KGLiDS_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## <b>KGLiDS APIs</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from api.api import KGLiDS\n",
    "import pandas as pd\n",
    "kglids = KGLiDS(endpoint='http://localhost:7200', db='kaggle_demo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information captured: \n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datasets</th>\n",
       "      <th>Pipelines</th>\n",
       "      <th>Tables</th>\n",
       "      <th>Columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>1064</td>\n",
       "      <td>425</td>\n",
       "      <td>13719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Datasets  Pipelines  Tables  Columns\n",
       "0       104       1064     425    13719"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.show_graph_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "<b> Running Adhoc-queries on our graph!</b><br><br> \n",
    "<b>1.  Customized queries </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kaggle_demo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        source\n",
       "0  kaggle_demo"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\" \n",
    "SELECT ?source {\n",
    "?source_id rdf:type    kglids:Source    ;\n",
    "           schema:name ?source          . } \"\"\"\n",
    "kglids.query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "<b>Not comfortable with Sparql? No worries!</b><br> \n",
    "<b> You can use pre-defined KGLiDS APIs to discover useful insights that remain locked inside your Data</b>\n",
    "\n",
    "Note: \n",
    "- All query result(s) are returned in a form of <code>pandas.DataFrame</code> or plots in some cases \n",
    "- KGLiDS is a transparent system, you can see the queries used by the system by setting <code>show_query = True</code> in case of every API you wish to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>2.  Retrieving dataset(s) </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Number_of_tables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COVID-19 Dataset</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COVID-19 Healthy Diet Dataset</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COVID-19 Indonesia Dataset</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COVID-19 World Vaccination Progress</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>unitednations.international-greenhouse-gas-emi...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>upadorprofzs.testes</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>vitaliymalcev.russian-passenger-air-service-20...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Dataset  Number_of_tables\n",
       "0                  COVID-19 Corona Virus India Dataset                 8\n",
       "1                                     COVID-19 Dataset                 6\n",
       "2                        COVID-19 Healthy Diet Dataset                 5\n",
       "3                           COVID-19 Indonesia Dataset                 1\n",
       "4                  COVID-19 World Vaccination Progress                 2\n",
       "..                                                 ...               ...\n",
       "99            uciml.red-wine-quality-cortez-et-al-2009                20\n",
       "100  unitednations.international-greenhouse-gas-emi...                 4\n",
       "101                                upadorprofzs.testes                 8\n",
       "102  vitaliymalcev.russian-passenger-air-service-20...                14\n",
       "103               ylchang.coffee-shop-sample-data-1113                13\n",
       "\n",
       "[104 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_datasets_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>3.  Retrieving table(s) </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing all available table(s): \n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Table</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tests_day_wise.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>state_level_latest.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>state_level_daily.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>complete.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tests_state_wise.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>product.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Dates.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>201904 sales reciepts.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>customer.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>sales_outlet.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>425 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Table                               Dataset  \\\n",
       "0           tests_day_wise.csv   COVID-19 Corona Virus India Dataset   \n",
       "1       state_level_latest.csv   COVID-19 Corona Virus India Dataset   \n",
       "2        state_level_daily.csv   COVID-19 Corona Virus India Dataset   \n",
       "3                 complete.csv   COVID-19 Corona Virus India Dataset   \n",
       "4         tests_state_wise.csv   COVID-19 Corona Virus India Dataset   \n",
       "..                         ...                                   ...   \n",
       "420                product.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "421                  Dates.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "422  201904 sales reciepts.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "423               customer.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "424           sales_outlet.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "\n",
       "                                         Path_to_table  \n",
       "0    /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "1    /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "2    /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "3    /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "4    /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "..                                                 ...  \n",
       "420  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "421  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "422  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "423  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "424  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "\n",
       "[425 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_tables_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing table(s) for 'UK COVID-19 Data' dataset: \n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Table</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UK_Local_Authority_UTLA_COVID_Dataset.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK_National_Total_COVID_Dataset.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NEW_Official_Population_Data_ONS_mid-2019.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UK_Devolved_Nations_COVID_Dataset.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>England_Regions_COVID_Dataset.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Populations_for_UK_and_Devolved_Nations.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Table           Dataset  \\\n",
       "0      UK_Local_Authority_UTLA_COVID_Dataset.csv  UK COVID-19 Data   \n",
       "1            UK_National_Total_COVID_Dataset.csv  UK COVID-19 Data   \n",
       "2  NEW_Official_Population_Data_ONS_mid-2019.csv  UK COVID-19 Data   \n",
       "3          UK_Devolved_Nations_COVID_Dataset.csv  UK COVID-19 Data   \n",
       "4              England_Regions_COVID_Dataset.csv  UK COVID-19 Data   \n",
       "5    Populations_for_UK_and_Devolved_Nations.csv  UK COVID-19 Data   \n",
       "\n",
       "                                       Path_to_table  \n",
       "0  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "1  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "2  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "3  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "4  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "5  /data/mossad/kglids/storage/data_sources/kaggl...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_tables_info(dataset='UK COVID-19 Data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>Discovering enrichment possibilities using KGLiDS's recommendations </b><br><br>\n",
    "<b>4. Recommending tables by column name search</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing recommendations as per the following conditions:\n",
      "Condition =  [['player', 'club']]\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Table</th>\n",
       "      <th>Number_of_columns</th>\n",
       "      <th>Number_of_rows</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_21.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>18944</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_20.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>18483</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_20.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>18278</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_19.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>18085</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_18.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>17954</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA19_official_data.csv</td>\n",
       "      <td>64</td>\n",
       "      <td>17943</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA18_official_data.csv</td>\n",
       "      <td>64</td>\n",
       "      <td>17927</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_19.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>17770</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_17.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>17597</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_18.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>17592</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA17_official_data.csv</td>\n",
       "      <td>63</td>\n",
       "      <td>17560</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA21_official_data.csv</td>\n",
       "      <td>65</td>\n",
       "      <td>17108</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA20_official_data.csv</td>\n",
       "      <td>65</td>\n",
       "      <td>17104</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_17.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>17009</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA22_official_data.csv</td>\n",
       "      <td>65</td>\n",
       "      <td>16710</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_15.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>16155</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_16.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>15623</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_15.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>15465</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_16.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>14881</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_draft.csv</td>\n",
       "      <td>11</td>\n",
       "      <td>9003</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_awards_players.csv</td>\n",
       "      <td>6</td>\n",
       "      <td>1719</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_player_allstar.csv</td>\n",
       "      <td>23</td>\n",
       "      <td>1609</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Dataset  \\\n",
       "0                   FIFA 21 complete player dataset   \n",
       "1                   FIFA 21 complete player dataset   \n",
       "2                   FIFA 20 complete player dataset   \n",
       "3                   FIFA 21 complete player dataset   \n",
       "4                   FIFA 21 complete player dataset   \n",
       "5                           FIFA22 OFFICIAL DATASET   \n",
       "6                           FIFA22 OFFICIAL DATASET   \n",
       "7                   FIFA 20 complete player dataset   \n",
       "8                   FIFA 21 complete player dataset   \n",
       "9                   FIFA 20 complete player dataset   \n",
       "10                          FIFA22 OFFICIAL DATASET   \n",
       "11                          FIFA22 OFFICIAL DATASET   \n",
       "12                          FIFA22 OFFICIAL DATASET   \n",
       "13                  FIFA 20 complete player dataset   \n",
       "14                          FIFA22 OFFICIAL DATASET   \n",
       "15                  FIFA 21 complete player dataset   \n",
       "16                  FIFA 21 complete player dataset   \n",
       "17                  FIFA 20 complete player dataset   \n",
       "18                  FIFA 20 complete player dataset   \n",
       "19  open-source-sports.mens-professional-basketball   \n",
       "20  open-source-sports.mens-professional-basketball   \n",
       "21  open-source-sports.mens-professional-basketball   \n",
       "\n",
       "                            Table  Number_of_columns  Number_of_rows  \\\n",
       "0                  players_21.csv                106           18944   \n",
       "1                  players_20.csv                106           18483   \n",
       "2                  players_20.csv                104           18278   \n",
       "3                  players_19.csv                106           18085   \n",
