UCI_ML_Crawler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook for crawling the UCI Machine Learning repository website + Convinient functions for downloading, summarizing or viewing information about various datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impoer libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import requests\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open the connection, read the HTML and parse it using BeautifulSoup library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening the file connection...\n",
      "HTTP status 200\n",
      "Reading done. Total 114429 characters read.\n"
     ]
    }
   ],
   "source": [
    "import urllib.request, urllib.parse, urllib.error\n",
    "from bs4 import BeautifulSoup\n",
    "import ssl\n",
    "\n",
    "# Ignore SSL certificate errors\n",
    "ctx = ssl.create_default_context()\n",
    "ctx.check_hostname = False\n",
    "ctx.verify_mode = ssl.CERT_NONE\n",
    "\n",
    "# Read the HTML from the URL and pass on to BeautifulSoup\n",
    "url = 'https://archive.ics.uci.edu/ml/datasets.html?format=&task=&att=&area=&numAtt=&numIns=&type=&sort=nameUp&view=list'\n",
    "print(\"Opening the file connection...\")\n",
    "uh= urllib.request.urlopen(url, context=ctx)\n",
    "print(\"HTTP status\",uh.getcode())\n",
    "html =uh.read()\n",
    "print(f\"Reading done. Total {len(html)} characters read.\")\n",
    "\n",
    "# Soupify!\n",
    "soup = BeautifulSoup(html, 'html5lib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read all _p_-tag contents and extract dataset name, url, and description from them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "425 datasets read\n"
     ]
    }
   ],
   "source": [
    "lst=[]\n",
    "for tag in soup.find_all('p'):\n",
    "    lst.append(tag.contents)\n",
    "\n",
    "i=0\n",
    "description_dict={}\n",
    "dataset_list=[]\n",
    "for l in lst:\n",
    "    if len(l)>2:\n",
    "        if str(l[1]).find('datasets/')!=-1:\n",
    "            string=str(l[1])\n",
    "            s=re.search('\">.*</a>',string)\n",
    "            x,y=s.span()\n",
    "            description_dict[string[x+2:y-4]]=(l[2])[2:]\n",
    "            s=re.search(\"\\\".*\\\"\",string)\n",
    "            x,y=s.span()\n",
    "            dataset_list.append(string[x+10:y-1])\n",
    "            i+=1\n",
    "print(f\"{i} datasets read\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for scraping through individual dataset pages to extract the _Abstract_ and _dataurl_ (the page where actual data files reside)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataset_page_crawl(dataset):\n",
    "    dataset_dict={}\n",
    "    baseurl='https://archive.ics.uci.edu/ml/datasets/'\n",
    "    url = baseurl+dataset\n",
    "    dataset_dict['Dataset Page']=url\n",
    "    #print(\"Opening the page:\", url)\n",
    "    try:\n",
    "        uh= urllib.request.urlopen(url, context=ctx)\n",
    "        html =uh.read().decode()\n",
    "        soup=BeautifulSoup(html,'html5lib')\n",
    "        #print(soup.text[:200])\n",
    "        if soup.text.find(\"does not appear to exist\")!=-1:\n",
    "            print(f\"{dataset} not found\")\n",
    "            return None\n",
    "        else:\n",
    "            dataurls = []\n",
    "            for pclass in soup.find_all('p'):\n",
    "                if (pclass.get_text().find('Abstract: '))!=-1:\n",
    "                    dataset_dict['Abstract']=str(pclass.get_text())[len('Abstract: '):]\n",
    "                    break # Breaking here is crucial. Otherwise the loop will progress through other p tags\n",
    "                    # and put 'Not found' in the Abstract field\n",
    "                else:\n",
    "                    dataset_dict['Abstract']='Not found!'\n",
    "            for link in soup.find_all('a'):\n",
    "                if link.attrs['href'].find('machine-learning-databases')!=-1:\n",
    "                    a=link.attrs['href']\n",
    "                    a=a[2:]\n",
    "                    dataurl=\"https://archive.ics.uci.edu/ml/\"+a\n",
    "                    dataurls.append(dataurl)\n",
    "            \n",
    "            # After finishing the for-loop with a-tags, the first dataurl is added to the dictionary \n",
    "            dataset_dict['dataurl']=dataurls[0]\n",
    "            \n",
    "        return dataset_dict\n",
    "    except:\n",
    "        print(\"Could not retrieve\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crawl through all the dataset pages in the dataset list, store the abstract and dataset urls in a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset 1 processed, Dataset 2 processed, Dataset 3 processed, Dataset 4 processed, Dataset 5 processed, Dataset 6 processed, Dataset 7 processed, Dataset 8 processed, Dataset 9 processed, Dataset 10 processed, Dataset 11 processed, Dataset 12 processed, Dataset 13 processed, Dataset 14 processed, Dataset 15 processed, Dataset 16 processed, Dataset 17 processed, Dataset 18 processed, Dataset 19 processed, Dataset 20 processed, Dataset 21 processed, Dataset 22 processed, Dataset 23 processed, Dataset 24 processed, Dataset 25 processed, Dataset 26 processed, Dataset 27 processed, Dataset 28 processed, Dataset 29 processed, Dataset 30 processed, Dataset 31 processed, Dataset 32 processed, Dataset 33 processed, Dataset 34 processed, Dataset 35 processed, Dataset 36 processed, Dataset 37 processed, Dataset 38 processed, Dataset 39 processed, Dataset 40 processed, Dataset 41 processed, Dataset 42 processed, Dataset 43 processed, Dataset 44 processed, Dataset 45 processed, Dataset 46 processed, Dataset 47 processed, Dataset 48 processed, Dataset 49 processed, Dataset 50 processed, Dataset 51 processed, Dataset 52 processed, Dataset 53 processed, Dataset 54 processed, Dataset 55 processed, Dataset 56 processed, Dataset 57 processed, Dataset 58 processed, Dataset 59 processed, Dataset 60 processed, Dataset 61 processed, Dataset 62 processed, Dataset 63 processed, Dataset 64 processed, Dataset 65 processed, Dataset 66 processed, Dataset 67 processed, Dataset 68 processed, Dataset 69 processed, chestnut+%E2%80%93+LARVIC not found\n",
      "Dataset 70 processed, Dataset 71 processed, Dataset 72 processed, Dataset 73 processed, Dataset 74 processed, Dataset 75 processed, Dataset 76 processed, Dataset 77 processed, Dataset 78 processed, Dataset 79 processed, Dataset 80 processed, Dataset 81 processed, Dataset 82 processed, Dataset 83 processed, Dataset 84 processed, Dataset 85 processed, Dataset 86 processed, Dataset 87 processed, Dataset 88 processed, Dataset 89 processed, Dataset 90 processed, Dataset 91 processed, Dataset 92 processed, Dataset 93 processed, Dataset 94 processed, Dataset 95 processed, Dataset 96 processed, Dataset 97 processed, Dataset 98 processed, Dataset 99 processed, Dataset 100 processed, Dataset 101 processed, Dataset 102 processed, Dataset 103 processed, Dataset 104 processed, Dataset 105 processed, Dataset 106 processed, Dataset 107 processed, Dataset 108 processed, Dataset 109 processed, Dataset 110 processed, Dataset 111 processed, Dataset 112 processed, Dataset 113 processed, Dataset 114 processed, Dataset 115 processed, Dataset 116 processed, Dataset 117 processed, Dataset 118 processed, Dataset 119 processed, Dataset 120 processed, Dataset 121 processed, Dataset 122 processed, Dataset 123 processed, Dataset 124 processed, Dataset 125 processed, Dataset 126 processed, Dataset 127 processed, Dataset 128 processed, Early+biomarkers+of+Parkinson%E2%80%99s+disease+based+on+natural+connected+speech not found\n",
      "Dataset 129 processed, Dataset 130 processed, Dataset 131 processed, Dataset 132 processed, Dataset 133 processed, Dataset 134 processed, Dataset 135 processed, Dataset 136 processed, Dataset 137 processed, Dataset 138 processed, Dataset 139 processed, Dataset 140 processed, Dataset 141 processed, Dataset 142 processed, Dataset 143 processed, Dataset 144 processed, Dataset 145 processed, Dataset 146 processed, Dataset 147 processed, Dataset 148 processed, Dataset 149 processed, Dataset 150 processed, Dataset 151 processed, Dataset 152 processed, Dataset 153 processed, Dataset 154 processed, Dataset 155 processed, Dataset 156 processed, Dataset 157 processed, Dataset 158 processed, Dataset 159 processed, Dataset 160 processed, Dataset 161 processed, Dataset 162 processed, Dataset 163 processed, Dataset 164 processed, Dataset 165 processed, Dataset 166 processed, Dataset 167 processed, Dataset 168 processed, Dataset 169 processed, Dataset 170 processed, Dataset 171 processed, Dataset 172 processed, Dataset 173 processed, Dataset 174 processed, Dataset 175 processed, Dataset 176 processed, Dataset 177 processed, Dataset 178 processed, Dataset 179 processed, Dataset 180 processed, Dataset 181 processed, Dataset 182 processed, Dataset 183 processed, Dataset 184 processed, Dataset 185 processed, Dataset 186 processed, Dataset 187 processed, Dataset 188 processed, Dataset 189 processed, Dataset 190 processed, Dataset 191 processed, Dataset 192 processed, Dataset 193 processed, Dataset 194 processed, Improved+Spiral+Test+Using+Digitized+Graphics+Tablet+for+Monitoring+Parkinson%E2%80%99s+Disease not found\n",
      "Dataset 195 processed, Dataset 196 processed, Dataset 197 processed, Dataset 198 processed, Dataset 199 processed, Dataset 200 processed, Dataset 201 processed, Dataset 202 processed, Dataset 203 processed, Dataset 204 processed, Dataset 205 processed, Dataset 206 processed, Dataset 207 processed, Dataset 208 processed, Dataset 209 processed, Dataset 210 processed, Dataset 211 processed, Dataset 212 processed, Dataset 213 processed, Dataset 214 processed, Dataset 215 processed, Dataset 216 processed, Dataset 217 processed, Dataset 218 processed, Dataset 219 processed, Dataset 220 processed, Dataset 221 processed, Dataset 222 processed, Dataset 223 processed, Dataset 224 processed, Dataset 225 processed, Dataset 226 processed, Dataset 227 processed, Dataset 228 processed, Dataset 229 processed, Dataset 230 processed, Dataset 231 processed, Dataset 232 processed, Dataset 233 processed, Dataset 234 processed, Mesothelioma%E2%80%99s+disease+data+set+ not found\n",
      "Dataset 235 processed, Dataset 236 processed, Dataset 237 processed, Dataset 238 processed, Dataset 239 processed, Dataset 240 processed, Dataset 241 processed, Dataset 242 processed, Dataset 243 processed, Dataset 244 processed, Dataset 245 processed, Dataset 246 processed, Dataset 247 processed, Dataset 248 processed, Dataset 249 processed, Dataset 250 processed, Dataset 251 processed, Dataset 252 processed, Dataset 253 processed, Dataset 254 processed, Dataset 255 processed, Dataset 256 processed, Dataset 257 processed, Dataset 258 processed, Dataset 259 processed, Dataset 260 processed, Dataset 261 processed, Dataset 262 processed, Dataset 263 processed, Dataset 264 processed, Dataset 265 processed, Dataset 266 processed, Dataset 267 processed, Dataset 268 processed, Dataset 269 processed, Dataset 270 processed, Dataset 271 processed, Dataset 272 processed, Dataset 273 processed, Dataset 274 processed, Dataset 275 processed, Dataset 276 processed, Dataset 277 processed, Dataset 278 processed, Dataset 279 processed, Dataset 280 processed, Dataset 281 processed, Dataset 282 processed, Dataset 283 processed, Dataset 284 processed, Dataset 285 processed, Dataset 286 processed, Dataset 287 processed, Dataset 288 processed, Dataset 289 processed, Dataset 290 processed, Dataset 291 processed, Dataset 292 processed, Dataset 293 processed, Dataset 294 processed, Dataset 295 processed, Dataset 296 processed, Dataset 297 processed, Dataset 298 processed, Dataset 299 processed, Dataset 300 processed, Dataset 301 processed, Dataset 302 processed, Dataset 303 processed, Dataset 304 processed, Could not retrieve\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset 305 processed, Dataset 306 processed, Dataset 307 processed, Dataset 308 processed, Dataset 309 processed, Dataset 310 processed, Dataset 311 processed, Dataset 312 processed, Dataset 313 processed, Dataset 314 processed, Dataset 315 processed, Dataset 316 processed, Dataset 317 processed, Dataset 318 processed, Dataset 319 processed, Dataset 320 processed, Dataset 321 processed, Dataset 322 processed, Dataset 323 processed, Dataset 324 processed, Dataset 325 processed, Dataset 326 processed, Dataset 327 processed, Dataset 328 processed, Dataset 329 processed, Dataset 330 processed, Dataset 331 processed, Dataset 332 processed, Dataset 333 processed, Dataset 334 processed, Dataset 335 processed, Dataset 336 processed, Dataset 337 processed, Dataset 338 processed, Dataset 339 processed, Dataset 340 processed, Dataset 341 processed, Dataset 342 processed, Dataset 343 processed, Dataset 344 processed, Dataset 345 processed, Dataset 346 processed, Dataset 347 processed, Dataset 348 processed, Dataset 349 processed, Dataset 350 processed, Dataset 351 processed, Dataset 352 processed, Dataset 353 processed, Dataset 354 processed, Dataset 355 processed, Dataset 356 processed, Dataset 357 processed, Dataset 358 processed, Dataset 359 processed, Dataset 360 processed, Dataset 361 processed, Dataset 362 processed, Dataset 363 processed, Dataset 364 processed, Dataset 365 processed, Dataset 366 processed, Dataset 367 processed, Dataset 368 processed, Dataset 369 processed, Dataset 370 processed, Dataset 371 processed, Dataset 372 processed, Dataset 373 processed, Dataset 374 processed, Dataset 375 processed, Dataset 376 processed, Dataset 377 processed, Dataset 378 processed, Dataset 379 processed, Dataset 380 processed, Dataset 381 processed, Dataset 382 processed, Dataset 383 processed, Dataset 384 processed, Dataset 385 processed, Dataset 386 processed, Dataset 387 processed, Dataset 388 processed, Dataset 389 processed, Dataset 390 processed, Dataset 391 processed, Dataset 392 processed, Dataset 393 processed, Dataset 394 processed, Dataset 395 processed, Dataset 396 processed, Dataset 397 processed, Dataset 398 processed, Dataset 399 processed, Dataset 400 processed, Dataset 401 processed, Dataset 402 processed, Dataset 403 processed, Dataset 404 processed, Dataset 405 processed, Dataset 406 processed, Dataset 407 processed, Dataset 408 processed, Dataset 409 processed, Dataset 410 processed, Dataset 411 processed, Dataset 412 processed, Dataset 413 processed, Dataset 414 processed, Dataset 415 processed, Dataset 416 processed, Dataset 417 processed, Dataset 418 processed, Dataset 419 processed, Dataset 420 processed, \n",
      "Total datasets analyzed:  420\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "baseurl='https://archive.ics.uci.edu/ml/datasets/'\n",
    "dataset_dicts=[]\n",
    "for dataset in dataset_list:\n",
    "    a=dataset_page_crawl(dataset)\n",
    "    if a!=None:\n",
    "        dataset_dicts.append(a)\n",
    "        i+=1\n",
    "        print(f\"Dataset {i} processed\",end=', ')\n",
    "\n",
    "print(\"\\nTotal datasets analyzed: \",i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dataframes to store abstracts and dataset url links for all the items, analyzed above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataframe with _Abstract_ and _dataurl_ (the url of the page where the actual data files reside)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dataset = pd.DataFrame(data=dataset_dicts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### dataframe with _name_ and _Abstract_ (description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_description=pd.DataFrame(data=list(description_dict.items()),columns=['Dataset','Abstract'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge these two dataframes on the common _Abstract_ column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "scrolled": false
   },
   "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>Dataset</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Dataset Page</th>\n",
       "      <th>dataurl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3D Road Network (North Jutland, Denmark)</td>\n",
       "      <td>3D road network with highly accurate elevation...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/3D+Roa...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAAI 2013 Accepted Papers</td>\n",
       "      <td>This data set compromises the metadata for the...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/AAAI+2...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAAI 2014 Accepted Papers</td>\n",
       "      <td>This data set compromises the metadata for the...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/AAAI+2...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abalone</td>\n",
       "      <td>Predict the age of abalone from physical measu...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Abalone</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abscisic Acid Signaling Network</td>\n",
       "      <td>The objective is to determine the set of boole...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Abscis...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Activities of Daily Living (ADLs) Recognition ...</td>\n",
       "      <td>This dataset comprises information regarding t...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Activity Recognition from Single Chest-Mounted...</td>\n",
       "      <td>The dataset collects data from a wearable acce...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Activity Recognition system based on Multisens...</td>\n",
       "      <td>This dataset contains temporal data from a Wir...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Activity recognition with healthy older people...</td>\n",
       "      <td>Sequential motion data from 14 healthy older p...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Acute Inflammations</td>\n",
       "      <td>The data was created by a medical expert as a ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Acute+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adult</td>\n",
       "      <td>Predict whether income exceeds $50K/yr based o...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Adult</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Air Quality</td>\n",
       "      <td>Contains the responses of a gas multisensor de...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Air+Qu...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Air Quality</td>\n",
       "      <td>Contains the responses of a gas multisensor de...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Air+qu...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Airfoil Self-Noise</td>\n",
