data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d38c7bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import load_data, convert_to_single_label\n",
    "import config as cfg\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9796884a",
   "metadata": {},
   "outputs": [],
   "source": [
    "users = load_data(format='pandas')\n",
    "features = load_data(cfg.USER_FEATURES_FILE, format='pandas')\n",
    "relations = load_data(cfg.USER_RELATIONS_FILE, format='pandas')\n",
    "labels = load_data(cfg.USER_LABELS_FILE, format='pandas')\n",
    "\n",
    "users = users.set_index('login')\n",
    "features = features.set_index('username')\n",
    "labels = labels.set_index('login')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d700e90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = labels.apply(convert_to_single_label, axis=1)\n",
    "labels = labels.idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99d34c45",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>languages_JavaScript</th>\n",
       "      <th>languages_Python</th>\n",
       "      <th>languages_TypeScript</th>\n",
       "      <th>languages_HTML</th>\n",
       "      <th>languages_Go</th>\n",
       "      <th>languages_Java</th>\n",
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       "      <th>languages_CSS</th>\n",
       "      <th>languages_Shell</th>\n",
       "      <th>...</th>\n",
       "      <th>company_uber</th>\n",
       "      <th>company_university</th>\n",
       "      <th>company_web</th>\n",
       "      <th>company_www</th>\n",
       "      <th>company_zhejiang</th>\n",
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       "      <td>0.000450</td>\n",
       "      <td>0.000323</td>\n",
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       "      <th>dennissiq</th>\n",
       "      <td>0.187</td>\n",
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       "      <td>0.335</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guoyoujin</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.179</td>\n",
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       "      <td>0.002462</td>\n",
       "      <td>0.004556</td>\n",
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       "      <td>0.004270</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.017</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007073</td>\n",
       "      <td>0.010934</td>\n",
       "      <td>0.004927</td>\n",
       "      <td>0.001082</td>\n",
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       "      <th>pedrofsn</th>\n",
       "      <td>0.147</td>\n",
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       "      <td>0.002851</td>\n",
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       "      <th>LukeMichaels</th>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032</td>\n",
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       "      <td>0.002278</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.000493</td>\n",
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       "      <th>sfate</th>\n",
       "      <td>0.005</td>\n",
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       "    <tr>\n",
       "      <th>DemoYolk</th>\n",
       "      <td>0.196</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>charukiewicz</th>\n",
       "      <td>0.103</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.048</td>\n",
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       "  </tbody>\n",
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       "<p>35909 rows × 304 columns</p>\n",
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      ],
      "text/plain": [
       "              languages_JavaScript  languages_Python  languages_TypeScript  \\\n",
       "username                                                                     \n",
       "SaiNageswarS                 0.000             0.000                 0.250   \n",
       "dennissiq                    0.187             0.000                 0.263   \n",
       "guoyoujin                    0.000             0.051                 0.000   \n",
       "redshiftzero                 0.000             0.423                 0.000   \n",
       "pedrofsn                     0.147             0.000                 0.204   \n",
       "...                            ...               ...                   ...   \n",
       "LukeMichaels                 0.373             0.000                 0.000   \n",
       "hemlok                       0.531             0.000                 0.452   \n",
       "sfate                        0.005             0.000                 0.000   \n",
       "DemoYolk                     0.196             0.000                 0.000   \n",
       "charukiewicz                 0.103             0.000                 0.000   \n",
       "\n",
       "              languages_HTML  languages_Go  languages_Java  languages_PHP  \\\n",
       "username                                                                    \n",
       "SaiNageswarS           0.000         0.500           0.000          0.000   \n",
       "dennissiq              0.335         0.000           0.000          0.000   \n",
       "guoyoujin              0.000         0.179           0.202          0.000   \n",
       "redshiftzero           0.071         0.000           0.000          0.000   \n",
       "pedrofsn               0.030         0.000           0.002          0.000   \n",
       "...                      ...           ...             ...            ...   \n",
       "LukeMichaels           0.091         0.000           0.000          0.252   \n",
       "hemlok                 0.015         0.000           0.000          0.000   \n",
       "sfate                  0.027         0.000           0.000          0.000   \n",
       "DemoYolk               0.199         0.244           0.000          0.000   \n",
       "charukiewicz           0.048         0.000           0.000          0.000   \n",
       "\n",
       "              languages_Jupyter Notebook  languages_CSS  languages_Shell  ...  \\\n",
       "username                                                                  ...   \n",
       "SaiNageswarS                         0.0          0.000            0.000  ...   \n",
       "dennissiq                            0.0          0.000            0.014  ...   \n",
       "guoyoujin                            0.0          0.000            0.154  ...   \n",
       "redshiftzero                         0.0          0.000            0.017  ...   \n",
       "pedrofsn                             0.0          0.019            0.001  ...   \n",
       "...                                  ...            ...              ...  ...   \n",
       "LukeMichaels                         0.0          0.032            0.000  ...   \n",
       "hemlok                               0.0          0.002            0.000  ...   \n",
       "sfate                                0.0          0.015            0.000  ...   \n",
       "DemoYolk                             0.0          0.066            0.003  ...   \n",
       "charukiewicz                         0.0          0.125            0.015  ...   \n",
       "\n",
       "              company_uber  company_university  company_web  company_www  \\\n",
       "username                                                                   \n",
       "SaiNageswarS           0.0                 0.0          0.0          0.0   \n",
       "dennissiq              0.0                 0.0          0.0          0.0   \n",
       "guoyoujin              0.0                 0.0          0.0          0.0   \n",
       "redshiftzero           0.0                 0.0          0.0          0.0   \n",
       "pedrofsn               0.0                 0.0          0.0          0.0   \n",
       "...                    ...                 ...          ...          ...   \n",
       "LukeMichaels           0.0                 0.0          0.0          0.0   \n",
       "hemlok                 0.0                 0.0          0.0          0.0   \n",
       "sfate                  0.0                 0.0          0.0          0.0   \n",
       "DemoYolk               0.0                 0.0          0.0          0.0   \n",
