Clustering - v2 - Harsha.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "983725fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "c4b356f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 12132707\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06b78eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_df = pd.concat([pd.read_csv(\"data/processed/ProcessedSurveyDataWithID_%d.csv\" % i) for i in range(2020,2023)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6dc81ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "SURVEY_YEAR = \"SurveyYear\"\n",
    "TITLE = \"CurrentTitle\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5244def8",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_df.loc[survey_df[\"CurrentTitle\"].isin([\"Machine Learning/ MLops Engineer\", \"Machine Learning Engineer\"]), \"CurrentTitle\"] = \"Machine Learning Engineer\"\n",
    "survey_df.loc[survey_df[\"CurrentTitle\"].isin([\"Data Analyst\", \"Business Analyst\", \"Data Analyst (Business, Marketing, Financial, Quantitative, etc)\"]), \"CurrentTitle\"] = \"Data Analyst\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0725127",
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASSES = [\"Research Scientist\", \"Data Scientist\", \"Data Analyst\", \"Machine Learning Engineer\", \"Data Engineer\"]\n",
    "CLASSES_FILTER = survey_df[TITLE].isin(CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2edd1e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def year_filter(years=[2022]):\n",
    "    return survey_df[SURVEY_YEAR].isin(years)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2dca3a5",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "- Age (Numeric?)\n",
    "- Country (Categorical)\n",
    "- MOOCLearning (#Platforms used - Numeric)\n",
    "- UniversityCourse (bool)\n",
    "- Highest Education (Cat)\n",
    "- Research (Should use only 2022 data for this ?) - Figure out\n",
    "- Programming Experience (Numeric)\n",
    "- PythonUsed?\n",
    "- SQL Used?\n",
    "- R Used?\n",
    "- Number of Programming Languages used (numeric)\n",
    "- IDEs Used\n",
    "- #HostedNBsUsed\n",
    "- VizInPython\n",
    "- VizInR\n",
    "- #VizLibraries\n",
    "- ML Exposure\n",
    "- MLFrameworks\n",
    "- #CV ALgos\n",
    "- #NLP Algos\n",
    "- TransferLearningDegree\n",
    "- InvestmentInML\n",
    "- AWS\n",
    "- GCP\n",
    "- Azure\n",
    "- #CloudProviders\n",
    "- ExperienceWithCloudCompute\n",
    "- ExperienceWithCloudStorage\n",
    "- ExperienceWithRelationalDB\n",
    "- ExperienceWithNonRelationalDB\n",
    "- ExperienceWithBITools\n",
    "- PowerBI\n",
    "- Tableau\n",
    "- ExperienceWithMLaas\n",
    "- ExperienceWithAutoML\n",
    "- ExperienceWithMLDeploymentTools\n",
    "- ExperienceWithMLMonitoringTools\n",
    "- ResponsibleAI\n",
    "- GPUUsed\n",
    "- TPUUsageFrequecy\n",
    "- ExperienceWithSpecializedhardware\n",
    "- EngagementWithDSMedia\n",
    "- ExperiencePipelineAutomation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0bd6321",
   "metadata": {},
   "source": [
    "#### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f31ed1b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_col_names(df, include_kws, exclude_kws=None):\n",
    "    \"\"\"\n",
    "    Return the column names that match any of include_kw and none of the exclude_kw\n",
    "    \"\"\"\n",
    "    res_cols = []\n",
    "    for col in df.columns:\n",
    "        inc = False\n",
    "        for include_kw in include_kws:\n",
    "            inc = inc or (include_kw in col)\n",
    "        exc = False\n",
    "        if exclude_kws:\n",
    "            for exclude_kw in exclude_kws:\n",
    "                exc = exc or (exclude_kw in col)\n",
    "        if inc and (not exc):\n",
    "            res_cols.append(col)\n",
    "    return res_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31d47093",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_col_to_df1(df1, df2, col_name, new_col_name, identifier=\"PID\"):\n",
    "    res = pd.merge(df1, df2[[identifier, col_name]], on=identifier, how=\"inner\")\n",
    "    res = res.rename({col_name: new_col_name}, axis=1)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "93755140",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_frequency_feature_in_df1(df1, df2, cols, new_col_name, identifier=\"PID\"):\n",
    "    df2 = df2.copy()\n",
    "    df2[\"AggCol\"] = df2[cols].sum(axis=1)\n",
    "    res = add_col_to_df1(df1, df2[[identifier, \"AggCol\"]], \"AggCol\", new_col_name)\n",
    "    del df2\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb90f59",
   "metadata": {},
   "source": [
    "#### Convert Age to Ordinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8fce3905",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/07/r04lbdjd0vj2ps53pqkc0b040000gn/T/ipykernel_1115/1204961570.py:2: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  survey_df[\"Age_categ\"] = enc_age.fit_transform(survey_df[\"Age\"].to_numpy().reshape(-1,1))\n"
     ]
    }
   ],
   "source": [
    "enc_age = OrdinalEncoder(categories=[np.sort(survey_df[\"Age\"].unique())])\n",
    "survey_df[\"Age_categ\"] = enc_age.fit_transform(survey_df[\"Age\"].to_numpy().reshape(-1,1))\n",
    "survey_df = survey_df.drop(\"Age\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a050e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Duration (in seconds)</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>IsStudent</th>\n",
       "      <th>MOOCPlatforms_Coursera</th>\n",
       "      <th>MOOCPlatforms_edX</th>\n",
       "      <th>MOOCPlatforms_Kaggle Learn Courses</th>\n",
       "      <th>MOOCPlatforms_DataCamp</th>\n",
       "      <th>MOOCPlatforms_Fast.ai</th>\n",
       "      <th>...</th>\n",
       "      <th>Q35_B_Part_4</th>\n",
       "      <th>Q35_B_Part_5</th>\n",
       "      <th>Q35_B_Part_6</th>\n",
       "      <th>Q35_B_Part_7</th>\n",
       "      <th>Q35_B_Part_8</th>\n",
       "      <th>Q35_B_Part_9</th>\n",
       "      <th>Q35_B_Part_10</th>\n",
       "      <th>Q35_B_OTHER</th>\n",
       "      <th>PID</th>\n",
       "      <th>Age_categ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49970</td>\n",
       "      <td>1838</td>\n",
       "      <td>Man</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TensorBoard</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>1_2020</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49971</td>\n",
       "      <td>289287</td>\n",
       "      <td>Man</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>2_2020</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49972</td>\n",
       "      <td>860</td>\n",
       "      <td>Man</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3_2020</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49973</td>\n",
       "      <td>507</td>\n",
       "      <td>Man</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>4_2020</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49974</td>\n",
       "      <td>78</td>\n",
       "      <td>Man</td>\n",
       "      <td>Japan</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>5_2020</td>\n",
       "      <td>3.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23992</th>\n",
       "      <td>23992</td>\n",
       "      <td>331</td>\n",
       "      <td>Man</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>23992_2022</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23993</th>\n",
       "      <td>23993</td>\n",
       "      <td>330</td>\n",
       "      <td>Man</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>23993_2022</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23994</th>\n",
       "      <td>23994</td>\n",
       "      <td>860</td>\n",
       "      <td>Man</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>23994_2022</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23995</th>\n",
       "      <td>23995</td>\n",
       "      <td>597</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Israel</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>23995_2022</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23996</th>\n",
       "      <td>23996</td>\n",
       "      <td>303</td>\n",
       "      <td>Man</td>\n",
       "      <td>India</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>23996_2022</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70006 rows × 566 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Duration (in seconds) Gender                   Country  \\\n",
       "0           49970                   1838    Man                  Colombia   \n",
       "1           49971                 289287    Man  United States of America   \n",
       "2           49972                    860    Man                 Argentina   \n",
       "3           49973                    507    Man  United States of America   \n",
       "4           49974                     78    Man                     Japan   \n",
       "...           ...                    ...    ...                       ...   \n",
       "23992       23992                    331    Man  United States of America   \n",
       "23993       23993                    330    Man  United States of America   \n",
       "23994       23994                    860    Man                    Turkey   \n",
       "23995       23995                    597  Woman                    Israel   \n",
       "23996       23996                    303    Man                     India   \n",
       "\n",
       "      IsStudent  MOOCPlatforms_Coursera  MOOCPlatforms_edX  \\\n",
       "0           Yes                       1                  0   \n",
       "1            No                       1                  0   \n",
       "2            No                       1                  1   \n",
       "3            No                       1                  0   \n",
       "4            No                       0                  0   \n",
       "...         ...                     ...                ...   \n",
       "23992       Yes                       0                  0   \n",
       "23993       Yes                       0                  0   \n",
       "23994        No                       0                  0   \n",
       "23995        No                       0                  0   \n",
       "23996       Yes                       0                  0   \n",
       "\n",
       "       MOOCPlatforms_Kaggle Learn Courses  MOOCPlatforms_DataCamp  \\\n",
       "0                                       1                       0   \n",
       "1                                       0                       1   \n",
       "2                                       0                       0   \n",
       "3                                       0                       1   \n",
       "4                                       0                       0   \n",
       "...                                   ...                     ...   \n",
       "23992                                   0                       0   \n",
       "23993                                   0                       0   \n",
       "23994                                   0                       1   \n",
       "23995                                   1                       0   \n",
       "23996                                   0                       0   \n",
       "\n",
       "       MOOCPlatforms_Fast.ai  ...  Q35_B_Part_4   Q35_B_Part_5  Q35_B_Part_6  \\\n",
       "0                          0  ...           NaN   TensorBoard            NaN   \n",
