Experiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data[:, 2:4]\n",
    "X = pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.columns = ['petal_length', 'petal_width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petal_length  petal_width\n",
       "0           1.4          0.2\n",
       "1           1.4          0.2\n",
       "2           1.3          0.2\n",
       "3           1.5          0.2\n",
       "4           1.4          0.2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m+sepCUhSP6DaA4kiynoGoesYnOZaz0BqWFidAkSb6x+nxiFJPYTWBZDTko4ZhK1joPUMRAivU4g61z9OTUCS+gHVHkgUE76byN3XFSMQkUq0vO0SdvXtfP237mBef9i+qH2Tti1kX6kdscYMzOydZnaLmX389JZWYCKVIOyd+1Hfwx/nff1J3u2vdQEkijjrGdwHnAv8NvB54FrgB+7+J+mFF05jBiIi8RWqzuCt7v4B4Ii73w6sAi4oRIAiIlJacZLBK8GfvzSzBWQXunlj4UMSEZFii7O4zaNmNgu4C9hFtuL486lEJSIiRRXnm8Gn3P0X7v4w8AayayBrJEoKohIKo7SYjFSzOAPIu9x9xUT7ikEDyNWnEgqjkhSYiZSDRAPIZjbfzFYCM80sY2Yrgu0KsrOLRBKrhMIoLSYj1SzKmMHvAWuBRcBnxux/CfhYCjFJDaqEwqgkBWYi5S7OY6I/CMYLSk6PiarPyMgIG2+9jT39+1jedgmfvvMOmpubSx3WGcJiBMo+bpHT8j0mipMM5gN/BSxw9yvN7GJglbt/oXChRqNkICISX6GKzh4AHgcWBJ9/DNw8wYXvN7PDZtaf47iZ2WfNbMDMnjGzog9Gi4hIvGTQ4u5fBU4BuPtJ4NUJ+nwR6Mxz/EpgSbCtBzbHiEdERAokTjI4ZmZzCJa3NLPLgKP5Orj7k0C+idfXAF8K1mr+HjDLzM6PEZMIg4ODLF2WoaF1IUuXZRgcHEzUDgq/mIzqEaTcxRkzWAHcA1wC7ANagWvd/ZkJ+i0GHnX3tpBjjwJ3uvt3gs87gb9w97wDAhozkLGWLssw0txGU6aTo33dNI/08+O9fZNuB4VfTAZUjyClV6gxgx8C24GngUPA58iOGySKLWRfaHYys/Vm1mNmPUNDQwkvK9XkwMHDNGU6md44h6ZMJwcOHk7UDgq/mIzqEaTcxUkGXyL7Coq/JvsNYQnwDwmvv58z33y6CDgQ1tDdt7h7h7t3tLa2JrysVJMF8+dytK+bE6PDHO3rZsH8uYnaQbam4FDfTo6PHuFQ306Wx1xMZnzfJOcTKYY4yeAid/9Td/9msK0Hlia8/iPAB4JZRZcBR939hYTnlBqzo2sbzSP9HNp6C80j/ezo2paoHRR+MRktMCPlLs6YwReB+4KBXszsLcAH3X1Dnj4PAVcALWQfLX0CmAbg7veZmQH3kp1x9Etg3UTjBaAxAxGRycg3ZhDnFdZvIftb/M+DzxcCz5rZXsDd/c3jO7j79flO6NlMdFOMGEREJAVxkkG+egEREalgkZOBu/8szUBERKR04gwgi4hIlVIyEBERJQMREVEyEBERlAxERAQlAxERQclARERQMhAREZQMREQEJQMREUHJQEREUDIQERGUDEREBCUDERFByUBERFAyEBERlAxERAQlAxERQclARERQMhAREZQMREQEJQMREUHJQEREUDIompGREdat38CKt76Ndes3MDIyUuqQREReo2RQJBtvvY1dR6bTctVH2XVkOhtvva3UIYmIvEbJoEj29O9jXmYNMxpnMy+zhj39+0odkojIa5QMimR52yUc6tvJ8dEjHOrbyfK2S0odkojIa5QMiuTTd97BitknePHRu1kx+wSfvvOOUockIvKaulIHUCuam5t5YMumUochIhJK3wxERCT9ZGBmnWb2IzMbMLNbQ46vNbMhM9sdbH+adkwiInKmVB8TmdlU4O+AtwP7gafN7BF3/+G4pl9x9w+lGYuIiOSW9jeDS4EBd/+Ju58Avgxck/I1K4YK0USkXKSdDBYCz4/5vD/YN94fmNkzZrbNzC5IOaayoUI0ESkXaScDC9nn4z5/HVjs7m8G/h/wYOiJzNabWY+Z9QwNDRU4zNJQIZqIlIu0k8F+YOxv+ouAA2MbuPuwux8PPn4OWBl2Inff4u4d7t7R2tqaSrDFpkI0ESkXaSeDp4ElZvZGM5sOXAc8MraBmZ0/5uPVwLMpx1Q2VIgmIuUi1dlE7n7SzD4EPA5MBe53931m9kmgx90fAf67mV0NnARGgLVpxlROVIgmIuXC3Mc/wi9/HR0d3tPTU+