       "4                  players_18.csv                106           17954   \n",
       "5        FIFA19_official_data.csv                 64           17943   \n",
       "6        FIFA18_official_data.csv                 64           17927   \n",
       "7                  players_19.csv                104           17770   \n",
       "8                  players_17.csv                106           17597   \n",
       "9                  players_18.csv                104           17592   \n",
       "10       FIFA17_official_data.csv                 63           17560   \n",
       "11       FIFA21_official_data.csv                 65           17108   \n",
       "12       FIFA20_official_data.csv                 65           17104   \n",
       "13                 players_17.csv                104           17009   \n",
       "14       FIFA22_official_data.csv                 65           16710   \n",
       "15                 players_15.csv                106           16155   \n",
       "16                 players_16.csv                106           15623   \n",
       "17                 players_15.csv                104           15465   \n",
       "18                 players_16.csv                104           14881   \n",
       "19           basketball_draft.csv                 11            9003   \n",
       "20  basketball_awards_players.csv                  6            1719   \n",
       "21  basketball_player_allstar.csv                 23            1609   \n",
       "\n",
       "                                        Path_to_table  \n",
       "0   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "1   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "2   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "3   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "4   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "5   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "6   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "7   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "8   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "9   /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "10  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "11  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "12  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "13  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "14  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "15  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "16  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "17  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "18  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "19  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "20  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "21  /data/mossad/kglids/storage/data_sources/kaggl...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = kglids.search_tables_on(conditions=[['player', 'club']])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    " <b>5. Recommending unionable tables</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing the top-5 unionable table recommendations:\n"
     ]
    },
    {
     "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>Recommended_table</th>\n",
       "      <th>Score</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_player_allstar.csv</td>\n",
       "      <td>1.00</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_awards_players.csv</td>\n",
       "      <td>0.92</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_teams.csv</td>\n",
       "      <td>0.92</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_coaches.csv</td>\n",
       "      <td>0.92</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_master.csv</td>\n",
       "      <td>0.67</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Dataset  \\\n",
       "0  open-source-sports.mens-professional-basketball   \n",
       "1  open-source-sports.mens-professional-basketball   \n",
       "2  open-source-sports.mens-professional-basketball   \n",
       "3  open-source-sports.mens-professional-basketball   \n",
       "4  open-source-sports.mens-professional-basketball   \n",
       "\n",
       "               Recommended_table  Score  \\\n",
       "0  basketball_player_allstar.csv   1.00   \n",
       "1  basketball_awards_players.csv   0.92   \n",
       "2           basketball_teams.csv   0.92   \n",
       "3         basketball_coaches.csv   0.92   \n",
       "4          basketball_master.csv   0.67   \n",
       "\n",
       "                                       Path_to_table  \n",
       "0  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "1  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "2  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "3  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "4  /data/mossad/kglids/storage/data_sources/kaggl...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_union = kglids.recommend_k_unionable_tables(df.iloc[19], k = 5)\n",
    "recommendations_union"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>6. Recommending joinable tables</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing the top-5 joinable table recommendations:\n"
     ]
    },
    {
     "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>Recommended_table</th>\n",
       "      <th>Score</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>proselotis.financial-ipo-data</td>\n",
       "      <td>IPODataFull.csv</td>\n",
       "      <td>1.00</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rajanand.key-indicators-of-annual-health-survey</td>\n",
       "      <td>Key_indicator_statewise.csv</td>\n",
       "      <td>0.13</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_teams.csv</td>\n",
       "      <td>0.08</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_series_post.csv</td>\n",
       "      <td>0.08</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mdabbert.ultimate-ufc-dataset</td>\n",
       "      <td>upcoming-event.csv</td>\n",
       "      <td>0.07</td>\n",
       "      <td>/data/mossad/kglids/storage/data_sources/kaggl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Dataset  \\\n",
       "0                    proselotis.financial-ipo-data   \n",
       "1  rajanand.key-indicators-of-annual-health-survey   \n",
       "2  open-source-sports.mens-professional-basketball   \n",
       "3  open-source-sports.mens-professional-basketball   \n",
       "4                    mdabbert.ultimate-ufc-dataset   \n",
       "\n",
       "             Recommended_table  Score  \\\n",
       "0              IPODataFull.csv   1.00   \n",
       "1  Key_indicator_statewise.csv   0.13   \n",
       "2         basketball_teams.csv   0.08   \n",
       "3   basketball_series_post.csv   0.08   \n",
       "4           upcoming-event.csv   0.07   \n",
       "\n",
       "                                       Path_to_table  \n",
       "0  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "1  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "2  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "3  /data/mossad/kglids/storage/data_sources/kaggl...  \n",
       "4  /data/mossad/kglids/storage/data_sources/kaggl...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_join = kglids.recommend_k_joinable_tables(df.iloc[19], k = 5)\n",
    "recommendations_join"
   ]
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   "source": [
    "<b>7. Visualizing the path between two joinable tables</b>"
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   "source": [
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  {
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   "execution_count": 11,
   "metadata": {
    "pycharm": {
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   "outputs": [],
   "source": [
    "# df1 = pd.read_csv(df.iloc[19].Path_to_table)\n",
    "# df1"
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  {
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   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# df2 = pd.read_csv(recommendations_join.iloc[2].Path_to_table)\n",
    "# df2"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# result_join = df1.merge(df2, left_on='tmID', right_on='tmID')\n",
    "# result_join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>8. Search pipelines</b> "
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Author</th>\n",
       "      <th>Written_on</th>\n",
       "      <th>Number_of_votes</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>How Models Work</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2023-04-21 13:39:23</td>\n",
       "      <td>5315</td>\n",
       "      <td>0.877727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Your First Machine Learning Model</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2023-04-21 13:38:55</td>\n",
       "      <td>2932</td>\n",
       "      <td>0.866003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Model Validation</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2023-04-21 13:38:50</td>\n",
       "      <td>2662</td>\n",
       "      <td>0.831276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Machine Learning Tutorial for Beginners</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "      <td>DATAI</td>\n",
       "      <td>2018-07-24 14:43:45</td>\n",
       "      <td>2532</td>\n",
       "      <td>0.754959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Underfitting and Overfitting</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2023-04-21 13:38:58</td>\n",
       "      <td>2503</td>\n",
       "      <td>0.604260</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>From ZZZs to Insights</td>\n",
       "      <td>mlomuscio.sleepstudypilot</td>\n",
       "      <td>Francis S. Domingo</td>\n",
       "      <td>2023-12-02 04:07:49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.524537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>Starter: SleepStudyPilot 10e5fcfb-e</td>\n",
       "      <td>mlomuscio.sleepstudypilot</td>\n",
       "      <td>Kaggle Kerneler</td>\n",
       "      <td>2019-09-05 18:57:59</td>\n",
       "      <td>0</td>\n",
       "      <td>0.661398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>JL COVID EDA PROJECT 1</td>\n",
       "      <td>anandhuh.covid19-india-statewise-vaccine-data</td>\n",
       "      <td>Yash Khandagale</td>\n",
       "      <td>2023-07-21 20:34:18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.548864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>Cleaning</td>\n",
       "      <td>supergus.multistage-continuousflow-manufacturi...</td>\n",
       "      <td>Pedro Yves Fracari</td>\n",
       "      <td>2021-06-03 03:27:32</td>\n",
       "      <td>0</td>\n",
       "      <td>0.610697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>Spotify Chart Trend + Seasonal ARIMA</td>\n",
       "      <td>nadintamer.top-tracks-of-2017</td>\n",
       "      <td>yasemin dogan</td>\n",
       "      <td>2018-12-03 04:24:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.721093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1064 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Pipeline  \\\n",
       "0                             How Models Work   \n",
       "1           Your First Machine Learning Model   \n",
       "2                            Model Validation   \n",
       "3     Machine Learning Tutorial for Beginners   \n",
       "4                Underfitting and Overfitting   \n",
       "...                                       ...   \n",
       "1059                    From ZZZs to Insights   \n",
       "1060      Starter: SleepStudyPilot 10e5fcfb-e   \n",
       "1061                   JL COVID EDA PROJECT 1   \n",
       "1062                                 Cleaning   \n",
       "1063     Spotify Chart Trend + Seasonal ARIMA   \n",
       "\n",
       "                                                Dataset              Author  \\\n",
       "0         iabhishekofficial.mobile-price-classification                DanB   \n",
       "1         iabhishekofficial.mobile-price-classification                DanB   \n",
       "2         iabhishekofficial.mobile-price-classification                DanB   \n",
       "3     uciml.biomechanical-features-of-orthopedic-pat...               DATAI   \n",
       "4         iabhishekofficial.mobile-price-classification                DanB   \n",
       "...                                                 ...                 ...   \n",
       "1059                          mlomuscio.sleepstudypilot  Francis S. Domingo   \n",
       "1060                          mlomuscio.sleepstudypilot     Kaggle Kerneler   \n",
       "1061      anandhuh.covid19-india-statewise-vaccine-data     Yash Khandagale   \n",
       "1062  supergus.multistage-continuousflow-manufacturi...  Pedro Yves Fracari   \n",
       "1063                      nadintamer.top-tracks-of-2017       yasemin dogan   \n",
       "\n",
       "               Written_on  Number_of_votes     Score  \n",
       "0     2023-04-21 13:39:23             5315  0.877727  \n",
       "1     2023-04-21 13:38:55             2932  0.866003  \n",
       "2     2023-04-21 13:38:50             2662  0.831276  \n",
       "3     2018-07-24 14:43:45             2532  0.754959  \n",
       "4     2023-04-21 13:38:58             2503  0.604260  \n",
       "...                   ...              ...       ...  \n",