       "1                          0  ...           NaN            NaN           NaN   \n",
       "2                          0  ...           NaN            NaN           NaN   \n",
       "3                          0  ...           NaN            NaN           NaN   \n",
       "4                          0  ...           NaN            NaN           NaN   \n",
       "...                      ...  ...           ...            ...           ...   \n",
       "23992                      0  ...           NaN            NaN           NaN   \n",
       "23993                      0  ...           NaN            NaN           NaN   \n",
       "23994                      0  ...           NaN            NaN           NaN   \n",
       "23995                      0  ...           NaN            NaN           NaN   \n",
       "23996                      0  ...           NaN            NaN           NaN   \n",
       "\n",
       "       Q35_B_Part_7  Q35_B_Part_8  Q35_B_Part_9  Q35_B_Part_10  Q35_B_OTHER  \\\n",
       "0               NaN           NaN           NaN            NaN          NaN   \n",
       "1               NaN           NaN           NaN            NaN          NaN   \n",
       "2               NaN           NaN           NaN           None          NaN   \n",
       "3               NaN           NaN           NaN            NaN          NaN   \n",
       "4               NaN           NaN           NaN            NaN          NaN   \n",
       "...             ...           ...           ...            ...          ...   \n",
       "23992           NaN           NaN           NaN            NaN          NaN   \n",
       "23993           NaN           NaN           NaN            NaN          NaN   \n",
       "23994           NaN           NaN           NaN            NaN          NaN   \n",
       "23995           NaN           NaN           NaN            NaN          NaN   \n",
       "23996           NaN           NaN           NaN            NaN          NaN   \n",
       "\n",
       "              PID  Age_categ  \n",
       "0          1_2020        4.0  \n",
       "1          2_2020        3.0  \n",
       "2          3_2020        4.0  \n",
       "3          4_2020        3.0  \n",
       "4          5_2020        3.0  \n",
       "...           ...        ...  \n",
       "23992  23992_2022        1.0  \n",
       "23993  23993_2022        9.0  \n",
       "23994  23994_2022        2.0  \n",
       "23995  23995_2022        4.0  \n",
       "23996  23996_2022        0.0  \n",
       "\n",
       "[70006 rows x 566 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "532c1e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = survey_df.loc[CLASSES_FILTER, [\"PID\", TITLE, \"Age_categ\", \"Country\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f17f2bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "CATEGORICAL_FEATURES = []\n",
    "BINARY_FEATURES = []\n",
    "NUMERICAL_FEATURES = [\"Age_categ\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dcc68b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_cluster_mapping = {\n",
    "    \"AsianFuture\": ['Russia', 'Egypt', 'Viet Nam', 'India', 'China', 'Nigeria', 'Pakistan', 'Indonesia', 'Bangladesh', 'Morocco', 'Iran, Islamic Republic of...', 'Tunisia'],\n",
    "    \"DevelopedGuards\" : ['Portugal', 'Israel', 'United Kingdom of Great Britain and Northern Ireland', 'Italy', 'Spain', 'Canada', 'Australia', 'United States of America', 'Poland', 'France', 'Germany', 'Netherlands', 'United Arab Emirates', 'Japan'],\n",
    "    \"SouthAmericaAndOthers\": ['Peru', 'Colombia', 'South Africa', 'Mexico', 'Brazil', 'Argentina', 'Chile', 'Turkey', 'Thailand', 'Taiwan', 'South Korea', 'Saudi Arabia', 'Philippines']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "154515cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_country_cluster(country):\n",
    "    for key in country_cluster_mapping.keys():\n",
    "        if country in country_cluster_mapping[key]:\n",
    "            return key\n",
    "    return \"Others\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5431c903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SouthAmericaAndOthers'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_country_cluster(\"Peru\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "14e79cb6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "adf[\"CountryClusters\"] = adf[\"Country\"].apply(lambda x:get_country_cluster(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "60868043",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "CATEGORICAL_FEATURES.append(\"CountryClusters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "283ada31",
   "metadata": {},
   "source": [
    "<center> Feature: <b>MOOC Learning Platforms</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a2f35abe",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"MOOCPlatforms_University Courses (resulting in a university degree)\", \"CompletedUniDegree\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe838df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "MOOC_COLS = get_col_names(survey_df, include_kws=[\"MOOCPlatforms_\"], exclude_kws=[\"Other\", \"None\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "43127aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = get_frequency_feature_in_df1(adf, survey_df, MOOC_COLS, \"NumCoursePlatforms\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ae0487e",
   "metadata": {},
   "outputs": [],
   "source": [
    "BINARY_FEATURES.append(\"CompletedUniDegree\")\n",
    "NUMERICAL_FEATURES.append(\"NumCoursePlatforms\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df09a0fc",
   "metadata": {},
   "source": [
    "<center> Feature: <b>Highest Education (Ordinal - Treated as numeric)</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2923da27",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"HighestEducation\", \"HighestEducation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b30420d",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = OrdinalEncoder(categories=[[\"Other\", \"Lower than Bachelor\", \"Bachelor\", \"Master\", \"Higher than Master\"]])\n",
    "adf[\"EduOrdinal\"] = enc.fit_transform(adf[\"HighestEducation\"].to_numpy().reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bc4c16a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERICAL_FEATURES.append(\"EduOrdinal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9e471f3",
   "metadata": {},
   "source": [
    "<center> Feature: <b>Programming Experience (Ordinal - Treated as Numeric)</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa20e463",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"Programming Experience\", \"Programming Experience\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "88023702",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf[\"Programming Experience\"] = adf[\"Programming Experience\"].fillna('I have never written code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fde6572e",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = OrdinalEncoder(categories=[['I have never written code', '< 1 years', '1-2 years', '1-3 years', '3-5 years', '5-10 years', '10-20 years', '20+ years']])\n",
    "adf[\"ProgExpOrdinal\"] = enc.fit_transform(adf[\"Programming Experience\"].to_numpy().reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "166757e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERICAL_FEATURES.append(\"ProgExpOrdinal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dde8e77",
   "metadata": {},
   "source": [
    "<center> Feature: <b>Programming Languages</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3a8e643a",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"ProgLanguage_Python\", \"UsedPython\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"ProgLanguage_R\", \"UsedR\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"ProgLanguage_SQL\", \"UsedSQL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8787cdc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "BINARY_FEATURES.extend([\"UsedPython\", \"UsedR\", \"UsedSQL\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "51999a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "PROGLANG_COLS = get_col_names(survey_df, include_kws=[\"ProgLanguage_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, PROGLANG_COLS, \"NumProgLanguages\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "11fff333",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERICAL_FEATURES.append(\"NumProgLanguages\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b165749b",
   "metadata": {},
   "source": [
    "<center> Feature: <b>Visualization Experience</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "35521853",
   "metadata": {},
   "outputs": [],
   "source": [
    "PYTHON_VIZ_COLNAMES = get_col_names(survey_df, include_kws=[\"VizLib_Altair\", \"VizLib_Bokeh\", \"VizLib_Geoplotlib\", \"VizLib_Matplotlib\", \"VizLib_Plotly\", \"VizLib_Seaborn\", \"VizLib_Pygal\"], exclude_kws=[\"Other\", \"None\"])\n",
    "JS_VIZ_COLNAMES = get_col_names(survey_df, include_kws=[\"VizLib_D3 js\"], exclude_kws=[\"Other\", \"None\"])\n",
    "R_VIZ_COLNAMES = get_col_names(survey_df, include_kws=[\"VizLib_Dygraphs\", \"VizLib_Ggplot\", \"VizLib_Highchart\", \"VizLib_Shiny\"], exclude_kws=[\"Other\", \"None\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "52ad5c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_df[\"PyVizUsage\"] = survey_df[PYTHON_VIZ_COLNAMES].fillna(0).sum(axis=1)\n",
    "survey_df.loc[survey_df[\"PyVizUsage\"]>1, \"PyVizUsage\"] = 1\n",
    "\n",
    "survey_df[\"RVizUsage\"] = survey_df[R_VIZ_COLNAMES].fillna(0).sum(axis=1)\n",
    "survey_df.loc[survey_df[\"RVizUsage\"]>1, \"RVizUsage\"] = 1\n",
    "\n",
    "survey_df[\"JSVizUsage\"] = survey_df[JS_VIZ_COLNAMES].fillna(0).sum(axis=1)\n",
    "survey_df.loc[survey_df[\"JSVizUsage\"]>1, \"JSVizUsage\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8a7c0de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"PyVizUsage\", \"UsedPyViz\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"RVizUsage\", \"UsedRViz\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"JSVizUsage\", \"UsedJSViz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ced5c9b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "BINARY_FEATURES.extend([\"UsedPyViz\", \"UsedRViz\", \"UsedJSViz\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9291b7be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CompletedUniDegree',\n",
       " 'UsedPython',\n",
       " 'UsedR',\n",
       " 'UsedSQL',\n",
       " 'UsedPyViz',\n",
       " 'UsedRViz',\n",
       " 'UsedJSViz']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BINARY_FEATURES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00d5407",
   "metadata": {},
   "source": [
    "<center> Feature: <b>ML Exposure</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f547a4c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"MLExposure\", \"MLExposure\")\n",
    "adf[\"MLExposure\"] = adf[\"MLExposure\"].fillna('I do not use machine learning methods')\n",
    "\n",
    "enc = OrdinalEncoder(categories=[['I do not use machine learning methods', 'Under 1 year', '1-2 years', '2-3 years', '3-4 years', '4-5 years', '5-10 years', '10-20 years', '20 or more years']])\n",
    "adf[\"MLExpOrdinal\"] = enc.fit_transform(adf[\"MLExposure\"].to_numpy().reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3e424939",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1-2 years', '3-4 years', 'I do not use machine learning methods',\n",
       "       '2-3 years', 'Under 1 year', '4-5 years', '5-10 years',\n",
