Problem5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Project5_HelperFeature import extract_feat \n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"tweets_#gohawks.txt\", \"tweets_#gopatriots.txt\", \\\n",
    "         \"tweets_#nfl.txt\", \"tweets_#patriots.txt\", \"tweets_#sb49.txt\", \"tweets_#superbowl.txt\"]\n",
    "\n",
    "columns= ['N','NR','SNF','MF','TD','NI','RS','UCF','LC','FOA','target']\n",
    "\n",
    "feat = [\"number of tweets\",\"number of retweets\", \"sum of followers\", \"max of followers\",\\\n",
    "        \"time of day\", \"number of impressions\", \"ranking score\", \"friend count of user\", \"listed count\", \"foll. of orig. author\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = extract_feat(names[0],flag = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "p2 = extract_feat(names[1],flag = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "p3 = extract_feat(names[2],flag = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "p4 = extract_feat(names[3],flag = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "p5 = extract_feat(names[4],flag = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "p6 = extract_feat(names[5],flag = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MfKm4vRvwf4E3AQ9k5qvr3ssaWrBgQa5evbrR3ZgyMpNp05b13961ayml+Yik2omINZm5oNH9mAizSbVg5jaXdsgmMJ80MeZScxptPo1mNtQOYJ+y2y8DOovi8flx9k9TQN9wg3J9wxAkSbVl5kpqNuZS6xtNsfgPwNqI+FJEXAfcC/xjRLwM+GE9O6fW1tu7k5Ur19PVNZ9du5bS1TWflSvX09u7s9Fdk6S2Y+ZKajbmUusbdhhqcf7CQ4AXgeOK5rsz8z8moW914VCKyeUMWJoM7TDUy2xSLZi5zaUdsgnMJ02MudScRptPw05wk5kZEd/LzNfiCe81DuVhEBGGgyTVkZkrqdmYS61tNMNQ74mIhXXviSRJkiSpaYzm1BlvAN4TEU8AvwaC0k7H36lrzyRJkiRJDTOaYvHUuvdCkiRJktRURiwWM/MJgIh4BeAZNCVJkiRpChjxmMWIWBQR64DHgDuAx4Hv17lfkiRJkqQGGs0EN5cCxwOPZuZhwMnAnXXtlTRKPT07+k/smpn09OxocI8kSfVg3ktqRa2eXaMpFv8rM7cC0yJiWmbeBrT8OYPU+np6drBw4QqWLLmdzGTJkttZuHBFy30IJUnDM+8ltaJ2yK7RTHCzLSL2AX4MfCUitgC/qm+3pJF1dMxg0aIj6e5eQ3f3GgC6uubT0eGhtZLUTsx7Sa2oHbJrNHsW7wOeAy4GfgBsAB6pZ6ek0YgIli8/cUDb8uUnEhGN6ZAkqS7Me0mtqB2yazTF4kmZuSszX8zM6zPzCmBhvTsmjaRvd365vt38kqT2Yd5LakXtkF1DFosR8WcR8QBwVETcX3Z5DLh/vCuMiN+OiLVll+0R0RURH4+Ip8ra31H2nA