Coding-challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": [
    "df = pd.read_csv(\"ClusterPlot.csv\", usecols=[1,2])\n",
    "df\n",
    "\n",
    "plt.scatter(df['V1'], df['V2'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.copy()\n",
    "kmeans=KMeans(2)\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster=x.copy()\n",
    "cluster['cluster_pred']=kmeans.fit_predict(x)"
   ]
  },
  {
   "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": [
    "plt.scatter(cluster['V1'], cluster['V2'], c=cluster['cluster_pred'], cmap='rainbow')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.34555875e-01, -1.34331530e+00],\n",
       "       [-1.12218742e+00, -1.34331530e+00],\n",
       "       [-1.12218742e+00, -1.38332667e+00],\n",
       "       [-1.34722501e+00, -1.30330392e+00],\n",
       "       [-3.34555875e-01, -1.34331530e+00],\n",
       "       [ 4.53075670e-01, -1.14325841e+00],\n",
       "       [-1.00966863e+00, -1.30330392e+00],\n",
       "       [-5.59593460e-01, -1.30330392e+00],\n",
       "       [-1.79730017e+00, -1.34331530e+00],\n",
       "       [-1.00966863e+00, -1.34331530e+00],\n",
       "       [ 2.28038086e-01, -1.30330392e+00],\n",
       "       [-7.84631044e-01, -1.26329254e+00],\n",
       "       [-1.23470621e+00, -1.38332667e+00],\n",
       "       [-1.79730017e+00, -1.50336080e+00],\n",
       "       [ 1.01566963e+00, -1.42333805e+00],\n",
       "       [ 1.35322601e+00, -1.22328116e+00],\n",
       "       [ 4.53075670e-01, -1.30330392e+00],\n",
       "       [-3.34555875e-01, -1.30330392e+00],\n",
       "       [ 6.78113254e-01, -1.18326979e+00],\n",
       "       [ 3.00050113e-03, -1.26329254e+00],\n",
       "       [-1.09518291e-01, -1.22328116e+00],\n",
       "       [-1.09518291e-01, -1.22328116e+00],\n",
       "       [-7.84631044e-01, -1.50336080e+00],\n",
       "       [-5.59593460e-01, -1.10324703e+00],\n",
       "       [-7.84631044e-01, -1.14325841e+00],\n",
       "       [-1.00966863e+00, -1.26329254e+00],\n",
       "       [-5.59593460e-01, -1.18326979e+00],\n",
       "       [-2.22037083e-01, -1.30330392e+00],\n",
       "       [-3.34555875e-01, -1.34331530e+00],\n",
       "       [-1.12218742e+00, -1.26329254e+00],\n",
       "       [-1.12218742e+00, -1.26329254e+00],\n",
       "       [-1.09518291e-01, -1.22328116e+00],\n",
       "       [ 4.53075670e-01, -1.34331530e+00],\n",
       "       [ 9.03150839e-01, -1.34331530e+00],\n",
       "       [-1.00966863e+00, -1.34331530e+00],\n",
       "       [-7.84631044e-01, -1.42333805e+00],\n",
       "       [ 1.15519293e-01, -1.38332667e+00],\n",
       "       [-1.00966863e+00, -1.34331530e+00],\n",
       "       [-1.68478138e+00, -1.38332667e+00],\n",
       "       [-4.47074668e-01, -1.30330392e+00],\n",
       "       [-4.47074668e-01, -1.34331530e+00],\n",
       "       [-2.35989414e+00, -1.34331530e+00],\n",
       "       [-1.45974380e+00, -1.38332667e+00],\n",
       "       [-4.47074668e-01, -1.10324703e+00],\n",
       "       [ 3.00050113e-03, -1.06323566e+00],\n",
       "       [-1.23470621e+00, -1.30330392e+00],\n",
       "       [ 3.00050113e-03, -1.26329254e+00],\n",
       "       [-1.23470621e+00, -1.34331530e+00],\n",
       "       [ 1.15519293e-01, -1.30330392e+00],\n",
       "       [-6.72112252e-01, -1.34331530e+00],\n",
       "       [ 1.46574480e+00,  4.57196667e-01],\n",
       "       [ 7.90632047e-01,  4.17185290e-01],\n",
       "       [ 1.24070722e+00,  5.77230798e-01],\n",
       "       [-1.23470621e+00,  1.37105652e-01],\n",
       "       [ 4.53075670e-01,  4.57196667e-01],\n",
       "       [-4.47074668e-01,  3.37162536e-01],\n",
       "       [ 7.90632047e-01,  5.37219421e-01],\n",
       "       [-1.79730017e+00, -2.63008118e-01],\n",
       "       [ 6.78113254e-01,  3.77173913e-01],\n",
       "       [-1.12218742e+00,  1.37105652e-01],\n",
       "       [-2.13485655e+00, -1.82985364e-01],\n",