       "       '20 or more years', '10-20 years'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf[\"MLExposure\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "acf65f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERICAL_FEATURES.append(\"MLExpOrdinal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "517b980e",
   "metadata": {},
   "source": [
    "<center> Feature: <b>ML Frameworks</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5b7897c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MLFramework_Scikit-learn',\n",
       " 'MLFramework_TensorFlow',\n",
       " 'MLFramework_Keras',\n",
       " 'MLFramework_PyTorch',\n",
       " 'MLFramework_Fast.ai',\n",
       " 'MLFramework_Xgboost',\n",
       " 'MLFramework_LightGBM',\n",
       " 'MLFramework_CatBoost',\n",
       " 'MLFramework_Caret',\n",
       " 'MLFramework_Tidymodels',\n",
       " 'MLFramework_JAX',\n",
       " 'MLFramework_PyTorch Lightning',\n",
       " 'MLFramework_Huggingface',\n",
       " 'MLFramework_None',\n",
       " 'MLFramework_Other',\n",
       " 'MLFramework_MXNet',\n",
       " 'MLFramework_Prophet',\n",
       " 'MLFramework_H2O 3',\n",
       " 'AutoMLFrameworksUsed_Automated data augmentation (e.g. imgaug, albumentations)',\n",
       " 'AutoMLFrameworksUsed_Automated feature engineering/selection (e.g. tpot, boruta_py)',\n",
       " 'AutoMLFrameworksUsed_Automated model selection (e.g. auto-sklearn, xcessiv)',\n",
       " 'AutoMLFrameworksUsed_Automated model architecture searches (e.g. darts, enas)',\n",
       " 'AutoMLFrameworksUsed_Automated hyperparameter tuning (e.g. hyperopt, ray.tune, Vizier)',\n",
       " 'AutoMLFrameworksUsed_Automation of full ML pipelines (e.g. Google AutoML, H2O Driverless AI)',\n",
       " 'AutoMLFrameworksUsed_No / None',\n",
       " 'AutoMLFrameworksUsed_Other',\n",
       " 'AutoMLFrameworksUsed_Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_col_names(survey_df, include_kws=[\"MLFramework\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1b70832d",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"MLFramework_Scikit-learn\", \"UsedSKlearn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "58612b83",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_df[\"TFKerasUsage\"] = survey_df[['MLFramework_TensorFlow','MLFramework_Keras']].fillna(0).sum(axis=1)\n",
    "survey_df.loc[survey_df[\"TFKerasUsage\"]>1, \"TFKerasUsage\"] = 1\n",
    "adf = add_col_to_df1(adf, survey_df, \"TFKerasUsage\", \"UsedTFKeras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9a539e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_df[\"PyTorchUsage\"] = survey_df[['MLFramework_PyTorch','MLFramework_PyTorch Lightning']].fillna(0).sum(axis=1)\n",
    "survey_df.loc[survey_df[\"PyTorchUsage\"]>1, \"PyTorchUsage\"] = 1\n",
    "adf = add_col_to_df1(adf, survey_df, \"PyTorchUsage\", \"UsedPyTorch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c4536266",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"MLFramework_Huggingface\", \"UsedHF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "39aec4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"MLFramework_Fast.ai\", \"UsedFastAI\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1b1ddf87",
   "metadata": {},
   "outputs": [],
   "source": [
    "BINARY_FEATURES.extend([\"UsedSKlearn\", \"UsedTFKeras\", \"UsedPyTorch\", \"UsedHF\", \"UsedFastAI\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b76b01fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "MLFRAMEWORK_COLS = get_col_names(survey_df, include_kws=[\"MLFramework_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, MLFRAMEWORK_COLS, \"NumMLFrameworks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7ee4ef9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERICAL_FEATURES.append(\"NumMLFrameworks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46aa1478",
   "metadata": {},
   "source": [
    "<center> Feature: <b>ML Algorithms</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3cc0fd19",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Linear or Logistic Regression\", \"UsedLinearModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Decision Trees or Random Forests\", \"UsedTreeModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Gradient Boosting Machines (xgboost, lightgbm, etc)\", \"UsedGBModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Bayesian Approaches\", \"UsedBayesianModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Evolutionary Approaches\", \"UsedEvolutionaryModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Dense Neural Networks (MLPs, etc)\", \"UsedNNModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Convolutional Neural Networks\", \"UsedCNNModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Generative Adversarial Networks\", \"UsedGANModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Transformer Networks (BERT, gpt-3, etc)\", \"UsedTransformerModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Autoencoder Networks (DAE, VAE, etc)\", \"UsedAEModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Recurrent Neural Networks\", \"UsedRNNModels\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"MLAlgo_Graph Neural Networks\", \"UsedGraphModels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c47a9c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "MLALGOCOLS = get_col_names(survey_df, include_kws=[\"MLAlgo_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, MLALGOCOLS, \"NumMLAlgos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "752fd8d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "BINARY_FEATURES.extend([\"UsedLinearModels\",\"UsedTreeModels\",\"UsedGBModels\",\"UsedBayesianModels\",\"UsedEvolutionaryModels\",\"UsedNNModels\",\"UsedCNNModels\",\"UsedGANModels\",\"UsedTransformerModels\",\"UsedAEModels\",\"UsedRNNModels\",\"UsedGraphModels\"])\n",
    "NUMERICAL_FEATURES.extend([\"NumMLAlgos\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef0b59f",
   "metadata": {},
   "source": [
    "<center> Feature: <b>Transfer Learning</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "25761641",
   "metadata": {},
   "outputs": [],
   "source": [
    "PRETRAINED_COLS = get_col_names(survey_df, include_kws=[\"DownloadPreTrainedWeights_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, PRETRAINED_COLS, \"NumTransferLearning\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9563e92e",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERICAL_FEATURES.append(\"NumTransferLearning\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91db363a",
   "metadata": {},
   "source": [
    "<center> Feature: <b>CloudProviders</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "90d280d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"CloudProviderUsed_Google Cloud Platform (GCP)\", \"UsedGCP\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"CloudProviderUsed_Amazon Web Services (AWS)\", \"UsedAWS\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"CloudProviderUsed_Microsoft Azure\", \"UsedAzure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4e2cc222",
   "metadata": {},
   "outputs": [],
   "source": [
    "BINARY_FEATURES.extend([\"UsedGCP\", \"UsedAWS\", \"UsedAzure\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "53f70a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "CLOUDPROVIDER_COLS = get_col_names(survey_df, include_kws=[\"CloudProviderUsed_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, CLOUDPROVIDER_COLS, \"NumCloudProvidersUsed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "931435f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERICAL_FEATURES.append(\"NumCloudProvidersUsed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a60265f",
   "metadata": {},
   "source": [
    "<center> Feature: <b>CloudCompute</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e1368f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "CLOUDCOMPUTE_COLS = get_col_names(survey_df, include_kws=[\"CloudComputeUsed_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, CLOUDCOMPUTE_COLS, \"NumCloudComputeUsed\")\n",
    "NUMERICAL_FEATURES.append(\"NumCloudComputeUsed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7af4c164",
   "metadata": {},
   "source": [
    "<center> Feature: <b>CloudStorage</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffc0c58a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9c8351ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "CLOUDSTORAGE_COLS = get_col_names(survey_df, include_kws=[\"CloudStorageUsed_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, CLOUDSTORAGE_COLS, \"NumCloudStorageUsed\")\n",
    "NUMERICAL_FEATURES.append(\"NumCloudStorageUsed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8adaf93c",
   "metadata": {},
   "source": [
    "<center> Feature: <b>DBUSed</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1428e40e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DBUsed_MySQL',\n",
       " 'DBUsed_PostgreSQL',\n",
       " 'DBUsed_SQLite',\n",
       " 'DBUsed_Oracle Database',\n",
       " 'DBUsed_MongoDB',\n",
       " 'DBUsed_Snowflake',\n",
       " 'DBUsed_IBM Db2',\n",
       " 'DBUsed_Microsoft SQL Server',\n",
       " 'DBUsed_Microsoft Azure SQL Database',\n",
       " 'DBUsed_Amazon Redshift',\n",
       " 'DBUsed_Amazon RDS',\n",
       " 'DBUsed_Amazon DynamoDB',\n",
       " 'DBUsed_Google Cloud BigQuery',\n",
       " 'DBUsed_Google Cloud SQL',\n",
       " 'DBUsed_Microsoft Azure Cosmos DB',\n",
       " 'DBUsed_Amazon Aurora',\n",
       " 'DBUsed_Google Cloud Firestore',\n",
       " 'DBUsed_Google Cloud BigTable',\n",
       " 'DBUsed_Google Cloud Spanner',\n",
       " 'DBUsed_PostgresSQL',\n",
       " 'DBUsed_Microsoft Access',\n",
       " 'DBUsed_Microsoft Azure Data Lake Storage',\n",
       " 'DBUsed_Amazon Athena']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_col_names(survey_df, include_kws=[\"DBUsed_\"], exclude_kws=[\"Other\", \"None\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0b5abeb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_db_type(db):\n",
    "    for key in rel_db_map.keys():\n",
    "        if db in rel_db_map[key]:\n",
    "            return key\n",
    "    return \"Others\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9d35a138",
   "metadata": {},
   "outputs": [],
   "source": [
    "RELATIONAL_DBS = [\"DBUsed_Microsoft Access\",\"DBUsed_Google Cloud Spanner\",\"DBUsed_Amazon Aurora\",\"DBUsed_Microsoft Azure Cosmos DB\",\"DBUsed_Google Cloud SQL\",\"DBUsed_Google Cloud BigQuery\",\"DBUsed_Amazon RDS\",\"DBUsed_Amazon Redshift\",\"DBUsed_Snowflake\", \"DBUsed_IBM Db2\", \"DBUsed_Microsoft SQL Server\", \"DBUsed_Microsoft Azure SQL Database\",\"DBUsed_MySQL\", \"DBUsed_PostgreSQL\", \"DBUsed_PostgresSQL\", \"DBUsed_SQLite\", 'DBUsed_Oracle Database']\n",
    "NON_RELATIONAL_DBS = [\"DBUsed_Google Cloud BigTable\",\"DBUsed_Google Cloud Firestore\",\"DBUsed_Microsoft Azure Cosmos DB\",\"DBUsed_Amazon DynamoDB\",\"DBUsed_MongoDB\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f74bf418",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_df[\"UsedRelationalDBs\"] = survey_df[RELATIONAL_DBS].fillna(0).sum(axis=1).apply(lambda x:1 if x>=1 else 0)\n",
    "survey_df[\"UsedNonRelationalDBs\"] = survey_df[NON_RELATIONAL_DBS].fillna(0).sum(axis=1).apply(lambda x:1 if x>=1 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b96e1043",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"UsedRelationalDBs\", \"UsedRelationalDBs\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"UsedNonRelationalDBs\", \"UsedNonRelationalDBs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8f3b5258",