       "      <td>NASA data set, obtained from a series of aerod...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Airfoi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Amazon Access Samples</td>\n",
       "      <td>Amazon's InfoSec is getting smarter about the ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Amazon...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Amazon Commerce reviews set</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Amazon...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazon Commerce reviews set</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Reuter...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Reuter_50_50</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Amazon...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Reuter_50_50</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Reuter...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Annealing</td>\n",
       "      <td>Steel annealing data</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Annealing</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Anonymous Microsoft Web Data</td>\n",
       "      <td>Log of anonymous users of www.microsoft.com; p...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Anonym...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Anuran Calls (MFCCs)</td>\n",
       "      <td>Acoustic features extracted from syllables of ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Anuran...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Appliances energy prediction</td>\n",
       "      <td>Experimental data used to create regression mo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Applia...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>APS Failure at Scania Trucks</td>\n",
       "      <td>The datasets' positive class consists of compo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/APS+Fa...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Arcene</td>\n",
       "      <td>ARCENE's task is to distinguish cancer versus ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Arcene</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Arrhythmia</td>\n",
       "      <td>Distinguish between the presence and absence o...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Arrhyt...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Artificial Characters</td>\n",
       "      <td>Dataset artificially generated by using first ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Artifi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Audiology (Original)</td>\n",
       "      <td>Nominal audiology dataset from Baylor</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Audiol...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Audiology (Standardized)</td>\n",
       "      <td>Standardized version of the original audiology...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Audiol...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Australian Sign Language signs</td>\n",
       "      <td>This data consists of sample of Auslan (Austra...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Austra...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Urban Land Cover</td>\n",
       "      <td>Classification of urban land cover using high ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Urban+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>URL Reputation</td>\n",
       "      <td>Anonymized 120-day subset of the ICML-09 URL d...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/URL+Re...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>US Census Data (1990)</td>\n",
       "      <td>The USCensus1990raw data set contains a one pe...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/US+Cen...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>User Identification From Walking Activity</td>\n",
       "      <td>The dataset collects data from an Android smar...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/User+I...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>User Knowledge Modeling</td>\n",
       "      <td>It is the real dataset about the students' kno...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/User+K...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>USPTO Algorithm Challenge, run by NASA-Harvard...</td>\n",
       "      <td>Data used for USPTO Algorithm Competition. Con...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/USPTO+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Vertebral Column</td>\n",
       "      <td>Data set containing values for six biomechanic...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Verteb...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Vicon Physical Action Data Set</td>\n",
       "      <td>The Physical Action Data Set includes 10 norma...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Vicon+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Volcanoes on Venus - JARtool experiment</td>\n",
       "      <td>The JARtool project was a pioneering effort to...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Volcan...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Wall-Following Robot Navigation Data</td>\n",
       "      <td>The data were collected as the SCITOS G5 robot...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wall-F...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Water Treatment Plant</td>\n",
       "      <td>Multiple classes predict plant state</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Water+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Waveform Database Generator (Version 1)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Waveform Database Generator (Version 1)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Waveform Database Generator (Version 2)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Waveform Database Generator (Version 2)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Wearable Computing: Classification of Body Pos...</td>\n",
       "      <td>A dataset with 5 classes (sitting-down, standi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wearab...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Website Phishing</td>\n",
       "      <td>\\n\\n</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Websit...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Weight Lifting Exercises monitored with Inerti...</td>\n",
       "      <td>Six young health subjects were asked to perfor...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Weight...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Wholesale customers</td>\n",
       "      <td>The data set refers to clients of a wholesale ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wholes...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>wiki4HE</td>\n",
       "      <td>Survey of faculty members from two Spanish uni...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/wiki4HE</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Wilt</td>\n",
       "      <td>High-resolution Remote Sensing data set (Quick...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wilt</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Wine</td>\n",
       "      <td>Using chemical analysis determine the origin o...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wine</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Wireless Indoor Localization</td>\n",
       "      <td>Collected in indoor space by observing signal ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wirele...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Yacht Hydrodynamics</td>\n",
       "      <td>Delft data set, used to predict the hydodynami...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Yacht+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>YearPredictionMSD</td>\n",
       "      <td>Prediction of the release year of a song from ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YearPr...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Yeast</td>\n",
       "      <td>Predicting the Cellular Localization Sites of ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Yeast</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>YouTube Comedy Slam Preference Data</td>\n",
       "      <td>This dataset provides user vote data on which ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YouTub...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>YouTube Multiview Video Games Dataset</td>\n",
       "      <td>This dataset contains about 120k instances, ea...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YouTub...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>YouTube Spam Collection</td>\n",
       "      <td>It is a public set of comments collected for s...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YouTub...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>Zoo</td>\n",
       "      <td>Artificial, 7 classes of animals</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Zoo</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>425 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Dataset  \\\n",
       "0             3D Road Network (North Jutland, Denmark)   \n",
       "1                            AAAI 2013 Accepted Papers   \n",
       "2                            AAAI 2014 Accepted Papers   \n",
       "3                                              Abalone   \n",
       "4                      Abscisic Acid Signaling Network   \n",
       "5    Activities of Daily Living (ADLs) Recognition ...   \n",
       "6    Activity Recognition from Single Chest-Mounted...   \n",
       "7    Activity Recognition system based on Multisens...   \n",
       "8    Activity recognition with healthy older people...   \n",
       "9                                  Acute Inflammations   \n",
       "10                                               Adult   \n",
       "11                                         Air Quality   \n",
       "12                                         Air Quality   \n",
       "13                                  Airfoil Self-Noise   \n",
       "14                               Amazon Access Samples   \n",
       "15                         Amazon Commerce reviews set   \n",
       "16                         Amazon Commerce reviews set   \n",
       "17                                        Reuter_50_50   \n",
       "18                                        Reuter_50_50   \n",
       "19                                           Annealing   \n",
       "20                        Anonymous Microsoft Web Data   \n",
       "21                                Anuran Calls (MFCCs)   \n",
       "22                        Appliances energy prediction   \n",
       "23                        APS Failure at Scania Trucks   \n",
       "24                                              Arcene   \n",
       "25                                          Arrhythmia   \n",
       "26                               Artificial Characters   \n",
       "27                                Audiology (Original)   \n",
       "28                            Audiology (Standardized)   \n",
       "29                      Australian Sign Language signs   \n",
       "..                                                 ...   \n",
       "395                                   Urban Land Cover   \n",
       "396                                     URL Reputation   \n",
       "397                              US Census Data (1990)   \n",
       "398          User Identification From Walking Activity   \n",
       "399                            User Knowledge Modeling   \n",
       "400  USPTO Algorithm Challenge, run by NASA-Harvard...   \n",
       "401                                   Vertebral Column   \n",
       "402                     Vicon Physical Action Data Set   \n",
       "403            Volcanoes on Venus - JARtool experiment   \n",
       "404               Wall-Following Robot Navigation Data   \n",
       "405                              Water Treatment Plant   \n",
       "406            Waveform Database Generator (Version 1)   \n",
       "407            Waveform Database Generator (Version 1)   \n",
       "408            Waveform Database Generator (Version 2)   \n",
       "409            Waveform Database Generator (Version 2)   \n",
       "410  Wearable Computing: Classification of Body Pos...   \n",
       "411                                   Website Phishing   \n",
       "412  Weight Lifting Exercises monitored with Inerti...   \n",
       "413                                Wholesale customers   \n",
       "414                                            wiki4HE   \n",
       "415                                               Wilt   \n",
       "416                                               Wine   \n",
       "417                       Wireless Indoor Localization   \n",
       "418                                Yacht Hydrodynamics   \n",
       "419                                  YearPredictionMSD   \n",
       "420                                              Yeast   \n",
       "421                YouTube Comedy Slam Preference Data   \n",
       "422              YouTube Multiview Video Games Dataset   \n",
       "423                            YouTube Spam Collection   \n",
       "424                                                Zoo   \n",
       "\n",
       "                                              Abstract  \\\n",
       "0    3D road network with highly accurate elevation...   \n",
       "1    This data set compromises the metadata for the...   \n",
       "2    This data set compromises the metadata for the...   \n",
       "3    Predict the age of abalone from physical measu...   \n",
       "4    The objective is to determine the set of boole...   \n",
       "5    This dataset comprises information regarding t...   \n",
       "6    The dataset collects data from a wearable acce...   \n",
       "7    This dataset contains temporal data from a Wir...   \n",
       "8    Sequential motion data from 14 healthy older p...   \n",
       "9    The data was created by a medical expert as a ...   \n",
       "10   Predict whether income exceeds $50K/yr based o...   \n",
       "11   Contains the responses of a gas multisensor de...   \n",
       "12   Contains the responses of a gas multisensor de...   \n",
       "13   NASA data set, obtained from a series of aerod...   \n",
       "14   Amazon's InfoSec is getting smarter about the ...   \n",
       "15   The dataset is used for authorship identificat...   \n",
       "16   The dataset is used for authorship identificat...   \n",
       "17   The dataset is used for authorship identificat...   \n",
       "18   The dataset is used for authorship identificat...   \n",
       "19                                Steel annealing data   \n",
       "20   Log of anonymous users of www.microsoft.com; p...   \n",
       "21   Acoustic features extracted from syllables of ...   \n",
       "22   Experimental data used to create regression mo...   \n",
       "23   The datasets' positive class consists of compo...   \n",
       "24   ARCENE's task is to distinguish cancer versus ...   \n",
       "25   Distinguish between the presence and absence o...   \n",
       "26   Dataset artificially generated by using first ...   \n",
       "27               Nominal audiology dataset from Baylor   \n",
       "28   Standardized version of the original audiology...   \n",
       "29   This data consists of sample of Auslan (Austra...   \n",
       "..                                                 ...   \n",
       "395  Classification of urban land cover using high ...   \n",
       "396  Anonymized 120-day subset of the ICML-09 URL d...   \n",
       "397  The USCensus1990raw data set contains a one pe...   \n",
       "398  The dataset collects data from an Android smar...   \n",
       "399  It is the real dataset about the students' kno...   \n",
       "400  Data used for USPTO Algorithm Competition. Con...   \n",
       "401  Data set containing values for six biomechanic...   \n",
       "402  The Physical Action Data Set includes 10 norma...   \n",
       "403  The JARtool project was a pioneering effort to...   \n",
       "404  The data were collected as the SCITOS G5 robot...   \n",
       "405               Multiple classes predict plant state   \n",
       "406                       CART book's waveform domains   \n",
       "407                       CART book's waveform domains   \n",
       "408                       CART book's waveform domains   \n",
       "409                       CART book's waveform domains   \n",
       "410  A dataset with 5 classes (sitting-down, standi...   \n",
       "411                                               \\n\\n   \n",
       "412  Six young health subjects were asked to perfor...   \n",
       "413  The data set refers to clients of a wholesale ...   \n",
       "414  Survey of faculty members from two Spanish uni...   \n",
       "415  High-resolution Remote Sensing data set (Quick...   \n",
       "416  Using chemical analysis determine the origin o...   \n",
       "417  Collected in indoor space by observing signal ...   \n",
       "418  Delft data set, used to predict the hydodynami...   \n",
       "419  Prediction of the release year of a song from ...   \n",
       "420  Predicting the Cellular Localization Sites of ...   \n",
       "421  This dataset provides user vote data on which ...   \n",
       "422  This dataset contains about 120k instances, ea...   \n",
       "423  It is a public set of comments collected for s...   \n",
       "424                   Artificial, 7 classes of animals   \n",
       "\n",
       "                                          Dataset Page  \\\n",
       "0    https://archive.ics.uci.edu/ml/datasets/3D+Roa...   \n",
       "1    https://archive.ics.uci.edu/ml/datasets/AAAI+2...   \n",
       "2    https://archive.ics.uci.edu/ml/datasets/AAAI+2...   \n",
       "3      https://archive.ics.uci.edu/ml/datasets/Abalone   \n",
       "4    https://archive.ics.uci.edu/ml/datasets/Abscis...   \n",
       "5    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "6    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "7    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "8    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "9    https://archive.ics.uci.edu/ml/datasets/Acute+...   \n",
       "10       https://archive.ics.uci.edu/ml/datasets/Adult   \n",
       "11   https://archive.ics.uci.edu/ml/datasets/Air+Qu...   \n",
       "12   https://archive.ics.uci.edu/ml/datasets/Air+qu...   \n",
       "13   https://archive.ics.uci.edu/ml/datasets/Airfoi...   \n",
