Graduate Admission Prediction ML project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graduate Admission Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Admission_Predict.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Display Top 5 Rows of The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0           1        337          118                  4  4.5   4.5  9.65   \n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit   \n",
       "0         1              0.92  \n",
       "1         1              0.76  \n",
       "2         1              0.72  \n",
       "3         1              0.80  \n",
       "4         0              0.65  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Check Last 5 Rows of The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>396</td>\n",
       "      <td>324</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>397</td>\n",
       "      <td>325</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>398</td>\n",
       "      <td>330</td>\n",
       "      <td>116</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.45</td>\n",
       "      <td>1</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>399</td>\n",
       "      <td>312</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>400</td>\n",
       "      <td>333</td>\n",
       "      <td>117</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "395         396        324          110                  3  3.5   3.5  9.04   \n",
       "396         397        325          107                  3  3.0   3.5  9.11   \n",
       "397         398        330          116                  4  5.0   4.5  9.45   \n",
       "398         399        312          103                  3  3.5   4.0  8.78   \n",
       "399         400        333          117                  4  5.0   4.0  9.66   \n",
       "\n",
       "     Research  Chance of Admit   \n",
       "395         1              0.82  \n",
       "396         1              0.84  \n",
       "397         1              0.91  \n",
       "398         0              0.67  \n",