   "metadata": {},
   "outputs": [],
   "source": [
    "BINARY_FEATURES.extend([\"UsedRelationalDBs\",\"UsedNonRelationalDBs\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e878bc",
   "metadata": {},
   "source": [
    "<center> Feature: <b>BI Tools</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ce1b0557",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_df[\"UsedTableau\"] = survey_df[[\"BIToolUsed_Tableau\", \"BIToolUsed_Tableau CRM\"]].fillna(0).sum(axis=1).apply(lambda x:1 if x>=1 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d14bd2c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"BIToolUsed_Microsoft Power BI\", \"UsedPowerBI\")\n",
    "adf = add_col_to_df1(adf, survey_df, \"UsedTableau\", \"UsedTableau\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a7809f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "BINARY_FEATURES.extend([\"UsedPowerBI\",\"UsedTableau\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "19ad3d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "BI_COLS = get_col_names(survey_df, include_kws=[\"BIToolUsed_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, BI_COLS, \"NumBIToolsUsed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "500bc23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERICAL_FEATURES.append(\"NumBIToolsUsed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a34f7f",
   "metadata": {},
   "source": [
    "<center> Features: <b>MLaaS, AutoML, Deployment, Monitoring Tools</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d49e68a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "COLS = get_col_names(survey_df, include_kws=[\"MLaaS_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, COLS, \"NumMLaaSToolsUsed\")\n",
    "NUMERICAL_FEATURES.append(\"NumMLaaSToolsUsed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3d160347",
   "metadata": {},
   "outputs": [],
   "source": [
    "COLS = get_col_names(survey_df, include_kws=[\"AutoMLUsed_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, COLS, \"NumAutoMLUsed\")\n",
    "NUMERICAL_FEATURES.append(\"NumAutoMLUsed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "aadb5e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "COLS = get_col_names(survey_df, include_kws=[\"MLDeploymentToolsUsed_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, COLS, \"NumMLDeploymentToolsUsed\")\n",
    "NUMERICAL_FEATURES.append(\"NumMLDeploymentToolsUsed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c7a52283",
   "metadata": {},
   "outputs": [],
   "source": [
    "COLS = get_col_names(survey_df, include_kws=[\"MLMonitoringToolUsed_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, COLS, \"NumMLMonitoringToolUsed\")\n",
    "NUMERICAL_FEATURES.append(\"NumMLMonitoringToolUsed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1b3ecac",
   "metadata": {},
   "source": [
    "<center> Feature: <b>Hardware</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6b6a2380",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_df[\"UsedGPU\"] = survey_df[[\"HardwareUsed_GPUs\", \"HardwareUsed_NVIDIA GPUs\"]].fillna(0).sum(axis=1).apply(lambda x:1 if x>=1 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9bafae55",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"UsedGPU\", \"UsedGPU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "699ec249",
   "metadata": {},
   "outputs": [],
   "source": [
    "BINARY_FEATURES.extend([\"UsedGPU\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b6267e60",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"TPUUsageFrequency\", \"TPUUsageFrequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "95f5db1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf[\"TPUUsageFrequency\"] = adf[\"TPUUsageFrequency\"].fillna('Never')\n",
    "\n",
    "enc = OrdinalEncoder(categories=[['Never', 'Once', '2-5 times', '6-25 times', 'More than 25 times']])\n",
    "adf[\"TPUUsageOrdinal\"] = enc.fit_transform(adf[\"TPUUsageFrequency\"].to_numpy().reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "63a01615",
   "metadata": {},
   "outputs": [],
   "source": [
    "COLS = get_col_names(survey_df, include_kws=[\"HardwareUsed_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, COLS, \"NumHardwareUsed\")\n",
    "NUMERICAL_FEATURES.append(\"NumHardwareUsed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "619d56a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERICAL_FEATURES.append(\"TPUUsageOrdinal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11adec17",
   "metadata": {},
   "source": [
    "<center> Feature: <b>DSMedia</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "002016a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "COLS = get_col_names(survey_df, include_kws=[\"DSContentMediaConsumed_\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, COLS, \"NumDSContentMediaConsumed\")\n",
    "NUMERICAL_FEATURES.append(\"NumDSContentMediaConsumed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6728a19",
   "metadata": {},
   "source": [
    "<center> Feature: <b>PipelineAutomation</b> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "fcdfdfe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_df[\"UsedE2EPipelineAutomation\"] = survey_df[[\"AutoMLFrameworksUsed_Automation of full ML pipelines (e.g. Google AutoML, H2O Driverless AI)\", \"AutoMLFrameworksUsed_Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)\"]].fillna(0).sum(axis=1).apply(lambda x:1 if x>=1 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a698f4d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "COLS = get_col_names(survey_df, include_kws=[\"AutoMLFrameworksUsed_Automated\"], exclude_kws=[\"Other\", \"None\"])\n",
    "adf = get_frequency_feature_in_df1(adf, survey_df, COLS, \"NumStepsOfPipelineAutomated\")\n",
    "NUMERICAL_FEATURES.append(\"NumStepsOfPipelineAutomated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "27cce0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "adf = add_col_to_df1(adf, survey_df, \"UsedE2EPipelineAutomation\", \"UsedE2EPipelineAutomation\")\n",
    "BINARY_FEATURES.extend([\"UsedE2EPipelineAutomation\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583c95e0",
   "metadata": {},
   "source": [
    "## <center> Clustering - KMeans </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d8296803",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age_categ',\n",
       " 'NumCoursePlatforms',\n",
       " 'EduOrdinal',\n",
       " 'ProgExpOrdinal',\n",
       " 'NumProgLanguages',\n",
       " 'MLExpOrdinal',\n",
       " 'NumMLFrameworks',\n",
       " 'NumMLAlgos',\n",
       " 'NumTransferLearning',\n",
       " 'NumCloudProvidersUsed',\n",
       " 'NumCloudComputeUsed',\n",
       " 'NumCloudStorageUsed',\n",
       " 'NumBIToolsUsed',\n",
       " 'NumMLaaSToolsUsed',\n",
       " 'NumAutoMLUsed',\n",
       " 'NumMLDeploymentToolsUsed',\n",
       " 'NumMLMonitoringToolUsed',\n",
       " 'NumHardwareUsed',\n",
       " 'TPUUsageOrdinal',\n",
       " 'NumDSContentMediaConsumed',\n",
       " 'NumStepsOfPipelineAutomated']"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NUMERICAL_FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "4c8a8f29",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = adf[NUMERICAL_FEATURES]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c015e9c",
   "metadata": {},
   "source": [
    "### Silhouette Score Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "7c84ba68",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 10/10 [01:03<00:00,  6.39s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = []\n",
    "for k in tqdm(range(2,12)):\n",
    "    km = KMeans(n_clusters=k, random_state=seed)\n",
    "    km.fit(X)\n",
    "    score = silhouette_score(X, km.labels_, metric='euclidean')\n",
    "    scores.append(score)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title('K Means Clustering with Numerical Features')\n",
    "plt.plot(range(2,12), scores);\n",
    "plt.xlabel(\"Number of Clusters (k)\");\n",
    "plt.ylabel(\"Silhouette Score\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6a16f8",
   "metadata": {},
   "source": [
    "### <center> Using PCA on numerical features </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "30a163b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3161ee35",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "61f6e827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23215, 21)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d3ba074e",
   "metadata": {},
   "outputs": [],
   "source": [
    "vec, val = np.linalg.eig(X_scaled.T @ X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "8fbbebae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 21 artists>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.title('Scree Plot of Eigenvalues')\n",
    "plt.xlabel('Eigenvalue #')\n",
    "plt.ylabel('Eigenvalue')\n",
    "plt.bar(np.arange(1, len(vec)+1,1),sorted(vec, reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a2390f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=4).fit_transform(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e7a66d67",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = plt.cm.coolwarm(pca[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "08314671",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a2411a00>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (6,6))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('3 component PCA (Third component represented as colour)', fontsize = 20)\n",
    "ax.scatter(pca[:, 0], pca[:, 1], edgecolors=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "74a4fda6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled_2 = pca[:, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "6c780d8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 10/10 [00:58<00:00,  5.87s/it]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = []\n",
    "for k in tqdm(range(2,12)):\n",
    "    km = KMeans(n_clusters=k, random_state=seed)\n",
    "    km.fit(X)\n",
    "    score = silhouette_score(pca[:, :3], km.labels_, metric='euclidean')\n",
    "    scores.append(score)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title('K Means Clustering with PCA (Top 3)')\n",
    "plt.plot(range(2,12), scores);\n",
    "plt.xlabel(\"Number of Clusters (k)\");\n",
    "plt.ylabel(\"Silhouette Score\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "185ef3a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = plt.cm.coolwarm(pca[:,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512c0316",
   "metadata": {},
   "source": [
    "## K Prototypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "d106761f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = adf[CATEGORICAL_FEATURES + BINARY_FEATURES + NUMERICAL_FEATURES]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "ee3d68cc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "missing_vals = pd.DataFrame()\n",
    "missing_vals['col'] = list(X.columns)\n",
    "missing_vals['num_missing_vals'] = [X[col].isnull().sum() for col in X.columns]\n",
    "\n",
    "X = X.drop(list(missing_vals[missing_vals['num_missing_vals'] > 0]['col']), axis=1)\n",
    "X = X.drop(['CountryClusters'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "df857c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CompletedUniDegree', 'UsedPython', 'UsedR', 'UsedSQL', 'UsedPyViz',\n",
       "       'UsedRViz', 'UsedJSViz', 'UsedSKlearn', 'UsedTFKeras', 'UsedPyTorch',\n",
       "       'UsedFastAI', 'UsedLinearModels', 'UsedTreeModels', 'UsedGBModels',\n",