       "14   https://archive.ics.uci.edu/ml/datasets/Amazon...   \n",
       "15   https://archive.ics.uci.edu/ml/datasets/Amazon...   \n",
       "16   https://archive.ics.uci.edu/ml/datasets/Reuter...   \n",
       "17   https://archive.ics.uci.edu/ml/datasets/Amazon...   \n",
       "18   https://archive.ics.uci.edu/ml/datasets/Reuter...   \n",
       "19   https://archive.ics.uci.edu/ml/datasets/Annealing   \n",
       "20   https://archive.ics.uci.edu/ml/datasets/Anonym...   \n",
       "21   https://archive.ics.uci.edu/ml/datasets/Anuran...   \n",
       "22   https://archive.ics.uci.edu/ml/datasets/Applia...   \n",
       "23   https://archive.ics.uci.edu/ml/datasets/APS+Fa...   \n",
       "24      https://archive.ics.uci.edu/ml/datasets/Arcene   \n",
       "25   https://archive.ics.uci.edu/ml/datasets/Arrhyt...   \n",
       "26   https://archive.ics.uci.edu/ml/datasets/Artifi...   \n",
       "27   https://archive.ics.uci.edu/ml/datasets/Audiol...   \n",
       "28   https://archive.ics.uci.edu/ml/datasets/Audiol...   \n",
       "29   https://archive.ics.uci.edu/ml/datasets/Austra...   \n",
       "..                                                 ...   \n",
       "395  https://archive.ics.uci.edu/ml/datasets/Urban+...   \n",
       "396  https://archive.ics.uci.edu/ml/datasets/URL+Re...   \n",
       "397  https://archive.ics.uci.edu/ml/datasets/US+Cen...   \n",
       "398  https://archive.ics.uci.edu/ml/datasets/User+I...   \n",
       "399  https://archive.ics.uci.edu/ml/datasets/User+K...   \n",
       "400  https://archive.ics.uci.edu/ml/datasets/USPTO+...   \n",
       "401  https://archive.ics.uci.edu/ml/datasets/Verteb...   \n",
       "402  https://archive.ics.uci.edu/ml/datasets/Vicon+...   \n",
       "403  https://archive.ics.uci.edu/ml/datasets/Volcan...   \n",
       "404  https://archive.ics.uci.edu/ml/datasets/Wall-F...   \n",
       "405  https://archive.ics.uci.edu/ml/datasets/Water+...   \n",
       "406  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "407  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "408  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "409  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "410  https://archive.ics.uci.edu/ml/datasets/Wearab...   \n",
       "411  https://archive.ics.uci.edu/ml/datasets/Websit...   \n",
       "412  https://archive.ics.uci.edu/ml/datasets/Weight...   \n",
       "413  https://archive.ics.uci.edu/ml/datasets/Wholes...   \n",
       "414    https://archive.ics.uci.edu/ml/datasets/wiki4HE   \n",
       "415       https://archive.ics.uci.edu/ml/datasets/Wilt   \n",
       "416       https://archive.ics.uci.edu/ml/datasets/Wine   \n",
       "417  https://archive.ics.uci.edu/ml/datasets/Wirele...   \n",
       "418  https://archive.ics.uci.edu/ml/datasets/Yacht+...   \n",
       "419  https://archive.ics.uci.edu/ml/datasets/YearPr...   \n",
       "420      https://archive.ics.uci.edu/ml/datasets/Yeast   \n",
       "421  https://archive.ics.uci.edu/ml/datasets/YouTub...   \n",
       "422  https://archive.ics.uci.edu/ml/datasets/YouTub...   \n",
       "423  https://archive.ics.uci.edu/ml/datasets/YouTub...   \n",
       "424        https://archive.ics.uci.edu/ml/datasets/Zoo   \n",
       "\n",
       "                                               dataurl  \n",
       "0    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "1    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "2    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "3    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "4    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "5    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "6    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "7    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "8    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "9    https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "10   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "11   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "12   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "13   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "14   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "15   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "16   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "17   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "18   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "19   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "20   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "21   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "22   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "23   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "24   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "25   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "26   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "27   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "28   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "29   https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "..                                                 ...  \n",
       "395  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "396  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "397  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "398  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "399  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "400  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "401  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "402  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "403  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "404  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "405  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "406  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "407  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "408  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "409  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "410  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "411  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "412  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "413  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "414  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "415  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "416  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "417  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "418  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "419  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "420  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "421  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "422  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "423  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "424  https://archive.ics.uci.edu/ml//machine-learni...  \n",
       "\n",
       "[425 rows x 4 columns]"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined=df_description.merge(df_dataset,on='Abstract')\n",
    "df_joined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Information Extraction from Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening the file connection...\n",
      "HTTP status 200\n",
      "Reading done. Total 364147 characters read.\n"
     ]
    }
   ],
   "source": [
    "# Read the HTML from the URL and pass on to BeautifulSoup\n",
    "url = 'https://archive.ics.uci.edu/ml/datasets.html'\n",
    "print(\"Opening the file connection...\")\n",
    "uh= urllib.request.urlopen(url, context=ctx)\n",
    "print(\"HTTP status\",uh.getcode())\n",
    "html =uh.read()\n",
    "print(f\"Reading done. Total {len(html)} characters read.\")\n",
    "\n",
    "soup = BeautifulSoup(html, 'html5lib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst=[]\n",
    "for tag in soup.find_all('p'):\n",
    "    lst.append(tag.contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_stats=[]\n",
    "lst_dataset=[]\n",
    "for l in lst:\n",
    "    if str(l).find('datasets/')!=-1:\n",
    "        \n",
    "        string=str(l)\n",
    "        s=re.search('\">.*</a>',string)\n",
    "        x,y=s.span()\n",
    "        lst_dataset.append(string[x+2:y-4])\n",
    "        \n",
    "        lst_stats.append([x[0].replace(u'\\xa0', u'') for x in lst[lst.index(l)+1:lst.index(l)+7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dataframes to store information about the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Info dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stats=pd.DataFrame(data=lst_stats,columns=['Data Type','Machine Learning Task',\n",
    "                                              'Feature Type(s)','Number of samples','Number of features','Year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Names dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_names=pd.DataFrame(data=lst_dataset,columns=['Dataset'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merged dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_stats_joined=pd.concat([df_names,df_stats],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, merge this name & info dataset with the previously created Abstract/dataurl dataframe. Perform an 'inner join' to match by _Abstract_ column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "scrolled": false
   },
   "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>Dataset</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Dataset Page</th>\n",
       "      <th>dataurl</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Machine Learning Task</th>\n",
       "      <th>Feature Type(s)</th>\n",
       "      <th>Number of samples</th>\n",
       "      <th>Number of features</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3D Road Network (North Jutland, Denmark)</td>\n",
       "      <td>3D road network with highly accurate elevation...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/3D+Roa...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Sequential, Text</td>\n",
       "      <td>Regression, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>434874</td>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAAI 2013 Accepted Papers</td>\n",
       "      <td>This data set compromises the metadata for the...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/AAAI+2...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td></td>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAAI 2014 Accepted Papers</td>\n",
       "      <td>This data set compromises the metadata for the...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/AAAI+2...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td></td>\n",
       "      <td>399</td>\n",
       "      <td>6</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abalone</td>\n",
       "      <td>Predict the age of abalone from physical measu...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Abalone</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer, Real</td>\n",
       "      <td>4177</td>\n",
       "      <td>8</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abscisic Acid Signaling Network</td>\n",
       "      <td>The objective is to determine the set of boole...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Abscis...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Causal-Discovery</td>\n",
       "      <td>Integer</td>\n",
       "      <td>300</td>\n",
       "      <td>43</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Activities of Daily Living (ADLs) Recognition ...</td>\n",
       "      <td>This dataset comprises information regarding t...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Sequential, Time-Series</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td></td>\n",
       "      <td>2747</td>\n",
       "      <td></td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Activity Recognition from Single Chest-Mounted...</td>\n",
       "      <td>The dataset collects data from a wearable acce...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Univariate, Sequential, Time-Series</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Activity Recognition system based on Multisens...</td>\n",
       "      <td>This dataset contains temporal data from a Wir...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Sequential, Time-Series</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>42240</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Activity recognition with healthy older people...</td>\n",
       "      <td>Sequential motion data from 14 healthy older p...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Sequential</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>75128</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Acute Inflammations</td>\n",
       "      <td>The data was created by a medical expert as a ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Acute+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer</td>\n",
       "      <td>120</td>\n",
       "      <td>6</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adult</td>\n",
       "      <td>Predict whether income exceeds $50K/yr based o...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Adult</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer</td>\n",
       "      <td>48842</td>\n",
       "      <td>14</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Air Quality</td>\n",
       "      <td>Contains the responses of a gas multisensor de...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Air+Qu...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>9358</td>\n",
       "      <td>15</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Air Quality</td>\n",
       "      <td>Contains the responses of a gas multisensor de...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Air+qu...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>9358</td>\n",
       "      <td>15</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Airfoil Self-Noise</td>\n",
       "      <td>NASA data set, obtained from a series of aerod...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Airfoi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>1503</td>\n",
       "      <td>6</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Amazon Access Samples</td>\n",
       "      <td>Amazon's InfoSec is getting smarter about the ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Amazon...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Time-Series, Domain-Theory</td>\n",
       "      <td>Regression, Clustering, Causal-Discovery</td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>20000</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Amazon Commerce reviews set</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Amazon...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text, Domain-Theory</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>1500</td>\n",
       "      <td>10000</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazon Commerce reviews set</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Reuter...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text, Domain-Theory</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>1500</td>\n",
       "      <td>10000</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Reuter_50_50</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Amazon...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text, Domain-Theory</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>2500</td>\n",
       "      <td>10000</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Reuter_50_50</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Reuter...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text, Domain-Theory</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>2500</td>\n",
       "      <td>10000</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Annealing</td>\n",
       "      <td>Steel annealing data</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Annealing</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer, Real</td>\n",
       "      <td>798</td>\n",
       "      <td>38</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Anonymous Microsoft Web Data</td>\n",
       "      <td>Log of anonymous users of www.microsoft.com; p...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Anonym...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td></td>\n",
       "      <td>Recommender-Systems</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>37711</td>\n",
       "      <td>294</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Anuran Calls (MFCCs)</td>\n",
       "      <td>Acoustic features extracted from syllables of ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Anuran...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>7195</td>\n",
       "      <td>22</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Appliances energy prediction</td>\n",
       "      <td>Experimental data used to create regression mo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Applia...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>19735</td>\n",
       "      <td>29</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>APS Failure at Scania Trucks</td>\n",
       "      <td>The datasets' positive class consists of compo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/APS+Fa...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>60000</td>\n",
       "      <td>171</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Arcene</td>\n",
       "      <td>ARCENE's task is to distinguish cancer versus ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Arcene</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>900</td>\n",
       "      <td>10000</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Arrhythmia</td>\n",
       "      <td>Distinguish between the presence and absence o...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Arrhyt...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer, Real</td>\n",
       "      <td>452</td>\n",
       "      <td>279</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Artificial Characters</td>\n",
       "      <td>Dataset artificially generated by using first ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Artifi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer, Real</td>\n",
       "      <td>6000</td>\n",
       "      <td>7</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Audiology (Original)</td>\n",
       "      <td>Nominal audiology dataset from Baylor</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Audiol...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>226</td>\n",
