Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Alpha_team.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyctU7Nsm6DT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "087bf939-4bcf-41c7-c17c-c790644f0e8f"
      },
      "source": [
        " from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjbfPg05m_1N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as  pd \n",
        "df = pd.read_csv(\"/content/drive/My Drive/developer_survey_2019/survey_results_public.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZaNx0ltlVN1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "37acae14-bbef-4876-f95c-4c2cd3ffb28f"
      },
      "source": [
        "list(df[\"OpenSource\"].unique()) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['The quality of OSS and closed source software is about the same',\n",
              " 'OSS is, on average, of HIGHER quality than proprietary / closed source software',\n",
              " nan,\n",
              " 'OSS is, on average, of LOWER quality than proprietary / closed source software']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cf3sinkjoF9T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        },
        "outputId": "db47732d-b5d1-48af-da32-2af9c145b5a1"
      },
      "source": [
        "df.head() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Respondent</th>\n",
              "      <th>MainBranch</th>\n",
              "      <th>Hobbyist</th>\n",
              "      <th>OpenSourcer</th>\n",
              "      <th>OpenSource</th>\n",
              "      <th>Employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Student</th>\n",
              "      <th>EdLevel</th>\n",
              "      <th>UndergradMajor</th>\n",
              "      <th>EduOther</th>\n",
              "      <th>OrgSize</th>\n",
              "      <th>DevType</th>\n",
              "      <th>YearsCode</th>\n",
              "      <th>Age1stCode</th>\n",
              "      <th>YearsCodePro</th>\n",
              "      <th>CareerSat</th>\n",
              "      <th>JobSat</th>\n",
              "      <th>MgrIdiot</th>\n",
              "      <th>MgrMoney</th>\n",
              "      <th>MgrWant</th>\n",
              "      <th>JobSeek</th>\n",
              "      <th>LastHireDate</th>\n",
              "      <th>LastInt</th>\n",
              "      <th>FizzBuzz</th>\n",
              "      <th>JobFactors</th>\n",
              "      <th>ResumeUpdate</th>\n",
              "      <th>CurrencySymbol</th>\n",
              "      <th>CurrencyDesc</th>\n",
              "      <th>CompTotal</th>\n",
              "      <th>CompFreq</th>\n",
              "      <th>ConvertedComp</th>\n",
              "      <th>WorkWeekHrs</th>\n",
              "      <th>WorkPlan</th>\n",
              "      <th>WorkChallenge</th>\n",
              "      <th>WorkRemote</th>\n",
              "      <th>WorkLoc</th>\n",
              "      <th>ImpSyn</th>\n",
              "      <th>CodeRev</th>\n",
              "      <th>CodeRevHrs</th>\n",
              "      <th>...</th>\n",
              "      <th>DatabaseWorkedWith</th>\n",
              "      <th>DatabaseDesireNextYear</th>\n",
              "      <th>PlatformWorkedWith</th>\n",
              "      <th>PlatformDesireNextYear</th>\n",
              "      <th>WebFrameWorkedWith</th>\n",
              "      <th>WebFrameDesireNextYear</th>\n",
              "      <th>MiscTechWorkedWith</th>\n",
              "      <th>MiscTechDesireNextYear</th>\n",
              "      <th>DevEnviron</th>\n",
              "      <th>OpSys</th>\n",
              "      <th>Containers</th>\n",
              "      <th>BlockchainOrg</th>\n",
              "      <th>BlockchainIs</th>\n",
              "      <th>BetterLife</th>\n",
              "      <th>ITperson</th>\n",
              "      <th>OffOn</th>\n",
              "      <th>SocialMedia</th>\n",
              "      <th>Extraversion</th>\n",
              "      <th>ScreenName</th>\n",
              "      <th>SOVisit1st</th>\n",
              "      <th>SOVisitFreq</th>\n",
              "      <th>SOVisitTo</th>\n",
              "      <th>SOFindAnswer</th>\n",
              "      <th>SOTimeSaved</th>\n",
              "      <th>SOHowMuchTime</th>\n",
              "      <th>SOAccount</th>\n",
              "      <th>SOPartFreq</th>\n",
              "      <th>SOJobs</th>\n",
              "      <th>EntTeams</th>\n",
              "      <th>SOComm</th>\n",
              "      <th>WelcomeChange</th>\n",
              "      <th>SONewContent</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Trans</th>\n",
              "      <th>Sexuality</th>\n",
              "      <th>Ethnicity</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>SurveyLength</th>\n",
              "      <th>SurveyEase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>I am a student who is learning to code</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Never</td>\n",
              "      <td>The quality of OSS and closed source software ...</td>\n",
              "      <td>Not employed, and not looking for work</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>No</td>\n",
              "      <td>Primary/elementary school</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Taught yourself a new language, framework, or ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>SQLite</td>\n",
              "      <td>MySQL</td>\n",
              "      <td>MacOS;Windows</td>\n",
              "      <td>Android;Arduino;Windows</td>\n",
              "      <td>Django;Flask</td>\n",
              "      <td>Flask;jQuery</td>\n",
              "      <td>Node.js</td>\n",
              "      <td>Node.js</td>\n",
              "      <td>IntelliJ;Notepad++;PyCharm</td>\n",
              "      <td>Windows</td>\n",
              "      <td>I do not use containers</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fortunately, someone else has that title</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>Online</td>\n",
              "      <td>Username</td>\n",
              "      <td>2017</td>\n",
              "      <td>A few times per month or weekly</td>\n",
              "      <td>Find answers to specific questions;Learn how t...</td>\n",
              "      <td>3-5 times per week</td>\n",
              "      <td>Stack Overflow was much faster</td>\n",
              "      <td>31-60 minutes</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No, I didn't know that Stack Overflow had a jo...</td>\n",
              "      <td>No, and I don't know what those are</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>Just as welcome now as I felt last year</td>\n",
              "      <td>Tech articles written by other developers;Indu...</td>\n",
              "      <td>14.0</td>\n",
              "      <td>Man</td>\n",
              "      <td>No</td>\n",
              "      <td>Straight / Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Appropriate in length</td>\n",
              "      <td>Neither easy nor difficult</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>No</td>\n",
              "      <td>Less than once per year</td>\n",
              "      <td>The quality of OSS and closed source software ...</td>\n",
              "      <td>Not employed, but looking for work</td>\n",
              "      <td>Bosnia and Herzegovina</td>\n",
              "      <td>Yes, full-time</td>\n",
              "      <td>Secondary school (e.g. American high school, G...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Taken an online course in programming or softw...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Developer, desktop or enterprise applications;...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I am actively looking for a job</td>\n",
              "      <td>I've never had a job</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Financial performance or funding status of the...</td>\n",
              "      <td>Something else changed (education, award, medi...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MySQL</td>\n",
              "      <td>Windows</td>\n",
              "      <td>Windows</td>\n",
              "      <td>Django</td>\n",
              "      <td>Django</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Atom;PyCharm</td>\n",
              "      <td>Windows</td>\n",
              "      <td>I do not use containers</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Useful across many domains and could change ma...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Instagram</td>\n",
              "      <td>Online</td>\n",
              "      <td>Username</td>\n",
              "      <td>2017</td>\n",
              "      <td>Daily or almost daily</td>\n",
              "      <td>Find answers to specific questions;Learn how t...</td>\n",
              "      <td>3-5 times per week</td>\n",
              "      <td>Stack Overflow was much faster</td>\n",
              "      <td>11-30 minutes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>A few times per month or weekly</td>\n",
              "      <td>No, I knew that Stack Overflow had a job board...</td>\n",
              "      <td>No, and I don't know what those are</td>\n",
              "      <td>Yes, somewhat</td>\n",
              "      <td>Just as welcome now as I felt last year</td>\n",
              "      <td>Tech articles written by other developers;Indu...</td>\n",
              "      <td>19.0</td>\n",
              "      <td>Man</td>\n",
              "      <td>No</td>\n",
              "      <td>Straight / Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Appropriate in length</td>\n",
              "      <td>Neither easy nor difficult</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>Yes</td>\n",
              "      <td>Never</td>\n",
              "      <td>The quality of OSS and closed source software ...</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Thailand</td>\n",
              "      <td>No</td>\n",
              "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Web development or web design</td>\n",
              "      <td>Taught yourself a new language, framework, or ...</td>\n",
              "      <td>100 to 499 employees</td>\n",
              "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>Slightly satisfied</td>\n",
              "      <td>Slightly satisfied</td>\n",
              "      <td>Not at all confident</td>\n",
              "      <td>Not sure</td>\n",
              "      <td>Not sure</td>\n",
              "      <td>I’m not actively looking, but I am open to new...</td>\n",
              "      <td>1-2 years ago</td>\n",
              "      <td>Interview with people in peer roles</td>\n",
              "      <td>No</td>\n",
              "      <td>Languages, frameworks, and other technologies ...</td>\n",
              "      <td>I was preparing for a job search</td>\n",
              "      <td>THB</td>\n",
              "      <td>Thai baht</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>Monthly</td>\n",
              "      <td>8820.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>There's no schedule or spec; I work on what se...</td>\n",
              "      <td>Distracting work environment;Inadequate access...</td>\n",
              "      <td>Less than once per month / Never</td>\n",
              "      <td>Home</td>\n",
              "      <td>Average</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>PostgreSQL</td>\n",
              "      <td>PostgreSQL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Other(s):</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Vim;Visual Studio Code</td>\n",
              "      <td>Linux-based</td>\n",
              "      <td>I do not use containers</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Reddit</td>\n",
              "      <td>In real life (in person)</td>\n",
              "      <td>Username</td>\n",
              "      <td>2011</td>\n",
              "      <td>A few times per week</td>\n",
              "      <td>Find answers to specific questions;Learn how t...</td>\n",
              "      <td>6-10 times per week</td>\n",
              "      <td>They were about the same</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Less than once per month or monthly</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No, I've heard of them, but I am not part of a...</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>Just as welcome now as I felt last year</td>\n",
              "      <td>Tech meetups or events in your area;Courses on...</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Man</td>\n",
              "      <td>No</td>\n",
              "      <td>Straight / Heterosexual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Appropriate in length</td>\n",
              "      <td>Neither easy nor difficult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>I am a developer by profession</td>\n",
              "      <td>No</td>\n",
              "      <td>Never</td>\n",
              "      <td>The quality of OSS and closed source software ...</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>United States</td>\n",
              "      <td>No</td>\n",
              "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>Taken an online course in programming or softw...</td>\n",
              "      <td>100 to 499 employees</td>\n",
              "      <td>Developer, full-stack</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>Less than 1 year</td>\n",
              "      <td>Very satisfied</td>\n",
              "      <td>Slightly satisfied</td>\n",
              "      <td>Very confident</td>\n",
              "      <td>No</td>\n",
              "      <td>Not sure</td>\n",
              "      <td>I am not interested in new job opportunities</td>\n",
              "      <td>Less than a year ago</td>\n",
              "      <td>Write code by hand (e.g., on a whiteboard);Int...</td>\n",
              "      <td>No</td>\n",
              "      <td>Languages, frameworks, and other technologies ...</td>\n",
              "      <td>I was preparing for a job search</td>\n",
              "      <td>USD</td>\n",
              "      <td>United States dollar</td>\n",
              "      <td>61000.0</td>\n",
              "      <td>Yearly</td>\n",
              "      <td>61000.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>There's no schedule or spec; I work on what se...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Less than once per month / Never</td>\n",
              "      <td>Home</td>\n",
              "      <td>A little below average</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>MySQL;SQLite</td>\n",
              "      <td>MySQL;SQLite</td>\n",
              "      <td>Linux;Windows</td>\n",
              "      <td>Linux;Windows</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>.NET</td>\n",
              "      <td>.NET</td>\n",
              "      <td>Eclipse;Vim;Visual Studio;Visual Studio Code</td>\n",
              "      <td>Windows</td>\n",
              "      <td>I do not use containers</td>\n",
              "      <td>Not at all</td>\n",
              "      <td>Useful for decentralized currency (i.e., Bitcoin)</td>\n",
              "      <td>Yes</td>\n",
              "      <td>SIGH</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Reddit</td>\n",
              "      <td>In real life (in person)</td>\n",
              "      <td>Username</td>\n",
              "      <td>2014</td>\n",
              "      <td>Daily or almost daily</td>\n",
              "      <td>Find answers to specific questions;Pass the ti...</td>\n",
              "      <td>1-2 times per week</td>\n",
              "      <td>Stack Overflow was much faster</td>\n",
              "      <td>31-60 minutes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Less than once per month or monthly</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No, and I don't know what those are</td>\n",
              "      <td>No, not really</td>\n",
              "      <td>Just as welcome now as I felt last year</td>\n",
              "      <td>Tech articles written by other developers;Indu...</td>\n",
              "      <td>22.0</td>\n",
              "      <td>Man</td>\n",
              "      <td>No</td>\n",
              "      <td>Straight / Heterosexual</td>\n",
              "      <td>White or of European descent</td>\n",
              "      <td>No</td>\n",
              "      <td>Appropriate in length</td>\n",
              "      <td>Easy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>I am a developer by profession</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Once a month or more often</td>\n",
              "      <td>OSS is, on average, of HIGHER quality than pro...</td>\n",
              "      <td>Employed full-time</td>\n",
              "      <td>Ukraine</td>\n",
              "      <td>No</td>\n",
              "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
              "      <td>Computer science, computer engineering, or sof...</td>\n",
              "      <td>Taken an online course in programming or softw...</td>\n",
              "      <td>10,000 or more employees</td>\n",
              "      <td>Academic researcher;Developer, desktop or ente...</td>\n",
              "      <td>16</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>Very dissatisfied</td>\n",
              "      <td>Slightly dissatisfied</td>\n",
              "      <td>Somewhat confident</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>I am not interested in new job opportunities</td>\n",
              "      <td>Less than a year ago</td>\n",
              "      <td>Write any code;Write code by hand (e.g., on a ...</td>\n",
              "      <td>No</td>\n",
              "      <td>Industry that I'd be working in;Languages, fra...</td>\n",
              "      <td>I was preparing for a job search</td>\n",