owREQqipn1untH2DFVIIuIiJJBVFFrAnp7e5k1bxHTzmtl1rxF9Pb25uwf9ZyqRxCRtOkxUUTr1m9g15HpzMus4VDfTlbMPhH6vH/WvEWw5HIa2zsZ3d0Nzz3JLw7tD+0PRDpn1GuLiOSjx0QFELUm4Ngrx2ls76SucQ6N7Z0ce+V4zv5Rz6l6BBFJm5JBRFFrAupnzmB0dzcnR4cZ3d1N/cwZOftHPafqEUQkbUoGEUWtCdj52L/Ac09y8Esb4bkns59z9I96TtUjiEjaNGYgIlIjNGYgIiJ5KRmIiIiSQVSDg4MsXZahoXUhS5dlGBwcDK0piFMToPoBESkXGjOIaOmyDCPNbTRlOjna103zSD+HDw+dVVPw7muujlwToPoBESkmjRkUwIGDh2nKdDK9cQ5NmU4OHDwcWlMQpyZA9QMiUi6UDCJaMH8uR/u6OTE6zNG+bhbMnxtaUxCnJkD1AyJSLpQMItrRtY3mkX4Obb2F5pF+dnRtC60piFMToPoBESkXGjMQEakRGjMQEZG8lAxERKS2kkHUef1hNQVPPPEE5zS1UtfYwjlNrTzxxBPcfffdTK2fxdTGFqbWz+Luu+9m8+bNZ+zbvHkzAF1dXUxraGZqYwvTGprp6urSegYiUjZqaswg6rz+sJqCn/98P+csezsNyzt5eU83v9r7DX598tc0Zn7/tX2jfY8BnLXv1WO/YFpDM/Xtna/tP7a7m/e99zqtZyAiRaMxg0DUef1hNQUnTzkNy7M1BQ3LOzl5ymFK3Rn7mFIXvg84ZVPO2H/Kpmg9AxEpGzWVDKLO6w+rKaibYry8J1tT8PKebuqmGJw6ecY+Tp0M3wdM8VNn7J/ip7SegYiUD3evuG3lypU+GcPDw772hhs9s+pyX3vDjT48PBzabmBgwJe0tXt9ywJf0tbuAwMDvnPnTp9xXotPbZjjM85r8Z07d/pdd93lU85t8ikNc3zKuU1+1113+aZNm87Yt2nTJnd33759u9fVz/YpDXO8rn62b9++PXI8UduJiOQD9HiOf1drasxARKSWacxARETyUjIQERElgyRz+MPqEcL2iYiUu5ofM0gyhz+sHgE4a9+P9/YVJFYRkSQ0ZpBHkjn8YfUIYftERMpdzSeDJHP4w+oRwvaJiJS7mk8GSdYUCFvjIGyfiEi5q/kxAxGRWqExAxERySv1ZGBmnWb2IzMbMLNbQ47PMLOvBMe/b2aL045JRETOlGoyMLOpwN8BVwIXA9eb2cXjmv0JcMTdfxP4W+Bv0oxJRETOlvY3g0uBAXf/ibufAL4MXDOuzTXAg8HP24A1ZmYpxyUiImOknQwWAs+P+bw/2Bfaxt1PAkeBOeNPZGbrzazHzHqGhoZSCldEpDalnQzCfsMfP30pShvcfYu7d7h7R2tra0GCExGRrLSTwX7ggjGfFwEHcrUxszqgCdAivyIiRVSX8vmfBpaY2RuBfweuA947rs0jwAeBp4BrgSd8guKH3t7eF83sZwniagFeTNC/nFTTvUB13U813QtU1/1U071A9Pt5Q64DqSYDdz9pZh8CHgemAve7+z4z+yTZFXceAb4A/IOZDZD9RnBdhPMmek5kZj25Ci8qTTXdC1TX/VTTvUB13U813QsU5n7S/maAuz8GPDZu38fH/Pwr4A/TjkNERHJTBbKIiNRsMthS6gAKqJruBarrfqrpXqC67qea7gUKcD8V+aI6EREprFr9ZiAiImMoGYiISG0lAzO738wOm1l/qWNJyswuMLNvmtmzZrbPzD5c6pgmy8zOMbMfmNme4F5uL3VMSZnZVDPrM7NHSx1LUmb2UzPba2a7zaziFxIxs1lmts3M/i34/2dVqWOaDDO7KPhvcnp7ycxunvT5amnMwMwuB14GvuTubaWOJwkzOx843913mVkj0Au8y91/WOLQYgteTFjv7i+b2TTgO8CH3f17JQ5t0szsI0AHcJ67X1XqeJIws58CHe5eFUVaZvYg8G13/7yZTQfOdfdflDquJII3RP878BZ3n1RBbk19M3D3J6mSV124+wvuviv4eRR4lrNfAlgRPOvl4OO0YKvY31LMbBHwTuDzpY5FzmRm5wGXky12xd1PVHoiCKwBBiebCKDGkkG1ChYEygDfL20kkxc8VtkNHAa+4e4Vey/A/wZuAU6VOpACceD/mlmvma0vdTAJ/QYwBDwQPMb7vJnVlzqoArgOeCjJCZQMKpyZNQAPAze7+0uljmey3P1Vd28n+zLDS82sIh/jmdlVwGF37y11LAW02t1XkF2k6qbgcWulqgNWAJvdPQMcA85agbGSBI+6rgb+Ocl5lAwqWPB8/WFgq7t/rdTxFELwlf1bQGeJQ5ms1cDVwXP2LwO/Y2b/WNqQknH3A8Gfh4HtZBetqlT7gf1jvnluI5scKtmVwC53P5TkJEoGFSoYdP0C8Ky7f6bU8SRhZq1mNiv4eSbwu8C/lTaqyXH3/+Xui9x9Mdmv7k+4+/tKHNakmVl9MEGB4HHKO4CKnY3n7geB583somDXGqDiJl2Mcz0JHxFBEV5UV07M7CHgCqDFzPYDn3D3L5Q2qklbDbwf2Bs8awf4WPBiwEpzPvBgMCNiCvBVd6/4KZlVYh6wPViJtg74J3fvLm1Iif03YGvweOUnwLoSxzNpZnYu8HbgzxKfq5amloqISDg9JhIRESUDERFRMhAREZQMREQEJQMREUHJQEREUDIQAcDM1prZggjtvmhm1+Y5/i0z6yhwbLPMbMOYz1dUw6uxpbwoGYhkrQUmTAYlMgvYMGErkQSUDKQqmdniYPGSB83smWAxk3PNbKWZ/WvwBs7Hzez84Df9DrJVqbvNbKaZfdzMnjazfjPbErz+I24M7zCzp8xsl5n9c/BSwdOLxdwe7N9rZm8K9rea2TeC/X9vZj8zsxbgTuA/BLHdFZy+YcwCLVsnE5/IWEoGUs0uAra4+5uBl4CbgHuAa919JXA/8Ffuvg3oAf7Y3dvd/RXgXnf/j8EiSDOBWAvUBP+I3wb8bvDGzx7gI2OavBjs3wx8NNj3CbLvMlpB9oVwFwb7byX7rvp2d/+fwb4McDNwMdnXMq+OE5/IeDX1biKpOc+7+3eDn/8R+BjQBnwj+EV6KvBCjr6/bWa3AOcCzcA+4Osxrn0Z2X+ovxtcazrw1Jjjp98y2wu8J/j5PwHvBnD3bjM7kuf8P3D3/QDBu6kWk10hTmRSlAykmo1/8dYosM/d8655a2bnAJvILvX4vJn9JXBOzGsb2UV6rs9x/Hjw56u8/v9hnEc9x8f8PPYcIpOix0RSzS4cs9j59cD3gNbT+8xsmpldEhwfBRqDn0//w/9i8Jw/5+yhPL4HrDaz3wyuda6ZLZ2gz3eAPwravwOYHRKbSCqUDKSaPQt80MyeIfuo5x6y/7D/jZntAXYDbw3afhG4L3jkchz4HLAX6AKejnthdx8iO0PpoeD63wPeNEG324F3mNkusguWvACMuvsw2cdN/WMGkEUKSq+wlqoUrAv9aDAAXBHMbAbwqrufDL69bA6WAhVJnZ4zipSPC4GvmtkU4ARwQ4njkRqibwYik2Bm24E3jtv9F+7+eCniEUlKyUBERDSALCIiSgYiIoKSgYiIoGQgIiLA/wefEopFCLgzMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X.petal_length, X.petal_width, s=15, edgecolors='k', alpha=0.8)\n",
    "plt.xlabel('petal_length')\n",
    "plt.ylabel('petal_width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = kmeans.predict(X)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petal_length  petal_width  pred\n",
       "0           1.4          0.2     0\n",
       "1           1.4          0.2     0\n",
       "2           1.3          0.2     0\n",
       "3           1.5          0.2     0\n",
       "4           1.4          0.2     0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['pred'] = pred \n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(X.petal_length, X.petal_width, s=15, c=X.pred, edgecolors='k', alpha=0.8)\n",
    "plt.xlabel('petal_length')\n",
    "plt.ylabel('petal_width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elbow Method for optimum K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "K_values = list(range(1,10))\n",
    "sse = []\n",
    "for K in K_values:\n",
    "    kmeans = KMeans(n_clusters=K)\n",
    "    kmeans.fit(X)\n",
    "    sse.append(kmeans.inertia_)\n",
    "plt.plot(K_values, sse)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Sum of Squared Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = KMeans(n_clusters=2)\n",
    "clf.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_clf_pred = clf.predict(X)\n",
    "y_clf_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>pred</th>\n",