       "charukiewicz           0.0                 0.0          0.0          0.0   \n",
       "\n",
       "              company_zhejiang  company_zhejiang university  \\\n",
       "username                                                      \n",
       "SaiNageswarS               0.0                          0.0   \n",
       "dennissiq                  0.0                          0.0   \n",
       "guoyoujin                  0.0                          0.0   \n",
       "redshiftzero               0.0                          0.0   \n",
       "pedrofsn                   0.0                          0.0   \n",
       "...                        ...                          ...   \n",
       "LukeMichaels               0.0                          0.0   \n",
       "hemlok                     0.0                          0.0   \n",
       "sfate                      0.0                          0.0   \n",
       "DemoYolk                   0.0                          0.0   \n",
       "charukiewicz               0.0                          0.0   \n",
       "\n",
       "              stats_public_repos  stats_public_gists  stats_followers  \\\n",
       "username                                                                \n",
       "SaiNageswarS            0.000995            0.000000         0.000450   \n",
       "dennissiq               0.002882            0.000000         0.000550   \n",
       "guoyoujin               0.002462            0.004556         0.001276   \n",
       "redshiftzero            0.007073            0.010934         0.004927   \n",
       "pedrofsn                0.005449            0.000911         0.002851   \n",
       "...                          ...                 ...              ...   \n",
       "LukeMichaels            0.001677            0.001822         0.000375   \n",
       "hemlok                  0.001729            0.002278         0.001251   \n",
       "sfate                   0.004034            0.027335         0.000600   \n",
       "DemoYolk                0.000367            0.000000         0.000075   \n",
       "charukiewicz            0.001781            0.001822         0.000800   \n",
       "\n",
       "              stats_following  \n",
       "username                       \n",
       "SaiNageswarS         0.000323  \n",
       "dennissiq            0.000209  \n",
       "guoyoujin            0.004270  \n",
       "redshiftzero         0.001082  \n",
       "pedrofsn             0.007363  \n",
       "...                       ...  \n",
       "LukeMichaels         0.002144  \n",
       "hemlok               0.000493  \n",
       "sfate                0.000664  \n",
       "DemoYolk             0.000076  \n",
       "charukiewicz         0.000493  \n",
       "\n",
       "[35909 rows x 304 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8af4727d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ebe6809",
   "metadata": {},
   "outputs": [],
   "source": [
    "relations_subset = relations[relations.following.isin(labels.index) & relations.follow.isin(labels.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0441299e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lang_cols = [col for col in features.columns if 'languages' in col]\n",
    "topic_cols = [col for col in features.columns if 'topics' in col]\n",
    "comp_cols =  [col for col in features.columns if 'company' in col]\n",
    "stat_cols =  [col for col in features.columns if 'stats' in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e1734a3",
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "    .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>languages_JavaScript</th>\n",
       "      <th>languages_Python</th>\n",
       "      <th>languages_TypeScript</th>\n",
       "      <th>languages_HTML</th>\n",
       "      <th>languages_Go</th>\n",
       "      <th>languages_Java</th>\n",
       "      <th>languages_PHP</th>\n",
       "      <th>languages_Jupyter Notebook</th>\n",
       "      <th>languages_CSS</th>\n",
       "      <th>languages_Shell</th>\n",
       "      <th>...</th>\n",
       "      <th>company_uber</th>\n",
       "      <th>company_university</th>\n",
       "      <th>company_web</th>\n",
       "      <th>company_www</th>\n",
       "      <th>company_zhejiang</th>\n",
       "      <th>company_zhejiang university</th>\n",
       "      <th>stats_public_repos</th>\n",
       "      <th>stats_public_gists</th>\n",
       "      <th>stats_followers</th>\n",
       "      <th>stats_following</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>username</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SaiNageswarS</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.000323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dennissiq</th>\n",
       "      <td>0.187</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pedrofsn</th>\n",
       "      <td>0.147</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005449</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>0.002851</td>\n",
       "      <td>0.007363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scazan</th>\n",
       "      <td>0.168</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.001901</td>\n",
       "      <td>0.002144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dantehemerson</th>\n",
       "      <td>0.408</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002253</td>\n",
       "      <td>0.014579</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>0.002903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emersonjsouza</th>\n",
       "      <td>0.379</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.037</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002515</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.000825</td>\n",
       "      <td>0.001423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShayBox</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002148</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.000950</td>\n",
       "      <td>0.001328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hsk81</th>\n",
       "      <td>0.200</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.001075</td>\n",
       "      <td>0.000266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>makiftutuncu</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004401</td>\n",
       "      <td>0.017768</td>\n",
       "      <td>0.005302</td>\n",
       "      <td>0.004402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChappIO</th>\n",
       "      <td>0.071</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.000550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5005 rows × 304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               languages_JavaScript  languages_Python  languages_TypeScript  \\\n",
       "username                                                                      \n",
       "SaiNageswarS                  0.000             0.000                 0.250   \n",
       "dennissiq                     0.187             0.000                 0.263   \n",
       "pedrofsn                      0.147             0.000                 0.204   \n",
       "scazan                        0.168             0.000                 0.107   \n",
       "dantehemerson                 0.408             0.000                 0.571   \n",
       "...                             ...               ...                   ...   \n",
       "emersonjsouza                 0.379             0.250                 0.096   \n",
       "ShayBox                       0.000             0.250                 0.000   \n",
       "hsk81                         0.200             0.116                 0.000   \n",
       "makiftutuncu                  0.000             0.006                 0.000   \n",
       "ChappIO                       0.071             0.191                 0.167   \n",
       "\n",
       "               languages_HTML  languages_Go  languages_Java  languages_PHP  \\\n",
       "username                                                                     \n",
       "SaiNageswarS            0.000         0.500           0.000            0.0   \n",
       "dennissiq               0.335         0.000           0.000            0.0   \n",