              "      <td>UAH</td>\n",
              "      <td>Ukrainian hryvnia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>There is a schedule and/or spec (made by me or...</td>\n",
              "      <td>Being tasked with non-development work;Inadequ...</td>\n",
              "      <td>A few days each month</td>\n",
              "      <td>Office</td>\n",
              "      <td>A little above average</td>\n",
              "      <td>Yes, because I see value in code review</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>Couchbase;MongoDB;MySQL;Oracle;PostgreSQL;SQLite</td>\n",
              "      <td>Couchbase;Firebase;MongoDB;MySQL;Oracle;Postgr...</td>\n",
              "      <td>Android;Linux;MacOS;Slack;Windows</td>\n",
              "      <td>Android;Docker;Kubernetes;Linux;Slack</td>\n",
              "      <td>Django;Express;Flask;jQuery;React.js;Spring</td>\n",
              "      <td>Flask;jQuery;React.js;Spring</td>\n",
              "      <td>Cordova;Node.js</td>\n",
              "      <td>Apache Spark;Hadoop;Node.js;React Native</td>\n",
              "      <td>IntelliJ;Notepad++;Vim</td>\n",
              "      <td>Linux-based</td>\n",
              "      <td>Outside of work, for personal projects</td>\n",
              "      <td>Not at all</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Also Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Facebook</td>\n",
              "      <td>In real life (in person)</td>\n",
              "      <td>Username</td>\n",
              "      <td>I don't remember</td>\n",
              "      <td>Multiple times per day</td>\n",
              "      <td>Find answers to specific questions</td>\n",
              "      <td>More than 10 times per week</td>\n",
              "      <td>Stack Overflow was much faster</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>A few times per month or weekly</td>\n",
              "      <td>No, I knew that Stack Overflow had a job board...</td>\n",
              "      <td>No, I've heard of them, but I am not part of a...</td>\n",
              "      <td>Yes, definitely</td>\n",
              "      <td>Just as welcome now as I felt last year</td>\n",
              "      <td>Tech meetups or events in your area;Courses on...</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Man</td>\n",
              "      <td>No</td>\n",
              "      <td>Straight / Heterosexual</td>\n",
              "      <td>White or of European descent;Multiracial</td>\n",
              "      <td>No</td>\n",
              "      <td>Appropriate in length</td>\n",
              "      <td>Easy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 85 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Respondent  ...                  SurveyEase\n",
              "0           1  ...  Neither easy nor difficult\n",
              "1           2  ...  Neither easy nor difficult\n",
              "2           3  ...  Neither easy nor difficult\n",
              "3           4  ...                        Easy\n",
              "4           5  ...                        Easy\n",
              "\n",
              "[5 rows x 85 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CL2tYuKqsvV3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6e652dc7-cdc5-44ed-aa7b-d6bad0146ec7"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(88883, 85)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dw6p9RJP5ciq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "79147cde-4976-41da-a731-23da2d7d7966"
      },
      "source": [
        "from tqdm import tqdm\n",
        "first_15_col_excl_yearcode = [ 'MainBranch'\t,'Hobbyist',\t'OpenSourcer',\t'OpenSource',\t'Employment',\t'Country' ,\n",
        "                'Student'\t,'EdLevel',\t'UndergradMajor',\t'OrgSize',\t\t'CareerSat']\n",
        "df[first_15_col_excl_yearcode].fillna(value =-1, inplace= True)\n",
        "for i in tqdm(first_15_col_excl_yearcode):\n",
        "  print('Converting '+ i +'\\n')\n",
        "  df[i].replace(to_replace = list(df[i].unique()), value = range(1,len(list(df[i].unique()))+1) , inplace = True)\n",
        "  print(df[i].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4153: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  downcast=downcast,\n",
            " 18%|█▊        | 2/11 [00:00<00:00,  9.36it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Converting MainBranch\n",
            "\n",
            "[1 2 3 4 5 6]\n",
            "Converting Hobbyist\n",
            "\n",
            "[1 2]\n",
            "Converting OpenSourcer\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 36%|███▋      | 4/11 [00:00<00:00, 10.07it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4]\n",
            "Converting OpenSource\n",
            "\n",
            "[1 2 3 4]\n",
            "Converting Employment\n",
            "\n",
            "[1 2 3 4 5 6 7]\n",
            "Converting Country\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 73%|███████▎  | 8/11 [00:01<00:00,  7.84it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18\n",
            "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
            "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
            "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
            "  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90\n",
            "  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108\n",
            " 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126\n",
            " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144\n",
            " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162\n",
            " 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180]\n",
            "Converting Student\n",
            "\n",
            "[1 2 3 4]\n",
            "Converting EdLevel\n",
            "\n",
            "[ 1  2  3  4  5  6  7  8  9 10]\n",
            "Converting UndergradMajor\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 11/11 [00:01<00:00,  8.20it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[ 1  2  3  4  5  6  7  8  9 10 11 12 13]\n",
            "Converting OrgSize\n",
            "\n",
            "[ 1  2  3  4  5  6  7  8  9 10]\n",
            "Converting CareerSat\n",
            "\n",
            "[1 2 3 4 5 6]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StP13b8Z_TJj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "24d7c42d-9907-406b-ef68-16fc92502718"
      },
      "source": [
        "from tqdm import tqdm\n",
        "first_15_col_excl_yearcode = ['WorkPlan',\t'WorkRemote',\t'WorkLoc',\t'ImpSyn' ,\n",
        "                'CodeRev'\t,'UnitTests',\t'PurchaseHow',\t'PurchaseWhat']\n",
        "df[first_15_col_excl_yearcode].fillna(value =-1, inplace= True)\n",
        "for i in tqdm(first_15_col_excl_yearcode):\n",
        "  print('Converting '+i +'\\n')\n",
        "  \n",
        "  df[i].replace(to_replace = list(df[i].unique()), value = range(1,len(list(df[i].unique()))+1) , inplace = True)\n",
        "  print(df[i].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4153: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  downcast=downcast,\n",
            " 25%|██▌       | 2/8 [00:00<00:00, 12.33it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Converting WorkPlan\n",
            "\n",
            "[1 2 3 4]\n",
            "Converting WorkRemote\n",
            "\n",
            "[1 2 3 4 5 6 7 8]\n",
            "Converting WorkLoc\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 50%|█████     | 4/8 [00:00<00:00, 12.35it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4]\n",
            "Converting ImpSyn\n",
            "\n",
            "[1 2 3 4 5 6]\n",
            "Converting CodeRev\n",
            "\n",
            "[1 2 3 4]\n",
            "Converting UnitTests\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8/8 [00:00<00:00, 12.64it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5]\n",
            "Converting PurchaseHow\n",
            "\n",
            "[1 2 3 4 5]\n",
            "Converting PurchaseWhat\n",
            "\n",
            "[1 2 3 4]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIh8zFlNCUTU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "53bec1af-bf12-4c15-a5ed-5dd7cd94ff30"
      },
      "source": [
        "from tqdm import tqdm\n",
        "first_15_col_excl_yearcode = ['OpSys'\t,'BlockchainOrg',\t'BlockchainIs',\t'ITperson']\n",
        "df[first_15_col_excl_yearcode].fillna(value =-1, inplace= True)\n",
        "for i in tqdm(first_15_col_excl_yearcode):\n",
        "  print('Converting '+i +'\\n')\n",
        "  \n",
        "  df[i].replace(to_replace = list(df[i].unique()), value = range(1,len(list(df[i].unique()))+1) , inplace = True)\n",
        "  print(df[i].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4153: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  downcast=downcast,\n",
            " 50%|█████     | 2/4 [00:00<00:00, 12.78it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Converting OpSys\n",
            "\n",
            "[1 2 3 4 5]\n",
            "Converting BlockchainOrg\n",
            "\n",
            "[1 2 3 4 5 6]\n",
            "Converting BlockchainIs\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 4/4 [00:00<00:00, 12.08it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5 6]\n",
            "Converting ITperson\n",
            "\n",
            "[1 2 3 4 5]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzY9IluzGCNX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "db642737-40a9-4e95-c374-9181be0b3cc4"
      },
      "source": [
        "from tqdm import tqdm\n",
        "last_15_col_excl_yearcode = ['OffOn','SocialMedia',\t'SOFindAnswer',\t'SOVisitFreq','SOTimeSaved' ,\n",
        "                'SOHowMuchTime'\t,'SOAccount',\t'SOPartFreq',\t'SOJobs',\t'WelcomeChange',\t'SONewContent',\t'Gender','Trans','Sexuality','Dependents']\n",
        "df[last_15_col_excl_yearcode].fillna(value = -1, inplace= True)\n",
        "for i in tqdm(last_15_col_excl_yearcode):\n",
        "  print('Converting '+i)\n",
        "  \n",
        "  df[i].replace(to_replace = list(df[i].unique()), value = range(1,len(list(df[i].unique()))+1) , inplace = True)\n",
        "  print(df[i].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4153: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  downcast=downcast,\n",
            "\r  0%|          | 0/15 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Converting OffOn\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 13%|█▎        | 2/15 [00:00<00:01, 12.01it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4]\n",
            "Converting SocialMedia\n",
            "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]\n",
            "Converting SOFindAnswer\n",
            "[1 2 3 4 5 6]\n",
            "Converting SOVisitFreq\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 27%|██▋       | 4/15 [00:00<00:00, 12.16it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5 6 7]\n",
            "Converting SOTimeSaved\n",
            "[1 2 3 4 5 6]\n",
            "Converting SOHowMuchTime\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 40%|████      | 6/15 [00:00<00:00, 12.56it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5]\n",
            "Converting SOAccount\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 53%|█████▎    | 8/15 [00:00<00:00, 12.94it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4]\n",
            "Converting SOPartFreq\n",
            "[1 2 3 4 5 6 7]\n",
            "Converting SOJobs\n",
            "[1 2 3 4]\n",
            "Converting WelcomeChange\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 67%|██████▋   | 10/15 [00:00<00:00, 13.26it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5 6 7]\n",
            "Converting SONewContent\n",
            "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16]\n",
            "Converting Gender\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 80%|████████  | 12/15 [00:00<00:00, 12.87it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5 6 7 8]\n",
            "Converting Trans\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 15/15 [00:01<00:00, 13.37it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3]\n",
            "Converting Sexuality\n",
            "[1 2 3 4 5 6 7 8]\n",
            "Converting Dependents\n",
            "[1 2 3]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tz8h1VbjcTO8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "6190ff8b-1b77-4246-920c-9b756f0d7b57"
      },
      "source": [
        "from tqdm import tqdm\n",
        "last_15_col_excl_yearcode = ['JobSat','MgrIdiot','MgrMoney','MgrWant','JobSeek','LastHireDate','CurrencySymbol']\n",
        "df[last_15_col_excl_yearcode].fillna(value = -1, inplace= True)\n",
        "for i in tqdm(last_15_col_excl_yearcode):\n",
        "  print( 'Converting '+ i )\n",
        "  \n",
        "  df[i].replace(to_replace = list(df[i].unique()), value = range(1,len(list(df[i].unique()))+1) , inplace = True)\n",
        "  print(df[i].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4153: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  downcast=downcast,\n",
            "\r  0%|          | 0/7 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Converting JobSat\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 29%|██▊       | 2/7 [00:00<00:00, 13.52it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5 6]\n",
            "Converting MgrIdiot\n",
            "[1 2 3 4 5]\n",
            "Converting MgrMoney\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 57%|█████▋    | 4/7 [00:00<00:00, 13.87it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4]\n",
            "Converting MgrWant\n",
            "[1 2 3 4 5]\n",
            "Converting JobSeek\n",
            "[1 2 3 4]\n",
            "Converting LastHireDate\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 86%|████████▌ | 6/7 [00:00<00:00, 13.88it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1 2 3 4 5 6 7]\n",
            "Converting CurrencySymbol\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 7/7 [00:00<00:00,  8.02it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18\n",
            "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
            "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
            "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
            "  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90\n",
            "  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108\n",
            " 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126\n",
            " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHmg6Oa2Sfmi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a1b27e44-1e81-4940-d954-4609057b5198"
      },
      "source": [
        "df = df.drop(axis=1,columns=[\"Respondent\", \"Age1stCode\", \"FizzBuzz\", \"ResumeUpdate\", \"BetterLife\", \"ScreenName\", \"SOVisit1st\",\n",
        "                             \"EntTeams\",\"SOComm\",\"Extraversion\",\"SurveyLength\",\"SurveyEase\",\"LastInt\",'CurrencyDesc',\n",
        "                             \"Ethnicity\", \"DevEnviron\", \"LanguageDesireNextYear\",\"DatabaseDesireNextYear\",\"PlatformDesireNextYear\",\n",
        "                             \"WebFrameDesireNextYear\",\"MiscTechDesireNextYear\",\"SOVisitTo\", \"WelcomeChange\",\n",
        "                             \"CurrencySymbol\",\"CompTotal\",\"CompFreq\" ,'EduOther',\t'WorkChallenge','JobFactors'])\n",
        "\n",
        "'''Dropped non - useful columns '''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dropped non - useful columns '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0l1vH5_RdKLH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "a5b1a395-86c9-4791-abc9-bba676549a1b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MainBranch</th>\n",
              "      <th>Hobbyist</th>\n",
              "      <th>OpenSourcer</th>\n",
              "      <th>OpenSource</th>\n",
              "      <th>Employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Student</th>\n",
              "      <th>EdLevel</th>\n",
              "      <th>UndergradMajor</th>\n",
              "      <th>OrgSize</th>\n",
              "      <th>DevType</th>\n",
              "      <th>YearsCode</th>\n",
              "      <th>YearsCodePro</th>\n",
              "      <th>CareerSat</th>\n",
              "      <th>JobSat</th>\n",
              "      <th>MgrIdiot</th>\n",
              "      <th>MgrMoney</th>\n",
              "      <th>MgrWant</th>\n",
              "      <th>JobSeek</th>\n",
              "      <th>LastHireDate</th>\n",
              "      <th>ConvertedComp</th>\n",
              "      <th>WorkWeekHrs</th>\n",
              "      <th>WorkPlan</th>\n",
              "      <th>WorkRemote</th>\n",
              "      <th>WorkLoc</th>\n",
              "      <th>ImpSyn</th>\n",
              "      <th>CodeRev</th>\n",
              "      <th>CodeRevHrs</th>\n",
              "      <th>UnitTests</th>\n",
              "      <th>PurchaseHow</th>\n",
              "      <th>PurchaseWhat</th>\n",
              "      <th>LanguageWorkedWith</th>\n",
              "      <th>DatabaseWorkedWith</th>\n",
              "      <th>PlatformWorkedWith</th>\n",
              "      <th>WebFrameWorkedWith</th>\n",
              "      <th>MiscTechWorkedWith</th>\n",
              "      <th>OpSys</th>\n",
              "      <th>Containers</th>\n",
              "      <th>BlockchainOrg</th>\n",
              "      <th>BlockchainIs</th>\n",
              "      <th>ITperson</th>\n",
              "      <th>OffOn</th>\n",
              "      <th>SocialMedia</th>\n",
              "      <th>SOVisitFreq</th>\n",
              "      <th>SOFindAnswer</th>\n",
              "      <th>SOTimeSaved</th>\n",
              "      <th>SOHowMuchTime</th>\n",
              "      <th>SOAccount</th>\n",