       "       [ 3.00050113e-03,  2.97151159e-01],\n",
       "       [-7.84631044e-01,  1.70715208e-02],\n",
       "       [ 1.15519293e-01,  4.57196667e-01],\n",
       "       [-4.47074668e-01, -2.29398561e-02],\n",
       "       [ 1.01566963e+00,  3.37162536e-01],\n",
       "       [-3.34555875e-01,  4.17185290e-01],\n",
       "       [-4.47074668e-01,  5.70828978e-02],\n",
       "       [-5.59593460e-01,  4.17185290e-01],\n",
       "       [-8.97149837e-01,  1.70715208e-02],\n",
       "       [ 2.28038086e-01,  6.57253552e-01],\n",
       "       [ 3.00050113e-03,  1.37105652e-01],\n",
       "       [-1.09518291e-01,  5.77230798e-01],\n",
       "       [ 3.00050113e-03,  3.77173913e-01],\n",
       "       [ 4.53075670e-01,  2.57139782e-01],\n",
       "       [ 7.90632047e-01,  3.37162536e-01],\n",
       "       [ 7.90632047e-01,  4.97208044e-01],\n",
       "       [ 9.03150839e-01,  6.97264929e-01],\n",
       "       [ 3.00050113e-03,  4.17185290e-01],\n",
       "       [-6.72112252e-01, -1.82985364e-01],\n",
       "       [-1.12218742e+00, -2.29398561e-02],\n",
       "       [-1.12218742e+00, -1.02962610e-01],\n",
       "       [-4.47074668e-01,  5.70828978e-02],\n",
       "       [-2.22037083e-01,  6.97264929e-01],\n",
       "       [-5.59593460e-01,  4.17185290e-01],\n",
       "       [ 5.65594462e-01,  4.57196667e-01],\n",
       "       [ 1.01566963e+00,  4.97208044e-01],\n",
       "       [-3.34555875e-01,  2.97151159e-01],\n",
       "       [-3.34555875e-01,  1.77117029e-01],\n",
       "       [-1.00966863e+00,  1.37105652e-01],\n",
       "       [-8.97149837e-01,  2.57139782e-01],\n",
       "       [ 2.28038086e-01,  4.17185290e-01],\n",
       "       [-5.59593460e-01,  9.70942747e-02],\n",
       "       [-1.79730017e+00, -2.63008118e-01],\n",
       "       [-6.72112252e-01,  2.17128406e-01],\n",
       "       [-2.22037083e-01,  1.77117029e-01],\n",
       "       [-3.34555875e-01,  2.17128406e-01],\n",
       "       [ 2.28038086e-01,  2.57139782e-01],\n",
       "       [-1.45974380e+00, -3.43030872e-01],\n",
       "       [-4.47074668e-01,  1.77117029e-01],\n",
       "       [ 7.90632047e-01,  1.41746971e+00],\n",
       "       [-4.47074668e-01,  8.17299060e-01],\n",
       "       [ 1.35322601e+00,  1.21741283e+00],\n",
       "       [ 3.40556878e-01,  9.77344567e-01],\n",
       "       [ 6.78113254e-01,  1.21741283e+00],\n",
       "       [ 1.91581997e+00,  1.49749247e+00],\n",
       "       [-1.68478138e+00,  4.97208044e-01],\n",
       "       [ 1.46574480e+00,  1.25742421e+00],\n",
       "       [ 3.40556878e-01,  1.05736732e+00],\n",
       "       [ 2.14085755e+00,  1.45748109e+00],\n",
       "       [ 9.03150839e-01,  8.57310437e-01],\n",
       "       [ 2.28038086e-01,  8.97321814e-01],\n",
       "       [ 1.01566963e+00,  1.05736732e+00],\n",
       "       [-7.84631044e-01,  8.17299060e-01],\n",
       "       [-3.34555875e-01,  1.01735594e+00],\n",
       "       [ 7.90632047e-01,  1.05736732e+00],\n",
       "       [ 6.78113254e-01,  9.37333190e-01],\n",
       "       [ 2.92848910e+00,  1.57751522e+00],\n",
       "       [ 1.57826359e+00,  1.69754935e+00],\n",
       "       [-7.84631044e-01,  6.17242175e-01],\n",
       "       [ 1.35322601e+00,  1.21741283e+00],\n",
       "       [-5.59593460e-01,  7.77287683e-01],\n",
       "       [ 1.80330118e+00,  1.49749247e+00],\n",
       "       [ 1.15519293e-01,  6.97264929e-01],\n",
       "       [ 1.24070722e+00,  1.13739008e+00],\n",
       "       [ 1.69078238e+00,  1.13739008e+00],\n",
       "       [ 1.15519293e-01,  6.57253552e-01],\n",
       "       [ 2.28038086e-01,  6.97264929e-01],\n",
       "       [ 3.40556878e-01,  1.09737870e+00],\n",
       "       [ 1.46574480e+00,  9.77344567e-01],\n",
       "       [ 1.46574480e+00,  1.21741283e+00],\n",
       "       [ 3.15352668e+00,  1.37745834e+00],\n",
       "       [ 3.40556878e-01,  1.13739008e+00],\n",
       "       [ 2.28038086e-01,  6.57253552e-01],\n",