       "1059  2023-12-02 04:07:49                0  0.524537  \n",
       "1060  2019-09-05 18:57:59                0  0.661398  \n",
       "1061  2023-07-21 20:34:18                0  0.548864  \n",
       "1062  2021-06-03 03:27:32                0  0.610697  \n",
       "1063  2018-12-03 04:24:00                0  0.721093  \n",
       "\n",
       "[1064 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_pipelines_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>How Models Work</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2023-04-21 13:39:23</td>\n",
       "      <td>5315</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Your First Machine Learning Model</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
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       "      <td>2932</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Model Validation</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2023-04-21 13:38:50</td>\n",
       "      <td>2662</td>\n",
       "      <td>0.831276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Underfitting and Overfitting</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2023-04-21 13:38:58</td>\n",
       "      <td>2503</td>\n",
       "      <td>0.604260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forests</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2023-04-21 13:39:22</td>\n",
       "      <td>1723</td>\n",
       "      <td>0.641919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Basic Data Exploration</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2023-04-21 13:38:55</td>\n",
       "      <td>1555</td>\n",
       "      <td>0.567306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Explore Your Data</td>\n",
       "      <td>dansbecker.home-data-for-ml-course</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2019-01-23 01:07:33</td>\n",
       "      <td>1337</td>\n",
       "      <td>0.899627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Basic Data Exploration Daily</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2019-08-24 02:41:50</td>\n",
       "      <td>95</td>\n",
       "      <td>0.811601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Exercise: Explore Your Data</td>\n",
       "      <td>dansbecker.home-data-for-ml-course</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2019-01-23 01:07:42</td>\n",
       "      <td>21</td>\n",
       "      <td>0.749186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Pipeline  \\\n",
       "0                    How Models Work   \n",
       "1  Your First Machine Learning Model   \n",
       "2                   Model Validation   \n",
       "3       Underfitting and Overfitting   \n",
       "4                     Random Forests   \n",
       "5             Basic Data Exploration   \n",
       "6                  Explore Your Data   \n",
       "7       Basic Data Exploration Daily   \n",
       "8        Exercise: Explore Your Data   \n",
       "\n",
       "                                         Dataset Author           Written_on  \\\n",
       "0  iabhishekofficial.mobile-price-classification   DanB  2023-04-21 13:39:23   \n",
       "1  iabhishekofficial.mobile-price-classification   DanB  2023-04-21 13:38:55   \n",
       "2  iabhishekofficial.mobile-price-classification   DanB  2023-04-21 13:38:50   \n",
       "3  iabhishekofficial.mobile-price-classification   DanB  2023-04-21 13:38:58   \n",
       "4  iabhishekofficial.mobile-price-classification   DanB  2023-04-21 13:39:22   \n",
       "5  iabhishekofficial.mobile-price-classification   DanB  2023-04-21 13:38:55   \n",
       "6             dansbecker.home-data-for-ml-course   DanB  2019-01-23 01:07:33   \n",
       "7  iabhishekofficial.mobile-price-classification   DanB  2019-08-24 02:41:50   \n",
       "8             dansbecker.home-data-for-ml-course   DanB  2019-01-23 01:07:42   \n",
       "\n",
       "   Number_of_votes     Score  \n",
       "0             5315  0.877727  \n",
       "1             2932  0.866003  \n",
       "2             2662  0.831276  \n",
       "3             2503  0.604260  \n",
       "4             1723  0.641919  \n",
       "5             1555  0.567306  \n",
       "6             1337  0.899627  \n",
       "7               95  0.811601  \n",
       "8               21  0.749186  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_pipelines_info(author='DanB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>9. Most recent pipeline</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pharma Sales Exploratory Data Analysis 💊</td>\n",
       "      <td>milanzdravkovic.pharma-sales-data</td>\n",
       "      <td>Aila Felix</td>\n",
       "      <td>2023-12-10 21:02:03</td>\n",
       "      <td>9</td>\n",
       "      <td>0.804345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Pipeline  \\\n",
       "0  Pharma Sales Exploratory Data Analysis 💊    \n",
       "\n",
       "                             Dataset      Author           Written_on  \\\n",
       "0  milanzdravkovic.pharma-sales-data  Aila Felix  2023-12-10 21:02:03   \n",
       "\n",
       "   Number_of_votes     Score  \n",
       "0                9  0.804345  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_most_recent_pipeline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hepatitis Prediction MKR</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Igor Gutsu</td>\n",
       "      <td>2023-12-08 17:09:35</td>\n",
       "      <td>2</td>\n",
       "      <td>0.66338</td>\n",
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      ],
      "text/plain": [
       "                   Pipeline                          Dataset      Author  \\\n",
       "0  Hepatitis Prediction MKR  fedesoriano.hepatitis-c-dataset  Igor Gutsu   \n",
       "\n",
       "            Written_on  Number_of_votes    Score  \n",
       "0  2023-12-08 17:09:35                2  0.66338  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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    "kglids.get_most_recent_pipeline(dataset='fedesoriano.hepatitis-c-dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>10.<i> Top-k</i> scoring pipelines</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Most powerful Superheroes &amp; Best Comic ?</td>\n",
       "      <td>claudiodavi.superhero-set</td>\n",
       "      <td>Debadri Dutta</td>\n",
       "      <td>2018-10-10 07:55:48</td>\n",
       "      <td>57</td>\n",
       "      <td>0.999687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Red Wine 🍷 Quality Assesment 📊 | Starter Pack</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>Aditi Mulye</td>\n",
       "      <td>2021-11-15 00:02:22</td>\n",
       "      <td>65</td>\n",
       "      <td>0.998915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Storesalesforecast_3 diff models</td>\n",
       "      <td>rohitsahoo.sales-forecasting</td>\n",
       "      <td>Alkan Erturan</td>\n",
       "      <td>2023-10-24 16:15:59</td>\n",
       "      <td>13</td>\n",
       "      <td>0.998100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Who said this line[EDA/Classification/Keras/ANN]</td>\n",
       "      <td>thec03u5.seinfeld-chronicles</td>\n",
       "      <td>Nafisur Rahman</td>\n",
       "      <td>2018-07-27 04:35:13</td>\n",
       "      <td>4</td>\n",
       "      <td>0.997422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Space Missions Simple EDA and Visualizations</td>\n",
       "      <td>agirlcoding.all-space-missions-from-1957</td>\n",
       "      <td>Rupesh Deshmukh</td>\n",
       "      <td>2020-09-13 05:46:06</td>\n",
       "      <td>27</td>\n",
       "      <td>0.997159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Pipeline  \\\n",
       "0          Most powerful Superheroes & Best Comic ?   \n",
       "1     Red Wine 🍷 Quality Assesment 📊 | Starter Pack   \n",
       "2                  Storesalesforecast_3 diff models   \n",
       "3  Who said this line[EDA/Classification/Keras/ANN]   \n",
       "4      Space Missions Simple EDA and Visualizations   \n",
       "\n",
       "                                    Dataset           Author  \\\n",
       "0                 claudiodavi.superhero-set    Debadri Dutta   \n",
       "1  uciml.red-wine-quality-cortez-et-al-2009      Aditi Mulye   \n",
       "2              rohitsahoo.sales-forecasting    Alkan Erturan   \n",
       "3              thec03u5.seinfeld-chronicles   Nafisur Rahman   \n",
       "4  agirlcoding.all-space-missions-from-1957  Rupesh Deshmukh   \n",
       "\n",
       "            Written_on  Number_of_votes     Score  \n",
       "0  2018-10-10 07:55:48               57  0.999687  \n",
       "1  2021-11-15 00:02:22               65  0.998915  \n",
       "2  2023-10-24 16:15:59               13  0.998100  \n",
       "3  2018-07-27 04:35:13                4  0.997422  \n",
       "4  2020-09-13 05:46:06               27  0.997159  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_k_scoring_pipelines_for_dataset(k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Author</th>\n",
       "      <th>Written_on</th>\n",
       "      <th>Number_of_votes</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HepC-ML-Submission</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Fritz Stevenson</td>\n",
       "      <td>2021-05-21 05:19:50</td>\n",
       "      <td>5</td>\n",
       "      <td>0.958015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hepatitis-c-prediction</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Mahmoud Elhusseni</td>\n",
       "      <td>2022-10-12 21:46:47</td>\n",
       "      <td>6</td>\n",
       "      <td>0.932045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Liver Disease Analysis: EDA➡️SMOTE➡️OPTUNA➡️SHAP</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Caleb Reigada</td>\n",
       "      <td>2022-02-14 12:51:35</td>\n",
       "      <td>77</td>\n",
       "      <td>0.918229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hepatitis C Predictions 🩺</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Krishna Bhatt</td>\n",
       "      <td>2021-09-25 11:36:06</td>\n",
       "      <td>30</td>\n",
       "      <td>0.892317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Klasifikasi Penyakit Hati (Liver)</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Lutfi Andriyanto</td>\n",
       "      <td>2022-09-14 07:20:19</td>\n",
       "      <td>9</td>\n",
       "      <td>0.855545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Pipeline  \\\n",
       "0                                HepC-ML-Submission   \n",
       "1                            hepatitis-c-prediction   \n",
       "2  Liver Disease Analysis: EDA➡️SMOTE➡️OPTUNA➡️SHAP   \n",
       "3                         Hepatitis C Predictions 🩺   \n",
       "4                 Klasifikasi Penyakit Hati (Liver)   \n",
       "\n",
       "                           Dataset             Author           Written_on  \\\n",
       "0  fedesoriano.hepatitis-c-dataset    Fritz Stevenson  2021-05-21 05:19:50   \n",
       "1  fedesoriano.hepatitis-c-dataset  Mahmoud Elhusseni  2022-10-12 21:46:47   \n",
       "2  fedesoriano.hepatitis-c-dataset      Caleb Reigada  2022-02-14 12:51:35   \n",
       "3  fedesoriano.hepatitis-c-dataset      Krishna Bhatt  2021-09-25 11:36:06   \n",
       "4  fedesoriano.hepatitis-c-dataset   Lutfi Andriyanto  2022-09-14 07:20:19   \n",
       "\n",
       "   Number_of_votes     Score  \n",
       "0                5  0.958015  \n",
       "1                6  0.932045  \n",
       "2               77  0.918229  \n",
       "3               30  0.892317  \n",
       "4                9  0.855545  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_k_scoring_pipelines_for_dataset(dataset='fedesoriano.hepatitis-c-dataset', k=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>11. Search classifiers used in a dataset</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>hepatitis-c-prediction</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.932045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>hepatitis-c-prediction</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.932045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Liver Disease Analysis: EDA➡️SMOTE➡️OPTUNA➡️SHAP</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.918229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Liver Disease Analysis: EDA➡️SMOTE➡️OPTUNA➡️SHAP</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.918229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Liver Disease Analysis: EDA➡️SMOTE➡️OPTUNA➡️SHAP</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.918229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predictions 🩺</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.892317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predictions 🩺</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.892317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predictions 🩺</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.892317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Easy Hepatitis C Prediction ACC=98%!!!</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.842262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Detection</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.829828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Detection</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.829828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>EDA and hepatitis C prediction using RFs</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.827178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>95% Accuracy XGB|LR|RF|DT</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.822377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>95% Accuracy XGB|LR|RF|DT</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.822377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C LR|KNN|RF with best accuracy of 96%</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.802531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C LR|KNN|RF with best accuracy of 96%</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.802531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C different models 94.3%</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.758405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C different models 94.3%</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.758405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C different models 94.3%</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.758405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Detection</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.749930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Detection</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.749930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis Prediction MKR</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.663380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis Prediction MKR</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.663380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis Prediction MKR</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.663380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis Prediction MKR</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.663380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Prediction: Achieving 94% Accuracy💉💻</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.594484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Prediction: Achieving 94% Accuracy💉💻</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.594484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Prediction: Achieving 94% Accuracy💉💻</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.594484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Prediction: Achieving 94% Accuracy💉💻</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.594484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predict</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.593907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predict</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.593907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predict</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.593907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predict</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.593907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>PDS Project - Hepatitis C Prediction (complete)</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.546918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>PDS Project - Hepatitis C Prediction (complete)</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.546918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Dataset  \\\n",
       "0   fedesoriano.hepatitis-c-dataset   \n",
       "1   fedesoriano.hepatitis-c-dataset   \n",
       "2   fedesoriano.hepatitis-c-dataset   \n",
       "3   fedesoriano.hepatitis-c-dataset   \n",
       "4   fedesoriano.hepatitis-c-dataset   \n",
       "5   fedesoriano.hepatitis-c-dataset   \n",
       "6   fedesoriano.hepatitis-c-dataset   \n",
       "7   fedesoriano.hepatitis-c-dataset   \n",
       "8   fedesoriano.hepatitis-c-dataset   \n",
       "9   fedesoriano.hepatitis-c-dataset   \n",
       "10  fedesoriano.hepatitis-c-dataset   \n",
       "11  fedesoriano.hepatitis-c-dataset   \n",
       "12  fedesoriano.hepatitis-c-dataset   \n",
       "13  fedesoriano.hepatitis-c-dataset   \n",
       "14  fedesoriano.hepatitis-c-dataset   \n",
       "15  fedesoriano.hepatitis-c-dataset   \n",
       "16  fedesoriano.hepatitis-c-dataset   \n",
       "17  fedesoriano.hepatitis-c-dataset   \n",
       "18  fedesoriano.hepatitis-c-dataset   \n",
       "19  fedesoriano.hepatitis-c-dataset   \n",
       "20  fedesoriano.hepatitis-c-dataset   \n",
       "21  fedesoriano.hepatitis-c-dataset   \n",
       "22  fedesoriano.hepatitis-c-dataset   \n",
       "23  fedesoriano.hepatitis-c-dataset   \n",
       "24  fedesoriano.hepatitis-c-dataset   \n",
       "25  fedesoriano.hepatitis-c-dataset   \n",
       "26  fedesoriano.hepatitis-c-dataset   \n",
       "27  fedesoriano.hepatitis-c-dataset   \n",
       "28  fedesoriano.hepatitis-c-dataset   \n",
       "29  fedesoriano.hepatitis-c-dataset   \n",
       "30  fedesoriano.hepatitis-c-dataset   \n",
       "31  fedesoriano.hepatitis-c-dataset   \n",
       "32  fedesoriano.hepatitis-c-dataset   \n",
       "33  fedesoriano.hepatitis-c-dataset   \n",
       "34  fedesoriano.hepatitis-c-dataset   \n",
       "\n",
       "                                            Pipeline  \\\n",
       "0                             hepatitis-c-prediction   \n",
       "1                             hepatitis-c-prediction   \n",
       "2   Liver Disease Analysis: EDA➡️SMOTE➡️OPTUNA➡️SHAP   \n",
       "3   Liver Disease Analysis: EDA➡️SMOTE➡️OPTUNA➡️SHAP   \n",
       "4   Liver Disease Analysis: EDA➡️SMOTE➡️OPTUNA➡️SHAP   \n",
       "5                          Hepatitis C Predictions 🩺   \n",
       "6                          Hepatitis C Predictions 🩺   \n",
       "7                          Hepatitis C Predictions 🩺   \n",
       "8             Easy Hepatitis C Prediction ACC=98%!!!   \n",
       "9                              Hepatitis C Detection   \n",
       "10                             Hepatitis C Detection   \n",
       "11          EDA and hepatitis C prediction using RFs   \n",
       "12                         95% Accuracy XGB|LR|RF|DT   \n",
       "13                         95% Accuracy XGB|LR|RF|DT   \n",
       "14   Hepatitis C LR|KNN|RF with best accuracy of 96%   \n",
       "15   Hepatitis C LR|KNN|RF with best accuracy of 96%   \n",
       "16                Hepatitis C different models 94.3%   \n",
       "17                Hepatitis C different models 94.3%   \n",
       "18                Hepatitis C different models 94.3%   \n",
       "19                             Hepatitis C Detection   \n",
       "20                             Hepatitis C Detection   \n",
       "21                          Hepatitis Prediction MKR   \n",
       "22                          Hepatitis Prediction MKR   \n",
       "23                          Hepatitis Prediction MKR   \n",
       "24                          Hepatitis Prediction MKR   \n",
       "25  Hepatitis C Prediction: Achieving 94% Accuracy💉💻   \n",
       "26  Hepatitis C Prediction: Achieving 94% Accuracy💉💻   \n",
       "27  Hepatitis C Prediction: Achieving 94% Accuracy💉💻   \n",
       "28  Hepatitis C Prediction: Achieving 94% Accuracy💉💻   \n",
       "29                               Hepatitis C Predict   \n",
       "30                               Hepatitis C Predict   \n",
       "31                               Hepatitis C Predict   \n",
       "32                               Hepatitis C Predict   \n",
       "33   PDS Project - Hepatitis C Prediction (complete)   \n",
       "34   PDS Project - Hepatitis C Prediction (complete)   \n",
       "\n",
       "                    Classifier     Score  \n",
       "0       RandomForestClassifier  0.932045  \n",
       "1           LogisticRegression  0.932045  \n",
       "2       RandomForestClassifier  0.918229  \n",
       "3                          SVC  0.918229  \n",
       "4           LogisticRegression  0.918229  \n",
       "5       RandomForestClassifier  0.892317  \n",
       "6   GradientBoostingClassifier  0.892317  \n",
       "7           LogisticRegression  0.892317  \n",
       "8       RandomForestClassifier  0.842262  \n",
       "9       RandomForestClassifier  0.829828  \n",
       "10                         SVC  0.829828  \n",
       "11      RandomForestClassifier  0.827178  \n",
       "12      RandomForestClassifier  0.822377  \n",
       "13          LogisticRegression  0.822377  \n",
       "14      RandomForestClassifier  0.802531  \n",
       "15          LogisticRegression  0.802531  \n",
       "16      RandomForestClassifier  0.758405  \n",
       "17                         SVC  0.758405  \n",
       "18          LogisticRegression  0.758405  \n",
       "19      RandomForestClassifier  0.749930  \n",
       "20                         SVC  0.749930  \n",
       "21      RandomForestClassifier  0.663380  \n",
       "22                         SVC  0.663380  \n",
       "23  GradientBoostingClassifier  0.663380  \n",
       "24          LogisticRegression  0.663380  \n",
       "25      RandomForestClassifier  0.594484  \n",
       "26                         SVC  0.594484  \n",
       "27  GradientBoostingClassifier  0.594484  \n",
       "28          LogisticRegression  0.594484  \n",
       "29      RandomForestClassifier  0.593907  \n",
       "30                         SVC  0.593907  \n",
       "31  GradientBoostingClassifier  0.593907  \n",
       "32          LogisticRegression  0.593907  \n",
       "33      RandomForestClassifier  0.546918  \n",
       "34  GradientBoostingClassifier  0.546918  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_info = kglids.search_classifier(dataset='fedesoriano.hepatitis-c-dataset')\n",
    "classifier_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>12. Fetch hyper-parameters </b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>RandomForestClassifier_hyperparameter</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_state</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n_estimators</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RandomForestClassifier_hyperparameter  Value\n",
       "0                          random_state     42\n",
       "1                          n_estimators    100"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_hyperparameters(classifier_info.iloc[8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "###### <b>13. Visualize library utilization</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.get_top_k_library_used(k=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.get_top_k_library_used(dataset='fedesoriano.hepatitis-c-dataset', k=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>14. Library discovery</b><br> \n",
    "tasks supported: classification, regression, visualization and clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single library was found for classification: \n",
      "sklearn\n"
     ]
    },
    {
     "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>Library</th>\n",
       "      <th>Module</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>Classification of Patients EDA</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>Gender Classification-II</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>Gender Classification : 95% accuracy</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>gender_classification</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>gender_classification</td>\n",
       "      <td>hb20007.gender-classification</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>136</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>Stellar Classification Dataset - SDSS17</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>Stellar Classification - SDSS17 (4 ML Models)</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>Twitter Sentiment Analysis: NLP, SMOTE</td>\n",
       "      <td>arkhoshghalb.twitter-sentiment-analysis-hatred...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>Stellar Classification - 98.4% Acc 100% AUC</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>Predict the winner - Data Visualization and Model</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Library                      Module  \\\n",
       "0    sklearn      RandomForestClassifier   \n",
       "1    sklearn      RandomForestClassifier   \n",
       "2    sklearn      RandomForestClassifier   \n",
       "3    sklearn  GradientBoostingClassifier   \n",
       "4    sklearn      RandomForestClassifier   \n",
       "..       ...                         ...   \n",
       "136  sklearn  GradientBoostingClassifier   \n",
       "137  sklearn      RandomForestClassifier   \n",
       "138  sklearn      RandomForestClassifier   \n",
       "139  sklearn      RandomForestClassifier   \n",
       "140  sklearn      RandomForestClassifier   \n",
       "\n",
       "                                              Pipeline  \\\n",
       "0                       Classification of Patients EDA   \n",
       "1                             Gender Classification-II   \n",
       "2                 Gender Classification : 95% accuracy   \n",
       "3                                gender_classification   \n",
       "4                                gender_classification   \n",
       "..                                                 ...   \n",
       "136            Stellar Classification Dataset - SDSS17   \n",
       "137      Stellar Classification - SDSS17 (4 ML Models)   \n",
