DC and ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data and do cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Stock Ticker',\n",
       " 'Previous Close',\n",
       " 'Open',\n",
       " 'Bid',\n",
       " 'Ask',\n",
       " \"Day's Range\",\n",
       " '52 Week Range',\n",
       " 'Volume',\n",
       " 'Avg. Volume',\n",
       " 'Market Cap',\n",
       " 'Beta (3Y Monthly)',\n",
       " 'PE Ratio (TTM)',\n",
       " 'EPS (TTM)',\n",
       " 'Forward Dividend',\n",
       " 'Forward Yeild',\n",
       " 'Ex-Dividend Date']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pylab import plot,show\n",
    "from numpy import vstack,array\n",
    "from numpy.random import rand\n",
    "from scipy.cluster.vq import kmeans,vq\n",
    "from math import sqrt\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "\n",
    "# File to Load (Remember to Change These)\n",
    "file_to_load = \"Resources/final_df.csv\"\n",
    "\n",
    "# Read File and store into Pandas data frame, dropna\n",
    "final_pre = pd.read_csv(file_to_load)\n",
    "final_drop = final_pre.drop('Earnings Date',axis=1).drop('1y Target Est',axis = 1).dropna()\n",
    "final_drop.head()\n",
    "list(final_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create column \"Dividend yield\"\n",
    "final_drop['Dividend_yield'] = final_drop['Forward Dividend']/final_drop['EPS (TTM)']\n",
    "data =final_drop\n",
    "data.count()\n",
    "data\n",
    "data.to_csv(\"data2019.csv\", index=True, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine learning K-means clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Using K-Means Algorithm in clustering stocks from S&P500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20511c88198>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RrLUxMbzMNwbFXGOQzDcGyXxjUMy15dm+ffuC25cVnrXW781+XUr5oySfWd6wAAAAGHbLup1KKeWqeU9/OcmB/gwHAACAYbOU26n8SZI3JhktpXw3yfuSvLGU8prMHGr7nST/puEYAQAAWMcWDc9a668vsPljDcYCAADAEFrWobYAAACwVMITAACApoQnAAAATQlPAAAAmhKeAAAANCU8AQAAaEp4AgAA0JTwBAAAoCnhCQAAQFPCEwAAgKaEJwAAAE0JTwAAAJoSngAAADQlPAEAAGhKeAIAANCU8AQAAKAp4QkAAEBTwhMAAICmhCcAAABNCU8AAACaEp4AAAA0JTwBAABoSngCAADQlPAEAACgKeEJAABAU8ITAACApoQnAAAATQlPAAAAmhKeAAAANCU8AQAAaEp4AgAA0JTwBAAAoCnhCQAAQFPCEwAAgKaEJwAAAE0JTwAAAJoSngAAADQlPAEAAGhKeAIAANCU8AQAAKAp4QkAAEBTwhMAAICmhCcAAABNCU8AAACaEp4AAAA0JTwBAABoSngCAADQlPAEAACgKeEJAABAUyOL7VBKuTfJLyY5Umsd623bmuRPk7wyyXeSlFrrZLthAgAAsF4tZcXzviS3nrXtt5N8sdZ6TZIv9p4DAADACywanrXW/Ukmztr8liT7el/vS/JLfR4XAAAAQ2K553i+rNZ6KEl6f760f0MCAABgmCx6judKlVL2JNmTJLXWjI6Otv7I8zIyMrLmxsTwMt8YFHONQTLfGCTzjUEx1/prueH5vVLKVbXWQ6WUq5IcOdeOtdZ7ktzTe9o9evToMj+yjdHR0ay1MTG8zDcGxVxjkMw3Bsl8Y1DMteXZvn37gtuXe6jtp5O8rff125L8xTLfBwAAgCG3lNup/EmSNyYZLaV8N8n7krw/SS2l3JHkH5L8astBAgAAsH4tGp611l8/x7d+vs9jAQAAYAgt91BbAAAAWBLhCQAAQFPCEwAAgKaEJwAAAE0JTwAAAJoSngAAADQlPAEAAGhKeAIAANCU8AQAAKAp4QkAAEBTwhMAAICmhCcAAABNCU8AAACaEp4AAAA0JTwBAABoSngCAADQlPAEAACgKeEJAABAU8ITAACApoQnAAAATQlPAAAAmhKeAAAANCU8AQAAaEp4AgAA0JTwBAAAoCnhCQAAQFPCEwAAgKaEJwAAAE0JTwAAAJoSngAAADQlPAEAAGhKeAIAANCU8AQAAKAp4QkAAEBTwhMAAICmhCcAAABNCU8AAACaEp4AAAA0JTwBAABoSngCAADQlPAEAACgKeEJAABAU8ITAACApoQnAAAATQlPAAAAmhKeAAAANCU8AQAAaEp4AgAA0JTwBAAAoCnhCQAAQFMjK3lxKeU7SX6Q5HSS6VrrDf0YFAAAAMNjReHZ86Za69E+vA8AAABDyKG2AAAANNXpdrvLfnEp5X8kmUzSTfKHtdZ7FthnT5I9SVJr3TU1NbXsz2thZGQk09PTqz0MLhDmG4NirjFI5huDZL4xKOba8lx00UVJ0jl7+0rDc3ut9WAp5aVJvpDk39Za97/IS7oHDx5c9ue1MDo6mqNHHSnMYJhvDIq5xiCZbwyS+cagmGvLs3379mSB8FzRoba11oO9P48k+VSS167k/QAAABg+yw7PUsqlpZQfnv06yT9NcqBfAwMAAGA4rOSqti9L8qlSyuz7/Pda6+f6MioAAACGxrLDs9b690le3cexAAAAMITcTgUAAICmhCcAAABNCU8AAACaEp4AAAA0JTzhAtXtdnPixIl0u93VHgoAAENOeA45ccFCut1ubr/99uzevTu33Xab+QEAQFMruY8na9xsXIyPj2dsbCx79+5Np9NZ7WGxBpw8eTLj4+M5dOjQ3PNLLrlklUcFAMCwEp5DTFxwLps3b87Y2FiSZGxsLJs3b17lEQEAMMyE5xATF5xLp9PJ3r17c/LkyWzevNlKOAAATQnPISYueDGdTscKOAAAAyE8h5y4AAAAVpur2gIAANCU8IQ1wG1vAAAYZsITVpl7agIAMOyc4wmrzG1vAAAYdsKTdaHb7Q7t1Xnd9gYAgGEnPFnzZg9FHR8fz9jYWPbu3TtU8em2NwAADDvhyZp3IRyK6rY3AAAMM+HJmudQVAAAWN+EJ2ueQ1EBAGB9E56sCw5FBQCA9ct9PAEAAGhKeLKudbvdnDhxIt1ut+/v06/3BgCAC53wZN2avc3K7t27c9ttty07EBd6n369NwAA4BxPVqjb7a7aRX/6dZuVhd4nydDfwgUAAAZFeLJss6uC4+PjGRsby969ewcan/26zcq53sctXAAAoD+EJ8u20hXHla6W9vM2Kx/+8IeTJJdccsnc+7iFCwAA9IdzPNeJtXihm9mVwquuuuq8VwXP9xzKc/39Z2+zstwwnB3HzTffnHe96119fW8AAGCGFc91YLUPaT2Xlaw4ns9qacu/f7/OEwUAAM5NeK4DazmOZlcFz9f5nJ/Z8u/fr/NEAQCAcxOe68AwxtH5rJa2/Pv38zxR/tFqXu0YAIC1R3iuA8MaR0tdLW3991/uqi0LW6uHhgMAsHqE5zrR6XSyefPmoYvPpRKH68daPjQcAIDV4aq268T5XgV2Ke+31q6SuxqW+nPw81q6lVztGACA4WTFc42bPVeu2+32bRXJoZAzlvpz8PM6P8N6aDgAAMsnPNeIhS7Gcnbw7NixI8nKL7DT4lDI9Xgxmfk/h263m2PHjmXbtm0vGL9DR8+fQ6MBAJhPeK4B51pROzt4HnzwwblzPVcSd/2+Sux6XRGc/Tl0u92cPn06b37zm3P99de/YPzDeFVhAAAYJOG5ihY6jHb2XMJLL730BcFzySWX9CXoXuxQyOWsXK7XFcHZn8OxY8fy5je/OYcPH54L/vnjd+goAACsjPAcgKUcRnvttddmYmIik5OTueuuu3Lfffc1DZ6FDoVc7srloFYEWxzO2+l0sm3btlx//fXpdDrnHL9DRwEAYPmEZ5+dHUdLOYx2YmIil112WbrdbqampvLYY4/lxIkTc4fVDip4lrty2SKQl/pz7AcrmgAA0JbbqfTRQrc8OX78eL75zW/m0KFDOXDgQE6ePJnkH1cJr7zyyiTJk08+mU6nkyuvvDJjY2O56667cuONN+btb397ut3u3CG4Z86cyfHjx3P8+PEXvbXH2bf/WMrtQFZyG4zZFcF+RefZP8f5UTz/59gv/Rw/AADwfFY8V2j+ytzJkyfz6KOP5vDhw3PReeONN85F5Y4dO+ZibnaV7fjx43nnO9+Zxx9/PGNjY/nIRz6Sbrebn/qpn8qpU6fy1FNP5Zlnnsldd92Vxx57LKdPn873v//9dDqd3HTTTdm7d2+effbZFz2M9957780dd9yx6GrhWln5W2jl1QV+lm49XmEYAIDhJjxX4OzA+9jHPpbTp09nw4YNOX36dE6cOJFjx47N7Ts1NZUTJ07MHb46G53j4+O59tpr89GPfjTPPffc81Ylu91u7rzzzuzfvz9nzpx53uc/8sgjeetb35onnngiO3bsyH333ZdTp06l2+0+L4AnJib6cvGfcwVNv0NnochcK1G81q3XKwwDADDchOd5ODuw5q/MnTlzJhMTE9mwYUPOnDkzd17i5ZdfnomJiSTJ3/7t32b37t0ZGxvLqVOn8pWvfCVTU1NJZg61veaaa3LZZZflx3/8x/OzP/uz+da3vpWf/MmfzOOPPz4XnbPvmySnT5/Ogw8+mG63myeffDI7d+7Mxo0bc91112VqamougLds2bKk1cIXi5Zzfe/Fti83Es8VmS7ws7j1eoVhAACGm/BcotnAevTRR+fu9bh58+a86lWvytGjRzMxMZF3v/vdee6555IkR44cya5du3L55ZfPvcf09HQOHz6cI0eOvGD1Mkmee+65HDt2bG6V9Ed+5Efy5S9/ee49k2TLli0ZGRnJtddemyeeeGIuQs+cOTP3uiNHjszF38aNG3Pq1KnnhVySnDhx4gVReK5o6Xa7OXbs2Nwq6vzvneuw2JWuuonM5XFIMgAAa5HwPIezV+xOnDiRBx98MKdOncrk5GSefPLJbN68OQ8//PBcGD7wwAMveI/JyckXvPdC0bmQp59++gXbJiYmsnv37uzbty833HBDkmRkZCTT09Mv+OyXvvSluf766+f+DrMRea4oXCha5gf36dOn5y5+NBs0C73GqtvqcUgyAABrkfBcwEJxNntl2SQ5depUbrjhhpw+fXpVxvflL385O3fuzFNPPZUkueyyy/LMM8/MHbbb6XQyOjqav/mbv8mll146t2K5ZcuWTE5OLrhyOfu6e++9NxMTE9m6detccM/uf+WVV+bzn/98tm3b9rzDX88OHatuq8tqMQAAa43wXMDZK3bHjx/PnXfeORd2SVYtOpOZQ3JnozOZCeUbb7wxjz32WHbs2JEPfOADGR0dTafTyTPPPJM3vOENmZiYyKZNm7J169YFVy5n3+fsq99efPHFz7tg0myQznd26Fh1AwAA5hOeC5hdset2u7n22mvzjne8I/v371/tYSV5/sWFZj3zzDM5cOBAzpw5k/Hx8bz73e/O/fffn3e84x154IEH5oJ5ampqbuXyc5/73Fwszh5WfPbVcGfvlblx48acOXMmGzduzLPPPruk1TSrbgAAwKwVhWcp5dYkH0qyMclHa63v78uo1oDf//3fzzvf+c489NBDz1vpbGWhoDzbtm3bcu211+ZLX/rS3LatW7fm+PHjz7ug0IMPPpif/umfzsaNG5839k2bNmXbtm25/vrr85u/+Zs5cOBAduzYkQ0bNmR8fDw7duzI9PT03OrmxRdfnE6nk+uvvz6dTsdhswAAwLIsOzxLKRuTfDjJP0ny3SRfLaV8utb6eL8GN2jdbjcnTpzI3XffnUceeSSTk5PNovPnfu7n8q1vfSuTk5PZsmVLksxdlXYhb3rTm/LBD34wl19+eXbt2pXJycls3bo1Dz30UO6+++48+uijmZ6enrt1y+TkZG666aY8/fTT6Xa7ef3rX5/7778/U1NT6Xa7ufnmm+duA9PpdHL48OG5Vc2zVzcXO2y23/fxBAAAhstKVjxfm+Tbtda/T5JSyieSvCXJugzP2QsKPfLII3nqqady6tSpJp8ze5/Pr33ta7npppvygQ98IFu3bs3tt9+e/fv357nnnnvByufu3buzcePG3HrrrTl9+nQ2btyYm266KR//+MezYcOGucfOnTvzyCOPZGJiItu2bcvHP/7xPPvss0mSSy65JJ1OJyMjI+l2u3MX/9mxY0c6nU46nc7c1wcOHHje6uaLHTb7YlfJBQAASFYWni9P8j/nPf9ukp9d2XBWz+wFhb73ve/lJS95yVwgjoyMpNPpZHp6etFDYefbsmVLNmzYkE2bNuW6667L7/7u72bLli25+eabc+TIkUxNTeXAgQO59NJLs3Hjxtx33305ceJEzpw5kzvvvDMPPfRQkuTGG2/MH/7hH+aNb3xjDh8+PDeu2ftzJsn4+HgOHz6cTqeTL33pS3n22WezdevWbNiwIZdeeukLxnb2xX9m//5nf72UgHTrFAAAYDErCc+FquQFZVZK2ZNkT5LUWjM6OrqCj+y/kZGRjI6OptvtZteuXfnGN76RV7/61Tl9+nTGx8ezc+fO7Nu3b27/breb3/iN38gDDzyQ5557Lps2bcott9ySj3zkI3nta1+bY8eOzd2/c/v27fnKV76SK664Yu78y5/5mZ/JX/7lX6bT6eSGG27I1Vdf/YLA+9znPpfjx48nyVw47tq1Kw8//HCmp6ezadOm7Ny5M1dfffXc977xjW9k586d+dEf/dGBrjjO/7nNjsmK57nNzjdozVxjkMw3Bsl8Y1DMtf7qnM8q3nyllNcn+Z1a65t7z/9jktRa/+8XeVn34MGDy/q8VkZHR3P06NEkzz9XMTn3yt/suaDdbnfuMNROp5MzZ87k2LFjec973pPHH398wUNPZ1+b/OPhr0sxO7aLL744zz777PPGtdrnWK72568n8+cbtGSuMUjmG4NkvjEo5trybN++PVlgkXIlK55fTXJNKeXHkvyvJL+W5F+t4P1W3dnnMp7rkNFOp7PgIawbNo5yNhgAAAWOSURBVGzIFVdckX379p0zxM712vMZ29njWu1bl6z25wMAAGvbhuW+sNY6neTuJJ9P8sTMpvpYvwa2ns1fBQUAALjQreg+nrXWzyb5bJ/GAgAAwBBa9oonAAAALIXwBAAAoCnhCQAAQFPCEwAAgKaEJwAAAE0JTwAAAJoSngAAADQlPAEAAGhKeAIAANCU8AQAAKAp4QkAAEBTwhMAAICmhCcAAABNCU8AAACaEp4AAAA0JTwBAABoSngCAADQVKfb7Q7y8wb6YQAAAAxc5+wNg17x7Ky1Rynl66s9Bo8L52G+eQzqYa55DPJhvnkM8mG+eQzqYa6t6PECDrUFAACgKeEJAABAU8IzuWe1B8AFxXxjUMw1Bsl8Y5DMNwbFXOujQV9cCAAAgAuMFU8AAACaGlntAayWUsqtST6UZGOSj9Za37/KQ2KdK6Xcm+QXkxyptY71tm1N8qdJXpnkO0lKrXWylNLJzPz750lOJHl7rfXh1Rg361Mp5eok9ye5MsmZJPfUWj9kztFvpZSLk+xP8pLM/H/DJ2ut7yul/FiSTyTZmuThJG+ttU6VUl6Smbm5K8mxJP9HrfU7qzJ41q1SysYkX0vyv2qtv2i+0Uop5TtJfpDkdJLpWusNfpe2cUGuePb+Y/bhJP8syauS/Hop5VWrOyqGwH1Jbj1r228n+WKt9ZokX+w9T2bm3jW9x54kfzCgMTI8ppO8p9Z6XZLXJXlX779j5hz9dirJLbXWVyd5TZJbSymvS/JfknywN9cmk9zR2/+OJJO11p9I8sHefnC+/l2SJ+Y9N99o6U211tfUWm/oPfe7tIELMjyTvDbJt2utf19rncrMv6C9ZZXHxDpXa92fZOKszW9Jsq/39b4kvzRv+/211m6t9f9Lcnkp5arBjJRhUGs9NPuvrLXWH2Tmf9BeHnOOPuvNmWd6Tzf1Ht0ktyT5ZG/72XNtdg5+MsnP91YJYElKKa9I8i+SfLT3vBPzjcHyu7SBCzU8X57kf857/t3eNui3l9VaDyUzoZDkpb3t5iB9U0p5ZZKdSb4Sc44GSikbSynfTHIkyReS/F2Sp2qt071d5s+nubnW+/7TSbYNdsSsc/8tyX/IzGkEycz8Md9opZvk/y2lfL2Usqe3ze/SBi7U8FzoX8Jc3pdBMgfpi1LKDyX5syTvrrV+/0V2NedYtlrr6Vrra5K8IjNHDV23wG6z88lcY9lKKbPXSvj6vM0vNqfMN1bqDbXWn87MYbTvKqXsfpF9zbcVuFDD87tJrp73/BVJDq7SWBhu35s9BKP355HednOQFSulbMpMdP5xrfXPe5vNOZqptT6V5IHMnFd8eSll9iKF8+fT3Fzrff9H8sLTEOBc3pDkX/Yu+PKJzBxi+99ivtFIrfVg788jST6VmX9c87u0gQs1PL+a5JpSyo+VUi5K8mtJPr3KY2I4fTrJ23pfvy3JX8zb/q9LKZ3eRTqenj2kA5aidw7Tx5I8UWv9vXnfMufoq1LKFaWUy3tfb07yC5k5p/ivk/xKb7ez59rsHPyVJH9Va7UiwJLUWv9jrfUVtdZXZub/z/6q1vp/xnyjgVLKpaWUH579Osk/TXIgfpc2cUHeTqXWOl1KuTvJ5zNzO5V7a62PrfKwWOdKKX+S5I1JRksp303yviTvT1JLKXck+Yckv9rb/bOZuRT3tzNzOe7bBj5g1rs3JHlrkvHeuXdJ8t6Yc/TfVUn29a4IvyFJrbV+ppTyeJJPlFL+c5JvZOYfQtL78+OllG9nZuXp11Zj0Ayd34r5Rv+9LMmnSinJTBf991rr50opX43fpX3X6Xb9oxAAAADtXKiH2gIAADAgwhMAAICmhCcAAABNCU8AAACaEp4AAAA0JTwBAABoSngCAADQlPAEAACgqf8NjeuMPynI/I0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the PE ratios and Dividend yield values and plotting it\n",
    "f1 = data['PE Ratio (TTM)'].values\n",
    "f2 = data['Dividend_yield'].values\n",
    "X = np.array(list(zip(f1, f2)))\n",
    "plt.scatter(f1, f2, c='black', s=7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There's one obvious outlier so we want to get rid of it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stock Ticker         305\n",