              "      <th>SOPartFreq</th>\n",
              "      <th>SOJobs</th>\n",
              "      <th>SONewContent</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Trans</th>\n",
              "      <th>Sexuality</th>\n",
              "      <th>Dependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>HTML/CSS;Java;JavaScript;Python</td>\n",
              "      <td>SQLite</td>\n",
              "      <td>MacOS;Windows</td>\n",
              "      <td>Django;Flask</td>\n",
              "      <td>Node.js</td>\n",
              "      <td>1</td>\n",
              "      <td>I do not use containers</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Developer, desktop or enterprise applications;...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>C++;HTML/CSS;Python</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Windows</td>\n",
              "      <td>Django</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>I do not use containers</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Designer;Developer, back-end;Developer, front-...</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8820.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>HTML/CSS</td>\n",
              "      <td>PostgreSQL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>I do not use containers</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Developer, full-stack</td>\n",
              "      <td>3</td>\n",
              "      <td>Less than 1 year</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>61000.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>C;C++;C#;Python;SQL</td>\n",
              "      <td>MySQL;SQLite</td>\n",
              "      <td>Linux;Windows</td>\n",
              "      <td>NaN</td>\n",
              "      <td>.NET</td>\n",
              "      <td>1</td>\n",
              "      <td>I do not use containers</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Academic researcher;Developer, desktop or ente...</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA</td>\n",
              "      <td>Couchbase;MongoDB;MySQL;Oracle;PostgreSQL;SQLite</td>\n",
              "      <td>Android;Linux;MacOS;Slack;Windows</td>\n",
              "      <td>Django;Express;Flask;jQuery;React.js;Spring</td>\n",
              "      <td>Cordova;Node.js</td>\n",
              "      <td>2</td>\n",
              "      <td>Outside of work, for personal projects</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MainBranch  Hobbyist  OpenSourcer  ...  Trans  Sexuality  Dependents\n",
              "0           1         1            1  ...      1          1           1\n",
              "1           1         2            2  ...      1          1           1\n",
              "2           2         1            1  ...      1          1           2\n",
              "3           3         2            1  ...      1          1           1\n",
              "4           3         1            3  ...      1          1           1\n",
              "\n",
              "[5 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23UniD334woM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d6cd50eb-88eb-4a87-c4ea-2b6797f6d1aa"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(88883, 56)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnoxbLF0ThbB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "2603eadb-1665-49eb-e33d-8d89dafb255b"
      },
      "source": [
        "def Convert_YearsCodePro_and_YearsCode ( df ):\n",
        "    df[\"YearsCode\"].replace(to_replace = ['Less than 1 year', 'More than 50 years'],\n",
        "                            value = [1,50] , inplace = True) \n",
        "    df[\"YearsCode\"].fillna(value= '-1', inplace = True )\n",
        "    df[\"YearsCode\"] = df[\"YearsCode\"].apply(pd.to_numeric) \n",
        "    df[\"YearsCodePro\"].replace(to_replace = ['Less than 1 year', 'More than 50 years'],\n",
        "                            value = [1,50] , inplace = True) \n",
        "    df[\"YearsCodePro\"].fillna(value= '-1', inplace = True )\n",
        "    df[\"YearsCodePro\"] = df[\"YearsCodePro\"].apply(pd.to_numeric) \n",
        "    return df\n",
        "Convert_YearsCodePro_and_YearsCode(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "      <td>4</td>\n",
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              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA</td>\n",
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              "      <td>Android;Linux;MacOS;Slack;Windows</td>\n",
              "      <td>Django;Express;Flask;jQuery;React.js;Spring</td>\n",
              "      <td>Cordova;Node.js</td>\n",
              "      <td>2</td>\n",
              "      <td>Outside of work, for personal projects</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>88878</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>HTML/CSS;JavaScript;Other(s):</td>\n",
              "      <td>Firebase;SQLite</td>\n",
              "      <td>Linux</td>\n",
              "      <td>jQuery</td>\n",
              "      <td>Node.js</td>\n",
              "      <td>1</td>\n",
              "      <td>I do not use containers</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>88879</th>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>180</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88880</th>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>180</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88881</th>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>180</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88882</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bash/Shell/PowerShell;HTML/CSS;Java;JavaScript...</td>\n",
              "      <td>DynamoDB;MariaDB;Microsoft SQL Server;MySQL</td>\n",
              "      <td>iOS;Linux;MacOS;WordPress</td>\n",
              "      <td>Django;jQuery;React.js</td>\n",
              "      <td>Unity 3D;Unreal Engine</td>\n",
              "      <td>2</td>\n",
              "      <td>Production</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>88883 rows × 56 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       MainBranch  Hobbyist  OpenSourcer  ...  Trans  Sexuality  Dependents\n",
              "0               1         1            1  ...      1          1           1\n",
              "1               1         2            2  ...      1          1           1\n",
              "2               2         1            1  ...      1          1           2\n",
              "3               3         2            1  ...      1          1           1\n",
              "4               3         1            3  ...      1          1           1\n",
              "...           ...       ...          ...  ...    ...        ...         ...\n",
              "88878           6         1            4  ...      1          3           1\n",
              "88879           6         2            1  ...      2          3           3\n",
              "88880           6         2            1  ...      2          3           3\n",
              "88881           6         2            1  ...      2          3           3\n",
              "88882           6         1            2  ...      1          1           1\n",
              "\n",
              "[88883 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6bKXaBnvAjU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.impute import SimpleImputer\n",
        "yrcode =df[\"YearsCode\"]\n",
        "imp = SimpleImputer(missing_values= -1, strategy='mean')\n",
        "yrcodepro =df[\"YearsCodePro\"]\n",
        "imp = SimpleImputer(missing_values= -1, strategy='mean')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qysF9mTHvywH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "outputId": "3cdecdb7-dabf-4007-ec4a-4b464131d8dc"
      },
      "source": [
        "df.corrwith(df['ConvertedComp'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MainBranch       -0.001112\n",
              "Hobbyist         -0.001855\n",
              "OpenSourcer       0.012241\n",
              "OpenSource       -0.018901\n",
              "Employment       -0.064174\n",
              "Country          -0.153479\n",
              "Student          -0.064535\n",
              "EdLevel           0.002915\n",
              "UndergradMajor    0.042989\n",
              "OrgSize          -0.020685\n",
              "YearsCode         0.105021\n",
              "YearsCodePro      0.102933\n",
              "CareerSat        -0.033745\n",
              "JobSat            0.027898\n",
              "MgrIdiot         -0.013628\n",
              "MgrMoney         -0.000137\n",
              "MgrWant          -0.016243\n",
              "JobSeek           0.046519\n",
              "LastHireDate      0.049782\n",
              "ConvertedComp     1.000000\n",
              "WorkWeekHrs       0.013908\n",
              "WorkPlan          0.018349\n",
              "WorkRemote        0.020159\n",
              "WorkLoc          -0.007189\n",
              "ImpSyn            0.033821\n",
              "CodeRev           0.016649\n",
              "CodeRevHrs       -0.021261\n",
              "UnitTests         0.038522\n",
              "PurchaseHow       0.014664\n",
              "PurchaseWhat      0.042321\n",
              "OpSys             0.050922\n",
              "BlockchainOrg     0.019280\n",
              "BlockchainIs      0.068031\n",
              "ITperson          0.000629\n",
              "OffOn            -0.055646\n",
              "SocialMedia      -0.050274\n",
              "SOVisitFreq      -0.016966\n",
              "SOFindAnswer      0.005363\n",
              "SOTimeSaved       0.013400\n",
              "SOHowMuchTime     0.002074\n",
              "SOAccount        -0.000102\n",
              "SOPartFreq       -0.018892\n",
              "SOJobs            0.009269\n",
              "SONewContent      0.001541\n",
              "Age               0.108268\n",
              "Gender           -0.005611\n",
              "Trans            -0.001672\n",
              "Sexuality        -0.011283\n",
              "Dependents       -0.007321\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgRpI369kbhh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "430befbd-a462-455f-e955-707b823ccca1"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 88883 entries, 0 to 88882\n",
            "Data columns (total 56 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   MainBranch          88883 non-null  int64  \n",
            " 1   Hobbyist            88883 non-null  int64  \n",
            " 2   OpenSourcer         88883 non-null  int64  \n",
            " 3   OpenSource          88883 non-null  int64  \n",
            " 4   Employment          88883 non-null  int64  \n",
            " 5   Country             88883 non-null  int64  \n",
            " 6   Student             88883 non-null  int64  \n",
            " 7   EdLevel             88883 non-null  int64  \n",
            " 8   UndergradMajor      88883 non-null  int64  \n",
            " 9   OrgSize             88883 non-null  int64  \n",
            " 10  DevType             81335 non-null  object \n",
            " 11  YearsCode           88883 non-null  int64  \n",
            " 12  YearsCodePro        88883 non-null  int64  \n",
            " 13  CareerSat           88883 non-null  int64  \n",
            " 14  JobSat              88883 non-null  int64  \n",
            " 15  MgrIdiot            88883 non-null  int64  \n",
            " 16  MgrMoney            88883 non-null  int64  \n",
            " 17  MgrWant             88883 non-null  int64  \n",
            " 18  JobSeek             88883 non-null  int64  \n",
            " 19  LastHireDate        88883 non-null  int64  \n",
            " 20  ConvertedComp       55823 non-null  float64\n",
            " 21  WorkWeekHrs         64503 non-null  float64\n",
            " 22  WorkPlan            88883 non-null  int64  \n",
            " 23  WorkRemote          88883 non-null  int64  \n",
            " 24  WorkLoc             88883 non-null  int64  \n",
            " 25  ImpSyn              88883 non-null  int64  \n",
            " 26  CodeRev             88883 non-null  int64  \n",
            " 27  CodeRevHrs          49790 non-null  float64\n",
            " 28  UnitTests           88883 non-null  int64  \n",
            " 29  PurchaseHow         88883 non-null  int64  \n",
            " 30  PurchaseWhat        88883 non-null  int64  \n",
            " 31  LanguageWorkedWith  87569 non-null  object \n",
            " 32  DatabaseWorkedWith  76026 non-null  object \n",
            " 33  PlatformWorkedWith  80714 non-null  object \n",
            " 34  WebFrameWorkedWith  65022 non-null  object \n",
            " 35  MiscTechWorkedWith  59586 non-null  object \n",
            " 36  OpSys               88883 non-null  int64  \n",
            " 37  Containers          85366 non-null  object \n",
            " 38  BlockchainOrg       88883 non-null  int64  \n",
            " 39  BlockchainIs        88883 non-null  int64  \n",
            " 40  ITperson            88883 non-null  int64  \n",
            " 41  OffOn               88883 non-null  int64  \n",
            " 42  SocialMedia         88883 non-null  int64  \n",
            " 43  SOVisitFreq         88883 non-null  int64  \n",
            " 44  SOFindAnswer        88883 non-null  int64  \n",
            " 45  SOTimeSaved         88883 non-null  int64  \n",
            " 46  SOHowMuchTime       88883 non-null  int64  \n",
            " 47  SOAccount           88883 non-null  int64  \n",
            " 48  SOPartFreq          88883 non-null  int64  \n",
            " 49  SOJobs              88883 non-null  int64  \n",
            " 50  SONewContent        88883 non-null  int64  \n",
            " 51  Age                 79210 non-null  float64\n",
            " 52  Gender              88883 non-null  int64  \n",
            " 53  Trans               88883 non-null  int64  \n",
            " 54  Sexuality           88883 non-null  int64  \n",
            " 55  Dependents          88883 non-null  int64  \n",
            "dtypes: float64(4), int64(45), object(7)\n",
            "memory usage: 38.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19oPNoFKqMEE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DevType = df[\"DevType\"]\n",
        "DevType.fillna(value= '-1', inplace = True )\n",
        "ls = []\n",
        "for i in DevType:    \n",
        "    if i != '-1':\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            if j not in ls:\n",
        "                ls.append(j)\n",
        "d = dict(zip(set(ls), [1]*len(ls)))\n",
        "rep_devtype = []\n",
        "for i in DevType:\n",
        "    if i == '-1':\n",
        "        rep_devtype.append(-1)\n",
        "    if i != '-1':\n",
        "        sum = 0\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            sum = d[ j ] + sum\n",
        "        rep_devtype.append(sum) \n",
        "        \n",
        "DevType = pd.DataFrame(rep_devtype)\n",
        "\n",
        "df[\"DevType\"] = DevType"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EuPBoLQwDhq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "LanguageWorkedWith = df[\"LanguageWorkedWith\"]\n",
        "LanguageWorkedWith.fillna(value= '-1', inplace = True )\n",
        "ls = []\n",
        "for i in LanguageWorkedWith  :    \n",
        "    if i != '-1':\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            if j not in ls:\n",
        "                ls.append(j)\n",
        "d = dict(zip(set(ls),[1]*len(ls)))\n",
        "rep_LanguageWorkedWith   = []\n",
        "for i in LanguageWorkedWith  :\n",
        "    if i == '-1':\n",
        "        rep_LanguageWorkedWith.append(-1)\n",
        "    if i != '-1':\n",
        "        sum = 0\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            sum = d[ j ] + sum\n",
        "        rep_LanguageWorkedWith.append(sum) \n",
        "LanguageWorkedWith = pd.DataFrame(rep_LanguageWorkedWith)\n",
        "\n",
        "df[\"LanguageWorkedWith\"] = LanguageWorkedWith"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kbUGW9vwpBT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DatabaseWorkedWith = df[\"DatabaseWorkedWith\"]\n",
        "DatabaseWorkedWith.fillna(value= '-1', inplace = True )\n",
        "ls = []\n",
        "for i in DatabaseWorkedWith  :    \n",
        "    if i != '-1':\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            if j not in ls:\n",
        "                ls.append(j)\n",
        "d = dict(zip(set(ls), [1]*len(ls)))\n",
        "rep_DatabaseWorkedWith   = []\n",
        "for i in DatabaseWorkedWith  :\n",
        "    if i == '-1':\n",
        "        rep_DatabaseWorkedWith.append(-1)\n",
        "    if i != '-1':\n",
        "        sum = 0\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            sum = d[ j ] + sum\n",
        "        rep_DatabaseWorkedWith.append(sum) \n",
        "DatabaseWorkedWith = pd.DataFrame(rep_DatabaseWorkedWith)\n",
        "\n",
        "df[\"DatabaseWorkedWith\"] = DatabaseWorkedWith"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vgzikub5woUw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PlatformWorkedWith = df[\"PlatformWorkedWith\"]\n",
        "PlatformWorkedWith.fillna(value= '-1', inplace = True )\n",
        "ls = []\n",
        "for i in PlatformWorkedWith  :    \n",
        "    if i != '-1':\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            if j not in ls:\n",
        "                ls.append(j)\n",
        "d = dict(zip(set(ls),[1]*len(ls)))\n",
        "rep_PlatformWorkedWith   = []\n",
        "for i in PlatformWorkedWith  :\n",
        "    if i == '-1':\n",
        "        rep_PlatformWorkedWith.append(-1)\n",
        "    if i != '-1':\n",
        "        sum = 0\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            sum = d[ j ] + sum\n",
        "        rep_PlatformWorkedWith.append(sum) \n",