       "      <td></td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Audiology (Standardized)</td>\n",
       "      <td>Standardized version of the original audiology...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Audiol...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>226</td>\n",
       "      <td>69</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Australian Sign Language signs</td>\n",
       "      <td>This data consists of sample of Auslan (Austra...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Austra...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Real</td>\n",
       "      <td>6650</td>\n",
       "      <td>15</td>\n",
       "      <td>1999</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Urban Land Cover</td>\n",
       "      <td>Classification of urban land cover using high ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Urban+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td>168</td>\n",
       "      <td>148</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>URL Reputation</td>\n",
       "      <td>Anonymized 120-day subset of the ICML-09 URL d...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/URL+Re...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>2396130</td>\n",
       "      <td>3231961</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>US Census Data (1990)</td>\n",
       "      <td>The USCensus1990raw data set contains a one pe...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/US+Cen...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>2458285</td>\n",
       "      <td>68</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>User Identification From Walking Activity</td>\n",
       "      <td>The dataset collects data from an Android smar...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/User+I...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Univariate, Sequential, Time-Series</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>User Knowledge Modeling</td>\n",
       "      <td>It is the real dataset about the students' kno...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/User+K...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Integer</td>\n",
       "      <td>403</td>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>USPTO Algorithm Challenge, run by NASA-Harvard...</td>\n",
       "      <td>Data used for USPTO Algorithm Competition. Con...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/USPTO+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Domain-Theory</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer</td>\n",
       "      <td>306</td>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Vertebral Column</td>\n",
       "      <td>Data set containing values for six biomechanic...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Verteb...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>310</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Vicon Physical Action Data Set</td>\n",
       "      <td>The Physical Action Data Set includes 10 norma...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Vicon+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Time-Series</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>3000</td>\n",
       "      <td>27</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Volcanoes on Venus - JARtool experiment</td>\n",
       "      <td>The JARtool project was a pioneering effort to...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Volcan...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Image</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Wall-Following Robot Navigation Data</td>\n",
       "      <td>The data were collected as the SCITOS G5 robot...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wall-F...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Sequential</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5456</td>\n",
       "      <td>24</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Water Treatment Plant</td>\n",
       "      <td>Multiple classes predict plant state</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Water+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>527</td>\n",
       "      <td>38</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Waveform Database Generator (Version 1)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Data-Generator</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Waveform Database Generator (Version 1)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Data-Generator</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Waveform Database Generator (Version 2)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Data-Generator</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5000</td>\n",
       "      <td>40</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Waveform Database Generator (Version 2)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Data-Generator</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5000</td>\n",
       "      <td>40</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Wearable Computing: Classification of Body Pos...</td>\n",
       "      <td>A dataset with 5 classes (sitting-down, standi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wearab...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Sequential</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>165632</td>\n",
       "      <td>18</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Website Phishing</td>\n",
       "      <td>\\n\\n</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Websit...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer</td>\n",
       "      <td>1353</td>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Weight Lifting Exercises monitored with Inerti...</td>\n",
       "      <td>Six young health subjects were asked to perfor...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Weight...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>39242</td>\n",
       "      <td>152</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Wholesale customers</td>\n",
       "      <td>The data set refers to clients of a wholesale ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wholes...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Integer</td>\n",
       "      <td>440</td>\n",
       "      <td>8</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>wiki4HE</td>\n",
       "      <td>Survey of faculty members from two Spanish uni...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/wiki4HE</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression, Clustering, Causal-Discovery</td>\n",
       "      <td></td>\n",
       "      <td>913</td>\n",
       "      <td>53</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Wilt</td>\n",
       "      <td>High-resolution Remote Sensing data set (Quick...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wilt</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td>4889</td>\n",
       "      <td>6</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Wine</td>\n",
       "      <td>Using chemical analysis determine the origin o...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wine</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>178</td>\n",
       "      <td>13</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Wireless Indoor Localization</td>\n",
       "      <td>Collected in indoor space by observing signal ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wirele...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>2000</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Yacht Hydrodynamics</td>\n",
       "      <td>Delft data set, used to predict the hydodynami...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Yacht+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>308</td>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>YearPredictionMSD</td>\n",
       "      <td>Prediction of the release year of a song from ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YearPr...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>515345</td>\n",
       "      <td>90</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Yeast</td>\n",
       "      <td>Predicting the Cellular Localization Sites of ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Yeast</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>1484</td>\n",
       "      <td>8</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>YouTube Comedy Slam Preference Data</td>\n",
       "      <td>This dataset provides user vote data on which ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YouTub...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Text</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td>1138562</td>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>YouTube Multiview Video Games Dataset</td>\n",
       "      <td>This dataset contains about 120k instances, ea...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YouTub...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>120000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>YouTube Spam Collection</td>\n",
       "      <td>It is a public set of comments collected for s...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YouTub...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Text</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td>1956</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>Zoo</td>\n",
       "      <td>Artificial, 7 classes of animals</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Zoo</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer</td>\n",
       "      <td>101</td>\n",
       "      <td>17</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>425 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Dataset  \\\n",
       "0             3D Road Network (North Jutland, Denmark)   \n",
       "1                            AAAI 2013 Accepted Papers   \n",
       "2                            AAAI 2014 Accepted Papers   \n",
       "3                                              Abalone   \n",
       "4                      Abscisic Acid Signaling Network   \n",
       "5    Activities of Daily Living (ADLs) Recognition ...   \n",
       "6    Activity Recognition from Single Chest-Mounted...   \n",
       "7    Activity Recognition system based on Multisens...   \n",
       "8    Activity recognition with healthy older people...   \n",
       "9                                  Acute Inflammations   \n",
       "10                                               Adult   \n",
       "11                                         Air Quality   \n",
       "12                                         Air Quality   \n",
       "13                                  Airfoil Self-Noise   \n",
       "14                               Amazon Access Samples   \n",
       "15                         Amazon Commerce reviews set   \n",
       "16                         Amazon Commerce reviews set   \n",
       "17                                        Reuter_50_50   \n",
       "18                                        Reuter_50_50   \n",
       "19                                           Annealing   \n",
       "20                        Anonymous Microsoft Web Data   \n",
       "21                                Anuran Calls (MFCCs)   \n",
       "22                        Appliances energy prediction   \n",
       "23                        APS Failure at Scania Trucks   \n",
       "24                                              Arcene   \n",
       "25                                          Arrhythmia   \n",
       "26                               Artificial Characters   \n",
       "27                                Audiology (Original)   \n",
       "28                            Audiology (Standardized)   \n",
       "29                      Australian Sign Language signs   \n",
       "..                                                 ...   \n",
       "395                                   Urban Land Cover   \n",
       "396                                     URL Reputation   \n",
       "397                              US Census Data (1990)   \n",
       "398          User Identification From Walking Activity   \n",
       "399                            User Knowledge Modeling   \n",
       "400  USPTO Algorithm Challenge, run by NASA-Harvard...   \n",
       "401                                   Vertebral Column   \n",
       "402                     Vicon Physical Action Data Set   \n",
       "403            Volcanoes on Venus - JARtool experiment   \n",
       "404               Wall-Following Robot Navigation Data   \n",
       "405                              Water Treatment Plant   \n",
       "406            Waveform Database Generator (Version 1)   \n",
       "407            Waveform Database Generator (Version 1)   \n",
       "408            Waveform Database Generator (Version 2)   \n",
       "409            Waveform Database Generator (Version 2)   \n",
       "410  Wearable Computing: Classification of Body Pos...   \n",
       "411                                   Website Phishing   \n",
       "412  Weight Lifting Exercises monitored with Inerti...   \n",
       "413                                Wholesale customers   \n",
       "414                                            wiki4HE   \n",
       "415                                               Wilt   \n",
       "416                                               Wine   \n",
       "417                       Wireless Indoor Localization   \n",
       "418                                Yacht Hydrodynamics   \n",
       "419                                  YearPredictionMSD   \n",
       "420                                              Yeast   \n",
       "421                YouTube Comedy Slam Preference Data   \n",
       "422              YouTube Multiview Video Games Dataset   \n",
       "423                            YouTube Spam Collection   \n",
       "424                                                Zoo   \n",
       "\n",
       "                                              Abstract  \\\n",
       "0    3D road network with highly accurate elevation...   \n",
       "1    This data set compromises the metadata for the...   \n",
       "2    This data set compromises the metadata for the...   \n",
       "3    Predict the age of abalone from physical measu...   \n",
       "4    The objective is to determine the set of boole...   \n",
       "5    This dataset comprises information regarding t...   \n",
       "6    The dataset collects data from a wearable acce...   \n",
       "7    This dataset contains temporal data from a Wir...   \n",
       "8    Sequential motion data from 14 healthy older p...   \n",
       "9    The data was created by a medical expert as a ...   \n",
       "10   Predict whether income exceeds $50K/yr based o...   \n",
       "11   Contains the responses of a gas multisensor de...   \n",
       "12   Contains the responses of a gas multisensor de...   \n",
       "13   NASA data set, obtained from a series of aerod...   \n",
       "14   Amazon's InfoSec is getting smarter about the ...   \n",
       "15   The dataset is used for authorship identificat...   \n",
       "16   The dataset is used for authorship identificat...   \n",
       "17   The dataset is used for authorship identificat...   \n",
       "18   The dataset is used for authorship identificat...   \n",
       "19                                Steel annealing data   \n",
       "20   Log of anonymous users of www.microsoft.com; p...   \n",
       "21   Acoustic features extracted from syllables of ...   \n",
       "22   Experimental data used to create regression mo...   \n",
       "23   The datasets' positive class consists of compo...   \n",
       "24   ARCENE's task is to distinguish cancer versus ...   \n",
       "25   Distinguish between the presence and absence o...   \n",
       "26   Dataset artificially generated by using first ...   \n",
       "27               Nominal audiology dataset from Baylor   \n",
       "28   Standardized version of the original audiology...   \n",
       "29   This data consists of sample of Auslan (Austra...   \n",
       "..                                                 ...   \n",
       "395  Classification of urban land cover using high ...   \n",
       "396  Anonymized 120-day subset of the ICML-09 URL d...   \n",
       "397  The USCensus1990raw data set contains a one pe...   \n",
       "398  The dataset collects data from an Android smar...   \n",
       "399  It is the real dataset about the students' kno...   \n",
       "400  Data used for USPTO Algorithm Competition. Con...   \n",
       "401  Data set containing values for six biomechanic...   \n",
       "402  The Physical Action Data Set includes 10 norma...   \n",
       "403  The JARtool project was a pioneering effort to...   \n",
       "404  The data were collected as the SCITOS G5 robot...   \n",
       "405               Multiple classes predict plant state   \n",
       "406                       CART book's waveform domains   \n",
       "407                       CART book's waveform domains   \n",
       "408                       CART book's waveform domains   \n",
       "409                       CART book's waveform domains   \n",
       "410  A dataset with 5 classes (sitting-down, standi...   \n",
       "411                                               \\n\\n   \n",
       "412  Six young health subjects were asked to perfor...   \n",
       "413  The data set refers to clients of a wholesale ...   \n",
       "414  Survey of faculty members from two Spanish uni...   \n",
       "415  High-resolution Remote Sensing data set (Quick...   \n",
       "416  Using chemical analysis determine the origin o...   \n",
       "417  Collected in indoor space by observing signal ...   \n",
       "418  Delft data set, used to predict the hydodynami...   \n",
       "419  Prediction of the release year of a song from ...   \n",
       "420  Predicting the Cellular Localization Sites of ...   \n",
       "421  This dataset provides user vote data on which ...   \n",
       "422  This dataset contains about 120k instances, ea...   \n",
       "423  It is a public set of comments collected for s...   \n",
       "424                   Artificial, 7 classes of animals   \n",
       "\n",
       "                                          Dataset Page  \\\n",
       "0    https://archive.ics.uci.edu/ml/datasets/3D+Roa...   \n",
       "1    https://archive.ics.uci.edu/ml/datasets/AAAI+2...   \n",
       "2    https://archive.ics.uci.edu/ml/datasets/AAAI+2...   \n",
       "3      https://archive.ics.uci.edu/ml/datasets/Abalone   \n",