9HxPqI+FlEnFrWflrRtj4iLhlvn9Saent3snLlerq65rNr11K6uuazcuV6ent3NrprkqQaMu8ltaJ2yK4YqrKNiH2BDuDvgPJC7JeZ2VOTlUdMB54C3gC8H/hVZn5m0GNeDXwNOA74LeCHwKuKux8F3gZsBu4Gzs7Mh4db54IFC3L16tW16L6aQE/PDjo6ZhARZCa9vTvp7Nyr0d3SJIuINZnZ0hNvmU3S8Fox79shm8B8kiaiWbNrtPk05J7FzHw2Mx/PzLMz84myS00KxcLJwIbMfGKYx5wB3JiZz2fmY8B6SoXjccD6zNyYmS8ANxaPVZOqx9TBnZ179Y/7joim+PBJUqtq5inezXupeTVzdjRaq2fXaI5ZrKezKO017HNRMdT12ojoKNoOBp4se8zmom2odjWhdpg6WJLamTktaTzMjvbWsGIxIvYAFgH/XDR9FjgCOBZ4GlhWw3WdFxHrIuIXmzZtqtViNQblUwdPm7aM7u41LFp0ZEtNHSzVmtmkZmJOq5z5pNEyO9pbI/csvh24JzN/DpCZP8/MlzJzF/B5SsNMoXRM46FlzzukaBuqvUJmXpOZczPzgNmzZ9d4MzQa7TB1sFRrZpOaiTmtcuaTRsvsaG+NLBbPpmwIakQcVHbfO4EHi+srgbMiYs+IOAyYC9xFaUKbuRFxWLGX8qzisWpC7TB1sCS1M3Na0niYHe2tIcViRLyM0iym3ypr/oeIeCAi7gdOAi4GyMyHgJuAh4EfABcWeyBfBC4CbgZ+CtxUPFZNqB2mDpakdmZOSxoPs6O9DXnqjHbl9M+N06xTB6v1tcP09GaTmoE5XVvtkE1gPmlkZkfrmfCpM9TcWnGK4lafOlhSbbRifk0V5rRalbnSWGZH+7JYbEFOUSypVZlfkmrNXJHqZ7dGd0BjVz5FcXf3GgC6uuY7RbGkpmd+Sao1c0WqH/cstiCnKJbUqswvSbVmrkj1Y7HYgpyiWFKrMr8k1Zq5ItWPxWILcopiSa3K/JJUa+aKVD+eOqNFOUWx9BvtMD19u2TTaJhfmiraIZugNfLJXJHGZrT55AQ3Lao8AJ2iWFIrMb8k1Zq5ItWHw1AlSZIkSRUsFiVJkiRJFSwWJUmSJEkVLBYlSZIkSRUsFiVJkiRJFSwWJUmSJEkVLBYlSZIkSRUsFiVJkiRJFSwWJUmSJEkVGlYsRsTjEfFARKyNiNVFW2dE3BIR64p/O4r2iIgrImJ9RNwfEfPKlnNu8fh1EXFuo7ZHkiRJktpJo/csnpSZx2bmguL2JcCtmTkXuLW4DfB2YG5xOQ/4LJSKS+BjwBuA44CP9RWYkiRJkqTxa3SxONgZwPXF9euBM8vab8iSO4FZEXEQcCpwS2b2ZGYvcAtw2mR3WpIkSZLaTSOLxQRWRcSaiDivaDswM58urv8ncGBx/WDgybLnbi7ahmpvCz09O8hMADKTnp4dDe6RJNWG+SZpspg30vg1slh8U2bOozTE9MKIeEv5nVn6VGctVhQR5xXHNP5i06ZNtVhk3fX07GDhwhUsWXI7mcmSJbezcOEKA05qI62YTbVgvknNr13yybyRJqZhxWJmPlX8uwX4F0rHHP68GF5K8e+W4uFPAYeWPf2Qom2o9sHruiYz52bmAbNnz671ptRFR8cMFi06ku7uNUybtozu7jUsWnQkHR0zGt01STXSitlUC+ab1PzaJZ/MG2liGlIsRsTLIuLlfdeBU4AHgZVA34ym5wLfKa6vBN5bzIp6PPBsMVz1ZuCUiOgoJrY5pWhreRHB8uUnDmhbvvxEIqIxHZKkGjHfJE0W80aamEbtWTwQ+LeIuA+4C/j/MvMHwN8Db4uIdcBbi9sA3wM2AuuBzwMXAGRmD3ApcHdx+UTR1vL6hkqU6xtC0ccx+JJaUbV8u/ji29i69bnGdEhS29q69Tkuvvi2AW2Dv09JGlpDisXM3JiZrysur8nMTxXtWzPz5GLYw1v7Cr9iFtQLM/OIzHxtZq4uW9a1mXlkcflSI7anHnp7d7Jy5Xq6uuaza9dSurrms3Llenp7dwKOwZfUunp7d/Ltb69j5sw9WLx4HosXz+O66x5k/vwvm2GSaqanZwfz53+Z6657sD9rZs7cg29/e13/9ylJw