BentoUniAcc/Stack_Overflow_Salary_Predicting_Model

1{2 "cells": [3  {4   "cell_type": "markdown",5   "metadata": {6    "id": "xzJRGBM0BNrr"7   },8   "source": [9    "# Part 1 – Imports and setup"10   ]11  },12  {13   "cell_type": "markdown",14   "metadata": {15    "id": "e68abf09"16   },17   "source": [18    "#### Load Stack Overflow Developer Survey Data\n"19   ]20  },21  {22   "cell_type": "markdown",23   "metadata": {},24   "source": [25    "#### Google Colab Setup\n",26    "\n",27    "Run the cell below if you are on **Google Colab** — it installs `kagglehub` and enables inline plots. On a local machine this cell is a no-op."28   ]29  },30  {31   "cell_type": "code",32   "execution_count": 152,33   "metadata": {},34   "outputs": [35    {36     "name": "stdout",37     "output_type": "stream",38     "text": [39      "Local environment detected.\n"40     ]41    }42   ],43   "source": [44    "%matplotlib inline\n",45    "%config InlineBackend.figure_format = 'retina'\n",46    "\n",47    "# ── Colab compatibility ───────────────────────────────────────────────────\n",48    "try:\n",49    "    import google.colab\n",50    "    IN_COLAB = True\n",51    "    import subprocess\n",52    "    subprocess.run(['pip', 'install', '-q', 'kagglehub'], check=True)\n",53    "    # To authenticate Kaggle on Colab, run once:\n",54    "    #   from google.colab import files; files.upload()  # upload kaggle.json\n",55    "    #   import os; os.makedirs(os.path.expanduser('~/.config/kaggle'), exist_ok=True)\n",56    "    #   !mv kaggle.json ~/.config/kaggle/ && chmod 600 ~/.config/kaggle/kaggle.json\n",57    "    print('Google Colab detected -- kagglehub ready.')\n",58    "except ImportError:\n",59    "    IN_COLAB = False\n",60    "    print('Local environment detected.')"61   ]62  },63  {64   "cell_type": "code",65   "execution_count": 153,66   "metadata": {67    "colab": {68     "base_uri": "https://localhost:8080/",69     "height": 70770    },71    "id": "e05bf83a",72    "outputId": "066ab01c-5ef4-4f2e-84fc-ab3f210e7610"73   },74   "outputs": [75    {76     "name": "stdout",77     "output_type": "stream",78     "text": [79      "First 5 records:\n"80     ]81    },82    {83     "data": {84      "text/html": [85       "<div>\n",86       "<style scoped>\n",87       "    .dataframe tbody tr th:only-of-type {\n",88       "        vertical-align: middle;\n",89       "    }\n",90       "\n",91       "    .dataframe tbody tr th {\n",92       "        vertical-align: top;\n",93       "    }\n",94       "\n",95       "    .dataframe thead th {\n",96       "        text-align: right;\n",97       "    }\n",98       "</style>\n",99       "<table border=\"1\" class=\"dataframe\">\n",100       "  <thead>\n",101       "    <tr style=\"text-align: right;\">\n",102       "      <th></th>\n",103       "      <th>ResponseId</th>\n",104       "      <th>MainBranch</th>\n",105       "      <th>Age</th>\n",106       "      <th>EdLevel</th>\n",107       "      <th>Employment</th>\n",108       "      <th>EmploymentAddl</th>\n",109       "      <th>WorkExp</th>\n",110       "      <th>LearnCodeChoose</th>\n",111       "      <th>LearnCode</th>\n",112       "      <th>LearnCodeAI</th>\n",113       "      <th>...</th>\n",114       "      <th>AIAgentOrchestration</th>\n",115       "      <th>AIAgentOrchWrite</th>\n",116       "      <th>AIAgentObserveSecure</th>\n",117       "      <th>AIAgentObsWrite</th>\n",118       "      <th>AIAgentExternal</th>\n",119       "      <th>AIAgentExtWrite</th>\n",120       "      <th>AIHuman</th>\n",121       "      <th>AIOpen</th>\n",122       "      <th>ConvertedCompYearly</th>\n",123       "      <th>JobSat</th>\n",124       "    </tr>\n",125       "  </thead>\n",126       "  <tbody>\n",127       "    <tr>\n",128       "      <th>0</th>\n",129       "      <td>1</td>\n",130       "      <td>I am a developer by profession</td>\n",131       "      <td>25-34 years old</td>\n",132       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",133       "      <td>Employed</td>\n",134       "      <td>Caring for dependents (children, elderly, etc.)</td>\n",135       "      <td>8.0</td>\n",136       "      <td>Yes, I am not new to coding but am learning ne...</td>\n",137       "      <td>Online Courses or Certification (includes all ...</td>\n",138       "      <td>Yes, I learned how to use AI-enabled tools for...</td>\n",139       "      <td>...</td>\n",140       "      <td>Vertex AI</td>\n",141       "      <td>NaN</td>\n",142       "      <td>NaN</td>\n",143       "      <td>NaN</td>\n",144       "      <td>ChatGPT</td>\n",145       "      <td>NaN</td>\n",146       "      <td>When I don’t trust AI’s answers</td>\n",147       "      <td>Troubleshooting, profiling, debugging</td>\n",148       "      <td>61256.0</td>\n",149       "      <td>10.0</td>\n",150       "    </tr>\n",151       "    <tr>\n",152       "      <th>1</th>\n",153       "      <td>2</td>\n",154       "      <td>I am a developer by profession</td>\n",155       "      <td>25-34 years old</td>\n",156       "      <td>Associate degree (A.A., A.S., etc.)</td>\n",157       "      <td>Employed</td>\n",158       "      <td>NaN</td>\n",159       "      <td>2.0</td>\n",160       "      <td>Yes, I am not new to coding but am learning ne...</td>\n",161       "      <td>Online Courses or Certification (includes all ...</td>\n",162       "      <td>Yes, I learned how to use AI-enabled tools for...</td>\n",163       "      <td>...</td>\n",164       "      <td>NaN</td>\n",165       "      <td>NaN</td>\n",166       "      <td>NaN</td>\n",167       "      <td>NaN</td>\n",168       "      <td>NaN</td>\n",169       "      <td>NaN</td>\n",170       "      <td>When I don’t trust AI’s answers;When I want to...</td>\n",171       "      <td>All skills. AI is a flop.</td>\n",172       "      <td>104413.0</td>\n",173       "      <td>9.0</td>\n",174       "    </tr>\n",175       "    <tr>\n",176       "      <th>2</th>\n",177       "      <td>3</td>\n",178       "      <td>I am a developer by profession</td>\n",179       "      <td>35-44 years old</td>\n",180       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",181       "      <td>Independent contractor, freelancer, or self-em...</td>\n",182       "      <td>None of the above</td>\n",183       "      <td>10.0</td>\n",184       "      <td>Yes, I am not new to coding but am learning ne...</td>\n",185       "      <td>Online Courses or Certification (includes all ...</td>\n",186       "      <td>Yes, I learned how to use AI-enabled tools for...</td>\n",187       "      <td>...</td>\n",188       "      <td>NaN</td>\n",189       "      <td>NaN</td>\n",190       "      <td>NaN</td>\n",191       "      <td>NaN</td>\n",192       "      <td>ChatGPT;Claude Code;GitHub Copilot;Google Gemini</td>\n",193       "      <td>NaN</td>\n",194       "      <td>When I don’t trust AI’s answers;When I want to...</td>\n",195       "      <td>Understand how things actually work, problem s...</td>\n",196       "      <td>53061.0</td>\n",197       "      <td>8.0</td>\n",198       "    </tr>\n",199       "    <tr>\n",200       "      <th>3</th>\n",201       "      <td>4</td>\n",202       "      <td>I am a developer by profession</td>\n",203       "      <td>35-44 years old</td>\n",204       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",205       "      <td>Employed</td>\n",206       "      <td>None of the above</td>\n",207       "      <td>4.0</td>\n",208       "      <td>Yes, I am not new to coding but am learning ne...</td>\n",209       "      <td>Other online resources (e.g. standard search, ...</td>\n",210       "      <td>Yes, I learned how to use AI-enabled tools for...</td>\n",211       "      <td>...</td>\n",212       "      <td>NaN</td>\n",213       "      <td>NaN</td>\n",214       "      <td>NaN</td>\n",215       "      <td>NaN</td>\n",216       "      <td>ChatGPT;Claude Code</td>\n",217       "      <td>NaN</td>\n",218       "      <td>When I don’t trust AI’s answers;When I want to...</td>\n",219       "      <td>NaN</td>\n",220       "      <td>36197.0</td>\n",221       "      <td>6.0</td>\n",222       "    </tr>\n",223       "    <tr>\n",224       "      <th>4</th>\n",225       "      <td>5</td>\n",226       "      <td>I am a developer by profession</td>\n",227       "      <td>35-44 years old</td>\n",228       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",229       "      <td>Independent contractor, freelancer, or self-em...