       "4    https://archive.ics.uci.edu/ml/datasets/Abscis...   \n",
       "5    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "6    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "7    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "8    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "9    https://archive.ics.uci.edu/ml/datasets/Acute+...   \n",
       "10       https://archive.ics.uci.edu/ml/datasets/Adult   \n",
       "11   https://archive.ics.uci.edu/ml/datasets/Air+Qu...   \n",
       "12   https://archive.ics.uci.edu/ml/datasets/Air+qu...   \n",
       "13   https://archive.ics.uci.edu/ml/datasets/Airfoi...   \n",
       "14   https://archive.ics.uci.edu/ml/datasets/Amazon...   \n",
       "15   https://archive.ics.uci.edu/ml/datasets/Amazon...   \n",
       "16   https://archive.ics.uci.edu/ml/datasets/Reuter...   \n",
       "17   https://archive.ics.uci.edu/ml/datasets/Amazon...   \n",
       "18   https://archive.ics.uci.edu/ml/datasets/Reuter...   \n",
       "19   https://archive.ics.uci.edu/ml/datasets/Annealing   \n",
       "20   https://archive.ics.uci.edu/ml/datasets/Anonym...   \n",
       "21   https://archive.ics.uci.edu/ml/datasets/Anuran...   \n",
       "22   https://archive.ics.uci.edu/ml/datasets/Applia...   \n",
       "23   https://archive.ics.uci.edu/ml/datasets/APS+Fa...   \n",
       "24      https://archive.ics.uci.edu/ml/datasets/Arcene   \n",
       "25   https://archive.ics.uci.edu/ml/datasets/Arrhyt...   \n",
       "26   https://archive.ics.uci.edu/ml/datasets/Artifi...   \n",
       "27   https://archive.ics.uci.edu/ml/datasets/Audiol...   \n",
       "28   https://archive.ics.uci.edu/ml/datasets/Audiol...   \n",
       "29   https://archive.ics.uci.edu/ml/datasets/Austra...   \n",
       "..                                                 ...   \n",
       "395  https://archive.ics.uci.edu/ml/datasets/Urban+...   \n",
       "396  https://archive.ics.uci.edu/ml/datasets/URL+Re...   \n",
       "397  https://archive.ics.uci.edu/ml/datasets/US+Cen...   \n",
       "398  https://archive.ics.uci.edu/ml/datasets/User+I...   \n",
       "399  https://archive.ics.uci.edu/ml/datasets/User+K...   \n",
       "400  https://archive.ics.uci.edu/ml/datasets/USPTO+...   \n",
       "401  https://archive.ics.uci.edu/ml/datasets/Verteb...   \n",
       "402  https://archive.ics.uci.edu/ml/datasets/Vicon+...   \n",
       "403  https://archive.ics.uci.edu/ml/datasets/Volcan...   \n",
       "404  https://archive.ics.uci.edu/ml/datasets/Wall-F...   \n",
       "405  https://archive.ics.uci.edu/ml/datasets/Water+...   \n",
       "406  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "407  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "408  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "409  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "410  https://archive.ics.uci.edu/ml/datasets/Wearab...   \n",
       "411  https://archive.ics.uci.edu/ml/datasets/Websit...   \n",
       "412  https://archive.ics.uci.edu/ml/datasets/Weight...   \n",
       "413  https://archive.ics.uci.edu/ml/datasets/Wholes...   \n",
       "414    https://archive.ics.uci.edu/ml/datasets/wiki4HE   \n",
       "415       https://archive.ics.uci.edu/ml/datasets/Wilt   \n",
       "416       https://archive.ics.uci.edu/ml/datasets/Wine   \n",
       "417  https://archive.ics.uci.edu/ml/datasets/Wirele...   \n",
       "418  https://archive.ics.uci.edu/ml/datasets/Yacht+...   \n",
       "419  https://archive.ics.uci.edu/ml/datasets/YearPr...   \n",
       "420      https://archive.ics.uci.edu/ml/datasets/Yeast   \n",
       "421  https://archive.ics.uci.edu/ml/datasets/YouTub...   \n",
       "422  https://archive.ics.uci.edu/ml/datasets/YouTub...   \n",
       "423  https://archive.ics.uci.edu/ml/datasets/YouTub...   \n",
       "424        https://archive.ics.uci.edu/ml/datasets/Zoo   \n",
       "\n",
       "                                               dataurl  \\\n",
       "0    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "1    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "2    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "3    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "4    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "5    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "6    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "7    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "8    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "9    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "10   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "11   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "12   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "13   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "14   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "15   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "16   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "17   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "18   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "19   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "20   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "21   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "22   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "23   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "24   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "25   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "26   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "27   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "28   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "29   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "..                                                 ...   \n",
       "395  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "396  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "397  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "398  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "399  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "400  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "401  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "402  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "403  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "404  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "405  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "406  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "407  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "408  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "409  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "410  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "411  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "412  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "413  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "414  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "415  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "416  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "417  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "418  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "419  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "420  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "421  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "422  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "423  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "424  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "\n",
       "                                 Data Type  \\\n",
       "0                         Sequential, Text   \n",
       "1                             Multivariate   \n",
       "2                             Multivariate   \n",
       "3                             Multivariate   \n",
       "4                             Multivariate   \n",
       "5    Multivariate, Sequential, Time-Series   \n",
       "6      Univariate, Sequential, Time-Series   \n",
       "7    Multivariate, Sequential, Time-Series   \n",
       "8                               Sequential   \n",
       "9                             Multivariate   \n",
       "10                            Multivariate   \n",
       "11               Multivariate, Time-Series   \n",
       "12               Multivariate, Time-Series   \n",
       "13                            Multivariate   \n",
       "14              Time-Series, Domain-Theory   \n",
       "15       Multivariate, Text, Domain-Theory   \n",
       "16       Multivariate, Text, Domain-Theory   \n",
       "17       Multivariate, Text, Domain-Theory   \n",
       "18       Multivariate, Text, Domain-Theory   \n",
       "19                            Multivariate   \n",
       "20                                           \n",
       "21                            Multivariate   \n",
       "22               Multivariate, Time-Series   \n",
       "23                            Multivariate   \n",
       "24                            Multivariate   \n",
       "25                            Multivariate   \n",
       "26                            Multivariate   \n",
       "27                            Multivariate   \n",
       "28                            Multivariate   \n",
       "29               Multivariate, Time-Series   \n",
       "..                                     ...   \n",
       "395                           Multivariate   \n",
       "396              Multivariate, Time-Series   \n",
       "397                           Multivariate   \n",
       "398    Univariate, Sequential, Time-Series   \n",
       "399                           Multivariate   \n",
       "400                          Domain-Theory   \n",
       "401                           Multivariate   \n",
       "402                            Time-Series   \n",
       "403                                  Image   \n",
       "404               Multivariate, Sequential   \n",
       "405                           Multivariate   \n",
       "406           Multivariate, Data-Generator   \n",
       "407           Multivariate, Data-Generator   \n",
       "408           Multivariate, Data-Generator   \n",
       "409           Multivariate, Data-Generator   \n",
       "410                             Sequential   \n",
       "411                           Multivariate   \n",
       "412                           Multivariate   \n",
       "413                           Multivariate   \n",
       "414                           Multivariate   \n",
       "415                           Multivariate   \n",
       "416                           Multivariate   \n",
       "417                           Multivariate   \n",
       "418                           Multivariate   \n",
       "419                           Multivariate   \n",
       "420                           Multivariate   \n",
       "421                                   Text   \n",
       "422                     Multivariate, Text   \n",
       "423                                   Text   \n",
       "424                           Multivariate   \n",
       "\n",
       "                        Machine Learning Task             Feature Type(s)  \\\n",
       "0                      Regression, Clustering                        Real   \n",
       "1                                  Clustering                               \n",
       "2                                  Clustering                               \n",
       "3                              Classification  Categorical, Integer, Real   \n",
       "4                            Causal-Discovery                     Integer   \n",
       "5                  Classification, Clustering                               \n",
       "6                  Classification, Clustering                        Real   \n",
       "7                              Classification                        Real   \n",
       "8                              Classification                        Real   \n",
       "9                              Classification        Categorical, Integer   \n",
       "10                             Classification        Categorical, Integer   \n",
       "11                                 Regression                        Real   \n",
       "12                                 Regression                        Real   \n",
       "13                                 Regression                        Real   \n",
       "14   Regression, Clustering, Causal-Discovery                               \n",
       "15                             Classification                        Real   \n",
       "16                             Classification                        Real   \n",
       "17                 Classification, Clustering                        Real   \n",
       "18                 Classification, Clustering                        Real   \n",
       "19                             Classification  Categorical, Integer, Real   \n",
       "20                        Recommender-Systems                 Categorical   \n",
       "21                 Classification, Clustering                        Real   \n",
       "22                                 Regression                        Real   \n",
       "23                             Classification               Integer, Real   \n",
       "24                             Classification                        Real   \n",
       "25                             Classification  Categorical, Integer, Real   \n",
       "26                             Classification  Categorical, Integer, Real   \n",
       "27                             Classification                 Categorical   \n",
       "28                             Classification                 Categorical   \n",
       "29                             Classification           Categorical, Real   \n",
       "..                                        ...                         ...   \n",
       "395                            Classification                               \n",
       "396                            Classification               Integer, Real   \n",
       "397                                Clustering                 Categorical   \n",
       "398                Classification, Clustering                        Real   \n",
       "399                Classification, Clustering                     Integer   \n",
       "400                            Classification                     Integer   \n",
       "401                            Classification                        Real   \n",
       "402                            Classification                        Real   \n",
       "403                            Classification                               \n",
       "404                            Classification                        Real   \n",
       "405                                Clustering               Integer, Real   \n",
       "406                            Classification                        Real   \n",
       "407                            Classification                        Real   \n",
       "408                            Classification                        Real   \n",
       "409                            Classification                        Real   \n",
       "410                            Classification               Integer, Real   \n",
       "411                            Classification                     Integer   \n",
       "412                            Classification                        Real   \n",
       "413                Classification, Clustering                     Integer   \n",
       "414  Regression, Clustering, Causal-Discovery                               \n",
       "415                            Classification                               \n",
       "416                            Classification               Integer, Real   \n",
       "417                            Classification                        Real   \n",
       "418                                Regression                        Real   \n",
       "419                                Regression                        Real   \n",
       "420                            Classification                        Real   \n",
       "421                            Classification                               \n",
       "422                Classification, Clustering               Integer, Real   \n",
       "423                            Classification                               \n",
       "424                            Classification        Categorical, Integer   \n",
       "\n",
       "    Number of samples Number of features  Year  \n",
       "0              434874                  4  2013  \n",
       "1                 150                  5  2014  \n",
       "2                 399                  6  2014  \n",
       "3                4177                  8  1995  \n",
       "4                 300                 43  2008  \n",
       "5                2747                     2013  \n",
       "6                                         2014  \n",
       "7               42240                  6  2016  \n",
       "8               75128                  9  2016  \n",
       "9                 120                  6  2009  \n",
       "10              48842                 14  1996  \n",
       "11               9358                 15  2016  \n",
       "12               9358                 15  2016  \n",
       "13               1503                  6  2014  \n",
       "14              30000              20000  2011  \n",
       "15               1500              10000  2011  \n",
       "16               1500              10000  2011  \n",
       "17               2500              10000  2011  \n",
       "18               2500              10000  2011  \n",
       "19                798                 38        \n",
       "20              37711                294  1998  \n",
       "21               7195                 22  2017  \n",
       "22              19735                 29  2017  \n",
       "23              60000                171  2017  \n",
       "24                900              10000  2008  \n",
       "25                452                279  1998  \n",
       "26               6000                  7  1992  \n",
       "27                226                     1987  \n",
       "28                226                 69  1992  \n",
       "29               6650                 15  1999  \n",
       "..                ...                ...   ...  \n",
       "395               168                148  2014  \n",
       "396           2396130            3231961  2009  \n",