       "138             Twitter Sentiment Analysis: NLP, SMOTE   \n",
       "139        Stellar Classification - 98.4% Acc 100% AUC   \n",
       "140  Predict the winner - Data Visualization and Model   \n",
       "\n",
       "                                               Dataset  \n",
       "0    uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "1                        hb20007.gender-classification  \n",
       "2                        hb20007.gender-classification  \n",
       "3                        hb20007.gender-classification  \n",
       "4                        hb20007.gender-classification  \n",
       "..                                                 ...  \n",
       "136  fedesoriano.stellar-classification-dataset-sdss17  \n",
       "137  fedesoriano.stellar-classification-dataset-sdss17  \n",
       "138  arkhoshghalb.twitter-sentiment-analysis-hatred...  \n",
       "139  fedesoriano.stellar-classification-dataset-sdss17  \n",
       "140                                    gdaley.hkracing  \n",
       "\n",
       "[141 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_used_libraries(k = 5, task = 'classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single library was found for regression: \n",
      "sklearn\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Library</th>\n",
       "      <th>Module</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>Classification of Patients EDA</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>gender_classification</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>Logistic Regression and Predicting Genders</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>Gender Classification - LR, DT, RF, SVM and KNN</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>Human Resources : who is going to quit ?</td>\n",
       "      <td>rhuebner.human-resources-data-set</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>72</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>Stellar Classification | Comparing 6 algorithms</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>Red Wine Quality Classification Analysis + EDA</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>Advanced Analysis with Resampling 🍷🍇🍀</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>Twitter Sentiment Analysis</td>\n",
       "      <td>arkhoshghalb.twitter-sentiment-analysis-hatred...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>Predict the winner - Data Visualization and Model</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Library              Module  \\\n",
       "0   sklearn  LogisticRegression   \n",
       "1   sklearn  LogisticRegression   \n",
       "2   sklearn  LogisticRegression   \n",
       "3   sklearn  LogisticRegression   \n",
       "4   sklearn  LogisticRegression   \n",
       "..      ...                 ...   \n",
       "72  sklearn  LogisticRegression   \n",
       "73  sklearn  LogisticRegression   \n",
       "74  sklearn  LogisticRegression   \n",
       "75  sklearn  LogisticRegression   \n",
       "76  sklearn  LogisticRegression   \n",
       "\n",
       "                                             Pipeline  \\\n",
       "0                      Classification of Patients EDA   \n",
       "1                               gender_classification   \n",
       "2         Logistic Regression and Predicting Genders    \n",
       "3     Gender Classification - LR, DT, RF, SVM and KNN   \n",
       "4            Human Resources : who is going to quit ?   \n",
       "..                                                ...   \n",
       "72    Stellar Classification | Comparing 6 algorithms   \n",
       "73     Red Wine Quality Classification Analysis + EDA   \n",
       "74              Advanced Analysis with Resampling 🍷🍇🍀   \n",
       "75                         Twitter Sentiment Analysis   \n",
       "76  Predict the winner - Data Visualization and Model   \n",
       "\n",
       "                                              Dataset  \n",
       "0   uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "1                       hb20007.gender-classification  \n",
       "2                       hb20007.gender-classification  \n",
       "3                       hb20007.gender-classification  \n",
       "4                   rhuebner.human-resources-data-set  \n",
       "..                                                ...  \n",
       "72  fedesoriano.stellar-classification-dataset-sdss17  \n",
       "73           uciml.red-wine-quality-cortez-et-al-2009  \n",
       "74           uciml.red-wine-quality-cortez-et-al-2009  \n",
       "75  arkhoshghalb.twitter-sentiment-analysis-hatred...  \n",
       "76                                    gdaley.hkracing  \n",
       "\n",
       "[77 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_used_libraries(k = 5, task = 'regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum 2 libraries were found for visualization: \n",
      "matplotlib, seaborn\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Library</th>\n",
       "      <th>Module</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>matplotlib</td>\n",
       "      <td>pyplot</td>\n",
       "      <td>What's this? Chai and DataScience?</td>\n",
       "      <td>rohanrao.chai-time-data-science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>matplotlib</td>\n",
       "      <td>pyplot</td>\n",
       "      <td>Food demand full EDA</td>\n",
       "      <td>kannanaikkal.food-demand-forecasting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>matplotlib</td>\n",
       "      <td>pyplot</td>\n",
       "      <td>Food Demand Forecasting</td>\n",
       "      <td>kannanaikkal.food-demand-forecasting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>matplotlib</td>\n",
       "      <td>pyplot</td>\n",
       "      <td>food demand prediction</td>\n",
       "      <td>kannanaikkal.food-demand-forecasting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>matplotlib</td>\n",
       "      <td>pyplot</td>\n",
       "      <td>Food-demand-forecasting 🧁🍟🍱🍺🍨</td>\n",
       "      <td>kannanaikkal.food-demand-forecasting</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>602</th>\n",
       "      <td>seaborn</td>\n",
       "      <td>seaborn</td>\n",
       "      <td>Finding dependent attributes using only EDA</td>\n",
       "      <td>becksddf.churn-in-telecoms-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>seaborn</td>\n",
       "      <td>seaborn</td>\n",
       "      <td>Lab 1. EDA. Lazor</td>\n",
       "      <td>becksddf.churn-in-telecoms-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>seaborn</td>\n",
       "      <td>seaborn</td>\n",
       "      <td>Steller-Classification with 97% Accuracy</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>seaborn</td>\n",
       "      <td>seaborn</td>\n",
       "      <td>Predict Covid 19 With EDA And ML In Python</td>\n",
       "      <td>anandhuh.covid19-india-statewise-vaccine-data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>seaborn</td>\n",
       "      <td>seaborn</td>\n",
       "      <td>India Trade</td>\n",
       "      <td>lakshyaag.india-trade-data</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>607 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Library   Module                                     Pipeline  \\\n",
       "0    matplotlib   pyplot           What's this? Chai and DataScience?   \n",
       "1    matplotlib   pyplot                         Food demand full EDA   \n",
       "2    matplotlib   pyplot                      Food Demand Forecasting   \n",
       "3    matplotlib   pyplot                       food demand prediction   \n",
       "4    matplotlib   pyplot                Food-demand-forecasting 🧁🍟🍱🍺🍨   \n",
       "..          ...      ...                                          ...   \n",
       "602     seaborn  seaborn  Finding dependent attributes using only EDA   \n",
       "603     seaborn  seaborn                            Lab 1. EDA. Lazor   \n",
       "604     seaborn  seaborn     Steller-Classification with 97% Accuracy   \n",
       "605     seaborn  seaborn   Predict Covid 19 With EDA And ML In Python   \n",
       "606     seaborn  seaborn                                  India Trade   \n",
       "\n",
       "                                               Dataset  \n",
       "0                      rohanrao.chai-time-data-science  \n",
       "1                 kannanaikkal.food-demand-forecasting  \n",
       "2                 kannanaikkal.food-demand-forecasting  \n",
       "3                 kannanaikkal.food-demand-forecasting  \n",
       "4                 kannanaikkal.food-demand-forecasting  \n",
       "..                                                 ...  \n",
       "602                 becksddf.churn-in-telecoms-dataset  \n",
       "603                 becksddf.churn-in-telecoms-dataset  \n",
       "604  fedesoriano.stellar-classification-dataset-sdss17  \n",
       "605      anandhuh.covid19-india-statewise-vaccine-data  \n",
       "606                         lakshyaag.india-trade-data  \n",
       "\n",
       "[607 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_used_libraries(k = 5, task = 'visualization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No library found for clustering\n"
     ]
    }
   ],
   "source": [
    "kglids.get_top_used_libraries(k = 5, task = 'clustering')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>15. Pipeline Discovery</b> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Author</th>\n",
       "      <th>Score</th>\n",
       "      <th>Number_of_votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stellar Classification and Supervised Learning⭐</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "      <td>Waleed Faheem</td>\n",
       "      <td>0.988913</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Red Wine Quality Classification</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>Nitesh Yadav</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Red Wine Quality | EDA | Models</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>Arsh Anwar</td>\n",
       "      <td>0.978748</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Classification_of_temp_reading_using_LR_and</td>\n",
       "      <td>atulanandjha.temperature-readings-iot-devices</td>\n",
       "      <td>Bhushan Rajput</td>\n",
       "      <td>0.978135</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mushroom Classes: with 100% accuracy</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "      <td>Ahmed Gado</td>\n",
       "      <td>0.963857</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Modeling-FeatureSelection-SMOTE-EDA-XAI-ModelTune</td>\n",
       "      <td>becksddf.churn-in-telecoms-dataset</td>\n",
       "      <td>Ali Galip Sekeroglu</td>\n",
       "      <td>0.960298</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Feature Engineering + Classifying heroes as hu...</td>\n",
       "      <td>claudiodavi.superhero-set</td>\n",
       "      <td>Pere Manresa</td>\n",
       "      <td>0.942909</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>All ML Algos</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "      <td>Izam Mohammed</td>\n",
       "      <td>0.922462</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>💫Stellar Classification : EDA + ML🌳</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "      <td>ANMOL BAJPAI</td>\n",
       "      <td>0.918764</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nueral Network Regression VS Sklearn Algorithms</td>\n",
       "      <td>alexteboul.heart-disease-health-indicators-dat...</td>\n",
       "      <td>Ahmed_Helal</td>\n",
       "      <td>0.893106</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>⭐🌃Stellar Entity Classification</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "      <td>Gerome Andrew</td>\n",
       "      <td>0.886222</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Red Wine Quality Prediction</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>Halime Doğan</td>\n",
       "      <td>0.883319</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FinNews sentiment for retail investors</td>\n",
       "      <td>ankurzing.sentiment-analysis-for-financial-news</td>\n",
       "      <td>Ekaterina Dranitsyna</td>\n",
       "      <td>0.875297</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>📚Predicting Depression from Tweets with 98% Acc🎯</td>\n",
       "      <td>gargmanas.sentimental-analysis-for-tweets</td>\n",
       "      <td>Akul_Malhotra</td>\n",
       "      <td>0.870942</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CARTs and LInear Classification Models &amp; Ensem...</td>\n",
       "      <td>uciml.glass</td>\n",
       "      <td>eric mutisya</td>\n",
       "      <td>0.854147</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>US Hurricane</td>\n",
       "      <td>noaa.hurricane-database</td>\n",
       "      <td>Rand Salem</td>\n",
       "      <td>0.845638</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sentimental Tweets Preprocessing Modeling Tuto...</td>\n",
       "      <td>gargmanas.sentimental-analysis-for-tweets</td>\n",
       "      <td>Abdulrahman Khalid</td>\n",
       "      <td>0.839083</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kirmizi Sarap Kalite Tahmini(Siniflandirmasi)</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>Serkan Peldek</td>\n",