        "PlatformWorkedWith = pd.DataFrame(rep_PlatformWorkedWith)\n",
        "\n",
        "df[\"PlatformWorkedWith\"] = PlatformWorkedWith"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-3Ng5WVw3mE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "WebFrameWorkedWith = df[\"WebFrameWorkedWith\"]\n",
        "WebFrameWorkedWith.fillna(value= '-1', inplace = True )\n",
        "ls = []\n",
        "for i in WebFrameWorkedWith  :    \n",
        "    if i != '-1':\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            if j not in ls:\n",
        "                ls.append(j)\n",
        "d = dict(zip(set(ls),[1]*len(ls)))\n",
        "rep_WebFrameWorkedWith   = []\n",
        "for i in WebFrameWorkedWith  :\n",
        "    if i == '-1':\n",
        "        rep_WebFrameWorkedWith.append(-1)\n",
        "    if i != '-1':\n",
        "        sum = 0\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            sum = d[ j ] + sum\n",
        "        rep_WebFrameWorkedWith.append(sum) \n",
        "WebFrameWorkedWith = pd.DataFrame(rep_WebFrameWorkedWith)\n",
        "\n",
        "df[\"WebFrameWorkedWith\"] = WebFrameWorkedWith"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVVtpPLIxLw0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MiscTechWorkedWith = df[\"MiscTechWorkedWith\"]\n",
        "MiscTechWorkedWith.fillna(value= '-1', inplace = True)\n",
        "ls = []\n",
        "for i in MiscTechWorkedWith  :    \n",
        "    if i != '-1':\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            if j not in ls:\n",
        "                ls.append(j)\n",
        "d = dict(zip(set(ls), [1]*len(ls)))\n",
        "rep_MiscTechWorkedWith   = []\n",
        "for i in MiscTechWorkedWith  :\n",
        "    if i == '-1':\n",
        "        rep_MiscTechWorkedWith.append(-1)\n",
        "    if i != '-1':\n",
        "        sum = 0\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            sum = d[ j ] + sum\n",
        "        rep_MiscTechWorkedWith.append(sum) \n",
        "MiscTechWorkedWith = pd.DataFrame(rep_MiscTechWorkedWith)\n",
        "\n",
        "df[\"MiscTechWorkedWith\"] = MiscTechWorkedWith"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1ivW7ZUxWu9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Containers = df[\"Containers\"]\n",
        "Containers.fillna(value= '-1', inplace = True )\n",
        "ls = []\n",
        "for i in Containers  :    \n",
        "    if i != '-1':\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            if j not in ls:\n",
        "                ls.append(j)\n",
        "d = dict(zip(set(ls),[1]*len(ls)))\n",
        "rep_Containers   = []\n",
        "for i in Containers  :\n",
        "    if i == '-1':\n",
        "        rep_Containers.append(-1)\n",
        "    if i != '-1':\n",
        "        sum = 0\n",
        "        cat = i.split(';')\n",
        "        for j in cat:\n",
        "            sum = d[ j ] + sum\n",
        "        rep_Containers.append(sum) \n",
        "Containers = pd.DataFrame(rep_Containers)\n",
        "\n",
        "df[\"Containers\"] = Containers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MnVrFg_hxflF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d14bf1f1-4c22-406d-bfb7-ea3d7d125df9"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 88883 entries, 0 to 88882\n",
            "Data columns (total 56 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   MainBranch          88883 non-null  int64  \n",
            " 1   Hobbyist            88883 non-null  int64  \n",
            " 2   OpenSourcer         88883 non-null  int64  \n",
            " 3   OpenSource          88883 non-null  int64  \n",
            " 4   Employment          88883 non-null  int64  \n",
            " 5   Country             88883 non-null  int64  \n",
            " 6   Student             88883 non-null  int64  \n",
            " 7   EdLevel             88883 non-null  int64  \n",
            " 8   UndergradMajor      88883 non-null  int64  \n",
            " 9   OrgSize             88883 non-null  int64  \n",
            " 10  DevType             88883 non-null  int64  \n",
            " 11  YearsCode           88883 non-null  int64  \n",
            " 12  YearsCodePro        88883 non-null  int64  \n",
            " 13  CareerSat           88883 non-null  int64  \n",
            " 14  JobSat              88883 non-null  int64  \n",
            " 15  MgrIdiot            88883 non-null  int64  \n",
            " 16  MgrMoney            88883 non-null  int64  \n",
            " 17  MgrWant             88883 non-null  int64  \n",
            " 18  JobSeek             88883 non-null  int64  \n",
            " 19  LastHireDate        88883 non-null  int64  \n",
            " 20  ConvertedComp       55823 non-null  float64\n",
            " 21  WorkWeekHrs         64503 non-null  float64\n",
            " 22  WorkPlan            88883 non-null  int64  \n",
            " 23  WorkRemote          88883 non-null  int64  \n",
            " 24  WorkLoc             88883 non-null  int64  \n",
            " 25  ImpSyn              88883 non-null  int64  \n",
            " 26  CodeRev             88883 non-null  int64  \n",
            " 27  CodeRevHrs          49790 non-null  float64\n",
            " 28  UnitTests           88883 non-null  int64  \n",
            " 29  PurchaseHow         88883 non-null  int64  \n",
            " 30  PurchaseWhat        88883 non-null  int64  \n",
            " 31  LanguageWorkedWith  88883 non-null  int64  \n",
            " 32  DatabaseWorkedWith  88883 non-null  int64  \n",
            " 33  PlatformWorkedWith  88883 non-null  int64  \n",
            " 34  WebFrameWorkedWith  88883 non-null  int64  \n",
            " 35  MiscTechWorkedWith  88883 non-null  int64  \n",
            " 36  OpSys               88883 non-null  int64  \n",
            " 37  Containers          88883 non-null  int64  \n",
            " 38  BlockchainOrg       88883 non-null  int64  \n",
            " 39  BlockchainIs        88883 non-null  int64  \n",
            " 40  ITperson            88883 non-null  int64  \n",
            " 41  OffOn               88883 non-null  int64  \n",
            " 42  SocialMedia         88883 non-null  int64  \n",
            " 43  SOVisitFreq         88883 non-null  int64  \n",
            " 44  SOFindAnswer        88883 non-null  int64  \n",
            " 45  SOTimeSaved         88883 non-null  int64  \n",
            " 46  SOHowMuchTime       88883 non-null  int64  \n",
            " 47  SOAccount           88883 non-null  int64  \n",
            " 48  SOPartFreq          88883 non-null  int64  \n",
            " 49  SOJobs              88883 non-null  int64  \n",
            " 50  SONewContent        88883 non-null  int64  \n",
            " 51  Age                 79210 non-null  float64\n",
            " 52  Gender              88883 non-null  int64  \n",
            " 53  Trans               88883 non-null  int64  \n",
            " 54  Sexuality           88883 non-null  int64  \n",
            " 55  Dependents          88883 non-null  int64  \n",
            "dtypes: float64(4), int64(52)\n",
            "memory usage: 38.0 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nd7XTHU1WELx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "5fb8b069-18e4-4586-ba10-ef9aae0ad17b"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MainBranch</th>\n",
              "      <th>Hobbyist</th>\n",
              "      <th>OpenSourcer</th>\n",
              "      <th>OpenSource</th>\n",
              "      <th>Employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Student</th>\n",
              "      <th>EdLevel</th>\n",
              "      <th>UndergradMajor</th>\n",
              "      <th>OrgSize</th>\n",
              "      <th>DevType</th>\n",
              "      <th>YearsCode</th>\n",
              "      <th>YearsCodePro</th>\n",
              "      <th>CareerSat</th>\n",
              "      <th>JobSat</th>\n",
              "      <th>MgrIdiot</th>\n",
              "      <th>MgrMoney</th>\n",
              "      <th>MgrWant</th>\n",
              "      <th>JobSeek</th>\n",
              "      <th>LastHireDate</th>\n",
              "      <th>ConvertedComp</th>\n",
              "      <th>WorkWeekHrs</th>\n",
              "      <th>WorkPlan</th>\n",
              "      <th>WorkRemote</th>\n",
              "      <th>WorkLoc</th>\n",
              "      <th>ImpSyn</th>\n",
              "      <th>CodeRev</th>\n",
              "      <th>CodeRevHrs</th>\n",
              "      <th>UnitTests</th>\n",
              "      <th>PurchaseHow</th>\n",
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              "      <th>LanguageWorkedWith</th>\n",
              "      <th>DatabaseWorkedWith</th>\n",
              "      <th>PlatformWorkedWith</th>\n",
              "      <th>WebFrameWorkedWith</th>\n",
              "      <th>MiscTechWorkedWith</th>\n",
              "      <th>OpSys</th>\n",
              "      <th>Containers</th>\n",
              "      <th>BlockchainOrg</th>\n",
              "      <th>BlockchainIs</th>\n",
              "      <th>ITperson</th>\n",
              "      <th>OffOn</th>\n",
              "      <th>SocialMedia</th>\n",
              "      <th>SOVisitFreq</th>\n",
              "      <th>SOFindAnswer</th>\n",
              "      <th>SOTimeSaved</th>\n",
              "      <th>SOHowMuchTime</th>\n",
              "      <th>SOAccount</th>\n",
              "      <th>SOPartFreq</th>\n",
              "      <th>SOJobs</th>\n",
              "      <th>SONewContent</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Trans</th>\n",
              "      <th>Sexuality</th>\n",
              "      <th>Dependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8820.0</td>\n",
              "      <td>40.0</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>61000.0</td>\n",
              "      <td>80.0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>3</td>\n",
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              "      <td>1</td>\n",
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>366420.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>95179.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>13293.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MainBranch  Hobbyist  OpenSourcer  ...  Trans  Sexuality  Dependents\n",
              "0           1         1            1  ...      1          1           1\n",
              "1           1         2            2  ...      1          1           1\n",
              "2           2         1            1  ...      1          1           2\n",
              "3           3         2            1  ...      1          1           1\n",
              "4           3         1            3  ...      1          1           1\n",
              "5           2         1            1  ...      1          1           1\n",
              "6           3         2            1  ...      1          1           2\n",
              "7           4         1            2  ...      1          1           3\n",
              "8           3         1            3  ...      1          2           1\n",
              "9           3         1            3  ...      2          3           2\n",
              "\n",
              "[10 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XmeGrhsnWwmN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "f608b4eb-6073-45e6-a6b7-2d493067ba7f"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(df.corr())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f59643b76d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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78QD7fwf4YbBeW1QrlPQyYHeKuMIDgNmSjghv7w6cYWb7ACuBN4ZM9nOBE4Lhd2lZZr3FWndPmmUgk8lkNhdW7Wn4NVzJHWE/BCuz2cBJwBLg/BCO0SgvAH4d/v3zuvKXhdetFLGFe1J0gADzzey28O+bgZkNnOeZZjbHzOa0tqQpeDKZTGazkTXCkU+wQbscuDwEzL/Dq1b371hY8wQIAV81sx+XCqWZQL3wVKXwQG2YE6bOTsoWRX6Lfc4peTkEPc/QZY4fYqwJ/vnWM5I6/zP7v5Oy+0jjDTsjLejk7V+Y1PFYifNrMw13c+PDYhrNm9ceaTOLnDyDXszj66alBkFxbkCAptEzBqzjaWCrnc/yqIl7JWVx/sE43hTgkd5VSdl4J0Yw9hHtdZ4TTyub7uhi3U7s4pgoBm6Z4yHq/ar37kWc29DDy93XoJRIW/Rntc3R8HYflWrCTzl66de2TSedrrbyZ7KGVFP1ci7u1OR8IYaCYTzl2Sh5RNgPkp4rafe6ogOABcAaYFxd+ZOS9pLUROFwU+Ma4C3h3yfUlV9CkSJqbDjODjWrt36Ij5nJZDJbnjwiHPGMBb4X0i31Ag9STJMeD1ws6fGgE36KIlvFEgotr/Yz9yPAryT9J/DHWqNmdmlIA3VdWG23Fngr9Js6+xzgR5I6gENjnTCTyWS2CCNgRJg7wn4ws5uBw5y3vkdddvqwmOV3zv7zgUPrij5b9953KBbTxMyqq/ONun9fwNNpmzKZTGZ4MIwT7jZK7ghHGF7OujjGq9OJ0+pyvAm9HIILelKfzDhG0NMDP3Xzl5Kyj8z5VFK2KtJ01ig911ZnRt/Tz7xcfY3oQ57u5u0Vn4eXY2614685wYmB84ivyTsvjwcdrXLvljTGsRE6Hf/LNkdnjfU/TxvtcJ6xSYmk7uuxcWsTnXu9tJrGyXnxn03R98HThL1YWy8foTcWWhzF63ntdzU4TdjufOTtTeVzW9ibrhRvbUrPv0ebaeQ2AkaEWSPMZDKZzMYzhBphSHV3n6QHJSW/lCXNkPRPSbeGGOxXDcUl5I4wk8lkMhvPEAXUhzyyZwCvBPYGjpcUWxt9FviNmR1IsRDxB0NxCbkjHABJO0r6o6QHJP1L0ncktQ6yjXcFd5l5ku6U9PpQ/kVJL908Z57JZDLPAEM3IjwEeNDMHjKzbuA84PXx0YBasPQEBrCcbJSsEfaDCnHt9xTuMK8Pv1jOpEii+4m6es1mjphSvLcj8BkKv9JVIWRiGoCZfW6oz/nVXanm8qvWsjYzvintx5c4rg/bOXnOVlZSD8nYM9SLD/T0wO/M/Z+k7AtzPlvabnL0lUl9adkUZ73ttr3pR9IctWdO+53O78OVlVQfWhdVO4/UO9XTld7WlZ7sAqWf2z6tZe1neUdaZ5uxqS720Wr6+R7TkX7VWyItbmxT+gx8pSWN89u9KY3iWRtp06MdffYJS58dTw+c4cS7zegrn/+tSq+72bnXax2Ntj2KSfQ8UFc5XqmeDh3nSfTwcjNu77gkLnQWgi927s+CKLazw4mVrDh6bGvzwOe6UQydRrgDUG+uuhB4XlTnFIpk6idTRAoPyUAid4T98xKg08zOhiK4XtLHgPmS5gOvoAiVqEh6JUWIwyzgPgo/0g9S6OlrKEIkam41awGCJ+mfgYeBn4ZjVoBZZiZJu1FMFUyjMPh+j5ndu3kvOZPJZAbBIFaNSjqJIgStxplmduYgjnY8cI6ZnR6SIvxc0iyzTQtSzB1h/+xDYXO2HjNbLekRint3ELCfmS2X9HFghZntLWkWULNJux14kqLzvAz4vZn9KWpzLkWwPpJOAy4Ob50JvM/MHpD0PIr58JfEJ1n/cJ00/hCOHv2cIbj0TCaTaYBBjAhDp7ehju8xYKe67R1DWT3vphiAYGbXSWoDpgINpqnxyRrhpvF3M1se/n04xZw2ZnYnMC/8u0rxwb0JuB/4lqRTvMYkHUfRuX4qTKEeBvxW0m3AjymyYSTUe43mTjCTyTyjmDX+6p+bgN1D1p5WisUwF0V1HgGOAgimJG0URiabRB4R9s/dFB3YeiSNB2ZQOM2kAVsOViR6uxG4UdLfgbMp5rrr250Vyo4IU7BNwEozS40p++H3ral28mhPWbt6YWua33etUp2hzfmdNMnxgYxzCMZ+oZDGB0KqBwJ8fm4pWxWnOHWWNaVfqIeb0l+lFSdc765qmgcuxtP1Dlaa8HCClTWjijnxjU483Xmj0q/dROc8bqasz81sTc9raW+qB+5cSe/1eW2pVhbnrNvOiek7vrsxq9ueKO6u6lh17keqN55ri5Ky3VrSnNf/aipfU+xtCtDiPK+e/ufFeyZtOZ+bF5Pofb7x8+Od1xJLPw8v5nSB8xDHXsETnO+kp6175zEkDJFGaGa9kj5EYUFZAc4ys7skfRGYa2YXAf8B/CRIVAacaF4izUGSR4T9cxkwWtLbYf3y3tMptMC4x7kGeHOotzewb/j39pIOqqtX8ytdT7Bw+zXwdjNbAsUULMV06rGhjiTtP6RXl8lkMpvKEOYjNLO/mtkeZrabmZ0ayj4XOkHM7G4ze4GZ7R9S0l06FJeQO8J+CL803gAcK+kBiqnNTuC/nOo/AKZJuhv4MnAXsApoAb4h6d4wxXkchQdpPa8Hdqb4pXNbqAeFUfe7Jd0e2ouXEmcymcyWJZtuj3zM7FHgtc5b54RXjU7grWbWGVZ7/h+wIMTDJAtcQtsn1m2e67xfW5naMAdbuvR8WRTy0OEYQy3pXZuUbVdJ27rtqUeTskdWl3VqL3WSZ5XmTd94U6GnRNOlvXdentT57xP/npRtY+lU4uIoJKTFSbfT68y09DpTfbM6y0vUZzGBC6NZqgV96ez5jL50ustpnr6mculzup172Jp+hS/pW5qUHdWSysvxVU5wwlKubE2n63qcJf1euMT91fIy/ylN6dTrVCdXVncDVnLPVbrf9aQhD0t60qnwFmeKeXUULuGlh4rTVgFUnWclnrr0/vyPcSzc4rAOgB+cGseTw/P/47LyeTnfLY8DmzfOZm9AqgOnNhvu5I5w6BgN/FNSC8XftQ+ETjCzCcSd4HAm7gSfzcSd4HAm7gQzg2QEeI3mjnCIMLM1wJwtfR6ZTCbzjJI7wkwmk8k8qxnG2l+jbDUdYbA7uwo41cz+FsqOBd5tZoPS0Ro4VgvwJeCNFK4wXcAXa8dtYP8TgTlm9qEB6pxGETDaCnzLzH6yaWcOY51nckpk8dTlqBZNzjJwz/7KWy7eCF7qJM8qLQ6N8PTA5llHJmXb2D/SMkc6Gd9SfuRHOedVVXrd3rlOaCnrZyf2wlnNA9vQbtOgpNIehWNMbUlDY6pOeMOY1vQcJvV5n2+Zyc55rZaTOsmx2o1TRI11bPw8PNsy789q/DmtcdKNxamgAEZX0lCJOBXX5ObRrHPCe2K6nfRTHqMpX7v3jfFCk1xaUt0w1tY9a7mqcxcnOOE9Q4H1bXL0whZnq+kIrfAcex9FgPk/Kc79KwxyMUmN/vxBKTrB7SiszrokTQdetDHHGYDzzexDkrYB7pJ0kZk92eA5ZoYZjXSCmeFHI51gph9GQGLerSp8Iji2/An4T+BzwC+Az0i6MeSnqmV1mCnpKkm3hNdhofzIUH4RcLekMZL+Iun2kBXiOEmjgfcAJ5sVUa9m9qSZ/Sa0cXzIJHGnpK/Vzk3SOyXdL+lG4AV15dMkXSDppvBa/17ddS0G/gXsLOkcST+SdAPwdUkHSLo+ZK64UNKkzXFvM5lMZqPos8Zfw5StZkRYxxeAW4BuCsPqf5jZu0JQ+o2S/o/Cd+7oEMqwO0Wwem0hy0EUI735kt4IPG5mrwaQNAF4DvBICGgvIWl74GvAbGAFhQv6McAN4bxmU8QO/hO4Nez2HYppz6slzaBwTdgrandXYFfgwVC0I3BYcJiZR9EpXxEcFj4PfDTaf73X6FsnHsIRY3YfzP3MZDKZjScvlnnmMbOnJJ1PkcHhzcBrg+E1FL5zMyhyVH1f0gFAFdijrokbQ3wewB3A6WFk92czu0pOXFkdBwOX19xfJP0SOCK8V19+ft0xXwrsXdfu+OAjCnCcpMMpNMj3BvNugN+GTnACMNHMrgj1zwV+69yT9Ua2X9r5BHsi0mtiTWFJX7pcfKJj0+Q93jNGObFIkfvYSlJdydOCvNRJsVWaHx+Y6oEfu/mLSdkDzzs5KWvqLA+o251fqWubHHsq58fsor7yPbvTUt/fdkcr26E3vT+LK+lX8fBK+bdYR3eqF+0yNvm9xpTe9LPcuTu9gKYoBq7Lue6Vju3XxEp6TauitD/TlGpznq2Y5461zLH7mxbpbvf1pfGBnlY2qZLatcXfh7FNo1BUttaZLvX+Nnjnvy6yGKw4+7natPONO/WTabKZKVFqrEYdxu5uzBFy8OSOcIvRF14C3mhm99W/GUytnwT2p5j+rf/Lv/5pMLP7g/3Zq4Avh+wQ3wBmSBrvjQo3gibg+WblZGzhS3X+BhbUbKYnNpPJxMSdYGaQbLrV5xZnq9IIHS4BTg4rSpF0YCifACwKOareBo7tBeunOteZ2S8oVnAeZGbrgP8F1meiDzrfsRTG2S+SNDX4jh4PXEExNfoiSVPCitNj6w5zKXBy3TEbNtE2s1XACkk1q5a3heNlMpnM8GAIvUa3FFvriLDGl4BvA/NCtob5wGsofD8vCGbZF7PhEda+wGmS+oAe4P2h/LMUfqF3S+oM+3/OzBZJ+hSFBijgL2b2R1g/Cr0OWMnTuQgBPgycEbS+ZuBK4H2DuMZ3AD8Ki3geAt45iH0zmUxm85It1rYMZnZK3eZ7nfcfAParK/rPUH45cHldvUsoRpXx/t3AJ8Mrfu/XFItv4vKzKdIrxeVLKYy24/JzKHuV1spPjLZvA54f19sQo5xpnjgt0mjH53BVtSMpG11JB9LLe9PfFKu7o7LUBpI2Z1C+bW+67DrOOuP5hXrxgZ4euPsN30vK7p9V9jId7aXzceKtZk1elpQ9tKysN05tdnwzndiznSan9mNaPj4pm7Jd+b52rU0/t/Hbp7rbunvGJWUzK+nntq63fLPHNae6WLPzubU4z9i46JnyUv54z8CoprTeNjhxkNFzMNFJpbSoml6j97yOjnRbL7WY97l5aZjGOucRa32NTr2Ocu7PWOdZjOMlO/pSzdljbYOxnYNmGK8GbZStsiPMZDKZzPDAhvGUZ6PkjjCTyWQyG88IGBFu7YtlNguSTNIv6rabJS2R9OeNaOthSVOd8lNqYR+SvijppQO089GgE2YymczwIecjHLE8BcyS1G5mHcDRFJ6gDRNWsjYkDpjZ5xqo9lEKJ53UcLKOnXrSX2c3RrrbCieO0PtN91A1jdXyaIli4DodfSX2dwRodm7PXdEx4/yBkPqFQhofCKkeCPDqO8tpnXp+/Y2kji1ZnpRd8eO0/UeiXICrnZg7L+eiWVp266hUv9llXVkzunfx5KTO3pVUu1zqaGW7HJD+EepeVf5M+qpOHOEjadlSJ2/eyihGcJrzua12spJVnHvR6uiGh3aV6908qjFbLy+2MCb2SQXotFR38/TAbscBsTf6g19tctp3YgbjPIYA92vgFFFdjkbY0pR+R9Y43/shoXfrXyyTR4Qb5q/Aq8O/j6dugUwIp/i7pLsk/VTSghBSMVPSfZJ+BtwJ7FTfoKTPBBu2q4Hn1pWfI+lN4d9HBbu4OySdJWmUpA8D21PkO/zn5r3sTCaTGQQjwGItd4Qb5jzgLZLaKFag3lD33ucprN32AX5H4WZTY3fgB2a2j5ktqBVKmg28BTiAIoD/4PiA4VjnAMeZ2b4UI/b3m9l3KdxyXmxmL3b2O0nSXElzL1v3YPx2JpPJbD5GwNRo7gg3gJnNA2ZSjAb/Gr19OEVHiZldTOE7WmOBmV3vNPlC4EIzWxccay5y6jwXmG9m94ftc3nawq2/cz3TzOaY2ZyjRj9noOqZTCYzdIyAEWHWCPvnIgrLtSMBx2TTZYvao/U5mlQc09XjaHitjpbi6R+eH2KjXofJfg1IqC0N+jR6nqFejGCsCbYc//G0zm+/lZQ91ZTqhrH/aIujg3q/gas9A+cGBLAoB+Iop5Yc/cnTJU47/YEAACAASURBVD1aJ5TPrnP5wHrahtqPy7yYOy+2sMvJK+i13xp9ll5M4lLnufae4bZIP/N8cCvOuXZ6bTkxuY3QqKmbF7MZ5wl1PVDdJ2rzMBLCJ/KIsH/OAr5gZndE5ddQGH4j6WVAI6mRrgSOkdQuaRzwWqfOfcBMSbVhXb2l2hogjZTOZDKZLUkeEY5szGwh8F3nrS8Av5b0NgpbtScoOqqxTt1aW7eErBS3U6SJusmp0ynpnRTJh5tDnR+Ft88ELpb0uKcTZjKZzBYhW6yNTMws6dAie7ZVwMvNrFfSocDBIYnvw8CsaL+Zdf8+FTjVafvEun9fBhzo1PkekHqGZTKZzJZkGI/0GiV3hBvHDOA3wei7myKj/bCg2xEf4hn8iqNlubqSo7GNqbQlZR2RR6XnrejGajkz87FW2etoTVU1mkPQ0eKiGEFPD2w59mNJ2fJT01DPzqj5Hud+edrrsqfSGLveNESNtWvKhWudttasTD+PSZX0/qx5zMmvF+mLXhyhHJWz1Xl+4njAZke38nRDT1/udHTD7kiLW+vE+Xm6mBf7NyqKg/S+D7GOCNDZl2qETc51xvkHPQ3SiyP0viOe+hbrl62OTtniPCvjmtJnZSiw3BE+Owmm3smoLZPJZJ51jICOMC+WyWQymczGM4T5CCW9IpiSPBhS3nl13izp7mBo8quhuITcEfaDpLX9vHfkhrxHJb0rOMPMk3SnpNdv5PHX+5FmMpnMsGSIVo2GZOdnAK8E9gaOl7R3VGd34NPAC4KhyUeH4hLy1OgQI2lH4DMU2e5XSRoLTHumjv/t6kNJ2X7N25a2xzu6yaM9K5OyN7bMSMp+1fWvpCzWfjytxvvFtdLJd3iwJpS2ex3pclKfpwem9bwcgrFnqBcf6OmB77rti0nZQ4d/sLR9fUeq/a1xtKztp6YerrstSz+T8dPL3pC7rU4vctL01Hp2/kNOzsiV2yRlSzrKHu7T2tO2pjihhV5sW5wfsLUB/RdgvFKP1WlOPsJ5o8rHnKL0fvU4itrDXekz0F4pfyZV0s9tRW96L6Y2p4vCvRi+rr6yxtnhxB+Oc/xavfszwfkTHXuSdvSl+m+3p+M65zoUWHXI4ggPAR40s4cAJJ0HvB64u67Oe4AzzGwFgJktHooD5xHhAKjgtDCyu0NSfZLd8ZL+EobyPwqLZ7ahCKVYC2Bma81sfmhrN0kXS7pZ0lWS9gzl0yRdIOmm8HqBcx7vkfQ3Sem3NpPJZLYUgxgR1ttBhtdJdS3tADxat70wlNWzB7CHpGskXS/pFUNxCXlEODD/j8IfdH9gKnCTpCvDe4dQDOEXABeHuhcCTwLzJV0G/N7M/hTqnwm8z8wekPQ84AfAS4DvAN8ys6slzQAuAfaqnYCkD1FkwDgmhGmUCA/TSQA7jNuVKaOnD+X1ZzKZzIYZxGIZMzuT4u/gxtJM4ed8JLAjcKWkfc0sndIaZKOZ/jkc+LWZVYEnJV1BYZi9Grixbhj/a+BwM/td+JVyMHAU8K1guP0N4DCKYPla27X5nZcCe9eVjw9TqgBvp/iVdIyZM89G+eHaf9vDtv4lXJlMZqthCMMnHqOcsWdH0vR3C4Ebwt/C+ZLup+gYE4OSwZA7wk0jfgIMwIrgqBuBGyX9HTgb+Caw0swOcNppAp5vZiVRKHSMd1CMSHcE5g90QpObxwzyEsKxHN3nSaXahlevs1oepLY7cU2eZrTOmZifEMWjzepM+/4JLWnev0V96YzxQ8sGziHoaYtxfCCkeiDArlefUS6Y/e9JHfe+LtugAVGJpUvLn+WY9lQLWvFkmqt5Wydf4+Md6XPREj2+a7tSbU6t6f33FKE4htLLreexqC/VM0c72vG4SDf07muHk5ev1YkHbI/0uV7Ho3SsEy/refS2OJ6nsf9oWwM5EQGqThzqKzvTsisr5eegEe9XgO0qjT13g2boOsKbgN0l7ULRAb4F+Leozh8oEiGcHRKe7wGkCyMGSdYIB+Yq4DhJFUnTKLJB3BjeO0TSLkEbPA64WtL2kg6q2/8AiowUqyl+wRwL67XH/UOdS4GTaztIqu8sbwXeC1wkafvNcYGZTCazsVivNfzqtx2zXuBDFNLQPcBvzOwuSV+U9LpQ7RJgmaS7gX8CnzCzdEXUIMkjwg0QvD67KDS/Qyk8Qg34pJk9ERa63AR8H3gOxYdyIcXQ/huh0+oElgDvC82eAPxQ0meBFopUTrcDHwbOkDSP4jO5sm4fgnb4ceAvko42s6Wb9eIzmUymUYYwoN7M/kqU9s7MPlf3bwP+PbyGDG1sCp2RThit/cTMDtnS5zIYXrnTKwf8QD1LKW9aaXVfZ1K2cF3aB+/QXs5QNc1ZZu6FbCytpkvU43PbozIhqeNNu93Zk/4onOpME6/uK0+reqmBvGX405oaW6z7s5u/Wdr+fwd9OKnT6DRMvOx+fFM6ddnlTKet7UunjqdW0inUzmiqb7Qzpe2l3VrjPCuTo893mfPseOfv3X+PddG9iMM1ABY7z9Myp8ybNhwTndsoZ0rVS83kTavGdDt12pvS8AlvanS3yvik7IHq6tK2dwc9uzaPSx+9eJNjKlYe9+KGO5GJ5/9z88RwbCJ5ROgg6X0Uo7QhCdbMPDuIO8HM1kHcCWYGR/YaHaGY2Y94Ov1RJpPJZDbE1p+XN3eEmUwmk9l48ojwGUDStyhWXX47bF8CPGpm/1/YPh14zMwGnJeSdDnwcTObW1f2EWAXM/to2P4xsJuZvTRsnwzsbmap2NP/sU4B1prZN6Lyc4A/m9nv6srWejkQNxdeuqBmZ4m3px/PGb9rUrYusnjyNMhYmwPfUired4GzvN6j3Zne8rSZWB/yfsx65+VZpcVL+D098Pe3pHmdX3dgGorhnUdzdC9iTa/YL/2MPD3Ws/mK6XTqePfQs+qK70987kX7aVvrcFIbeSnBIjw90GOSo41urNWYp5d6n1t85Z7e6H0HPa3v0Qaf/0bwnuuhoIFHa9izNYRPXEMRiE4IU5gK7FP3/mHAtQM1Egxd+20/sD8woa5+Q+0PNWHVaiaTyQxv+gbxGqZsDR3htRThC1B0gHcCayRNkjSKwopsgqRbgxfoWaEcSQ9L+pqkW4Bjaw1KapJ0jqQvA7dReNe1S5oAdISyfUP1w4BrngmfUBUZLa6SdBFwt6Qxwcv09uB1elx/+2cymcwzjfU1/hquDPtRh5k9Lqk3eHAeBlxHYcR6KLAKeAD4KXCUmd0v6WfA+4FvhyaWmdlBsH41aDPwS+BOMzs1lN9KYYnWDtwQ2jxM0hKKEJNHg2/oJvuEBreY00IsocdBwCwzmy/pjcDjZvbq0FYaS0DZa3Sfifuw09idvGqZTCYz9AzjDq5Rhn1HGLiWohM8jMKqbIfw71UU3nMdZnZ/qHsu8EGe7gjPj9r6MYVjwalO++0UHe0DwH9RBMNfG3w/h9In9BOxRlj33o21bBUU9mqnS/oaha54lXdz6r1Gf7zjW5MsSH+vlOOO2pyP/b6eND5wv1GpefetnYuSsgdWP17aft201EVughM39bauVDM6b1T53Gb0pftt44Ru7dCbang7TV6VlFlk4VbtSSdFlj2VDty91EmxVdrXHOs3Tw+86NYzkrLrZ30yKdvv6BWl7Y4FjhXYoZOTsqN/sSIpu2DnpMi99pgPPZneiz2bxiVlndFD1+bofE+QWsR5KZ326kvtzcZHf2xvb04/739F8XUAjzhpmMY2l9ufXEnjTRc5ackmOnpjk6M3xmnJtnHiWfdXeg/nkaY/3Y906cCfeheWtlf0pPt51nLbtKQxiUPBcB7pNcrWMDUKT+t4+1JMjV5PMSI8DLh8gH1jtfla4MWS6r8NtfYPpegI76HIKlHTB5sIPqF1r9qor+YTWivfwcxqT+YdwEwKn9BGWX++oXM/KLTzZUlporxMJpPZgoyEqdGtpSO8FngNsNzMqma2HJhI0XFdAMyU9JxQ923AFf209b8UFj6/qVuQch3wfGCamS0ONj5LKJJCXrOlfELDPuvM7BfAaRSdYiaTyQwbrKqGX8OVraUjvINitej1UdkqM1sIvJNi2vIOihnrfoPhQ6jFrcDPJTWFbMdLgLvqql1HkWT39rB9AvBuSbeHeq8P5R8G5kiaF4xg31fXBmZ2NVDzCZ06uMtmX4oMFrcBnwe+PMj9M5lMZrMyEkaE2Wt0hPHpmf+WfKC395X1jp2aUs3inp7lSdkhLdskZTf0LE7KlvSUtZmD29PFOmOdaJAjulP9754oHHBCX/orcpqjEbY4z/H0aqoj3TqqfADv6e91frju1j3w9+QXLakm6cXOfbYnvf/Pv/PrSdmV+3y6tD22kl7PlIlpnNnX1qVa0At7nNjC6DqnVNNrvKA1bX+Gs/g5vkovvnEZ6fm3Ob/Fp5I+FzHzrSMpW9qXlnkeunGMY4sTWeWlXErEd/z42zheb4zS65mm9PN4rC+NjTyCiUnZn/ueLG33Oj2MFyu5fSXVJX+14MJNHqYtOrxxr9Htrs5eo5lMJpMZYQznkV6j5I4wk8lkMhtNvBJ7a2Rr0QifMSR9S9JH67YvkfTTuu3TJTWUC0vS5ZLmOOUPb4RemMlkMsOOvl41/Bqu5BFhyjXAm4Fv11m61YsuhwEfG6iRfizdNis7V9PfNgsi78nllsZzefnL1iXKD3Q5xoKLO8oaZNPoGUmdihMvtk9rGvd1cxQ31deU7tdu6TUeXknbmrJdqm/tsq78sZijQa5dk+o346en+fWWLi1rfZ6fp+e5GccHQqoHAhxx11dL26tOeGdSp/2QNNZzwTnpvfjy7kuSsmp3+dorrekzcNZDaexc1dEI10SeoWPwdDfPqzM9ZkWpb+yuPeVzXdSSth97vwI85Xjcjq+U4wi9z2hJTxo3OraSxjfKzddYvhfjmtPrGefcH88LdLHS72BHFDO7qjfVFtsr6TG9+zMUjIRlJnlEmLK5Ld1cJM2U9I+w+vSy4FKDpOmSLgw2a7dLOmxDbWQymcwzjfWp4ddwJXeEEWb2OBBbut1A0TnO4WlLt+PMbF+KUfX765pYZmYHmdl5Ybtm6faAmW3IVg3ge8C5ZrZfqF9LW/Bd4Aoz258ijvCueEdJJ0maK2nuVWsf2KjrzmQymY0hd4Qjl3pLt+vCq7a9EJgfWbodUbevZ+m23te0Hw4FfhX+/XPg8PDvlwA/BAhmAskafTM708zmmNmcF47dvYHLy2QymaHBrPHXcCVrhD6xpdujwH8Aqyks3d7Yz74bsnQ73cxSoWmI8VYyL+srH3afSurdvUSpntbhaISdfam+2BXF63l6oKdBLu9INZeZrWWd5Dndqe42tSXVRDqcmMSutenjfe/isjfnKOe81jpazW6r03pj2sv3Ynx3qst4cYSuZ6gTIxhrghN+eXba1qffl5S1Oxrb8sdSrS/+hT5mQqqnTW9K9cAplt6f0ZHO5n3eU53zWuXkIxzvjBzWRT/ZJzp/uh5LSqDq6JKNaGWjHG/cXufb1e7ECMaMdp6nsY7O7bFfd7rv3yLd04tv7HLiJ8e3pDGJQ8FwHuk1Sh4R+mxuS7cNHfMt4d8nADWD7csIU6+SKhvKQJHJZDJbgr6qGn4NV3JH6LNZLd1C8TxJC8PrmxR+pe+UNI+ic/1IqPcRihHlHcDNFGbgmUwmMyzoMzX8Gq7kqVEHM6tSDpnAzE6s+/dlwIHOfjOj7SPr/v35urdK9ep4idPmkzzta5rJZDLDipEQUJ87whHGlN5ULxjfWtZm1jjanxfj1ebEOnlU+8r7evqQxzZjU61vaW/5XJta00e02p3qVruMTWPnxm+fal57V8r56dSUnuualal2OWl6eq4rnizrbl0Nxsl5OQSnLE012jhG0NUDv5pORnRfnOZA3Ha/1IezN9I9vYn7dcvSZ8X7sxd/5qOdP46L5T1j6aTUaOfxmRCdxppKel5VR8PzfEQ93TCm24kJHeXogV69OB9hj7NKpNm5i57Wt9j5TNqi70hPX3ovWp0Pc3M5oY0EjTB3hJlMJpPZaIbzatBGyRphJpPJZDaaoYwjlPQKSfdJelDSp/qp90ZJ5llYbgwjpiOUtK2k8yT9S9LNkv4qaY8G9z1H0psaqDNf0m3B4eWojTzPIyX9ebDHz2QymeFIta+p4Vd/BFvKM4BXUiwKPF5SsjhQ0jiKRYQ3DNU1jIipUUkCLqRwZnlLKNsfmA7c39++g+QTZvY7SS8GzgQ2a/S6pEpYuNMw57akPpZd1bKO0VZJdZMdm9Mcdgud/Ghz2nZIyvafvl1pe7UTa/hgNdXAPlpN48p2rpT3vaRvaVJnTGu635TeVDdcd0+af21pdB5NjlYzqZLO9cx/KD3/bVvKXx9PDxzflPqWHv2L9DOa1ZJ6sMeeoV58oKcHXnTrGUnZUfu/JymLz3ZsU9r+ROdv1/1Kw2GfirQy709ep6NlrXM0tieU6pl90V+qx7pWJnUmVdJYyQNHbZuUdURfqR5HPZvQmn4eKx3fUk8Pn1Apf+brnFjJv/UuSsoqjufpRaT3Ynpz+bnevzXNG+rFPG4FXqOHAA+a2UMAks6jWCh4d1TvS8DXgE8M1YFHyojwxUCPma1fOWBmtwNXSzpN0p3BF/Q4KDpOSd8PQ/D/o8hET3hvtqQrwqjyEknbJUcrnGZ2CPUr4Rg3BZ/Q94by8yS9uq7dhkZ9sV+ppA9Luju0fd5A+2cymcwzyWDCJ+rtIMPrpLqmdqAwL6mxMJStR9JBwE5m9pehvIYRMSIEZlHE2MX8P+AAYH+KuMCbJF1JERj/XIrh93SKXxxnSWqh8Px8vZktCR3nqcC7onZfAfwh/PvdFPGFBwfz7WskXUphtfZm4C+SWoGjKALjnwe8UNJtde3NAOqnS5eZ2UEAkh4HdjGzLkmuNUR4mE4C2GfSPswYm2Z/yGQymc3BYMInzOxMitm0QRNisL8JnLgx+/fHSOkIN8ThwK/D9OKTkq4ADqbwBq2VPy7pH6H+cyk61b8Xs61UgPo5jNMkfQXYkaczVLwM2K9utDeBYsr0b8B3Quf4CuBKM+sI7V5lZq+pNSrpnOi86/1K5wG/lPQHnu58S9Q/XF/f+a3JRMWNUYqiDmeqZlHv2qTMSwtzX8cTSdmq7vK+R03cK6mzd8uUpOyYjvTxO6+tPP30nOZJ7KxyOMMkR2vYuTudn5lZcdIwHTDwIvI1j6VTu6tXptNPj3eMScr+t608leWlZrpg5/SY/7cgnUKNUyd5NmleWIQ3DXrZ7T9JytZ9rFyvaUx6D4//a2rVNcmZovViL+JPyZuaG+OEJOxFOs09u7M8nfnH9vRe/KuahtDM7Xw8KWtzUhSNbyo/Y8udqfyxzjS3N7X+RPRdmupM2c5umZaULbBUithV6TN2Y8/i8n7dy5I6zc53d0Ilva9DwRBOjT4G7FS3vSNl57xxFH+fLw9/R7cFLpL0OjObuykHHikd4V3AUCw2EXCXmR26gfdrGuHJwFnA7LDPyWZ2SdKYdDnwcuA4YDDTmvXfwldTdNyvBT4jaV8z56/rCCXuBIczcSf4bGZr0lziTjAzOAZaBDMIbgJ2l7QLRQf4FuDfam+GhAPrxdvw9/Xjm9oJwtb1vPbHP4BR9fPNkvYDVgLHBR1vGkWHciNwZV35dhQaI8B9wDRJh4Y2WiTt4xzv+0CTpJcDlwDvD9OqSNpDWv8z7nwKO7YXAhcP9qLCVMBOZvZP4D8pRptj+98rk8lknjmGymIt/MD/EMXf1HuA35jZXZK+KOl1m/MaRsSI0MxM0hsossr/J9AJPAx8lKLjuJ1ikdwnzewJSRdS2JndDTxCsfgFM+sOU5zfVWFu3Qx8mygHYDjel4FPAkdTWKbdElavLgGOCVUvpUip9EczJy38wFSAX4RzEfBdM0uXy2UymcwWYijj6c3srxRJCurLPreBukcO1XFHREcI6xPqvtl56xNEy2zNzCh+eXjt3EY5v2Ct/MRo+wKKTBQA/xVe8T49wOSo7HKKVE5u2/V+pWH/wxkEXqxFrIqZM6nvpU7qdiI32iqpprMi+ip4y9E9WpyvUFukbXhfMq+sybmmdb2OJdaq8iRI6wRnmblju7akI9V54vN/X2cb3x6Vaksx1Z50IqbD+bFc7S4XegHJsU0a+Pcn1gMBRn+rrBt2fvYDSZ0WZ9JoXAPWe551mrd6f5kXkqBUyxqjshrQ4pyDZ53W4zzDo6MTWdvXxYRKeXq01wn16FZa1uKEPMRUlF54S4OhDG4YSqSMdFZTHbfinOvm0giHs5l2o4yYjjCT2dI00glmhh9xJ5gZHNl0O5PJZDLPajaXmfczSe4IM5lMJrPRVPOIcPMiqUqRELeZYhXRO8ycYJvBtXkKsNbMvrHpZ5i0PRH4FzA1LKg5lCLz/E5mtjAseplPsQT4Hwxi6a+kmcBhZvarwZ5XR19ZQ5hRSReePlFN4whHObFIXixYrDmOajB909imVNvYjvI01QRHF5vsCKFdTWm9cc3p+qQ4S3bn8vRcvUza09rTx25tVzkebbQTSxfrORtiStXRbVvLZWMmpHqalzrJs0rzYgRjTbDtyz9I6oz528fTMkuVq5ZIB+vx9F/nuZjonOs45zNvi9IuyfnTJUeL84h1w6W9TzG1uRyv59mdeVRcLbQ8Rmp1rts7U++Io5173RdpoU3OdXvn3+g1DZa+zWTd9kwy3MMnOszsADObBXQDaUK2DRAMXJ9RworORUAtovwwisz0h4Xt5wM3mjWQEC1lJnUxNZlMZmiIO8HM4DDU8Gu4Mtw7wnquAp4TZ28InqEnhn/HPp2vkHRLyBZxWV1be0u6XNJDkj5c19YfgsfoXbWYxBBreE6dX+nHQvluki4O9a+StGdo5lqe7vgOA74VbV9Tdx7HSrpR0v2SXhjanRnauyW8avv+D8GarXYOmUwms6XpG8RruDKsp0ZrSGqmSM3RSFD6MjM7KATQ3wIcYWbzJdWHMexJEUQ/DrhP0g9DqMK7zGy5pHYKX9ILKEZiO4RRaW36EwpLs/eZ2QOSngf8gCI28RrgRcBPgV2B3wLvDfscRtGh1Wg2s0MkvQr4PPBSYDFwtJl1Stod+DUwB/gUxVTqa4io9xo9ZvIhHDJ2sybFyGQymfUM55Feowz3jrC9zpz6KuB/eXp0tSFqPp3Pp/D3nA9gZsvr6vzFzLqALkmLKYy3FwIfDoH5UHje7U7hNrOrpO8BfwEulTQ2nMdv63SJmhHhtcCng03Qw6FDU9hnNuUcWr8P/7+ZosMFaAG+L+kAirDAAXMq1nuNHrnjS+2SatkPdH5H2Zvw4HH7Jm2MdjwfJznLyvuceL09J5VThj3Suyqp42llX2lJtcrju8uxTle2prrYaqXaopcip9nRZlY+0kByUOe36xRnol2t5fPwple8WMwPPZnGc41uTUMvznqoHLs4vclJNbUsbd9LneR5hsYxgp4eePbNqZT++MtOSsriGMd1a1Ltz7Pi+mRv+lzs48QRfrO5/NxNcj4jT69rd3xFx0WeoV196Tl4euMYR8/04m9XR+09Zem9f8CJ+I21fIA/9z6SlLVH5zHaOa/WpvRP+9pq+h0ZCkaC3+Nw7wg7zOyA+gJJvZT/5sR/rRsJ5qp/IqpAs6QjKUZkh5rZuuBj12ZmK1TkNnw5hUb5ZgrHmpXxuQGEEeJECm/Q60LxzRRWaw+bWf2qlNp5VHn6s/gY8CRFxowmCpecTCaTGZaMhBHh1qQR1lhAofGNCh3OhjLFXw8cEUZmRFOjHhOAFaET3JNiRImkqUBTcJL5LHCQma0G5ks6NtRR6Czrj/0Rnu4Ir6PoPOv1wf7OY1FYUPM2WD+sWUMxlZvJZDLDhj41/hqubHUdoZk9CvwGuDP8/9YN1FtCoZv9XtLtlFMbeVxMMTK8h0LHuz6U70CR9uM24BfAp0P5CcC7Q9t3UWRSrnENxdRqLTTiOgq98NoGLvEHwDtCu3vy9Ah3HlANC3/yYplMJjMs6EMNv4Yr8nwnM1sv75l5bPKBxjnZXts2M9nvpurypMyLRzMnPmx1Xzlez8tj6Gll+1TSPMNzusuz9Ze1pjPDno+l9xR7fo5LI+9zL59cqxNv5bUVq1SPOLGYns/kvk0TkjKP+I5Nscbi0e5XY/cs9gz14gPfMeXJpGz7S9O8qkte9+7SdmVUquE9cn86KfP9lvSYJ3SkevXFkWy4xPGwf7IvjfX0vEbbm8rttznBmIt61yRl3rPieY22R3r7RCeP4UQnp+NC5/y9kcriKFeiF+/rMdnJi/j7BRdtcu/0h23/reFO5JgnfjUse8PhrhFmMplMZhgznMMiGiV3hJlMJpPZaKoNOvoMZwbUCCVVQxD3nZJ+K2l0KE/ngcr7TZT0gajstBCsftqmnXZyrI9I+nbd9o8l/V/d9smSvjuI9s4JeQk35lxOkfRxSfvXhX4g6XhJHXo6ge++kuaFf18b/j9T0r/V7XOipO9vzHlkMpnMM8GzJaB+fQiDpF9ShBB8s4H9JgIfoFj8UeMkYLKZM3HvIKk5ZC0eiGsoFq/U2B+oSKqEYx0G/LHRYzZSrwHuAGZIGmdma8I53AMcCNwYtq8FMLNabORMChu1QfuJ1uh0bu2q3rL2MM3x0qw2mF5zRTXVn+KYqx7HQc7LFbfWiaXqiW7/aC/vnHOuq5xYrXHOR7nSyrFUnu5TcUyEPZ0nvs7JTp01znl1OufvaZBrogit0Y4e5d2Lp7yvTAOPdewXCn4OxFgPBJh20f+Wtu88KF3P1daantdOli6EntTckZ4H5Xs7wYl7XezdQ+d5jXVuU2PPvqcHtjr3NdbDu5wuoLfB75v3fe6J4x6d4UyzU7iimt7XoWA4rwZtlMGuGr0KeE59gaSxki4LdmB3SKqtnvwfYLcwmjxN0kUU2eJvlnRcGP38Q9K8sP+M0N45kn4k5WCEKgAAIABJREFU6Qbg62H7h5KuV2GJdqSksyTdI+mccKzbgD0ktaswtu4IZbXI8cOAayQdENqZJ+lCSZPCMS+X9G1JcynCHuqv70vhHCqSPiHpprD/F+rqfEaFTdrVwHMBQvjDXOB5odps4Awcu7W60bVno7a9Ciu3ByR9vfGPKpPJZDY/I2HVaMMdoZ62ObsjeqsTeIOZHURhW3a6iiHCp4B/BdPsT5jZ63jaRPt84HvAuWa2H/BLoH7qckeKTAv/HrYnAYdSBJtfROHfuQ+wr6QDwqjxVuBgivi/GyjCHw6TtAPF6thHgZ8B/xmOeQeFrVmNVjObY2an113zacA0imD4oyicZg4BDgBmSzpC0mzgLaHsVeEcalwTzmEMxczA5ZQ7wjic4lPAVeEefSuUHQAcR9GpHydpp2gfJJ0kaa6kuQ+snR+/nclkMpsNG8RruNJIR1izOZsLPEJhc1aPgK8Evev/KOLupjfQ7qE8PQX4c+Dwuvd+G02f/smKOI87gCfN7I4w4rqLp63JambXh1HE7V1Xt31tGClONLMrQv1zgSPqjhHHGf43MMHM3heO/bLwupXCw3RPio7xhcCFZrYuBNpfVNdG7ZwOAW4ys39RGIdPA8aG7YG4zMxWmVkncDewc1zBzM4Mnfic3cfu0kCTmUwmMzSMhID6QWmEG+AEilHTbDPrkfQwqe3ZYIlt0mrCTh9le7Q+nr6Gayj0yzaKKcglwN7h/40EssfHvIli1Dc5+JQK+KqZ/bi+kqSP9tPm9RQjxBfwtMvMQooR5HUb2ikisYPrr7KnnUxsKaeZeaCS6laOXOfGDHr+o7Fu2Os05uVCc/W/6MtyfzX1LfXiG6cp1ediL02AaZFfp+ed2uxoZa1OW33R/XmyL9Vgmp3rbnOmiLz8fWOi++PpgaMdPdP7deuVxbGF3jl4nqHtk9IYvlgTnHXLt5I6C454f1J271PpmrudND4p66R8zCdtYO0PfB/O+DMfU0m/M0t60/PytMTmSvoMj4p8Pkc5d3+s8+x757/K0fXGVtJnvRFaNlNmuoYWfAxzhsJZZgKwOHSCL+bpEctAlmDXUnQIUHSmV23ieVxHMS06zcwWh1HcEgrHl2vMbBWwQiHdEYV92RV+U0DhNPM/wF8kjQMuAd6lwjwbSTtI2ga4Ejgm6JPjKDxGAQiLZB6lmFptxG4t26hlMpmtimfLiHAgfgn8SdIdFNOn9wKY2TJJ10i6E/ibmX0i2u9k4GxJn6DosN65KScRzLGXUEyX1riOYjR2e9h+B/CjEALy0EDHNLPfhs7tIgr971fAdWGV5FrgrWZ2i6TzwzEWU4wk67kGeH3QKGvn9BX8Uep6GzXgHGDFQNedyWQyW5LhHBbRKAN2hGaW5sqpKzezpRR6n1fn37x9wr8XUOTvi/c5cUPbZvYwMKufuvtE26cAp9Rt30Yw047qHdnPMc8Czgqb3wmveP9TgVPj8vDeB4EP1m1fTuSMVXcve0jvyTl19ZJchJlMJrMleVZ0hJmtiyWW5hzrjuKOvBxq8nQrJ/bPy60Wezd6MYMdzn5PODrPfpR/d01pakxu9q67zdFhVkcelZ6O6OmGnn9qzHhHj/LiwJ4g1dg84vs/1fGnXKz0Xnf2pcf0Pt+4yLtfXg5BzzM0jhH09MCdr/xhUrZgvxOTslUtqUYY67GTnXvRqTROcV1feq8nNpc9N7uc57XZ+by9Z8CLQ61Gz0+no6ItdZ6B+BrB19bj59PLPdjrxR82Fr49aByZeqsjd4SZTCaT2WhGQmLerS4NUyaTyWSGD0MZRyjpFZLuk/SgpE857/+7pLvrjFiScLKNIXeEm4CkHSX9Mbi+/EvSdyRnzqa8z2dU+K3OCw4yz+uvfiaTyQxnhmrVqKQKRejbKylC346XtHdU7VZgTjBF+R0wJG5beWp0IwnuOb8Hfmhmrw8f4pkUi2biFbK1fQ4FXkOR5b5L0lSg345zsExx+uHpkebiaYReDJPnrdjr6E8r+sr63/TmdH3VJCe01DvmubaotD2VMUkd9/wdXW9UU3r+sY9ol6PfeG2Nd+7ror5y6Gkcowiwzpk48nxFvWuKNaNVTltensF1jtfoGCe+dFlfWVed6Gicn+xN29qmJa0Xe4Z68YGeHnjtvHOSsq/O/u+kbKWjMcdMdGJJ11VSLW55b/lzm9maap7TnGd4gvMMrHLyIsb1HqumuQ13ak5zA65wdO7tm9NoqliX7HGWq3g6vVdvKBjCVg8BHjSzhwAknUcR/nZ3rYKZ/bOu/vXAW4fiwHlEuPG8BOg0s7MBghPOxyhiDT8QRoqXh9FizcptO2CpWfHEm9lSM3tc0ksk/aHWsKSjgxdqJfic3hl8XHNm+kwmM6wYTPaJejvI8DqprqkdKOKuaywMZRvi3cDfhuIa8ohw49kHuLm+wMxWS3qE4r4eQhHqsQ64SdJfgEuBz0m6n8KO7vxg+fZP4AeSpplZLabyLAqf0R3MbBYUqa28EwkP00kAL5o8m73H7TrkF5vJZDIeg/EQNbMzKWbONglJbwXmAC/a1LYgd4Sbk7+b2TIASb8HDjezucGk+4UUBuXnS/qUmZ0j6efAWyWdTRGX+XYKl5ldJX0PqHWkCfUP13tnHmuxFdcTPWWbsm2btknauKEvnXoa5aSYWefUi9PCdFfSKUJvGnRGUzrtuVtLecqo29nPm4pZpnSKahtn1rk1mi71lr97y92nOW2Nduy1VkdTeF77e/Wl08TLm9KrqkRTbOMdkWW081foCaW2XHuRfiZVlcvGOe3vo3S//TrS6dI4dZJnk+aFRXjToJ+++UtJ2RfmfLa07T0Xt1VT/4mlPekU7bjm8jUt6l3D6GjqeE1fOk3Z5TzXXljKI5Et4JRKOg3q2dl5oTyjnJCW+3uWlba9UAkvPZQ2UwLd3qFr9jGgPqnAjqGshKSXAp8BXlSbXdtU8tToxnM3RWql9UgaD8ygWFEcP+kGxRSqmV1uZp8HPgS8Mbx/NsV89/EUpuO9ZraCIrfi5RQ+qj/dPJeSGQriTjCzdRB3gpnBMYSrRm8Cdpe0S1h0+BbKSQyQdCDwY+B1ZrZ4qK4hd4Qbz2XAaElvh/Urnk6ncIJZBxwtabKkduAYinyIz5W0e10bBwALAMzsceBx4LMUnSJhMU2TmV0Qyg96Ji4sk8lkGqUPa/jVHyGd3ocofJ3vAX5jZndJ+qKk14Vqp1Hktf1tWHV/0QaaGxR5anQjMTOT9AYKbe+/KX5U/BX4L4pR3Y3ABRTD+1/UTYt+L2h9vcCDBG0v8EsK0/B7wvYOFH6stR8sn97c15XJZDKDYSjXoprZXyn+jtaXfa7u3y8dwsOtJ3eEm0Aw0n5tXB7m4hea2TFR/Zt5OjGvx+HAT+rq384gR4GTSad5doyWh9+mdUkdL13Qmr7UAs1L5dITLev3lup7Uw8z+tLH719NA9uPeWltPA1vkqXnemhXWdBodZaZdzv6yrxRqRAyLjrmXEu1OY/xzl+O5c4N2rWnfMx1Tp0JjmuWc1uZ3ZlWHBNZkrVV0jrfbE5/xT/e7oXClEMX4rRJ4FuIeWERsR4I8Pm5Xy5te9qiR1tT+ix6U6Fxaq9qgyEJnhVbe9RWr3PdjYYytDvtj06uKb0ezx5xbNPGpW8aiOGccLdRckc4TJB0M0VOxP/Y0ueSyTyb8PJbZhonm25nXMzsHOqyRjS4z+yBa2UymczwotdJWLy1kTvCTCaTyWw0W383OAI6QklV4A6KpDJV4ENmdq2kmcCfa8Hog2zzYQo/u6UN1D0nHOd3g2j/WjPrTytE0uXAx81sbqPtAjxsqf63OtL6dqqk8XvdTizShEqqBT3UuSQpGxVpFsv6Uq1soqNP3OpolbG28Vyl57rGifO7ry+1sfKOefOoSBdz4rTWOrrVFMe+K44h8yy+FlfTa7y9OW1/uWPVtailfG4Tna/rGkfXe6xrZVL2x/Y0lq0lunY57U9yJr6WuLZi5WfgSSfFlpc6ycOLEYw1QS/W8M0HfSQpu9e5F91RyqglwLgoRtCL/5zcnH4fOpy42hXV8vMfpykD367NS/PkpfGK0zx1OZZ6Xvqmhd3Lk7KhIE+NDg86zOwAAEkvB77KELkNbC4G6gQzmcwzR9wJZgbHQGERWwMjLY5wPJDYS0hqk3R28Ou8VdKLQ3lF0jeCl+c8SSdH+7VL+puk94Ttt4d6twcnmBpHSLpW0kOS3hTqjg1pQm4Jx319Xbtrw/+PDH6kv5N0r6RfKrJ/yH6jmUxmODOUaZi2FCNhRNgu6TagjcLU+iVOnQ9ShP7tK2lP4FJJe1B4es4EDjCzXkn1cQZjgfOAn5nZzyTtQxHUfpiZLY3qbkcR+rAnhRPC74BO4A3Bf3QqcL2kiyxNbXAghW/p48A1wAuAq+veH9BvtN5rdM7k/XnO2Jn93K5MJpMZOrzwkK2NkdAR1k+NHgr8TFKsCx4OfA/AzO6VtADYA3gp8KPgaICZ1U+i/xH4upn9Mmy/hML6bKlT9w9m1gfcLWl6KBPwFUlHUEyj7wBMB56Izu1GM1sYzv82io65viN8iAH8Ruu9Rt8w47UW6wrjm8raxkpH4xnjLCH3vA/3ats2KYu/CN40w1JHK/NisOJjXk+qNTUaz7Wo+lRSlpyXo8F4vqhe3FesD3kaj8e/qquTstgfFlINMjFdxI93m+R4W7rHjO6j50VZcT5N7/4sjs7Vq9OpVMvydFXPMzTG0wN/c8t3krLjZn80KVsd+Yh6ca+e7jXG+XPZQaoR7txS/q26qDfVrz3WON/LdX1pWRzusVNT6uHqPa/dztqAoWDr7wZH2NSomV0HTAWmDUFz1wCviKcqN0D9N6tW/4RwHrNDR/0kOEn5yvtWiX6cZL/RTCYznBlMGqbhyojqCMO0ZwVYFr11FUXHRJgSnQHcB/wdeK9UWIlE052fo9Abzwjb/wCOlTTFqesxAVhsZj1Bk9x5I68p+41mMplhiw3iv+HKSJgarWmEUIzG3mFm1Wgg9wPgh5LuoPD4PDFkiP8pxRTpPEk9FPZm36/b7yPAWZK+bmaflHQqcEUI2bgVOLGf8/ol8KdwzLnAvRt5fdlvNJPJDFuG80ivUZSu3chszRy148uSD3RSFA+40sm1tp2TG3CpEw+4naMzxPrWakfXWOZohF7sYhxztaQn1VdGV1JdydPFlvWmuehiLbHbicHyPBmX96Z6Y2tT+Xekp1N65/VIVzxhAVNbxiVlT0Wfk6eNet6vB45Kddy5nY8nZT3O/Y9pr6TasXd/1lTLWu5oR3N29S7ns/RyCMaeoYud+MA54/7/9s47Xo6q7OPf372pkBBC7wSlSO9I05cuKCgKiAhSLCAixdfyiqggFpoIgoJYqCKGqqiA1NAREwglSJXeS4CEkP68fzxnc+fOnN2d3dv3nm8++8md2TlzzrY5c57ye1Yp7Bs/6fTCvm9u0vle8vVISbsRkff1rcjvJuaLW0ydf2/vRHx/b0T818MjfUa1THM+zRXbit+xWB7k6EjO7AnP/KnL1QS/Nu6zpSeRs565tGeKInaRVlgRJhKJRKKPiAV6DTTSRJhIJBKJpmkF02irBcscI2lKSHqfLOnDDbbfRNIZdY7ZRtLfw98HSjJJO2Se3z3s27OBfsdJerjsGBKJRKK/kIJl+hEhh3BXYKMQCLMERIrU1SDoejak7YnrnH4OuDFs7wM80OA5ujqGBcQ0BmflfEtzI76hmRH9zti5YjmI+fqAsdqGbZEslLyvA2CRnP9p6LCY3yRy/ohPJOanyjOirfgTGB4Z18j2Yr5Yfvwxn2d75HWPimhWxl7TIjnfbj6vEOJ+w/cj4xgR8fUtlDtfzGc4OuIPHB55z/IXufmR2INFhxR9WTHf6+ghRcmzfA3BvF4oFPMDoegPBDh14gmdtj+/cVGsaVpEb/a9yL5Yru20XC3GIZHPraw/cGxb8buSz8l9PdIuNum0qWek5NKKsH+xLPCGmXu+zewNM3tJ0vZBVu0hSedKnsEradMgi/aApHsljc6t9jaTdHdoe5ekNar0ezuwmaShkkYBqwKVKFYkbSzpVkmTJP1T0rKZ/Q9IegBXvqkc38wYEolEok9ohRVhK02E1wMrSnpc0lmS/kfSCLwu4N5mti6+Aj5U0jBgPHCkma2PK8zkQyQfBT5iZhviOYU/q9Kv4avBjwGfwiXWAJA0FFe02TPUGzwX+Gl4+jzg8NB/NUqNQdLBkiZKmvjC9OdrnC6RSCS6l1ZIqG8Z06iZTZe0MfARYFt8ojsBeNrMHg+HXYCvvm4CXjazf4e270JBYmoMcIGk1fDJrmgv6+DPwBGhzTeB74X9awDrADeEc7cDLwe90EXN7LZw3EXALpHzlhpDVmJtpxV37r+3XYlEouXIl4UaiLTMRAhgZvNwKbIJIZH9sNotavJj4BYz+7S8tuGEGv3eK2ldYIaZPZ6ZUAVMMbMtssfHhLO7OoYKS7QV/QBv5/KkYr65GDH/R94fCOVEd2P+rVidtjzvzitqjcb8eqMjvpSxkfI6+VDv9si4Yr7ReRTPlfe1Llyy3t5ikVzMmNko5jcsQyy3La83C3H/ZZ5Z84u+uBGK+AhzVcoXbi9+tnlfNcC4YUWBpncin3leX3Nm5LONaYbGcgTzPsE/TTqtcMyhm3ynsG9qpMbisLZIHqfGdNp+qmBsglGR9+fl+UV/6VvzYrU9O3+W+VqQAO9E/JlTI/u6g1SGqR8haY2wcqqwAfAUME7SqmHfF4BbcXm1ZSVtGtqOrsisZRhDh87xgSWG8F06VoIVHgOWDIE8BD/i2mb2NvC2pK3DcftWOWejY0gkEoleJfkI+xejcDPiI5IeBNbCJ6eDgMvCCnE+Xm1iNrA3cGYIVrmBoiD2ycAJku6nxMrZzK41s1ty+2YDewInhX4mA5WivAcBvw7ycNVuzRsaQyKRSPQ2yUfYjzCzSXRMMlluwmv+5Y//N7B5bveE8KhUslg989z3w/7sMefjwTj5cx+Y+Xsy8NEq480Gynwncv7oGBKJRKK/0Aqm0ZaZCBPO+xHtzLyv732Kx8Rynd6L5BaW+c7HfE/DIn6lmC9oaM4vtvSQUfU7BKZHdCxjeX0zc36SWL29mA9y6tyiVuqoXJ7fQpFcvRgvzynqZC4eeZ15ndXhEZ9qTCt1zLAlCvveimhbzp3f+f2J+UZjVchmlPA1vV5C5xVgycjrnhbJB8zXXYzljcZWHDHN0HyOYMwfePbEkwv7Yse9F3n/X1Tn7+ILkXqE8fEXf1zLthffn7wPOObnHh7RFc3/trqLJLGWSCQSiUFNKxRuSBNhIpFIJJommUZ7EUnHAJ/Hq7jPBw7BawKejEurGfAInjLxPu4bBFgmtHk9bB8M7GdmR3TDmDYHfgkMD4/xZnZcV89bo78JwLeCDFuUWTGptJz5b7iGFMwksRIzwyJmpRkRU1DB/BT5XcTOFTPp5KW5ps57v1CqJ2byjJnwYjJf+RJCMyOvZ2YkZWCJiAkvL0k2fd4sxuTC+vMmPYBFI6WZYhF1edMrwNzc+WJycLEyW7HSSbNVvwzTwhGZuvciZuj892l4e3