       "Previous Close       305\n",
       "Open                 305\n",
       "Bid                  305\n",
       "Ask                  305\n",
       "Day's Range          305\n",
       "52 Week Range        305\n",
       "Volume               305\n",
       "Avg. Volume          305\n",
       "Market Cap           305\n",
       "Beta (3Y Monthly)    305\n",
       "PE Ratio (TTM)       305\n",
       "EPS (TTM)            305\n",
       "Forward Dividend     305\n",
       "Forward Yeild        305\n",
       "Ex-Dividend Date     305\n",
       "Dividend_yield       305\n",
       "dtype: int64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Dividend_yield'].max()\n",
    "data_final=data[data.Dividend_yield != 38.0]\n",
    "data_final.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Dividend yield')"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replot the data\n",
    "f1 = data_final['PE Ratio (TTM)'].values\n",
    "f2 = data_final['Dividend_yield'].values\n",
    "X = np.array(list(zip(f1, f2)))\n",
    "plt.scatter(f1, f2, c='black', s=7)\n",
    "plt.title('P/E ratios vs. Dividend yields for S&P 500')\n",
    "plt.xlabel('P/E ratios')\n",
    "plt.ylabel('Dividend yield')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Euclidean Distance Caculator\n",
    "def dist(a, b, ax=1):\n",
    "    return np.linalg.norm(a - b, axis=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6.  2.]\n",
      " [36.  4.]\n",
      " [70.  4.]\n",
      " [82.  1.]\n",
      " [83.  0.]]\n"
     ]
    }
   ],
   "source": [
    "# Number of clusters\n",
    "k = 5\n",
    "# X coordinates of random centroids\n",
    "C_x = np.random.randint(0, np.max(X)-20, size=k)\n",
    "# Y coordinates of random centroids\n",
    "C_y = np.random.randint(0, np.max(X)-133, size=k)\n",
    "C = np.array(list(zip(C_x, C_y)), dtype=np.float32)\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20511633ba8>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting along with the Centroids\n",
    "plt.scatter(f1, f2, c='#050505', s=7)\n",
    "plt.scatter(C_x, C_y, marker='*', s=100, c='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To store the value of centroids when it updates\n",
    "C_old = np.zeros(C.shape)\n",
    "# Cluster Lables(0, 1, 2)\n",
    "clusters = np.zeros(len(X))\n",
    "# Error func. - Distance between new centroids and old centroids\n",
    "error = dist(C, C_old, None)\n",
    "# Loop will run till the error becomes zero\n",