       "pedrofsn                0.030         0.000           0.002            0.0   \n",
       "scazan                  0.042         0.000           0.000            0.0   \n",
       "dantehemerson           0.000         0.000           0.000            0.0   \n",
       "...                       ...           ...             ...            ...   \n",
       "emersonjsouza           0.000         0.234           0.000            0.0   \n",
       "ShayBox                 0.000         0.000           0.000            0.0   \n",
       "hsk81                   0.000         0.000           0.000            0.0   \n",
       "makiftutuncu            0.009         0.000           0.000            0.0   \n",
       "ChappIO                 0.327         0.000           0.000            0.0   \n",
       "\n",
       "               languages_Jupyter Notebook  languages_CSS  languages_Shell  \\\n",
       "username                                                                    \n",
       "SaiNageswarS                          0.0          0.000            0.000   \n",
       "dennissiq                             0.0          0.000            0.014   \n",
       "pedrofsn                              0.0          0.019            0.001   \n",
       "scazan                                0.0          0.079            0.000   \n",
       "dantehemerson                         0.0          0.006            0.008   \n",
       "...                                   ...            ...              ...   \n",
       "emersonjsouza                         0.0          0.000            0.037   \n",
       "ShayBox                               0.0          0.000            0.000   \n",
       "hsk81                                 0.0          0.000            0.482   \n",
       "makiftutuncu                          0.0          0.004            0.008   \n",
       "ChappIO                               0.0          0.067            0.000   \n",
       "\n",
       "               ...  company_uber  company_university  company_web  \\\n",
       "username       ...                                                  \n",
       "SaiNageswarS   ...           0.0                 0.0          0.0   \n",
       "dennissiq      ...           0.0                 0.0          0.0   \n",
       "pedrofsn       ...           0.0                 0.0          0.0   \n",
       "scazan         ...           0.0                 0.0          0.0   \n",
       "dantehemerson  ...           0.0                 0.0          0.0   \n",
       "...            ...           ...                 ...          ...   \n",
       "emersonjsouza  ...           0.0                 0.0          0.0   \n",
       "ShayBox        ...           0.0                 0.0          0.0   \n",
       "hsk81          ...           0.0                 0.0          0.0   \n",
       "makiftutuncu   ...           0.0                 0.0          0.0   \n",
       "ChappIO        ...           0.0                 0.0          0.0   \n",
       "\n",
       "               company_www  company_zhejiang  company_zhejiang university  \\\n",
       "username                                                                    \n",
       "SaiNageswarS           0.0               0.0                          0.0   \n",
       "dennissiq              0.0               0.0                          0.0   \n",
       "pedrofsn               0.0               0.0                          0.0   \n",
       "scazan                 0.0               0.0                          0.0   \n",
       "dantehemerson          0.0               0.0                          0.0   \n",
       "...                    ...               ...                          ...   \n",
       "emersonjsouza          0.0               0.0                          0.0   \n",
       "ShayBox                0.0               0.0                          0.0   \n",
       "hsk81                  0.0               0.0                          0.0   \n",
       "makiftutuncu           0.0               0.0                          0.0   \n",
       "ChappIO                0.0               0.0                          0.0   \n",
       "\n",
       "               stats_public_repos  stats_public_gists  stats_followers  \\\n",
       "username                                                                 \n",
       "SaiNageswarS             0.000995            0.000000         0.000450   \n",
       "dennissiq                0.002882            0.000000         0.000550   \n",
       "pedrofsn                 0.005449            0.000911         0.002851   \n",
       "scazan                   0.003458            0.000456         0.001901   \n",
       "dantehemerson            0.002253            0.014579         0.002151   \n",
       "...                           ...                 ...              ...   \n",
       "emersonjsouza            0.002515            0.001822         0.000825   \n",
       "ShayBox                  0.002148            0.001822         0.000950   \n",
       "hsk81                    0.001729            0.001367         0.001075   \n",
       "makiftutuncu             0.004401            0.017768         0.005302   \n",
       "ChappIO                  0.001415            0.001367         0.000525   \n",
       "\n",
       "               stats_following  \n",
       "username                        \n",
       "SaiNageswarS          0.000323  \n",
       "dennissiq             0.000209  \n",
       "pedrofsn              0.007363  \n",
       "scazan                0.002144  \n",
       "dantehemerson         0.002903  \n",
       "...                        ...  \n",
       "emersonjsouza         0.001423  \n",
       "ShayBox               0.001328  \n",
       "hsk81                 0.000266  \n",
       "makiftutuncu          0.004402  \n",
       "ChappIO               0.000550  \n",
       "\n",
       "[5005 rows x 304 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[(features[topic_cols] > 0).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12f5ddda",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>languages_JavaScript</th>\n",
       "      <th>languages_Python</th>\n",
       "      <th>languages_TypeScript</th>\n",
       "      <th>languages_HTML</th>\n",
       "      <th>languages_Go</th>\n",
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       "      <th>company_zhejiang</th>\n",
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       "      <th>stats_public_repos</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SaiNageswarS</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.000323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dennissiq</th>\n",
       "      <td>0.187</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guoyoujin</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002462</td>\n",
       "      <td>0.004556</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.004270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>redshiftzero</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007073</td>\n",
       "      <td>0.010934</td>\n",
       "      <td>0.004927</td>\n",
       "      <td>0.001082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pedrofsn</th>\n",
       "      <td>0.147</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005449</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>0.002851</td>\n",
       "      <td>0.007363</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>LukeMichaels</th>\n",
       "      <td>0.373</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.002144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hemlok</th>\n",
       "      <td>0.531</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sfate</th>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004034</td>\n",
       "      <td>0.027335</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.000664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DemoYolk</th>\n",
       "      <td>0.196</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charukiewicz</th>\n",