vhfYx9B8ZEUk5mxdJecqkXi0Vk6haOXM5i3+u8VNrbzCqsasqaS7+8ybcL+97NnX+p9oV5eObLnfYtM7RzigXE01LejUgaLp5Ln5gd+Y7Fou+mRt6L7qA7g2Ak7YxfU9uB35vZibnnhwMXAhvjBdj3NrNnutrvgJgIa+iI/gwYDawRivEeBFwJfNjMNghtjwOmm9nPM6e8t5uGdgHwWTN7QFI7nkDf7+kpX0FPEKtX11/JT4LdTX4S7M/Ebib6Kz1t2stPgq1Gd6VFhGvor4EdgReAf0u62sweyRz2JWCqma0q6XPASXgGQJcYKFfEgo4o8DaegvCNkEiPmZ0HzAK2q3ainJbncZIukHS7pGclfUbSyUGX9LogkVZVLxRYCng59D2v8oFV0wiVdI+ktTNjmRCqTSwcdFDvDW0+FZ4fKenPkv4j6SqIZHUnEolEHzIfK/2ow2bAk2b235B69mdctjLLp/AFCMDlwPaKmYMaZKBMhAUdUVzc+rmKPFqGicDahTNU54P4xPlJ4I+4ksu6uHn1E3X0Qk8DHpN0laRDgrYpVNcIHQ98FiBMpssGM+cxwM1mthkuD3eKpIWBQ3G1mjWBY3FzQIGs1uhz059r4KUnEolE15hnVvqRvVaFx8GZUy0PZMWSXwj7iB1jZnOBd4DFu/oaBoRptIqOaDUR7Ea51szmhIT7duC6sP8hYBxV9ELDuI6XdDGwE+6/3AfYhuoaoZfik/qx+IR4edi/E/BJSd8K2yOAlfD8wzNCXw8GoYACWa3RXVbcxfKmnrk5v2HMlBELM4/54mJtY8fliRn1hsb8hjmzbVkdw5h5K+oXy/kEY36ZWMmo2E3n0FyI+oz5swt+sNjrjp0/llqgXOuYNF4sfSIWzh4L18+byGOpJDE/bsy0nk+PGdJefD2xMbwT8YHF5Pjy78/784spHO9T3Bf/3HLjV9F/GUuLiPkDfz/xlMK+z2zUOfxgpYiMXOx7Efvc5kTG8VquDFPsuxMjVn6tO2jENJq9VvUnBsRECFEd0UOAlSSNNrNsos7GwN8bOHXF3Dpf0pzMLDIff3+ieqGZcT0FnC3pd8Drkhanikaomb0o6U1J6+F27a+G0wjYw8wey567G1b8iV4kFgyS6P+kz61rdGPU6IvAipntFeiQmMwf80KQxRyDB810iQFhGq2iI/oYbiv+RXCyIml/YCHg5m7sPqoXGv7+RMY+vRoenfo2tTVCx+MqMmPMrLLC+ydweOVckipKOLfhK00krQOs142vK5FIJLqMmZV+1OHfwGqSVpGXyvscmbJ2gauBA8Lfe+IupS7PxANlRTgK1wVdFJgLPImnQUwDfg48Lmk+7pv7dHe8MRXMbLakPYEzJI3B37PTgSm4iPdpkmaEce0boldPxk2j3wf+kTvl5Xh48I8z+34czvmgpDbgaTxK9mzgPEn/Af4DTOqu15VIJBLdQXetCM1srqSv4wuDduBcM5si6XhgopldDfwBuEjSk8Bb+GTZZdQKqgCJDvZb+TOFDzRfhinmD4z54mKm2UUjeV/5XKcRkfur1+YXJcryuWFQLM0U+5HF5KNi+Y2xHL5YWaE8MYm4WRH/St6/GPM3xojl4cXSRKbN6/y5xcoyxd7DmCzdKxHJszyx1z0ncv5For7Xzu/P8IhMXew7tmSkbNhzc98p7BuZy2d8J1KeaOWhxepmsVJi0yJ+yXzppLxMGhTzAyH+vbvyvjM6bR8c8S2OiHyHn5hXfN3LthVLdk3PjWOZaOm14vjHRn67Zz1zaZf9Lx9dfvvSk8htL97UL/09A2VFmEgkEj1CfhJMNEYrLKXSRJhIJBKJpkkSa4lEIpEY1KSJcADQiEapmb0Q2szD8wiH4EEqB5hZ0ckV728DYDkzuyZsHwicQkcU6YNmtn+3vLgIeR1FgDdzeUfrDC2W6XnRYmV6iv6hd4mUyMn5kWK+slhu2NiIlmbepxPNw4vsi/m3Yn3Oa6v/o43lzsXKW+V9rVMjZaVivrKlhhT9PrGxjh7S2aezUMS3Oyfid5sRKbO1RETfNP+eDYv4rWLfp4Uin++svAZq5FOaGdHBfXFesUTR4pGxzs1dbPO+ZICXI+WOYrJ3+ZJjT1H0N8ZKJ702J6/dEc8RzPsEfxvJNfzhJsXSojF/Zqxk2vO5Ml4vqzjWqE5wD0kAtkKcyYBIn2iWnEbpesAOuCpBVqN0NeAvwJWZVIj3zWwDM1sHmE1Hvl+9/obgqR0fzz01Ppxvg/wkGNokEonEgKQbJdb6jFa/CBc0SiUthGuUrpLVKJX0RVxq7abcOW4H1pO0G14hfhiewLmvmb0aRL0/CHwAeA7YChgpaWvghNig8m0kHQH8BleTATjKzO4MyfmX4LJCd+NitBsHrdVEIpHoc+ZHIosHGi29IqSLGqVhtbYLbia9A9g86If+GU+Kr7AWsIOZ7YNri1ZWgOPD83tLmhweB0Xa/BI4zcw2BfYAfh+OORa4w8zWBq6iY6LsRFa/75npzzby/iQSiUSXSCvCfk4XNEpHSpoc/r4dT+JcAxgfxLKH4UnvFa42s6KjoYPxZvb1ykZYEWbb7ACslcnbW0TSKFxr9DPhtfxD0tQqr3OBft9xK+9bzCNUZ99VzH8Ty6+L5YvF8qZez/lTVhte9EHOitw1Lqeiz+L1XM7jwhHL8YjI+GOvKcbMnC8rltSUPwZgdETns9CuregDi+Xhra/RhX0vq+iLG53z2Y2y4mvM+7sArp1bLPuz8dAlC/uG5trG3osnIn69WC5p3oc3KuJvfINibtuKQ4r+wDkxzc3cZxKrYxjjqbnFn8zwnP9sVHvxs43posZqCMbWQvkcwZg/8PiJPynsy2uUQtxvOzznH43VjIxp0PZU+bVW8BG29EQITWuUvl+pZ1hB0pnAL8zsaknbAMdlni5GmtQn26YNX212mrGS1mgikejv9OeVXlla2jTazRqlWf3QA2ocNw0PxGmE64HDM+OuTMJZrdFdgLENnjeRSCR6FGvgX3+lpSdCXKP0AkmPhBJGa+EruaOBmbhG6RPAXtTXKD0OuEzSJKBWsMotuJlzsqSylZOPADaR9KCkR+iIUv0R8FFJU3ATaSo2mEgk+hXzzUo/+itJa3QAIekZYJNaUaOfWmnXwgea96/EtDpj+ouxXKRYHbi8P6VsWZtYLti78zv7CGM1+GLEdEVjr7OMGSdWFy72mvL5k2XqMgKMivp0im3LvI+xu+w3IzqcY0vkkMXuit+LfN5l6t/FxhV776N1Ekvcn8fem9h3eEZE17XM+GMarrH82Nh3Jf86Y68nNoa8RinAJzc8LHL+zpR9r2P1CCe8cGOX/S9rLrVZ6UmzrbRfAAAgAElEQVTkP6/d2y/9PS3vI0wkEolEz9GfTZ5lSRPhAMLMxvX1GBKJRCJLfzZ5lqXVfYT9Dkm7SzJJH+rrsSQSiURXaYVgmbQi7H32wZPz98ET5ruVj88v1mT795DOeYSvzi9qYk6fV9QQ3WTY0oV9E2YVE/Yfnfp8p+2Tltm2cMzIyG/gtcgP49lcTtdZP12r2HBo0W/40+88WtgXy7t7PJdTmc+lg3hu2JjIT2WXmZ2PvCrihnt+fjGzZj2K9QIXiozjNXX26aw3u+hXei3yC746op35ARX1TfPvzkKR9+vvc4vxWctH8ulm5vxPsXqB7ZE8tuWGFAOsY77dkTmfWr4/KO8PHNvWWeP2rchYl20vfkb5upsAcyL5evkagjG90Fh+YMwfePX9vy7s2379r3Tajr2vMV/+V7VCYV93kFaEiYYISfJbA18iVFaW1BZUbx6VdIOkayTtGZ7bWNKtkiZJ+mdI5k8kEol+w3ybV/rRX0kTYe/yKeA6M3sceDOo3nwGGIendnwB2AJA0lDgTGBPM9sYOBf4aeykWYm12997oudfRSKRSASSxFqiUSq6ouB6pfvgn8FlZjYfeEXSLeH5NYB1gBuCwkw7UNTOorPE2jkr7Nd/v22JRKLlaIUUvJRH2EtIWgx4AXgdr4HYHv6/CphsZueF464E/oQr4PzWzLZopJ+dVty58IGulPN3TJnzZqHdom3F2oAx30Ms322RXF7cvMh3amQkB+vZue8U9uXvGmdEfJex3LPFhxZ9OrFcsDwxB357xFASu5t9L+eTGhupoxdj6rxiacuYXmTezzNjfvG9GBFpt3TE7xbz2c3M+bdiVQRGRs4/N+JFnZPTqh3VXtSpjfmSlo2M9ek5RX3QhXI5p7HvWCw/M+YjnJ57H2Pf/SGR736sDuNrkc8yn2+Yrx8IRb1QgFERbd/3I37Pmx74Xaftf6xT1DJ9dUhx/Ne0Fcfx1+f+3uW8vhUWW6f0JPLCWw/3yzzCZBrtPfYELjKzlc1snJmtiAt3vwXsEXyFSwPbhOMfA5YMNRWRNFTS2rETJxKJRF9hZqUf/ZVkGu099gFOyu27AlgTXyk+ghcNvg94x8xmh6CZMySNwT+r04EpvTfkRCKRqE0rRI2mibCXMLNCToGZnQEeTRpKRi0O3IvXP8TMJuOlmBKJRKJf0gqFedNE2D/4u6RF8TqHPzazV5o9Ucy38dL8zn6MmO5krL7b9EiO1BJtxbajcz6RaZG8qRfm5usgx/0fY3Jjm6PiuWJ+n5jZJZZLNSu3L1bqaljEFxQba95X2VZOYpWpc6YX9g0fVsz/fGdu588t5s+cM7/os11/2FKFfc/OLvqFZ87r/F60Rd6LhSJ+t3w9P29c3JVnWFvxfc3r4EJcExM6+9RmRb6bK7YtUti3aESr9vXc+MdEjmmP+KFnR8Ya+y4uk/uNvKxphWNiPuGovzrye877BD/xcLG24S1rf6+wb5vhxe9Yd9Cfo0HLkibCfoCZbdPXY0gkEolm6M++v7KkiTCRSCQSTZN8hC2CpGPwArjzcIWtQ8zsXz3c54F4SaWvS/oqMMPMLgz7rzezl5o5b5lyPrH0gJhkVcxUM82K5sYRefkrImOImMXaI+eKpUbkiZVcKsvQ3Dhir3FoxNw1O2Kiyo/19bnTWWxIUcosT/S9iJglR7Z3Np/lzboQN+PG0htiJrz2nIRbzAwXG2vMdDmkhG001i6W4hJ7TXNyx8XGGjOzxr5P+c/8bZuNcsfFZN7Kxv2/nZNii5WMmh0x7cY+o5h5P58aETODbjvlZ4V9129yTHGw3UBvrQhDCtp4XIDkGeCzZjY1d8wGwNnAIvj1/KdmNr7euQd9+kRIT9gV2MjM1gN2wKM3ew0z+42ZXRg2DwSW683+E91DmUkw0f/IT4KJxuhFZZnvAjeZ2WrATWE7zwxgfzNbG9gZOD3EX9Rk0E+EwLLAG2Y2C8DM3jCzl2I6n5LGSHpM0hoAki6R9JXw94IICEl7Sjo//L2bpH9Jul/SjSFXsBOSjpP0rZAusQlwcahw/wlJf8kct6Okq3ryzUgkEolGmDd/fulHF/kUcEH4+wJg9/wBZva4mT0R/n4JeA1Yst6J00QI1wMrSno8iF//TzWdTzN7B/g6cL6kzwFjzex31U8NeKWJzc1sQ1xW7TvVDjSzy4GJwL5mtgFwDfAhSZUP8qAwlk5ktUZfnP5CI689kUgkukQjZZiy16rwOLiBrpY2s4rM5CtAsTxOBkmb4ZH4T9U78aD3EYb8vY2BjwDb4jbon1BF59PMbpC0F/BrYP0SXawAjA+VI4bhajJlx2aSLgL2k3QeLsi9f+S4BVqjB4zbo2B/eC/iiys3gOJ9Ukwq7ZVcesaKbUUT4RxFfEFDij6Robl7sw2HLF44ZkxkXI9QLHc0PRKiPi1SgirP6KjcXNF8li/V82bk3DH/0FJDi2H+sbI/eZPdIkOLFp7YPXbM1JdPS4nti/ndYuW5louMf2pOwi3mZ50T8RHG/HqxlJa8/NgLs98qHDO7vfi9Gx0ZR5uK78XU3G9kaOS9mBqRuIv5Pce2dZbamxF572PnfyMi1xYrnXRVTiotlhYR8weeMDGq2d9lGgmWyV6rYki6EVgm8lSnFxSujVU7Dtfbi4ADgo5zTQb9RAhgZvOACcAESQ8BhwFTYjqfktpwNZgZwFhcFQboZADPXknPBH5hZldL2gY4rsHhnQf8DZiJi3MXveyJRKJp8pNgojG6M1jGzHao9pykVyUta2Yvh4nutSrHLQL8AzjGzO4p0++gN41KWkPSapldGwD/obrO5zfC858HzgtmVIBXJa0ZJspPZ843Bngx/H1AiSFNAxYoEQc790vA9/FJMZFIJPoNvVih/mo6rqEHAH/NHyBpGF7I4MLgairFoJ8IgVHABZIekfQgXhfwh7hI9kmSHgAmA1uGIJkvA980s9uB2/AJCjyC6e/AXXQul3QccJmkScAbJcZzPvCbECxTsalcDDxvZv9p/mUmEolE9zN//vzSjy5yIrCjpCfw6P4TASRtIun34ZjP4rKUB4Zr6OSQUlGTQW8aNbNJwJaRp94grvO5Zqbt/2b+vhwo3IGY2V+J3LmY2fn4pIeZHZfZfwUuxp1la6BeUE4ikUj0Or2VTm9mbwLbR/ZPxBcomNkfgT82c/L06McPYBK+8hzeRNuDm+yzV9ulsfa/PtNY+1e7vupzsDySabSfY2Ybm9lHLeQ5Nkgjocl92a4v+hxIY+2LPtNY+1e7vupzUJAmwkQikUgMatJEmEgkEolBTZoIW5uqiav9rF1f9DmQxtoXfaax9q92fdXnoEDBmZpIJBKJxKAkrQgTiUQiMahJE2EikUgkBjVpIkwkaiBpeGTfYn0xlkQi0TOkiTDRCUkL1T+q0/GxiaKwr7sINSFPy5RxOVXSmJ7qD7gyoydbUbW/oQf76zYktQUB4rLHH1lmXyLRaqSJsMWQNFzS5yV9T9IPK48S7baU9AjwaNheX9JZJbq8u+S+fH+rS7pJ0sNhez1J36/XDq/H+C6uKfjZ8HcpMXJJN5XZl+MvwKWS2iWNA/4JHF2yv70kjQ5/f1/SlZI2Ktl280rbsL2IpA+XaPencOzCwMPAI5K+XaZP4qLwB5Zsi6Tlw/foo5VHneOPDGOVpD9Iuk/STiX7aqqtpNUkXR60hf9bedRps1GtR4k+F6v1qNFu6fDarg3ba0n6Ur3+wrFXygt7p2t8Gfpa2iY9uvcBXIfXVPwO8M3Ko0S7fwErAvdn9j1c4/hlgI3xShwbAhuFxzbAoyX6uxXYrGx/mWMml9mXe34EsBjwAF46a7HwGFdyrIfhpbAeArZs4LN4MPy/NV7m6xPAv0q2vZ8Q1R2224D7yr4/wL7AqcDQyjhqtNknvL6puMJ/5XELcFPJ8Z4EPIMXk/5beFxdp80D4f+PAVcCa5d5jV1pixfK3h54EFgZF8U/vk6bW8LjbmAOXjx7Uvj77hJ9PgPMw/WL3wx/Px0e/63R7lr8Zq/yWocAD5V8f3bAxfqfwsWp1yj7vR2Mj0Evut2CrGBmOzfT0MyeV+eiqMWqox18DF8trAD8IrN/GvC9Et0tZGb35vorU2vxfUlbm9kdAJK2At6v0+YQ4ChgOeC+zP53gV/FGkj63+wmsBJehWRzSZub2S9i7XJU3r9PAL81s39I+kmJduCT4ILcJjObL0WqIhcZGky5uwO/MrM5qlHANFCpmLIEPnlWmIZPGGXYHb/YNiIFWPnwPw5cZGZTpEhV3u5tO9LMbpIkM3sWOC5UhqlqNTGzbcFXWcBGZvZQ2F6HcvVFbwCuMrNrQrtdgN3N7JA67ZYws0slHR3GMVdSrd9kdsw3AjcGt8E+4e/ncfH+P5qlIoxZ0kTYetwlad3Kj7UBnpe0JWDhQnokvtqLYmYX4OWr9jCvmNEob0j6IEG8XtKedC5fVY2vAhdm/IJTqVPn0cx+CfxS0uFmdmbJ8Y3ObV9ZZX8tXpR0DrAjXtJrOOXdEf+VdARwdtj+GlDThBf4Db4CeQC4TdLK+IRflTAhPAsUClE3wH/x1WcjE+EkSdcDqwBHB1Nw2Vo9zbadFcyFT0j6Ol4rdFTJPtfI/q7M7GFJa9ZqENjczL6SaXetpJNLtHtP0uJ0/EY2B94pOVZC2/2AL+AWhotx68QBuOUmEUgJ9S2CpIfwH8wQYDX8wjQLv3M2M1uvTvslgF/iJhUB1wNHmpc+qdVuOLAHbmZccGNlZsfXafcBXPFiS3wyexrYz8yeqdGmHTjJzL6lEARiZjUv8rn2w/CJtOK7mgCcU+buWNJCZjajbF+VNsDOuDnrCXmgzbpmdn2JtksBZwDbhV03AkeZWbQqd2jTBuxpZpdm9gloN7O6q+1woT0TLzU2DGgH3jOzqgE3ks7Ev3fLA+sDN5GZDM3siDrj3QA3D74dLtzLm1ndVWizbSVtit/gLQr8GC+cfbKVqGQu6RLgPTrK/OwLjDKzfeq0+ydwe67dR83sY3XabYR/Huvg/t4l8c+3zPtzFbAGcBFwvpm9nHluopltUu8cg4k0EbYI4c6/KuGuvyf6vQ6/S51ExpRqZqdWbdS5/cJAm5lNK3n8PWa2eZNj/T2+arkg7PoCMM/MvlyjzRbAH/AL3kqS1gcOMbOv1emrHZhiZh9qZqzN0pWLnKSJwOeAy4BNgP2B1c2sanCQpFqrcTOzC2u0vQIPfrrWzEqtBOsFp5jZfbWe7wqSRgCH0nEjdRtwtpnNrNNuMeDY0M5Cu+PN7K0SfQ7BJzQBj5U1aUr6eMUUm9k3vEHT9aAhTYQtRrirn1KZWMLKaU0z+1eddhfgK8C3w/ZY4FQz+2Kddg+b2TpNjHMecApwdMUXJuk+M6t5oZN0Nr76uAy/OwfAzK6s2qij7QNmtn69fbnn/wXsiQd+bBj2lXrNkv4KHG5mz9U7NtJ2BXw1sFXYdTv++bxQp92JeFDGeDq/P2UuuhPNbBNJD1YsCJLur7zuOm2PDCbomvtyz+8AHARsjn+e55nZY3X6uaXG02Zm28WekHS6mR0l6W9Easma2Sdr9Zs5z0hgpXrjrNJ2YTN7r/6RC47/TGT3O7iFoaplILQt/JbK/L4GK8lH2HqcjUdvVpge2RdjvcokCGBmUyXVvQDSvE9yCu4vu17S3uFCXSbYYQQeeZe94BkdPrxazJP0QTN7ChaYZ+sGHzQYRJRlLDBF0r10npTKXHTPA/4E7BW29wv7dqzTbu/w/2GZfQZ8oESfM4L5eHLwYb1MeZ/mAbhpPcuBkX0dg2oioKMSuNIEF4X/f95keyR9Er95GwasImkDfGVX8/MMvvff477I0lYF4Eu437Yy+W+DW15WkXS8mV2UbyBpGfxGcWT4/Va+uIsADeUIDybSRNh6NBtt2CZprJlNhQXmnDLttgYOlPQ0Dfgkgblm9h1JewO3S9qfyJ16FklLAr8GnsxO2g3wbeAWed6Y8PD5g+q0aSiIKMcPmhhjhSXNLJsfeb6ko+o1MrNVutDnF/CJ7+vAN/B0mj1qNZC0D/B5/OJ8deap0UCZVWjTAR3hcxlHZ9901BRrZpPC/7fWG1MNjsVTfiaEc02WVOb9Pg2Psr46tHtAdXIsA0Nwa86r4HmFwIXAh3HzamEipOvR3IOSNBG2Hs1GG54K3C3psrC9F/DTEu12aXyIQLhTNbPxkqbgq5+Vqh4sfRn4GZ4XtYqkg83s6mrHxzAPm18N97mA+1zq+Uy+iq9qlscjDK+n82qrVn9duei+KWk/4JKwvQ++Eo5SxYyWHUvdFXPGjzwT+FHJcTadepEL6NgtE9AxPvgra7W9CPggntJSWaEbPlHEjq8Ek0UpceMGMMfM3slZB0r5lpq0KqxYmQQDr4V9b0mK+gqt69Hcg5I0EbYeX8WjDb+P/0hvAg6u1SBE4D0JfIYOk+NnzOyRep2Z2bOStgZWM7PzwqqtTDj6ggAV8zD0jwCfqnH8UcDaZvZ6MGleTLjDroek7czs5shksaqkmpOEmb2BR/mVRtI04hfIymq5jOzZF3Ef4WnhXHdRW+Vlt9zff8tslzIdy3Myj8NXytlVVlWzqnUt9eIMM4v6/EoE/GwCrJW1ftRh1/B/5Samspraj5KTGW7m/jzQHm6ojsA/l3o0a1WYIOnvuP8UfHU+IQSYRS0ikvYzsz8C49Q5FxYAK5f/OuhIwTIJoHxQRKTdsfhFaQ0zW13ScsBlZrZVleOrTUpA9ZVL3tHfiONf0o/M7FhJMSk2qxUQJGl1fHW9tJmtI2k94JNmVioxvtn3tcq5fm5m3+qpPiU9iptE8xHAtVaid5jZ1pHJv+qkL09heN7MXgnb++MX+WeB40oG9lwGHJFZRZYi9t6U/S7J02GOASpSbv8EflIiarTZ1CThN6dbh11T8e9hVYuEpEPM7JzwuyxgZmVX+oOKNBG2GGFF9hWKvpN60Z8/xyWkrmzgLhtJk3GJtfsyUZULog4jxzc1KUl6DfhzZtfnsttWI1+tK0i6FfctntNo1Gg4ttsi9SQ9Z2ZVzcdd7VPSv8ysrp5pV5F0H7BDMPF9FP8cD8fzAtc0sz1LnOOWcPy9dM5brBe4Mhk4zMzuDNtbAmeZ2QYl+tzIGkzPkKfRXGhmDVkVMu03xH2we+G5tleYWVQNKdE8yTTaevwVD7W/kfLRjeAyZP8LzJU0k/JmvNlmZgoyXsFsUxUzOzb8Xy9IJU9eOHpS2YYxE1FuTLXMRc1KwfUEZeXHGjtpR27eLZJOwc2o2cml6sVf0gPAnZWH1RBEyNCeWfXtjcvPXQFcESaqMhxX8rg8XwLOVYcy0du4GboMp4aozMuB8Wb2cL0GZjZP0sqShpnZ7DKdBCvEPuFRSYVRmYhZSWfUGU+P3DAOdNJE2HosZGb/12gjM2tEOizLpXIZsUUlfQW/qPyu2sGSdsNFoJ8N2z+kwyx2pJk9XWV8F2S31ZjSS+W1rQFsSodvcTd8RVGLhqXgcmbfRfNm4Fo+SVWvRiBqTITqyI8T8IFcBGe9lVJe/CDrnzM6p6rk2RdXB9oRODbcCN2NT4x3WTx/tV3SEHO1m+3p7MMudU1qNhApRI+uX5kIzay0ZJmZbRsmws8C58hzdMeXMJP/F7gzfCbZNJpqN2CP4jezu5rZkwCSvlFymKVvEBMdJNNoiyEXdb7LcqoSJdpFw7nN7LYSbXfE/SYC/mlmVev1SXoQ116cIWlXPMR7H9y8upfVl51qSukltL0N+IR1iA2MBv5hZlVD2RWXgtvXaij1VDH7Vqjnk3yajgkt1jgari/pf2r02dUI1tIEf9jn8OCmVcysPXLMMbhY9ht4pPBGwaqwKnBBNf9yaNuwTzLXfmk8+ng5M9tF0lrAFmb2hwZf57p4hZe9zWxYnWMb8tdJ2h1/D7fCq8n8Gfh9tc8+0XXSRNhihAvEwrhpaw7lLxDZKMMReL7UJKui1NGF8S1QcpF0Lp7CcFLYLqMs0xWll8dw4YBZYXs4vjpdo3bLBSbf0lJwlfNbLj1D0mJlgkGqnK9TjmiVYz6DT+6lpbQkvYmX4boTj4L8V9nVdvCBbYjfKGyFpzS8iK8K7642AcsVkJYFrregthJMgqMa9cM1gry233nAMWa2vjzH9n4zW7dE2zVxU+4eeCrLeNxnF1V5kXS+mR0Y/j4gb9Uo0d/CeCT1Pviq/EK8ikUZrdolgf8D1sJ/zwB09++5VUhFG1sMMxttZm1mNtLMFgnbdcP1zWy3zGNHXOh3arXjJU2T9G61R42uJGmUPGVjezy9o8KIKm3yY30+t6usL/RC4F5Jx0k6Dr/4n19lkLups37rN4E7JF2tcknU4NXtF5j6glmtVHV7ScfnttvoEG2uxW7A45IukrSryokprAKcjuuwHo2H+0+U9EtJn63Tdhpe8WIa8F0z28TMPmVmJ9ZahZrZPWZ2FbCGpCMkHU4Tk6CkpSStVHmUaLKEuSj5/DCOuZT//pyL/yY+ZmbbmNnZ1SbBQFa678iSfSzAzN4zsz+Z2W54gvz9+ORWhovxFI1V8JzQZ4B/NzqGQYP1g6KI6dG9D1zaazNc5PejuNJ9o+cQ8EiJ436MJ+2PxmWcDqVGoVPch/gkXhfwusz+DSlRBBYPVNgytB8KfAv4cwOvayP8onQksGGN4x7E/a3gOWiP44WIv4ybf8v09RXgKryKw7hwzp1Ktj0P12EFGI4HQR1Xsu1Q4JP4xfBZ3KzWyGe/MK4u8yQuSl7r2H3wvNU7cbWTU/EV+/Il+vkBXuz4R+HxAPD9kmP8JPAE7nN7Gp/YppRoNwFYnFDEF9c5vbXR30fJMd4X+7s3Hrg1BzJFmYF/9+YYBtIjmUZbDLkCy5H4HeRk/Id+t9UxiaijnA64pWAD4Bkz269Ou2aErJcHlsIrb88P+5YFhlodgWo1kZNVIwAFiAtSd9WEmznPYXgppnG4L7NMAnYlh+xifKLYFrjGzE4v0za0Hxr6PQi/EVqixrHL4TcXW+LBROBBF/fg351SlUvkeXabhfMcBAwzs6pVUYKpen0LeXhyQevJVs5U/QBuLrzRzDaUtC1exutLddp1pbTRasAJFM2NUcEBdaT8CDepZtN/sB6M4FSo0iIvAXUG8BJwuZl9sKf6HMikqNHW40j8YnaPeZTbh/DggHpkJa3mApdYyLWqw3uS9sV/5IavEGoq7JvZiyFoYQN1Tkt4JxNNWK1tw0ov+EU9G4BSmfBFdUFqSRoFzMBNuGdlnqtpwlUXqturc5mhXwLnEFZbKpHHJq9+vjeu0zkBF3uuZ958AV9hn4abN0uF+Wf6XBjXv6z4CTcFng/jrsVL+HtZSUgfjvsXyzDHzN6U1CapzcxukVT3RsHM7guBRQ2XNsJX6cfi79O2+GRfy72UTfmpKRnXA/xEHhn7TXziXwQXS0hESCvCFkPSv81sU3k+1ofNbJakKWa2dom2w4DVw2apC4SkcfgFeyt8UrkTLyD7TJ129+BmygfxC9I6eEWKMcChlgsIyK1YC9S7uw4rrBXrrTgzx38RFyl+F3jNzHYO+zcEfm5m29doG40SzIy1qrqHmiwzlGl/CR7Eca2VDJiRR+JugU9kq+D+pLvDY2Kt80i6HxfnnkRHsM09Zja9RL9/wSfNit90Bzyd5QWoW9T3RmB3fIW2BK7DuamZbVnl+C5rsUqaZGYbS3rIQnBNZV+ddnuZ2WX19iX6jjQRthhyIeOD8PD17XDn/lAz+3iddtvgBWufwSemFYEDrET6RJPjvBL4gZlNCdtrAcfjIelXWk7pQx0FYLfCTVPjw/ZeuC/zqyX6XHABKznGpk24ufM0VN0+BMbsZWbj6x4cb78yrv16YzA3DrHGol3H4UE3RwIrmFnVFbBccu4ha+JCIulQ3CpluBXi/ezzViPKMqxCK8IP++I3UBdXM5FLmo+vyisJ+1lThFkd5aVwjrtwubPLgZvx1euJ9Uy5MVN6I+b1ZpCn8MTqLpYVDxhUpImwhQkmoDF4UEpNc5ekScDnLRQclYeyX1LibrepH5wiKQ+VfZIm5yfCzDH3AFtXzKfBF3a7lahaLy8+/Cszayh6LqwmtsZf5x3m0Y5l2nUl57GpSvNyUYODgcXM7IPBr/WbWivY0O5DdPgJtwIWxX2Ed5pZ1Rp+akK1J0Sy/gwPnHqWDvPxecD3GjBVlkYduXmr4oFHl1hIVm/gHJvikZiL4kFiY4CTzeyeKsfvgudLfpaOGzdwM+VaZrZZo6+jgbFmy2eNAD4NvNSTfsmBTPIRthDynK4pZvYhaDiJeqhlqm6b2eNhkqnH3zN/L/jBlWj3iLzafCWAYO+wbzie/1iNsfiFpBLgMirsK8OHgX0lPYv7MevWTpR0Fn7xrJRDOkTSDlZD+DjD6TRXhw68SO23aLzS/GF4wMq/wvFPSFqqVgNJb+Cf2d145OeJDUwSzaj2nBLarWId4gaL4EVzT8GtGTUJNycn4Sv2iuqOWZVUITP7C/AXdeTmnSqvhXhM2d9J5gZqergBeLvOSvgl3Df4STorvkyjh/11livBFEzmd/RknwOZNBG2EOa6ho9JWqkR011goqTf05Grti8lHPxd+MEdgKddVC56d+KpEHPwQIRqnAjcH3xpwtNDjivRH/ik1Cjb4ULQFYm1C4C65akqWPPV7ZutND/LzGZX+gyrr3pmnw9aA1JjWSr+Trlqz0aZie044B9Vmu0KrJ6dRMzs3WAqfZQSEyFwMl7DsGyR5AozgXdw3+/KlMhdlcsAXmpmj4YbtevwHMG5kj5vZjfG2oUbn4fxvMOGkul7gNXwm4ZEhDQRth5j8bpp99J5JVFTlR/P/zsMr7EGrnV4VvXDq1L3BxdWrteYiwjndS4BqgZamNc8vBZf3RnwfxbK+ZQg5ier5zt7EjfbVVIIVsTz18rQdHV7a15O61ZJ3wNGyrTeoGwAABkMSURBVKXvvkbn2oSxvt6BBebwZktOLQ1kze+zw74qXRZXUuFGrqyv5tVGJkFJ2+Gm0c1wQfpfmlnZSM69cVMo+A0ceNrF6rhfPToRwoLXtKIaEN3uDlSUoHuF8sn4g440EbYeP2imUYgM/IWkXwFrAy+WiTps5gcXLg7zJY1pciWyGfCRyumoc6HPcB8+kU3FV5OLAq9IehX4irkgMwDqELEeDfwn3FhU+q4n1F2h6er2YeI8FF/xgqdCnFPCf/ZdvMLCQ3hFkWvMrKoIeo7fEUpOAZjZg5L+BJSZCCuqPRX/6e74JBHjEUn7m1mnavKS9sNXhFXJRH9OlDQe+AudK2VUi/68EY9QvgNP09hfXgex0q6W72x2ZuL+GC7gMA//XpS5hj5NY6LbXcaaF9EflKRgmRZGnnz+Zi0/hqTfAGea2RR53tHduPluMeBbZnZJtbZdHNtfcTWZG+h8caiXBnEi7ou6OOzaB1fM+F6JPn+HJxX/M2zvhOtGnoevED6cObZPRayDmXooHZPJF3CVly83eJ6dgG+by+bVO7aSerOgeG2twKVI+43ouEG5zczur3Lc8nipp/fp8J1tAowEPm1mVXMJ1aSguaQDqZ1+UytC9R5cUehV4DFgYwtVUiQ9WvHJ12jf60VyJX3JMkLiwQrz/Z7sc0Bj/UDeJj26/sAVZCbgF5gNcdWMV/D8qp1rtJuS+fso4C/h72VwMeJ6/RZk0WL7Isccihdj/Tq+cjoAT9eo1+5BXPy6st1ORkaqTtuHYucL/0+u0W5p3K+1K7BUA5/J6riW6sNhez3KS4g9UGZf5rntcBm46bifd13cxzsJ+EzJPq/FRbMr8mN74vmIZV/v1sBB4e8l8WCYWsdvF74DhwPbl+2ntx+4Gf5RXGj7B5n9H8ejT/t8jJEx/wm4Bhc2XwfXGf15X4+rvz76fADp0U0fpF/0dsLz6qbipY4APlRrQss+hwc3HBh7LtJuBL5qfAD3Sy4WHuOAR2u0G4IHOrwRLtL3hb9PwSNX673OB/HUgMr2Yg1MhNfjZtuVw+M7+Iq0nSpakHjo+7P4yuxC3My1Z8n+bsVNqdn3+OGSbe/Dg1gq2x+oNsbKZ4WryQzHzZLTga83+B36AG5CnIGbcu8AxpVseyxuon48bC+Hp1701Pf9AmDRzPZY4Nwax/8Nj2iNPnpqnKHvJcP3+xo8//Bm4Oae7DP0u3f4bT0LbNXT/Q3kR/IRtg5DLKixSDreQm6TeaRbrXZvy+sCvojnj30pnGMIbqqqxiH4CnI5iqHhv6rRrquh8ydQjBr9bp02FT6PX7D/ErbvDPvaqS5DdgyuWPJaGOuS+GRxeYn+Gq5uL+koXJ3lu8DN8vqE4DcYtXIzzcwmhL//IulFM6v1OcRO8F9gBzVRcgpPm9kQn8Axs5fk9R57ivXM7O3KhplNlav+VKNqLmRZ1Hwtw4vxNJhd6bB+vN7V8dQZ62p4cNYVwJrAF4LJu7Sww2AiTYStw/zM3+/nnqvlCD4EF+VdBpdGq0Rgbk/18Hfwi/Wl+OroTLnyyx64Ms2farTrUui8mV0iaQId4tClo0bNdUoPr/J0tby5NutcaudNypcva7i6PS6Wfjp+8XoCz5e8Ba97Vys/c1F1lhEbkt22chJiR+L+0mnA74LP77tWov4dIaCkEvUZJtOepE3SWDObGvpbjBrXM+sen+75hFqGYftxfIKrNxEubmZ/kHRkGMetknq6JNLfcIvAjfI7sf/FzaN1pRYHIylYpkWQNI+OJPGRuHmLsD3CzMokxzfS333ADmb2VkgS/zM+yWyA593tWaXd42a2ehPPfQwYbWaX5/bvCbxjZlXr/IVovapYjdQSSafgvr1K0NDncFPsd2qdM7RtuLp9pu0wPIBkS1wHdAs8gXutKsc3FUSSO8cD5sVqP4avXL4PXGQlpMDkyf+rATviq/Yv4v6zM+q1bYYQ8fk9oKLXuRfwM8tFokbaNVRBIte2qWAi9UElCEmLmNm7uX2rm9njPdXnQCatCFsEM2vvSvtg8vsKboJb8L2ocQFttw6Vk72B35on118hF/yuRrOh8z/EfV95JuB3v7UK3m6BV0O4BFdcqWkrzmJm3w4rq63Crt+Yq5RURV4F4S7cR9asqXEkrqAzJjxewlMiqo3zoAbOXY3K+/Jx4ELzSOKy79WpuGj2u7jKzA9xlZoewcwulDQRD7gBDwgqI3TQaAWJLO/J1Wgqq97N8eT8evRFJYiRkk7D60LuXDHj4qvYRI60ImxBQqj00nSe0OrV+bsLT6KfREb9xHLKMZnjHwY2MLO5kh4FDrYg0K2IjmimXVOh86qhvSnpQastk9aOr1T2wVd3/8BXK1NqtMnmR+Yng5nAU7g8102Rtl+nQ7cTfFK8C/dJLhDwrtLvb3Hz1TR80r4Hr+YwtVqbXPtm/ViVVeXywCq4cko7MMHq6M2Gtudmb5rkJaz+anU0Tpslnx4Q9p1oZjX9xWqygkQ4rqFahpJG4CvrVfGbmD9YjRJj3YlcdOI8/Du6fvD5328NiM4PJtKKsMWQdDh+x/sqHX5DwyeAWixkZo0oT1yC+zrewCe120P/q1LjLjlMdB+WK31U/BXXxCaUHIsoUqtQnnheK6gH8+Tn64Dr5BJZ+wATJP2oWkCJ1UhIDhPrOngQRGHCD+f8VTg2W/T2G/jFM6qHGVgJj/x8Ag9gegF4u8bxec6nOT8WeKDUBsB/zWxGWP2UXWm+KOksM/uapLH4zUbZRP5m2EPSTDO7GEDSrykhlwbMklf3eCLcsLyI69XWxRqvZXgBLhl4O7ALbo49skxf3cASZnappKMBwg1rWXm/QUdaEbYYkp7E6xBWrdhepd1PgLvM7JoG2myO5yldb2bvhX2r49UWahaQbRR5Iv3SeABApa9RuHLLG/Um8TABfgKfBMfhYfPnVluBlhzTIWZ2TpXnhOfyVao5rIVHCt5tdZKaQ9u16ZhA18GDZu42s5q1Dpv1Y2Xaj8V9fVn/WSkTp6ST8Ul+Y1y4O2pN6A7k5aWuBs4Fdsb9p3UnGTVYQSLXth3/Do2js7UlqhCTW3UOAe4t42/tDkJA2R7ADWa2UfitnmRmNYUiBitpImwx5GkFOzZqggmmwIVxjcjZUFvNv7cJF5Kf4AoflWCTlfCVzg9q3ZlLuhCfTK7B5bEe7uGx3oBPCJPpMG02Kg6NpBXwSXRLPNp2cTNbtE6bCTR5AZT0ZUINwjD2zfHJt2oxYHWOVBUu8XcvvgIvFa3aCCE6tMJoPBXmTtwnidWvztGVvq/BzeIPkYnSrnZjo1zNwfx2T9KoGXewkybCFkPSH3DTzT/orMHYY7qGvUlYCawaNp80s3yqSKzNfDpk3LJf+B6Z7CWdg5ui38cnwrvxCeWNEm2PoGMlOIcO/+JduDJOVf9iaN/0BVDSQ3hayj1mtoG8RuHPzKxqdffuiFZtBHlepRE+OygU2I1Gf0o63cyOUoeGbH6g9UTp6/qiI8dnI7mhI5q7x24yw4r3eTN7Jdw8HoLfGD0C/LAnbxQGMmkibDHUpK5hMMftiye6/1jSisCyZlZWYLpXkLQXXmh4mqTvAxsBP+luU2x3IBcK2Byf1DbHJ6WHzeyAGm1+ga9w7jKzejmH1c4xhPJ+rGy7ill1Mm5enyVpipnVzD0LJsMjzOy0ZsbbG0ja2MwmqYqGrJXIM5R0Ei4fWCavsk9Qk2lNg500ESYAkBfJnQ9sZ2ZrBl/R9Wa2aZ2mvUrlrlzS1rip9BT8TvfDdZr2OsEvuSkd5s3Ngdd6InIvZ6IsUMZEKa8ccRAuarAdnvs41Mw+XqLtvdaDFdcj/R0GXGxBXSZ8X/cxs5qlw+SpLO9XVtZhEh9uJRRXJH0a13Ftw1frpVZ2kralIzDsYetQAOp2FHJBw9+/Bl43s+PCdmlf8WAjTYQtQldNPxX/RS7IYsGPqr9QGZ+kE3BT4Z+yY+4PyPO3tsRFt+/DTaN34ubRRiJAG+mzYqJcKvR9c9jeFl9d7trg+f4HDyS5zkrU0QuveSgeoZqtJtIjK/XYRb3M90BeSWIHM5setkfhN3xb1moXjn0ar27/kJW4cKojVWgmHalCG1OiykazqMm0psFOSp9oHS4K/zerqTgn3B1XkoWXpLNsW3/hxeCD2xE4Kay6yiZE9xZP4yuHyeapGz2OhYR6SdcDa1XMqpKWxVMqSqGOHNSKxukyQM0c1EBlUjo+Oyw6Et67m3ZJqkxIYdzDSrQbUZkEAcxsuqSFSvb5PL6iK7t6+BVwtpmdn90pV8U5C59Uu5um0poGO2lFmABA0r64QszG+IVzT7xk0GW12vU24aK1M35X/kS40K/bH/02cvHzH2a223HFln17sM//mNmame02vNTWmjWaVY6N5qA2EiDSW8il71YmFBHGg0KeN7Nv1ml3J3B4ZaUqaWPgV2a2RYk+z8crdFxLiUA0SY+Z2RqNPtdV1ItpTa1CWhG2GGpSS9HMLpY0CRfbBti9mZD/niRMJPdZphBqWPk0FVTSC6wo6WgzOyGsXC/FyyX1JDfJNS0r2qh749UyynAksIY1mIMKIJcQOxavBgJegup4M+upVcj/4ZPfoWH7BuD3JdodBVwm6SXcx7cM/h6V4enwGEa51WfUUhFuTrokiVgLi+REWtIYrUlaEbYYku6gQ0txN4KWYnZlUqPtRnhxVcN1Mvvd3aO8sv3hVkcyrj8QInEvxvPOtsUVdE7vhX4/TceEdJuZXVWyXVM5qKHtFXi6RqXS+xeA9WulXnQVuTD5Gvj3tZHo2KGhHY20a2J8p+O5uUdlVmYL47/NmWZ2RE/0m2icNBG2GGpSS1HSD3EF/yvwO+XdgcvM7Cc9PugGkHQbXvfuXjoHZdTNA+stwg1FhaG4+e5OgsxZT99gyPVGN8MniHutcxmp2PH/G/5cmyZzUKsEr/RYlKKkbfBJ9xn8+7oicIDVUcEJk+ChdNwoTADOqTUZNhuIFvr6GX4zmhWBuAD4XpkgpETvkEyjrUezWor74nfwMwHkkmaT8RSF/sQP+noAJTg1tz0VN1WfSs8GkCDps3hKyQR8gjhT0rctV74qR0VX9bnwKGv6y/K+pK3N7I4wjq0o1sXsTk4FdjKzx0J/q+Pm4Hri2WfjNyeVNIsvhH1frtHmj+H/RgPRNgB+gaverApsg1tphuG/yZTc3k9IK8IWQ01qKQaz2KczeVmLAldaDXmtRHXCzcheZja+l/t9ADdvvha2lwRu7Ok0GEkb4CudMfgE/Ba+QusRSS9FVF5i+yLtCilB9dKE1KQ0WkpuHzikFWGLYWaVytfTKV85ADy0eopcJxO8tty9ks4I5+0X/owQEXcmXsF9GB508F69pObexszmS/o2nlfXm7TlTKFvUjK9JHz2e+WS1P9sZh+r0SZbe3F9uZoOlisK2wNMlPR7OlZr+wITS7SbJ+mDZvYUgLx4cr0Ul9L1K3M0W7Mz0cukibBFUBeqsAf+CdyEm+7mArd009C6m1/hVeIvw+sY7o8nrvdHbpRXbs8nmfekSey6SNRo2YoiS2YT/s1sqqSl6rR5Evcnn+yxQa6LGtIUatZe7CKHAocBlRu02+kwd9bi28Atkv4btsdR/4ZxyYwftUANH2q7OkqHbQ8cnHkuXXv7Eck02iJIep0aVditipaiXJfyZ8AXcYe+cIf+ebhDv0ci6ppFoUBv1gzW35RlKgQlkjxWL5Wlyb5WBZY2szvlcmtbh6fexqXInipxjkm4efy5sL0ycFVZs6A61178FD6x9ouVujqLUQ/HUy92xyfy79a6OZH0Mu5HjK4MrXr1iWOAjwNv4L+pjczMwmd1gZlt1ZXXlOg+0kTYIqiJKuyh3Wl4sMQ3zGxa2LcIHhgww8yO6tGBN0iIGt0Bzxl7Bc8hPLCnfWD9HUl/B442s4dy+9fFK0jsVuIcH8OL6d6KX/Q/gkt0/bNOu6ZrLzaKvEJG1YtWNR9hV/x1zfoIQ9uU3D4ASBNhC6KOKuynAFWrsIdjnwBWt9wXIUysj5rZaj062AYJq5RXcf/gN/DgjLPM7Mk+HViEZkL1u9DXv62KQHo2laZG+zZcTehmXBwcvBxTzdJR6qbai2UJn39VzOzZ2H51QYy6v1ocEt1HslO3ECpWYT8DqJdMbflJMOycJ6nf3SWZ2bPymoTLdvdqowdoJlS/WWoV7B1Zr3EI7vmOmV0K/L2Bfv+LWyBWwwNz3pD0er0JtFliE52kJYA3Y9/jDF3x120f+slWkZhiZv3Vj55okDQRtgjqXIX9R1a+CvsjkvY3swtz59sPeLSbh9llJO2Gm22HAauEsP3j+1NCfYZNcybbm0N6Q08wUdJXzOx32Z3yqvOTqrTJ03Bwj5kdEvrJ1l48LKRt1Ky92AzB1Hginp7xY1xsfgmgLXyPr6vStCti1CMl/YvOVST2ktcn7JEqEoneJZlGWwQ1WYVdHaVi3qfjR74JPVgqpiuEgI7tgAnWUS6qrumvLwh+qb1yofqXN+tvqtPX0vjqfzadP8dh+Of4SolzNB3co16qvShpIvA93CT+W2AXM7tH0odwn3hVE2az/jp5nca/WryKxB5m1hNVJBK9SJoIEwBI2o4Os88jZnZTX46nGpLuMbPN1bluYt1E6t5E0lF4GsGiePBJZYIZB3zRzG6u0rQ7+t4WtwyAm+96rK/QX6X24mq4oHiP1l7M+vNUrLTRI7489VEViUTvkUyjCQDCBbNHL5pdQdI1eN7YFEmfx30+q+F5ZHf16eCKrACcjif9P4Gb8W4BrjCzl3qy4+C3asp3FVY4sXNeGNsf6O3ai9m8xLyEW0/d1fdJFYlE75FWhIkBgaS9gJ/iPqGReKoIuBDAj81sVrW2fYW8OsIm+Ippi/B428zW6tOBVUHSmZnNEXiQyH21Ugty7dfDV70LbrDN7MpuHuM83AUg/Hswo/IUXnR3aHf2F/pMVSRanDQRJgYMkkbhots74xNi5ctrNdQ9+gx5jb4tcL/ZFrip9CEL1eT7O3K92T+b2c4ljj0Xjx6dQueivl/swSH2CkpVJFqeZBpNDCRm46uB4bh6f7+8i5P0W9zfOg1X+bkL+IWZTe3TgTXOe8AqJY/dvL+udLuBVEWixUkTYWJAIGln/GJ0NS5VNaNOk75kJXyyfgIvg/UCLnXWr1HnenttuELMpSWb3y1pLTN7pEcG17ecg6vSvC8XIj+aDlWa3+JCBIkBTDKNJgYEkm4HvlpPMq6/EGTH1qZDe3MdfOVwt5kd25djq4ak/8lszgWeNbMXGmh7NS57N4uOtJ1+E83bLF1RpUkMDNKKMDEgMLOP9PUYGiGonDws6W08YfsdYFe8cny/mggljQC+ipv9HgL+EBRYGuEPuHLOQ3SO7GwFUhWJFid9iIlENyPpCDpWgnMIpYmAc/GJor9xAT7O24FdcJPokQ2e43Uzq1kKbADTFVWaxAAgmUYTiW5G0i/wpPK7zOzlvh5PPbLKPPKyXPc2qn4j6Sw8KvZvuGkU6P70ib4iVZFobdKKMJHoZsysahHXfsqCahhmNtfdmw0zEp8Ad8rsM1y+b8BjZvdE9j3eF2NJdD9pIkwkEutLejf8LVxk+l3q6NRWCCW73jSzb/XwOBOJHiFNhInEIMfMuiQTFkp2pWrriQFLmggTiUR3MFnS1cBldC7h1BKm0URrkybCRCLRHYzAC/Nul9nXMj7CRGuTokYTiUQiMaiJlhdJJBKJRpC0gqSrJL0WHldIWqGvx5VIlCFNhIlEojs4D5dYWy48/hb2JRL9nmQaTSQSXSamuZl0OBMDhbQiTCQS3cGbkvaT1B4e++HBM4lEvyetCBOJRJeRtDJwJl6A2HBt1SPM7Lk+HVgiUYKUPpFIJJpG0klm9n/AZmb2yb4eTyLRDMk0mkgkusLHQ+3Fo/t6IIlEs6QVYSKR6ArXAVOBUVl9UkrqlCYS/YHkI0wkEl1G0l/N7FN9PY5EohmSaTSRSHQHj+Z3SDqpLwaSSDRKmggTiUR3sGNk3y69PopEogmSjzCRSDSNpEOBrwEflPRg5qnRwJ19M6pEojGSjzCRSDSNpDHAWOAE4LuZp6aZ2Vt9M6pEojHSRJhIJLoNSUvhJZkASAn1iYFA8hEmEokuI2k3SU8ATwO3As8A1/bpoBKJkqSJMJFIdAc/ATYHHjezVYDtgXv6dkiJRDnSRJhIJLqDOWb2JtAmqc3MbgE26etBJRJlSFGjiUSiO3hb0ijgNuBiSa8B7/XxmBKJUqRgmUQi0WUkLQzMxKXV9gXGABeHVWIi0a9JE2EikUgkBjXJNJpIJJpG0jQ6RLYJf0MS3U4MINKKMJFIJBKDmrQiTCQSTSNpBPBVYFXgQeBcM5vbt6NKJBojrQgTiUTTSBoPzAFux0W2nzWzI/t2VIlEY6SJMJFINI2kh8xs3fD3EOBeM9uoj4eVSDRESqhPJBJdYU7lj2QSTQxU0oowkUg0jaR5dCTOCxgJzCBFjSYGEGkiTCQSicSgJplGE4lEIjGoSRNhIpFIJAY1aSJMJBKJxKAmTYSJRCKRGNT8P2li3KLx6+b+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69h1iLlNbdsZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "11cac4fa-5cae-4823-a5ec-3338c21ab034"
      },
      "source": [
        "import numpy as np\n",
        "np.std(df['ConvertedComp'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "284149.75871214515"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHRRpU8ycMqe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e43a1a40-ea1f-458b-ad13-1599ef619607"
      },
      "source": [
        "np.std(df['Hobbyist'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.39872360324634243"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKTh4_G_cqLd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['WorkWeekHrs'] = df['WorkWeekHrs'].fillna((df['WorkWeekHrs'].mean()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2JIyYXHdfBH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['CodeRevHrs'] = df['CodeRevHrs'].fillna((df['CodeRevHrs'].mean()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqTlzpS1d1Sd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Age'] = df['Age'].fillna((df['Age'].median()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXsUy9B4eGbF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "outputId": "95c2d8a8-4163-40ee-b6f7-de2821e0daff"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MainBranch                0\n",
              "Hobbyist                  0\n",
              "OpenSourcer               0\n",
              "OpenSource                0\n",
              "Employment                0\n",
              "Country                   0\n",
              "Student                   0\n",
              "EdLevel                   0\n",
              "UndergradMajor            0\n",
              "OrgSize                   0\n",
              "DevType                   0\n",
              "YearsCode                 0\n",
              "YearsCodePro              0\n",
              "CareerSat                 0\n",
              "JobSat                    0\n",
              "MgrIdiot                  0\n",
              "MgrMoney                  0\n",
              "MgrWant                   0\n",
              "JobSeek                   0\n",
              "LastHireDate              0\n",
              "ConvertedComp         33060\n",
              "WorkWeekHrs               0\n",
              "WorkPlan                  0\n",
              "WorkRemote                0\n",
              "WorkLoc                   0\n",
              "ImpSyn                    0\n",
              "CodeRev                   0\n",
              "CodeRevHrs                0\n",
              "UnitTests                 0\n",
              "PurchaseHow               0\n",
              "PurchaseWhat              0\n",
              "LanguageWorkedWith        0\n",
              "DatabaseWorkedWith        0\n",
              "PlatformWorkedWith        0\n",
              "WebFrameWorkedWith        0\n",
              "MiscTechWorkedWith        0\n",
              "OpSys                     0\n",
              "Containers                0\n",
              "BlockchainOrg             0\n",
              "BlockchainIs              0\n",
              "ITperson                  0\n",
              "OffOn                     0\n",
              "SocialMedia               0\n",
              "SOVisitFreq               0\n",
              "SOFindAnswer              0\n",
              "SOTimeSaved               0\n",
              "SOHowMuchTime             0\n",
              "SOAccount                 0\n",
              "SOPartFreq                0\n",
              "SOJobs                    0\n",
              "SONewContent              0\n",
              "Age                       0\n",
              "Gender                    0\n",
              "Trans                     0\n",
              "Sexuality                 0\n",
              "Dependents                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVFCyFD4eJhN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "107a9d81-a7af-4a04-d0bd-3d11cecf6de6"
      },
      "source": [
        "np.std(df['Age'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.674512549180495"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOn1iq1qgmFZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "outputId": "c03ddac5-15ba-4b74-ff7c-45029bde9f49"
      },
      "source": [
        "df.corrwith(df['ConvertedComp'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MainBranch           -0.001112\n",
              "Hobbyist             -0.001855\n",
              "OpenSourcer           0.012241\n",
              "OpenSource           -0.018901\n",
              "Employment           -0.064174\n",
              "Country              -0.153479\n",
              "Student              -0.064535\n",
              "EdLevel               0.002915\n",
              "UndergradMajor        0.042989\n",
              "OrgSize              -0.020685\n",
              "DevType              -0.010583\n",
              "YearsCode             0.105021\n",
              "YearsCodePro          0.102933\n",
              "CareerSat            -0.033745\n",
              "JobSat                0.027898\n",
              "MgrIdiot             -0.013628\n",
              "MgrMoney             -0.000137\n",
              "MgrWant              -0.016243\n",
              "JobSeek               0.046519\n",
              "LastHireDate          0.049782\n",
              "ConvertedComp         1.000000\n",
              "WorkWeekHrs           0.013849\n",
              "WorkPlan              0.018349\n",
              "WorkRemote            0.020159\n",
              "WorkLoc              -0.007189\n",
              "ImpSyn                0.033821\n",
              "CodeRev               0.016649\n",
              "CodeRevHrs           -0.019345\n",
              "UnitTests             0.038522\n",
              "PurchaseHow           0.014664\n",
              "PurchaseWhat          0.042321\n",
              "LanguageWorkedWith    0.034940\n",
              "DatabaseWorkedWith   -0.005600\n",
              "PlatformWorkedWith    0.048180\n",
              "WebFrameWorkedWith   -0.009086\n",
              "MiscTechWorkedWith    0.032905\n",
              "OpSys                 0.050922\n",
              "Containers            0.047793\n",
              "BlockchainOrg         0.019280\n",
              "BlockchainIs          0.068031\n",
              "ITperson              0.000629\n",
              "OffOn                -0.055646\n",
              "SocialMedia          -0.050274\n",
              "SOVisitFreq          -0.016966\n",
              "SOFindAnswer          0.005363\n",
              "SOTimeSaved           0.013400\n",
              "SOHowMuchTime         0.002074\n",
              "SOAccount            -0.000102\n",
              "SOPartFreq           -0.018892\n",
              "SOJobs                0.009269\n",
              "SONewContent          0.001541\n",
              "Age                   0.104982\n",
              "Gender               -0.005611\n",
              "Trans                -0.001672\n",
              "Sexuality            -0.011283\n",
              "Dependents           -0.007321\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZuQItz_hx6l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "outputId": "91e6d309-1145-4485-bc02-cf0bef2ec06a"
      },
      "source": [
        "df.head(15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MainBranch</th>\n",
              "      <th>Hobbyist</th>\n",
              "      <th>OpenSourcer</th>\n",
              "      <th>OpenSource</th>\n",
              "      <th>Employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Student</th>\n",
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              "      <th>UndergradMajor</th>\n",
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              "      <th>DevType</th>\n",
              "      <th>YearsCode</th>\n",
              "      <th>YearsCodePro</th>\n",
              "      <th>CareerSat</th>\n",
              "      <th>JobSat</th>\n",
              "      <th>MgrIdiot</th>\n",
              "      <th>MgrMoney</th>\n",
              "      <th>MgrWant</th>\n",
              "      <th>JobSeek</th>\n",
              "      <th>LastHireDate</th>\n",
              "      <th>ConvertedComp</th>\n",
              "      <th>WorkWeekHrs</th>\n",
              "      <th>WorkPlan</th>\n",
              "      <th>WorkRemote</th>\n",
              "      <th>WorkLoc</th>\n",
              "      <th>ImpSyn</th>\n",
              "      <th>CodeRev</th>\n",
              "      <th>CodeRevHrs</th>\n",
              "      <th>UnitTests</th>\n",
              "      <th>PurchaseHow</th>\n",
              "      <th>PurchaseWhat</th>\n",
              "      <th>LanguageWorkedWith</th>\n",
              "      <th>DatabaseWorkedWith</th>\n",
              "      <th>PlatformWorkedWith</th>\n",
              "      <th>WebFrameWorkedWith</th>\n",
              "      <th>MiscTechWorkedWith</th>\n",
              "      <th>OpSys</th>\n",
              "      <th>Containers</th>\n",
              "      <th>BlockchainOrg</th>\n",
              "      <th>BlockchainIs</th>\n",
              "      <th>ITperson</th>\n",
              "      <th>OffOn</th>\n",
              "      <th>SocialMedia</th>\n",
              "      <th>SOVisitFreq</th>\n",
              "      <th>SOFindAnswer</th>\n",
              "      <th>SOTimeSaved</th>\n",
              "      <th>SOHowMuchTime</th>\n",
              "      <th>SOAccount</th>\n",
              "      <th>SOPartFreq</th>\n",
              "      <th>SOJobs</th>\n",
              "      <th>SONewContent</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Trans</th>\n",
              "      <th>Sexuality</th>\n",
              "      <th>Dependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
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              "      <td>-1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.127197</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.127197</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8820.0</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>61000.0</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>366420.0</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.127197</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>95179.0</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>13293.0</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.127197</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.127197</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>-1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>21.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>57060.0</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.127197</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5.084308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    MainBranch  Hobbyist  OpenSourcer  ...  Trans  Sexuality  Dependents\n",
              "0            1         1            1  ...      1          1           1\n",
              "1            1         2            2  ...      1          1           1\n",
              "2            2         1            1  ...      1          1           2\n",
              "3            3         2            1  ...      1          1           1\n",
              "4            3         1            3  ...      1          1           1\n",
              "5            2         1            1  ...      1          1           1\n",
              "6            3         2            1  ...      1          1           2\n",
              "7            4         1            2  ...      1          1           3\n",
              "8            3         1            3  ...      1          2           1\n",
              "9            3         1            3  ...      2          3           2\n",
              "10           4         1            3  ...      3          4           1\n",
              "11           1         2            1  ...      1          1           1\n",
              "12           3         1            4  ...      1          1           2\n",
              "13           3         1            2  ...      1          1           1\n",
              "14           1         1            1  ...      1          3           2\n",
              "\n",
              "[15 rows x 56 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBeISWY_iWT2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "2de0d7f6-5a6a-48a3-a48d-fc6573eea784"
      },
      "source": [
        "ls = []\n",
        "for i in range(len(df[\"MainBranch\"])):\n",
        "  if df[\"MainBranch\"][i] == 1:\n",
        "    ls.append(i)\n",
        "len(ls)\n",
        "for i in range(len(df[\"ConvertedComp\"])):\n",
        "  if i in ls:\n",
        "    df[\"ConvertedComp\"][i] = 0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PprCuWZTj8fs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2d851ac6-178a-460e-e114-f4cb1a780203"
      },
      "source": [
        "df[\"ConvertedComp\"] = df[\"ConvertedComp\"].fillna(value = df[\"ConvertedComp\"].mean())\n",