       "399         1              0.95  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Find Shape of Our Dataset (Number of Rows And Number of Columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\karti\\AppData\\Local\\Temp\\ipykernel_15692\\2644703078.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(data['GRE Score'],color='green')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='GRE Score', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['GRE Score'],color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\karti\\AppData\\Local\\Temp\\ipykernel_15692\\2316146702.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(data['TOEFL Score'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='TOEFL Score', ylabel='Density'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z8aHwc1fCUNeI93afRV2DWeyyiOwOAxAR2Rx9bQP2nasA0DT3h7d+N+eskGHW6DBonJ1QVm3CpqzzEARB7LKI7AoDEBHZnF0nSmG2CAj1dkEfH1exy7FJGmcn3D0yBDKJBIcLDUg/c0HskojsCgMQEdkUQ10D9p0rBwDcGMnRn6sJ9nLBpCgtAOC7QzocLtCLXBGR/WAAIiKb8vOJUjRamub+9PXl6M+1xPfxxsAANcyCgKc+P4gGs0XskojsAgMQEdkMY30j9lpHf/w4+tMKEokEk2MC4ewkw9EiA9btOiN2SUR2gQGIiGzGb2cuoMEsIFCjQoSfm9jl2A13lRNuGRIAAFiVdhKnSqpFrojI9jEAEZFNMDVarBN5r+/ny9GfNooJ9sAN/X1harRg6ddHeFcY0TUwABGRTcjILUeNyQwvVwUGBWrELsfuSCQSPDt5MBQyKX45VYYfj5eIXRKRTWMAIiLRNZot1lWfx4T7cNXndgr2csH9Y8IAAM9/ewymRk6IJroSBiAiEt33R4tRWdMAF4UMw3s79p5fHfWPcX3h46bAmTIjPv4tV+xyiGwWAxARie79X88BAOLCvOAk44+ljnBXOeGJCf0BAK/9eBJVdQ0iV0Rkm/iThohEdbhAj73nyiGVAHFh3mKX0yP8dXgv9PF1RWVNAz7YfU7scohsEgMQEYnq/Yu/oAcHaaB2dhK3mB5CLpNi7vgIAMC6XWegr+UoENGfMQARkWjKquvxdXYhACChr4/I1fQstwwJRISfGwx1jXj3l7Nil0NkcxiAiEg0n+7Jg8lsQXSwB0K8XMQup0eRSSV4LLEfAODdX85yFIjoTxiAiEgUpkYLPrp4l9KshFBxi+mhJg3Wor+/O6rqG3lHGNGfMAARkSi+O1yEkqp6+LorcVNUgNjl9EhSqQQPje0DAHjv13OoazCLXBGR7ZCLXQARtc36PXnd9rXujgvpstd+7+Kt73+L6w2FnP8W6yq3RgdixfcnUFBZiy+yzuOeuN5il0RkE/hTh4i63f68CmTnV0Ihk3ZpyCLASSbFA9c1rQ791q4zMFu4RxgRwABERCK4dOv7LdEB8HVXiluMA5gaGwxPFyecu1CD7Ud0YpdDZBNsIgCtWbMGoaGhUKlUiIuLw969e6/afuPGjYiMjIRKpUJUVBS2bt3a7Pn77rsPEomk2WPixIld2QUiaqViQx2+PVgEAJiVECZyNY7BRSHHvaOaLn299ytviScCbCAAbdiwASkpKViyZAmysrIQHR2NpKQklJS0vJPx7t27MX36dMyePRv79+9HcnIykpOTcfjw4WbtJk6ciKKiIuvj008/7Y7uENE1fPJbLhotAkb09kRUL+763l3+Nqo35FIJ9p2rwOECvdjlEIlO9AC0cuVKPPjgg5g1axYGDhyItWvXwsXFBe+++26L7VetWoWJEydi3rx5GDBgAJ599lkMGzYMq1evbtZOqVRCq9VaH56e3GCRSGz1jWZ8cnES96zRHP3pTn5qFW4e0nS33aUJ6ESOTNQAZDKZkJmZicTEROsxqVSKxMREpKent3hOenp6s/YAkJSUdFn7nTt3ws/PD/3798fDDz+MCxcuXLGO+vp6GAyGZg8i6nzfHCjCBaMJARoVJgzyF7sch3MpdH5zoBBl1fUiV0MkLlEDUFlZGcxmM/z9m/8g9Pf3h07X8kQ9nU53zfYTJ07Ehx9+iLS0NCxfvhw//fQTJk2aBLO55TUwli1bBo1GY30EBwd3sGdE9GeCIFjnn9wb35u7vosgJtgDMcEeMJkt3bqcApEt6pE/gaZNm4bbbrsNUVFRSE5OxpYtW7Bv3z7s3LmzxfYLFiyAXq+3PvLz87u3YCIHkJFbgSOFBijlUkyP5a3vYpk1OhQA8NnePN4STw5N1ADk4+MDmUyG4uLiZseLi4uh1WpbPEer1bapPQD06dMHPj4+OHXqVIvPK5VKqNXqZg8i6lzvX5x3cvvQIHi6KsQtxoElDdLC08UJhfo67DpRKnY5RKIRNQApFAoMHz4caWlp1mMWiwVpaWmIj49v8Zz4+Phm7QEgNTX1iu0B4Pz587hw4QICArjcPpEYCitrse3i+jP3XRyBIHGonGS4Y1gvAMD6vbwMRo5L9EtgKSkpeOutt/DBBx/g2LFjePjhh2E0GjFr1iwAwIwZM7BgwQJr+7lz52Lbtm1YsWIFjh8/jqVLlyIjIwOPPvooAKC6uhrz5s3Db7/9hnPnziEtLQ2TJ09GeHg4kpKSROkjkaP76LdcmC0C4vt4I1LLEVaxTRvZdAnyx+Ml0OnrRK6GSByiB6CpU6fi5ZdfxuLFixETE4Ps7Gxs27bNOtE5Ly8PRUVF1vYJCQlYv3491q1bh+joaHz++efYvHkzBg8eDACQyWQ4ePAgbrvtNvTr1w+zZ8/G8OHD8fPPP0Op5IqzRN2trsGMTy+ONHD0xzaE+7lhZJgXzBYBGzM455Eck0QQBM6C+xODwQCNRgO9Xs/5QGRz7G0z1PV78rDwy0Po5emMn+aNg0wquWI76pi2vF9f7j+PxzccQJCHM3Y9deX3hcietOX3t+gjQETUcwmCgHcv3vp+X0Iof8nakEmDA6BxdkJBZS1+PsnJ0OR4GICIqMvsOlmGUyXVcFPKMTWW62vZEpWTDFOGBQGA9RIlkSNhACKiLvPOL02jP3eNCIa7yknkaujPpl+cDP3DsRKUGDgZmhwLAxARdYmTxVXYdaIUEknT5S+yPf383TGit2fTZOjM82KXQ9StGICIqEu8e3HhwwkD/RHi7SJuMXRFl0aBPt2bBwtXhiYHwgBERJ2u3GjCpqymEYXZY/qIXA1dzc1DAqBWyXG+oha7T19502iinoYBiIg63fo9uahvtCAqSIPYUE+xy6GrUDnJcGt0IADgiyxeBiPHwQBERJ3K1GjBh+m5AID7x4RCIuGt77buzuFNW2N8d7gIVXUNIldD1D0YgIioU317qBAlVfXwc1fi5qhAscuhVogJ9kAfX1fUNVjw3SGd2OUQdQsGICLqNIIg4M2fzgAAZiaEQiHnjxh7IJFIrKNAn/NuMHIQ/OlERJ3mx+MlOK6rgqtChr/F9Ra7HGqD24cGQSIB9p4rR+4Fo9jlEHU5BiAi6hSCIGD1jlMAgL/F94bGhQsf2pMAjTPGhPsAAL7IKhC5GqKuxwBERJ0i/cwF7M+rhFIuxQO89d0uXboMtinrPNcEoh6PAYiIOsV/d5wGAEyNDYavu1Lkaqg9JgzUwl3ZtCbQnrPlYpdD1KUYgIiow7LzK/HLqTLIpRL83/Uc/bFXzgoZbh4SAIBrAlHPxwBERB225uLcn8kxQejlyW0v7Nmly2BbDxXBWN8ocjVEXYcBiIg6JEdXhdSjxZBIgIdv6Ct2OdRBw3t7ItTbBTUmM7Yd5ppA1HMxABFRh7yxs2n0Z9JgLcL93ESuhjpKIpHgjmFNo0C8DEY9GQMQEbXbuTIjvj5QCAB45IZwkauhznL7sCAATXf2FVTWilwNUddgACKidnvlhxOwCMC4/r4YHKQRuxzqJL08XTCqjxcEAdi8n2sCUc/EAERE7ZKjq7KO/jwxob/I1VBnmzLs9zWBBIFrAlHPwwBERO2y4vscCAJwU5SWoz890KTBWqicpDhdasTB83qxyyHqdAxARNRm2fmV+P5oMaQSIOUv/cQuh7qAu8oJEwZqAQBf8jIY9UAMQETUJoIgYNnWYwCA24f2Qrifu8gVUVeZcnEy9NcHCmFqtIhcDVHnYgAiojZJPVqMPWfLoZRL8cQEjv70ZGPCfeDrrkS50YSfTpSKXQ5Rp2IAIqJWazBb8MJ3xwEAD1wXhkAPZ5Eroq4kl0mRHBMIoGkyNFFPwgBERK22fk8ezpQZ4eOmwN/HctVnR3DpbrC0YyWorDGJXA1R52EAIqJWKTeasDL1BABgbmI/uKucRK6IusOAADUGBKhhMluw5WCR2OUQdRoGICJqlZe2H4e+tgEDAtSYHhssdjnUje64OBmal8GoJ2EAIqJrOpBfic/25QMA/jN5EOQy/uhwJLfFBEIqAbLyKnG2zCh2OUSdol0/xc6cOdPZdRCRjTJbBCz+6jAEAZgyNAixoV5il0TdzM9dhev7+QIAvuQoEPUQ7QpA4eHhGDduHD7++GPU1dV1dk1EZEM+TD+HA+f1cFfKMX9SpNjlkEisW2PsL4DFwq0xyP61KwBlZWVhyJAhSElJgVarxUMPPYS9e/d2dm1EJLLzFTV4aXsOAODpSZHwU6tErojEMmGgP9yVcpyvqMW+c+Vil0PUYe0KQDExMVi1ahUKCwvx7rvvoqioCGPGjMHgwYOxcuVKlJZywSwieycIAhZtPowakxmxoZ64e2SI2CWRiFROMtwUFQAA2JTFrTHI/nVoJqNcLseUKVOwceNGLF++HKdOncKTTz6J4OBgzJgxA0VFvGWSyF5l51diR04pFDIplk0ZAqlUInZJJLJLW2N8e6gIdQ1mkash6pgOBaCMjAw88sgjCAgIwMqVK/Hkk0/i9OnTSE1NRWFhISZPntxZdRJRN9LXNuCbg4UAgLmJEQj3cxO5IrIFsaFe6OXpjOr6Rnx/tFjscog6pF0BaOXKlYiKikJCQgIKCwvx4YcfIjc3F8899xzCwsJw3XXX4f3330dWVlZn10tEXUwQBGzKOo+6Bgtigj3w0PV9xC6JbIRUKsGUoVwTiHqGdgWgN954A3fffTdyc3OxefNm3HLLLZBKm7+Un58f3nnnnU4pkoi6z56z5ThZUg25VIIVd0VzzR9q5vaLd4PtOlGKkireBUz2S96ek1JTUxESEnJZ6BEEAfn5+QgJCYFCocDMmTNb9Xpr1qzBSy+9BJ1Oh+joaLz++usYOXLkFdtv3LgRixYtwrlz5xAREYHly5fjpptuarHt3//+d7z55pt45ZVX8Nhjj7W6j0SOqEhfi62HmubuTRysRV9fXvqyJ+v35HXL1wn2dEZ+RS2Wfn0UY8J9uvzr3R3HCfjU+dr1T7u+ffuirKzssuPl5eUICwtr02tt2LABKSkpWLJkCbKyshAdHY2kpCSUlJS02H737t2YPn06Zs+ejf379yM5ORnJyck4fPjwZW2//PJL/PbbbwgMDGxTTUSOyNRowad789FoERCpdUd8H2+xSyIbNTTEEwCwP69C5EqI2q9dAUgQWl4Eq7q6GipV29YJWblyJR588EHMmjULAwcOxNq1a+Hi4oJ33323xfarVq3CxIkTMW/ePAwYMADPPvsshg0bhtWrVzdrV1BQgDlz5uCTTz6BkxM3bSS6lq8PFKKsuh5qlRx3DOsFiYR3fVHLhvTSQCaRoEhfhyJ9rdjlELVLmy6BpaSkAAAkEgkWL14MFxcX63Nmsxl79uxBTExMq1/PZDIhMzMTCxYssB6TSqVITExEenp6i+ekp6db