    "while error != 0:\n",
    "    # Assigning each value to its closest cluster\n",
    "    for i in range(len(X)):\n",
    "        distances = dist(X[i], C)\n",
    "        cluster = np.argmin(distances)\n",
    "        clusters[i] = cluster\n",
    "    # Storing the old centroid values\n",
    "    C_old = copy.deepcopy(C)\n",
    "    # Finding the new centroids by taking the average value\n",
    "    for i in range(k):\n",
    "        points = [X[j] for j in range(len(X)) if clusters[j] == i]\n",
    "        C[i] = np.mean(points, axis=0)\n",
    "    error = dist(C, C_old, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20511c5bb00>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['r', 'g', 'b', 'y', 'c', 'm']\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(k):\n",
    "        points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])\n",
    "        ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])\n",
    "ax.scatter(C[:, 0], C[:, 1], marker='*', s=200, c='#050505')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# computing K-Means with K = 5 (5 clusters)\n",
    "centroids,_ = kmeans(f1,5)\n",
    "# assign each sample to a cluster\n",
    "idx,_ = vq(f1,centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(69.31, 0), (68.51, 0), (72.44, 0), (116.9, 0), (123.13, 0), (68.97, 0), (89.33, 0), (104.98, 0), (83.09, 0), (66.11, 0), (84.23, 0), (84.37, 0), (83.27, 0), (138.2, 0), (87.73, 0)]\n"
     ]
    }
   ],
   "source": [
    "details = [(PE,cluster) for PE, cluster in zip(f1,idx)]\n",
    "selected = []\n",
    "detailDict = {}\n",
    "for detail in details:\n",
    "    if detail[1]==0:\n",
    "        selected.append(detail)\n",
    "print(selected)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_df = pd.DataFrame(selected)\n",
    "# selected_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "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>PE Ratio (TTM)</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Stock Ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69.31</td>\n",
       "      <td>0</td>\n",
       "      <td>AMT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.51</td>\n",
       "      <td>0</td>\n",
       "      <td>CPB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72.44</td>\n",
       "      <td>0</td>\n",
       "      <td>CCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>116.90</td>\n",
       "      <td>0</td>\n",
       "      <td>XRAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123.13</td>\n",