9ut0R1QdZ2de3H33efQ0TGjfwjFRz96PJ2dewEDx+B3d68BoKtrvmPwJTW9zs69WL36HC699E4uv/ye/vb3v/+1ZpikmunomME73/kqurvX9GfN4sXzBnyfkjS8ZjvPosp0du7VP6Y+IgYEm2PwJbWy/fbbm8suO2lAmxkmqZaqfVe67LKT2G+/vRvTIakFWSy2qNEc0yhJzcoMk1Rv5ow0cRaLLWqkYxrHwolyJE22WmZYPZiLUutrtpwxV9SKYqr9urJgwYJcvXr1yA9sAT09O/qPacxMent3jnkMft9EOYsWHcny5SeyZMntrFy5nrvvPsfx/GoZEbEmMxc0uh8T0U7ZNFq1yLB69ctcVC20QzZBa+dTs+SMuaJmM9p8coKbFjb4GMbxhI0T5UhqlFpkWD2Yi1L7aJacMVfUqhyGWmOtNsTAiXLUbFrtMzRVTKX3xVxUNVPpM9DMWvV9MFdUT/X8XFgs1lC9zn1Yzz8AD/5WM/H8oc2p3u9Ls335Mxc1mNnUHMrfh61bn+Pii29j4cIVbN36XNO/F+aK6qXe+WSxWEPlQwymTVtGd/caFi06csQhBsN9Uar3H0CzHfytqW28nyHVVy3el6Fyrhm/hJuLGsxsag7l78P++1/F5Zffw+mnH8Gll97JwoUr2LCht6l+eCpnrqhe6p1PTnBTY5nJtGnL+m/v2rV02CEGIx3w3PflqW98O5TGuNdy6EKzHPwtwdg/Q9Aek0g0+wQS43lf+gyXcx0dM+qeceNhLmqwqZpN0Fz5NPh96HP++a/j5psf44wz5jbtBDLmiuqlnvnknsUaGs8Qg5F+DZiMMe6dnXv1L6/awd/NNkRM7cthOs1pou/LcDnXrMfxjJSLY2GGtj6zqTlUex/6XHnlyZxxxtym3vtbniu9vTv7+2YuaCLqnU8WizU0niEGI31RavR/UM04REzty2E6zWmi78twOdfojKs3M7Q9mE3Noe99WLx4HosXzxtw39Kld7Bs2QkD2prhh6dqzAXVUr3zyWGoNTbWIQYjDTNt9Hl5JmMYrFRuPMN02mGoVzMN86pmIsOnhsuR3t6dbX3uMTO0fUzVbILmyqe+PfXHHfcVTj/9CD760eP55Cd/wsqV6znllDlcffV9/Y9t1s+auaBaq2c+WSw22GiKwUaPcZ/IsUrSZGiHL2TNlk21NFLONTrj6s0MnbraIZugOfNpcG5s3LiNU075Rsv88GQuqNE8ZrFFdHbuxapV72LZshOICJYtO4FVq941INhqeezMWLX7EDFJtTf4GD2Au+8+p/9X8+XLTxzwBa6RGVdvZqhUe9V+YDriiI5hc6aZmAtqJRaLo1SvCQp6enZwyinfYOnSO8hMli69g1NO+UbTjFv3OA2p9TRyQpWhjsUB2rYgHI4ZKtU2k4Y73q9VfngyF9RKHIY6CvU8brAVxq23+xAxtb52GOpVq2FeHufcfMzQqasdsgkmlk+1zqR2yRhzQY3WtMNQI+LQiLgtIh6OiIciYnHR/vGIeCoi1haXd5Q958MRsT4ifhYRp5a1n1a0rY+IS+rV53qe7LJZp40v1yq/1Elq/MnDWyHTJpsZqqms1pnULhljLqhVNGIY6ovA0sx8NXA8cGFEvLq477LMPLa4fA+guO8s4DXAacBVETE9IqYDVwJvB14NnF22nJqqZzA5bl1SLTX6i5SZJqlcrTPJjJEm16QXi5n5dGbeU1z/JfBT4OBhnnIGcGNmPp+ZjwHrgeOKy/rM3JiZLwA3Fo+tR5/rFkyOW5dUS43+ImWmSSpX60wyY6TJ1dAJbiJiDvB64CdF00URcX9EXBsRHUXbwcCTZU/bXLQN1V5z9Qymzs69Wmb2LknNr9FfpMw0SeVqnUlmjDS5GjbBTUTsA9wBfCozvxURBwLPAAlcChyUmR+IiP8D3JmZK4rnfRH4frGY0zLzj4v2/wW8ITMvqrKu84C/AGYdcMAB+2/ZsmXM/fVAZKl5teokErXIpmrMK6k5tGo2QW3zyUySmk/TTnADEBG7A98EvpKZ3wLIzJ9n5kuZuQv4PKVhpgBPAYeWPf2Qom2o9gqZeU1mzs3MA2bPnj2uPnsgsqRaq0U2VWNeSZqoWuaTmSS1rkbMhhrAF4GfZubysvaDyh72TuDB4vpK4KyI2DMiDgPmAncBdwNzI+KwiNiD0iQ4KydjGyRJkiSp3e3WgHW+EfhfwAMRsbZo+38pzWZ6LKVhqI8DfwqQmQ9FxE3Aw5RmUr0wM18CiIiLgJuB6cC1mfnQZG6IJEmSJLWrSS8WM/PfgGrzJX9vmOd8CvhUlfbvDfc8SZIkSdL4NHQ2VEmSJElSc7JYbGE9PTv6z1OUmfT07GhwjySpccxESfVkxmgqslisoVqHyHDL6+nZwcKFK/pPbLtkye0sXLjC4JI0IdVypxW+IJmJUmuqd77UavlmjKYqi8UaqXWIjLS8jo4ZLFp0JN3da5g2bRnd3WtYtOhIOjpm1HKzJE0h1XJn3rwbmD//y03/BclMlFpPvQuwWi7fjNFUFX2/tkwVCxYsyNWrV9d8uZnJBRf8kKuvvq+/7fzzX8eVV57Mtm3Pj/mcQn2h1t29pr+tq2s+y5ef2H+uosxk2rRl/ffv2rW0/z5pKmnlE1/3qVc2DTb45NgbN27j8MNnERHs2rWLCy+8dUCOLV48D4DLL7+nv21wFjULM1HNph2yCeqTTz09O5g1a0+WLr1jwHedxYvncdllJwHQ27tzQudkHM13qbEuz4xRuxhtPrlnsUZ6e3eyatXjA9pWrXqcCy+8dVy/YkUEy5efOKBtcKG4ZMntA+7v++VMkqoZ/Cv7BRf8kKOO+hIXXPBDMpOlS++oyLHLLjup/4tbn2YtFM1EqTX0ZdHSpXewbNkJVR9Ti72MI32XGgszRlOVxWKN7Nq1i5kz9xzQtnHjs1x99X3jGqYwUij19u5k5cr1dHXNZ9eupXR1zWflyvX09u6c0HZIal+Dh1FdffV9HHPM/lx99X39w6r23Xdgjl188W1cfPFtA9qa8QuSmSi1jszk9NOPoLt7DdOnLx9w3+WX31OzYZ61LPDMGE1VFos10NOzgwULVrBxY2/V+8fzK9ZIodTZuRefXim/AAAVTklEQVR3331O/7KXLz+Ru+8+Z0LDNSS1t4jgIx95w4C2t7zl4AG3t23bOSB3vv3tdXznO83/BclMlFpDT88OjjvuKxXtv/jFn/UPe+8z0VEMtSzwzBhNVR6zWAOZyZ/92S187nP3V71/NOPjBx9H1Bdkg9sMJalSOxwXNBnHLG7d+hyf+MS/c8UV9w75mL5jradNm2YWSRPUDtkEtc2noXLoz//89QAD2mtxfHS171fml+QxizW1YUMvu3btAkrDTTdsGLgHsbd3Jzff/PiAtj32mMYrX/lyzj//dSP+ijXUbF1Af0BGhOEmaYDBU8Jv2NA77Ol2FixYwZe+9CAzZ+4xYDkzZ+7BM89cQFfXfFatepxt254HfpM7nZ17mUWSRmW40+/0fd959tnnBzznta/dn29/ex3XXfcQ++77mzyqxSgG80uaGIvFEWzY0MtRR32JBQtWsGvXLhYsWMFRR31pQME4a9aeFWPqjz66k9Wrz+HKK09m1ap3DRtOU3E65lY4b5vUzEaarOaCC37YPzlEZpKZnHbaYfzyl//F9u0vDFjW4YfPoqNjhsOqJE3ISKffmTVrT/bddwbXX//wgOc98MAzbNr0K7Zvf4GzzjoKgGXLTuDuu8+ho2OG3xGkBrJYHMFhh+3La1+7P/feu4Xp05dz771bOOaY/TnssH37i5xt255n27aBv3w99th2IoKlS+/glFO+MWzQ1XK2rlbgiW2lias2Wc3RR3f2T1Zz9dX38bKX7c6sWXuyZMntzJ//ZX7wg8eqLuvZZ3eybdvz/uouaUKq/fh9xhlHcsYZR/ZPZrN27ZYBz5k5c/cBt7///cdYsKA0U2pHxwy/I0gNZrE4gmnTprF69TkD2t785t8iIujq+hHHHnsDmcmppx424DHbt7/A/vtfNaq9hM0+HXOt9wJOxT2pUq1V+5HpV78auMfwgQeeYfr05f1f2GbNqv4ZO/XUw8b8+XN0gKTBquUSULWtz5577jbgdmfnjP7ishHfEcw2aSCLxRGsW7eVo4++dkDbP/3TWqZNW8YVV9zL5s2/ZO3aLaxa9Xj/bFtjnc2rmadjrsdewKm2J1Wqh76hpuUGHwdUbt68V/DsswMzZc6c0nHVq1Y9Pqa8cXSApGq2