       "      <td>0.833402</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Hepatitis C LR|KNN|RF with best accuracy of 96%</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>AkshayRamakrishnan28</td>\n",
       "      <td>0.802531</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Glass Classification Analysis with EDA</td>\n",
       "      <td>uciml.glass</td>\n",
       "      <td>tolgahancepel</td>\n",
       "      <td>0.799320</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HertDisease_EDA+Prediction</td>\n",
       "      <td>alexteboul.heart-disease-health-indicators-dat...</td>\n",
       "      <td>Farid Rizqi S</td>\n",
       "      <td>0.794994</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Whats in your beer?</td>\n",
       "      <td>nickhould.craft-cans</td>\n",
       "      <td>hemen boro</td>\n",
       "      <td>0.786128</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Basic Machine Learning with Red Wine Quality data</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>Madhuri Sivalenka</td>\n",
       "      <td>0.781366</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Advanced Analysis with Resampling 🍷🍇🍀</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>Ishara Neranjana</td>\n",
       "      <td>0.773672</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Heart Disease Binary Classification</td>\n",
       "      <td>alexteboul.heart-disease-health-indicators-dat...</td>\n",
       "      <td>Mahdi Shirmohammadi</td>\n",
       "      <td>0.766771</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Hepatitis C different models 94.3%</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Abdelrhman_Yasser</td>\n",
       "      <td>0.758405</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Stellar Classification - SDSS17 (4 ML Models)</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "      <td>Wessam Salah Walid</td>\n",
       "      <td>0.756920</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>multiple-model-gender-classification[75 percent]</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "      <td>supasun kumpraphan</td>\n",
       "      <td>0.741190</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Classification for Zoo Animals by Random Forest</td>\n",
       "      <td>agajorte.zoo-animals-extended-dataset</td>\n",
       "      <td>Mohamed Bakrey Mahmoud</td>\n",
       "      <td>0.729831</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Gender Classification - LR, DT, RF, SVM and KNN</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "      <td>Snehal Sanjay Mankar</td>\n",
       "      <td>0.727761</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>irisdataset</td>\n",
       "      <td>prasertk.animal-crossing-new-horizons-with-ima...</td>\n",
       "      <td>IYLI MAITREYI</td>\n",
       "      <td>0.726991</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Predict the winner - Data Visualization and Model</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "      <td>Kentucky44</td>\n",
       "      <td>0.723761</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Zoo Animals - classification</td>\n",
       "      <td>agajorte.zoo-animals-extended-dataset</td>\n",
       "      <td>AI Maverick</td>\n",
       "      <td>0.707461</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Model Evaluation Tutorial</td>\n",
       "      <td>alexteboul.heart-disease-health-indicators-dat...</td>\n",
       "      <td>Mahmoud Limam</td>\n",
       "      <td>0.690653</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Mobile price. EDA, Guide to using optuna with ...</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>Georgy Zubkov</td>\n",
       "      <td>0.672867</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Mushroom_Classification_99.75%ac_RF_PCA</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "      <td>Sanchita Karmakar</td>\n",
       "      <td>0.667779</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Hepatitis Prediction MKR</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Igor Gutsu</td>\n",
       "      <td>0.663380</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Learn machine learning faster 1</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "      <td>Karim Negm</td>\n",
       "      <td>0.661662</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Stacked learning method (78% accuracy)</td>\n",
       "      <td>agirlcoding.all-space-missions-from-1957</td>\n",
       "      <td>Overfitted</td>\n",
       "      <td>0.659931</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Churn Predication Model _LR_XGB_ADA_GBC_ETC_DTC</td>\n",
       "      <td>becksddf.churn-in-telecoms-dataset</td>\n",
       "      <td>NikitPatel</td>\n",
       "      <td>0.658925</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Quality of Red Wine with different Classifiers.</td>\n",
       "      <td>piyushgoyal443.red-wine-dataset</td>\n",
       "      <td>venu</td>\n",
       "      <td>0.646916</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>🔥🔥Depression Killer🔥🔥</td>\n",
       "      <td>gargmanas.sentimental-analysis-for-tweets</td>\n",
       "      <td>Chandler Bing</td>\n",
       "      <td>0.625143</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Zoo Dataset - Classification</td>\n",
       "      <td>agajorte.zoo-animals-extended-dataset</td>\n",
       "      <td>Omar E.A.</td>\n",
       "      <td>0.623636</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Mushroom Classification</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "      <td>Raghu</td>\n",
       "      <td>0.623345</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Mushroom EDA || Classification📊📈</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "      <td>Mina Emil</td>\n",
       "      <td>0.599870</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Hepatitis C Prediction: Achieving 94% Accuracy💉💻</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Ragi Shehab</td>\n",
       "      <td>0.594484</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Hepatitis C Predict</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Ömer Said YİLMAZ</td>\n",
       "      <td>0.593907</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>gender_classification_16_ml_models</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "      <td>Suleyman Zeynul</td>\n",
       "      <td>0.588589</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>churn data analysis</td>\n",
       "      <td>becksddf.churn-in-telecoms-dataset</td>\n",
       "      <td>Mayank Phanse</td>\n",
       "      <td>0.583264</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Mobile Price Prediction 🏷️📱</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>Sercan Yeşilöz</td>\n",
       "      <td>0.569651</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Text Classification Financial News</td>\n",
       "      <td>ankurzing.sentiment-analysis-for-financial-news</td>\n",
       "      <td>Shivam Burnwal</td>\n",
       "      <td>0.566943</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Stellar Classification Dataset - SDSS17</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "      <td>Kemal Akay</td>\n",
       "      <td>0.563263</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Red Wine Quality Classification Analysis + EDA</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>tolgahancepel</td>\n",
       "      <td>0.562225</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Stellar Classification Dataset - RandomForest</td>\n",
       "      <td>fedesoriano.stellar-classification-dataset-sdss17</td>\n",
       "      <td>Haines City</td>\n",
       "      <td>0.560362</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>heart diseases prediction system</td>\n",
       "      <td>alexteboul.heart-disease-health-indicators-dat...</td>\n",
       "      <td>Mohammad Abd ElNasser</td>\n",
       "      <td>0.559073</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Dealing with unbalance: EDA,PCA,SMOTE,LR,SVM,D...</td>\n",
       "      <td>becksddf.churn-in-telecoms-dataset</td>\n",
       "      <td>Alexander Abstreiter</td>\n",
       "      <td>0.549287</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Glass Classification EDA&amp;12 models(GCEDA12M)</td>\n",
       "      <td>uciml.glass</td>\n",
       "      <td>Yasir Hussein Shakir</td>\n",
       "      <td>0.522184</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Alti Özellige Göre Hastalarin Siniflandirilmasi</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "      <td>Serkan Peldek</td>\n",
       "      <td>0.511412</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Twitter Sentiment Analysis: NLP, SMOTE</td>\n",
       "      <td>arkhoshghalb.twitter-sentiment-analysis-hatred...</td>\n",
       "      <td>Pradnesh Lachake</td>\n",
       "      <td>0.503465</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Pipeline  \\\n",
       "0     Stellar Classification and Supervised Learning⭐   \n",
       "1                    Red Wine Quality Classification    \n",
       "2                     Red Wine Quality | EDA | Models   \n",
       "3         Classification_of_temp_reading_using_LR_and   \n",
       "4               Mushroom Classes: with 100% accuracy    \n",
       "5   Modeling-FeatureSelection-SMOTE-EDA-XAI-ModelTune   \n",
       "6   Feature Engineering + Classifying heroes as hu...   \n",
       "7                                        All ML Algos   \n",
       "8                 💫Stellar Classification : EDA + ML🌳   \n",
       "9     Nueral Network Regression VS Sklearn Algorithms   \n",
       "10                    ⭐🌃Stellar Entity Classification   \n",
       "11                        Red Wine Quality Prediction   \n",
       "12             FinNews sentiment for retail investors   \n",
       "13   📚Predicting Depression from Tweets with 98% Acc🎯   \n",
       "14  CARTs and LInear Classification Models & Ensem...   \n",
       "15                                       US Hurricane   \n",
       "16  Sentimental Tweets Preprocessing Modeling Tuto...   \n",
       "17      Kirmizi Sarap Kalite Tahmini(Siniflandirmasi)   \n",
       "18    Hepatitis C LR|KNN|RF with best accuracy of 96%   \n",
       "19             Glass Classification Analysis with EDA   \n",
       "20                         HertDisease_EDA+Prediction   \n",
       "21                                Whats in your beer?   \n",
       "22  Basic Machine Learning with Red Wine Quality data   \n",
       "23              Advanced Analysis with Resampling 🍷🍇🍀   \n",
       "24                Heart Disease Binary Classification   \n",
       "25                 Hepatitis C different models 94.3%   \n",
       "26      Stellar Classification - SDSS17 (4 ML Models)   \n",
       "27   multiple-model-gender-classification[75 percent]   \n",
       "28    Classification for Zoo Animals by Random Forest   \n",
       "29    Gender Classification - LR, DT, RF, SVM and KNN   \n",
       "30                                        irisdataset   \n",
       "31  Predict the winner - Data Visualization and Model   \n",
       "32                       Zoo Animals - classification   \n",
       "33                          Model Evaluation Tutorial   \n",
       "34  Mobile price. EDA, Guide to using optuna with ...   \n",
       "35            Mushroom_Classification_99.75%ac_RF_PCA   \n",
       "36                           Hepatitis Prediction MKR   \n",
       "37                   Learn machine learning faster 1    \n",
       "38             Stacked learning method (78% accuracy)   \n",
       "39    Churn Predication Model _LR_XGB_ADA_GBC_ETC_DTC   \n",
       "40    Quality of Red Wine with different Classifiers.   \n",
       "41                              🔥🔥Depression Killer🔥🔥   \n",
       "42                       Zoo Dataset - Classification   \n",
       "43                            Mushroom Classification   \n",
       "44                   Mushroom EDA || Classification📊📈   \n",
       "45   Hepatitis C Prediction: Achieving 94% Accuracy💉💻   \n",
       "46                                Hepatitis C Predict   \n",
       "47                 gender_classification_16_ml_models   \n",
       "48                                churn data analysis   \n",
       "49                        Mobile Price Prediction 🏷️📱   \n",
       "50                 Text Classification Financial News   \n",
       "51            Stellar Classification Dataset - SDSS17   \n",
       "52     Red Wine Quality Classification Analysis + EDA   \n",
       "53      Stellar Classification Dataset - RandomForest   \n",
       "54                   heart diseases prediction system   \n",
       "55  Dealing with unbalance: EDA,PCA,SMOTE,LR,SVM,D...   \n",
       "56       Glass Classification EDA&12 models(GCEDA12M)   \n",
       "57    Alti Özellige Göre Hastalarin Siniflandirilmasi   \n",
       "58             Twitter Sentiment Analysis: NLP, SMOTE   \n",
       "\n",
       "                                              Dataset                  Author  \\\n",
       "0   fedesoriano.stellar-classification-dataset-sdss17           Waleed Faheem   \n",
       "1            uciml.red-wine-quality-cortez-et-al-2009            Nitesh Yadav   \n",
       "2            uciml.red-wine-quality-cortez-et-al-2009              Arsh Anwar   \n",