       "      <td>0</td>\n",
       "      <td>DLR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68.97</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>89.33</td>\n",
       "      <td>0</td>\n",
       "      <td>FDX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>104.98</td>\n",
       "      <td>0</td>\n",
       "      <td>FE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>83.09</td>\n",
       "      <td>0</td>\n",
       "      <td>MGM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>66.11</td>\n",
       "      <td>0</td>\n",
       "      <td>MCHP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>84.23</td>\n",
       "      <td>0</td>\n",
       "      <td>NI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>84.37</td>\n",
       "      <td>0</td>\n",
       "      <td>PRGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>83.27</td>\n",
       "      <td>0</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>138.20</td>\n",
       "      <td>0</td>\n",
       "      <td>SYMC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>87.73</td>\n",
       "      <td>0</td>\n",
       "      <td>UDR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PE Ratio (TTM)  Cluster Stock Ticker\n",
       "0            69.31        0          AMT\n",
       "1            68.51        0          CPB\n",
       "2            72.44        0          CCI\n",
       "3           116.90        0         XRAY\n",
       "4           123.13        0          DLR\n",
       "5            68.97        0            D\n",
       "6            89.33        0          FDX\n",
       "7           104.98        0           FE\n",
       "8            83.09        0          MGM\n",
       "9            66.11        0         MCHP\n",
       "10           84.23        0           NI\n",
       "11           84.37        0         PRGO\n",
       "12           83.27        0           PG\n",
       "13          138.20        0         SYMC\n",
       "14           87.73        0          UDR"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_df.rename(columns={0:'PE Ratio (TTM)',\n",
    "                            1:'Cluster'},inplace =True)\n",
    "selected_df\n",
    "C0= pd.merge(selected_df, data, on='PE Ratio (TTM)', how='inner')\n",
    "C0_df=C0.loc[:,'PE Ratio (TTM)':'Stock Ticker']\n",
    "C0_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "C0_df.to_csv(\"Cluster0.csv\", index=True, header=True)"
   ]
  }
 ],
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