dummy_data_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "672ee0e6",
   "metadata": {},
   "source": [
    "## 🔘 Create dummy data for `Clustering`\n",
    "\n",
    "`sklearn.datasets.make_blobs()` Generates isotropic Gaussian blobs for clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e343277",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b4791b",
   "metadata": {},
   "source": [
    "**Make Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1104be50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the features (X) and output (y) with 100 samples,\n",
    "X, y = make_blobs(n_samples = 100,\n",
    "                  n_features = 2, # dimension of each data point,\n",
    "                  centers = 3, # number of clusters,\n",
    "                  cluster_std = 0.5, # std deviation of each cluster\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81072c52",
   "metadata": {},
   "source": [
    "**Plot Blobs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f7785d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wP4PuMLM9ZjZjZjMLCwsdKkdEpD+0tU/ezB4DLg+46y53/3rpMXcB54FDQc/h7geBg7D8zVDt1CMiIqu1FfLufkPU/Wb2S8DNwPXeTd8zKCLSJ2I749XM3g98GvhZdz8X1zgiIhIuzp78fwbeBHzbzObM7L/EOJaIiASIbSbv7j8R13OLiEhjdMariEiKKeRFRFJMIS8ikmIKeRGRFFPIi4ikmEJeRCTFFPIiIimmkBcRSTGFvIhIiinkRURSTCEvIpJiCnkRkRRTyIuIpJhCXkQkxRTyIiIpppAXEUmx2EPezD5lZm5ml8U9loiIrBZryJvZlcCNwJk4xxERkWBxz+Q/x/KXeXvM44iISIDYQt7MbgXy7v50ncftMbMZM5tZWFiIqxwRkb7U1hd5m9ljwOUBd90F/HuWWzWR3P0gcBBgdHRUM34RkTXUVsi7+w1Bt5vZNmAr8LSZAVwBPGVm17n799sZU0REGtdWyIdx9xPAW8q/m9nzwKi7/yCO8UREJJj2yYuIpFgsM/lq7r6lE+OIiMhqmsmLiKSYQl5EJMUU8iIiKaaQFxFJMYW8iEiKKeRFRFJMIS8ikmIKeRGRFFPIi4ikmEJeRCTFFPIiIimmkBcRSTGFvIhIiinkRURSTCEvIpJisYa8mX3SzJ41s5Nm9ttxjiUiIrVi+9IQM9sJ3Apc4+4/MrO31PsbERFZW3HO5H8VOODuPwJw95diHEtERALEGfJvB37GzJ40s++Y2U8GPcjM9pjZjJnNLCwsxFiOiEj/aatdY2aPAZcH3HVX6bnfDLwX+EngQTP7R+7ulQ9094PAQYDR0VGvfiIREWldWyHv7jeE3WdmvwocKYX6n5vZ68BlgKbrIiIdEme7ZgrYCWBmbwcuAn4Q43giIlIltt01wJeAL5nZM8BrwMerWzUiIhKv2ELe3V8DPhbX84uISH0641