       "      <td>0.103</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.015</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001781</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35307 rows × 304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              languages_JavaScript  languages_Python  languages_TypeScript  \\\n",
       "username                                                                     \n",
       "SaiNageswarS                 0.000             0.000                 0.250   \n",
       "dennissiq                    0.187             0.000                 0.263   \n",
       "guoyoujin                    0.000             0.051                 0.000   \n",
       "redshiftzero                 0.000             0.423                 0.000   \n",
       "pedrofsn                     0.147             0.000                 0.204   \n",
       "...                            ...               ...                   ...   \n",
       "LukeMichaels                 0.373             0.000                 0.000   \n",
       "hemlok                       0.531             0.000                 0.452   \n",
       "sfate                        0.005             0.000                 0.000   \n",
       "DemoYolk                     0.196             0.000                 0.000   \n",
       "charukiewicz                 0.103             0.000                 0.000   \n",
       "\n",
       "              languages_HTML  languages_Go  languages_Java  languages_PHP  \\\n",
       "username                                                                    \n",
       "SaiNageswarS           0.000         0.500           0.000          0.000   \n",
       "dennissiq              0.335         0.000           0.000          0.000   \n",
       "guoyoujin              0.000         0.179           0.202          0.000   \n",
       "redshiftzero           0.071         0.000           0.000          0.000   \n",
       "pedrofsn               0.030         0.000           0.002          0.000   \n",
       "...                      ...           ...             ...            ...   \n",
       "LukeMichaels           0.091         0.000           0.000          0.252   \n",
       "hemlok                 0.015         0.000           0.000          0.000   \n",
       "sfate                  0.027         0.000           0.000          0.000   \n",
       "DemoYolk               0.199         0.244           0.000          0.000   \n",
       "charukiewicz           0.048         0.000           0.000          0.000   \n",
       "\n",
       "              languages_Jupyter Notebook  languages_CSS  languages_Shell  ...  \\\n",
       "username                                                                  ...   \n",
       "SaiNageswarS                         0.0          0.000            0.000  ...   \n",
       "dennissiq                            0.0          0.000            0.014  ...   \n",
       "guoyoujin                            0.0          0.000            0.154  ...   \n",
       "redshiftzero                         0.0          0.000            0.017  ...   \n",
       "pedrofsn                             0.0          0.019            0.001  ...   \n",
       "...                                  ...            ...              ...  ...   \n",
       "LukeMichaels                         0.0          0.032            0.000  ...   \n",
       "hemlok                               0.0          0.002            0.000  ...   \n",
       "sfate                                0.0          0.015            0.000  ...   \n",
       "DemoYolk                             0.0          0.066            0.003  ...   \n",
       "charukiewicz                         0.0          0.125            0.015  ...   \n",
       "\n",
       "              company_uber  company_university  company_web  company_www  \\\n",
       "username                                                                   \n",
       "SaiNageswarS           0.0                 0.0          0.0          0.0   \n",
       "dennissiq              0.0                 0.0          0.0          0.0   \n",
       "guoyoujin              0.0                 0.0          0.0          0.0   \n",
       "redshiftzero           0.0                 0.0          0.0          0.0   \n",
       "pedrofsn               0.0                 0.0          0.0          0.0   \n",
       "...                    ...                 ...          ...          ...   \n",
       "LukeMichaels           0.0                 0.0          0.0          0.0   \n",
       "hemlok                 0.0                 0.0          0.0          0.0   \n",
       "sfate                  0.0                 0.0          0.0          0.0   \n",
       "DemoYolk               0.0                 0.0          0.0          0.0   \n",
       "charukiewicz           0.0                 0.0          0.0          0.0   \n",
       "\n",
       "              company_zhejiang  company_zhejiang university  \\\n",
       "username                                                      \n",
       "SaiNageswarS               0.0                          0.0   \n",
       "dennissiq                  0.0                          0.0   \n",
       "guoyoujin                  0.0                          0.0   \n",
       "redshiftzero               0.0                          0.0   \n",
       "pedrofsn                   0.0                          0.0   \n",
       "...                        ...                          ...   \n",
       "LukeMichaels               0.0                          0.0   \n",
       "hemlok                     0.0                          0.0   \n",
       "sfate                      0.0                          0.0   \n",
       "DemoYolk                   0.0                          0.0   \n",
       "charukiewicz               0.0                          0.0   \n",
       "\n",
       "              stats_public_repos  stats_public_gists  stats_followers  \\\n",
       "username                                                                \n",
       "SaiNageswarS            0.000995            0.000000         0.000450   \n",
       "dennissiq               0.002882            0.000000         0.000550   \n",
       "guoyoujin               0.002462            0.004556         0.001276   \n",
       "redshiftzero            0.007073            0.010934         0.004927   \n",
       "pedrofsn                0.005449            0.000911         0.002851   \n",
       "...                          ...                 ...              ...   \n",
       "LukeMichaels            0.001677            0.001822         0.000375   \n",
       "hemlok                  0.001729            0.002278         0.001251   \n",
       "sfate                   0.004034            0.027335         0.000600   \n",
       "DemoYolk                0.000367            0.000000         0.000075   \n",
       "charukiewicz            0.001781            0.001822         0.000800   \n",
       "\n",
       "              stats_following  \n",
       "username                       \n",
       "SaiNageswarS         0.000323  \n",
       "dennissiq            0.000209  \n",
       "guoyoujin            0.004270  \n",
       "redshiftzero         0.001082  \n",
       "pedrofsn             0.007363  \n",
       "...                       ...  \n",
       "LukeMichaels         0.002144  \n",
       "hemlok               0.000493  \n",
       "sfate                0.000664  \n",
       "DemoYolk             0.000076  \n",
       "charukiewicz         0.000493  \n",
       "\n",
       "[35307 rows x 304 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[(features[lang_cols] > 0).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e41ffdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>languages_JavaScript</th>\n",
       "      <th>languages_Python</th>\n",
       "      <th>languages_TypeScript</th>\n",
       "      <th>languages_HTML</th>\n",
       "      <th>languages_Go</th>\n",
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       "      <th>company_uber</th>\n",
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       "      <th>stats_followers</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SaiNageswarS</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.000323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zendq1998</th>\n",