       "       [-2.22037083e-01,  8.17299060e-01],\n",
       "       [ 2.02833876e+00,  1.37745834e+00],\n",
       "       [ 9.03150839e-01,  1.21741283e+00],\n",
       "       [ 6.78113254e-01,  9.37333190e-01],\n",
       "       [ 1.15519293e-01,  6.57253552e-01],\n",
       "       [ 1.24070722e+00,  1.01735594e+00],\n",
       "       [ 1.01566963e+00,  1.21741283e+00],\n",
       "       [ 1.24070722e+00,  9.77344567e-01],\n",
       "       [-4.47074668e-01,  8.17299060e-01],\n",
       "       [ 1.24070722e+00,  1.29743558e+00],\n",
       "       [ 1.24070722e+00,  1.29743558e+00],\n",
       "       [ 9.03150839e-01,  1.01735594e+00],\n",
       "       [-1.09518291e-01,  7.77287683e-01],\n",
       "       [ 6.78113254e-01,  8.97321814e-01],\n",
       "       [ 7.90632047e-01,  1.09737870e+00],\n",
       "       [ 3.00050113e-03,  7.77287683e-01]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaled= preprocessing.scale(x)\n",
    "x_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[300.0,\n",
       " 128.1313015023252,\n",
       " 77.06914260423744,\n",
       " 54.152376736977665,\n",
       " 40.118991086518434,\n",
       " 31.814834974518323,\n",
       " 26.789102027884116,\n",
       " 21.800865131669852,\n",
       " 18.959864636770273,\n",
       " 16.254466184641725,\n",
       " 14.431789209617444,\n",
       " 12.428719243873426,\n",
       " 10.664685394048988,\n",
       " 9.237580491185577,\n",
       " 8.246354949925522,\n",
       " 7.450670820606328,\n",
       " 6.931529533675638,\n",
       " 6.376718181634388,\n",
       " 5.898706380637183,\n",
       " 5.4485735463288965,\n",
       " 5.315293897881143,\n",
       " 4.800584026750359,\n",
       " 4.298391956991633,\n",
       " 4.130547438657414,\n",
       " 3.895956352545999,\n",
       " 3.7340879179510496,\n",
       " 3.474939887527766,\n",
       " 3.3621713317429536,\n",
       " 3.1630450976059046]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss=[]\n",
    "\n",
    "for i in range(1,30):\n",
    "    kmeans=KMeans(i)\n",
    "    kmeans.fit(x_scaled)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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.plot(range(1,30),wcss)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>cluster_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.30</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.95</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.95</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.30</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>4.85</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>4.40</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>4.75</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>4.80</td>\n",
       "      <td>3.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>4.45</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       V1    V2  cluster_pred\n",
       "0    4.30  0.80             3\n",
       "1    3.95  0.80             2\n",
       "2    3.95  0.75             2\n",
       "3    3.85  0.85             2\n",
       "4    4.30  0.80             3\n",
       "..    ...   ...           ...\n",
       "145  4.85  3.75             1\n",
       "146  4.40  3.45             0\n",
       "147  4.75  3.60             1\n",
       "148  4.80  3.85             1\n",
       "149  4.45  3.45             0\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_new=KMeans(4)\n",
    "kmeans.fit(x_scaled)\n",
    "cluster_new=x.copy()\n",
    "cluster_new['cluster_pred']=kmeans_new.fit_predict(x_scaled)\n",
    "cluster_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v52cTJnB0hVOVjuHjd7nYZxMreTve0EesMuIYA7cdCk3vQTwM0U6Ih+DJ78PSl0dHJhEhmMMSL5TlWeFyDeiPf7i7m1i6yUQsHTHSlnROzE9iXR+ZGGM4Y9UqFsXj9BhDtzGEjeGi1lbeH6BUgFKcqEJXRpym92DtIjAZuireA69dOioiAXBCRUVWtqYPODFHdme+VLvd7BoIkBl8GBAhAllvCFGgKZkk0/PtAiZ7PGzpEMb4bjRKSyKRFa0TNYbbc/jdAVbE4/yyuZmjly3je6tX884wQzWVsYUqdGXECbdYN0sWBrpWb3Rx1rFvaSmZW5NJYL8CuhUunjiR7f1+SvpsWH61oiKnbz0JnF1dvW5+QITNvF6uyZEpujaVyroHsL73VTk2XhfHYnxp+XLu7+piYTzOs+Ewp69axbM5Cokp4wf1oSsjzqRdIBnLHvcEYKsjssc3Fpe0tmZZtkng4rY27pycVVtuUGKpFPd0ddGZSnFceTl1Hg9Vbje3TZ7Mp7EYTckkW/t8VLvdzI/FeKWnJ+sam3m9nFFdzUmVlbwXjVLtcrGVz5czvX6ax5MzKidXHPqlbW2E02UCYP3G629aWti/tFRT+ccxaqErI46/Ag74bf+oFk8JlDXArmePjkzGGObHHJ4ywAfD8D0/1tXFzosX87vWVq5Yu5b9li7l501N645v7vOxRyCwrt74BRMmEBRZZ1G5sK6YX6R7gJa5XOwRCLC13z+ggl0Sj2e5jXrpzFEK9/VIxDGhqj2VoiWHD18ZH6iFrmwU9vw+1H8WXr0UQs2w9VGw23etsh8NRIQyl8ux/ne+2Y/hVIoLmpuzkm/u6+5m32CQLzhkhM70+bhvyhRu6ujg3UiELX0+/qeqii3zKAoGMMHtxiOSlbzkAibnKB1Q7XbbGPlMjMlZbVEZH6hCVzYam3/BfkaD+ffBy3+C7jWw+YGw7y/hqxUV/KOjo9/mZIkIp+QZ7ndzR4djJiXA1W1tjgodbL2Wn6ct8uGyg99PvcfD0ni8X+MKX9pX78Q3Kiv5XWvrusqJYDNLDwkGCahCH9fot6cUPS//Ce4/FVbMgY4l8NY/4NpZcEqomiPKyvClNyB9IhxZVsa3qqvzun6uUEOw6f8jiaQzRbf2+ShJh2KWuVz8traWbXMU3jqmvJyvVVbi73PfnwsENvjhoow+eZXPLSRaPlfZGMRCcEkdJDL2H11em9R02BXQnkyyLB5nqtdL1TB6aj4bCnHOmjWOx/bw+7lxgA3Wj2IxPopGmeb18plB/OWDsTQepzuVYqbPN6TOQ92pFIvjcerdbm0MPY4YkfK5ijIeaFkAbm+2Qk/FYfFz9ucqt3tYiryXcCqVs3JjRY7rRlMpzkkX3nKnz93c6+WGxsac5wzGtCGU2+1LmcvlWLpXGb+oy0UpasoaIOEczELV9MKsMdHjcWwb5wWm51CyV61dy9yeHiLGEDKGHmP4KBbjopaWwgilbJIMpadoiYjMEZG3ReR9Efm1w5z9RKRDRN5Kf34xMuIqI8UH98K1O8LFE+DWg2HlQB1kB6B7NVy/O1zktp+/7QKdgzQj/OhRuG4Xu/bN+8GyV4a3thMVk2Gz/cGdYYh6S2HvHxVmjdklJdS43WTa1R4RTsyxMXlvV1dWU4o48HQoREJ7dSrDZCgWehQ4wBizIzALOERE9nCY96IxZlb6c1EhhVRGltevhQe+BmvegZ42+PRpuPnzsOrN/K6TSsBlW8DKOWBS9rN6Hly+eW4r+d3b4Z4TYdU8u/aS/8AtB8HSlzb8vno5/g7Y8hCr1L1BKKmGI66F6Z8vzPVdItw8aRI7+P34sJEyDW43Vzc0MCmHhZ4ZZthLCmfXjaIMhUF96OluRN3pX73pj5oQRUIqAc9eaItm9SXeA8/+FE5+LPe5bQvh039bBbn1kfDfv0IinD0vGYUXfgMH/Kb/uDHw1A+z106E4ekL4Buv2DeFFa9BxVSYeej6EgLti+GTp6yC3voo8A9QfsVfASc9YB8Y4Vao3ixHKYINoMHj4fbJk2lOJIgYwxSPZ8ANzr0DAZ4Jh7OU9/Z+Pz7N1FSGyZD+WYuIG3gD2BK4yhjzmsO0PUXkbWAltpH0+4UTUxkpuldDwikx0sCqHG4XY+DJ8+GNvwFileMjLpiwde51nCzuWDeEm53nr37blttd/Jxdz+Wxivn0F+Gtm+GVi0Fc9vPoWXDSQ9a1MhCBGvsZSYYaLXJBbS1vLF9OOF3G1w94Rfi1hg4qG8CQ/vUZY5LALBGpAu4XkR2MMe/1mTIPmG6M6RaRw4AHgJmZ1xGRM4EzAaZNm7aBoiuFIDAh97HKHF/Rx4/BvBsgkVGgb807ua9Vs2X2mLfU1nOJdTkfW/Rs/+iUeAhuOxw6l2SvfcfR8MM14M0uFz4mmeTx8OjUqdzX1cU70SgzfT5OKC+nVsMHlQ0grygXY0w7tkn0IRnjncaY7vTPjwFeEckyNYwx1xljZhtjZtfV1Q1baKVweAOwyzezuwd5S202pRPzbrDKNROXB+eGnAIH/M5hvtuWBHBa2+3JDjU0KWj90LqDspYQ6/4ZT1S43Xy9qoq/1NdzdnW1KnNlgxlKlEtd2jJHRALAF4AFGXMaJO0wFJHd0tdtLbi0yohwcLpzkKfUFs0qrYVDr8pdCTGZo3S2ywUHXQK+Pn2DvUE4+XEom+h8zr6/gD3Ot/M8JVBSBV+42P7shAg5d3CcKjoqyqbEUEyCRuAfaT+6C7jLGPOIiJwFYIy5FjgeOFtEEkAPcJIZrRRUJW9cHvjiX+DAP0Ck3Sp01wC5LZ85GZa8mG2lmxTs+m3Y6wfQvsSODRbrLS6r1Ou2h6Z3YcqeMPMw6FxmC3klM/z7wYnrOx71JRm3NVq6VtoQzGTMbtRO2Moej3bB/HvtnsH0z9t1RnvvMWkML/X0rMsU3T8Y1A1RZYPQ1H8lb1IJu2G57GW7sen2gbjh2Fthu+Pyu1bHMrhxz7SSDlsXUNVm8NVH4bZDoGOpXcMTsA+eU5+G//7Z+vHjITvm8sJhV1s3zcPftNc1KSvT3j+ybxq3HGjlTkTB44cZ+9nIl0JHuwyVzmSSU1euZGW6h6hfhAqXi9snTy54g2eluBgo9V8VujIsTMqGDX70iLXodzzNhgPmy60H283Pvu3p3D7Y6Qw45K/W2l76ElTNgFmnWQvdGBv9suBBG/my46k2dPLSadmbpZ4AlFRay7wv3lLrapp9Vv4yF4JfNzdzf1dXv+YUbmDPQIC/NTaOjlDKuEAVujImSUTgD+XWcs6kpAZ+lMcuzJs3waPfgaTThqkHjMMak2bDN18f+hqFZI/Fix1rsbuBuZttpq4XJScDKXSt5aKMTfJMl0zFnZU5OCtzsIlGo4VuMSkjgSp0BYBIB8y91mZofnCv3WQcaTwlMO1zdmO0Ly4fbP/l/K7VsSL/9XvW5n9OoTg4GMyKSHABuwUCap0rw0Z3XxSa3oOb9rGRIfGwDTusnG5T70e6RdxRf7ebovGQ3fz0lUN5Ixz4+/yu0/Zh/ms7xdJvLH4wYQJzIxFakknCxlAqQqnLxUWaKapsAKrQFe472YYr9hLrtnVaXviNjSsfSao3g3MXwQf32DUbdoStjrQ1zMFugEY7wRccOCJlyh7w/p35rV0+afhybyhVbjcPTZ3Kc6EQH8ViTPd6OSgYpGQDWsDFjCFuDEFtI7fJopuimzihZvjrFOeknPLJ8P3lG1+mXj64F544F0JNNvJl17NtrLyTYk+l4A/B7CgXXLbsQNtH2eec9KAt7DXe6U6luKi5madCIVLAZl4vv66rY1ZJjuwsZVyjm6JKTjL9130ZrRhtsKGMD3wNulbYDc94COZcDU9+33m+ywXf/RBqtlo/VtZg3UZnvwszDmBdWQJv0Lp6ikGZA3x79WqeDoWIA0lgYTzON1etYnl8I2yEKGMKdbls4pROgMZdbIla0yeyxBOAWV8fNbH4z6+dy+rOu8Fa6b5g9jmV0+CcHL70054pvIxDZU0iwX1dXayIx5kdCHBoMIi/QG6RhbEY70ejZL5gxYzhnx0d/Fh98psUaqErfOk2CNbbDUm3z1qwk3aBz/149GRq+8R53OWGkHM/5jHJvEiEw5ct47q1a7m/u5vftrRw3IoVjjHow2FZPI7HISomAXyiFvomh1roBSCVtDVEAtX9C1ONF6o3g/MWw4cPQ8cSmLRrOpxwFKPnGne2f6dOhbjKJ290cYaFMYYfNTXR02efqscYVsTj3LB2LedPyF27OGUMTckkZS4XZQNY81v5fMQd9sF8wI7aAHqTQxX6BvLu7XbjLhYGUrDdiba92Xipy92L25d/HZaRZP+LYNEz/d0u3lL4/M9sLZbxwIpEgtZEdlZTDHgiFMqp0J8PhfhVSwudqRTGGD5fWspvJ06k3EGxT/Z6ObC0lGfDYSJpxe4CAi4XX6msLOTtKOMAdblsAIues8Wgwi3Wv5uIwAd3wYOnj7Zk45+GWXDa8zB9P/vWU7MlHHYV7HXBKAuWB34Rcjk9kjmiy+ZHo/ygqYnmZJKoMcSA/4TDnLt6teN8gD9MnMiZVVVMdLsJivCFYJC7Jk9mgnuAkplKUaIW+gbw4u8dNu4itmhUuMUWrVKGz+Rd4evP5XfOoufgrb/bptQ7nATbHD1wJE8uYiF4+x+2AFnlVJj9bajb1rpCngmHeay7mxIRvlRezq4B59exEpcrZ/NdJ783wE3t7VkNpOPAW9EoS+Nxpjk0nfaI8K3qar5VXT3k++tMJrmnq4s3IhE283r5SkUFk3M0tFbGD6rQN4D2Rc7jbq+t7qcKfePy7wthzhXrM0A/fhS2+CKceE9++wGRDrh+tvXhx8O2uNebf4dj/2W4arc1vNrTQ48xCPBUKMRplZV8rya7WWlbMolfZJ0rpC9Ofm+ApYmEYxkbLzZaxkmh50tTIsEJK1bQnUoRMYaXgDs6O7musZGdNXZ9XKMulw1g2udsze1MTAqqt9j48mxset9GVuaRH9a1Epred66wmA+pJDTPt/XSAdZ+Cq9d2j+dPx6CT560Me0A3WtsmYPBOhv99y+2Tnvv25dJ2J+vva6H19LKHOx+bcQYburoYKVDREmjx4OT08MFOZN+ZpeU4KSyY8Yw0+cbWPAhckVbG+3J5LoHTQK7WfuzpiYtGjbOGdRCF5ES4AXAn55/jzHmlxlzBLgMOAwIA183xswrvLhji8//DBbcb1Ple2O4vaW2A8942xTNl6cvgFf+j3VRKP4KOP1FqP+s8/xQM9x9Aix/zTaicHnh8GtghzyLcAEsfNImHcXCVtlO3AG2+RKO/UzjIfjgbivr4ufSzTgEvvhX2Ol/nK8//97sTkkAS/cKE06ZrHXcwCs9PRyfYT37RDirupq/trX1s7rdwHdyuEdOq6zkvq4uulKpdecERDipooKqAvnEnw+HcXqerkokaEul1Pc+jhmKhR4FDjDG7AjMAg4RkT0y5hwKzEx/zgSuKaSQY5WaLW097W2Ps3HcDbPg6Jtg73G0cTcc3r8LXrmEfiGF0U64YXebgu/E7YfDsldsP9JYN0TWwkP/k591D7bey11fsuUA4t32LWHVm9Y6d/KVu7zwydOw+FmrpGNdVtbHz7H+didyFSTzdLrwODw0BHLWT3kvGs36TybA4hwx4nUeD/dMnsyRZWXUut1s6fXy0wkT+IGDS2e4BHLIaoASrfQ4rhnUQk/3Bu1O/+pNfzLfy44GbknPfVVEqkSk0RizqqDSjkEmbAUn3DXaUmxcnv2Z83giYq3hTKu7eT40v29T+PsS74H//hWOu23oa8+9Nru0r0lal4hxeJiI25YPyHSzxMPwysWw2f7Z5+z+PVjzTn/3jbhh9ntlfCIdJDLcEgLsW1rKi+Ewd3Z2EkqlODQYZP9gkOdCoSxrOAbc0N7OAUGHdFdgktfL7yfm6KpdAL5SUcGVa9f28+17gL0CAS3sNc4Z0rcnIm4ReQtoAp42xryWMWUysKzP78vTY5nXOVNE5orI3Obm5mGKrIw24Zbcx1odUu+7V1lLOQtjE5nyYe2n2Q8GsAp7rx+Cv9Ja2P4K8JTCPj/JHbfekaPw2PZfhp3PAHeJvY6vzLbAO+tqH7+srcUvQjD9KXe5uKahgeva2zl/zRqeC4eZE4lwcVsbZ61enfM/WFMymePIyHNqZSUHlJauu4+ACFv6fCP6EFE2DkOKcjHGJIFZIlIF3C8iOxhj3uszxek9LWt3xRhzHXAd2GqL+YurjAUm7w6fPOF8bNvjrKW8+i37Z8NO1hXl5JP2lMAWB+e3dkmV83gyBjOPgH1+an3lyRjM2N+WCnj5j9nzXV7Y/EDna4nAIZfaB8Ty12yRr6l72fGjKefAYJA5PT14Rdg9EGBtMsk/Vq/uF27YYwxL4nF7UoZF7wJ2GySapCeVYkEsRrXLxYwCbYb24hHhkvp6lsbjLIhGmeT1sr3Ph6i7ZdyTV9iiMaZdRJ4HDgH6KvTlwNQ+v08BVm6wdMqY5Ihr4fIt+jd2Bpi0m/VR/2WK/RNsXZgT74G9/tdGj/S6MVw+29h51+/kt3bfuu19cXltGOmU3WDLQ/of2/+38NzP+6ztsZb3YHsdFVNguynZ42UuVz93yeuRCF7IKpDVYwzb+XzMj8X6WTdu4Kyqqpzr3tnRwSVtbbixESibe71c1dDARE9ho4yneb0FCYNUxg6DulxEpC5tmSMiAeALwIKMaQ8BXxPLHkDHpuA/31Spmg7fft/WfHF5rdLe7Rw4+Qm49WDrYol1209oDdx2KOz2PTjmZpiyJ9TMhN2+C2e9Zas95kP5ZBz/1br9EMixb7jn+XD8HTDt83Yje5dvwVlvF67BRWUOv7Mb6EilskIX3SK8H3OOnZzb08PFbW30GEO3MUSM4cNYjLMHyBRVlF6G8shvBP4hIm7sf6W7jDGPiMhZAMaYa4HHsCGLC7Fhi5r8XuTUbg3fnNN/bN6NzhuTqZQtiVA53YZ19n6GU2999rfgzRshkdEQ2hd03uDsZasj7Gck2DMQwC9CKMO14sEm8WRuikaM4ab2dg4ty67kdktHR1YiUhIbFbMwFmPLArtflOJiKFEu7wA7OYxf2+dnA+T58qwUG+FmZ195ogfm3wfLX13v9mhZAO/cYi3lXH5xJ2q2hJIa6O7bFFpg84NGryGHR4S/T5rEt1atoiuVwoVVwudUV3PF2rWOWaFrc8R3NufYLPWI0D6KG6nK+EBjlDYxTMrGgy980sZjF5Lp+1rXRyaeACx9sX8YYDJqY8lfvzq/Nd69HaLtGYMG5t9jsztzYQysmgcfPz5wlE5fQs12/qo3s/Y1s5jp8/HvadO4sbGRyxsaeGn6dE6prMTvsNHYGyLoxL7p6JNMEsawrZbDVQZBa7lsQjR/AP/8ot1YFJeN5z7kr9anXAim7AGbfwE+/fd65e0NQt32NpwxMxY8EYGPH7OhhUPl48f7Pxh6cXlh2ctQeVL2sc4V9r7bF9uol2QM9vyhLdHrFNhhjI21f/UvNrM0lbQ14095cmC/u0uEz2ZEr/y8tpafNjcTNQaDTeIIulycnSNT9KsVFdzb1UVLMrkuaiYgwjnV1RojrgyKKvRNhFQSbj0o3TSiD09837agm+TYcjY/RODEe+GdW62f2xiYdbq99t/3cjrBRpLkQ+UU61pxqgUTrHc+585jrIunb1TOq3+1XZm2OSZ7/vz74LXL7AOnt+l083y480twxqv5yXtIWRmNHg83dXSwIh5n90CA0yorqcsRsVLhdnPP5Mnc1tnJ86EQtW43p1ZWsmdpaX4LK5skqtA3EZa+BNGu7PFkBF6/Bo6+0fm8pvdtf88Vc2zBsX1/DjP2y72Oy217kWb2I62eCU3vQt+iJp4S2OO8vG6DXc6CuX/rr9DFZbtFzdg3e/7aT+09ZIZYxkPw6mXOCj2zyBfY89e8De1LbJRPPuxYUsKleVQxrHS7+XZ1Nd/OoxyuooD60DcZImtzuBdSdjPTidVv2/os8++1GZ2Ln7U1Wd6/O//1p38+O/vM5bWbnPlQu7UNQSyptj1QvaVQuw187VnnWi6R9tybpT2tOcbbnMdd3txx8IoyFlALfRNh2j626UMm3qDN7nTi3xdkW6rxMDxxHmx3fO4a47Fu60c3xvrUkzF48/rskMZkFF67Avb7Fax83TaGrv8sTNx+4HvZ+ij44Rpr8XuDVsnnYuIOzore7Ydtjs1x/WOsLJkROy431G03sGyKMpqoQt9EKJ1gNwH/8ytbFAuT3rDc1nb2cWLFHOfxnhZrxTolBX34MNz7lfVK1CRtlqiTlZyM2nrlHz8GLfPtOakEbHaA9cUP1DvU7bWNpAfD7YMjroGHzrD+cJOyUTdlDbndPXv9AN69zUbhJHqsXJ4SOOJvdl1FGavIaBW0nz17tpk7N8/aqcoGs+QFGyrYsxa2PwE+e4pVVk5cuY1zsS1PAH7cbpVlX0JNcOmM7KQfd4n11TtRWgfRjv4RMJ4SG4VywG+GeleDs2oezLnSNsSYeZgtvpWrTC7YkM43roOFT0DlNFuBsWFW4eRRlOEiIm8YYxzDGFShjwOWvgTP/MSGHdZsCfv/BrY4yNYGf+antiNPaY1VgjufYRXk87+ydctdbtjxNFu0Kt+mG2/fAo+e3b9vqicAO38DDr0ie/7rV8NTP3RQ6L50yds8/qkFJ1q3ihPdq21Y4UcP24qKu54Ne35/9BKLFGVjMpBC1/8CY5xFz8LtR0IirVRXtNowvC9ean3c0U7rRuhpgSfPs+F5Cx+z0R29Vu9//wyLn7cdhfIpqPfZU22Y4wu/teclE/CZr8LBf3aeHws5hxMmE9ZVMVjrt77Ee5zHo51w3S72baB3red/ZX3wJwxjs1ZRiglV6GOcJ3+wXpn3Eg/DUz9Y7xPuOz7nirRF3Ed5JiK2nO3SF220yVARgc/92Pqa25dYv3NJ5frjbZ/Aomds6v5WR9gqh8//MrteubfEOdPSWwqBCdCZkeEpbusWceLNm6y7qO+DI9EDHz0CLR8OvEGqKMWOhi2OcVo+cB6PdTk3ekCcMylTcVj5xvBk8JRYRdmrzI2BJ86Ha3aAJ8+3G45/nmQfKDt/w2629uINwmdOhqOutwq8t6m2N2gjRk64y4Yf9pYM8ASskj/oEmdZlr6Q7dIBG1K4+s3h3Z+iFAtqoY9xyhrWd7bvi8ubtlIzLd90FEeWH9tvu+4UgoWPw7zr12dR9vKvI+D7K2044Nu3Wlk+c4oNXRSB+lnwxt+su2SrI2D7E+3bxHc/tONN78OU3WGnb9hEIScmbJP9BgKAsdUcFWVTRhX6GGefn1nfeN+NSW+pTal/66b+426/damsfD2tbNPKXtw2oqNQ5WPn3eD8FpCIwoL7rU+7Zb4dW/ZfOPlxqNnCxpcfenn2eeWNNhZ9KMw+C+Zc3l+hu7xQtZmtJaMomzLqchnj7HwG7HeR7ZXpCdj+lntdYKNMTrjb1kJx++1nu+Pgy/fD6S/Z1m9un1V2U/eE/3m5cDHUuTYsReC+k9crc4C2j+GqbSEWdj4nXyqnwqlPw4St7f25ffYN4GvP5LfhqyjFiIYtjhOScVv2tXRC//hvY6wLw1dmmzz0Jdxqwxb71hsPt8IH91gf/BYH28xMsBb9/Pute2fSbJvck0tBvvNPeOSsbCvd5c3h18eGVB6cwy8+XELNNvlooHhyRSk2NihsUUSmArcADdjSStcZYy7LmLMf8CCwKD10nzHmog2QWcnA7bWuiUxEoCxHlcHMTM5PnoI70+nuqQQ89wsbo77n9+Gmz1n3TSJiN0En7mCtXq9Dkb8dTrIVFZe+AvFuq8hdHttarukdZ1lWvj70ex0qwbrCX1NRxjND8aEngB8YY+aJSDnwhog8bYzJjL940RgzQk2+lIEwZnB3QyICdx3f3+cOVjEvfs5a/70hkLFuG+b44h+cszVdHusXX/gkfPyojUqZdZrNxMyl0LUGiqKMPIP60I0xq4wx89I/dwHzgckjLZgyOCvmwPW7wkVu+GMl/PvCdEamA4ufd1b68RC0fpRdOCsRsZmiuRAXzDwUDrsS9v81VG+ebhjh9C/KBQf+fqh3pSjKcMlrU1REZmD7i77mcHhPEXlbRB4XEcd6eSJypojMFZG5zc05arYqQ6JlAfzjAFg5FzA2g/K1y+ChbzjPd2rePCh5nuP2OzeZCNbaDV1FUUaWISt0ESkD7gXOM8ZkdqOcB0w3xuwIXAE84HQNY8x1xpjZxpjZdXXqAN0QXr44Ow480WPrt3Svzp4/Yz/btSgTb9CG/GUWK3f7YYev5ifTR49Yd00m8bANZ1QUZWQZkkIXES9Wmd9mjLkv87gxptMY053++THAKyK1BZVU6cfqt7K78IDd0Gz7JHvcWwrH3W4tZXcJIOla6MfCVx62iTy9GZ6+Mts04vM/y0+mlgXZPnqwSr5lQX7XUhQlf4YS5SLAjcB8Y8xfcsxpANYYY4yI7IZ9UOToB6MUgsadYc072Uo9EcndBWjro+B7C+G9O21Fxi0Pgcm7W9/6uYutdd++GCbvCjMPtyGP+VC3rX1wxDJa3fnKoXbb/K6lKEr+DCXKZW/gVOBdEXkrPfYTYBqAMeZa4HjgbBFJAD3ASWa0Atw3Efa+AN65DZJ9FLq4YbsTcocxgu1av+f52eP+cluHZUPY6ghb9jYRWR+P7vJAoMa+CSiKMrIMqtCNMS+R3Q4yc86VwJWFEkoZnETE4UvJUZhrY+HywDf+C0+cC/PTjrmtj7bp/pnNMBRFKTxay2Wc8vIl2QWqTMIWzupcARWjFFgarLO+ekVRNj5ay2Wc0vyBcyii2w8dSza+PIqijD5Fr9CNMcQTXSRTOZpajlOm7J678fKErQq7VjIOnctzF+VSFGVsUNQul1DPElY0P0gi2QkGgoEZTJ54LB53cPCTxzh7/a8tkhXrZl2Z3N6yuqUFDBidc6Xt39nbE3SXb9kiW9q/U1HGHkVrocfia1my+jbiibUYk8SQpLtnEYtX3UoxBOBUbwZnvGpDD31lUDHVNo92qjc+XN6/C/79IxvimAjbxKV519kSA4qijD2K1s5q63wdk5V5kyIebyMSXUmgZPyXo6nbDk5+bOSu/5+LshOF4mGYezUc+DuNXFGUsUbRWujReCvOxUiEWKJjY4szLula4TyeStraMYqijC2KVqEHS6Yjkv0CYkgR8DsUFh8CsUQHnaEFxBKbhjZr2Nl5vKTSJgspijK2KFqXS3XFzrR2vEoiGaLXUhfxUhHcFp83RwfiHKRSCT5dcT3ReNO6sRLfJDab9A1crqJ9JnLQn+DmfbP7mR78fznK5CqKMqoU7X9Lt6uEzSefSXX5TnjcZfg8NdRXH8DkuqPzvtbiVbf2U+YAkdhKlq25o1DijkkmzYbTX7Qbr8GJMHk328f0s6eOtmSKojhRtBY6gNdTxqS6I4DhN1JKpVL0RJc6HuvuWTjs644XGne23YkURRn7FK2FXjgG6vIw/sMfFUUpHlShD4LL5QGc68jaMvH5Y4whEltDOLLCIbRSURRleBS1y6VQTKzZn6a2f2eNN0z4Yt7XisaaWbL6XySS3QgC4mJK3bGUBwucr68oyiaHWuhDoK5qbybVHo3bFQRcuF1lTJl4PDUVu+R1HWOSLFr1j3T2apyUiZFKRVjWdA+x+NqREV5RlE0GtdCHSHXFLKorZm3QNbp7PsX0dn7ogzFJ1nbOo37CgRt0fUVRNm0GtdBFZKqIPCci80XkfRE512GOiMjlIrJQRN4RkRwpKZs2yWQoxzZqiniyy/GIoijKUBmKhZ4AfmCMmSci5cAbIvK0MeaDPnMOBWamP7sD16T/VPpQWjIdp6gZES/lpTkagSqKogyRQS10Y8wqY8y89M9dwHwgs7LV0cAtxvIqUCUiw8uvL2J83mqqymb1i44R8eD31lIe1C7KiqJsGHn50EVkBrAT8FrGocnAsj6/L0+Prco4/0zgTIBp06blKWpx0Fh7GMHADNo655JKxags+ww1FbvgEufQSEVRlKEyZIUuImXAvcB5xpjM6lROTaSz3MXGmOuA6wBmz569SWbliAiVZdtTWbb9aIuiKEqRMaSwRbE+gnuB24wx9zlMWQ5M7fP7FGDlhounKIqiDJWhRLkIcCMw3xjzlxzTHgK+lo522QPoMMasyjFXURRFGQGG4nLZGzgVeFdE3kqP/QSYBmCMuRZ4DDgMWAiEgdMLLqmiKIoyIIMqdGPMSzj7yPvOMcB3CiWUoiiKkj+a+q8oilIkqEJXFEUpElShK4qiFAmq0BVFUYoEVeiKoihFgip0RVGUIkEVuqIoSpGgCl1RFKVIUIWuKIpSJKhCVxRFKRJUoSuKohQJqtAVRVGKBFXoiqIoRYIqdEVRlCJBFbqiKEqRMJSORX8XkSYReS/H8f1EpENE3kp/flF4MRVFUZTBGErHopuBK4FbBpjzojHmiIJIpCiKogyLQS10Y8wLQNtGkEVRFEXZAArlQ99TRN4WkcdFZPtck0TkTBGZKyJzm5ubC7S0oiiKAoVR6POA6caYHYErgAdyTTTGXGeMmW2MmV1XV1eApRVFUZReNlihG2M6jTHd6Z8fA7wiUrvBkimKoih5scEKXUQaRETSP++Wvmbrhl5XURRFyY9Bo1xE5F/AfkCtiCwHfgl4AYwx1wLHA2eLSALoAU4yxpgRk1hRFEVxZFCFboz5yiDHr8SGNSqKoiijiGaKKoqiFAmq0BVFUYoEVeiKoihFgip0RVGUIkEVuqIoSpGgCl1RFKVIUIWuKIpSJKhCVxRFKRJUoSvjj1AILr4Ydt0VDjgA7r0XNDlZUYbU4EJRxg6RCOy5JyxcCD09dmzOHHj5ZfjLX0ZXNkUZZdRCV4ZPZyc89RS8/np/Czkeh//8B55/HmKxwq55xx3w6afrlTlYi/2aa2D58sKupSjjDLXQleFx1VXwv/8LXi+kUjBxIjz5pFWqxx0HiYSd53LBXXfBQQcVZt1HH7UKPBOvF156CU46qTDrKMo4RBW6kj8vvwwXXGCt5F5LORSCAw+E1tZshXvMMbBokVX6G8rkyeDxrH9g9KUQ11eUcYy6XBQAunsWsXjlLXy09DKWN91HNDZASfurrurv8gDrclmzBpLJ7PnGwJ13FkbQs84Cn6//mAhUVcG++xZmDYClS+HMM2GLLWCffeDhhwt3bUUZIdRCV2jvepeVLQ9jTByAju4OukIfsvnkM/D7HFoFNjfnjiqJx7PHIhFYu7Ywwm6zDdx6K3zjG/bhkUrBlCnwyCPgdhdmjeXLYaed7B5BImF99vPmwe9+B+edV5g1FGUEUAt9E8eYFKtbn1inzNOjpEyMNW3POp90zDFQWup0sWzrGezcL3yhEOJavvQl+zbwzDM2wmX+fNhyy8Jd/49/XK/MewmH4Wc/y34z6UssBk88YcMo29oKJ4+iDJFBFbqI/F1EmkTkvRzHRUQuF5GFIvKOiOxceDGVkSKR7CZlnCNRwpFlziedfjqUlWWPn3eeVbbB4PqxYBAOO8yGGhYSn8/GoW+3nXW5FJJnn3X20bvd8OGHzue89ho0NsKXv2z/fiZPhquvLqxcijIIQ3G53IztSHRLjuOHAjPTn92Ba9J/KuMAt6sk5zGPx0Fpg1Ve3d3Z47fdBosXwwMPwE03WYv9tNPghBMKr3RHkqlTrdWfSSwG9fXZ49EoHHpotlvphz+EvfaCWbNGRExFyWRQC90Y8wIw0Pvj0cAtxvIqUCUijYUSUBlZXC4flcEdkIxnu4iXuqrPOZ90zTXWBZFJZ6d1gRx/vA0vfOwxa7G6hunZ+8tfwO+3DwOXC44+enjXyZcLLsh2Kfn9Niu10eGf9tNPO28Gx2Jwww0jI+NQefhh+yYzcaJ96MybN7ryKCNKIXzok4G+7+bL02NZiMiZIjJXROY2NzcXYGmlEDTWHk5FcFsENy7xIeJlYtV+VJbt4HxCR4fzuAh0dRVGqCuugB/8YH1ikjHw0EMbx9o98EC7fmWldS35/XDwwfCvfznP7+py3iROJnP/XW0Mbr7ZxuXPnWs3sp94wkbsvPHG6MmkjChihlADQ0RmAI8YY7L+h4vIo8AfjDEvpX9/BrjAGDPgv5rZs2ebuXPnDktoZT1doYV0hN7F666gtnof3C67KZlIhugOLwSE8tKtcLtzu1bAbo52hhYQiTVRVroFwZKpuSffeCN873vZVnppqd2sdPKv50sgYKNjnGhthY8/hr/9zSrdCy8cmRj0WAw++QRqa6HOIdqnl6YmmD49W95gEP75T7uJvLFJJqGhAVpaso8ddJDN8FXGJSLyhjFmttOxQoQtLgf6/u+fAqwswHWVAUilUnyy4mpi8fXx4i0dLzGl/kSSyTCrW59AcGEf1w8zue5LVJZt63iteKKTRStvIpkMY0jR2vEKwZLpTG04CZc4hAKecgpcfz28955NInK7rRV7+eWFUeaQW5mDtTI/+GD975deaj/nnluYtXvx+WBb57+zfkycCBddBL/6lY2CMcYq8733hiOPLKxMQ6WlxXmfA9RCL2IKYaEfDnwXOAy7GXq5MWa3wa6pFvqGsarlcdo65zgckfQn1X9UPGw17Xw87uxww0UrbyYcWQqYfvPrqj5PXfU+zgLEYnD33XD//dZ6/da3CusOcbttjHk+rF1rE4xGi1dftT7zri67EXzssYWLjc+XaBRqapz3Oj77WXj77Y0vk1IQBrLQhxK2+C/gv8DWIrJcRL4hImeJyFnpKY8BnwILgeuBbxdI7k0KYwxtHXP5aOllfLDo9yxa+Q96ovZFpzv8CZ8s/xsfLPo9C5ddTWdoAe1d7+S6EpnK3CJ0hRZkjSaSPfREltNXmVt5EqztGmADbelSW6PlySfhwQftJmgiAe3t8O1vW2VSXW0VfVubdQH86U82nK+8HI44Yn0I4N13W0s4GLQbeM8+a98CnBgoWuaPf4Tvf9+6SKqqbITNmjX2wXD55TBtmn2DOOggeCfX31+at96ysfNlZdadcsUVg5fo3WMPq9DvvNNuDI+WMgf7xnTmmdmbu6Wl8Mtfjo5MyogzJAt9JFALvT9Nbc/R0vHffgk+Il7qaw5iTdtTGJPoN25MEmfFnZuGCYcwobJ/RGki0c1HSy/FkB2l4XGXsfX0H2RfaNUqG//d2bneii4thaOOsm6Yjz5av5np88GMGTYO/e6711uMIlBRAT//OfziF/0tyUDAZn7++tfwwgvrx6uqrILNVVWxsdE+PKLR9A147Ngxx1i/f981yspsxMfMmdnXWbDAPlj6uixKS+E737F12McLiYQNnbzuOvu73w9/+IMtn6CMWway0MeVQk+ZJN3hj4jF11LiayAY2AzZSPHNxqToCn9MLN6K31dHWWALRAqTaJtKxViw5JJ+StsiuMRHykSzzhHxOMwfmEl1x1BdvmPW+MfLriIWz9w8c1NTsQsNEw4hFFlEJLoan6eKsuDWuH76cxtSGM2Qy+u1CjyzOFcwaOdmJut4vVbpOmVfzp5ty/L29Filvu221sL+6U/h9793vsHS0mwXQ6613W449VQbL5/JqafC7bdnu3xKSqzFX1HhvH6+pFI25PHdd22m6+GH27+TQhOJ2AfdxIn271sZ14z0puhGIZ7o4NMVfyeVipAyCUQ8+L0TmDHp6+siO0aKRKKbT1f+nWQyRMokcIkHr6eSzSadjtsd2ODrxxIdfTYw+2IclTmQU5n7PROJJpocjyWTDv5UYMrEY1m88h8YUhiTQMSLx13GhMq9+HTlDURjLZj0fbta/Wz20Zv4MpU52Fhxp9K2oZBzLHo87lz7BdYn9gQC8MUvrh//9a+tEl61qv/83Xd39guHQs5KMpm0DwwnXn/d2X/vdtuol512cj4vHzo64POft3ViolH7sKipgVdegUmTNvz6fSkpKfw1lTHJuKnlsqLpQRLJrnSaegpjYkTjTTSvfX7E117Z8ijxRMe6tVMmRjTeyurWwoR+eT3lji4PS643EMHp64smcldJTKWcU/wD/knMnPY96qr2pap8JybVHsaWU86mrXMO0dgaTJ/7TiS7WXHOzrmVZO7Fc9xGjvubNs153OOx/vvf/x62394q8ocftoreSaZAwHltkdwRLH6/83goZAuBFYILL7Sune5u+