       "3       atulanandjha.temperature-readings-iot-devices          Bhushan Rajput   \n",
       "4                       uciml.mushroom-classification              Ahmed Gado   \n",
       "5                  becksddf.churn-in-telecoms-dataset     Ali Galip Sekeroglu   \n",
       "6                           claudiodavi.superhero-set            Pere Manresa   \n",
       "7                       uciml.mushroom-classification           Izam Mohammed   \n",
       "8   fedesoriano.stellar-classification-dataset-sdss17            ANMOL BAJPAI   \n",
       "9   alexteboul.heart-disease-health-indicators-dat...             Ahmed_Helal   \n",
       "10  fedesoriano.stellar-classification-dataset-sdss17           Gerome Andrew   \n",
       "11           uciml.red-wine-quality-cortez-et-al-2009            Halime Doğan   \n",
       "12    ankurzing.sentiment-analysis-for-financial-news    Ekaterina Dranitsyna   \n",
       "13          gargmanas.sentimental-analysis-for-tweets           Akul_Malhotra   \n",
       "14                                        uciml.glass            eric mutisya   \n",
       "15                            noaa.hurricane-database              Rand Salem   \n",
       "16          gargmanas.sentimental-analysis-for-tweets      Abdulrahman Khalid   \n",
       "17           uciml.red-wine-quality-cortez-et-al-2009           Serkan Peldek   \n",
       "18                    fedesoriano.hepatitis-c-dataset    AkshayRamakrishnan28   \n",
       "19                                        uciml.glass           tolgahancepel   \n",
       "20  alexteboul.heart-disease-health-indicators-dat...           Farid Rizqi S   \n",
       "21                               nickhould.craft-cans              hemen boro   \n",
       "22           uciml.red-wine-quality-cortez-et-al-2009       Madhuri Sivalenka   \n",
       "23           uciml.red-wine-quality-cortez-et-al-2009        Ishara Neranjana   \n",
       "24  alexteboul.heart-disease-health-indicators-dat...     Mahdi Shirmohammadi   \n",
       "25                    fedesoriano.hepatitis-c-dataset       Abdelrhman_Yasser   \n",
       "26  fedesoriano.stellar-classification-dataset-sdss17      Wessam Salah Walid   \n",
       "27                      hb20007.gender-classification      supasun kumpraphan   \n",
       "28              agajorte.zoo-animals-extended-dataset  Mohamed Bakrey Mahmoud   \n",
       "29                      hb20007.gender-classification    Snehal Sanjay Mankar   \n",
       "30  prasertk.animal-crossing-new-horizons-with-ima...           IYLI MAITREYI   \n",
       "31                                    gdaley.hkracing              Kentucky44   \n",
       "32              agajorte.zoo-animals-extended-dataset             AI Maverick   \n",
       "33  alexteboul.heart-disease-health-indicators-dat...           Mahmoud Limam   \n",
       "34      iabhishekofficial.mobile-price-classification           Georgy Zubkov   \n",
       "35                      uciml.mushroom-classification       Sanchita Karmakar   \n",
       "36                    fedesoriano.hepatitis-c-dataset              Igor Gutsu   \n",
       "37                      uciml.mushroom-classification              Karim Negm   \n",
       "38           agirlcoding.all-space-missions-from-1957              Overfitted   \n",
       "39                 becksddf.churn-in-telecoms-dataset              NikitPatel   \n",
       "40                    piyushgoyal443.red-wine-dataset                    venu   \n",
       "41          gargmanas.sentimental-analysis-for-tweets           Chandler Bing   \n",
       "42              agajorte.zoo-animals-extended-dataset               Omar E.A.   \n",
       "43                      uciml.mushroom-classification                   Raghu   \n",
       "44                      uciml.mushroom-classification               Mina Emil   \n",
       "45                    fedesoriano.hepatitis-c-dataset             Ragi Shehab   \n",
       "46                    fedesoriano.hepatitis-c-dataset        Ömer Said YİLMAZ   \n",
       "47                      hb20007.gender-classification         Suleyman Zeynul   \n",
       "48                 becksddf.churn-in-telecoms-dataset           Mayank Phanse   \n",
       "49      iabhishekofficial.mobile-price-classification          Sercan Yeşilöz   \n",
       "50    ankurzing.sentiment-analysis-for-financial-news          Shivam Burnwal   \n",
       "51  fedesoriano.stellar-classification-dataset-sdss17              Kemal Akay   \n",
       "52           uciml.red-wine-quality-cortez-et-al-2009           tolgahancepel   \n",
       "53  fedesoriano.stellar-classification-dataset-sdss17             Haines City   \n",
       "54  alexteboul.heart-disease-health-indicators-dat...   Mohammad Abd ElNasser   \n",
       "55                 becksddf.churn-in-telecoms-dataset    Alexander Abstreiter   \n",
       "56                                        uciml.glass    Yasir Hussein Shakir   \n",
       "57  uciml.biomechanical-features-of-orthopedic-pat...           Serkan Peldek   \n",
       "58  arkhoshghalb.twitter-sentiment-analysis-hatred...        Pradnesh Lachake   \n",
       "\n",
       "       Score  Number_of_votes  \n",
       "0   0.988913               31  \n",
       "1   0.981929               74  \n",
       "2   0.978748               65  \n",
       "3   0.978135                5  \n",
       "4   0.963857               41  \n",
       "5   0.960298               12  \n",
       "6   0.942909                2  \n",
       "7   0.922462               34  \n",
       "8   0.918764               13  \n",
       "9   0.893106                6  \n",
       "10  0.886222               10  \n",
       "11  0.883319               85  \n",
       "12  0.875297               12  \n",
       "13  0.870942                3  \n",
       "14  0.854147                8  \n",
       "15  0.845638               13  \n",
       "16  0.839083                6  \n",
       "17  0.833402              139  \n",
       "18  0.802531                9  \n",
       "19  0.799320               40  \n",
       "20  0.794994               18  \n",
       "21  0.786128                2  \n",
       "22  0.781366              145  \n",
       "23  0.773672               95  \n",
       "24  0.766771                5  \n",
       "25  0.758405                8  \n",
       "26  0.756920                7  \n",
       "27  0.741190                5  \n",
       "28  0.729831                7  \n",
       "29  0.727761                4  \n",
       "30  0.726991                1  \n",
       "31  0.723761                4  \n",
       "32  0.707461                4  \n",
       "33  0.690653               26  \n",
       "34  0.672867               87  \n",
       "35  0.667779               47  \n",
       "36  0.663380                2  \n",
       "37  0.661662              166  \n",
       "38  0.659931               16  \n",
       "39  0.658925               20  \n",
       "40  0.646916                2  \n",
       "41  0.625143               16  \n",
       "42  0.623636                1  \n",
       "43  0.623345               65  \n",
       "44  0.599870               35  \n",
       "45  0.594484               15  \n",
       "46  0.593907                3  \n",
       "47  0.588589               13  \n",
       "48  0.583264                3  \n",
       "49  0.569651               94  \n",
       "50  0.566943               16  \n",
       "51  0.563263                6  \n",
       "52  0.562225               71  \n",
       "53  0.560362                8  \n",
       "54  0.559073                3  \n",
       "55  0.549287               27  \n",
       "56  0.522184               53  \n",
       "57  0.511412               82  \n",
       "58  0.503465               34  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipelines_info = kglids.get_pipelines_calling_libraries(['pandas.read_csv', \n",
    "                                                         'sklearn.linear_model.LogisticRegression', \n",
    "                                                         'sklearn.metrics.accuracy_score'])\n",
    "pipelines_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>16. Search deep learning pipelines</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Author</th>\n",
       "      <th>Written_on</th>\n",
       "      <th>Score</th>\n",
       "      <th>Number_of_votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Inference] Birdsong Baseline: ResNeSt50-fast</td>\n",
       "      <td>shonenkov.birdcall-check</td>\n",
       "      <td>Tawara</td>\n",
       "      <td>2020-07-15 19:41:11</td>\n",
       "      <td>0.980886</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Classification_of_temp_reading_using_LR_and</td>\n",
       "      <td>atulanandjha.temperature-readings-iot-devices</td>\n",
       "      <td>Bhushan Rajput</td>\n",
       "      <td>2019-12-30 19:37:51</td>\n",
       "      <td>0.978135</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K-Nearest Neighbors (KNN) Classification (75.2...</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "      <td>Furkan Gulsen</td>\n",
       "      <td>2020-05-15 00:20:26</td>\n",
       "      <td>0.921151</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Birdcall ResNestSED Effnet b0 EMA All th04</td>\n",
       "      <td>shonenkov.birdcall-check</td>\n",
       "      <td>Hidehisa Arai</td>\n",
       "      <td>2020-09-15 00:53:38</td>\n",
       "      <td>0.909425</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Biomechanical features - 20 popular models</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "      <td>Vitalii Mokin</td>\n",
       "      <td>2020-10-11 19:52:55</td>\n",
       "      <td>0.898329</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Epidemic Model COVID-19🦠 India + Visualizations</td>\n",
       "      <td>danofer.india-census</td>\n",
       "      <td>Adarsh C</td>\n",
       "      <td>2020-10-22 05:34:49</td>\n",
       "      <td>0.897995</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>〽️|3️⃣Ways to Deal with Time Series Forecasting</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "      <td>M Faarisul Ilmi</td>\n",
       "      <td>2022-12-28 13:46:20</td>\n",
       "      <td>0.894920</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>🎯 Twitter sentiment analysis: Hatred speech</td>\n",
       "      <td>arkhoshghalb.twitter-sentiment-analysis-hatred...</td>\n",
       "      <td>Mustanger</td>\n",
       "      <td>2022-12-01 13:03:09</td>\n",
       "      <td>0.866724</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Trends in 2020 with Advice from Top Kagglers</td>\n",
       "      <td>rohanrao.chai-time-data-science</td>\n",
       "      <td>Leonie</td>\n",
       "      <td>2020-12-20 21:06:57</td>\n",
       "      <td>0.833010</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cell Phones Reviews Sentiment Analysis - Title</td>\n",
       "      <td>grikomsn.amazon-cell-phones-reviews</td>\n",
       "      <td>Gaurav Dutta</td>\n",
       "      <td>2021-09-03 07:50:20</td>\n",
       "      <td>0.814392</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[Training] Birdsong Baseline: ResNeSt50-fast</td>\n",
       "      <td>shonenkov.birdcall-check</td>\n",
       "      <td>Tawara</td>\n",
       "      <td>2020-07-05 10:54:52</td>\n",
       "      <td>0.804337</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Electricity price forecasting with DNNs (+ EDA)</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "      <td>Dimitrios Roussis</td>\n",
       "      <td>2021-03-25 16:02:26</td>\n",
       "      <td>0.795653</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Who said this line[EDA/Classification/Keras/ANN]</td>\n",
       "      <td>thec03u5.seinfeld-chronicles</td>\n",
       "      <td>Siddharth Yadav</td>\n",
       "      <td>2018-06-09 05:28:57</td>\n",
       "      <td>0.767137</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Self-Supervised Beer Similarity API</td>\n",
       "      <td>nickhould.craft-cans</td>\n",
       "      <td>Dionisio Chiuratto Agourakis</td>\n",
       "      <td>2021-01-07 17:08:25</td>\n",
       "      <td>0.762804</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Introduction to Sound Event Detection</td>\n",
       "      <td>shonenkov.birdcall-check</td>\n",
       "      <td>Hidehisa Arai</td>\n",
       "      <td>2020-08-14 10:26:19</td>\n",
       "      <td>0.761661</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>VGG16-Honey Bee Health Classification</td>\n",
       "      <td>jenny18.honey-bee-annotated-images</td>\n",
       "      <td>xan</td>\n",
       "      <td>2018-12-03 13:18:17</td>\n",
       "      <td>0.756804</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Twitter Sentiment Analysis LSTM</td>\n",
       "      <td>ankurzing.sentiment-analysis-for-financial-news</td>\n",
       "      <td>Kritanjali Jain</td>\n",
       "      <td>2021-06-15 14:09:49</td>\n",
       "      <td>0.753004</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mushroom Classification using Genetic Algorithm</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "      <td>Alin Cijov</td>\n",
       "      <td>2021-03-07 15:51:55</td>\n",
       "      <td>0.731023</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Twitter Sentiment Analysis Using RoBERTa</td>\n",
       "      <td>arkhoshghalb.twitter-sentiment-analysis-hatred...</td>\n",