       "      <th>pred_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petal_length  petal_width  pred  pred_new\n",
       "0           1.4          0.2     0         1\n",
       "1           1.4          0.2     0         1\n",
       "2           1.3          0.2     0         1\n",
       "3           1.5          0.2     0         1\n",
       "4           1.4          0.2     0         1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['pred_new'] = y_clf_pred \n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(X.petal_length, X.petal_width, s=15, c=X.pred_new, edgecolors='k', alpha=0.8)\n",
    "plt.xlabel('petal_length')\n",
    "plt.ylabel('petal_width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing using min max scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "scaler.fit(X[['petal_length']])\n",
    "X['petal_length'] = scaler.transform(X[['petal_length']])\n",
    "\n",
    "scaler.fit(X[['petal_width']])\n",
    "X['petal_width'] = scaler.transform(X[['petal_width']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>pred</th>\n",
       "      <th>pred_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petal_length  petal_width  pred  pred_new\n",
       "0      0.067797     0.041667     0         1\n",
       "1      0.067797     0.041667     0         1\n",
       "2      0.050847     0.041667     0         1\n",
       "3      0.084746     0.041667     0         1\n",
       "4      0.067797     0.041667     0         1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(columns=['pred', 'pred_new'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petal_length  petal_width\n",
       "0      0.067797     0.041667\n",
       "1      0.067797     0.041667\n",
       "2      0.050847     0.041667\n",
       "3      0.084746     0.041667\n",
       "4      0.067797     0.041667"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeansclf = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = kmeansclf.fit_predict(X)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(X.petal_length, X.petal_width, s=15, c=pred, edgecolors='k', alpha=0.8)\n",
    "plt.xlabel('petal_length')\n",
    "plt.ylabel('petal_width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "K_values_new = list(range(1,10))\n",
    "sse_new = []\n",
    "for K_new in K_values_new:\n",
    "    kmeans_new = KMeans(n_clusters=K_new)\n",
    "    kmeans_new.fit(X)\n",
    "    sse_new.append(kmeans_new.inertia_)\n",
    "plt.plot(K_values_new, sse_new)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Sum of Squared Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}