       "397           2458285                 68        \n",
       "398                                       2014  \n",
       "399               403                  5  2013  \n",
       "400               306                  5  2013  \n",
       "401               310                  6  2011  \n",
       "402              3000                 27  2011  \n",
       "403                                             \n",
       "404              5456                 24  2010  \n",
       "405               527                 38  1993  \n",
       "406              5000                 21  1988  \n",
       "407              5000                 21  1988  \n",
       "408              5000                 40  1988  \n",
       "409              5000                 40  1988  \n",
       "410            165632                 18  2013  \n",
       "411              1353                 10  2016  \n",
       "412             39242                152  2013  \n",
       "413               440                  8  2014  \n",
       "414               913                 53  2015  \n",
       "415              4889                  6  2014  \n",
       "416               178                 13  1991  \n",
       "417              2000                  7  2017  \n",
       "418               308                  7  2013  \n",
       "419            515345                 90  2011  \n",
       "420              1484                  8  1996  \n",
       "421           1138562                  3  2012  \n",
       "422            120000            1000000  2013  \n",
       "423              1956                  5  2017  \n",
       "424               101                 17  1990  \n",
       "\n",
       "[425 rows x 10 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined=df_joined.merge(df_stats_joined,on='Dataset')\n",
    "df_combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform the type of _Number of samples_, _Number of features_, and _Year_ to integers for sorting and easy searching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combined['Year']=pd.to_numeric(df_combined['Year'],errors='coerce')\n",
    "df_combined['Number of samples']=pd.to_numeric(df_combined['Number of samples'],errors='coerce')\n",
    "df_combined['Number of features']=pd.to_numeric(df_combined['Number of features'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "scrolled": false
   },
   "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>Dataset</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>Dataset Page</th>\n",
       "      <th>dataurl</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Machine Learning Task</th>\n",
       "      <th>Feature Type(s)</th>\n",
       "      <th>Number of samples</th>\n",
       "      <th>Number of features</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3D Road Network (North Jutland, Denmark)</td>\n",
       "      <td>3D road network with highly accurate elevation...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/3D+Roa...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Sequential, Text</td>\n",
       "      <td>Regression, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>434874.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAAI 2013 Accepted Papers</td>\n",
       "      <td>This data set compromises the metadata for the...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/AAAI+2...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td></td>\n",
       "      <td>150.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAAI 2014 Accepted Papers</td>\n",
       "      <td>This data set compromises the metadata for the...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/AAAI+2...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td></td>\n",
       "      <td>399.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abalone</td>\n",
       "      <td>Predict the age of abalone from physical measu...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Abalone</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer, Real</td>\n",
       "      <td>4177.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abscisic Acid Signaling Network</td>\n",
       "      <td>The objective is to determine the set of boole...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Abscis...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Causal-Discovery</td>\n",
       "      <td>Integer</td>\n",
       "      <td>300.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Activities of Daily Living (ADLs) Recognition ...</td>\n",
       "      <td>This dataset comprises information regarding t...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Sequential, Time-Series</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td></td>\n",
       "      <td>2747.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Activity Recognition from Single Chest-Mounted...</td>\n",
       "      <td>The dataset collects data from a wearable acce...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Univariate, Sequential, Time-Series</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Activity Recognition system based on Multisens...</td>\n",
       "      <td>This dataset contains temporal data from a Wir...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Sequential, Time-Series</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>42240.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Activity recognition with healthy older people...</td>\n",
       "      <td>Sequential motion data from 14 healthy older p...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Activi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Sequential</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>75128.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Acute Inflammations</td>\n",
       "      <td>The data was created by a medical expert as a ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Acute+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer</td>\n",
       "      <td>120.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adult</td>\n",
       "      <td>Predict whether income exceeds $50K/yr based o...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Adult</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer</td>\n",
       "      <td>48842.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Air Quality</td>\n",
       "      <td>Contains the responses of a gas multisensor de...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Air+Qu...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>9358.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Air Quality</td>\n",
       "      <td>Contains the responses of a gas multisensor de...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Air+qu...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>9358.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Airfoil Self-Noise</td>\n",
       "      <td>NASA data set, obtained from a series of aerod...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Airfoi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Amazon Access Samples</td>\n",
       "      <td>Amazon's InfoSec is getting smarter about the ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Amazon...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Time-Series, Domain-Theory</td>\n",
       "      <td>Regression, Clustering, Causal-Discovery</td>\n",
       "      <td></td>\n",
       "      <td>30000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Amazon Commerce reviews set</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Amazon...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text, Domain-Theory</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazon Commerce reviews set</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Reuter...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text, Domain-Theory</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Reuter_50_50</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Amazon...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text, Domain-Theory</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Reuter_50_50</td>\n",
       "      <td>The dataset is used for authorship identificat...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Reuter...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text, Domain-Theory</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Annealing</td>\n",
       "      <td>Steel annealing data</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Annealing</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer, Real</td>\n",
       "      <td>798.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Anonymous Microsoft Web Data</td>\n",
       "      <td>Log of anonymous users of www.microsoft.com; p...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Anonym...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td></td>\n",
       "      <td>Recommender-Systems</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>37711.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Anuran Calls (MFCCs)</td>\n",
       "      <td>Acoustic features extracted from syllables of ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Anuran...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>7195.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Appliances energy prediction</td>\n",
       "      <td>Experimental data used to create regression mo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Applia...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>19735.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>APS Failure at Scania Trucks</td>\n",
       "      <td>The datasets' positive class consists of compo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/APS+Fa...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Arcene</td>\n",
       "      <td>ARCENE's task is to distinguish cancer versus ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Arcene</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>900.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Arrhythmia</td>\n",
       "      <td>Distinguish between the presence and absence o...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Arrhyt...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer, Real</td>\n",
       "      <td>452.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Artificial Characters</td>\n",
       "      <td>Dataset artificially generated by using first ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Artifi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer, Real</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1992.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Audiology (Original)</td>\n",
       "      <td>Nominal audiology dataset from Baylor</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Audiol...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>226.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Audiology (Standardized)</td>\n",
       "      <td>Standardized version of the original audiology...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Audiol...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>226.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1992.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Australian Sign Language signs</td>\n",
       "      <td>This data consists of sample of Auslan (Austra...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Austra...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Real</td>\n",
       "      <td>6650.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Urban Land Cover</td>\n",
       "      <td>Classification of urban land cover using high ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Urban+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td>168.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>URL Reputation</td>\n",
       "      <td>Anonymized 120-day subset of the ICML-09 URL d...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/URL+Re...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Time-Series</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>2396130.0</td>\n",
       "      <td>3231961.0</td>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>US Census Data (1990)</td>\n",
       "      <td>The USCensus1990raw data set contains a one pe...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/US+Cen...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>Categorical</td>\n",
       "      <td>2458285.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>User Identification From Walking Activity</td>\n",
       "      <td>The dataset collects data from an Android smar...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/User+I...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Univariate, Sequential, Time-Series</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Real</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>User Knowledge Modeling</td>\n",
       "      <td>It is the real dataset about the students' kno...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/User+K...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Integer</td>\n",
       "      <td>403.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>USPTO Algorithm Challenge, run by NASA-Harvard...</td>\n",
       "      <td>Data used for USPTO Algorithm Competition. Con...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/USPTO+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Domain-Theory</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer</td>\n",
       "      <td>306.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Vertebral Column</td>\n",
       "      <td>Data set containing values for six biomechanic...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Verteb...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>310.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Vicon Physical Action Data Set</td>\n",
       "      <td>The Physical Action Data Set includes 10 norma...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Vicon+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Time-Series</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Volcanoes on Venus - JARtool experiment</td>\n",
       "      <td>The JARtool project was a pioneering effort to...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Volcan...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Image</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Wall-Following Robot Navigation Data</td>\n",
       "      <td>The data were collected as the SCITOS G5 robot...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wall-F...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Sequential</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5456.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Water Treatment Plant</td>\n",
       "      <td>Multiple classes predict plant state</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Water+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Clustering</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>527.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Waveform Database Generator (Version 1)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Data-Generator</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Waveform Database Generator (Version 1)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Data-Generator</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Waveform Database Generator (Version 2)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Data-Generator</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Waveform Database Generator (Version 2)</td>\n",
       "      <td>CART book's waveform domains</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wavefo...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Data-Generator</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Wearable Computing: Classification of Body Pos...</td>\n",
       "      <td>A dataset with 5 classes (sitting-down, standi...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wearab...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Sequential</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>165632.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Website Phishing</td>\n",
       "      <td>\\n\\n</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Websit...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Weight Lifting Exercises monitored with Inerti...</td>\n",
       "      <td>Six young health subjects were asked to perfor...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Weight...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>39242.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Wholesale customers</td>\n",
       "      <td>The data set refers to clients of a wholesale ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wholes...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Integer</td>\n",
       "      <td>440.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>wiki4HE</td>\n",
       "      <td>Survey of faculty members from two Spanish uni...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/wiki4HE</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression, Clustering, Causal-Discovery</td>\n",
       "      <td></td>\n",
       "      <td>913.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Wilt</td>\n",
       "      <td>High-resolution Remote Sensing data set (Quick...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wilt</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td>4889.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Wine</td>\n",
       "      <td>Using chemical analysis determine the origin o...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wine</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>178.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1991.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Wireless Indoor Localization</td>\n",
       "      <td>Collected in indoor space by observing signal ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Wirele...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Yacht Hydrodynamics</td>\n",
       "      <td>Delft data set, used to predict the hydodynami...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Yacht+...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>308.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>YearPredictionMSD</td>\n",
       "      <td>Prediction of the release year of a song from ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YearPr...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Regression</td>\n",
       "      <td>Real</td>\n",
       "      <td>515345.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Yeast</td>\n",