       "      <td>0.268</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001781</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000850</td>\n",
       "      <td>0.000835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>haczqyf</th>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000675</td>\n",
       "      <td>0.000873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sydnever</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scazan</th>\n",
       "      <td>0.168</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.001901</td>\n",
       "      <td>0.002144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rohitnair987</th>\n",
       "      <td>0.007</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>devpato</th>\n",
       "      <td>0.330</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010164</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.008703</td>\n",
       "      <td>0.010475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>matrush</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000576</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huangshiyu13</th>\n",
       "      <td>0.059</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005577</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charukiewicz</th>\n",
       "      <td>0.103</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.015</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001781</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9666 rows × 304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              languages_JavaScript  languages_Python  languages_TypeScript  \\\n",
       "username                                                                     \n",
       "SaiNageswarS                 0.000             0.000                 0.250   \n",
       "Zendq1998                    0.268             0.250                 0.457   \n",
       "haczqyf                      0.000             0.078                 0.000   \n",
       "sydnever                     0.000             0.116                 0.000   \n",
       "scazan                       0.168             0.000                 0.107   \n",
       "...                            ...               ...                   ...   \n",
       "rohitnair987                 0.007             0.001                 0.223   \n",
       "devpato                      0.330             0.000                 0.048   \n",
       "matrush                      0.000             0.209                 0.000   \n",
       "huangshiyu13                 0.059             0.827                 0.000   \n",
       "charukiewicz                 0.103             0.000                 0.000   \n",
       "\n",
       "              languages_HTML  languages_Go  languages_Java  languages_PHP  \\\n",
       "username                                                                    \n",
       "SaiNageswarS           0.000           0.5           0.000            0.0   \n",
       "Zendq1998              0.006           0.0           0.000            0.0   \n",
       "haczqyf                0.004           0.0           0.048            0.0   \n",
       "sydnever               0.000           0.0           0.000            0.0   \n",
       "scazan                 0.042           0.0           0.000            0.0   \n",
       "...                      ...           ...             ...            ...   \n",
       "rohitnair987           0.293           0.0           0.001            0.0   \n",
       "devpato                0.572           0.0           0.000            0.0   \n",
       "matrush                0.000           0.0           0.000            0.0   \n",
       "huangshiyu13           0.060           0.0           0.000            0.0   \n",
       "charukiewicz           0.048           0.0           0.000            0.0   \n",
       "\n",
       "              languages_Jupyter Notebook  languages_CSS  languages_Shell  ...  \\\n",
       "username                                                                  ...   \n",
       "SaiNageswarS                       0.000          0.000            0.000  ...   \n",
       "Zendq1998                          0.000          0.006            0.001  ...   \n",
       "haczqyf                            0.347          0.000            0.222  ...   \n",
       "sydnever                           0.000          0.000            0.274  ...   \n",
       "scazan                             0.000          0.079            0.000  ...   \n",
       "...                                  ...            ...              ...  ...   \n",
       "rohitnair987                       0.000          0.000            0.000  ...   \n",
       "devpato                            0.000          0.049            0.000  ...   \n",
       "matrush                            0.000          0.000            0.000  ...   \n",
       "huangshiyu13                       0.000          0.044            0.008  ...   \n",
       "charukiewicz                       0.000          0.125            0.015  ...   \n",
       "\n",
       "              company_uber  company_university  company_web  company_www  \\\n",
       "username                                                                   \n",
       "SaiNageswarS           0.0                 0.0          0.0          0.0   \n",
       "Zendq1998              0.0                 0.0          0.0          0.0   \n",
       "haczqyf                0.0                 1.0          0.0          0.0   \n",
       "sydnever               0.0                 0.0          0.0          0.0   \n",
       "scazan                 0.0                 0.0          0.0          0.0   \n",
       "...                    ...                 ...          ...          ...   \n",
       "rohitnair987           0.0                 0.0          0.0          0.0   \n",
       "devpato                0.0                 0.0          0.0          0.0   \n",
       "matrush                0.0                 0.0          0.0          0.0   \n",
       "huangshiyu13           0.0                 1.0          0.0          0.0   \n",
       "charukiewicz           0.0                 0.0          0.0          0.0   \n",
       "\n",
       "              company_zhejiang  company_zhejiang university  \\\n",
       "username                                                      \n",
       "SaiNageswarS               0.0                          0.0   \n",
       "Zendq1998                  0.0                          0.0   \n",
       "haczqyf                    0.0                          0.0   \n",
       "sydnever                   0.0                          0.0   \n",
       "scazan                     0.0                          0.0   \n",
       "...                        ...                          ...   \n",
       "rohitnair987               0.0                          0.0   \n",
       "devpato                    0.0                          0.0   \n",
       "matrush                    0.0                          0.0   \n",
       "huangshiyu13               0.0                          0.0   \n",
       "charukiewicz               0.0                          0.0   \n",
       "\n",
       "              stats_public_repos  stats_public_gists  stats_followers  \\\n",
       "username                                                                \n",
       "SaiNageswarS            0.000995            0.000000         0.000450   \n",
       "Zendq1998               0.001781            0.000000         0.000850   \n",
       "haczqyf                 0.000733            0.000000         0.000675   \n",
       "sydnever                0.000367            0.000000         0.000250   \n",
       "scazan                  0.003458            0.000456         0.001901   \n",
       "...                          ...                 ...              ...   \n",
       "rohitnair987            0.000210            0.000000         0.000125   \n",
       "devpato                 0.010164            0.001367         0.008703   \n",
       "matrush                 0.000576            0.000456         0.002051   \n",
       "huangshiyu13            0.002200            0.000000         0.005577   \n",
