{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "SN1jQ6BDYXyCeJWOHW5N",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9d9c17f50>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn import *\n",
    "\n",
    "url = \"https://gist.githubusercontent.com/seankross/a412dfbd88b3db70b74b/raw/5f23f993cd87c283ce766e7ac6b329ee7cc2e1d1/mtcars.csv\"\n",
    "cardf = pd.read_csv(url)\n",
    "\n",
    "cardf.head(3)\n",
    "\n",
    "sns.scatterplot(wt, mpg, data=cardf)\n",
    "plt.title(\"Milage vs weight\")\n",
    "plt.xlabel(\"Weight\")\n",
    "plt.ylabel(\"Milage\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "title: This is a paper\n",
    "author: Arin\n",
    "bibliography:biblio.bib\n",
    "---\n",
    "\n",
    "## Abstract\n",
    "\n",
    "The goal of this paper is to predict milage with wt\n",
    "\n",
    "## Introduction\n",
    "X has written on predictive models [@zhao2021]. \n",
    "\n",
    "| X | Y |\n",
    "|---|---|\n",
    "| A | B |\n",
    "\n",
    "## References"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
   "version": "3.7.6"
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