   "execution_count": 57
  },
  {
   "cell_type": "code",
   "source": "# transform the above data to numerical data using Labelencoder\n\nX[:, 0] = le_country.transform(X[:, 0])\nX[:, 1] = le_education.transform(X[:,1])\nX = X.astype(float)\nX",
   "metadata": {
    "cell_id": "b9bde280-7265-4955-ae60-3cba2b8376a2",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "4998fcf8",
    "execution_start": 1646129101440,
    "execution_millis": 11,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 58,
     "data": {
      "text/plain": "array([[17.,  2., 15.]])"
     },
     "metadata": {}
    }
   ],
   "execution_count": 58
  },
  {
   "cell_type": "code",
   "source": "new_y_pred = regressor.predict(X)\nnew_y_pred",
   "metadata": {
    "cell_id": "71a8f115-db10-4a20-ad01-948489c2b6dc",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9742b280",
    "execution_start": 1646129185844,
    "execution_millis": 41,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "name": "stderr",
     "text": "/shared-libs/python3.7/py/lib/python3.7/site-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n  \"X does not have valid feature names, but\"\n",
     "output_type": "stream"
    },
    {
     "output_type": "execute_result",
     "execution_count": 59,
     "data": {
      "text/plain": "array([136336.16137395])"
     },
     "metadata": {}
    }
   ],
   "execution_count": 59
  },
  {
   "cell_type": "code",
   "source": "import pickle\ndata = {\"model\": regressor, \"le_country\": le_country, \"le_education\": le_education}\nwith open('saved_steps.pkl', 'wb') as file:\n    pickle.dump(data, file)",
   "metadata": {
    "cell_id": "9226f41a-7593-4035-bb11-b070f1606575",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c6210d3a",
    "execution_start": 1646129254227,
    "execution_millis": 18,
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "execution_count": 61
  },
  {
   "cell_type": "code",
   "source": "input_11 = 'I am using pickle to save the file and \\'wb\\' means write binary'",
   "metadata": {
    "cell_id": "dc3d8d6b-a362-4bcd-8910-c798cb66f130",
    "deepnote_variable_name": "input_11",
    "deepnote_variable_value": "I am using pickle to save the file and 'wb' means write binary",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8072727e",
    "execution_start": 1646129379286,
    "execution_millis": 2,
    "deepnote_cell_type": "input-text"
   },
   "outputs": [],
   "execution_count": 63
  },
  {
   "cell_type": "code",
   "source": "with open('saved_steps.pkl', 'rb') as file:\n    data = pickle.load(file)\n\nregressor_loaded = data[\"model\"]\nle_country = data[\"le_country\"]\nle_education = data[\"le_education\"]",
   "metadata": {
    "cell_id": "580a6422-6108-4e0e-91a9-d1df13de5126",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1f132eb1",
    "execution_start": 1646129299044,
    "execution_millis": 6,
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "execution_count": 62
  },
  {
   "cell_type": "code",
   "source": "y_pred = regressor_loaded.predict(X)\ny_pred",
   "metadata": {
    "cell_id": "1bc5e614-2e4b-4a2b-bee4-64b0dca0eea4",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "67b71d5d",
    "execution_start": 1646129474800,
    "execution_millis": 0,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "name": "stderr",
     "text": "/shared-libs/python3.7/py/lib/python3.7/site-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n  \"X does not have valid feature names, but\"\n",
     "output_type": "stream"
    },
    {
     "output_type": "execute_result",
     "execution_count": 64,
     "data": {
      "text/plain": "array([136336.16137395])"
     },
     "metadata": {}
    }
   ],
   "execution_count": 64
  },
  {
   "cell_type": "code",
   "source": "",
   "metadata": {
    "cell_id": "4c1f53e1-5202-4485-b1d7-7e168351918d",
    "tags": [],
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": "",
   "metadata": {
    "cell_id": "dbe58258-94fc-40f8-a54f-c71c832c9068",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b623e53d",
    "execution_start": 1646128709037,
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "execution_count": 47
  },
  {
   "cell_type": "code",
   "source": "",
   "metadata": {
    "cell_id": "9109d35f-abcb-49c0-857f-cb5f266f8092",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b623e53d",
    "execution_start": 1646128709037,
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "execution_count": 47
  },
  {
   "cell_type": "markdown",
   "source": "<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=2133f0b7-2345-40af-98fb-f08e93556bc5' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>",
   "metadata": {
    "tags": [],
    "created_in_deepnote_cell": true,
    "deepnote_cell_type": "markdown"
   }
  }
 ],
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "orig_nbformat": 2,
  "deepnote": {
   "is_reactive": false
  },
  "deepnote_notebook_id": "27cd07a4-8aa8-4304-993a-9bb08cb357b9",
  "deepnote_execution_queue": []
 }
}