       "       'UsedBayesianModels', 'UsedEvolutionaryModels', 'UsedNNModels',\n",
       "       'UsedCNNModels', 'UsedGANModels', 'UsedTransformerModels',\n",
       "       'UsedRNNModels', 'UsedGCP', 'UsedAWS', 'UsedAzure', 'UsedRelationalDBs',\n",
       "       'UsedNonRelationalDBs', 'UsedPowerBI', 'UsedTableau', 'UsedGPU',\n",
       "       'UsedE2EPipelineAutomation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "f4ed4fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "catColumnsPos = [X.columns.get_loc(col) for col in list(X.columns[:31])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "ca6bfe86",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|██████▍                                      | 1/7 [01:11<07:10, 71.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster initiation: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 29%|████████████▌                               | 2/7 [03:21<08:50, 106.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster initiation: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 43%|██████████████████▊                         | 3/7 [06:58<10:27, 156.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster initiation: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 57%|█████████████████████████▏                  | 4/7 [12:31<11:18, 226.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster initiation: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 71%|███████████████████████████████▍            | 5/7 [17:00<08:03, 241.73s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster initiation: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 86%|█████████████████████████████████████▋      | 6/7 [27:40<06:16, 376.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster initiation: 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████| 7/7 [36:20<00:00, 311.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster initiation: 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Choose optimal K using Elbow method\n",
    "cost = []\n",
    "for cluster in tqdm(range(2,9)):\n",
    "    try:\n",
    "        kprototype = KPrototypes(n_jobs = -1, n_clusters = cluster, init = 'Huang', random_state = seed)\n",
    "        kprototype.fit_predict(X, categorical = catColumnsPos)\n",
    "        cost.append(kprototype.cost_)\n",
    "        print('Cluster initiation: {}'.format(cluster))\n",
    "    except:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "e4a7a5d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "L1 = cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "c26e4901",
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [744791.280573488,\n",
    " 678006.6782623926,\n",
    " 635372.8338874627,\n",
    " 604516.7016284579,\n",
    " 580150.1533840792,\n",
    " 559985.3729226808,\n",
    " 545307.0813085189,\n",
    " 533418.556521331]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "f297f78f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x297fb1e50>]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title('Elbow Plot - Cost vs Number of Clusters - KPrototypes')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Cost')\n",
    "plt.plot(range(2,9), L1, '.-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "a4c3ea3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, ..., 2, 1, 1], dtype=uint16)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kprototype_3clusters = KPrototypes(n_jobs = -1, n_clusters = 3, init = 'Huang', random_state = 1)\n",
    "kprototype_3clusters.fit_predict(X, categorical = catColumnsPos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "5ff62b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the cluster to the dataframeX_3cluster = X.copu\n",
    "X_3cluster = X.copy()\n",
    "\n",
    "X_3cluster['Cluster Labels'] = kprototype_3clusters.labels_\n",
    "X_3cluster['Segment'] = X_3cluster['Cluster Labels'].map({0:'First', 1:'Second', 2:'Third'})\n",
    "# Order the cluster\n",
    "X_3cluster['Segment'] = X_3cluster['Segment'].astype('category')\n",
    "X_3cluster['Segment'] = X_3cluster['Segment'].cat.reorder_categories(['First','Second','Third'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "ace16fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_3cluster_v2 = X_3cluster.join(adf['CurrentTitle'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "ed4eeea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_3cluster_v2['isDataScientist'] = X_3cluster_v2['CurrentTitle'] == 'Data Scientist'\n",
    "X_3cluster_v2['isDataAnalyst'] = X_3cluster_v2['CurrentTitle'] == 'Data Analyst'\n",
    "X_3cluster_v2['isMLE'] = X_3cluster_v2['CurrentTitle'] == 'Machine Learning Engineer'\n",
    "X_3cluster_v2['isResearchScientist'] = X_3cluster_v2['CurrentTitle'] == 'Research Scientist'\n",
    "X_3cluster_v2['isDataEngineer'] = X_3cluster_v2['CurrentTitle'] == 'Data Engineer'\n",
    "X_3cluster_v2 = X_3cluster_v2.drop(['CurrentTitle'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "c06bf371",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['CompletedUniDegree', 'UsedPython', 'UsedR', 'UsedSQL', 'UsedPyViz',\n",
    "       'UsedRViz', 'UsedJSViz', 'UsedSKlearn', 'UsedTFKeras', 'UsedPyTorch',\n",
    "       'UsedFastAI', 'UsedLinearModels', 'UsedTreeModels', 'UsedGBModels',\n",
    "       'UsedBayesianModels', 'UsedEvolutionaryModels', 'UsedNNModels',\n",
    "       'UsedCNNModels', 'UsedGANModels', 'UsedTransformerModels',\n",
    "       'UsedRNNModels', 'UsedGCP', 'UsedAWS', 'UsedAzure', 'UsedRelationalDBs',\n",
    "       'UsedNonRelationalDBs', 'UsedPowerBI', 'UsedTableau', 'UsedGPU',\n",
    "       'UsedE2EPipelineAutomation', 'Age_categ', 'NumCoursePlatforms',\n",
    "       'EduOrdinal', 'ProgExpOrdinal', 'NumProgLanguages', 'MLExpOrdinal',\n",
    "       'NumMLFrameworks', 'NumMLAlgos', 'NumTransferLearning',\n",
    "       'NumCloudProvidersUsed', 'NumCloudComputeUsed', 'NumCloudStorageUsed',\n",
    "       'NumBIToolsUsed', 'NumMLaaSToolsUsed', 'NumAutoMLUsed',\n",
    "       'NumMLDeploymentToolsUsed', 'NumMLMonitoringToolUsed',\n",
    "       'NumHardwareUsed', 'TPUUsageOrdinal', 'NumDSContentMediaConsumed',\n",
    "       'NumStepsOfPipelineAutomated','isDataScientist', 'isDataAnalyst', 'isMLE', 'isResearchScientist',\n",
    "       'isDataEngineer']\n",
    "agg_dict = {col: 'mean' for col in cols}\n",
    "agg_dict['Cluster Labels'] = 'count'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "709a90ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_3cluster_gb = X_3cluster_v2.groupby('Segment').agg(agg_dict).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "d06577f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_3cluster_gb['DataScientistProportion'] = X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isDataScientist']/(sum(X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isDataScientist'])) \n",
    "X_3cluster_gb['DataAnalystProportion'] = X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isDataAnalyst']/(sum(X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isDataAnalyst']))\n",
    "X_3cluster_gb['MLEProportion'] = X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isMLE']/(sum(X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isMLE']))\n",
    "X_3cluster_gb['ResearchScientistProportion'] = X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isResearchScientist']/(sum(X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isResearchScientist']))\n",
    "X_3cluster_gb['DataEngineerProportion'] = X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isDataEngineer']/(sum(X_3cluster_gb['Cluster Labels']*X_3cluster_gb['isDataEngineer']))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "6c4a8768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Segment</th>\n",
       "      <th>CompletedUniDegree</th>\n",
       "      <th>UsedPython</th>\n",
       "      <th>UsedR</th>\n",
       "      <th>UsedSQL</th>\n",
       "      <th>UsedPyViz</th>\n",
       "      <th>UsedRViz</th>\n",
       "      <th>UsedJSViz</th>\n",
       "      <th>UsedSKlearn</th>\n",
       "      <th>UsedTFKeras</th>\n",
       "      <th>...</th>\n",
       "      <th>isDataAnalyst</th>\n",
       "      <th>isMLE</th>\n",
       "      <th>isResearchScientist</th>\n",
       "      <th>isDataEngineer</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>DataScientistProportion</th>\n",
       "      <th>DataAnalystProportion</th>\n",
       "      <th>MLEProportion</th>\n",
       "      <th>ResearchScientistProportion</th>\n",
       "      <th>DataEngineerProportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>First</td>\n",
       "      <td>0.395324</td>\n",
       "      <td>0.985414</td>\n",
       "      <td>0.366152</td>\n",
       "      <td>0.619691</td>\n",
       "      <td>0.976405</td>\n",
       "      <td>0.405620</td>\n",
       "      <td>0.105749</td>\n",
       "      <td>0.925568</td>\n",
       "      <td>0.865723</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115616</td>\n",
       "      <td>0.199271</td>\n",
       "      <td>0.132990</td>\n",
       "      <td>0.052767</td>\n",
       "      <td>4662</td>\n",
       "      <td>0.283177</td>\n",
       "      <td>0.076130</td>\n",
       "      <td>0.294734</td>\n",
       "      <td>0.187595</td>\n",
       "      <td>0.168840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second</td>\n",
       "      <td>0.095056</td>\n",
       "      <td>0.688818</td>\n",
       "      <td>0.188140</td>\n",
       "      <td>0.384170</td>\n",
       "      <td>0.532363</td>\n",
       "      <td>0.150660</td>\n",
       "      <td>0.013808</td>\n",
       "      <td>0.270620</td>\n",
       "      <td>0.133029</td>\n",
       "      <td>...</td>\n",
       "      <td>0.510048</td>\n",
       "      <td>0.094316</td>\n",
       "      <td>0.115399</td>\n",
       "      <td>0.064480</td>\n",
       "      <td>8111</td>\n",
       "      <td>0.212869</td>\n",
       "      <td>0.584322</td>\n",
       "      <td>0.242703</td>\n",
       "      <td>0.283207</td>\n",
       "      <td>0.358957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Third</td>\n",
       "      <td>0.260391</td>\n",
       "      <td>0.926068</td>\n",
       "      <td>0.279448</td>\n",
       "      <td>0.516568</td>\n",
       "      <td>0.893890</td>\n",
       "      <td>0.297836</td>\n",
       "      <td>0.040605</td>\n",
       "      <td>0.774468</td>\n",
       "      <td>0.555353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.230224</td>\n",
       "      <td>0.139628</td>\n",
       "      <td>0.167497</td>\n",
       "      <td>0.065888</td>\n",
       "      <td>10442</td>\n",
       "      <td>0.503953</td>\n",
       "      <td>0.339548</td>\n",
       "      <td>0.462563</td>\n",
       "      <td>0.529198</td>\n",
       "      <td>0.472203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Segment  CompletedUniDegree  UsedPython     UsedR   UsedSQL  UsedPyViz  \\\n",
       "0   First            0.395324    0.985414  0.366152  0.619691   0.976405   \n",
       "1  Second            0.095056    0.688818  0.188140  0.384170   0.532363   \n",
       "2   Third            0.260391    0.926068  0.279448  0.516568   0.893890   \n",
       "\n",
       "   UsedRViz  UsedJSViz  UsedSKlearn  UsedTFKeras  ...  isDataAnalyst  \\\n",
       "0  0.405620   0.105749     0.925568     0.865723  ...       0.115616   \n",
       "1  0.150660   0.013808     0.270620     0.133029  ...       0.510048   \n",
       "2  0.297836   0.040605     0.774468     0.555353  ...       0.230224   \n",