bn2u4lQ7V1xxb8XpMY45Zj8effT9nH/+6yqyZ/v2F7joomMHtH3kI2+YlO8IZptUyWJxGJ/5zF286lVf4tFHtwFQLac6O2fwwQ/+gJtuOn3IX84G7yUc/KsVULPpmFthL2Cz70mVml1Pzw5+9KMn+Nzn7mO33YI99oC9955GT8/QxeL73ndzxWfshBMO5dJLf2/MeePoAEmD9fTs4PWv/zJf+ML97Lkn/PZvz6z6uEMP3ZsHH9zKUUd9ieOOewV/9EdzB9z/5jcfzPz5Kwa0HX74F9i69bm67/Uz26RKFotDuOSS2/mLv/jxgLZqtczWrTvZZ589OPPMb9PTs6N/L+H557+u6l7C8l+t+n6BW7hwxYAp6sd73FCr7AVs5j2pUrPr6dnB3Llf5K1v/QaZ8OKLyQsvwHPP7RrxuY8//sv+68ccsx//8i/rWbiw8nxnI3F0gKTBMpMnn/wlv/71izz/PPzsZ9urPu7JJ58DYNcu+MAHbuHrX38UgL333o2Xv3x3br11U8VzIkrfHeq9189skypZLFbxox89wac/PfrzCT30UA//8R+/4q//+t+YNWtPTjjh0P5hXYP3Epb/arX//ldx+eX3cPrpR3DppXdOOPRaZS+gJ7aVxq+r61Z6eib+w8qDD25l+/YXOO20sR+v6OgASYO96U1fndDzn3vuRQ4+eB/uuecc3vGOgfNA3H33ezjiiA5OP/2IAd9xTj/9CDo6ZtRsL6PZJlVq+WIxIk6LiJ9FxPqIuKQWyzz55H8e83P2228vPve5+5k+fTnXXfcQp5wyp/8ksOVFULVfrS6//J7+onEigVRt2R/5yBv6r48nTOu1F9DzHkljN2/edXz5y4/UdJnf//7GMX+eHR0gqdzixT/kkUe2TXg5jzzSyytecTU33vizAe3HHHM9W7c+N+TzarWX0WyTKkUr/1oSEdOBR4G3AZuBu4GzM/PhoZ6zYMGCXL166L2GEZ+pSd9eemkJ06ZV1uJ9v1p1d6+puG/x4nl897sbxr2Xrdqy9913D973vmO47LKTWLLkdlauXD/m5ff07OgvfPuGy1rcqZlExJrMXNDofkzESNl07rn/yg031LZQBDj22FewZs05VfNqOOaCNLJ2yCaYvO9OE9HVNb8mQ0bNNk0Vo82nVt+zeBywPjM3ZuYLwI3AGQ3uEwBLl95RdS9h369WixfPq5g1tW/v4niHjQ7+RWzx4nlklpY7kWGp7gWUGq8ehSLA9u3Ps23b0BPjDMVckNQIixfPG9fM86NltkkDtXqxeDDwZNntzUXbuP3pn47t8XvvPZ2jj+4c0Pb6179iyGELfcfrffSjx/Pd726oCLuPfvT4cYfd4GMBL7vsJDZu/OMBj/FAbak1feELHXVZ7tve9kpn+pM0IeeeO7nrq/ZjvMcWSvXR6sXiqETEeRGxLiJ+sWlT5Sxb5d74xgPHtOyZM2fw61+/wPve9xpeemkJXV3z2bZtJ6tWvWvIX6M6O/div/325q673lNx3yc/+ZMJTx5TXgx+8pM/GXC/YSo1j7Fk07vf/e4arROOPrqTfffdg5e/fHduvnls51iUNDWMJZ9uuaX269999+DRR9/PzJl7sO++e/DMMxfQ1TWf7353A4899izf/e4Gjy2UJkGrH7P4u8DHM/PU4vaHATLz74Z6Ti3G3X/602/iAx94Ldu2PT9gltOxjm/vO9XFokVHsnz5ieM+prBRy5eaRTscFzRSNk2b9pmqp++pJqJ0qp+ZM3dn992DV7xiHx59tJcf/OAPmTNnXw4/fNaET9UjaWTtkE1Q+2MWDzhgL5577r/49a9fLFsGnHfe73DVVW/lAx/4Abff/iT33PPe/h+499tv7wHfsTy2UJqY0eZTqxeLu1Ga4OZk4ClKE9z8P5n50FDPmWjgrV59DvPn/7dx9beaeoedYaqpoB2+kNXqy9hVV53Meef9DnfcsZkTTjiEZ599gVmz9uSxx57liCPqM5RVUnXtkE0w8Xx605sO4v77t7LPPrvzne+cSUfHDE4++SbOPHMul112EhdffBvf/OajrF373oqiUFJ9TIliESAi3gF0A9OBazPzU8M9fqTAKy3zMxx8MKxZcx5/8zfXcM018K1vLeKEEw41uKQm1A5fyEabTQDd3Udx552PcOONpfabb34n06ZN47DDZlkQSk2kHbIJRp9Pc+bAnXeex9/+7TV89rNw3XWn8gd/cET/nsDykQz+mC011pQpFsdqNIEnqbW0wxcys0lqP+2QTWA+Se1oqpw6Q5IkSZJUBxaLkiRJkqQKFouSJEmSpAoWi5IkSZKkChaLkiRJkqQKFouSJEmSpAoWi5IkSZKkClPuPIsR8QvgiVE+fH/gmTp2x/U39/qboQ+uf3Trf2VmHlDvztRTi2VTLbT6NrR6/8FtmAwtn00wJfNpLKba9sLU2+Z23d5R5dOUKxbHIiJWN/Jkuq6/setvhj64/sb/DTSjdnhdWn0bWr3/4DaoPqbaezLVthem3jZPte0dzGGokiRJkqQKFouSJEmSpAoWi8O7xvVP6fVD4/vg+lVNO7wurb4Nrd5/cBtUH1PtPZlq2wtTb5un2vYO4DGLkiRJkqQK7lmUJEmSJFWwWBxCRJwWET+LiPURcUkNl/t4RDwQEWsjYnXR1hkRt0TEuuLfjqI9IuKKog/3R8S8suWcWzx+XUScO8I6r42ILRHxYFlbzdYZEfOLbVpfPDdGsf6PR8RTxeuwNiLeUXbfh4tl/SwiTi1rr/qeRMRhEfGTov3rEbHHoPUfGhG3RcTDEfFQRCyezNdgmPVPymsQETMi4q6IuK9Y/98O95yI2LO4vb64f854+zXC+q+LiMfKtv/Yerz+7Wa413qS1t+QPBlqHePof8PyoIbb0LDP9FDrGOd2TI+IeyPiX1ux/xqo0dk02aplYTsbKjvb2VBZO+VkppdBF2A6sAE4HNgDuA94dY2W/Tiw/6C2fwAuKa5fAny6uP4O4PtAAMcDPynaO4GNxb8dxfWOYdb5FmAe8GA91gncVTw2iue+fRTr/zjwoSp9fXXxeu8JHFa8D9OHe0+Am4CziutXA382aJkHAfOK6y8HHi3WMymvwTDrn5TXoOjTPsX13YGfFH2t+hzgAuDq4vpZwNfH268R1n8d8K4q21/zv8F2uYz0Wk9SHxqSJ0OtYxz9b1ge1HAbGvaZHmod49yOJcBXgX8dbtnN2n8vA97LhmdTA7a5Igvb+TJUdja6X3Xe5qpZ2+h+TfbFPYvVHQesz8yNmfkCcCNwRh3XdwZwfXH9euDMsvYbsuROYFZEHAScCtySmT2Z2QvcApw21MIz88dATz3WWdw3MzPvzNKn6YayZQ23/uFeixsz8/nMfAxYT+n9qPqeFL/Y/z7wjSrb0rf+pzPznuL6L4GfAgdP1mswzPon5TUotuNXxc3di0sO85zy1+UbwMnFOsbUr1Gsf7jtr+nfYBuZ7Gyq0MA8GWodY+1/I/OgVtvQkM/0aPJ2tCLiEOC/A18obg+37Kbrvyo0PJsm2xi/27S8cXyXaXnj+P7SliwWqzsYeLLs9mZq94FIYFVErImI84q2AzPz6eL6fwIHjtCPWvSvVus8uLg+nr5cVAzrurZsONZY178fsC0zXxzN+ovhS6+n9OvQpL8Gg9YPk/QaFMO91gJbKH2p3TDMc/rXU9z/bLGOcf89Dl5/ZvZt/6eK7b8sIvYc5/ZP5G+w1dQzmyZiMj5LQ61j3BqQBzXbhgZ9pseUtyPoBv4S2FXcHm7Zzdh/DdSs2aQ6qPJdpm0N8/1lyrBYnHxvysx5wNuBCyPiLeV3Fr9ET+qvFo1YJ/BZ4AjgWOBpYFm9VxgR+wDfBLoyc3v5fZPxGlRZ/6S9Bpn5UmYeCxxC6Rfgo+q1rtGsPyKOAT5c9GMhpaF8fzWZfVJ9TMZnqRbraHQeTHQdjf5MT0RE/AGwJTPXNLovksZmuOxsR0N8f5lSLBarewo4tOz2IUXbhGXmU8W/W4B/ofSf/M+LoUsU/24ZoR+16F+t1vlUcX1MfcnMnxcfwF3A5ym9DuNZ/1ZKw8J2G279EbE7pXD7SmZ+q2ietNeg2von+zUo1rkNuA343WGe07+e4v59i3VM+O+xbP2nFUNaMjOfB740ge0f199gi6pbNk3QZHyWhlrHmDUwD2q2DX0m+TM96qwZwRuBRRHxOKXhir8PXN5C/VelZs0m1dAQ2TkllH9/aXRfJpvFYnV3A3OLWdP2oHRA/cqJLjQiXhYRL++