feature_discreatization.ipynb

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   "source": [
    "# Load libraries\n",
    "from sklearn.preprocessing import Binarizer\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create feature\n",
    "age = np.array([[6], \n",
    "                [12], \n",
    "                [20], \n",
    "                [36], \n",
    "                [65]])"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<h1>Process 1: Feature Binarize</h1>"
   ]
  },
  {
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1]])"
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     "execution_count": 3,
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   "source": [
    "# Create binarizer\n",
    "binarizer = Binarizer(18)\n",
    "\n",
    "# Transform feature\n",
    "binarizer.fit_transform(age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Option 2: Break Up Feature Into Bins\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3]], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bin feature\n",
    "np.digitize(age, bins=[20,30,64])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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