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   "source": [
    "## <center> Label Encoding</center>"
   ]
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   "cell_type": "markdown",
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    "- setosa -> 0\n",
    "- versicolor -> 1\n",
    "- virginica -> 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "labels = ['setosa', 'versicolor', 'virginica']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa => 0\n",
      "versicolor => 1\n",
      "virginica => 2\n"
     ]
    }
   ],
   "source": [
    "encoder = preprocessing.LabelEncoder()\n",
    "encoder.fit(labels)\n",
    "\n",
    "for i, item in enumerate(encoder.classes_):\n",
    "    print(item, '=>', i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "More labels = ['versicolor', 'versicolor', 'virginica', 'setosa', 'versicolor']\n",
      "More labels encoded = [1, 1, 2, 0, 1]\n"
     ]
    }
   ],
   "source": [
    "more_labels = ['versicolor', 'versicolor', 'virginica', 'setosa', 'versicolor']\n",
    "more_labels_encoded = encoder.transform(more_labels)\n",
    "\n",
    "print('More labels =', more_labels)\n",
    "print('More labels encoded =', list(more_labels_encoded))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "X, y"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "cell_type": "code",
   "execution_count": null,
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