       "      <td>Sadik Al Jarif</td>\n",
       "      <td>2023-06-08 16:18:20</td>\n",
       "      <td>0.713442</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>A Story about Unsupervised Learning ✨</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>Frank Mollard</td>\n",
       "      <td>2023-05-21 18:36:04</td>\n",
       "      <td>0.614188</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SDSS Classification with Deep Neural Networks</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "      <td>Kevin Trinh</td>\n",
       "      <td>2020-05-22 23:15:09</td>\n",
       "      <td>0.602062</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[inference, PyTorch] Birdcall ResNet Baseline</td>\n",
       "      <td>shonenkov.birdcall-check</td>\n",
       "      <td>Hidehisa Arai</td>\n",
       "      <td>2020-06-22 16:02:37</td>\n",
       "      <td>0.559938</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Cell Phones Reviews Sentiment Analysis - Body</td>\n",
       "      <td>grikomsn.amazon-cell-phones-reviews</td>\n",
       "      <td>Gaurav Dutta</td>\n",
       "      <td>2021-09-03 12:31:00</td>\n",
       "      <td>0.554809</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NLP : Financial News Sentiment Analysis</td>\n",
       "      <td>ankurzing.sentiment-analysis-for-financial-news</td>\n",
       "      <td>Siti Khotijah</td>\n",
       "      <td>2020-10-12 12:26:30</td>\n",
       "      <td>0.511446</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Univariate Time Series Forecasting With Keras</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "      <td>Nicholas Jhana</td>\n",
       "      <td>2020-03-26 18:27:49</td>\n",
       "      <td>0.504292</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Pipeline  \\\n",
       "0       [Inference] Birdsong Baseline: ResNeSt50-fast   \n",
       "1         Classification_of_temp_reading_using_LR_and   \n",
       "2   K-Nearest Neighbors (KNN) Classification (75.2...   \n",
       "3          Birdcall ResNestSED Effnet b0 EMA All th04   \n",
       "4          Biomechanical features - 20 popular models   \n",
       "5     Epidemic Model COVID-19🦠 India + Visualizations   \n",
       "6     〽️|3️⃣Ways to Deal with Time Series Forecasting   \n",
       "7         🎯 Twitter sentiment analysis: Hatred speech   \n",
       "8        Trends in 2020 with Advice from Top Kagglers   \n",
       "9      Cell Phones Reviews Sentiment Analysis - Title   \n",
       "10       [Training] Birdsong Baseline: ResNeSt50-fast   \n",
       "11    Electricity price forecasting with DNNs (+ EDA)   \n",
       "12   Who said this line[EDA/Classification/Keras/ANN]   \n",
       "13                Self-Supervised Beer Similarity API   \n",
       "14              Introduction to Sound Event Detection   \n",
       "15              VGG16-Honey Bee Health Classification   \n",
       "16                    Twitter Sentiment Analysis LSTM   \n",
       "17    Mushroom Classification using Genetic Algorithm   \n",
       "18           Twitter Sentiment Analysis Using RoBERTa   \n",
       "19              A Story about Unsupervised Learning ✨   \n",
       "20      SDSS Classification with Deep Neural Networks   \n",
       "21      [inference, PyTorch] Birdcall ResNet Baseline   \n",
       "22      Cell Phones Reviews Sentiment Analysis - Body   \n",
       "23            NLP : Financial News Sentiment Analysis   \n",
       "24      Univariate Time Series Forecasting With Keras   \n",
       "\n",
       "                                              Dataset  \\\n",
       "0                            shonenkov.birdcall-check   \n",
       "1       atulanandjha.temperature-readings-iot-devices   \n",
       "2   uciml.biomechanical-features-of-orthopedic-pat...   \n",
       "3                            shonenkov.birdcall-check   \n",
       "4   uciml.biomechanical-features-of-orthopedic-pat...   \n",
       "5                                danofer.india-census   \n",
       "6   nicholasjhana.energy-consumption-generation-pr...   \n",
       "7   arkhoshghalb.twitter-sentiment-analysis-hatred...   \n",
       "8                     rohanrao.chai-time-data-science   \n",
       "9                 grikomsn.amazon-cell-phones-reviews   \n",
       "10                           shonenkov.birdcall-check   \n",
       "11  nicholasjhana.energy-consumption-generation-pr...   \n",
       "12                       thec03u5.seinfeld-chronicles   \n",
       "13                               nickhould.craft-cans   \n",
       "14                           shonenkov.birdcall-check   \n",
       "15                 jenny18.honey-bee-annotated-images   \n",
       "16    ankurzing.sentiment-analysis-for-financial-news   \n",
       "17                      uciml.mushroom-classification   \n",
       "18  arkhoshghalb.twitter-sentiment-analysis-hatred...   \n",
       "19           uciml.red-wine-quality-cortez-et-al-2009   \n",
       "20         muhakabartay.sloan-digital-sky-survey-dr16   \n",
       "21                           shonenkov.birdcall-check   \n",
       "22                grikomsn.amazon-cell-phones-reviews   \n",
       "23    ankurzing.sentiment-analysis-for-financial-news   \n",
       "24  nicholasjhana.energy-consumption-generation-pr...   \n",
       "\n",
       "                          Author           Written_on     Score  \\\n",
       "0                         Tawara  2020-07-15 19:41:11  0.980886   \n",
       "1                 Bhushan Rajput  2019-12-30 19:37:51  0.978135   \n",
       "2                  Furkan Gulsen  2020-05-15 00:20:26  0.921151   \n",
       "3                  Hidehisa Arai  2020-09-15 00:53:38  0.909425   \n",
       "4                  Vitalii Mokin  2020-10-11 19:52:55  0.898329   \n",
       "5                       Adarsh C  2020-10-22 05:34:49  0.897995   \n",
       "6                M Faarisul Ilmi  2022-12-28 13:46:20  0.894920   \n",
       "7                      Mustanger  2022-12-01 13:03:09  0.866724   \n",
       "8                         Leonie  2020-12-20 21:06:57  0.833010   \n",
       "9                   Gaurav Dutta  2021-09-03 07:50:20  0.814392   \n",
       "10                        Tawara  2020-07-05 10:54:52  0.804337   \n",
       "11             Dimitrios Roussis  2021-03-25 16:02:26  0.795653   \n",
       "12               Siddharth Yadav  2018-06-09 05:28:57  0.767137   \n",
       "13  Dionisio Chiuratto Agourakis  2021-01-07 17:08:25  0.762804   \n",
       "14                 Hidehisa Arai  2020-08-14 10:26:19  0.761661   \n",
       "15                           xan  2018-12-03 13:18:17  0.756804   \n",
       "16               Kritanjali Jain  2021-06-15 14:09:49  0.753004   \n",
       "17                    Alin Cijov  2021-03-07 15:51:55  0.731023   \n",
       "18                Sadik Al Jarif  2023-06-08 16:18:20  0.713442   \n",
       "19                 Frank Mollard  2023-05-21 18:36:04  0.614188   \n",
       "20                   Kevin Trinh  2020-05-22 23:15:09  0.602062   \n",
       "21                 Hidehisa Arai  2020-06-22 16:02:37  0.559938   \n",
       "22                  Gaurav Dutta  2021-09-03 12:31:00  0.554809   \n",
       "23                 Siti Khotijah  2020-10-12 12:26:30  0.511446   \n",
       "24                Nicholas Jhana  2020-03-26 18:27:49  0.504292   \n",
       "\n",
       "    Number_of_votes  \n",
       "0               183  \n",
       "1                 5  \n",
       "2                28  \n",
       "3                57  \n",
       "4                64  \n",
       "5                77  \n",
       "6                88  \n",
       "7                96  \n",
       "8                81  \n",
       "9                 4  \n",
       "10              261  \n",
       "11              220  \n",
       "12               76  \n",
       "13                3  \n",
       "14              657  \n",
       "15                2  \n",
       "16              107  \n",
       "17               87  \n",
       "18               40  \n",
       "19               85  \n",
       "20                3  \n",
       "21              398  \n",
       "22                1  \n",
       "23               73  \n",
       "24               76  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_pipelines_for_deep_learning()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>17. Search for possible transformations</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Transformation</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Table</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Starter: India - Trade Data 75aadc10-1</td>\n",
       "      <td>2018-2010_import.csv</td>\n",
       "      <td>lakshyaag.india-trade-data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Starter: India - Trade Data 75aadc10-1</td>\n",
       "      <td>2018-2010_export.csv</td>\n",
       "      <td>lakshyaag.india-trade-data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>Biomechanical features - 20 popular models</td>\n",
       "      <td>column_2C_weka.csv</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Alti Özellige Göre Hastalarin Siniflandirilmasi</td>\n",
       "      <td>column_2C_weka.csv</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Alti Özellige Göre Hastalarin Siniflandirilmasi</td>\n",
       "      <td>column_3C_weka.csv</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</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>588</th>\n",
       "      <td>sklearn.preprocessing.OneHotEncoder</td>\n",
       "      <td>deep_learning_model_for_horse_racing</td>\n",
       "      <td>races.csv</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>deep_learning_model_for_horse_racing</td>\n",
       "      <td>runs.csv</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>deep_learning_model_for_horse_racing</td>\n",
       "      <td>races.csv</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>naive_bayes_classifier_for_horse_racing</td>\n",
       "      <td>runs.csv</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>naive_bayes_classifier_for_horse_racing</td>\n",
       "      <td>races.csv</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Transformation  \\\n",
       "0    sklearn.preprocessing.StandardScaler   \n",
       "1    sklearn.preprocessing.StandardScaler   \n",
       "2      sklearn.preprocessing.LabelEncoder   \n",
       "3    sklearn.preprocessing.StandardScaler   \n",
       "4    sklearn.preprocessing.StandardScaler   \n",
       "..                                    ...   \n",
       "588   sklearn.preprocessing.OneHotEncoder   \n",
       "589  sklearn.preprocessing.StandardScaler   \n",
       "590  sklearn.preprocessing.StandardScaler   \n",
       "591    sklearn.preprocessing.LabelEncoder   \n",
       "592    sklearn.preprocessing.LabelEncoder   \n",
       "\n",
       "                                            Pipeline                 Table  \\\n",
       "0             Starter: India - Trade Data 75aadc10-1  2018-2010_import.csv   \n",
       "1             Starter: India - Trade Data 75aadc10-1  2018-2010_export.csv   \n",
       "2         Biomechanical features - 20 popular models    column_2C_weka.csv   \n",
       "3    Alti Özellige Göre Hastalarin Siniflandirilmasi    column_2C_weka.csv   \n",
       "4    Alti Özellige Göre Hastalarin Siniflandirilmasi    column_3C_weka.csv   \n",
       "..                                               ...                   ...   \n",
       "588             deep_learning_model_for_horse_racing             races.csv   \n",
       "589             deep_learning_model_for_horse_racing              runs.csv   \n",
       "590             deep_learning_model_for_horse_racing             races.csv   \n",
       "591          naive_bayes_classifier_for_horse_racing              runs.csv   \n",
       "592          naive_bayes_classifier_for_horse_racing             races.csv   \n",
       "\n",
       "                                               Dataset  \n",
       "0                           lakshyaag.india-trade-data  \n",
       "1                           lakshyaag.india-trade-data  \n",
       "2    uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "3    uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "4    uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "..                                                 ...  \n",
       "588                                    gdaley.hkracing  \n",
       "589                                    gdaley.hkracing  \n",
       "590                                    gdaley.hkracing  \n",
       "591                                    gdaley.hkracing  \n",
       "592                                    gdaley.hkracing  \n",
       "\n",
       "[593 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.recommend_transformations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 18. Visualize pipeline usage by tasks</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.show_pipeline_usage_by_task()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 19. Most used ML models by tasks</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum 3 classifier(s) were found\n",
      "Showing top-3 classifiers\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.show_top_k_models_by_task(k=10, task='classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.show_top_k_models_by_task(k=1, task='regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}