       "charukiewicz            0.001781            0.001822         0.000800   \n",
       "\n",
       "              stats_following  \n",
       "username                       \n",
       "SaiNageswarS         0.000323  \n",
       "Zendq1998            0.000835  \n",
       "haczqyf              0.000873  \n",
       "sydnever             0.000304  \n",
       "scazan               0.002144  \n",
       "...                       ...  \n",
       "rohitnair987         0.000076  \n",
       "devpato              0.010475  \n",
       "matrush              0.000209  \n",
       "huangshiyu13         0.000209  \n",
       "charukiewicz         0.000493  \n",
       "\n",
       "[9666 rows x 304 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[(features[comp_cols] > 0).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "adc5161a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>languages_JavaScript</th>\n",
       "      <th>languages_Python</th>\n",
       "      <th>languages_TypeScript</th>\n",
       "      <th>languages_HTML</th>\n",
       "      <th>languages_Go</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SaiNageswarS</th>\n",
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       "      <td>0.000323</td>\n",
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       "    <tr>\n",
       "      <th>dennissiq</th>\n",
       "      <td>0.187</td>\n",
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       "      <td>0.000550</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guoyoujin</th>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005449</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>0.002851</td>\n",
       "      <td>0.007363</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>LukeMichaels</th>\n",
       "      <td>0.373</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.002144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hemlok</th>\n",
       "      <td>0.531</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sfate</th>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004034</td>\n",
       "      <td>0.027335</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.000664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DemoYolk</th>\n",
       "      <td>0.196</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charukiewicz</th>\n",
       "      <td>0.103</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.015</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001781</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35679 rows × 304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              languages_JavaScript  languages_Python  languages_TypeScript  \\\n",
       "username                                                                     \n",
       "SaiNageswarS                 0.000             0.000                 0.250   \n",
       "dennissiq                    0.187             0.000                 0.263   \n",
       "guoyoujin                    0.000             0.051                 0.000   \n",
       "redshiftzero                 0.000             0.423                 0.000   \n",
       "pedrofsn                     0.147             0.000                 0.204   \n",
       "...                            ...               ...                   ...   \n",
       "LukeMichaels                 0.373             0.000                 0.000   \n",
       "hemlok                       0.531             0.000                 0.452   \n",
       "sfate                        0.005             0.000                 0.000   \n",
       "DemoYolk                     0.196             0.000                 0.000   \n",
       "charukiewicz                 0.103             0.000                 0.000   \n",
       "\n",
       "              languages_HTML  languages_Go  languages_Java  languages_PHP  \\\n",
       "username                                                                    \n",
       "SaiNageswarS           0.000         0.500           0.000          0.000   \n",
       "dennissiq              0.335         0.000           0.000          0.000   \n",
       "guoyoujin              0.000         0.179           0.202          0.000   \n",
       "redshiftzero           0.071         0.000           0.000          0.000   \n",
       "pedrofsn               0.030         0.000           0.002          0.000   \n",
       "...                      ...           ...             ...            ...   \n",
       "LukeMichaels           0.091         0.000           0.000          0.252   \n",
       "hemlok                 0.015         0.000           0.000          0.000   \n",
       "sfate                  0.027         0.000           0.000          0.000   \n",
       "DemoYolk               0.199         0.244           0.000          0.000   \n",
       "charukiewicz           0.048         0.000           0.000          0.000   \n",
       "\n",
       "              languages_Jupyter Notebook  languages_CSS  languages_Shell  ...  \\\n",
       "username                                                                  ...   \n",
       "SaiNageswarS                         0.0          0.000            0.000  ...   \n",
       "dennissiq                            0.0          0.000            0.014  ...   \n",
       "guoyoujin                            0.0          0.000            0.154  ...   \n",
       "redshiftzero                         0.0          0.000            0.017  ...   \n",
       "pedrofsn                             0.0          0.019            0.001  ...   \n",
       "...                                  ...            ...              ...  ...   \n",
       "LukeMichaels                         0.0          0.032            0.000  ...   \n",
       "hemlok                               0.0          0.002            0.000  ...   \n",
       "sfate                                0.0          0.015            0.000  ...   \n",
       "DemoYolk                             0.0          0.066            0.003  ...   \n",
       "charukiewicz                         0.0          0.125            0.015  ...   \n",
       "\n",
       "              company_uber  company_university  company_web  company_www  \\\n",
       "username                                                                   \n",
       "SaiNageswarS           0.0                 0.0          0.0          0.0   \n",
       "dennissiq              0.0                 0.0          0.0          0.0   \n",
       "guoyoujin              0.0                 0.0          0.0          0.0   \n",
       "redshiftzero           0.0                 0.0          0.0          0.0   \n",
       "pedrofsn               0.0                 0.0          0.0          0.0   \n",
       "...                    ...                 ...          ...          ...   \n",
       "LukeMichaels           0.0                 0.0          0.0          0.0   \n",
       "hemlok                 0.0                 0.0          0.0          0.0   \n",
       "sfate                  0.0                 0.0          0.0          0.0   \n",
       "DemoYolk               0.0                 0.0          0.0          0.0   \n",
       "charukiewicz           0.0                 0.0          0.0          0.0   \n",
       "\n",
       "              company_zhejiang  company_zhejiang university  \\\n",
       "username                                                      \n",
       "SaiNageswarS               0.0                          0.0   \n",
       "dennissiq                  0.0                          0.0   \n",
       "guoyoujin                  0.0                          0.0   \n",
       "redshiftzero               0.0                          0.0   \n",
       "pedrofsn                   0.0                          0.0   \n",
       "...                        ...                          ...   \n",
       "LukeMichaels               0.0                          0.0   \n",
       "hemlok                     0.0                          0.0   \n",
       "sfate                      0.0                          0.0   \n",
       "DemoYolk                   0.0                          0.0   \n",
       "charukiewicz               0.0                          0.0   \n",