       "\n",
       "      isMLE  isResearchScientist  isDataEngineer  Cluster Labels  \\\n",
       "0  0.199271             0.132990        0.052767            4662   \n",
       "1  0.094316             0.115399        0.064480            8111   \n",
       "2  0.139628             0.167497        0.065888           10442   \n",
       "\n",
       "   DataScientistProportion  DataAnalystProportion  MLEProportion  \\\n",
       "0                 0.283177               0.076130       0.294734   \n",
       "1                 0.212869               0.584322       0.242703   \n",
       "2                 0.503953               0.339548       0.462563   \n",
       "\n",
       "   ResearchScientistProportion  DataEngineerProportion  \n",
       "0                     0.187595                0.168840  \n",
       "1                     0.283207                0.358957  \n",
       "2                     0.529198                0.472203  \n",
       "\n",
       "[3 rows x 63 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_3cluster_gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "173e6d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_3cluster_gb.to_csv('data/3clusterkprototypes_v4.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "88bc5de0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Third     10442\n",
       "Second     8111\n",
       "First      4662\n",
       "Name: Segment, dtype: int64"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_3cluster['Segment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "95f02ea8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, ..., 3, 1, 1], dtype=uint16)"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kprototype_4clusters = KPrototypes(n_jobs = -1, n_clusters = 4, init = 'Huang', random_state = 1)\n",
    "kprototype_4clusters.fit_predict(X, categorical = catColumnsPos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1f785c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['CompletedUniDegree', 'UsedPython', 'UsedR', 'UsedSQL', 'UsedPyViz',\n",
    "       'UsedRViz', 'UsedJSViz', 'UsedSKlearn', 'UsedTFKeras', 'UsedPyTorch',\n",
    "       'UsedFastAI', 'UsedLinearModels', 'UsedTreeModels', 'UsedGBModels',\n",
    "       'UsedBayesianModels', 'UsedEvolutionaryModels', 'UsedNNModels',\n",
    "       'UsedCNNModels', 'UsedGANModels', 'UsedTransformerModels',\n",
    "       'UsedRNNModels', 'UsedGCP', 'UsedAWS', 'UsedAzure', 'UsedRelationalDBs',\n",
    "       'UsedNonRelationalDBs', 'UsedPowerBI', 'UsedTableau', 'UsedGPU',\n",
    "       'UsedE2EPipelineAutomation', 'Age_categ', 'NumCoursePlatforms',\n",
    "       'EduOrdinal', 'ProgExpOrdinal', 'NumProgLanguages', 'MLExpOrdinal',\n",
    "       'NumMLFrameworks', 'NumMLAlgos', 'NumTransferLearning',\n",
    "       'NumCloudProvidersUsed', 'NumCloudComputeUsed', 'NumCloudStorageUsed',\n",
    "       'NumBIToolsUsed', 'NumMLaaSToolsUsed', 'NumAutoMLUsed',\n",
    "       'NumMLDeploymentToolsUsed', 'NumMLMonitoringToolUsed',\n",
    "       'NumHardwareUsed', 'TPUUsageOrdinal', 'NumDSContentMediaConsumed',\n",
    "       'NumStepsOfPipelineAutomated','isDataScientist', 'isDataAnalyst', 'isMLE', 'isResearchScientist',\n",
    "       'isDataEngineer']\n",
    "agg_dict = {col: 'mean' for col in cols}\n",
    "agg_dict['Cluster Labels'] = 'count'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "e512c0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the cluster to the dataframeX_4cluster = X.copu\n",
    "X_4cluster = X.copy()\n",
    "\n",
    "X_4cluster['Cluster Labels'] = kprototype_4clusters.labels_\n",
    "X_4cluster['Segment'] = X_4cluster['Cluster Labels'].map({0:'First', 1:'Second', 2:'Third', 3:'Fourth'})\n",
    "# Order the cluster\n",
    "X_4cluster['Segment'] = X_4cluster['Segment'].astype('category')\n",
    "X_4cluster['Segment'] = X_4cluster['Segment'].cat.reorder_categories(['First','Second','Third','Fourth'])\n",
    "\n",
    "X_4cluster_v2 = X_4cluster.join(adf['CurrentTitle'])\n",
    "X_4cluster_v2['isDataScientist'] = X_4cluster_v2['CurrentTitle'] == 'Data Scientist'\n",
    "X_4cluster_v2['isDataAnalyst'] = X_4cluster_v2['CurrentTitle'] == 'Data Analyst'\n",
    "X_4cluster_v2['isMLE'] = X_4cluster_v2['CurrentTitle'] == 'Machine Learning Engineer'\n",
    "X_4cluster_v2['isResearchScientist'] = X_4cluster_v2['CurrentTitle'] == 'Research Scientist'\n",
    "X_4cluster_v2['isDataEngineer'] = X_4cluster_v2['CurrentTitle'] == 'Data Engineer'\n",
    "X_4cluster_v2 = X_4cluster_v2.drop(['CurrentTitle'],axis=1)\n",
    "\n",
    "X_4cluster_gb = X_4cluster_v2.groupby('Segment').agg(agg_dict).reset_index()\n",
    "\n",
    "X_4cluster_gb['DataScientistProportion'] = X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isDataScientist']/(sum(X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isDataScientist'])) \n",
    "X_4cluster_gb['DataAnalystProportion'] = X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isDataAnalyst']/(sum(X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isDataAnalyst']))\n",
    "X_4cluster_gb['MLEProportion'] = X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isMLE']/(sum(X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isMLE']))\n",
    "X_4cluster_gb['ResearchScientistProportion'] = X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isResearchScientist']/(sum(X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isResearchScientist']))\n",
    "X_4cluster_gb['DataEngineerProportion'] = X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isDataEngineer']/(sum(X_4cluster_gb['Cluster Labels']*X_4cluster_gb['isDataEngineer']))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "d2ce6928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Segment</th>\n",
       "      <th>CompletedUniDegree</th>\n",
       "      <th>UsedPython</th>\n",
       "      <th>UsedR</th>\n",
       "      <th>UsedSQL</th>\n",
       "      <th>UsedPyViz</th>\n",
       "      <th>UsedRViz</th>\n",
       "      <th>UsedJSViz</th>\n",
       "      <th>UsedSKlearn</th>\n",
       "      <th>UsedTFKeras</th>\n",
       "      <th>...</th>\n",
       "      <th>isDataAnalyst</th>\n",
       "      <th>isMLE</th>\n",
       "      <th>isResearchScientist</th>\n",
       "      <th>isDataEngineer</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>DataScientistProportion</th>\n",
       "      <th>DataAnalystProportion</th>\n",
       "      <th>MLEProportion</th>\n",
       "      <th>ResearchScientistProportion</th>\n",
       "      <th>DataEngineerProportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>First</td>\n",
       "      <td>0.407767</td>\n",
       "      <td>0.988072</td>\n",
       "      <td>0.378086</td>\n",
       "      <td>0.654369</td>\n",
       "      <td>0.980860</td>\n",
       "      <td>0.427462</td>\n",
       "      <td>0.118724</td>\n",
       "      <td>0.936477</td>\n",
       "      <td>0.885992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112067</td>\n",
       "      <td>0.205825</td>\n",
       "      <td>0.116782</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>3605</td>\n",
       "      <td>0.222844</td>\n",
       "      <td>0.057062</td>\n",
       "      <td>0.235406</td>\n",
       "      <td>0.127383</td>\n",
       "      <td>0.141386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second</td>\n",
       "      <td>0.081124</td>\n",
       "      <td>0.650709</td>\n",
       "      <td>0.184320</td>\n",
       "      <td>0.371076</td>\n",
       "      <td>0.471836</td>\n",
       "      <td>0.141178</td>\n",
       "      <td>0.013186</td>\n",
       "      <td>0.198509</td>\n",
       "      <td>0.090010</td>\n",
       "      <td>...</td>\n",
       "      <td>0.518418</td>\n",
       "      <td>0.090583</td>\n",
       "      <td>0.121112</td>\n",
       "      <td>0.066504</td>\n",
       "      <td>6977</td>\n",
       "      <td>0.172607</td>\n",
       "      <td>0.510876</td>\n",
       "      <td>0.200508</td>\n",
       "      <td>0.255673</td>\n",
       "      <td>0.318463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Third</td>\n",
       "      <td>0.275313</td>\n",
       "      <td>0.906107</td>\n",
       "      <td>0.324249</td>\n",
       "      <td>0.471255</td>\n",
       "      <td>0.863736</td>\n",
       "      <td>0.317088</td>\n",
       "      <td>0.051920</td>\n",
       "      <td>0.736821</td>\n",
       "      <td>0.528944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.117366</td>\n",
       "      <td>0.141237</td>\n",
       "      <td>0.240103</td>\n",
       "      <td>0.044162</td>\n",
       "      <td>5027</td>\n",
       "      <td>0.279528</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.225254</td>\n",
       "      <td>0.365204</td>\n",
       "      <td>0.152368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fourth</td>\n",
       "      <td>0.251512</td>\n",
       "      <td>0.945832</td>\n",
       "      <td>0.246121</td>\n",
       "      <td>0.536682</td>\n",
       "      <td>0.924796</td>\n",
       "      <td>0.276492</td>\n",
       "      <td>0.032606</td>\n",
       "      <td>0.806206</td>\n",
       "      <td>0.582829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.324612</td>\n",
       "      <td>0.140415</td>\n",
       "      <td>0.109387</td>\n",
       "      <td>0.074283</td>\n",
       "      <td>7606</td>\n",
       "      <td>0.325021</td>\n",
       "      <td>0.348729</td>\n",
       "      <td>0.338832</td>\n",
       "      <td>0.251740</td>\n",
       "      <td>0.387783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Segment  CompletedUniDegree  UsedPython     UsedR   UsedSQL  UsedPyViz  \\\n",
       "0   First            0.407767    0.988072  0.378086  0.654369   0.980860   \n",
       "1  Second            0.081124    0.650709  0.184320  0.371076   0.471836   \n",
       "2   Third            0.275313    0.906107  0.324249  0.471255   0.863736   \n",
       "3  Fourth            0.251512    0.945832  0.246121  0.536682   0.924796   \n",
       "\n",
       "   UsedRViz  UsedJSViz  UsedSKlearn  UsedTFKeras  ...  isDataAnalyst  \\\n",
       "0  0.427462   0.118724     0.936477     0.885992  ...       0.112067   \n",
       "1  0.141178   0.013186     0.198509     0.090010  ...       0.518418   \n",
       "2  0.317088   0.051920     0.736821     0.528944  ...       0.117366   \n",
       "3  0.276492   0.032606     0.806206     0.582829  ...       0.324612   \n",
       "\n",
       "      isMLE  isResearchScientist  isDataEngineer  Cluster Labels  \\\n",
       "0  0.205825             0.116782        0.057143            3605   \n",
       "1  0.090583             0.121112        0.066504            6977   \n",
       "2  0.141237             0.240103        0.044162            5027   \n",
       "3  0.140415             0.109387        0.074283            7606   \n",
       "\n",
       "   DataScientistProportion  DataAnalystProportion  MLEProportion  \\\n",
       "0                 0.222844               0.057062       0.235406   \n",
       "1                 0.172607               0.510876       0.200508   \n",
       "2                 0.279528               0.083333       0.225254   \n",
       "3                 0.325021               0.348729       0.338832   \n",
       "\n",
       "   ResearchScientistProportion  DataEngineerProportion  \n",
       "0                     0.127383                0.141386  \n",
       "1                     0.255673                0.318463  \n",
       "2                     0.365204                0.152368  \n",