</td>\n",230       "      <td>Caring for dependents (children, elderly, etc.)</td>\n",231       "      <td>21.0</td>\n",232       "      <td>No, I am not new to coding and did not learn n...</td>\n",233       "      <td>NaN</td>\n",234       "      <td>Yes, I learned how to use AI-enabled tools for...</td>\n",235       "      <td>...</td>\n",236       "      <td>NaN</td>\n",237       "      <td>NaN</td>\n",238       "      <td>NaN</td>\n",239       "      <td>NaN</td>\n",240       "      <td>NaN</td>\n",241       "      <td>NaN</td>\n",242       "      <td>When I don’t trust AI’s answers</td>\n",243       "      <td>critical thinking, the skill to define the tas...</td>\n",244       "      <td>60000.0</td>\n",245       "      <td>7.0</td>\n",246       "    </tr>\n",247       "  </tbody>\n",248       "</table>\n",249       "<p>5 rows × 170 columns</p>\n",250       "</div>"251      ],252      "text/plain": [253       "   ResponseId                      MainBranch              Age  \\\n",254       "0           1  I am a developer by profession  25-34 years old   \n",255       "1           2  I am a developer by profession  25-34 years old   \n",256       "2           3  I am a developer by profession  35-44 years old   \n",257       "3           4  I am a developer by profession  35-44 years old   \n",258       "4           5  I am a developer by profession  35-44 years old   \n",259       "\n",260       "                                           EdLevel  \\\n",261       "0  Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",262       "1              Associate degree (A.A., A.S., etc.)   \n",263       "2     Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",264       "3     Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",265       "4  Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",266       "\n",267       "                                          Employment  \\\n",268       "0                                           Employed   \n",269       "1                                           Employed   \n",270       "2  Independent contractor, freelancer, or self-em...   \n",271       "3                                           Employed   \n",272       "4  Independent contractor, freelancer, or self-em...   \n",273       "\n",274       "                                    EmploymentAddl  WorkExp  \\\n",275       "0  Caring for dependents (children, elderly, etc.)      8.0   \n",276       "1                                              NaN      2.0   \n",277       "2                                None of the above     10.0   \n",278       "3                                None of the above      4.0   \n",279       "4  Caring for dependents (children, elderly, etc.)     21.0   \n",280       "\n",281       "                                     LearnCodeChoose  \\\n",282       "0  Yes, I am not new to coding but am learning ne...   \n",283       "1  Yes, I am not new to coding but am learning ne...   \n",284       "2  Yes, I am not new to coding but am learning ne...   \n",285       "3  Yes, I am not new to coding but am learning ne...   \n",286       "4  No, I am not new to coding and did not learn n...   \n",287       "\n",288       "                                           LearnCode  \\\n",289       "0  Online Courses or Certification (includes all ...   \n",290       "1  Online Courses or Certification (includes all ...   \n",291       "2  Online Courses or Certification (includes all ...   \n",292       "3  Other online resources (e.g. standard search, ...   \n",293       "4                                                NaN   \n",294       "\n",295       "                                         LearnCodeAI  ...  \\\n",296       "0  Yes, I learned how to use AI-enabled tools for...  ...   \n",297       "1  Yes, I learned how to use AI-enabled tools for...  ...   \n",298       "2  Yes, I learned how to use AI-enabled tools for...  ...   \n",299       "3  Yes, I learned how to use AI-enabled tools for...  ...   \n",300       "4  Yes, I learned how to use AI-enabled tools for...  ...   \n",301       "\n",302       "  AIAgentOrchestration  AIAgentOrchWrite AIAgentObserveSecure AIAgentObsWrite  \\\n",303       "0            Vertex AI               NaN                  NaN             NaN   \n",304       "1                  NaN               NaN                  NaN             NaN   \n",305       "2                  NaN               NaN                  NaN             NaN   \n",306       "3                  NaN               NaN                  NaN             NaN   \n",307       "4                  NaN               NaN                  NaN             NaN   \n",308       "\n",309       "                                    AIAgentExternal AIAgentExtWrite  \\\n",310       "0                                           ChatGPT             NaN   \n",311       "1                                               NaN             NaN   \n",312       "2  ChatGPT;Claude Code;GitHub Copilot;Google Gemini             NaN   \n",313       "3                               ChatGPT;Claude Code             NaN   \n",314       "4                                               NaN             NaN   \n",315       "\n",316       "                                             AIHuman  \\\n",317       "0                    When I don’t trust AI’s answers   \n",318       "1  When I don’t trust AI’s answers;When I want to...   \n",319       "2  When I don’t trust AI’s answers;When I want to...   \n",320       "3  When I don’t trust AI’s answers;When I want to...   \n",321       "4                    When I don’t trust AI’s answers   \n",322       "\n",323       "                                              AIOpen  ConvertedCompYearly  \\\n",324       "0              Troubleshooting, profiling, debugging              61256.0   \n",325       "1                          All skills. AI is a flop.             104413.0   \n",326       "2  Understand how things actually work, problem s...              53061.0   \n",327       "3                                                NaN              36197.0   \n",328       "4  critical thinking, the skill to define the tas...              60000.0   \n",329       "\n",330       "   JobSat  \n",331       "0    10.0  \n",332       "1     9.0  \n",333       "2     8.0  \n",334       "3     6.0  \n",335       "4     7.0  \n",336       "\n",337       "[5 rows x 170 columns]"338      ]339     },340     "metadata": {},341     "output_type": "display_data"342    }343   ],344   "source": [345    "# Importing necessary libraries\n",346    "import os\n",347    "import random\n",348    "\n",349    "import numpy as np\n",350    "import pandas as pd\n",351    "\n",352    "import seaborn as sns\n",353    "import matplotlib.pyplot as plt\n",354    "\n",355    "import kagglehub\n",356    "from kagglehub import KaggleDatasetAdapter\n",357    "\n",358    "# Setting the filepath for the dataset\n",359    "file_path =  \"survey_results_public.csv\"\n",360    "\n",361    "# Load the latest version\n",362    "df = kagglehub.dataset_load(\n",363    "  KaggleDatasetAdapter.PANDAS,\n",364    "  \"edoardogalli/stack-overflow-annual-developer-survey-2025\",\n",365    "  file_path\n",366    ")\n",367    "\n",368    "# Printing the first 5 records\n",369    "print(\"First 5 records:\")\n",370    "display(df.head())"371   ]372  },373  {374   "cell_type": "markdown",375   "metadata": {376    "id": "8Pt3TANYLBik"377   },378   "source": [379    "#### Setting the seeds and warning handling"380   ]381  },382  {383   "cell_type": "code",384   "execution_count": 154,385   "metadata": {386    "id": "HiwAyJrdLAZr"387   },388   "outputs": [],389   "source": [390    "SEED = 42\n",391    "\n",392    "random.seed(SEED)\n",393    "np.random.seed(SEED)\n",394    "os.environ['PYTHONHASHSEED'] = str(SEED)\n",395    "\n",396    "import warnings\n",397    "warnings.filterwarnings('ignore')"398   ]399  },400  {401   "cell_type": "markdown",402   "metadata": {403    "id": "8c37197d"404   },405   "source": [406    "#### Dataset Description\n",407    "\n",408    "This dataset is the Stack Overflow Annual Developer Survey 2025, sourced from Kaggle. It contains survey responses from 49,123 developers, covering 170 features related to their demographics, professional roles, education, technology usage, compensation, and job satisfaction.