       "      <td>Predicting the Cellular Localization Sites of ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Yeast</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Real</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>YouTube Comedy Slam Preference Data</td>\n",
       "      <td>This dataset provides user vote data on which ...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YouTub...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Text</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td>1138562.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>YouTube Multiview Video Games Dataset</td>\n",
       "      <td>This dataset contains about 120k instances, ea...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YouTub...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate, Text</td>\n",
       "      <td>Classification, Clustering</td>\n",
       "      <td>Integer, Real</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>YouTube Spam Collection</td>\n",
       "      <td>It is a public set of comments collected for s...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/YouTub...</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Text</td>\n",
       "      <td>Classification</td>\n",
       "      <td></td>\n",
       "      <td>1956.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>Zoo</td>\n",
       "      <td>Artificial, 7 classes of animals</td>\n",
       "      <td>https://archive.ics.uci.edu/ml/datasets/Zoo</td>\n",
       "      <td>https://archive.ics.uci.edu/ml//machine-learni...</td>\n",
       "      <td>Multivariate</td>\n",
       "      <td>Classification</td>\n",
       "      <td>Categorical, Integer</td>\n",
       "      <td>101.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>425 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Dataset  \\\n",
       "0             3D Road Network (North Jutland, Denmark)   \n",
       "1                            AAAI 2013 Accepted Papers   \n",
       "2                            AAAI 2014 Accepted Papers   \n",
       "3                                              Abalone   \n",
       "4                      Abscisic Acid Signaling Network   \n",
       "5    Activities of Daily Living (ADLs) Recognition ...   \n",
       "6    Activity Recognition from Single Chest-Mounted...   \n",
       "7    Activity Recognition system based on Multisens...   \n",
       "8    Activity recognition with healthy older people...   \n",
       "9                                  Acute Inflammations   \n",
       "10                                               Adult   \n",
       "11                                         Air Quality   \n",
       "12                                         Air Quality   \n",
       "13                                  Airfoil Self-Noise   \n",
       "14                               Amazon Access Samples   \n",
       "15                         Amazon Commerce reviews set   \n",
       "16                         Amazon Commerce reviews set   \n",
       "17                                        Reuter_50_50   \n",
       "18                                        Reuter_50_50   \n",
       "19                                           Annealing   \n",
       "20                        Anonymous Microsoft Web Data   \n",
       "21                                Anuran Calls (MFCCs)   \n",
       "22                        Appliances energy prediction   \n",
       "23                        APS Failure at Scania Trucks   \n",
       "24                                              Arcene   \n",
       "25                                          Arrhythmia   \n",
       "26                               Artificial Characters   \n",
       "27                                Audiology (Original)   \n",
       "28                            Audiology (Standardized)   \n",
       "29                      Australian Sign Language signs   \n",
       "..                                                 ...   \n",
       "395                                   Urban Land Cover   \n",
       "396                                     URL Reputation   \n",
       "397                              US Census Data (1990)   \n",
       "398          User Identification From Walking Activity   \n",
       "399                            User Knowledge Modeling   \n",
       "400  USPTO Algorithm Challenge, run by NASA-Harvard...   \n",
       "401                                   Vertebral Column   \n",
       "402                     Vicon Physical Action Data Set   \n",
       "403            Volcanoes on Venus - JARtool experiment   \n",
       "404               Wall-Following Robot Navigation Data   \n",
       "405                              Water Treatment Plant   \n",
       "406            Waveform Database Generator (Version 1)   \n",
       "407            Waveform Database Generator (Version 1)   \n",
       "408            Waveform Database Generator (Version 2)   \n",
       "409            Waveform Database Generator (Version 2)   \n",
       "410  Wearable Computing: Classification of Body Pos...   \n",
       "411                                   Website Phishing   \n",
       "412  Weight Lifting Exercises monitored with Inerti...   \n",
       "413                                Wholesale customers   \n",
       "414                                            wiki4HE   \n",
       "415                                               Wilt   \n",
       "416                                               Wine   \n",
       "417                       Wireless Indoor Localization   \n",
       "418                                Yacht Hydrodynamics   \n",
       "419                                  YearPredictionMSD   \n",
       "420                                              Yeast   \n",
       "421                YouTube Comedy Slam Preference Data   \n",
       "422              YouTube Multiview Video Games Dataset   \n",
       "423                            YouTube Spam Collection   \n",
       "424                                                Zoo   \n",
       "\n",
       "                                              Abstract  \\\n",
       "0    3D road network with highly accurate elevation...   \n",
       "1    This data set compromises the metadata for the...   \n",
       "2    This data set compromises the metadata for the...   \n",
       "3    Predict the age of abalone from physical measu...   \n",
       "4    The objective is to determine the set of boole...   \n",
       "5    This dataset comprises information regarding t...   \n",
       "6    The dataset collects data from a wearable acce...   \n",
       "7    This dataset contains temporal data from a Wir...   \n",
       "8    Sequential motion data from 14 healthy older p...   \n",
       "9    The data was created by a medical expert as a ...   \n",
       "10   Predict whether income exceeds $50K/yr based o...   \n",
       "11   Contains the responses of a gas multisensor de...   \n",
       "12   Contains the responses of a gas multisensor de...   \n",
       "13   NASA data set, obtained from a series of aerod...   \n",
       "14   Amazon's InfoSec is getting smarter about the ...   \n",
       "15   The dataset is used for authorship identificat...   \n",
       "16   The dataset is used for authorship identificat...   \n",
       "17   The dataset is used for authorship identificat...   \n",
       "18   The dataset is used for authorship identificat...   \n",
       "19                                Steel annealing data   \n",
       "20   Log of anonymous users of www.microsoft.com; p...   \n",
       "21   Acoustic features extracted from syllables of ...   \n",
       "22   Experimental data used to create regression mo...   \n",
       "23   The datasets' positive class consists of compo...   \n",
       "24   ARCENE's task is to distinguish cancer versus ...   \n",
       "25   Distinguish between the presence and absence o...   \n",
       "26   Dataset artificially generated by using first ...   \n",
       "27               Nominal audiology dataset from Baylor   \n",
       "28   Standardized version of the original audiology...   \n",
       "29   This data consists of sample of Auslan (Austra...   \n",
       "..                                                 ...   \n",
       "395  Classification of urban land cover using high ...   \n",
       "396  Anonymized 120-day subset of the ICML-09 URL d...   \n",
       "397  The USCensus1990raw data set contains a one pe...   \n",
       "398  The dataset collects data from an Android smar...   \n",
       "399  It is the real dataset about the students' kno...   \n",
       "400  Data used for USPTO Algorithm Competition. Con...   \n",
       "401  Data set containing values for six biomechanic...   \n",
       "402  The Physical Action Data Set includes 10 norma...   \n",
       "403  The JARtool project was a pioneering effort to...   \n",
       "404  The data were collected as the SCITOS G5 robot...   \n",
       "405               Multiple classes predict plant state   \n",
       "406                       CART book's waveform domains   \n",
       "407                       CART book's waveform domains   \n",
       "408                       CART book's waveform domains   \n",
       "409                       CART book's waveform domains   \n",
       "410  A dataset with 5 classes (sitting-down, standi...   \n",
       "411                                               \\n\\n   \n",
       "412  Six young health subjects were asked to perfor...   \n",
       "413  The data set refers to clients of a wholesale ...   \n",
       "414  Survey of faculty members from two Spanish uni...   \n",
       "415  High-resolution Remote Sensing data set (Quick...   \n",
       "416  Using chemical analysis determine the origin o...   \n",
       "417  Collected in indoor space by observing signal ...   \n",
       "418  Delft data set, used to predict the hydodynami...   \n",
       "419  Prediction of the release year of a song from ...   \n",
       "420  Predicting the Cellular Localization Sites of ...   \n",
       "421  This dataset provides user vote data on which ...   \n",
       "422  This dataset contains about 120k instances, ea...   \n",
       "423  It is a public set of comments collected for s...   \n",
       "424                   Artificial, 7 classes of animals   \n",
       "\n",
       "                                          Dataset Page  \\\n",
       "0    https://archive.ics.uci.edu/ml/datasets/3D+Roa...   \n",
       "1    https://archive.ics.uci.edu/ml/datasets/AAAI+2...   \n",
       "2    https://archive.ics.uci.edu/ml/datasets/AAAI+2...   \n",
       "3      https://archive.ics.uci.edu/ml/datasets/Abalone   \n",
       "4    https://archive.ics.uci.edu/ml/datasets/Abscis...   \n",
       "5    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "6    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "7    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "8    https://archive.ics.uci.edu/ml/datasets/Activi...   \n",
       "9    https://archive.ics.uci.edu/ml/datasets/Acute+...   \n",
       "10       https://archive.ics.uci.edu/ml/datasets/Adult   \n",
       "11   https://archive.ics.uci.edu/ml/datasets/Air+Qu...   \n",
       "12   https://archive.ics.uci.edu/ml/datasets/Air+qu...   \n",
       "13   https://archive.ics.uci.edu/ml/datasets/Airfoi...   \n",
       "14   https://archive.ics.uci.edu/ml/datasets/Amazon...   \n",
       "15   https://archive.ics.uci.edu/ml/datasets/Amazon...   \n",
       "16   https://archive.ics.uci.edu/ml/datasets/Reuter...   \n",
       "17   https://archive.ics.uci.edu/ml/datasets/Amazon...   \n",
       "18   https://archive.ics.uci.edu/ml/datasets/Reuter...   \n",
       "19   https://archive.ics.uci.edu/ml/datasets/Annealing   \n",
       "20   https://archive.ics.uci.edu/ml/datasets/Anonym...   \n",
       "21   https://archive.ics.uci.edu/ml/datasets/Anuran...   \n",
       "22   https://archive.ics.uci.edu/ml/datasets/Applia...   \n",
       "23   https://archive.ics.uci.edu/ml/datasets/APS+Fa...   \n",
       "24      https://archive.ics.uci.edu/ml/datasets/Arcene   \n",
       "25   https://archive.ics.uci.edu/ml/datasets/Arrhyt...   \n",
       "26   https://archive.ics.uci.edu/ml/datasets/Artifi...   \n",
       "27   https://archive.ics.uci.edu/ml/datasets/Audiol...   \n",
       "28   https://archive.ics.uci.edu/ml/datasets/Audiol...   \n",
       "29   https://archive.ics.uci.edu/ml/datasets/Austra...   \n",
       "..                                                 ...   \n",
       "395  https://archive.ics.uci.edu/ml/datasets/Urban+...   \n",
       "396  https://archive.ics.uci.edu/ml/datasets/URL+Re...   \n",
       "397  https://archive.ics.uci.edu/ml/datasets/US+Cen...   \n",
       "398  https://archive.ics.uci.edu/ml/datasets/User+I...   \n",
       "399  https://archive.ics.uci.edu/ml/datasets/User+K...   \n",
       "400  https://archive.ics.uci.edu/ml/datasets/USPTO+...   \n",
       "401  https://archive.ics.uci.edu/ml/datasets/Verteb...   \n",
       "402  https://archive.ics.uci.edu/ml/datasets/Vicon+...   \n",
       "403  https://archive.ics.uci.edu/ml/datasets/Volcan...   \n",
       "404  https://archive.ics.uci.edu/ml/datasets/Wall-F...   \n",
       "405  https://archive.ics.uci.edu/ml/datasets/Water+...   \n",
       "406  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "407  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "408  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "409  https://archive.ics.uci.edu/ml/datasets/Wavefo...   \n",
       "410  https://archive.ics.uci.edu/ml/datasets/Wearab...   \n",
       "411  https://archive.ics.uci.edu/ml/datasets/Websit...   \n",
       "412  https://archive.ics.uci.edu/ml/datasets/Weight...   \n",
       "413  https://archive.ics.uci.edu/ml/datasets/Wholes...   \n",
       "414    https://archive.ics.uci.edu/ml/datasets/wiki4HE   \n",
       "415       https://archive.ics.uci.edu/ml/datasets/Wilt   \n",
       "416       https://archive.ics.uci.edu/ml/datasets/Wine   \n",
       "417  https://archive.ics.uci.edu/ml/datasets/Wirele...   \n",
       "418  https://archive.ics.uci.edu/ml/datasets/Yacht+...   \n",
       "419  https://archive.ics.uci.edu/ml/datasets/YearPr...   \n",
       "420      https://archive.ics.uci.edu/ml/datasets/Yeast   \n",
       "421  https://archive.ics.uci.edu/ml/datasets/YouTub...   \n",
       "422  https://archive.ics.uci.edu/ml/datasets/YouTub...   \n",
       "423  https://archive.ics.uci.edu/ml/datasets/YouTub...   \n",
       "424        https://archive.ics.uci.edu/ml/datasets/Zoo   \n",
       "\n",
       "                                               dataurl  \\\n",
       "0    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "1    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "2    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "3    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "4    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "5    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "6    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "7    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "8    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "9    https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "10   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "11   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "12   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "13   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "14   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "15   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "16   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "17   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "18   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "19   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "20   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "21   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "22   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "23   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "24   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "25   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "26   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "27   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "28   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "29   https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "..                                                 ...   \n",
       "395  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "396  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "397  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "398  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "399  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "400  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "401  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "402  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "403  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "404  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "405  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "406  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "407  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "408  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "409  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "410  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "411  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "412  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "413  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "414  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "415  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "416  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "417  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "418  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "419  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "420  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "421  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "422  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "423  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "424  https://archive.ics.uci.edu/ml//machine-learni...   \n",
       "\n",
       "                                 Data Type  \\\n",
       "0                         Sequential, Text   \n",
       "1                             Multivariate   \n",
       "2                             Multivariate   \n",
       "3                             Multivariate   \n",