67gkKSkJmzdvtv7ZYrHg3nvvxbx58zBo0KBr1lFfX4/6+nrrnw0GQ6v7QNQT7M+rQFZeBSQA7ooNhquyXVfHyUG4KOSIDHDHkUIDsvMqERDlLHZJRG3Wpp9y+/fvB9A0AnTo0CEoFArrcwqFAtHR0XjyySdb/XplZWUwm83w9/dvdtzf3x/Hjx9v8RydTtdie51OZ/3z8uXLIZfL8c9//rNVdSxbtgz//ve/W103UU9SVl2Prw403fJ+Y6Qf+vhw3g9d29Bgz6YAlF+JCYO0kHGdKLIzbQpAO3bsAADMmjULq1atglqt7pKiOiIzMxOrVq1CVlZWq4fwFyxY0GxUyWAwIDg4uKtKJLIZjWYLPtuXB1OjBWE+rhgX6Sd2SWQn+mnd4KKQoaq+EadLq9HP313skojapF1zgN57771OCT8+Pj6QyWQoLm6+oFZxcTG0Wm2L52i12qu2//nnn1FSUoKQkBDI5XLI5XLk5ubiiSeeQGhoaIuvqVQqoVarmz2IHMG2IzoUVtbBRSHDXSOCIeW8H2oluVSKIb08AHAyNNmnVo8ATZkyBe+//z7UajWmTJly1babNm1q1WsqFAoMHz4caWlpSE5OBtA0fyctLQ2PPvpoi+fEx8cjLS2t2S3tqampiI+PBwDce++9SExMbHZOUlIS7r33XsyaNatVdRE5gmNFBuw+fQEAcOfwXtA482YBapthIR747cwFHC0yoK7BDJWTTOySiFqt1QFIo9FYLylpNJpOKyAlJQUzZ87EiBEjMHLkSLz66qswGo3WsDJjxgwEBQVh2bJlAIC5c+di7NixWLFiBW6++WZ89tlnyMjIwLp16wAA3t7e8PZufvuuk5MTtFot+vfv32l1E9mzyhoTPs9sWsl3TLgPIrUc9aS2C/Jwhq+bEqXV9ThSqMfw3l5il0TUaq0OQO+9916L/99RU6dORWlpKRYvXgydToeYmBhs27bNOtE5Ly+v2YKLCQkJWL9+PZ555hksXLgQERER2Lx5MwYPHtxpNRH1ZGaLgA0Z+ahtMCPIwxkTBvlf+ySiFkgkEgwN8cD3R4uRlVfJAER2RSJcaVGfq6itrYUgCNbb4HNzc/Hll19i4MCBmDBhQqcX2d0MBgM0Gg30ej3nA5HN6ehqv6lHi7EjpwRKuRSPjguHt5vyim27cwXe7lrFmDpXZY0JL23PgQDgyQn94eWquOY5bcWVoKm12vL7u12ToCdPnowPP/wQAFBZWYmRI0dixYoVmDx5Mt544432vCQRdYPTpdXYmdO0ynry0KCrhh+i1vBwUaCvX9PSCZm5nAxN9qNdASgrKwvXXXcdAODzzz+HVqtFbm4uPvzwQ7z22mudWiARdY5akxkbM/IhABjR2xPRF+/gIeqoEb2btsbIyquApe0XFYhE0a4AVFNTA3f3pjUfvv/+e0yZMgVSqRSjRo1Cbm5upxZIRJ3jqwMFMNQ1wttVgVuGcH886jwDA9RwdpJBX9uAk8XVYpdD1CrtCkDh4eHYvHkz8vPzsX37duu8n5KSEs6ZIbJBB/IrcfC8HlIJcNeIYCjk7froE7VILpNiaIgHACAjt1zcYohaqV0/BRcvXownn3wSoaGhiIuLs67B8/3332Po0KGdWiARdUxljQlfHSgAAIzr74dgL5drnEHUdiMu3gF2vKgK1fWNIldDdG3tCkB33nkn8vLykJGRgW3btlmPjx8/Hq+88kqnFUdEHWMRBHyeeR51DRb08nTGDf251QV1Da1GhV6ezjALAleGJrvQ7i2ftVrtZdtVjBw5ssMFEVHn2X36As6UGeEkk+CuEcHcsJK61PDenjhfUYuM3AqMCfdp9X6MRGJoVwAyGo144YUXkJaWhpKSElgslmbPnzlzplOKI6L2K62qx/dHdACAm6IC4MNb3qmLRffywNZDRSitqkd+eQ1CvF3FLonoitoVgB544AH89NNPuPfeexEQEMCUT2RjLIKAL/cXoNEiIMLPDSNDuUIvdT2VkwxRQRpk5VUiI7eCAYhsWrsC0HfffYdvv/0Wo0eP7ux6iKgTZOZW4NyFpktfyTFB/EcKdZvhvb2Qldd01