7DpwCPFgsu28mvXOB7xTXVwLvjZLjgWeLIUw3A6dEREcxdPGUom0sarLO4r7tEXF8cTzIe8uWNdxrcVDZzXcWr0Pf+s+K0ux3hwFzKU0WUfU9KX6dvw14V5Vt6VtXAF8EfpqZyyf7NRhq/ZP1GkTEARExq7i+F/A2SscaDPWc8tflXcCPinWMqV8jrP+Rsi/NQem4ofLtr/vfYIuqSzbVwGR8loZax5g0OA9qtQ0N+UyPJm9HIzM/nJmHZOacYtk/ysz3tEr/VVWzZpNqZJjsbFtDfX9pbK8aIJtglp1mvFCaAe9RSseB/HWNlnk4pRnC7gMe6lsupeMobgXWAT8EOov2AK4s+vAAsKBsWR+gdDD/euD9I6z3a5SGOf4XpeMIPljLdQILKH3R3wD8HyBGsf4vF8u/n9J/KAeVPf6vi2X9jIGzilZ9T4rX9a6iX/8M7Dlo/W+iNNzrfmBtcXnHZL0Gw6x/Ul4D4HeAe4v1PAj8zXDPAWYUt9cX9x8+3n6NsP4fFdv/ILCC38w4VvO/wXa6DPdaT9L6G5InQ61jHP1vWB7UcBsa9pkeah0T+Hs6kd/Mhtpy/fcy4L1saDY1YHsrsrDRfarz9lbNzkb3q87bXDVrp9ql7z8wSZIkSZL6OQxVkiRJklTBYlGSJEmSVMFiUZIkSZJUwWJRkiRJklTBYlGSJEmSWkBEXBsRWyLiwVE89rKIWFtcHo2IbWNdn8WiWl5EZEQsK7v9oYj4eHH94xHxVPEheTgizm5YRyVNORHxqyHa3xsRD0bEAxFxb0R8aLL7JmlqiYiXygqHtRExp2h/U0TcFRGPFJfzqjx3bUTcONl9VlXXAaeN5oGZeXFmHpuZxwL/BHxrrCuzWFQ7eB74w4jYf4j7Lys+JGcAn4uI3Seva5I0UES8HegCTsnM1wLHA882tleSpoAdfYVDcXk8Iv4b8FXg/Mw8itL5FP80Iv5735Mi4mhgOvDmiHhZY7quPpn5Y6CnvC0ijoiIH0TEmoj4vxFxVJWnnk3p/KBjYrGodvAicA1w8XAPysx1wHNAx2R0SpKG8GHgQ5n5HwCZ+Xxmfr7BfZI0NV0IXJeZ9wBk5jPAXwKXlD3mbODLwCpKP7yr+VwD/O/MnA98CLiq/M6IeCVwGPCjsS54t5p0T2q8K4H7I+IfhnpARMwD1mXmlsnrliRVOAZY0+hOSJpy9oqItcX1xzLzncBrgOsHPW510d7nfwJvA44C/jelPZFqEhGxD/B7wD9HRF/znoMedhbwjcx8aazLt1hUW8jM7RFxA/DnwI5Bd18cEe8HXgWcPumdkyRJarwdxWE5oxYRC4BnMnNTRDwFXBsRnZnZM9JzNWmmAdtGeG/PorQXeVwLl9pFN/BBYPB4+ssy8zXAHwFfjIgZk94zSfqNh4D5je6EJAEPU5lH8ynlFJSGoB4VEY8DG4CZlL5PqUlk5nbgsYj4HwBR8rq++4vjFzuAfx/P8i0W1TaKX7luolQwVrt/JaWhFedOZr8kaZC/A/6xmFiCiNgjIv64wX2SNDVdCbwvIo4FiIj9gE8D/xAR04B3A6/NzDmZOYfSMYvOLN9AEfE1SoXfb0fE5oj4IPAe4IMRcR+lQr/82NKzgBszM8ezPoehqt0sAy4a5v5PAF+NiM9n5q5J6pOkqWvviNhcdnt5Zi6PiAOBH0bpAJMErm1M9yRNZZn5dEScA3w+Il4OBNCdmd+NiBOAp/om4yr8GHh1RByUmU83os9TXWYOVaxXPZ1GZn58IuuLcRaZkiRJkqQ25jBUSZIkSVIFi0VJkiRJUgWLRUmSJElSBYtFSZIkSVIFi0VJkiRJUgWLRUmSJElSBYtFSZIkSVIFi0VJkiRJUoX/H2ifGmFpYd+0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,3, sharey=True, figsize = (15,4))\n",
    "ax1 = p1.plot(x='NR',y='target',c='DarkBlue', marker = \"x\", kind = \"scatter\",ax=axes[0])\n",
    "ax2 = p1.plot(x='LC',y='target',c='DarkBlue',marker = \"x\",kind = \"scatter\",ax=axes[1])\n",