VEJMUU8iIiKaaQFxFJMYW8iEiKKeRFRFJMIS8ikmIKeRGRFFPIi4ikmEJeRCTFFPIiIimmkBcRSTGFvIhIisV5FcquMjWbZ3J6nrOLBTYNZpkYG2Z8JJd0WSIiseqLkJ+azbPvyAkKxSUA8osF9h05AaCgF5FU64t2zeT0/ErAlxWKS0xOzydUkYhIZ/RFyJ9dLDR1u4hIWsQW8ma23cyOmdmcmc2Y2XVxjVXPpsFsU7eLiKRFnDP53wbuc/ftwGdKvydiYmyYbGZg1W3ZzAATY8MJVdRdpmbz7DhwlK17H2HHgaNMzeaTLklE1kicC68O/IPSzxuBszGOFam8uKrdNbW0KC2SbhbXd2ub2TuAacBY/sTw0+5+OuBxe4A9AJs3b7729Omah0iMdhw4Sj5gbSI3mOWJvbsSqEhEmmVmx919NPC+dkLezB4DLg+46y7geuA77v6wmf0isMfdb4h6vtHRUZ+ZmWm5njTvhY/rtW3d+whh/wvIpew9FEmrqJBvq10TFdpm9t+A20u/fhW4v52x6klz2yHO17ZpMBs4k1/rcUQkGXEuvJ4Ffrb08y7gr2Ica832wjeyCNnsQmWrC5vlv7vj8Fxs+/yDFqXjGEdEkhHnwuuvAF8ws/XA31Pqu8dlLfbCB82Y7zw8x8zpl/mN8W2hj4ma7bY6C6/+u7DX1m4bp3JROmxGr/MJRHpXbDN5d/8zd7/W3a9x9/e4+/G4xoK12Qsf9GnAgUPHzqzMvsM+Mdz3zZOBs/V7v3GypVl40DjVBjdk2HfkBPnFAs6FA0iznywAnti7i5zOJxBJndSc8boWe+HDZqwOK6Ec9phXzhVrwvbuqRMsFopNjdXo/dnMAO40fQApf0IIOjDofAKR9ElNyI+P5Ni/exu5wSzG8s6Q/bu3NdW6iJqxlkO30VltobjEA0++EDlWVK8+apzyaws7gIS1XSB67WIt3kMR6S6pugrl+EiurUCaGBvmzsNzgVsKy6E7MTZct1dethSxPXXnVUORvfqgcbKZgVWh+6kHnw4cY8AsdNx6axftvoci0l1SM5NfC+MjOf7lezdTHZGVLYvK2S5EB2rYXZduyPD4swuRrZZGZtVhB5Gog4uu4yPSXxTyVX5jfBuf+8j2yHAtz7SzmYHIQDUgM7A66bOZAe75havrzqgb2TUTtlBavj2oHaS+u0h/ie2yBq1o94zXTgq7HEC1wWyGS96wviasoy4n0EirZmo2z33fPMkr51b35cuPA0KfA3QdH5E0ie2M137W6N7xHxaKzN1zY83tYUE+MTZcd3E0bA/9YDbDvbdcvXIQCXuOJ/buUqiL9Am1a1rUaA877HFRPfd6rZywPfSXvGH9Snjri1JEBBTyQGuXHWi0h/3qj86HXhqhfJbpOjPyiwUmp+eZms3XXRxtJMC1wCoi0MchXw72LXsf4c7Dc02fNTo+kuPSDZm64ywWijXPV3lCElzYDVMee+dVQ5GLo40EuBZYRQT6NOSrQ7Z66bnRi3Ld8wtX1+yeCVL9fFGXLCgUl3j82YXI7ZM7rxqK3OYJa3NymIj0vp5feG3lAl2NXBemkd71+EiOe79xMvTM07Dna+SSBmEnJU3N5nn4eH7VgcmAD15b+3id2CQiPR3yrV7hsZEAb7R3/cMGAr76+aKu4V5v7LCLqD3+7EJDdcQtzV/cItKLerpd0+o15OsFeDO960YOBtXPV+8a7mGLtdDdu2aiLn4mIsno6ZBvNfCCQrbc447qXTd6BmlmnXHphkzkGbOVl0aovvxB0GJtWTfvmlmrL24RkbXT0+2asLZHvcCr/KKMRtsKYa2h/bu3sX/3tqZbFJX98qCzXytPfqoUdRJV0rr5U4ZIv2or5M3sw8C9wDuA69x9puK+fcAvA0vAr7n7dDtjBWkn8JpdlIyapbZ7Bmkz4djKAapTWj3oikh82p3JPwPsBv5r5Y1m9k7gNuBqYBPwmJm93d3rX5+3CZ0MvLAgzpe+gq+dMZsNx27dNdPNnzJE+lVbIe/upwCs9pq6twJfcfcfAc+Z2V8D1wH/q53xgnQq8KJ2xDSyoydKWsKxmz9liPSruHryOeBYxe8vlm6rYWZ7KH3J9+bNm2Mqp31RXxYS1j9vVJrCsVs/ZYj0q7ohb2aPAZcH3HWXu3+93QLc/SBwEJYvNdzu88WlHFx3HJ4LvL/dxUWFo4jEoW7Iu/sNLTxvHriy4vcrSrf1tPGR3MpFxappcVFEulFc++S/AdxmZm8ws63A24A/j2msjtKFv0Skl7S7hfKfA78DDAGPmNmcu4+5+0kzexD4LnAe+MRa76xJSpr65yKSfvr6PxGRHhf19X89fVkDERGJppAXEUkxhbyISIop5EVEUkwhLyKSYl21u8bMFoDTHRzyMuAHHRyvVb1QZy/UCL1RZy/UCL1RZy/UCO3X+ePuPhR0R1eFfKeZ2UzYtqNu0gt19kKN0Bt19kKN0Bt19kKNEG+dateIiKSYQl5EJMX6PeQPJl1Ag3qhzl6oEXqjzl6oEXqjzl6oEWKss6978iIiadfvM3kRkVRTyIuIpFhfh7yZDZjZrJn996RrCWJmz5vZCTObM7OuvTynmQ2a2UNm9qyZnTKzn0q6pkpmNlx6D8v//J2Z3ZF0XUHM7E4zO2lmz5jZA2Z2cdI1VTOz20v1neym99HMvmRmL5nZMxW3vdnMvm1mf1X696VdWOOHS+/l62a25tso+zrkgduBU0kXUcdOd9/e5Xt9vwB8y92vAq6hy95Td58vvYfbgWuBc8DXkq2qlpnlgF8DRt39XcAAcFuyVa1mZu8CfgW4juX/1jeb2U8kW9WKPwDeX3XbXuCP3f1twB+Xfk/SH1Bb4zPAbuBP4xiwb0PezK4AbgLuT7qWXmZmG4H3AV8EcPfX3H0x0aKiXQ/8H3fv5JnVzVgPZM1sPbABOJtwPdXeATzp7ufc/TzwHZYDKnHu/qfAy1U33wr8YennPwTGO1lTtaAa3f2Uu8/HNWbfhjzweeDTwOsJ1xHFgUfN7LiZ7Um6mBBbgQXg90utr/vN7JKki4pwG/BA0kUEcfc88B+BM8D3gB+6+6PJVlXjGeBnzOzHzGwD8POs/j7nbvNWd/9e6efvA29Nspgk9GXIm9nNwEvufjzpWur4p+7+buADwCfM7H1JFxRgPfBu4PfcfQR4leQ/Egcys4uAW4CvJl1LkFK/+FaWD5ybgEvM7GPJVrWau58Cfgt4FPgWMAf0xFd7+vJ+8b7bM96XIQ/sAG4xs+eBrwC7zOzLyZZUqzSzw91fYrmHfF2yFQV6EXjR3Z8s/f4Qy6HfjT4APOXuf5t0ISFuAJ5z9wV3LwJHgJ9OuKYa7v5Fd7/W3d8HvAL8ZdI1RfhbM/uHAKV/v5RwPR3XlyHv7vvc/Qp338Lyx/ej7t5VMyYzu8TM3lT+GbiR5Y/KXcXdvw+8YGbDpZuuZ/kL3LvRR+nSVk3JGeC9ZrbBzIzl97KrFrEBzOwtpX9vZrkf/0fJVhTpG8DHSz9/HPh6grUkYn3SBUiotwJfW/7/OuuBP3L3byVbUqhPAodK7ZC/Af51wvXUKB0ofw74t0nXEsbdnzSzh4CngPPALN15Wv7DZvZjQBH4RLcstJvZA8A/Ay4zsxeBe4ADwINm9sssX8b8F5OrMLTGl4HfAYaAR8xszt3H1mxMXdZARCS9+rJdIyLSLxTyIiIpppAXEUkxhbyISIop5EVEUkwhLyKSYgp5EZEU+//GN9+2H/JQAQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "694ef174",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_twitter",
   "language": "python",
   "name": "env_twitter"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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