\n",409    "\n",410    "#### Question to Answer\n",411    "\n",412    "Can we predict a software developer's annual salary from their professional profile — and what factors matter most?\n",413    "\n",414    "\n",415    "The ability to predict the developer's salary can assist companies in developing competitive compensation strategies, attracting and retaining top talent, and ensuring fair pay practices."416   ]417  },418  {419   "cell_type": "markdown",420   "metadata": {421    "id": "oNesOq9DUBNZ"422   },423   "source": [424    "# Part 2 – EDA"425   ]426  },427  {428   "cell_type": "markdown",429   "metadata": {430    "id": "01XxRegbpdwX"431   },432   "source": [433    "#### Overview"434   ]435  },436  {437   "cell_type": "code",438   "execution_count": 155,439   "metadata": {440    "colab": {441     "base_uri": "https://localhost:8080/",442     "height": 661443    },444    "id": "7A-uoH8fLo3R",445    "outputId": "44971f6f-05db-4551-e521-76945de87571"446   },447   "outputs": [448    {449     "name": "stdout",450     "output_type": "stream",451     "text": [452      "Dataset shape: 49,123 rows × 170 columns\n",453      "\n",454      "Numeric columns    : 51\n",455      "Categorical columns: 119\n",456      "\n",457      "First 3 rows:\n"458     ]459    },460    {461     "data": {462      "text/html": [463       "<div>\n",464       "<style scoped>\n",465       "    .dataframe tbody tr th:only-of-type {\n",466       "        vertical-align: middle;\n",467       "    }\n",468       "\n",469       "    .dataframe tbody tr th {\n",470       "        vertical-align: top;\n",471       "    }\n",472       "\n",473       "    .dataframe thead th {\n",474       "        text-align: right;\n",475       "    }\n",476       "</style>\n",477       "<table border=\"1\" class=\"dataframe\">\n",478       "  <thead>\n",479       "    <tr style=\"text-align: right;\">\n",480       "      <th></th>\n",481       "      <th>ResponseId</th>\n",482       "      <th>MainBranch</th>\n",483       "      <th>Age</th>\n",484       "      <th>EdLevel</th>\n",485       "      <th>Employment</th>\n",486       "      <th>EmploymentAddl</th>\n",487       "      <th>WorkExp</th>\n",488       "      <th>LearnCodeChoose</th>\n",489       "      <th>LearnCode</th>\n",490       "      <th>LearnCodeAI</th>\n",491       "      <th>...</th>\n",492       "      <th>AIAgentOrchestration</th>\n",493       "      <th>AIAgentOrchWrite</th>\n",494       "      <th>AIAgentObserveSecure</th>\n",495       "      <th>AIAgentObsWrite</th>\n",496       "      <th>AIAgentExternal</th>\n",497       "      <th>AIAgentExtWrite</th>\n",498       "      <th>AIHuman</th>\n",499       "      <th>AIOpen</th>\n",500       "      <th>ConvertedCompYearly</th>\n",501       "      <th>JobSat</th>\n",502       "    </tr>\n",503       "  </thead>\n",504       "  <tbody>\n",505       "    <tr>\n",506       "      <th>0</th>\n",507       "      <td>1</td>\n",508       "      <td>I am a developer by profession</td>\n",509       "      <td>25-34 years old</td>\n",510       "      <td>Master’s degree (M.A., M.S., M.Eng., MBA, etc.)</td>\n",511       "      <td>Employed</td>\n",512       "      <td>Caring for dependents (children, elderly, etc.)</td>\n",513       "      <td>8.0</td>\n",514       "      <td>Yes, I am not new to coding but am learning ne...</td>\n",515       "      <td>Online Courses or Certification (includes all ...</td>\n",516       "      <td>Yes, I learned how to use AI-enabled tools for...</td>\n",517       "      <td>...</td>\n",518       "      <td>Vertex AI</td>\n",519       "      <td>NaN</td>\n",520       "      <td>NaN</td>\n",521       "      <td>NaN</td>\n",522       "      <td>ChatGPT</td>\n",523       "      <td>NaN</td>\n",524       "      <td>When I don’t trust AI’s answers</td>\n",525       "      <td>Troubleshooting, profiling, debugging</td>\n",526       "      <td>61256.0</td>\n",527       "      <td>10.0</td>\n",528       "    </tr>\n",529       "    <tr>\n",530       "      <th>1</th>\n",531       "      <td>2</td>\n",532       "      <td>I am a developer by profession</td>\n",533       "      <td>25-34 years old</td>\n",534       "      <td>Associate degree (A.A., A.S., etc.)</td>\n",535       "      <td>Employed</td>\n",536       "      <td>NaN</td>\n",537       "      <td>2.0</td>\n",538       "      <td>Yes, I am not new to coding but am learning ne...</td>\n",539       "      <td>Online Courses or Certification (includes all ...</td>\n",540       "      <td>Yes, I learned how to use AI-enabled tools for...</td>\n",541       "      <td>...</td>\n",542       "      <td>NaN</td>\n",543       "      <td>NaN</td>\n",544       "      <td>NaN</td>\n",545       "      <td>NaN</td>\n",546       "      <td>NaN</td>\n",547       "      <td>NaN</td>\n",548       "      <td>When I don’t trust AI’s answers;When I want to...</td>\n",549       "      <td>All skills. AI is a flop.</td>\n",550       "      <td>104413.0</td>\n",551       "      <td>9.0</td>\n",552       "    </tr>\n",553       "    <tr>\n",554       "      <th>2</th>\n",555       "      <td>3</td>\n",556       "      <td>I am a developer by profession</td>\n",557       "      <td>35-44 years old</td>\n",558       "      <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",559       "      <td>Independent contractor, freelancer, or self-em...</td>\n",560       "      <td>None of the above</td>\n",561       "      <td>10.0</td>\n",562       "      <td>Yes, I am not new to coding but am learning ne...</td>\n",563       "      <td>Online Courses or Certification (includes all ...</td>\n",564       "      <td>Yes, I learned how to use AI-enabled tools for...</td>\n",565       "      <td>...</td>\n",566       "      <td>NaN</td>\n",567       "      <td>NaN</td>\n",568       "      <td>NaN</td>\n",569       "      <td>NaN</td>\n",570       "      <td>ChatGPT;Claude Code;GitHub Copilot;Google Gemini</td>\n",571       "      <td>NaN</td>\n",572       "      <td>When I don’t trust AI’s answers;When I want to...</td>\n",573       "      <td>Understand how things actually work, problem s...</td>\n",574       "      <td>53061.0</td>\n",575       "      <td>8.0</td>\n",576       "    </tr>\n",577       "  </tbody>\n",578       "</table>\n",579       "<p>3 rows × 170 columns</p>\n",580       "</div>"581      ],582      "text/plain": [583       "   ResponseId                      MainBranch              Age  \\\n",584       "0           1  I am a developer by profession  25-34 years old   \n",585       "1           2  I am a developer by profession  25-34 years old   \n",586       "2           3  I am a developer by profession  35-44 years old   \n",587       "\n",588       "                                           EdLevel  \\\n",589       "0  Master’s degree (M.A., M.S., M.Eng., MBA, etc.)   \n",590       "1              Associate degree (A.A., A.S., etc.)   \n",591       "2     Bachelor’s degree (B.A., B.S., B.Eng., etc.)   \n",592       "\n",593       "                                          Employment  \\\n",594       "0                                           Employed   \n",595       "1                                           Employed   \n",596       "2  Independent contractor, freelancer, or self-em...   \n",597       "\n",598       "                                    EmploymentAddl  WorkExp  \\\n",599       "0  Caring for dependents (children, elderly, etc.)      8.0   \n",600       "1                                              NaN      2.0   \n",601       "2                                None of the above     10.0   \n",602       "\n",603       "                                     LearnCodeChoose  \\\n",604       "0  Yes, I am not new to coding but am learning ne...   \n",605       "1  Yes, I am not new to coding but am learning ne...   \n",606       "2  Yes, I am not new to coding but am learning ne...   \n",607       "\n",608       "                                           LearnCode  \\\n",609       "0  Online Courses or Certification (includes all ...   \n",610       "1  Online Courses or Certification (includes all ...   \n",611       "2  Online Courses or Certification (includes all ...   \n",612       "\n",613       "                                         LearnCodeAI  ...  \\\n",614       "0  Yes, I learned how to use AI-enabled tools for...  ...   \n",615       "1  Yes, I learned how to use AI-enabled tools for...  ...   \n",616       "2  Yes, I learned how to use AI-enabled tools for...  ...   \n",617       "\n",618       "  AIAgentOrchestration  AIAgentOrchWrite AIAgentObserveSecure AIAgentObsWrite  \\\n",619       "0            Vertex AI               NaN                  NaN             NaN   \n",620       "1                  NaN               NaN                  NaN             NaN   \n",621       "2                  NaN               NaN                  NaN             NaN   \n",622       "\n",623       "                                    AIAgentExternal AIAgentExtWrite  \\\n",624       "0                                           ChatGPT             NaN   \n",625       "1                                               NaN             NaN   \n",626       "2  ChatGPT;Claude Code;GitHub Copilot;Google Gemini             NaN   \n",627       "\n",628       "                                             AIHuman  \\\n",629       "0                    When I don’t trust AI’s answers   \n",630       "1  When I don’t trust AI’s answers;When I want to...   \n",631       "2  When I don’t trust AI’s answers;When I want to...   \n",632       "\n",633       "                                              AIOpen  ConvertedCompYearly  \\\n",634       "0              Troubleshooting, profiling, debugging              61256.0   \n",635       "1                          All skills. AI is a flop.             104413.0   \n",636       "2  Understand how things actually work, problem s...              53061.0   \n",637       "\n",638       "   JobSat  \n",639       "0    10.0  \n",640       "1     9.0  \n",641       "2     8.0  \n",642       "\n",643       "[3 rows x 170 columns]"644      ]645     },646     "metadata": {},647     "output_type": "display_data"648    },649    {650     "name": "stdout",651     "output_type": "stream",652     "text": [653      "\n",654      "Dataset info:\n",655      "<class 'pandas.DataFrame'>\n",656      "RangeIndex: 49123 entries, 0 to 49122\n",657      "Columns: 170 entries, ResponseId to JobSat\n",658      "dtypes: float64(50), int64(1), str(119)\n",659      "memory usage: 63.7 MB\n"660     ]661    }662   ],663   "source": [664    "# ── Shape and column types ────────────────────────────────────\n",665    "print(f\"Dataset shape: {df.shape[0]:,} rows × {df.shape[1]} columns\\n\")\n",666    "\n",667    "# How many numeric vs. categorical columns do we have?\n",668    "numeric_cols     = df.select_dtypes(include='number').columns.tolist()\n",669    "categorical_cols = df.select_dtypes(include='object').columns.tolist()\n",670    "print(f\"Numeric columns    : {len(numeric_cols)}\")\n",671    "print(f\"Categorical columns: {len(categorical_cols)}\")\n",672    "\n",673    "# ── Quick look at the first few rows ─────────────────────────\n",674    "print(\"\\nFirst 3 rows:\")\n",675    "display(df.head(3))\n",676    "\n",677    "# ── Column-level summary ──────────────────────────────────────\n",678    "print(\"\\nDataset info:\")\n",679    "df.info()"680   ]681  },682  {683   "cell_type": "markdown",684   "metadata": {},685   "source": [686    "#### 2.1 – Data Cleaning\n",687    "\n",688    "We identify the salary target, drop rows without a salary, then remove extreme outliers."689   ]690  },691  {692   "cell_type": "code",693   "execution_count": 156,694   "metadata": {},695   "outputs": [696    {697     "name": "stdout",698     "output_type": "stream",699     "text": [700      "Non-null salary rows: 23,928  /  49,123 total\n",701      "Working shape after dropping null salary: (23928, 170)\n"702     ]703    }704   ],705   "source": [706    "TARGET = \"ConvertedCompYearly\"\n",707    "\n",708    "print(f\"Non-null salary rows: {df[TARGET].notna().sum():,}  /  {len(df):,} total\")\n",709    "\n",710    "# Keep only rows with a reported salary\n",711    "df_salary = df[df[TARGET].notna()].copy()\n",712    "print(f\"Working shape after dropping null salary: {df_salary.shape}\")\n"713   ]714  },715  {716   "cell_type": "code",717   "execution_count": 157,718   "metadata": {},719   "outputs": [720    {721     "name": "stdout",722     "output_type": "stream",723     "text": [724      "Salary  1st pct: $65\n",725      "Salary 99th pct: $440,856\n",726      "Shape after outlier removal: (23455, 170)\n"727     ]728    }729   ],730   "source": [731    "# ── Outlier removal on salary ─────────────────────────────────────────────────\n",732    "salary_q01 = df_salary[TARGET].quantile(0.01)\n",733    "salary_q99 = df_salary[TARGET].quantile(0.99)\n",734    "print(f\"Salary  1st pct: ${salary_q01:,.0f}\")\n",735    "print(f\"Salary 99th pct: ${salary_q99:,.0f}\")\n",736    "\n",737    "df_salary = df_salary[\n",738    "    (df_salary[TARGET] >= salary_q01) &\n",739    "    (df_salary[TARGET] <= salary_q99)\n",740    "].copy()\n",741    "print(f\"Shape after outlier removal: {df_salary.shape}\")\n"742   ]743  },744  {745   "cell_type": "code",746   "execution_count": 158,747   "metadata": {},748   "outputs": [749    {750     "name": "stdout",751     "output_type": "stream",752     "text": [753      "Duplicate rows: 0\n"754     ]755    }756   ],757   "source": [758    "# ── Duplicate rows ────────────────────────────────────────────────────────────\n",759    "n_dupes = df_salary.duplicated().sum()\n",760    "print(f\"Duplicate rows: {n_dupes}\")\n",761    "if n_dupes > 0:\n",762    "    df_salary = df_salary.drop_duplicates()\n",763    "    print(f\"Shape after dropping duplicates: {df_salary.shape}\")\n"764   ]765  },766  {767   "cell_type": "markdown",768   "metadata": {},769   "source": [770    "#### 2.2 – Identifying Essential Columns (Top-15 Salary Correlates)\n",771    "\n",772    "Before dropping any high-missingness columns we first find the **top 15 numeric columns most correlated with salary**. These are protected — they will **not** be removed even if they have many nulls, because they carry the strongest predictive signal for our target.\n"773   ]774  },775  {776   "cell_type": "code",777   "execution_count": 159,778   "metadata": {},779   "outputs": [780    {781     "name": "stdout",782     "output_type": "stream",783     "text": [784      "Top-15 numeric columns most correlated with salary:\n",785      "YearsCode          0.367432\n",786      "WorkExp            0.337665\n",787      "JobSatPoints_11    0.148940\n",788      "JobSatPoints_4     0.138901\n",789      "TechOppose_1       0.123555\n",790      "JobSatPoints_16    0.108617\n",791      "JobSatPoints_6     0.107968\n",792      "TechEndorse_1      0.093544\n",793      "SO_Actions_10      0.092648\n",794      "TechEndorse_4      0.085696\n",795      "JobSatPoints_10    0.084602\n",796      "JobSat             0.084474\n",797      "TechOppose_13      0.081299\n",798      "TechOppose_9       0.076532\n",799      "TechEndorse_8      0.068539\n",800      "\n",801      "ESSENTIALS list: ['YearsCode', 'WorkExp', 'JobSatPoints_11', 'JobSatPoints_4', 'TechOppose_1', 'JobSatPoints_16', 'JobSatPoints_6', 'TechEndorse_1', 'SO_Actions_10', 'TechEndorse_4', 'JobSatPoints_10', 'JobSat', 'TechOppose_13', 'TechOppose_9', 'TechEndorse_8']\n"802     ]803    }804   ],805   "source": [806    "# ── Compute correlations of ALL numeric columns with salary ───────────────────\n",807    "num_cols = df_salary.select_dtypes(include='number').columns.tolist()\n",808    "num_cols = [c for c in num_cols if c != TARGET]\n",809    "\n",810    "# Correlate each numeric column with salary (drop NaNs per pair)\n",811    "corr_with_salary = (\n",812    "    df_salary[num_cols + [TARGET]]\n",813    "    .corr()[TARGET]\n",814    "    .drop(TARGET)\n",815    "    .abs()               # absolute correlation\n",816    "    .sort_values(ascending=False)\n",817    "    .dropna()\n",818    ")\n",819    "\n",820    "# Top-15 essentials\n",821    "TOP_N = 15\n",822    "essentials = corr_with_salary.head(TOP_N).index.tolist()\n",823    "\n",824    "print(\"Top-15 numeric columns most correlated with salary:\")\n",825    "print(corr_with_salary.head(TOP_N).to_string())\n",826    "print()\n",827    "print(\"ESSENTIALS list:\", essentials)\n"828   ]829  },830  {831   "cell_type": "markdown",832   "metadata": {},833   "source": [834    "#### 2.3 – Missing-Value Analysis & High-Null Column Removal\n",835    "\n",836    "We plot the missingness across all columns, then drop any column with **> 60 % missing values** — *except* those in `essentials`. Columns in `essentials` are imputed later during feature engineering (Part 4).