    "ax3 = p1.plot(x='FOA',y='target',c='DarkBlue',marker = \"x\", kind = \"scatter\",ax=axes[2])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,3, sharey=True, figsize = (15,4))\n",
    "ax1 = p2.plot(x='RS',y='target',c='DarkBlue', marker = \"x\", kind = \"scatter\",ax=axes[0])\n",
    "ax2 = p2.plot(x='LC',y='target',c='DarkBlue',marker = \"x\",kind = \"scatter\",ax=axes[1])\n",
    "ax3 = p2.plot(x='FOA',y='target',c='DarkBlue',marker = \"x\", kind = \"scatter\",ax=axes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,3, sharey=True, figsize = (15,4))\n",
    "ax1 = p3.plot(x='N',y='target',c='DarkBlue', marker = \"x\", kind = \"scatter\",ax=axes[0])\n",
    "ax2 = p3.plot(x='RS',y='target',c='DarkBlue',marker = \"x\",kind = \"scatter\",ax=axes[1])\n",
    "ax3 = p3.plot(x='LC',y='target',c='DarkBlue',marker = \"x\", kind = \"scatter\",ax=axes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,3, sharey=True, figsize = (15,4))\n",
    "ax1 = p4.plot(x='RS',y='target',c='DarkBlue', marker = \"x\", kind = \"scatter\",ax=axes[0])\n",
    "ax2 = p4.plot(x='LC',y='target',c='DarkBlue',marker = \"x\",kind = \"scatter\",ax=axes[1])\n",
    "ax3 = p4.plot(x='FOA',y='target',c='DarkBlue',marker = \"x\", kind = \"scatter\",ax=axes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,3, sharey=True, figsize = (15,4))\n",
    "ax1 = p5.plot(x='N',y='target',c='DarkBlue', marker = \"x\", kind = \"scatter\",ax=axes[0])\n",
    "ax2 = p5.plot(x='RS',y='target',c='DarkBlue',marker = \"x\",kind = \"scatter\",ax=axes[1])\n",
    "ax3 = p5.plot(x='UCF',y='target',c='DarkBlue',marker = \"x\", kind = \"scatter\",ax=axes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,3, sharey=True, figsize = (15,4))\n",
    "ax1 = p6.plot(x='SNF',y='target',c='DarkBlue', marker = \"x\", kind = \"scatter\",ax=axes[0])\n",
    "ax2 = p6.plot(x='RS',y='target',c='DarkBlue',marker = \"x\",kind = \"scatter\",ax=axes[1])\n",
    "ax3 = p6.plot(x='LC',y='target',c='DarkBlue',marker = \"x\", kind = \"scatter\",ax=axes[2])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}