       "3                     0.251740                0.387783  \n",
       "\n",
       "[4 rows x 63 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_4cluster_gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "51548092",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_4cluster_gb.to_csv('data/4clusterkprototypes.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "f2611f12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fourth    7606\n",
       "Second    6977\n",
       "Third     5027\n",
       "First     3605\n",
       "Name: Segment, dtype: int64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_4cluster['Segment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "b3de76eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 1, ..., 3, 1, 1], dtype=uint16)"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kprototype_5clusters = KPrototypes(n_jobs = -1, n_clusters = 5, init = 'Huang', random_state = 1)\n",
    "kprototype_5clusters.fit_predict(X, categorical = catColumnsPos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "deaca416",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the cluster to the dataframeX_5cluster = X.copu\n",
    "X_5cluster = X.copy()\n",
    "\n",
    "X_5cluster['Cluster Labels'] = kprototype_5clusters.labels_\n",
    "X_5cluster['Segment'] = X_5cluster['Cluster Labels'].map({0:'First', 1:'Second', 2:'Third', 3:'Fourth', 4:'Fifth'})\n",
    "# Order the cluster\n",
    "X_5cluster['Segment'] = X_5cluster['Segment'].astype('category')\n",
    "X_5cluster['Segment'] = X_5cluster['Segment'].cat.reorder_categories(['First','Second','Third','Fourth', 'Fifth'])\n",
    "\n",
    "X_5cluster_v2 = X_5cluster.join(adf['CurrentTitle'])\n",
    "X_5cluster_v2['isDataScientist'] = X_5cluster_v2['CurrentTitle'] == 'Data Scientist'\n",
    "X_5cluster_v2['isDataAnalyst'] = X_5cluster_v2['CurrentTitle'] == 'Data Analyst'\n",
    "X_5cluster_v2['isMLE'] = X_5cluster_v2['CurrentTitle'] == 'Machine Learning Engineer'\n",
    "X_5cluster_v2['isResearchScientist'] = X_5cluster_v2['CurrentTitle'] == 'Research Scientist'\n",
    "X_5cluster_v2['isDataEngineer'] = X_5cluster_v2['CurrentTitle'] == 'Data Engineer'\n",
    "X_5cluster_v2 = X_5cluster_v2.drop(['CurrentTitle'],axis=1)\n",
    "\n",
    "X_5cluster_gb = X_5cluster_v2.groupby('Segment').agg(agg_dict).reset_index()\n",
    "\n",
    "X_5cluster_gb['DataScientistProportion'] = X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isDataScientist']/(sum(X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isDataScientist'])) \n",
    "X_5cluster_gb['DataAnalystProportion'] = X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isDataAnalyst']/(sum(X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isDataAnalyst']))\n",
    "X_5cluster_gb['MLEProportion'] = X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isMLE']/(sum(X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isMLE']))\n",
    "X_5cluster_gb['ResearchScientistProportion'] = X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isResearchScientist']/(sum(X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isResearchScientist']))\n",
    "X_5cluster_gb['DataEngineerProportion'] = X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isDataEngineer']/(sum(X_5cluster_gb['Cluster Labels']*X_5cluster_gb['isDataEngineer']))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "b898c19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_5cluster_gb.to_csv('data/5clusterprototypes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "e5e09d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_4cluster_gb.to_csv('data/4clusterprototypes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "fddf90bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_3cluster_gb.to_csv('data/3clusterprototypes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12dca936",
   "metadata": {},
   "source": [
    "### Visualizing Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "048e2d9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols_to_visualize = ['CompletedUniDegree', 'UsedPython', 'UsedR', 'UsedSQL',\n",
    "       'UsedPyViz', 'UsedRViz', 'UsedJSViz', 'UsedSKlearn', 'UsedTFKeras',\n",
    "       'UsedPyTorch', 'UsedFastAI', 'UsedLinearModels', 'UsedTreeModels',\n",
    "       'UsedGBModels', 'UsedBayesianModels', 'UsedEvolutionaryModels',\n",
    "       'UsedNNModels', 'UsedCNNModels', 'UsedGANModels',\n",
    "       'UsedTransformerModels', 'UsedRNNModels', 'UsedGCP', 'UsedAWS',\n",
    "       'UsedAzure', 'UsedRelationalDBs', 'UsedNonRelationalDBs', 'UsedPowerBI',\n",
    "       'UsedTableau', 'UsedGPU', 'UsedE2EPipelineAutomation']\n",
    "                         \n",
    "                         \n",
    "num_cols_to_visualize = ['Age_categ',\n",
    "       'NumCoursePlatforms',,\n",
    "       'NumProgLanguages', 'MLExpOrdinal', 'NumMLFrameworks', 'NumMLAlgos',\n",
    "       'NumTransferLearning', 'NumCloudProvidersUsed', 'NumCloudComputeUsed',\n",
    "       'NumCloudStorageUsed', 'NumBIToolsUsed', 'NumMLaaSToolsUsed',\n",
    "       'NumAutoMLUsed', 'NumMLDeploymentToolsUsed', 'NumMLMonitoringToolUsed',\n",
    "       'NumHardwareUsed', 'TPUUsageOrdinal', 'NumDSContentMediaConsumed', 'NumStepsOfPipelineAutomated']\n",
    "\n",
    "\n",
    "occupation_cols = ['isDataScientist', 'isDataAnalyst',\n",
    "       'isMLE', 'isResearchScientist', 'isDataEngineer',\n",
    "       'DataScientistProportion', 'DataAnalystProportion', 'MLEProportion',\n",
    "       'ResearchScientistProportion', 'DataEngineerProportion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "c9280f36",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster3_numfeatures = X_3cluster_gb[num_cols_to_visualize].copy()\n",
    "s = StandardScaler()\n",
    "cluster3_numfeatures = pd.DataFrame(s.fit_transform(cluster3_numfeatures), columns=num_cols_to_visualize)\n",
    "\n",
    "plt.figure(figsize=(8,10))\n",
    "c3nf = cluster3_numfeatures.T[[0,2,1]]\n",
    "c3nf.columns = ['Segment 1', 'Segment 2', 'Segment 3']\n",
    "sns.heatmap(c3nf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "5ea70def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EduOrdinal</th>\n",
       "      <td>0.803871</td>\n",
       "      <td>0.605707</td>\n",
       "      <td>-1.409578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProgExpOrdinal</th>\n",
       "      <td>0.834365</td>\n",
       "      <td>0.571693</td>\n",
       "      <td>-1.406059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumMLFrameworks</th>\n",
       "      <td>1.259209</td>\n",
       "      <td>-0.072116</td>\n",
       "      <td>-1.187093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumDSContentMediaConsumed</th>\n",
       "      <td>1.293414</td>\n",
       "      <td>-0.151418</td>\n",
       "      <td>-1.141996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumMLAlgos</th>\n",
       "      <td>1.234903</td>\n",
       "      <td>-0.020577</td>\n",
       "      <td>-1.214327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  0         2         1\n",
       "EduOrdinal                 0.803871  0.605707 -1.409578\n",
       "ProgExpOrdinal             0.834365  0.571693 -1.406059\n",
       "NumMLFrameworks            1.259209 -0.072116 -1.187093\n",
       "NumDSContentMediaConsumed  1.293414 -0.151418 -1.141996\n",
       "NumMLAlgos                 1.234903 -0.020577 -1.214327"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster3_numfeatures.T[[0,2,1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c543efa",
   "metadata": {},
   "source": [
    "### Picking features to showcase in report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "1a95f21f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp_num_calls = ['EduOrdinal', 'ProgExpOrdinal', 'NumMLFrameworks', 'NumDSContentMediaConsumed', 'NumMLAlgos']\n",
    "cluster3_numfeatures = X_3cluster_gb[imp_num_calls].copy()\n",
    "s = StandardScaler()\n",
    "cluster3_numfeatures = pd.DataFrame(s.fit_transform(cluster3_numfeatures), columns=imp_num_calls)\n",
    "\n",
    "plt.figure(figsize=(4,5))\n",
    "c3nf = cluster3_numfeatures.T[[0,2,1]]\n",
    "c3nf.columns = ['Segment 1', 'Segment 2', 'Segment 3']\n",
    "sns.heatmap(c3nf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "d994d24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x3gb = X_3cluster_gb[cat_cols_to_visualize].copy()\n",
    "for col in X_3cluster_gb[cat_cols_to_visualize].columns:\n",
    "    x3gb[col] = x3gb[col]/X_3cluster_v2[col].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "096c5393",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UsedFastAI</th>\n",
       "      <th>UsedLinearModels</th>\n",
       "      <th>UsedTreeModels</th>\n",
       "      <th>UsedGBModels</th>\n",
       "      <th>UsedBayesianModels</th>\n",
       "      <th>UsedEvolutionaryModels</th>\n",
       "      <th>UsedNNModels</th>\n",
       "      <th>UsedCNNModels</th>\n",
       "      <th>UsedGANModels</th>\n",
       "      <th>UsedTransformerModels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.149936</td>\n",
       "      <td>0.872801</td>\n",
       "      <td>0.848563</td>\n",
       "      <td>0.781853</td>\n",
       "      <td>0.452381</td>\n",
       "      <td>0.143072</td>\n",
       "      <td>0.542900</td>\n",
       "      <td>0.735521</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.386958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003945</td>\n",
       "      <td>0.295031</td>\n",
       "      <td>0.203797</td>\n",
       "      <td>0.065590</td>\n",
       "      <td>0.051165</td>\n",
       "      <td>0.007274</td>\n",
       "      <td>0.023548</td>\n",
       "      <td>0.058069</td>\n",
       "      <td>0.006904</td>\n",
       "      <td>0.009247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.028060</td>\n",
       "      <td>0.763838</td>\n",
       "      <td>0.677361</td>\n",
       "      <td>0.451255</td>\n",
       "      <td>0.245930</td>\n",
       "      <td>0.045202</td>\n",
       "      <td>0.220456</td>\n",
       "      <td>0.369948</td>\n",
       "      <td>0.047213</td>\n",
       "      <td>0.100747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UsedFastAI  UsedLinearModels  UsedTreeModels  UsedGBModels  \\\n",
       "0    0.149936          0.872801        0.848563      0.781853   \n",
       "1    0.003945          0.295031        0.203797      0.065590   \n",
       "2    0.028060          0.763838        0.677361      0.451255   \n",
       "\n",
       "   UsedBayesianModels  UsedEvolutionaryModels  UsedNNModels  UsedCNNModels  \\\n",
       "0            0.452381                0.143072      0.542900       0.735521   \n",
       "1            0.051165                0.007274      0.023548       0.058069   \n",
       "2            0.245930                0.045202      0.220456       0.369948   \n",
       "\n",
       "   UsedGANModels  UsedTransformerModels  \n",
       "0       0.216216               0.386958  \n",
       "1       0.006904               0.009247  \n",
       "2       0.047213               0.100747  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_3cluster_gb[cat_cols_to_visualize[10:20]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "2341a926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UsedFastAI</th>\n",
       "      <th>UsedLinearModels</th>\n",
       "      <th>UsedTreeModels</th>\n",
       "      <th>UsedGBModels</th>\n",
       "      <th>UsedBayesianModels</th>\n",
       "      <th>UsedEvolutionaryModels</th>\n",
       "      <th>UsedNNModels</th>\n",