\n"837   ]838  },839  {840   "cell_type": "code",841   "execution_count": 160,842   "metadata": {},843   "outputs": [844    {845     "name": "stdout",846     "output_type": "stream",847     "text": [848      "Columns with > 60% missing: 44\n",849      "\n",850      "Top 30 columns by % missing:\n"851     ]852    },853    {854     "data": {855      "text/html": [856       "<div>\n",857       "<style scoped>\n",858       "    .dataframe tbody tr th:only-of-type {\n",859       "        vertical-align: middle;\n",860       "    }\n",861       "\n",862       "    .dataframe tbody tr th {\n",863       "        vertical-align: top;\n",864       "    }\n",865       "\n",866       "    .dataframe thead th {\n",867       "        text-align: right;\n",868       "    }\n",869       "</style>\n",870       "<table border=\"1\" class=\"dataframe\">\n",871       "  <thead>\n",872       "    <tr style=\"text-align: right;\">\n",873       "      <th></th>\n",874       "      <th>missing_count</th>\n",875       "      <th>missing_pct</th>\n",876       "    </tr>\n",877       "  </thead>\n",878       "  <tbody>\n",879       "    <tr>\n",880       "      <th>AIAgentObsWrite</th>\n",881       "      <td>23271</td>\n",882       "      <td>99.22</td>\n",883       "    </tr>\n",884       "    <tr>\n",885       "      <th>SOTagsWant Entry</th>\n",886       "      <td>23190</td>\n",887       "      <td>98.87</td>\n",888       "    </tr>\n",889       "    <tr>\n",890       "      <th>SOTagsHaveEntry</th>\n",891       "      <td>23166</td>\n",892       "      <td>98.77</td>\n",893       "    </tr>\n",894       "    <tr>\n",895       "      <th>AIModelsWantEntry</th>\n",896       "      <td>23136</td>\n",897       "      <td>98.64</td>\n",898       "    </tr>\n",899       "    <tr>\n",900       "      <th>AIAgentOrchWrite</th>\n",901       "      <td>23108</td>\n",902       "      <td>98.52</td>\n",903       "    </tr>\n",904       "    <tr>\n",905       "      <th>JobSatPoints_15_TEXT</th>\n",906       "      <td>22973</td>\n",907       "      <td>97.95</td>\n",908       "    </tr>\n",909       "    <tr>\n",910       "      <th>SO_Actions_15_TEXT</th>\n",911       "      <td>22942</td>\n",912       "      <td>97.81</td>\n",913       "    </tr>\n",914       "    <tr>\n",915       "      <th>AIModelsHaveEntry</th>\n",916       "      <td>22901</td>\n",917       "      <td>97.64</td>\n",918       "    </tr>\n",919       "    <tr>\n",920       "      <th>AIAgentKnowWrite</th>\n",921       "      <td>22896</td>\n",922       "      <td>97.62</td>\n",923       "    </tr>\n",924       "    <tr>\n",925       "      <th>AIAgentExtWrite</th>\n",926       "      <td>22815</td>\n",927       "      <td>97.27</td>\n",928       "    </tr>\n",929       "    <tr>\n",930       "      <th>CommPlatformWantEntr</th>\n",931       "      <td>22671</td>\n",932       "      <td>96.66</td>\n",933       "    </tr>\n",934       "    <tr>\n",935       "      <th>CommPlatformHaveEntr</th>\n",936       "      <td>22471</td>\n",937       "      <td>95.80</td>\n",938       "    </tr>\n",939       "    <tr>\n",940       "      <th>TechOppose_15_TEXT</th>\n",941       "      <td>22466</td>\n",942       "      <td>95.78</td>\n",943       "    </tr>\n",944       "    <tr>\n",945       "      <th>DatabaseWantEntry</th>\n",946       "      <td>22400</td>\n",947       "      <td>95.50</td>\n",948       "    </tr>\n",949       "    <tr>\n",950       "      <th>OfficeStackWantEntry</th>\n",951       "      <td>22387</td>\n",952       "      <td>95.45</td>\n",953       "    </tr>\n",954       "    <tr>\n",955       "      <th>TechEndorse_13_TEXT</th>\n",956       "      <td>22235</td>\n",957       "      <td>94.80</td>\n",958       "    </tr>\n",959       "    <tr>\n",960       "      <th>DevEnvWantEntry</th>\n",961       "      <td>22014</td>\n",962       "      <td>93.86</td>\n",963       "    </tr>\n",964       "    <tr>\n",965       "      <th>DatabaseHaveEntry</th>\n",966       "      <td>21913</td>\n",967       "      <td>93.43</td>\n",968       "    </tr>\n",969       "    <tr>\n",970       "      <th>WebframeWantEntry</th>\n",971       "      <td>21625</td>\n",972       "      <td>92.20</td>\n",973       "    </tr>\n",974       "    <tr>\n",975       "      <th>OfficeStackHaveEntry</th>\n",976       "      <td>21606</td>\n",977       "      <td>92.12</td>\n",978       "    </tr>\n",979       "    <tr>\n",980       "      <th>DevEnvHaveEntry</th>\n",981       "      <td>21562</td>\n",982       "      <td>91.93</td>\n",983       "    </tr>\n",984       "    <tr>\n",985       "      <th>AIAgentObserveSecure</th>\n",986       "      <td>21489</td>\n",987       "      <td>91.62</td>\n",988       "    </tr>\n",989       "    <tr>\n",990       "      <th>LanguagesWantEntry</th>\n",991       "      <td>21465</td>\n",992       "      <td>91.52</td>\n",993       "    </tr>\n",994       "    <tr>\n",995       "      <th>PlatformWantEntry</th>\n",996       "      <td>21223</td>\n",997       "      <td>90.48</td>\n",998       "    </tr>\n",999       "    <tr>\n",1000       "      <th>WebframeHaveEntry</th>\n",1001       "      <td>21120</td>\n",1002       "      <td>90.04</td>\n",1003       "    </tr>\n",1004       "    <tr>\n",1005       "      <th>AIAgentKnowledge</th>\n",1006       "      <td>21077</td>\n",1007       "      <td>89.86</td>\n",1008       "    </tr>\n",1009       "    <tr>\n",1010       "      <th>LanguagesHaveEntry</th>\n",1011       "      <td>20860</td>\n",1012       "      <td>88.94</td>\n",1013       "    </tr>\n",1014       "    <tr>\n",1015       "      <th>AIAgentOrchestration</th>\n",1016       "      <td>20821</td>\n",1017       "      <td>88.77</td>\n",1018       "    </tr>\n",1019       "    <tr>\n",1020       "      <th>AIAgentImpactStrongly disagree</th>\n",1021       "      <td>20627</td>\n",1022       "      <td>87.94</td>\n",1023       "    </tr>\n",1024       "    <tr>\n",1025       "      <th>PlatformHaveEntry</th>\n",1026       "      <td>20547</td>\n",1027       "      <td>87.60</td>\n",1028       "    </tr>\n",1029       "  </tbody>\n",1030       "</table>\n",1031       "</div>"1032      ],1033      "text/plain": [1034       "                                missing_count  missing_pct\n",1035       "AIAgentObsWrite                         23271        99.22\n",1036       "SOTagsWant Entry                        23190        98.87\n",1037       "SOTagsHaveEntry                         23166        98.77\n",1038       "AIModelsWantEntry                       23136        98.64\n",1039       "AIAgentOrchWrite                        23108        98.52\n",1040       "JobSatPoints_15_TEXT                    22973        97.95\n",1041       "SO_Actions_15_TEXT                      22942        97.81\n",1042       "AIModelsHaveEntry                       22901        97.64\n",1043       "AIAgentKnowWrite                        22896        97.62\n",1044       "AIAgentExtWrite                         22815        97.27\n",1045       "CommPlatformWantEntr                    22671        96.66\n",1046       "CommPlatformHaveEntr                    22471        95.80\n",1047       "TechOppose_15_TEXT                      22466        95.78\n",1048       "DatabaseWantEntry                       22400        95.50\n",1049       "OfficeStackWantEntry                    22387        95.45\n",1050       "TechEndorse_13_TEXT                     22235        94.80\n",1051       "DevEnvWantEntry                         22014        93.86\n",1052       "DatabaseHaveEntry                       21913        93.43\n",1053       "WebframeWantEntry                       21625        92.20\n",1054       "OfficeStackHaveEntry                    21606        92.12\n",1055       "DevEnvHaveEntry                         21562        91.93\n",1056       "AIAgentObserveSecure                    21489        91.62\n",1057       "LanguagesWantEntry                      21465        91.52\n",1058       "PlatformWantEntry                       21223        90.48\n",1059       "WebframeHaveEntry                       21120        90.04\n",1060       "AIAgentKnowledge                        21077        89.86\n",1061       "LanguagesHaveEntry                      20860        88.94\n",1062       "AIAgentOrchestration                    20821        88.77\n",1063       "AIAgentImpactStrongly disagree          20627        87.94\n",1064       "PlatformHaveEntry                       20547        87.60"1065      ]1066     },1067     "metadata": {},1068     "output_type": "display_data"1069    }1070   ],1071   "source": [1072    "# ── Missing-value overview ────────────────────────────────────────────────────\n",1073    "missing = df_salary.isnull().sum()\n",1074    "missing_pct = (missing / len(df_salary) * 100).round(2)\n",1075    "missing_df = (\n",1076    "    pd.DataFrame({'missing_count': missing, 'missing_pct': missing_pct})\n",1077    "    .sort_values('missing_pct', ascending=False)\n",1078    ")\n",1079    "\n",1080    "print(f\"Columns with > 60% missing: {(missing_pct > 60).sum()}\")\n",1081    "print(\"\\nTop 30 columns by % missing:\")\n",1082    "display(missing_df.head(30))\n"1083   ]1084  },1085  {1086   "cell_type": "code",1087   "execution_count": 161,1088   "metadata": {},1089   "outputs": [1090    {1091     "data": {1092      "image/png": 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jRrjLV61atUwz76yy/jMLbT9nPWq7pna6lG7D4Et6tSsgJQYMGOC23VatWoWd7pVXXgnb5nUBkBirVq0K2Nd0OzPMOzOdl2UFfs8tE3EO6t2GoS7xbld8PpB9+d32xYsXD9sedd6TFdGXZi4Z4fibWQ0bNsxdb88991x6Lw4AIIshbAsAAAAAmdDAgQPN2rVr7fWnn37aFChQIEVfaOlSuHBhU6lSJXP++eebRx55xMyZMyeNXw0AAACQtlasWGFee+01c+mll5pTTz3VlCpVyuTLl89UrFjRnH322aZbt27myy+/NHv37mXTAACAhPP++C/cJU+ePPac5YwzzjC33HKLGT16tDl8+DBbB/Do3LmzOe2009zP0NevX8/6AQDETe74zQoAAAAAkBbWrFljXnrpJXu9Zs2aplOnTqma3/79++1lw4YNZuLEiWbw4MGmXbt25t1337VVcwHE50uz1atX2+vDhw+nSimQDSnApy/FHeXKlUvX5ckoVKGpevXq7u2VK1dmuaqF2ekYoNfatm1b9/aOHTvMjBkzTEaiNtavXz/z6aefmqSkpGT/37hxo738+++/dnuVLFnSPProo+aee+6xYVwAQOrkz5/ftGzZMuC+cD8gBhKlTZs2Zt++fe7t6dOnm507d2b4FX7s2DF7fqXLvHnzzKhRo+zIV2+//bb9LA/ISlREomvXrvb6SSed5Lu6b65cuWyF6muvvdZ+5v3YY4/FbfQaAAAI2wIAAABAJqMPC52qFapEqw8Q/apfv74NDHipWtfixYvN9u3b3fvGjh1rmjdvbqZOnWrDQQAAIHUuvPBCvuBDlte+fXt7cWj45tatW5uMYsyYMaZjx47m4MGDAfeXLl3aBlWKFi1qtmzZYqveHjp0yP5PYZZevXqZjz76yPz44492RAgAQMrpB0fjxo1jFSJdqXq9V6tWrcykSZNMRlGiRAnToEGDZPfr/GTTpk1m2bJl5vjx4+6P8i+//HLzySefmOuuuy4dlhbIeDp06GBOOeUUs2TJEhtK12foGs0CAIDUypnqOQAAAAAA0oyqoo0cOdINBWjIuFioaq2+1PJeFKjdtm2b+e2330ydOnXcaVUtoEePHnF/DQD8U+UNVd3ThSocaUuVJ51177d6CgAg49KoDddcc01A0FbB27/++ssGbGfNmmVHeZg/f749N9awzM7ws6JKt40aNTJLly5Np1cApF/FauecSJesVn0cADKiM888M9nnd7roh0yLFi2yo1P17t3bnV7B2zvuuMNs3bo1XZcbyChy5sxp7rvvPnf/eOaZZ9J7kQAAWQRhWwAAAADIRF555RW3coWCYPEcyvb88883kydPDqjW9c033xAoABA3CgznyJHDXrLyMOoAgIzlzz//tD8iO3HihL1dsGBB89NPP9kqVwrQ6rjkVahQITvs7Jw5c8y9997r3r9u3Tp7v1P1FgAAID2ULVvW/qC+b9++7n27du0yn376KRsE+D8333yzPe8X/ZBu/fr1rBsAQKoRtgUAAACATEJVuIYPH+7e7tSpU9yfQ9VyH3zwwYD7fv3117g/DwAAAJAW9u/fb2644QZz7NgxeztXrlzmhx9+MO3atYv62Dx58pjXXnvNdO/ePaDCbZ8+fRK6zAAAAH489NBDtoKnY9q0aaw44P8UK1bMtG/f3l4/evSoHekCAIDUImwLAAAAAJmEqszu3r3bXq9du7Y544wzEvI8zZs3D7i9cuVK32HgYcOG2WpfNWvWtB9oFihQwFStWtVceuml5s033zQHDhyIeXk0TN6NN95oqlevbvLnz2/KlStn6tevbwYOHGiHzUNyf/zxh+nZs6epV6+eXV958+a1VU+03hQO0ZCDsfjtt99syOTss882JUuWtMETbYsyZcqYc845x26fV1991axevTrifFRNTu34lltuscMyFy9e3OTOndu2k/Lly9vlU7VTffi9efNmX8umoWx//PFHO1zi6aefbgPjer2aX9OmTc2TTz5pq9D5oSFxnaqrqsDqGDt2rLn++uttu9aylihRwq5brctNmzaFnZ+Gd3Tm5103Xbt2de8PvqxatSpgHloffivBHjlyxPzyyy+2ss2FF15oTjrpJFuZT+vD2W/ULmbMmGGyOm2XQYMGmQYNGti2r/ZauXJl06FDBzNmzJiYq/D6HS55586d5vnnnzeNGze2+4eeV33XZZddZj777DP75U5K5+01d+5cOxyi9iP1tdrOtWrVMrfddputwpgorVq1cpd7wIAB7v1Tp0413bp1M3Xq1DGFCxc2RYsWtcumZVy2bJmveXv3A+07qVmerCBcG9Hw9drH1R+rv1OVHvVNWv9TpkzxNW+tK2feWoeO//77zzzyyCPusUOBRE2jClmh/PXXX+aBBx6wy6L27vS92u8ee+wxs2DBAl/LoX3ES7dD9Y/R9pV4Hg+C/f3337bPVwVUjQCgfVvrXv2sApvqb+bPnx/3Y0Aw7d/a/lrHFStWtCMclCpVyg4zfP/996eof9dQwxpSVccIzUuvS/3JddddZ9en1mtm995775m1a9cGhFJat24d0zxefvllU6NGDff222+/HfYcwLvtvRVzNf2QIUPsuXaVKlVs+9T/dW4UTPuPzq20HdSedc6k8y/91fbRudfIkSPd40pK+5R4HC/T8jioPvCpp56y61DLqflrnZx66qn2XFXnrAj9g802bdrYdqd+Q9ta/b/amJ8qzeqbYumrRKOxfPjhh+aSSy6x20rPq36rWbNmdrSWHTt2pHjeXvFuw36FO5auWLHCPProo+77Jr13UBvv2LGjmThxYsLOb8ItT1YUz88H4tFfi9rt008/bdu3zhPU3rX9dT585513xvQD5lDnxPoc5YMPPrD7sT5f0evW/qTbeu+s94HRhNvXtC+qL3CWXfPWa7788svNJ5984laEjwWfDaUtnb/pmOvYtm1bqucZy+cQDo7tGeMHZjrncradPm975513wk6vKq8vvPCC7Uv0vkbvA/ReXuea+txOx1E/7wUS8d7VORbqvZND76nCvY+K1E6vueYa97rOn7PC+xsAQDpLAgAAAABkCu3bt9engfbywAMP+HrM8OHD3cfoMnHixKiPWbp0acBjbrvttqiPGTVqVFLFihUDHhfqoml++OEHX8u+e/fugNcc6lK0aNGkL7/80k4f6+vMirTtLrjggqjbIVeuXEn33Xdf0tGjRyPOb/PmzUnnn39+1Pl5L/v27Qs5ryVLliTVq1fP93zKlCkT9fVOnz496dxzz406rwIFCiQ988wzUed30kknuY/RvrNt27akyy+/POK8CxcunPTjjz+GnJ/aYSzrTpeVK1cGzKNz587u/3Q9nO+//z6pRIkSvp/nqquuStq1a1fUdeL3+f3y9knxnp+2n3z++edJxYoVi/j6L7300qQDBw7EPO9I1LeVLVs24vM2btw4ac2aNb7nHbz+jx07lvTYY48l5cyZM+xz5MiRI2nAgAFJidCyZUv3eZ544omk/fv322NEpNecJ0+epPfffz/qvFPShwcvj999O7XTxSKl+1CoNvLGG28k5c2bN+L67tatW9KhQ4cizlvryple61Dt6tFHHw3brnbu3Bnw+K1btyZdeeWVvo413bt3Tzp48GDU5fBzibSvxPt44Fi2bFlS27ZtfS/jyy+/HNdjgPd4fM011/iax4033hj2WBzs66+/TipVqlTE+ek4uGPHjmTtxq/g9ZDW1L6rVq3qPn+RIkWS9u7dm6J5jRw5MuC1aL/x+5p1bCpevHjIdTxmzBj3sdp/TzvtNN9tRq9typQp6Xq8TORx0KFtpv4kd+7cUdfJhRdemLRp06ak7Eb9R3B/8u+//yadeuqpEddXzZo1k5YvXx7zvCPRef/ZZ58d9X3hpEmTfM87LdpwLEL1ia+99lpSvnz5Ii7P7bffbvuleJzfRFue1JyXxPs9gMS636fF5wOp6a9F27JPnz5Rt7surVq1irrvhFru//77L6l27doR5639XPt7JKH2Ne2DFSpUiNpvr1692vd2ysyfDaVk34s37/uRWM63vJ8F6L1+OCl9H+oHx/b4i+X4u2XLlqT69esHvO/55ptvQk6rzwJ1Hqtpou2reo+l43pav3f17o9+LpHaqd4b6XMJZ9pp06ZFfD0AAESTO73DvgAAAACA6FSpZPz48e7tWKtxxWL79u0Bt1XVIBJV7nn22WcD7lMVOVX/UgUuVUxxKrqp0syVV15pK+B26tQpYjUGVYr7888/A+5X1SrNe8uWLbbi1549e2wlXVW3ye5UYVDVZ7zbT1Vp6tatayt+qWLNvHnz7BDKqjSlIZFVZeK7776z1S6CHT582FxwwQX2MQ5tz1NOOcVWTdEwhap2qKqV2g6OUBUiVFmlZcuWZuPGjQHLpgrNqsKi5dG8tDxO9eNoFXS+/fZbW9FIVXMcRYoUsW1ElTVVGVdtRMujaVRlUW3R75BxWo6LLrrIzJ49295Wuzv55JNtxQxVL1TlNtm3b5+56qqrzKxZs2zlOS9VM2rbtq29PmnSJLdymKZT1aBQVP0qJfTanGVy9lunwrTWr9a9tpWzfVShRJW31G5S+pwZkarmqV2I2rWqSamNqc/QdnNev6o13nrrrbZaUzxoP1K1FG+FQac9qq1r3av/0/pW1Zh77703Rc9zzz332GqKonau16ftpwrkTj+r16jqZhUqVAgY9jzetI86lS+d9q59WpW/Fi9e7FZ81DpRxV1VmXP2B8ROfZe2v6jirKrbq29XpVZv9WAdX9XetY+H6ttD6dWrl60yKNp+TuVxbcPgSuh6Ph0blixZ4t6n44Eeo2pF6mucx6jv0XKrL/7pp5/sPuGlPkptQn20KrI7WrRoEbJfUuW6tDweaJnUvzvVFx06FqgP1/y0jrx9q7cKcLyOAdqfLr744oBqjzoe6/iu/k3HYFUl1nFbPv30U/sYVcMLXudeX375pbnhhhvsdnKocrsqVOtYp+2o1/7999/boVe1XTIjHZ/XrFnj3lZbUbtICfXzqtjtbGftZ6oKHI0qIWpdq51o3aqvVB+t47a2lZf6TG+VZO3HanPav3Q80WO0bZzzJb02vS9Q1USda6XX8TKRx0Htw3pf4K3crn5HbVX9gvZrna/qnEy0LlRZV/uwjj3ZldqW2p3TXlWlTn2P2s6///7r9hnaNjrnVdV8VZxLreXLl9tqdN4Ko2pj6vvUx+h+LZv+qo/8/PPPM9U5XzjPPfecrd4nqmqq16vzcR039R7HW2lb+7Oq8cK/tPp8INb+Wv2e+j8vVdxVhUjn+Oz0izouN2nSxPZRaq9+qOqkzrdVhV5U1VbzV3+nfs/ZjxcuXGjOP/98+xzB70nD0T6v0Vuc8xOdl6nP1GvVvJ3zA/XbOs5MnjzZVtONhM+G0ofOC7yfBWjEg4yOY3t86dir9wvO+0K9D9E5vPqcYOo/rr76ajsqkpf6AJ0nqM9y3gc459Kaj0YP8Nu24vHeVRXrdQ6pftD5XFK3w53vRhr9TSMBqQK6cwzRuUHDhg19vRYAAEKKGscFAAAAAKQ7Vazy/mLfb7WmlFS2HTx4cMBjIlX3e+uttwKmveyyy5JmzZqVbDrdp4ooznT58+dPmjt3btj53nPPPQHzvfjii211OS9VdNDzOVVQU1q9JCtYu3ZtUunSpd3XX7lyZVtR5vDhwwHTqTLdww8/bCtfOtOqSmYor776qjuNKkA8//zzSXv27Ak57cKFC+3/Tz755JDV4lSJ2VsJVlUuQ1U6PH78eNLs2bOT+vfvn3TGGWeEfb1z5syxbciZZ926dW1l1+AqUevXr0/q2rVrQNt47733fFWRcdbnWWedlfT7778HTKfnUeUqVW10pm/Tpk3Y+aamYqbfijKvv/66rRz8yiuv2ArHoWzcuDGpb9++AVXholXJzkyVbQsVKmTbhbZLv379klXj1Hpp2LBhQHuIVA3Qb2WeDRs2BFS/KliwoK3iEtzGJ0yYkHTKKack67P8VhRy2qSqUI4YMSLZ/j1+/PiAioKq9Oa3uqVf3uoyzvJUq1Yt6dtvv7X7r+PEiRO2KpjWhTN9rVq17P3hpKQPzy6VbdVvOn3erbfeaquceqmSmfcYq8vAgQN9Vb9TlU/9VUU2Hf+D+/BVq1YlHTlyxF7XNm7evHnA83Tp0sXuA8H7Wrt27QKm69SpU9wqJqbF8UBVHr0VwpyK8KEqu6lK+Mcff5zUunXrsFWlU9q2dNzVvuM8Vn3N0KFDk+3bqjKt7eetHtWxY8ew89V2VbvyVoPTsdnZ1qLr6muc/s3bb2WmyrYvvPBCxKqEsbriiisC5qdKz9Fes9av/qoN6pwtuFqfKpE5tA+WLFkyqWfPnnY+wX29s21Gjx5t+19vhcBI1TsTebxM5HFQldeaNWvmTqvlVv+2ffv2ZOvkgw8+cNe1Li1atAg4NmV1wX2pc5y++uqrk50bqt9SNTm/xw2//bTWd/DxqEePHsn2E+9xIvh9nJ/KtvFuwynhPZZqn9X7K1UIVIVz9cnB74W9fbnOxdUPh0Nl27T7fCA1/bWqQnof26hRI/teNvj9d+/evQPef9epUydif+2dp/M6VLn2jz/+SLYf672dd96qjB7quOGsn1Dz1j4bXBVXnzcF9xGqGh5JVvhsKDNWttU57yWXXBJwzrp48eIMXdmWY7t/fo6/M2bMCPgcQOten9GFc8MNN7jTamSTBx98MGndunXJjuc6Z/ZWqdY5XLjPFxL53jW1VdFDfS7ZtGnTFM8HAAAhbAsAAAAAmYC+tHM+FNSHqH7FGrbVl0feD1P1Aem2bdtCTqsvCL0Bl0ceeSTivPWlj4ZOdKbXFwKhaJhE71DW+tIk3FCb+gBYXyB7X2OsX6jE8sF2PC/x/PJGXzh5v2ALFfzweueddwKCtMEfrMsFF1wQdZjkUNsjVJhOIVxnXu+++66veUXa5gpTOfNTm4o2NKy+hPQGD8JN7/1iSxcNwRdpqOmnn37anVZfckb60jzRYdtYhsT+9NNPA8IKwQGFlDx/RgjbOpfPPvss7PT6wtv7Bay+RE7tlzreL6LVdyn0Go6+aKpevXrA8vr9klMXhZkWLVoUdvrJkycHTK8hz+MpeChHBb0i/fjjo48+Cpg+OCTglZI+PLuEbf2E49Wveb+0VHg2VN8eHBBy2u24ceOiLpPCqd7H9erVK+y0oY7PCtrFM2ybyOOBt20pFKVAuR/h+uKUtq0777zTfZyGeY421PvYsWMDzqGmT58ecrprr7024DxAfUc4GmbVe76X2cK23teqS7j9wi/vsV+Xn376KeprjvTjplDtOjioF+mYUrVqVV/nWIk8XibyOPjcc88F/Djg77//TooWwPf+0EOh5HgItf7idUnk+5a77ror4mP0YzFn2ho1avied7h+Wucd3ukiHZt1rh8cXo8070S24ZQIPpZG60sVANax2Zn+qaeeCjstYdu0+3wgpf21zse9y6QfI4X6MWmoH7Lqoh+XhhO8PApqh/tMRvRjS+/0Q4YM8d1HNGnSJOJy64cf3um//PLLLP3ZUGYJ22r96Qdgn3zySdJ5550X8Hoj9S0ZJWzLsd2/aMdfnYfqMx3n/2eeeab9kWE4Om563+N89913EZ9/zZo1SeXKlXMfox/3pfV713iFbfUjQmc+OlcM15cAAOBHztD1bgEAAAAAGYmGRPQO7ZUIEydOtMNxeYf8fPzxx+1wnKG8/PLL7pCDGlLs2WefjTh/DU2tIcKcocHGjh1rhzoL9tZbb9nhyUXD/Gr4MQ07FoqGkNWQ6sWKFTPZ1T///OMOk6l1q6FYNTRpJBpaXsNMioaIe+edd5JNs3btWvd68+bNfS2LtoeG24zHvMJtcw3T6ewP2u56vaGG3fZ6+umn7dC5sm3bNjvEtp/X8tFHH0UcalpDSWsYO9H3olOnTjXpJZYhsTU0qjOcoIZk/fnnn01WcdNNN9nhUMPR8MW33Xabe1vDoaaGhmb2tqfbb7/dXHDBBWGnL1u2rHn11VdT/HyDBw+2w9mG06xZMzt0drxeXzTqnzWEd6TtoaEo02p5sjINI68hqsNRP+g9xmpY4Q8++MDXvLVPaCjvaLxtt27duhGP++pDNVS29xzilVdeMfGUqOPBlClTzKRJk9zb/fv3N1dccUXc++JoNm3aZIYPH+7eHjFihKlRo0bEx2j42C5duri3X3/99WTTbNy40Q7V6j2Wqe8IR0OsPvTQQyaz0tC0jjx58gT0SSmhocHDzT8c7S9PPvmkr/lr3ylYsKCvaXVM6devn3v766+/Nml9vEzkcVD9mN5vOIYMGWKHFI7k7LPPNn369Im4D2QX1apVi9rv9u7d272+YsWKgPeBKaH3cd52r/eSkc719R5EQztnhnO+aLQuI/Wleg/foUOHNFuerCQtPx/w21+/8cYb7jLly5fPHqOd94Wh6FjrvP8WLaP6OL+vP9xnMnL//fcHtD1N/7/MbmQ6Z9S5Y6Tl1rmn99xj6NChIafjs6HE0PmoPl8JvqjN6XxE/eDMmTPdPubDDz+M2O9mBBzb40f7r96j6DMdad26tT22VKxYMexjvO8nddy6/PLLIz5HlSpVzAsvvBDzeVUi37umlPez9AMHDtjzHgAAUup/RzAAAAAAQIa2cuVK93pqQgIPP/ywKVmyZMB9+/btM4sXL7ahk+Avbfr27RtyPvpiaeTIke5thTBChSyDVa9e3YYtFezVF0C//fab/RDWyxsUuOaaa0yFChUizlPBUn3J4P1yN170AbCfAFJKxCs0rS/2HO3atTOnnXaar8d17tzZTJgwwV4fP368eeqppwL+7w0szZkzxwZ4UkrzOnLkiDuvOnXqxOX1durUyYY2otGXsTfffLN54okn3NfbrVu3iI9RUCRSqFGKFi1qQx3Tpk2zt+fPn28yCwUy//zzT3t9+vTpEcMKmUmPHj2iTqMfFTghwWXLltm2qR8DpIT2oYMHD7q377rrrqiPueyyy0zVqlXNmjVrYnouhQvU5v28vr/++ivhbVKBxQsvvDDiNAo8qM//7LPPEr48Wd2dd94ZtZ2qb9U20Y9Z5KuvvrJBUT/zjmbp0qVm3rx5AaER58vRSEEn9bXOF7RaLv1IJ1KoIyMcDz7++GP3evHixc2DDz5o0oP2GyeIc8YZZ5iLLrrI9/FdX16LzrNChZSPHTtmr+vc7Z577vHVt+oL8+PHj5vMZseOHe71ePw4S20i3PzDUeAvXDAstbw/sNDxPK2Pl4k8DqrP2Lx5s72uoFnXrl2N333A6ft0PFSgwm+AORy9/0rUe4Lg92bxouBztOOGAno6VjuhQR2nI4V0Itm+fbt7/uH8uC9auy9fvry56qqrzKhRozL8OV80d999t6/lccLpnBP5l5afD/jtr73LpDYc7ccwzmcmzvtv/VBDP+6J9OME0Xv7aNPIvffea+cn+kGzfpBbr169iI9Rnxbt/a5CnXfccYd55JFH7G19jqPjnrffyi6fDWVkWm/qE9u3b28yOo7t8aEfEHrf5+kH1QpbRzrGqV/QRfQ+rmfPnr6e67rrrrPHdL2P0w/plyxZYk455ZR0e++aUpUrVw64vWrVKvdHmAAAxIqwLQAAAABkAlu3bo3LF7IzZsyIOo2+dFGVrEiVef777z+zc+dO93abNm18L8NZZ51lv1ARVeHQh7YOfemuSm7e8Kgfl156aUK+UFHFRqdqbEblrb4XLfgWvB0cs2bNsl9web8UUyW7f//9115XdR+FHBRmSklISvP65Zdf7HWFevTBvio7xRo80TJ6q0Cl9PU61V8iadq0acwf2Ku6W0bpL3799Ve7/VShbM+ePckqJylw4Fi3bl2qn1NhGIVbolm/fr17XcvoJ8StYJwu0ahiYf369WPaZmpTu3fvNmXKlDEp8ffffwd8uettZ+FoP2vVqlXAl9J+nHfeefYL74zSJjPzPpIZXXLJJb6Ph84XlgryqNJRpKqBzo8GovEGqCRaFSTHlVde6YZtVUld5yF+K5yn1/HAe1xVKDClVRczwvFdxwBdvOE5b7+lL7lV/TIahVsU2vFz/MxonFEYxE8fGk3weZA3aBpOixYtUvRcCkX//vvv9jxNwQYdr7RPeysWep9fASjdjlbhOZ7Hy0QeB737gEKKWm4/FORVKFrHHK1DBUuciv4ppX0wlv0wI/BznFYIWe8tnR9dpuY4HRz29hMQFFX7jDVsmx7nfJGoH/Xzg1jOiWKX1p8P+OmvtUzeKtB+z4n0oxkdQ5zjkn74GG0/8Xv+p/WivtU5Pqhvjha2jeXc0gnbOvu69z1cdvlsKD3oh2uhKror4Kz3+PrRvPptBawfeOABM3DgQDsqgt82mR44tqeOfvimALwqfDu07V988cWoIXfvuj/zzDN9/VDROX/WewYnqKt9NVrYNlHvXVO7P8U6OgUAAOEQtgUAAACATMAZFkyifYGeWvrgVEMMRzJ37lz3ugKTqnTglzfk5w0Ri4IEXqrk5off6bIafZnmrTSoSnY//vijr8d6wxmq8qQva7wV31S1UJUxFNLURR/o9+rVywYN9CVko0aNzDnnnOMr+KDHKVip5VUQRO1FgQwFuxW6UhhXH/arslYkCoV6K8ipUlW4oSyDeR8X3O7CVdryw/tFgCqnpafVq1fboQA1PLhTtdCPeAQg1Z5+/vnnmB7jhMCiUVvzQ4FwP+0x+Mub1Gw3rXNHLBWbNURtrDJam8xoy5OVqV1HqzwW6nioL2P15bv610hVxfxUH1NlW4f6b7+VD4OfW+cA8QjbJup4oPCCggsOP2GuRPGea+nYntIqiHqN3u3lPdeK5fxJ02bGsK33i3UF7VIreB5+fgQXXKkvGgXTX3nlFRtU93POEnxMj/ZeIZ7Hy0QeB737gNpeLKMseEPWsa7DrCKW47QTto3XOZGOK37bQ0rOidLjnC8SzokSJ60/H/DTX3vPicTPjwxEPzhVe589e3ayz0VS+zqKFCliQ9/OiEjB6y0189Y5qPY3HZuceXv7Yz4bShydR0f68bc+X/njjz9sNWH9MEcVxlVp+ZtvvrE/GMuIOLanTseOHd0fOulYq3NFbf9Y170q1MZyXuU9xkc7r0rke9fUCB7lwPs5OwAAsSJsCwAAAACZjLeaVaxUNUSVpLwfLupDVlUSePnll+11fUCvIcj0POFCtJrG+4ForCG7cIEFb0UU54tUP/xOl9Vo/XlDlU6liZTOyxu21ZCVo0ePthVF9+7da+/TXw3l6AznqC+wW7dubYdqvPbaa8MOKa6ArqrLKMCrYK/oS30N1e0M160KZKr2o+q5qoIRKvzlbXehKi3G8lqjSUn1u9Tsm6mlapFafykJzgZXvc2sUlqxMDXbzbu+g4cWjySWaePdJvUl28MPPxz1sYMHD474RVdm20cyM7UXv5XAg4+HwcfVUJVt/fDOJ5aqgAp/qK04/Uy05fErUccD7dPOcOrit+JTInhfowLA3hBwvM61Yjl/yqznWt4w7L59+2xbTE2F2+C25yds63c/c368csUVV5jx48enaPn8HNPjebxM5HHQu65V4VCXlIhHyDozSuvjtLct6D1CuPcF6XVOFO71xWOUhXhUzUZoaf35gJ/+OniZYjkv8k7r55wo1uO0E7aN57y1L+tzAieUHzxvPhtKP/rMRJXfFbht3LixfZ+nz+duvfVWs2LFinQbnSESju2p4x1RQD9gVFXblKx7BWbj9TluWr53TQ0+iwAAxFPkkjEAAAAAgAzB+yG5n+FqY5mvqg7pA1oFNZ2qLAqa6AP64Kot8a4A4A20iBPEdOTNm9fXfLLrF5zxrMQQvC1EYQ9V3Onbt6+pUqVKyOf/4YcfbNj21FNPjRh2UmXcRYsWmXvuuSfkF5L6gl7hXlVgUSVBTRvq+eIhq33IrvXSoUMHN+SgSiIKBHz22Wd2WE9VcVSFN71u5/LEE0/EdRlUSck7/3AXDWvp6Ny5s6/HDBgwIK7Lmt2pPeiLtWgXb/VPpC+/x8JQx8NowbtoFcVDzSeW5QleJm+1ydRI1PEgePnS8/wiLc61UtO2MovgKoX//vtvquYX/MOmmjVrRn2M3/1MHn300YCgrUYRePXVV+05lqrBq10oSOMcI51gVVaUqH0A8PJzTuSnAikSI60/H/DTXwefW6X0WOrnnCil8/bzw4t4zZvPhtKfKnY+/vjj7u0tW7aYzz//3GREHNtTx1uxWCFrfaai88KMtO4T+d41NYI/S8+IYXQAQOZBZVsAAAAAyAS8ldUSFYBSZa6vvvrKVjLUEJuq/tWjRw/zyy+/RKxApA/24/WhbXAlGVVS9VPtaM+ePSYRNm/ebD+8ToRoFZL8CF43Cquqwmy8296gQYPsRUNGTpkyxV5+//33gICHvoRu06aN/V+9evXCDlf++uuvm9dee82GQKdOnWomT55s57Vx40Z3Og2B2KJFC/vXG/INfr3Tp09P1yG+MwoFWDWkuhO0/fXXX22Fm0icasVIHW+bjKWqcEoqECM5v18sZnax7K/Bx0NvxfJ4tfVYlkdhQJ1PhJpPvJYnnseDEiVKZJiKmHqNThU5VZru3bt33M+1UtO2MgtV/Ro6dKh7W6HVBg0axKWimNal3+HD/VA1L++y6odKGhkgVLX/jHI8T+Rx0Dvvu+++O2DdpDWdW7344osJmXe0SvKZhXd76b2hRt/wU92Wc6L4yKrnRBnt8wEJfn6/yxS8XH4ek9LjtJ/zv3jNO7t8NpTR6fMTL33O0q1btwzXD3FsTx19luZ8riajRo0yR48etX+jHXO9616jSf34448mq753DSX4s/T0HMEEAJD5EbYFAAAAgExAH6Y6nFBdoqp/9enTx/Tv39/9Ynns2LGmXbt2AdOVL1/eve4EcwsXLpzq5y9XrlzAbYU5Q1VUDaYh8hJV+SClQ6tF06hRo1TPQ5UYtN6dIJPCwYl0yimn2IvzpY2qw7300ktm5MiRbltQ+4m2zhQaUahAl7vuusuGsRR+UaDX+cBfw9oNHDjQvPPOOyHbXVq83sxi3Lhx7vUbb7wxatBW1q5dm+Clyh5OOukk93qoaszhLFiwwKSXVq1aZcjqzgqK64tCcf5Gk8hhJjMSfQmpLwf9DFcffDwMPq6mlPfLSPUfqjTnp2qRjuPe6kfx+lIzUceDAgUK2C95nZDt4sWLTXrRa3TCtvE83nnbRCxVURN1rpVoOiaqUqHTDhVGuP/++1M0r/nz55s5c+YEBHn9DpPrx2+//eb2fwosKdwZKWibEY7niTwOevfz9D7nW79+fcLeE+jcOSvwtgWdZ6g9nH766Rn6nEgy4jmR9/ia3c+JMtrnA6HOZZYvX+5rmZxpw80nFL/HabXjVatWxXT+p3mH+5Gsl85FvOG54Hlnl8+GMrrgH4x5f8yckfohju2po/NCBW61fZwfAamKsbaRRjbSe+r0XvcZ4b1rKMGfpWt0JgAAUsr/+EUAAAAAgHTj/aIy0cNYPvjggwFf/PTt2zfZl5CNGzcOuK2gZDwofOn9cNhbPSwSv9NlRU2aNIn7dvBL1dw+/PBD07FjR/e+iRMnxjz0m74w0Ov47rvvAiqyeEOkUqpUKRv2Ta/XmxreIUnj/aX+6tWr3et+qvXp+f/888+4LkN21bBhw4Avov0MT671r2rOCF+9yk8Fd/UzS5cuzTarcdq0aTEfD9VnxutLxHPPPde9ri9zZ86c6etxwX3NeeedF3XIZj99ZCKPB97jqo5p6XUMSNTx3bstZ8+e7StAoaCq322e0ShYcOWVV7q39TpUWT8lXn311YDbd955p0nU8bxu3bq+hrfViAJZ9Tjo3Qf89oFIP8HnoAqP+zFhwoQELVH2OScSjRqSFWXEzwfOOOOMgCCi3/dVCnp5fyAR6pwomN++T6F1byDWe6xP7byD12XwvPlsKGMIDrrqB2QZsR/i2B4fQ4YMMY8++qh7++uvvzZXX311xM/ivOt+7ty5NhyfGd67xuuzNO9n6fpRW40aNVI8LwAACNsCAAAAQCbgHRpZX2SntkpFJPpi/+GHH3Zv60vzr776KmCaihUrBgyb+8EHH8TlufPnzx/wRe2nn37q63GffPKJSQR90KsPcxNxGTBgQFyW0Vt1+NtvvzXbt283aU0f6jsU2vH7ZUyoD9Hbt2/v3t60aVPE1/vRRx/5rrKS3rzVfVQxOZ5iXQcKMatCG1Lv/PPPD/giU8N9R/PDDz+YNWvWsPojVMTTl39+1qOqq2YXfo5zGrJ79OjRYYeTTW2gztvW1f/6oR9keLdxqC9Qg6uf+e0jE3U8uPjiiwO+AFYgNT2OAd7Xp7DtwoULTTx4q5+rgq9GMIhm/Pjx6V5ZNDW857XSvXv3mPsPBXTff/999/app55qLr30UhNPsbZhTe+MLpAVj4PefUDnLcE/wkpLXbp0Sdh7AlWczwoUkvGG7t59992oQ4rrXH/MmDFpsHRZ+5xInw1k1UB6Rvt8wFkm7w8NPv74Y18BMO85kfgZjUSjvjjV9iPRMjgUTvaG6sLROWO0fTR43qVLl7Y/BsmOnw1ldJMmTQo7OlZa9EOHDh3ydU7JsT1+nnnmmYDPFb///nv7eZq2RSht2rRxf7yQ6HPIeL53jddnad4fhJ1zzjlxHZ0CAJD9ELYFAAAAgExA1UO8lSUSXcn1rrvuChi+Sx/geoeBlt69e7vXv/jiC/tFUDx07drVva6hejUkWiQKmGbnKp233nqrOzzb/v37TY8ePeIy31gqRmioyHBDGMZaecI7r1DDzvXs2dP9gkCVgfr162cygwoVKrjXlyxZEtd56wtOxx9//BFxWlUveeCBB+L6/NlZ8eLFzY033ujefu+99yJWctuyZUuKhy/P6rzVvb788stkxxwvfYH4xBNPmOxEAYNooc+hQ4cGBNh0fIjnD3FuuummgCDF/PnzIz5GX7grpOm44447wu5H3rCe3z4yUccDnYdomRy33357zBXb43EMuOKKK0zt2rXdY6nWXzwCxQpHegMUWm+R5qsvwh977DGTVoFGVbt3Lt4hsVOjUaNGAfuDKgDecMMNvtfnokWLzDXXXOOe0+jL+XfeeccuY6KO56oOF2045qeffjrdfzyTyOPg2WefbYMh3n3eT+gM6UfvIb37mdpoOAr5qV/T+xeEPydSiDZaOP3xxx/P0j9AyoifD+hHG9627v0xRrhA9AsvvODevuiii3yFIfXeLdqPdFUx9/XXX3dvq5q7wu9+qqm/8cYbEafRuad3fes4HSqglt0+G9L5ifd8ReslPamdBPe3qf1BkLcf2rBhQ9RK+qq0unXr1qjz5dgeX3pP/Oyzz7q39cOkyy67LGTV2jJlypjOnTu7t/v375+wHwHH872r932U2tiuXbtStEzez9Fbt26donkAAOAgbAsAAAAAmYDCJBdeeGHchzUOR0NqeauAKVAT/MWGvlhv2rSpva5Q1HXXXWeGDx8eNVypSgSqjhJuaEOFebzDed12221m8uTJIadVpTfvh8XZUZEiRcygQYPc29pOCpBEC2g4Qyl36tQpZNUJDY85bNiwZEHaYPqw2/vhvipaqQqN90s83afwXLSw0vLly+0H7pE+AFdVxIceesi9PXjwYBsejVbhQm1U+42+fIwWSE0Eb3vXNvIOFR2P4JRD61kV40JR1WN98bJ48eK4PTeMGThwoBvMUztTG3vzzTeTtXcNma1qLStXrrRfdCGQgmzeMKIq9YSiPknHm2hBz6xGbUuVisIFNRUu8AYd1Od4K0fFg84LnFCsQor6En/p0qVhj8/eAF758uXNnXfeGbaqub54dyj44WdY00QdD3Rc9QZb9EWxqt1GG1VA8wrX/6bkGKD18vLLL7uBTp0LaTn8hCtVBfeee+4JCPZ459u3b9+AUOctt9wSsgqW+rFu3brZ84XMTkGk008/3b2tapoKcipIGy0soL7bW21f7aN58+ZxX0ZVOHW2t9a9tmGoqoM611bb0PEnqx8H1Yad80qdv6gSZLRtJtrHFBL39hFIvI4dOwZUt33yySfNvffea0dmCR7KWT8o+O677zgnCuGSSy5xj7fap8L92EJ9gQJ28aokmlFlxM8HdC6qCucOtXNVlQxF5w86J3N+LKDjsEJufr3yyithq4br2KTzMec9u4Kwffr0iencTueQoeicU/258wM4nR+F+7EEnw2lH51/6tio8zmHPqfzfkaQEqreXLly5YA2vmfPnpDT6nMj9fd+cWyPL+3zL730kntbP3rScSTUZ3kK56pCtfNZntqO+s1o9GOp559/3h7n0/q965lnnun+wNJpP7H+qF8/7JkxY4Z7W+sHAIDUyJ2qRwMAAAAA0sy1115rvvrqK3tdlUJeffXVhFcmUnDFGTZYH57rSyWnkom+JNLy6EN4faGtYIwCGfrg8+qrr7Yflqqiir4YVPBTwQ99uKkqd5FCNPpiUVWxVO1FAQN9QKzwgT7Uvfzyy23FXX3Qq3WgYaM1jb6Ay67DBYq+gFWlF1VZc4I8Wj/XX3+9DYNUqlTJ5MuXz37Bp8oR//zzj/n111/dinGhvohRkE2VJVQpV0FvbWd9oahqs2oDahf6UH7EiBFmx44d7uNCVd5RRSa1X1Vnbtu2ralfv7455ZRT3Aq4Cg5p2MNRo0a51a3y5s1rHn300ZCvVyE8DWX4008/uV9A6rEKGWvITIW6tIyqeLFixQq7bn7++We3LadHZVe1X305oS8d9KVorVq1TL169UzZsmUDqgNpyF3dF2tlJc1b+4oTclFwytlftP/pC2l9CabArbaDvpT1OxQnIlOlFQ0Lq7Co+junwrS+9DrttNPsvqdQiROQU9vXl5W6iP4PYy644ALbNzhfgimEMH36dNu/64tefbmrvkSVw7QP6QcB6idmzZqV5VefXr+qn2k/VihVfbNCgupDVc1Mx+Kvv/7anV7BNAVvdJyOJ7VdfZHrVC7UsV/DBqvqmJZHx3xtGwVOdUx2QoLq43Ss8FY9D6Yffjhf9OoLYu1X6iOLFSvmhg/VN6qPTIvjgcIkqozmDG2qkGDNmjVtmETHTFUg1Ze8er1qgwqMKXisL7D1o4Z4HQP0pbN+UOOEYydMmGADRzrP0g9SVKFWP5DS/qGqYzq+axqdc0m4CtA6bui1aVrnvEGhYt2vbap1rvWq8zEFG9WmtCyZebh3nV/qHFTbxwkPK6CiL/DVfnVc1PrUMVLnmfPmzbP7lv4GV5BMVFX9qlWr2vMlp91pP9K21HbROZiOMbqt81+nr1SI/e233zZZ9TioPk/9mc5rtP9oCGDNU9tR56faLxUA27t3r92v9X9tV+fYkN1/lJfWdNxR+9T7D+cHCvoBhdqojtsKZet+JzCtvkXHdZ27OjgvMvbYd/fdd5sXX3zRrVSo99a6T32Bqtiqb9IQ4Or3tQ/ovbPzWUFWkxE/H9A5qH5ArHMN/VhFPy5QgFzhsg4dOtj+XP2S+iMtu7cSo47pzo+Wo9F5h96zadtr++o16zxA60CVRnXe4H0vrvBsuB81h5u3s8w6t9A5p9476nipvtf7AyZVLvWGL734bCgxdC6mH1oF0/FQ7Us/Qgn+kbXOZXQunNrq+9qmjzzyiHusVl+jc8T77rvP/tV5sIKUn332mW3nuXPnNjfffLN97mg4tsef3tPo+Kkfamnb6PM1tR29R/KOkqZ9WD/Q1udy6rf0maD6Mb2/UQBVxxhNr89sFcZViFvviaZOnWrbncK5af3eVSOsqI9yCkDovZHeW+p8UO+DHHoNap+h6PNPjdbh7CP6fBMAgFRJAgAAAABkCgcPHkwqXry4fr5vL3PmzIn6mOHDh7vT6zJx4sSYnvOll14KePyIESOSTbNp06ak5s2bB0zn9xLJqFGjknLlyhV1Hueee27S3r17U/U6s4ITJ04kDRw4MClnzpwxbwe1k2CxzkPbaujQocnms3LlypjnVaBAgaQxY8ZEfL1HjhxJ6tGjR4raXbj2cdJJJ0VcJ6F07tzZfYyuRzJo0KCkHDlyRFw2ra+UzP+rr75Kyp07d9TXXqhQoaSffvop6YknnnDva9myZVxenx/ePine89P28yO4TQav85TO+/vvv08qU6ZMxPXfqFGjpDVr1iS99dZb7n3169eP6/pPyTrxS23FmbfakB9+25osWLAg6jrUpXr16na7+V0ev/t2SvqAaFK6DwVvx3Xr1iXVqFEj6rrJnz9/0rhx4+K2TUJ55ZVXovZl3uWJ1p/LsWPHki699NKI8wrXnhNxPHCW6e67745pfpHaYUqOAY5hw4bZdRnr64u0PLt3705q0KBB1Hno2DJy5MgUtxutY+/8Irn44ovd6cqVK2fPfeNt//79SbfeemvM50tly5ZN+uijj+L+moNt27YtqVatWr6WqVu3bgk7ponfeSfyOOj48ccfA94H+b3E41wjs4h1e8Vy7It13osXL04666yzIm6bChUqJE2aNClp4cKFAfdrH02vNhyLlPSJsfQNWg8NGzZMt3P7eL8HiMc5aiI+H0hNfy1//PFHTH1Tnz59os4zeLm7d+/u+5igzwT87g/Lli1LuuSSS3zN+8knn/S1PjL7Z0N+31tMmzYt4DlefvnlpHjx9smxXq655pqkDRs2xG0/PH78eNIVV1zh61zx/fffj3kf59ge/+PYe++9F3COq3P9nTt3Jptu+vTpSZUrV465jYU7viTyvausX78+6vwiHatuuukmd7p+/fpFfT4AAKKJb3kDAAAAAEDC6Nf+qh7nUNWWRFO1LFWFczz11FNuNQCHKsmo4psqWpx33nlRK2jUqVPHDumqyhiRqGqLqtyp0mEohQsXNj179rQVFnQ9u9N613C5qjyhdeet8BCKqkqoApmqSqjyT7ChQ4fayhbeKhihaDg3VfFRlThV3Amm9qEqOKoy4QyHGo7+r2VXpSZV2In2vKqUpTaiikaqLhSJ2rEqJ6oyVCKGfvZDVYxUFeT222+31b1Utcpb0TA1VOlDFYi8Q2R76XlUDUqVC+M9tDz+R1X2VF3o2WeftZVSVOVT1WVUOUX7kqprqepPlSpV3Kqa4nco7exAlXTUp6taYShanzoOqjpptWrVTHaiCuWq1KjqjuGq/qmf1T6uSkWJpKqvqqqpPiVcBSL1yaqu7qc/d/ooDb+sip46Np188sn22O6nKleijgdaJh0LdY6jKrKRKgWrqqCOpVr2RBwD1O7Vv6haqB4XidabqrSq0qh3eNZgOr6rT9K5Q7jzKFWj0utXu0s0nV9q//dW59O5b7zp/EiVNFUtThXYVLE/Wr+k89/ly5fb6RNNxw5V8db5ULj2oaqGquiV0YaOT+RxUI9X9WjtR9FGANBzqj/U/usdVhlpR9WL9d5g+PDhtrKeqh+rb1b/qwp62i46PrRo0SKgLWj/jPYeJrvQelAlPr0fV8XIYDo+qrqr1nN2ObfPiJ8P6DxiwYIF9tge6b1u48aNzcSJE23/GCuNXqOLzgVDUbV97Ws6JsRSzdQ593r66adtfx2KKvFrxAKNOOFHdvlsSK/R+5o10lBa0nrV69TxVOf9GslAFcO/+OIL29/Gi859VQVV54rh2vc555xjj+2qXhorju3xd9ttt9n+wHnfopFiNIKMtwK2aF/SaAkaeUOVuCPRMUh9mKb1WzE83u9d1c+pn9AoJvqsQPOP9vmiQyOAfPvtt+5r0YgRAACkVg4lblM9FwAAAABAmtCQzRouVYEEfSGi4Vgz2lCbGmpMX3JoiFANaacPMzVkqIIBCgJ6w7t+6UNgBQ/0ZazCIfowWMEXDSeG0DS86N9//22H7d22bZsd1ldfyOhDaX2ppfCInyHGNVSc1r/CExryTUNW6ssdbVN9+aYv0XTdDy2DvlhXWELDXWteCkkp+Ktl0pCXKd2mGuZOISYNg7d9+3a73Ao/qa3otWpZswN9zKMvLPTFu9aD1oG+cGvWrFmK9j0khr5YHDt2rL2uLy8HDhzIqg6ycuVK+8Xtpk2b7HH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     "text/plain": [1094       "<Figure size 1400x900 with 1 Axes>"1095      ]1096     },1097     "metadata": {1098      "image/png": {1099       "height": 887,1100       "width": 13891101      }1102     },1103     "output_type": "display_data"1104    }1105   ],1106   "source": [1107    "# ── Bar chart: top-40 columns by % missing ───────────────────────────────────\n",1108    "top_missing = missing_df.head(40).reset_index()\n",1109    "top_missing.columns = ['Column', 'missing_count', 'missing_pct']\n",1110    "\n",1111    "# Colour-code: red if essential + high-null, orange if high-null, blue otherwise\n",1112    "NULL_THRESH = 60\n",1113    "colors = []\n",1114    "for _, row in top_missing.iterrows():\n",1115    "    if row['Column'] in essentials and row['missing_pct'] > NULL_THRESH:\n",1116    "        colors.append('crimson')      # essential but high-null → protected\n",1117    "    elif row['missing_pct'] > NULL_THRESH:\n",1118    "        colors.append('tomato')       # high-null → will be dropped\n",1119    "    else:\n",1120    "        colors.append('steelblue')    # low-null → kept\n",1121    "\n",1122    "fig, ax = plt.subplots(figsize=(14, 9))\n",1123    "bars = ax.barh(top_missing['Column'], top_missing['missing_pct'], color=colors)\n",1124    "ax.axvline(NULL_THRESH, color='black', linestyle='--', linewidth=1.5,\n",1125    "           label=f'{NULL_THRESH}% threshold')\n",1126    "ax.set_xlabel(\"% Missing\", fontsize=13)\n",1127    "ax.set_title(\"Top-40 Columns by % Missing Values\\n\"\n",1128    "             \"(Red = essential+high-null [protected], Orange = high-null [dropped], Blue = kept)\",\n",1129    "             fontsize=13)\n",1130    "ax.invert_yaxis()\n",1131    "ax.legend(fontsize=11)\n",1132    "ax.tick_params(axis='y', labelsize=9)\n",1133    "plt.tight_layout()\n",1134    "plt.show()\n"1135   ]1136  },1137  {1138   "cell_type": "code",1139   "execution_count": 162,1140   "metadata": {},1141   "outputs": [1142    {1143     "name": "stdout",1144     "output_type": "stream",1145     "text": [1146      "Dropping 44 columns (>60% null, not essential):\n",1147      "['TechEndorse_13_TEXT', 'TechOppose_15_TEXT', 'JobSatPoints_15_TEXT', 'LanguagesHaveEntry', 'LanguagesWantEntry', 'DatabaseHaveEntry', 'DatabaseWantEntry', 'PlatformHaveEntry', 'PlatformWantEntry', 'WebframeHaveEntry', 'WebframeWantEntry', 'DevEnvHaveEntry', 'DevEnvWantEntry', 'SOTagsAdmired', 'SOTagsHaveEntry', 'SOTagsWant Entry', 'OfficeStackHaveEntry', 'OfficeStackWantEntry', 'CommPlatformHaveEntr', 'CommPlatformWantEntr'] ...\n",1148      "\n",1149      "Shape after dropping high-null columns: (23455, 126)\n"1150     ]1151    }1152   ],1153   "source": [1154    "# ── Drop high-null columns, but NEVER drop essentials or the target ───────────\n",1155    "NULL_THRESH = 60   # percent\n",1156    "\n",1157    "cols_to_drop = [\n",1158    "    col for col in df_salary.columns\n",1159    "    if col != TARGET\n",1160    "    and col not in essentials\n",1161    "    and missing_pct[col] > NULL_THRESH\n",1162    "]\n",1163    "\n",1164    "print(f\"Dropping {len(cols_to_drop)} columns (>{NULL_THRESH}% null, not essential):\")\n",1165    "print(cols_to_drop[:20], \"...\" if len(cols_to_drop) > 20 else \"\")\n",1166    "\n",1167    "df_salary.drop(columns=cols_to_drop, inplace=True)\n",1168    "print(f\"\\nShape after dropping high-null columns: {df_salary.shape}\")\n"1169   ]1170  },1171  {1172   "cell_type": "markdown",1173   "metadata": {},1174   "source": [1175    "#### 2.4 – Descriptive Statistics"1176   ]1177  },1178  {1179   "cell_type": "code",1180   "execution_count": 163,1181   "metadata": {},1182   "outputs": [1183    {1184     "name": "stdout",1185     "output_type": "stream",1186     "text": [1187      "ConvertedCompYearly – summary statistics:\n",1188      "count     $23,455\n",1189      "mean      $88,220\n",1190      "std       $69,256\n",1191      "min           $65\n",1192      "10%       $10,000\n",1193      "25%       $39,313\n",1194      "50%       $75,320\n",1195      "75%      $120,000\n",1196      "90%      $180,000\n",1197      "max      $440,856\n",1198      "Name: ConvertedCompYearly, dtype: str\n"1199     ]1200    }

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