        "df[\"ConvertedComp\"].isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wx5_C_zepPrY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "4f2fc5af-089d-4d8a-fe7f-3bfff4d93144"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(df.corr())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5965d47f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUPTKpbWr7Kd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "outputId": "131abba7-f02c-46a9-877b-7245928ccfc0"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "k = 20 \n",
        "corrmat = df.corr()\n",
        "cols = corrmat.nlargest(k, 'ConvertedComp')['ConvertedComp'].index \n",
        "  \n",
        "cm = np.corrcoef(df[cols].values.T) \n",
        "f, ax = plt.subplots(figsize =(12, 10)) \n",
        "  \n",
        "sns.heatmap(cm, ax = ax, cmap =\"YlGnBu\", \n",
        "            linewidths = 0.1, yticklabels = cols.values,  \n",
        "                              xticklabels = cols.values) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f59614ba5f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qezR2N1Dia6N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "newConvComp=[]\n",
        "for i in df[\"ConvertedComp\"]:\n",
        "  newConvComp.append(i//200000)\n",
        "df[\"ConvertedComp\"]=newConvComp"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIq6WJVJorgG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "2c03eba0-7d96-4480-a78b-0e6b960e4a7d"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "# Create a minimum and maximum processor object\n",
        "min_max_scaler = preprocessing.MinMaxScaler( feature_range= (0,1))\n",
        "\n",
        "# Create an object to transform the data to fit minmax processor\n",
        "x_scaled = min_max_scaler.fit_transform(df.drop(axis = 1, columns= \"ConvertedComp\"))\n",
        "# Run the normalizer on the dataframe\n",
        "df_normalized = pd.DataFrame(x_scaled)\n",
        "df_normalized"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.098039</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008482</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.172414</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.132653</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.005587</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.008482</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.137931</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.183673</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.011173</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.078431</td>\n",
              "      <td>0.039216</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.008043</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.068966</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>0.275510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.078431</td>\n",
              "      <td>0.039216</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.016292</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.206897</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.022346</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.196078</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.011136</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.275862</td>\n",
              "      <td>0.466667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>0.295918</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88878</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.027933</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008482</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.137931</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88879</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008482</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88880</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008482</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88881</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008482</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88882</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.139665</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.078431</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008482</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.051357</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.275862</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.277778</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.173469</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>88883 rows × 55 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        0    1         2         3   ...        51   52        53   54\n",
              "0      0.0  0.0  0.000000  0.000000  ...  0.000000  0.0  0.000000  0.0\n",
              "1      0.0  1.0  0.333333  0.000000  ...  0.000000  0.0  0.000000  0.0\n",
              "2      0.2  0.0  0.000000  0.000000  ...  0.000000  0.0  0.000000  0.5\n",
              "3      0.4  1.0  0.000000  0.000000  ...  0.000000  0.0  0.000000  0.0\n",
              "4      0.4  0.0  0.666667  0.333333  ...  0.000000  0.0  0.000000  0.0\n",
              "...    ...  ...       ...       ...  ...       ...  ...       ...  ...\n",
              "88878  1.0  0.0  1.000000  0.000000  ...  0.000000  0.0  0.285714  0.0\n",
              "88879  1.0  1.0  0.000000  0.000000  ...  0.142857  0.5  0.285714  1.0\n",
              "88880  1.0  1.0  0.000000  0.666667  ...  0.142857  0.5  0.285714  1.0\n",
              "88881  1.0  1.0  0.000000  0.333333  ...  0.142857  0.5  0.285714  1.0\n",
              "88882  1.0  0.0  0.333333  0.333333  ...  0.000000  0.0  0.000000  0.0\n",
              "\n",
              "[88883 rows x 55 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Smd0hytpfz2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        },
        "outputId": "42ca6862-7556-409e-fa90-9e887db83f8f"
      },
      "source": [
        "df_normalized.corrwith(df['ConvertedComp'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0.028749\n",
              "1     0.004083\n",
              "2     0.008414\n",
              "3    -0.015863\n",
              "4    -0.014030\n",
              "5    -0.089052\n",
              "6    -0.056736\n",
              "7     0.008173\n",
              "8     0.037736\n",
              "9     0.042895\n",
              "10    0.013639\n",
              "11    0.064308\n",
              "12    0.069045\n",
              "13    0.030446\n",
              "14    0.071754\n",
              "15    0.055331\n",
              "16    0.064001\n",
              "17    0.045577\n",
              "18    0.070515\n",
              "19    0.067839\n",
              "20    0.008605\n",
              "21    0.076928\n",
              "22    0.052626\n",
              "23    0.076958\n",
              "24    0.075457\n",
              "25    0.082526\n",
              "26   -0.012726\n",
              "27    0.084645\n",
              "28    0.075806\n",
              "29    0.088048\n",
              "30    0.024633\n",
              "31    0.013819\n",
              "32    0.043679\n",
              "33    0.015767\n",
              "34    0.029484\n",
              "35    0.033055\n",
              "36    0.047033\n",
              "37    0.047050\n",
              "38    0.051901\n",
              "39   -0.001212\n",
              "40   -0.046391\n",
              "41   -0.041121\n",
              "42   -0.005929\n",
              "43   -0.008314\n",
              "44   -0.001701\n",
              "45   -0.003609\n",
              "46   -0.004068\n",
              "47   -0.004103\n",
              "48    0.026534\n",
              "49    0.004016\n",
              "50    0.062648\n",
              "51   -0.015171\n",
              "52   -0.022942\n",
              "53   -0.026947\n",
              "54   -0.024212\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-MmtjWr9B4S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b00206b0-165f-4dcd-baef-89b489343a90"
      },
      "source": [
        "list(df[\"ConvertedComp\"].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0, 1.0, 2.0, 5.0, 10.0, 4.0, 3.0, 6.0, 8.0, 7.0, 9.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yrt-Ipalr2BT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "1ccc1361-f520-421b-8c60-576dcb0d92a8"
      },
      "source": [
        "import keras\n",
        "import tensorflow as tf\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout\n",
        "from tensorflow.keras import regularizers\n",
        "\n",
        "cols = corrmat.nlargest(20, 'ConvertedComp')['ConvertedComp'].index \n",
        "X = df[cols].drop( axis =1  , columns = 'ConvertedComp' )\n",
        "y = df[\"ConvertedComp\"]\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
        "\n",
        "my_callbacks = [\n",
        "    tf.keras.callbacks.EarlyStopping(patience=2),\n",
        "    tf.keras.callbacks.ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.2f}.h5'),\n",
        "    tf.keras.callbacks.TensorBoard(log_dir='./logs'),\n",
        "]\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(units= 5, kernel_initializer = 'uniform', activation = 'relu', input_dim = X.shape[1]))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(units= 5, kernel_initializer = 'uniform', activation = 'relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(units = 1 , kernel_initializer = 'uniform', activation = 'softmax'))\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
        "\n",
        "model.fit(X_train, y_train, validation_data= (X_test,y_test) , batch_size = 10, epochs = 20, validation_split = 0.33, callbacks = my_callbacks)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "   1/3990 [..............................] - ETA: 0s - loss: 0.0000e+00 - accuracy: 0.0000e+00WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0047s vs `on_train_batch_end` time: 0.0361s). Check your callbacks.\n",
            "3990/3990 [==============================] - 5s 1ms/step - loss: 0.0000e+00 - accuracy: 0.0188 - val_loss: 0.0000e+00 - val_accuracy: 0.0187\n",
            "Epoch 2/20\n",
            "3990/3990 [==============================] - 5s 1ms/step - loss: 0.0000e+00 - accuracy: 0.0188 - val_loss: 0.0000e+00 - val_accuracy: 0.0187\n",
            "Epoch 3/20\n",
            "3990/3990 [==============================] - 5s 1ms/step - loss: 0.0000e+00 - accuracy: 0.0188 - val_loss: 0.0000e+00 - val_accuracy: 0.0187\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f5926fcef60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axd8RqXNjvka",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "058ee005-d2e1-47af-d203-9c855ce5b3f8"
      },
      "source": [
        "print(\"Generate predictions for samples\")\n",
        "predictions = model.predict(X_test)\n",
        "print(\"predictions shape:\", predictions.shape)\n",
        "print(\"predictions :\", pd.DataFrame(predictions).head(20))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Generate predictions for samples\n",
            "predictions shape: (29332, 1)\n",
            "predictions :       0\n",
            "0   1.0\n",
            "1   1.0\n",
            "2   1.0\n",
            "3   1.0\n",
            "4   1.0\n",
            "5   1.0\n",
            "6   1.0\n",
            "7   1.0\n",
            "8   1.0\n",
            "9   1.0\n",
            "10  1.0\n",
            "11  1.0\n",
            "12  1.0\n",
            "13  1.0\n",
            "14  1.0\n",
            "15  1.0\n",
            "16  1.0\n",
            "17  1.0\n",
            "18  1.0\n",
            "19  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ze7hN15kPlkp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "01a82ac6-b7d6-42d3-a152-ff0cc33bc747"
      },
      "source": [
        "\n",
        "X = df.drop( axis =1  , columns = 'ConvertedComp' )\n",
        "y = df[\"ConvertedComp\"]\n",
        "\n",
        "from keras.models import load_model\n",
        "model = load_model('/content/drive/My Drive/developer_survey_2019/model1.h5')\n",
        "model.load_weights(\"/content/drive/My Drive/developer_survey_2019/model_weights1.h5\")\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_15 (Dense)             (None, 5)                 280       \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 5)                 0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 5)                 30        \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 5)                 0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 1)                 6         \n",
            "=================================================================\n",
            "Total params: 316\n",
            "Trainable params: 316\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUOMxuIzVJB3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5f39eb65-8031-4bd8-d683-d85d6f62feb2"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
        "\n",
        "pred = model.predict(X_test,verbose = 0)\n",
        "results = model.evaluate(X_test, y_test, batch_size=10)\n",
        "\n",
        "results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2934/2934 [==============================] - 2s 770us/step - loss: 2.6787e-08 - accuracy: 0.9413\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.6786732831851623e-08, 0.9412927627563477]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxZo40ZJQS-U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "f8fb166c-9e70-472b-e792-5baae4ccc9ad"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "my_callbacks = [\n",
        "    tf.keras.callbacks.EarlyStopping(patience=2),\n",
        "    tf.keras.callbacks.ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.2f}.h5'),\n",
        "    tf.keras.callbacks.TensorBoard(log_dir='./logs')]\n",
        "loss = model.fit(X, y, batch_size = 10, epochs = 20, validation_split = 0.33, callbacks = my_callbacks)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "   2/5956 [..............................] - ETA: 2:40 - loss: 3.5763e-08 - accuracy: 0.9500WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0046s vs `on_train_batch_end` time: 0.0496s). Check your callbacks.\n",
            "5956/5956 [==============================] - 8s 1ms/step - loss: 2.5601e-08 - accuracy: 0.9364 - val_loss: 2.5389e-08 - val_accuracy: 0.9444\n",
            "Epoch 2/20\n",
            "5956/5956 [==============================] - 8s 1ms/step - loss: 2.5601e-08 - accuracy: 0.9358 - val_loss: 2.5389e-08 - val_accuracy: 0.9444\n",
            "Epoch 3/20\n",
            "5956/5956 [==============================] - 8s 1ms/step - loss: 2.5601e-08 - accuracy: 0.9362 - val_loss: 2.5389e-08 - val_accuracy: 0.9444\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OM38yL4jRIya",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "68d3142e-6b77-4204-8365-683ca71075e8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(pd.DataFrame(loss.history) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f59272a3e48>,\n",
              " <matplotlib.lines.Line2D at 0x7f59272a3f60>,\n",
              " <matplotlib.lines.Line2D at 0x7f59272af0f0>,\n",
              " <matplotlib.lines.Line2D at 0x7f59272af240>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UldJN9wrps1B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "9b941337-d8f0-40f1-8959-2d5a821ec0a3"
      },
      "source": [
        "print(\"Generate predictions for samples\")\n",
        "predictions = model.predict(X_test)\n",
        "print(\"predictions shape:\", predictions.shape)\n",
        "print(\"predictions :\", pd.DataFrame(predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Generate predictions for samples\n",
            "predictions shape: (29332, 1)\n",
            "predictions :               0\n",
            "0      0.500000\n",
            "1      0.500000\n",
            "2      0.500000\n",
            "3      0.499872\n",
            "4      0.500000\n",
            "...         ...\n",
            "29327  0.500000\n",
            "29328  0.500000\n",
            "29329  0.500000\n",
            "29330  0.500000\n",
            "29331  0.500000\n",
            "\n",
            "[29332 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzwLaMLVKb6g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9c43f365-4769-47f9-e36a-f0c8422da869"
      },
      "source": [
        "test =   { 'Age':18.0\n",
        ", 'BlockchainIs':2\n",
        ", 'BlockchainOrg':2\n",
        ", 'CareerSat':3\n",
        ", 'CodeRev':4\n",
        ", 'CodeRevHrs':5.0\n",
        ", 'Containers':1\n",
        ", 'Country':36\n",
        ", 'DatabaseWorkedWith': 10\n",
        ", 'Dependents':2\n",
        ", 'DevType': 20 \n",
        ", 'EdLevel':3\n",
        ", 'Employment':3\n",
        ", 'Gender':1\n",
        ", 'Hobbyist':2\n",
        ", 'ITperson':2\n",
        ", 'ImpSyn':4\n",
        ", 'JobSat':5\n",
        ", 'JobSeek':3\n",
        ", 'LanguageWorkedWith':30 \n",
        ", 'LastHireDate':3\n",
        ", 'MainBranch':3\n",
        ", 'MgrIdiot':3\n",
        ", 'MgrMoney':3\n",
        ", 'MgrWant':3\n",
        ", 'MiscTechWorkedWith':20\n",
        ", 'OffOn':1\n",
        ", 'OpSys':1\n",
        ", 'OpenSource':1\n",
        ", 'OpenSourcer':2\n",
        ", 'OrgSize':8\n",
        ", 'PlatformWorkedWith': 18\n",
        ", 'PurchaseHow':2\n",
        ", 'PurchaseWhat': 2\n",
        ", 'SOAccount':2\n",
        ", 'SOFindAnswer':4\n",
        ", 'SOHowMuchTime':4\n",
        ", 'SOJobs':3\n",
        ", 'SONewContent':9\n",
        ", 'SOPartFreq':2\n",
        ", 'SOTimeSaved':1\n",
        ", 'SOVisitFreq':2\n",
        ", 'Sexuality': 2\n",
        ", 'SocialMedia':4\n",
        ", 'Student': 1\n",
        ", 'Trans': 1\n",
        ", 'UndergradMajor':3\n",
        ", 'UnitTests':5\n",
        ", 'WebFrameWorkedWith': 20\n",
        ", 'WorkLoc':3\n",
        ", 'WorkPlan':3\n",
        ", 'WorkRemote':3\n",
        ", 'WorkWeekHrs':50.0\n",
        ", 'YearsCode':9\n",
        ", 'YearsCodePro': 1} \n",
        "test = pd.DataFrame(test, index=[0])\n",
        "pred = model.predict(test)\n",
        "pred*200000"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[99819.98205185]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgr4OYHkMZHq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}