       "      <th>UsedCNNModels</th>\n",
       "      <th>UsedGANModels</th>\n",
       "      <th>UsedTransformerModels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.399176</td>\n",
       "      <td>1.403386</td>\n",
       "      <td>1.553334</td>\n",
       "      <td>2.041931</td>\n",
       "      <td>2.062456</td>\n",
       "      <td>2.772461</td>\n",
       "      <td>2.508643</td>\n",
       "      <td>2.199552</td>\n",
       "      <td>3.223802</td>\n",
       "      <td>3.064906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.089443</td>\n",
       "      <td>0.474384</td>\n",
       "      <td>0.373061</td>\n",
       "      <td>0.171298</td>\n",
       "      <td>0.233267</td>\n",
       "      <td>0.140958</td>\n",
       "      <td>0.108812</td>\n",
       "      <td>0.173654</td>\n",
       "      <td>0.102942</td>\n",
       "      <td>0.073239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.636140</td>\n",
       "      <td>1.228183</td>\n",
       "      <td>1.239941</td>\n",
       "      <td>1.178521</td>\n",
       "      <td>1.121222</td>\n",
       "      <td>0.875932</td>\n",
       "      <td>1.018687</td>\n",
       "      <td>1.106318</td>\n",
       "      <td>0.703952</td>\n",
       "      <td>0.797967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UsedFastAI  UsedLinearModels  UsedTreeModels  UsedGBModels  \\\n",
       "0    3.399176          1.403386        1.553334      2.041931   \n",
       "1    0.089443          0.474384        0.373061      0.171298   \n",
       "2    0.636140          1.228183        1.239941      1.178521   \n",
       "\n",
       "   UsedBayesianModels  UsedEvolutionaryModels  UsedNNModels  UsedCNNModels  \\\n",
       "0            2.062456                2.772461      2.508643       2.199552   \n",
       "1            0.233267                0.140958      0.108812       0.173654   \n",
       "2            1.121222                0.875932      1.018687       1.106318   \n",
       "\n",
       "   UsedGANModels  UsedTransformerModels  \n",
       "0       3.223802               3.064906  \n",
       "1       0.102942               0.073239  \n",
       "2       0.703952               0.797967  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3gb[cat_cols_to_visualize[10:20]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "a2a75da2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,10))\n",
    "c3cf = x3gb[cat_cols_to_visualize].T[[0,2,1]]\n",
    "c3cf.columns = ['Segment 1', 'Segment 2', 'Segment 3']\n",
    "sns.heatmap(c3cf, cmap=\"crest_r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1643c759",
   "metadata": {},
   "source": [
    "### Picking features to showcase in report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "1ec2175c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp_cat_cols = ['UsedNNModels',  'UsedCNNModels', 'UsedGANModels', 'UsedTransformerModels', 'UsedE2EPipelineAutomation', 'UsedCNNModels']\n",
    "plt.figure(figsize=(4,5))\n",
    "c3cf = x3gb[imp_cat_cols].T[[0,2,1]]\n",
    "c3cf.columns = ['Segment 1', 'Segment 2', 'Segment 3']\n",
    "sns.heatmap(c3cf, cmap=\"crest_r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "7a059fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_3cluster['Country'] = adf['Country']\n",
    "X_3cluster['PID'] = adf['PID']\n",
    "USA_salaries_clustered = X_3cluster[X_3cluster['Country'] == 'United States of America'].merge(survey_df[['AnnualComp(USD)', 'PID']], on='PID').dropna().drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b79df30",
   "metadata": {},
   "source": [
    "### Salary Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "26a34266",
   "metadata": {},
   "outputs": [],
   "source": [
    "USA_salaries_clustered['AnnualComp(USD)'].value_counts().to_dict()\n",
    "salary_map = {'100,000-124,999': 'c. 100K+',\n",
    " '150,000-199,999': 'd. 150K+',\n",
    " '125,000-149,999': 'c. 100K+',\n",
    " '200,000-249,999': 'e. 200K+',\n",
    " '90,000-99,999': 'b. 50K+',\n",
    " '70,000-79,999': 'b. 50K+',\n",
    " '80,000-89,999': 'b. 50K+',\n",
    " '$0-999': 'a. 0K+',\n",
    " '60,000-69,999': 'b. 50K+',\n",
    " '50,000-59,999': 'b. 50K+',\n",
    " '250,000-299,999': 'e. 200K+',\n",
    " '40,000-49,999': 'a. 0K+',\n",
    " '300,000-499,999': 'b. 50K+',\n",
    " '30,000-39,999': 'a. 0K+',\n",
    " '25,000-29,999': 'a. 0K+',\n",
    " '20,000-24,999': 'a. 0K+',\n",
    " '300,000-500,000': 'f. 300K+',\n",
    " '10,000-14,999': 'a. 0K+',\n",
    " '$500,000-999,999': 'g. 500K+',\n",
    " '5,000-7,499': 'a. 0K+',\n",
    " '7,500-9,999': 'a. 0K+',\n",
    " '>$1,000,000': 'g. 500K+',\n",
    " '15,000-19,999': 'a. 0K+',\n",
    " '1,000-1,999': 'a. 0K+',\n",
    " '2,000-2,999': 'a. 0K+',\n",
    " '4,000-4,999': 'a. 0K+',\n",
    " '> $500,000': 'g. 500K+',\n",
    " '3,000-3,999': 'a. 0K+'}\n",
    "USA_salaries_clustered['AnnualComp(USD)'] = USA_salaries_clustered['AnnualComp(USD)'].apply(lambda x: salary_map[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "180d6ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "USA_Salaries_segment = pd.DataFrame(USA_salaries_clustered['Segment']).join(pd.get_dummies(USA_salaries_clustered[['AnnualComp(USD)']]))\n",
    "a = USA_Salaries_segment.groupby('Segment').agg({col: 'mean' for col in USA_Salaries_segment.columns[1:]}).reset_index()\n",
    "a.columns = ['Segment', 'a. 0K+',\n",
    " 'b. 50K+',\n",
    " 'c. 100K+',\n",
    " 'd. 150K+',\n",
    " 'e. 200K+',\n",
    " 'f. 300K+',\n",
    " 'g. 500K+']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "47d694dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
    "fig.suptitle('Salary Distribution')\n",
    "\n",
    "axes[0].title.set_text('Segment 1 Salary Distribution (USA)')\n",
    "axes[1].title.set_text('Segment 3 Salary Distribution (USA)')\n",
    "axes[2].title.set_text('Segment 2 Salary Distribution (USA)')\n",
    "\n",
    "plt.setp(axes, yticks=np.arange(0,0.6,0.1), ylim = (0, 0.5))\n",
    "\n",
    "# plt.title('Segment 1 Salary Distribution (USA)')\n",
    "sns.barplot(ax=axes[0], data=a[a['Segment'] == 'First'], palette=\"ch:s=.25,rot=-.25\")\n",
    "sns.barplot(ax=axes[1], data=a[a['Segment'] == 'Third'], palette=\"ch:s=.25,rot=-.25\")\n",
    "sns.barplot(ax=axes[2], data=a[a['Segment'] == 'Second'], palette=\"ch:s=.25,rot=-.25\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "3d0f7356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2600x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title_cols = ['Segment', 'isDataScientist', 'isDataAnalyst', 'isMLE', 'isResearchScientist', 'isDataEngineer']\n",
    "a = X_3cluster_gb[title_cols]\n",
    "a.columns = ['Segment', 'DataScientist', 'DataAnalyst', 'MLE', 'ResearchScientist', 'DataEngineer']\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(26, 5))\n",
    "fig.suptitle('Job Title Distribution')\n",
    "\n",
    "axes[0].title.set_text('Segment 1 Job Title Distribution')\n",
    "axes[1].title.set_text('Segment 3 Job Title Distribution')\n",
    "axes[2].title.set_text('Segment 2 Job Title Distribution')\n",
    "\n",
    "plt.setp(axes, yticks=np.arange(0,0.7,0.1), ylim = (0, 0.6))\n",
    "\n",
    "# plt.title('Segment 1 Salary Distribution (USA)')\n",
    "sns.barplot(ax=axes[0], data=a[a['Segment'] == 'First'], palette=\"ch:s=.25,rot=-.25\")\n",
    "sns.barplot(ax=axes[1], data=a[a['Segment'] == 'Third'], palette=\"ch:s=.25,rot=-.25\")\n",
    "sns.barplot(ax=axes[2], data=a[a['Segment'] == 'Second'], palette=\"ch:s=.25,rot=-.25\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "5189a965",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/07/r04lbdjd0vj2ps53pqkc0b040000gn/T/ipykernel_1115/2016857516.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  a['Segment'] = a['Segment'].cat.reorder_categories(['First','Third','Second'])\n",
      "/var/folders/07/r04lbdjd0vj2ps53pqkc0b040000gn/T/ipykernel_1115/2016857516.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  a['Segment'] = a['Segment'].map({'First':'First', 'Second':'Third', 'Third': 'Second'})\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2600x700 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title_cols = ['Segment', 'DataScientistProportion', 'DataAnalystProportion', 'MLEProportion',\n",
    "       'ResearchScientistProportion', 'DataEngineerProportion']\n",
    "a = X_3cluster_gb[title_cols]\n",
    "a.columns = ['Segment', 'DataScientist', 'DataAnalyst', 'MLE', 'ResearchScientist', 'DataEngineer']\n",
    "\n",
    "a['Segment'] = a['Segment'].cat.reorder_categories(['First','Third','Second'])\n",
    "a['Segment'] = a['Segment'].map({'First':'First', 'Second':'Third', 'Third': 'Second'})\n",
    "\n",
    "fig, axes = plt.subplots(1, 5, figsize=(26, 7))\n",
    "fig.suptitle('Job Title Distribution')\n",
    "\n",
    "axes[0].title.set_text('Data Scientist Segment Distribution')\n",
    "axes[1].title.set_text('Data Analyst Segment Distribution')\n",
    "axes[2].title.set_text('MLE Segment Distribution')\n",
    "axes[3].title.set_text('Research Scientist Segment Distribution')\n",
    "axes[4].title.set_text('Data Engineer Segment Distribution')\n",
    "\n",
    "plt.setp(axes, yticks=np.arange(0,0.7,0.1), ylim = (0, 0.6))\n",
    "\n",
    "# plt.title('Segment 1 Salary Distribution (USA)')\n",
    "sns.barplot(ax=axes[0], data=a[a['Segment'] == 'Second'], x=a['Segment'], y=a['DataScientist'], palette=\"ch:s=.25,rot=-.25\")\n",
    "sns.barplot(ax=axes[1], data=a[a['Segment'] == 'Second'], x=a['Segment'], y=a['DataAnalyst'], palette=\"ch:s=.25,rot=-.25\")\n",
    "sns.barplot(ax=axes[2], data=a[a['Segment'] == 'Second'], x=a['Segment'], y=a['MLE'], palette=\"ch:s=.25,rot=-.25\")\n",
    "sns.barplot(ax=axes[3], data=a[a['Segment'] == 'Second'], x=a['Segment'], y=a['ResearchScientist'], palette=\"ch:s=.25,rot=-.25\")\n",
    "sns.barplot(ax=axes[4], data=a[a['Segment'] == 'Second'], x=a['Segment'], y=a['DataEngineer'], palette=\"ch:s=.25,rot=-.25\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "21b8eb1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Segment</th>\n",
       "      <th>DataScientist</th>\n",
       "      <th>DataAnalyst</th>\n",
       "      <th>MLE</th>\n",
       "      <th>ResearchScientist</th>\n",
       "      <th>DataEngineer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>First</td>\n",
       "      <td>0.283177</td>\n",
       "      <td>0.076130</td>\n",
       "      <td>0.294734</td>\n",
       "      <td>0.187595</td>\n",
       "      <td>0.168840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second</td>\n",
       "      <td>0.212869</td>\n",
       "      <td>0.584322</td>\n",
       "      <td>0.242703</td>\n",
       "      <td>0.283207</td>\n",
       "      <td>0.358957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Third</td>\n",
       "      <td>0.503953</td>\n",
       "      <td>0.339548</td>\n",
       "      <td>0.462563</td>\n",
       "      <td>0.529198</td>\n",
       "      <td>0.472203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Segment  DataScientist  DataAnalyst       MLE  ResearchScientist  \\\n",
       "0   First       0.283177     0.076130  0.294734           0.187595   \n",
       "1  Second       0.212869     0.584322  0.242703           0.283207   \n",
       "2   Third       0.503953     0.339548  0.462563           0.529198   \n",
       "\n",
       "   DataEngineer  \n",
       "0      0.168840  \n",
       "1      0.358957  \n",
       "2      0.472203  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50046153",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}