1Dr6oIVK6zfW1GGybiw0FOpGKHIErI37pK0d71Nw4SDR2xtYwxd4Y/I9len6Ai9T1XPLFa3PkEktga3q4Sayj2oq9onpysomYqyuvUpOrrfxZgkZYHNaKw9nMqyHenofifDV+4BY3Ioe0Pm30d6hZz30hX+iIk1ziVmPe4gddX9M0M7ut9O++r7rxue4idZGcTd0r5+2O+3KfMLF+ZcPyfBYH/LvrQUfvOb3PM9HqsQL7xw/VgyaV0K3d39Ny/9fuvKePTR/q6dQKD/+X3J1WXJ7baui4Fi0ofKv/6VvU4iYTeZ4/GRcb0oRc+4sNAH9vJvjD0A5zWMSbFk9W1EYqsBQzLVQ0v7S6xufTLHfMOSVf+ko+vt9OZniu6eT/l0xfXUV+9PTcWuiHgBF15PFVPrT9go95Fzdq79FRF45GEb/tYbg37SSfA//+NcbXGgfQ6321YxrKqyirq+3tZbP+64vGTF5XK2rN1uuPZaW243ELBrbLmlrTezc446crks9JKSgd9C8mGgkExteK0Mk3FhobtdPgL+SfREM6Mb3FSUbT+ia4sIZYEt6O75hP4K0YXHXUoi2T/V3Zg4a7veYGLN/rhd/RVDT3QFkdiaDIvbkDJx2rvfpWHCwdTXfCHtp/daK1/ckGUlD4+66v1yHkumonR2v08s0U7AP4ny0q2oKNue9s55WW8IAX8j7t0/Z33W4bBV4h6P9S87WdZ+f+5EoX32gR//2NZPCYettT6cje7nn7eumExlGInYcrZXXGGTjyKR/tUgnTjtNLv5mrlZW1MztESjofClL9liZn33EFwu61f3euG55+ynttY+LLUbkzIExoWFDjB54jG4XaVpCxZc4sPnraK++oARX7ux7gg87iAu8a1b2+spzxnlIriJJ9qzxqNx5/o1xiTSVj6IuHC7fOtcNvlGsoh4cbuyk4d83olUBLdyPCcSa+bjpZexqvUJWtpfZHnT/Xyy4jpqK/fG661ed9/22gEm1x2z/uTS0vWRE1tsYbMley1hj8f+/JOfWOs9SyifdYWAVWZlZcOvyjh/vnPJ21Bo/Wap2z24MgcbR7/LLuuzXgMBGzt/552Fqxp5ySXWRdW7RjBolfe119oY/SOPtA/HH/0INt/cxuYryiCMCwsdwO+dwMxp59LZ/Z4NW/Q3UBHcBnFKTS8wPk8lM6d+j87QB0Rjzfj99VQEt2X5mnuJJ7KLLxmSeD1VDvfg7HsV8VDia3Be21vdL72/7znWv51hkZoUW0w9m/bud2jrmAMIdVX7UFOZu0z9iqb7SKbWW6PGxIjFW2jrnMOWU86iM/QhkegqfN5qKsp2GDiq6IILrDK65x5rLR93nN28BDj5ZJuev3atjVm/4orCJd9ss41zSF4waF1D+eD3w3/+Y+udvPCCjRD5yldgwoTCyApWec+fbzNt33oLtt7aZpfefbddu3dPoffN5sQTYfVqDTtUBmRcxaGPNXqiq1i08qasZKDq8p1prD0ka74xhkUrbyQSXd3HjSG4XSXMnHqOYwhkZ2g+y5vuz1qjtnJvWjpezhj3UBHcjikTjx3yPSSSIT5a8tcciUUVbD39/CFfa1RJpWw44YIF6zcb3W6rhBcutBb2eGC//axCz6S83G6YFrpRiDLu2KDUfyU3AX8j0xtOxu+zTQ/crgC1VZ/LGXUjIkxvPJXK8s/aCBaEYGBzNp98Rs549orgtkyuO3qdxe9xV9A44VAm1uzLjMbTCPgnA4LL5WdCxR5Mrjsqz7sYR40nBsLlsn70k06ym5ceDxxyiK3PPl6UOQz8xjLcuvLKJoNa6EMkmYrQ3vU20VgTJb5GKss/02/T0xiTd9Zqvufkmj+ctfvy6Yob0nVj+hTnwsOEqr2or9l/2NcdVYwZX12Sern9dhuLnpmgVV9v49RHsz6MMiZQC30DicXb+Hjp5axpe4a1XfNY3fYUHy+7gniic92c4SjUfM/JNX9Dyx/UVu1Ddkijobp8lw267qgyHpU52DeMI4+0m81er900LS+3vnZV5sog6A7LEFjZ8gjJVIRepWdMnGQywaqWx5nW8OXRFa4ANLU9kzVmMLR2vOK4F6CMIC6XTTqaO9e6kCZMsBvLhaofoxQ1qtAHwRhDqGcxThZsd88wsiLHGIlEN7G4U8vYFJ2h91WhjxazZ9uPouSBulyGRA5XRzH89Q0Q9mk3bhVFGS8UgUYaWUSEiuB2ZP5VCW4qyz4zOkIVEI87QKDERsr0RcRDdXkBqgoqirLRGJJCF5FDRORDEVkoIj92OL6fiHSIyFvpzy8KL+ro0Vh7GH5vLS7xIeLBJT78/nrqJxw02qIVhCkTj8PrqUjfnxcRL6Ul05lQtfdoi6YoSh4M+k4tNhXzKuAgbEPo10XkIWPMBxlTXzTGHDECMo46HneALaacRTiyhGi8hRLfRAL+qRutucZI4/VUMHPq9+ju+ZR4upZLwK/1sxVlvDEUJ+luwEJjzKcAInIHcDSQqdCLGhEhGJhBMDBjtEUZEURclJduOdpiKIqyAQzF5TIZWNbn9+XpsUz2FJG3ReRxEXEsgSgiZ4rIXBGZ29zsXKhKURRFGR5DUehOfoXMGL55wHRjzI7AFcADThcyxlxnjJltjJldV4gmAYqiKMo6hqLQlwNT+/w+BVjZd4IxptMY053++THAKyK1BZNSURRFGZShKPTXgZkispmI+ICTgIf6ThCRBknvEIrIbunr5m5uqSiKohScQTdFjTEJEfku8CTgBv5ujHlfRM5KH78WOB44W0QSQA9wkhmtql+KoiibKKNWbVFEmoElG3HJWqBlI643VtD73rTQ+y5+phtjHDchR02hb2xEZG6ukpPFjN73poXe96aNpv4riqIUCarQFUVRioRNSaFfN9oCjBJ635sWet+bMJuMD11RFKXY2ZQsdEVRlKJGFbqiKEqRUFQKXURKRGROukjY+yLya4c5RVm7XUTcIvKmiDzicExE5PJ0Pft3RGTn0ZBxpBjk3ov1+14sIu+m72muw/Gi/M6HcN9F+X0PlWLrMRYFDjDGdIuIF3hJRB43xryaMa8Ya7efC8wHnLoJHwrMTH92B65J/1ksDHTvUJzfN8D+xphcyTTF/J0PdN9QvN/3oBSVhW4s3elfvelP0e/6isgU4HDghhxTjgZuSf/9vApUiUjjRhNwBBnCvW+qFO13ruSmqBQ6rHv9fgtoAp42xrzmMG3Q2u3jjEuBC4BUjuNDrWk/HrmUge8diu/7BmuoPCUib4jImQ7Hi/U7H+y+oTi/7yFRbC4XjDFJYJaIVAH3i8gOxpj3+kzprd3eLSKHYWu3z9z4khYGETkCaDLGvCEi++Wa5jA27t9chnjvRfV992FvY8xKEZkIPC0iC4wxL/Q5XpTfOYPfd7F+30Oi6Cz0Xowx7cDzwCEZ48VWu31v4CgRWQzcARwgIv/MmDNoTftxyqD3XoTfNwDGmJXpP5uA+7GtIvtSlN/5YPddrN/3UCkqhS4idWnLHBEJAF8AFmTMKara7caYC40xU4wxM7C16p81xpySMe0h4GvpyIc9gA5jzKqNLWuhGcq9F9v3DSAiQREp7/0ZOBh4L2Na0X3nQ7nvYvy+86HYXC6NwD9ExI39Iu8yxjwim2Dt9ox7fgw4DFgIhIHTR1G0EWcT+L7rse5EsP+HbzfGPLEJfOdDue9i/L6HjKb+K4qiFAlF5XJRFEXZlFGFriiKUiSoQlcURSkSVKEriqIUCarQFUVRigRV6IqiKEWCKnRFUZQi4f8ByZPQNxtlx08AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cluster_new['V1'], cluster_new['V2'], c=cluster_new['cluster_pred'], cmap='rainbow')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}