       "4                             Multivariate   \n",
       "5    Multivariate, Sequential, Time-Series   \n",
       "6      Univariate, Sequential, Time-Series   \n",
       "7    Multivariate, Sequential, Time-Series   \n",
       "8                               Sequential   \n",
       "9                             Multivariate   \n",
       "10                            Multivariate   \n",
       "11               Multivariate, Time-Series   \n",
       "12               Multivariate, Time-Series   \n",
       "13                            Multivariate   \n",
       "14              Time-Series, Domain-Theory   \n",
       "15       Multivariate, Text, Domain-Theory   \n",
       "16       Multivariate, Text, Domain-Theory   \n",
       "17       Multivariate, Text, Domain-Theory   \n",
       "18       Multivariate, Text, Domain-Theory   \n",
       "19                            Multivariate   \n",
       "20                                           \n",
       "21                            Multivariate   \n",
       "22               Multivariate, Time-Series   \n",
       "23                            Multivariate   \n",
       "24                            Multivariate   \n",
       "25                            Multivariate   \n",
       "26                            Multivariate   \n",
       "27                            Multivariate   \n",
       "28                            Multivariate   \n",
       "29               Multivariate, Time-Series   \n",
       "..                                     ...   \n",
       "395                           Multivariate   \n",
       "396              Multivariate, Time-Series   \n",
       "397                           Multivariate   \n",
       "398    Univariate, Sequential, Time-Series   \n",
       "399                           Multivariate   \n",
       "400                          Domain-Theory   \n",
       "401                           Multivariate   \n",
       "402                            Time-Series   \n",
       "403                                  Image   \n",
       "404               Multivariate, Sequential   \n",
       "405                           Multivariate   \n",
       "406           Multivariate, Data-Generator   \n",
       "407           Multivariate, Data-Generator   \n",
       "408           Multivariate, Data-Generator   \n",
       "409           Multivariate, Data-Generator   \n",
       "410                             Sequential   \n",
       "411                           Multivariate   \n",
       "412                           Multivariate   \n",
       "413                           Multivariate   \n",
       "414                           Multivariate   \n",
       "415                           Multivariate   \n",
       "416                           Multivariate   \n",
       "417                           Multivariate   \n",
       "418                           Multivariate   \n",
       "419                           Multivariate   \n",
       "420                           Multivariate   \n",
       "421                                   Text   \n",
       "422                     Multivariate, Text   \n",
       "423                                   Text   \n",
       "424                           Multivariate   \n",
       "\n",
       "                        Machine Learning Task             Feature Type(s)  \\\n",
       "0                      Regression, Clustering                        Real   \n",
       "1                                  Clustering                               \n",
       "2                                  Clustering                               \n",
       "3                              Classification  Categorical, Integer, Real   \n",
       "4                            Causal-Discovery                     Integer   \n",
       "5                  Classification, Clustering                               \n",
       "6                  Classification, Clustering                        Real   \n",
       "7                              Classification                        Real   \n",
       "8                              Classification                        Real   \n",
       "9                              Classification        Categorical, Integer   \n",
       "10                             Classification        Categorical, Integer   \n",
       "11                                 Regression                        Real   \n",
       "12                                 Regression                        Real   \n",
       "13                                 Regression                        Real   \n",
       "14   Regression, Clustering, Causal-Discovery                               \n",
       "15                             Classification                        Real   \n",
       "16                             Classification                        Real   \n",
       "17                 Classification, Clustering                        Real   \n",
       "18                 Classification, Clustering                        Real   \n",
       "19                             Classification  Categorical, Integer, Real   \n",
       "20                        Recommender-Systems                 Categorical   \n",
       "21                 Classification, Clustering                        Real   \n",
       "22                                 Regression                        Real   \n",
       "23                             Classification               Integer, Real   \n",
       "24                             Classification                        Real   \n",
       "25                             Classification  Categorical, Integer, Real   \n",
       "26                             Classification  Categorical, Integer, Real   \n",
       "27                             Classification                 Categorical   \n",
       "28                             Classification                 Categorical   \n",
       "29                             Classification           Categorical, Real   \n",
       "..                                        ...                         ...   \n",
       "395                            Classification                               \n",
       "396                            Classification               Integer, Real   \n",
       "397                                Clustering                 Categorical   \n",
       "398                Classification, Clustering                        Real   \n",
       "399                Classification, Clustering                     Integer   \n",
       "400                            Classification                     Integer   \n",
       "401                            Classification                        Real   \n",
       "402                            Classification                        Real   \n",
       "403                            Classification                               \n",
       "404                            Classification                        Real   \n",
       "405                                Clustering               Integer, Real   \n",
       "406                            Classification                        Real   \n",
       "407                            Classification                        Real   \n",
       "408                            Classification                        Real   \n",
       "409                            Classification                        Real   \n",
       "410                            Classification               Integer, Real   \n",
       "411                            Classification                     Integer   \n",
       "412                            Classification                        Real   \n",
       "413                Classification, Clustering                     Integer   \n",
       "414  Regression, Clustering, Causal-Discovery                               \n",
       "415                            Classification                               \n",
       "416                            Classification               Integer, Real   \n",
       "417                            Classification                        Real   \n",
       "418                                Regression                        Real   \n",
       "419                                Regression                        Real   \n",
       "420                            Classification                        Real   \n",
       "421                            Classification                               \n",
       "422                Classification, Clustering               Integer, Real   \n",
       "423                            Classification                               \n",
       "424                            Classification        Categorical, Integer   \n",
       "\n",
       "     Number of samples  Number of features    Year  \n",
       "0             434874.0                 4.0  2013.0  \n",
       "1                150.0                 5.0  2014.0  \n",
       "2                399.0                 6.0  2014.0  \n",
       "3               4177.0                 8.0  1995.0  \n",
       "4                300.0                43.0  2008.0  \n",
       "5               2747.0                 NaN  2013.0  \n",
       "6                  NaN                 NaN  2014.0  \n",
       "7              42240.0                 6.0  2016.0  \n",
       "8              75128.0                 9.0  2016.0  \n",
       "9                120.0                 6.0  2009.0  \n",
       "10             48842.0                14.0  1996.0  \n",
       "11              9358.0                15.0  2016.0  \n",
       "12              9358.0                15.0  2016.0  \n",
       "13              1503.0                 6.0  2014.0  \n",
       "14             30000.0             20000.0  2011.0  \n",
       "15              1500.0             10000.0  2011.0  \n",
       "16              1500.0             10000.0  2011.0  \n",
       "17              2500.0             10000.0  2011.0  \n",
       "18              2500.0             10000.0  2011.0  \n",
       "19               798.0                38.0     NaN  \n",
       "20             37711.0               294.0  1998.0  \n",
       "21              7195.0                22.0  2017.0  \n",
       "22             19735.0                29.0  2017.0  \n",
       "23             60000.0               171.0  2017.0  \n",
       "24               900.0             10000.0  2008.0  \n",
       "25               452.0               279.0  1998.0  \n",
       "26              6000.0                 7.0  1992.0  \n",
       "27               226.0                 NaN  1987.0  \n",
       "28               226.0                69.0  1992.0  \n",
       "29              6650.0                15.0  1999.0  \n",
       "..                 ...                 ...     ...  \n",
       "395              168.0               148.0  2014.0  \n",
       "396          2396130.0           3231961.0  2009.0  \n",
       "397          2458285.0                68.0     NaN  \n",
       "398                NaN                 NaN  2014.0  \n",
       "399              403.0                 5.0  2013.0  \n",
       "400              306.0                 5.0  2013.0  \n",
       "401              310.0                 6.0  2011.0  \n",
       "402             3000.0                27.0  2011.0  \n",
       "403                NaN                 NaN     NaN  \n",
       "404             5456.0                24.0  2010.0  \n",
       "405              527.0                38.0  1993.0  \n",
       "406             5000.0                21.0  1988.0  \n",
       "407             5000.0                21.0  1988.0  \n",
       "408             5000.0                40.0  1988.0  \n",
       "409             5000.0                40.0  1988.0  \n",
       "410           165632.0                18.0  2013.0  \n",
       "411             1353.0                10.0  2016.0  \n",
       "412            39242.0               152.0  2013.0  \n",
       "413              440.0                 8.0  2014.0  \n",
       "414              913.0                53.0  2015.0  \n",
       "415             4889.0                 6.0  2014.0  \n",
       "416              178.0                13.0  1991.0  \n",
       "417             2000.0                 7.0  2017.0  \n",
       "418              308.0                 7.0  2013.0  \n",
       "419           515345.0                90.0  2011.0  \n",
       "420             1484.0                 8.0  1996.0  \n",
       "421          1138562.0                 3.0  2012.0  \n",
       "422           120000.0           1000000.0  2013.0  \n",
       "423             1956.0                 5.0  2017.0  \n",
       "424              101.0                17.0  1990.0  \n",
       "\n",
       "[425 rows x 10 columns]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File download helper function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_file(url,directory):\n",
    "    local_filename = directory+'/'+url.split('/')[-1]\n",
    "    # NOTE the stream=True parameter\n",
    "    r = requests.get(url, stream=True)\n",
    "    with open(local_filename, 'wb') as f:\n",
    "        for chunk in r.iter_content(chunk_size=1024): \n",
    "            if chunk: # filter out keep-alive new chunks\n",
    "                f.write(chunk)\n",
    "                #f.flush()\n",
    "    #return local_filename"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Function for downloading the data set from a page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_from_url(url,dataset):    \n",
    "    \n",
    "    cwd = os.getcwd()\n",
    "    local_directory = cwd+'\\\\'+dataset\n",
    "    if not os.path.exists(local_directory):\n",
    "        os.makedirs(local_directory)\n",
    "\n",
    "    uh= urllib.request.urlopen(url, context=ctx)\n",
    "    html =uh.read().decode()\n",
    "    soup=BeautifulSoup(html,'html5lib')\n",
    "\n",
    "    links=[]\n",
    "    for link in soup.find_all('a'):\n",
    "        links.append(link.attrs['href'])\n",
    "    \n",
    "    links_to_download=[]\n",
    "\n",
    "    if 'Index' in links:\n",
    "        idx=links.index('Index')\n",
    "    else:\n",
    "        idx=len(links)-2\n",
    "    for i in range(idx+1,len(links)):\n",
    "        links_to_download.append(url+str(links[i]))\n",
    "\n",
    "    for file_url in links_to_download:\n",
    "        download_file(file_url,local_directory)\n",
    "    \n",
    "    print (f\"Downloaded dataset from {url}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to download data by dataset name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_dataset(dataset):\n",
    "    \n",
    "    try:\n",
    "        url=df_combined[df_combined['Dataset']==dataset]['Dataset Page'].values[0]\n",
    "    except:\n",
    "        print(\"Something wrong in the dataset name!\")\n",
    "        return None\n",
    "    \n",
    "    print(f\"Downloading the dataset: {dataset}\")\n",
    "    download_from_url(url, dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the download function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading the dataset: Wireless Indoor Localization\n",
      "Downloaded dataset from https://archive.ics.uci.edu/ml/datasets/Wireless+Indoor+Localization\n"
     ]
    }
   ],
   "source": [
    "download_dataset('Wireless Indoor Localization')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for finding more information for any dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def dataset_information(dataset):\n",
    "    \n",
    "    try:\n",
    "        url=df_combined[df_combined['Dataset']==dataset]['Dataset Page'].values[0]\n",
    "    except:\n",
    "        print(\"Something wrong in the dataset name!\")\n",
    "        return None\n",
    "    \n",
    "    try:\n",
    "        dataset_info={}\n",
    "        uh= urllib.request.urlopen(url, context=ctx)\n",
    "        html =uh.read()\n",
    "        soup = BeautifulSoup(html, 'html5lib')\n",
    "        \n",
    "        tag=soup.find('p',text='Data Set Information:')\n",
    "        if tag!=None:\n",
    "            text=tag.find_next('p').get_text()\n",
    "            print(\"DATASET INFORMATION\")\n",
    "            print(\"=\"*50)\n",
    "            print(text)\n",
    "            print(\"=\"*50)\n",
    "            print(\"\\n\")\n",
    "            dataset_info['Dataset info']=text\n",
    "        else:\n",
    "            print(\"No information about the dataset could be found on its page!\")\n",
    "        \n",
    "        tag=soup.find('p',text='Source:')\n",
    "        if tag!=None:\n",
    "            text=tag.find_next('p').get_text()\n",
    "            print(\"SOURCE\")\n",
    "            print(\"=\"*50)\n",
    "            print(text)\n",
    "            print(\"=\"*50)\n",
    "            print(\"\\n\")\n",
    "            dataset_info['Dataset Source']=text\n",
    "        else:\n",
    "            print(\"No information about the source of dataset could be found on its page!\")\n",
    "        \n",
    "        return dataset_info\n",
    "    except:\n",
    "        print(\"Could not connect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATASET INFORMATION\n",
      "==================================================\n",
      "This is perhaps the best known database to be found in the pattern recognition literature.  Fisher's paper is a classic in the field and is referenced frequently to this day.  (See Duda & Hart, for example.)  The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.  One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.\n",
      "\n",
      "Predicted attribute: class of iris plant.\n",
      "\n",
      "This is an exceedingly simple domain.\n",
      "\n",
      "This data differs from the data presented in Fishers article (identified by Steve Chadwick,  spchadwick '@' espeedaz.net ).  The 35th sample should be: 4.9,3.1,1.5,0.2,\"Iris-setosa\" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,\"Iris-setosa\" where the errors are in the second and third features.  \n",
      "==================================================\n",
      "\n",
      "\n",
      "SOURCE\n",
      "==================================================\n",
      "Creator: \n",
      "\n",
      "R.A. Fisher\n",
      "\n",
      "Donor: \n",
      "\n",
      "Michael Marshall (MARSHALL%PLU '@' io.arc.nasa.gov)\n",
      "==================================================\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "d=dataset_information('Iris')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}