       "\n",
       "              stats_public_repos  stats_public_gists  stats_followers  \\\n",
       "username                                                                \n",
       "SaiNageswarS            0.000995            0.000000         0.000450   \n",
       "dennissiq               0.002882            0.000000         0.000550   \n",
       "guoyoujin               0.002462            0.004556         0.001276   \n",
       "redshiftzero            0.007073            0.010934         0.004927   \n",
       "pedrofsn                0.005449            0.000911         0.002851   \n",
       "...                          ...                 ...              ...   \n",
       "LukeMichaels            0.001677            0.001822         0.000375   \n",
       "hemlok                  0.001729            0.002278         0.001251   \n",
       "sfate                   0.004034            0.027335         0.000600   \n",
       "DemoYolk                0.000367            0.000000         0.000075   \n",
       "charukiewicz            0.001781            0.001822         0.000800   \n",
       "\n",
       "              stats_following  \n",
       "username                       \n",
       "SaiNageswarS         0.000323  \n",
       "dennissiq            0.000209  \n",
       "guoyoujin            0.004270  \n",
       "redshiftzero         0.001082  \n",
       "pedrofsn             0.007363  \n",
       "...                       ...  \n",
       "LukeMichaels         0.002144  \n",
       "hemlok               0.000493  \n",
       "sfate                0.000664  \n",
       "DemoYolk             0.000076  \n",
       "charukiewicz         0.000493  \n",
       "\n",
       "[35679 rows x 304 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[(features[stat_cols] > 0).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a78fbe90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(features.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c61880d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "languages_JavaScript    0.057984\n",
       "languages_Python        0.054502\n",
       "company_university      0.048341\n",
       "languages_TypeScript    0.030342\n",
       "topics_react            0.022223\n",
       "                          ...   \n",
       "languages_Cuda          0.000128\n",
       "languages_Blade         0.000110\n",
       "stats_following         0.000086\n",
       "stats_public_repos      0.000086\n",
       "languages_Less          0.000071\n",
       "Length: 304, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.var().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "46ed8e54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "login\n",
       "juankuquintana          Web Developer\n",
       "josete89             Mobile Developer\n",
       "addappcn             Mobile Developer\n",
       "mavisland               Web Developer\n",
       "babakasse         AI/ML/Data Engineer\n",
       "                         ...         \n",
       "Davidnet          AI/ML/Data Engineer\n",
       "AgarwalPragy      AI/ML/Data Engineer\n",
       "sundapeng         AI/ML/Data Engineer\n",
       "SOUMAJYOTI        AI/ML/Data Engineer\n",
       "everget           AI/ML/Data Engineer\n",
       "Name: label, Length: 100, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "44f59521",
   "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>languages_JavaScript</th>\n",
       "      <th>languages_Python</th>\n",
       "      <th>languages_TypeScript</th>\n",
       "      <th>languages_HTML</th>\n",
       "      <th>languages_Go</th>\n",
       "      <th>languages_Java</th>\n",
       "      <th>languages_PHP</th>\n",
       "      <th>languages_Jupyter Notebook</th>\n",
       "      <th>languages_CSS</th>\n",
       "      <th>languages_Shell</th>\n",
       "      <th>...</th>\n",
       "      <th>company_uber</th>\n",
       "      <th>company_university</th>\n",
       "      <th>company_web</th>\n",
       "      <th>company_www</th>\n",
       "      <th>company_zhejiang</th>\n",
       "      <th>company_zhejiang university</th>\n",
       "      <th>stats_public_repos</th>\n",
       "      <th>stats_public_gists</th>\n",
       "      <th>stats_followers</th>\n",
       "      <th>stats_following</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>username</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SaiNageswarS</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.000323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dennissiq</th>\n",
       "      <td>0.187</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guoyoujin</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002462</td>\n",
       "      <td>0.004556</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.004270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>redshiftzero</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007073</td>\n",
       "      <td>0.010934</td>\n",
       "      <td>0.004927</td>\n",
       "      <td>0.001082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pedrofsn</th>\n",
       "      <td>0.147</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005449</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>0.002851</td>\n",
       "      <td>0.007363</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>LukeMichaels</th>\n",
       "      <td>0.373</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.002144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hemlok</th>\n",
       "      <td>0.531</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sfate</th>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004034</td>\n",
       "      <td>0.027335</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.000664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DemoYolk</th>\n",
       "      <td>0.196</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charukiewicz</th>\n",
       "      <td>0.103</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.015</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001781</td>\n",
       "      <td>0.001822</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35909 rows × 304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              languages_JavaScript  languages_Python  languages_TypeScript  \\\n",
       "username                                                                     \n",
       "SaiNageswarS                 0.000             0.000                 0.250   \n",
       "dennissiq                    0.187             0.000                 0.263   \n",
       "guoyoujin                    0.000             0.051                 0.000   \n",
       "redshiftzero                 0.000             0.423                 0.000   \n",
       "pedrofsn                     0.147             0.000                 0.204   \n",
       "...                            ...               ...                   ...   \n",
       "LukeMichaels                 0.373             0.000                 0.000   \n",
       "hemlok                       0.531             0.000                 0.452   \n",
       "sfate                        0.005             0.000                 0.000   \n",
       "DemoYolk                     0.196             0.000                 0.000   \n",
       "charukiewicz                 0.103             0.000                 0.000   \n",
       "\n",
       "              languages_HTML  languages_Go  languages_Java  languages_PHP  \\\n",
       "username                                                                    \n",
       "SaiNageswarS           0.000         0.500           0.000          0.000   \n",
       "dennissiq              0.335         0.000           0.000          0.000   \n",
       "guoyoujin              0.000         0.179           0.202          0.000   \n",
       "redshiftzero           0.071         0.000           0.000          0.000   \n",
       "pedrofsn               0.030         0.000           0.002          0.000   \n",
       "...                      ...           ...             ...            ...   \n",
       "LukeMichaels           0.091         0.000           0.000          0.252   \n",
       "hemlok                 0.015         0.000           0.000          0.000   \n",
       "sfate                  0.027         0.000           0.000          0.000   \n",
       "DemoYolk               0.199         0.244           0.000          0.000   \n",
       "charukiewicz           0.048         0.000           0.000          0.000   \n",
       "\n",
       "              languages_Jupyter Notebook  languages_CSS  languages_Shell  ...  \\\n",
       "username                                                                  ...   \n",
       "SaiNageswarS                         0.0          0.000            0.000  ...   \n",
       "dennissiq                            0.0          0.000            0.014  ...   \n",
       "guoyoujin                            0.0          0.000            0.154  ...   \n",
       "redshiftzero                         0.0          0.000            0.017  ...   \n",
       "pedrofsn                             0.0          0.019            0.001  ...   \n",
       "...                                  ...            ...              ...  ...   \n",
       "LukeMichaels                         0.0          0.032            0.000  ...   \n",
       "hemlok                               0.0          0.002            0.000  ...   \n",
       "sfate                                0.0          0.015            0.000  ...   \n",
       "DemoYolk                             0.0          0.066            0.003  ...   \n",
       "charukiewicz                         0.0          0.125            0.015  ...   \n",
       "\n",
       "              company_uber  company_university  company_web  company_www  \\\n",
       "username                                                                   \n",
       "SaiNageswarS           0.0                 0.0          0.0          0.0   \n",
       "dennissiq              0.0                 0.0          0.0          0.0   \n",
       "guoyoujin              0.0                 0.0          0.0          0.0   \n",
       "redshiftzero           0.0                 0.0          0.0          0.0   \n",
       "pedrofsn               0.0                 0.0          0.0          0.0   \n",
       "...                    ...                 ...          ...          ...   \n",
       "LukeMichaels           0.0                 0.0          0.0          0.0   \n",
       "hemlok                 0.0                 0.0          0.0          0.0   \n",
       "sfate                  0.0                 0.0          0.0          0.0   \n",
       "DemoYolk               0.0                 0.0          0.0          0.0   \n",
       "charukiewicz           0.0                 0.0          0.0          0.0   \n",
       "\n",
       "              company_zhejiang  company_zhejiang university  \\\n",
       "username                                                      \n",
       "SaiNageswarS               0.0                          0.0   \n",
       "dennissiq                  0.0                          0.0   \n",
       "guoyoujin                  0.0                          0.0   \n",
       "redshiftzero               0.0                          0.0   \n",
       "pedrofsn                   0.0                          0.0   \n",
       "...                        ...                          ...   \n",
       "LukeMichaels               0.0                          0.0   \n",
       "hemlok                     0.0                          0.0   \n",
       "sfate                      0.0                          0.0   \n",
       "DemoYolk                   0.0                          0.0   \n",
       "charukiewicz               0.0                          0.0   \n",
       "\n",
       "              stats_public_repos  stats_public_gists  stats_followers  \\\n",
       "username                                                                \n",
       "SaiNageswarS            0.000995            0.000000         0.000450   \n",
       "dennissiq               0.002882            0.000000         0.000550   \n",
       "guoyoujin               0.002462            0.004556         0.001276   \n",
       "redshiftzero            0.007073            0.010934         0.004927   \n",
       "pedrofsn                0.005449            0.000911         0.002851   \n",
       "...                          ...                 ...              ...   \n",
       "LukeMichaels            0.001677            0.001822         0.000375   \n",
       "hemlok                  0.001729            0.002278         0.001251   \n",
       "sfate                   0.004034            0.027335         0.000600   \n",
       "DemoYolk                0.000367            0.000000         0.000075   \n",
       "charukiewicz            0.001781            0.001822         0.000800   \n",
       "\n",
       "              stats_following  \n",
       "username                       \n",
       "SaiNageswarS         0.000323  \n",
       "dennissiq            0.000209  \n",
       "guoyoujin            0.004270  \n",
       "redshiftzero         0.001082  \n",
       "pedrofsn             0.007363  \n",
       "...                       ...  \n",
       "LukeMichaels         0.002144  \n",
       "hemlok               0.000493  \n",
       "sfate                0.000664  \n",
       "DemoYolk             0.000076  \n",
       "charukiewicz         0.000493  \n",
       "\n",
       "[35909 rows x 304 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5cfeba2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels.name = 'label'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a967ec5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = features.merge(labels, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2012e964",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = X.iloc[:, :-1], X['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bf064f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import f1_score, classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "95212f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = PCA(n_components=50).fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d4ec8a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b3cca398",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "73f20beb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "13f4d615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9998703319502075"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "9176cb3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "26d85706",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           precision    recall  f1-score   support\n",
      "\n",
      "            Data Engineer       1.00      0.05      0.09        21\n",
      "           Data Scientist       0.58      0.09      0.15        81\n",
      "Machine Learning Engineer       0.68      0.70      0.69       748\n",
      "         Mobile Developer       0.68      0.45      0.55       222\n",
      "          UI/UX Developer       0.00      0.00      0.00        17\n",
      "            Web Developer       0.69      0.81      0.74       839\n",
      "\n",
      "                 accuracy                           0.68      1928\n",
      "                macro avg       0.60      0.35      0.37      1928\n",
      "             weighted avg       0.67      0.68      0.66      1928\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tugrul/development/python/pr-course/github-job-title-prediction/.env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/tugrul/development/python/pr-course/github-job-title-prediction/.env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/home/tugrul/development/python/pr-course/github-job-title-prediction/.env/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1c31123b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4196.,    0.,    0., 1107.,    0.,    0., 4253.,    0.,    0.,\n",
       "          87.]),\n",
       " array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8, 2.1, 2.4, 2.7, 3. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4048b22c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('.env': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "7fb3b9acd036f0bb40a19178a4a4e0c104f45e4cf2c749b0af2e81dcdbc701d9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}