+HNUQFQcoNUslHtmgTt6ekJLy/+i5LIFlXVNeC7w0UAgL8M1MKzC7YmILqSUG8X+LgpYDJbcKhAL3Y5RFfUrgD07LPPYvHixaipqenseoiog7Yd1qGuwYIgD2fE9/EWuxxyMBKJxHpL/N5zXBOIbFe7LoGtWLECp0+fhr+/P0JDQ+Hk5NTs+aysrE4pjojaJveCEfvzKwEAk2MCedcXiWJYb0+kHivG+YpanK+oQS9Prj1FtqddASg5ObmTyyCijrIIArYcbLr0Nby3J3/pkGjclHJEBWmQnV+JPWfL+b1INqldAWjJkiWdXQcRdVBmbgUKKmuhlEuRNEh77ROIulBcmBey8ytx8HwlbhocAGcFJ0OTbWn3QoiVlZX4/PPPcfr0acybNw9eXl7IysqCv78/goKCOrNGIroGU6MFPxwtBgCMH+APN2W7P9rNrN+T1ymvQ44nxMsFWrUKOkMdsvIqMDrcR+ySiJpp1yTogwcPol+/fli+fDlefvllVFZWAgA2bdqEBQsWdGZ9RNQKv5wqRVV9I7xcFRjVh3dokvgkEgniLn4v/nbmAiyCIHJFRM21KwClpKTgvvvuw8mTJ6FSqazHb7rpJuzatavTiiOia6uub8Suk2UAgL8M9Idcyp3eyTbEBHtAKZfigtGEM6VGscshaqZdPyn37duHhx566LLjQUFB0Ol0HS6KiFpvx/ESmBqbbnuPCtKIXQ6RlVIuw9AQDwDAnrMXxC2G6E/aFYCUSiUMBsNlx0+cOAFfX98OF0VErVNZY8Les01rrSQN0kLKFZ/JxsSFNa1FdazIAH1tg8jVEP2uXQHotttuw3/+8x80NDR9M0skEuTl5eHpp5/GHXfc0akFEtGV/XSiFGZBQJiPK8L93MQuh+gy/moVQr1dYRGAfVwYkWxIuwLQihUrUF1dDV9fX9TW1mLs2LEIDw+Hu7s7nn/++c6ukYhaUFljQsa5CgDA+Eg/kashurJLE/P3nSuH2cLJ0GQb2nWvrEajQWpqKn799VccOHAA1dXVGDZsGBITEzu7PiK6gj+O/vTx5egP2a6BgWq4K+WoqmvEoQI9YoI9xC6JqO0ByGKx4P3338emTZtw7tw5SCQShIWFQavVQhAE7jpN1A30tQ0c/SG7IZdKEdfHGz8cK8Yvp0oR3UvD3xUkujZdAhMEAbfddhseeOABFBQUICoqCoMGDUJubi7uu+8+3H777V1VJxH9wa+nymAWBIR6u3D0h+xCXJgX5FIJCivrcO4CN9Im8bVpBOj999/Hrl27kJaWhnHjxjV77scff0RycjI+/PBDzJgxo1OLJKLf1ZrM1l22x/bjXZdkH1yVcgwL8cTec+X45VQZwnxcxS6JHFybRoA+/fRTLFy48LLwAwA33ngj5s+fj08++aTTiiOiy+09ewGmRgv81Ur083cXuxyiVksIb7ol/niRAWXV9SJXQ46uTQHo4MGDmDhx4hWfnzRpEg4cONDhooioZfWNZuw+3bSg3HURvpxHQXbFz12F/v7uEADsPl0mdjnk4NoUgMrLy+Hv73/F5/39/VFRUdHhooioZV/tL0RVfSM0zk4Y0ourPpP9GRPRtClqZm4FakyNIldDjqxNAchsNkMuv/K0IZlMhsZGfkMTdQVBEPDur2cBAAl9vbnnF9mlPj6uCNCo0GAWrKuYE4mhTZOgBUHAfffdB6VS2eLz9fW8pkvUVfacLcdxXRWcZBKM6M0d38k+SSQSjA73weeZ55F+5gLGRPgwzJMo2hSAZs6cec02vAOMqGu8/+s5AMDQYE84K2TiFkPUAUN6abD9iA5VdY04eF6PYSGeYpdEDqhNAei9997rqjqI6CoKKmvx/VEdACC+r7fI1RB1jFwqRXwfb3x/tBi7TpQiJtiDG/lSt+O4I5Ed+Cg9FxYBGB3uDX+1SuxyiDosLswbSrkUJVX1OFpoELscckAMQEQ2rr7RjP9l5AMAZsaHilsMUSdxVsiQcHE0c0dOCQSBm6RS92IAIrJx248Uo9xoglatwo3c94t6kNF9faCQS1Gkr8NxXZXY5ZCDYQAisnGf/JYLAJgaGwy5jB9Z6jlclHKMCmu6o5GjQNTd+NOUyIadKqnGnrPlkEqAaSODxS6HqNONifCFk0yC8xW1OFlSLXY55EBsIgCtWbMGoaGhUKlUiIuLw969e6/afuPGjYiMjIRKpUJUVBS2bt3a7PmlS5ciMjISrq6u8PT0RGJiIvbs2dOVXSDqEp/uzQMA3BjphwCNs8jVEHU+N6UcI0MvjgId5ygQdR/RA9CGDRuQkpKCJUuWICsrC9HR0UhKSkJJSUmL7Xfv3o3p06dj9uzZ2L9/P5KTk5GcnIzDhw9b2/Tr1w+rV6/GoUOH8MsvvyA0NBQTJkxAaWlpd3WLqMPqG834Ius8AODuuBCRqyHqOtf184VcKkFueQ3OlBnFLocchEQQOW7HxcUhNjYWq1evBgBYLBYEBwdjzpw5mD9//mXtp06dCqPRiC1btliPjRo1CjExMVi7dm2LX8NgMECj0eCHH37A+PHjr1nTpfZ6vR5qtbqdPSPqmO8OFeHhT7KgVavw6/wbIZM2rZOyfk+eyJURdb6vDxTgtzPl6OPjigeu69PsOf4DgFqrLb+/RR0BMplMyMzMRGJiovWYVCpFYmIi0tPTWzwnPT29WXsASEpKumJ7k8mEdevWQaPRIDo6usU29fX1MBgMzR5EYvs8s2n05/ZhQdbwQ9RTXR/hC5lEgjNlRpwu5Vwg6nqiBqCysjKYzebLdpj39/eHTqdr8RydTteq9lu2bIGbmxtUKhVeeeUVpKamwsfHp8XXXLZsGTQajfURHMzJpiSu0qp67DzRdMn2jmG9RK6GqOt5uCgQG9a0Jca2wzpYOBeIupjoc4C6yrhx45CdnY3du3dj4sSJuOuuu644r2jBggXQ6/XWR35+fjdXS9TcV9kFMFsExAR7INzPTexyiLrFjZH+UMilKKisxeECvdjlUA8nagDy8fGBTCZDcXFxs+PFxcXQarUtnqPValvV3tXVFeHh4Rg1ahTeeecdyOVyvPPOOy2+plKphFqtbvYgEtOly193DOfoDzkON6Uc10U0jdR/f7QYjRaLyBVRTyZqAFIoFBg+fDjS0tKsxywWC9LS0hAfH9/iOfHx8c3aA0BqauoV2//xdevr6zteNFEXO1Kox3FdFRQyKW4dEiB2OUTdaky4D9yUcpQbTdh7tlzscqgHE/0SWEpKCt566y188MEHOHbsGB5++GEYjUbMmjULADBjxgwsWLDA2n7u3LnYtm0bVqxYgePHj2Pp0qXIyMjAo48+CgAwGo1YuHAhfvvtN+Tm5iIzMxP3338/CgoK8Ne//lWUPhK1xReZBQCAxIF+8HBRiFwNUfdSymUYP6Bpy5cfj5egrsEsckXUU8nFLmDq1KkoLS3F4sWLodPpEBMTg23btlknOufl5UEq/T2nJSQkYP369XjmmWewcOFCREREYPPmzRg8eDAAQCaT4fjx4/jggw9QVlYGb29vxMbG4ueff8agQYNE6SNRazWYLfgquykA3cnLX+SgRvT2wq+nylBWbcLPJ0tx/5gwsUuiHkj0dYBsEdcBIrGkHi3Ggx9mwMdNid8W3Nji3l9cB4gcweECPdbvzYOTTIJfnr4R/mqV2CWRHbCbdYCIqLkvLk5+To4J5Man5NAGBaoR4uWCBrOAld+fELsc6oH4E5bIRlQYTUg73nSHI+/+IkcnkUgwaXDT3b3/y8zHgfxKcQuiHocBiMhGfHOwEA1mAQMD1BgQwEuvRL29XRET7AFBABZ/fQQWC2dsUOcRfRI0ETX5OrsQADBlWJDIlRDZjomDtThVUo0D+ZX4PPM87ort2pX6u3OOHfc4ExdHgIhsQGFlLTJyKwAAN3PtHyIrtcoJjyVGAABe2HYcFUaTyBVRT8EARGQDth4qAgDEhnoiQOMscjVEtmVmQij6+buh3GjC81uPiV0O9RAMQEQ24JuDTQHoliGBIldCZHucZFIsmzIEEknTNjG7T5WJXRL1AAxARCLLL6/BgfxKSCXApKiW98AjcnTDe3vib3G9AQALvzzEFaKpwxiAiES25eLoT1yYN/zcudgb0ZXMm9gf/molzl2owSupXBuIOoYBiEhkWw423f11SzQnPxNdjVrlhOeTowAA634+g8xcbpZK7ccARCSis2VGHCk0QCaVYNJgBiCia0kc6I87hvWCIABPbjyIWhMvhVH7MAARiWjLgabRn9HhPvBy5c7vRK2x+NaB8FcrcbbMiBe+411h1D4MQEQi2mK9+4ujP0StpXF2wot3RgMAPkjPxY8Xt5AhagsGICKRnCyuQk5xFZxkEiQN5N1fRG0xtp8v7h8dBqDpUlhJVZ3IFZG9YQAiEsmltX+uj/CFxsVJ5GqI7M/Tk/pjQIAa5UYTHt+QDTP3CqM2YAAiEoEgCLz7i6iDlHIZXp8eA2cnGX49dQEvf58jdklkRxiAiERwrKgKZ0qNUMilSBzgL3Y5RHYr3M8dL945BADwxs7T2HZYJ3JFZC8YgIhEcGn0Z1x/X7irePmLqCNujQ7E7DGX5gMdwKmSapErInvAAETUzZouf3HvL6LONH9SJEaGeaG6vhEPfZSB6vpGsUsiG8cARNTNDhXokVdeA2cnGcYP8BO7HKIewUkmxZq7h8FfrcTpUiPmbTwAQeCkaLoyBiCibvbNxcUPbxzgBxeFXORqiHoOX3cl/nvPcDjJJPjusA6v/nBS7JLIhjEAEXUji0XAtxcvf93KxQ+JOt3w3p54dvJgAMCqtJP4X0a+yBWRrWIAIupG+/MrUKivg6tChhv68/IXUVeYNjIE/xjXFwCwcNMh7DpRKnJFZIsYgIi60TcHmkZ//jLQHyonmcjVEPVcT07oj8kxgWi0CHjkkywcLTSIXRLZGAYgom5itgjYeoh3fxF1B4lEghfvHIJRfZruDJv1/l4UVtaKXRbZEAYgom6y71w5Sqrq4a6S47p+PmKXQ9TjKeUyvPm3EYjwc0OxoR4z392LcqNJ7LLIRjAAEXWTS4sfJg3SQinn5S+i7qBxccJ7s2Lhr1biZEk1Zr67F1V1DWKXRTaAAYioGzSaLfjuUNMS/bfw7i+ibtXL0wWfPBAHL1cFDhXoMfv9DNSazGKXRSJjACLqBr+dKccFowmeLk4YHc7LX0TdLdzPHR/ePxLuKjn2nivHQx9nor6RIciRMQARdYNLl78mDg6Ak4wfOyIxDA7S4P1ZsXB2kmHXiVLM/TQbjWaL2GWRSPiTmKiLNZgt2Hak6fIXFz8kEtfw3l54a8YIKGRSbDuiw1NfHITFwi0zHBEDEFEX++VUGSprGuDjpkRcH2+xyyFyeGMifLD67qGQSSXYlFWApd8c4b5hDogBiKiLbbm4+OFNUVrIpBKRqyEiAJgwSIuVd0VDIgE+TM/FS9tzxC6Juhl3YiTqQvWNZnx/9NLdX1z8kMiWTI4JgrHejIVfHsJ/d56G2tkJapWT2GVRN+EIEFEX2nWiDFV1jdCqVRjR21PscojoT+6OC8GCSZEAgBe+O469Z8tFroi6CwMQURe6dPfXTVEBkPLyF5FNemhsXzxyQ9PmqV9lF+DA+UpxC6JuwQBE1EVqTWb8cLQYAHBLNO/+IrJl85L642+jQiAA2JiRjxwdN0/t6RiAiLrIjpwSGE1mBHk4Y2iwh9jlENFVSCQS/Oe2wYjupYFFAD7Zk4e88hqxy6IuZBMBaM2aNQgNDYVKpUJcXBz27t171fYbN25EZGQkVCoVoqKisHXrVutzDQ0NePrppxEVFQVXV1cEBgZixowZKCws7OpuEDVz6fLXLUMCIJHw8heRrZNKJbhzeDD6+7uj0SLgo/Rz3Dy1BxM9AG3YsAEpKSlYsmQJsrKyEB0djaSkJJSUlLTYfvfu3Zg+fTpmz56N/fv3Izk5GcnJyTh8+DAAoKamBllZWVi0aBGysrKwadMm5OTk4LbbbuvObpGDM9Y34sfjTd/DvPuLyH7IpBJMGxmMQA8VjCYz3t99DjWmRrHLoi4gEURe/SkuLg6xsbFYvXo1AMBisSA4OBhz5szB/PnzL2s/depUGI1GbNmyxXps1KhRiImJwdq1a1v8Gvv27cPIkSORm5uLkJCQa9ZkMBig0Wig1+uhVqvb2TNyZF9lF2DuZ9no7e2CnU/e0KkjQOv35HXaaxHZg7vjrv1zu7Nc+nwZ6hrwxs7T0Nc2IMzHFbMSQiHv5G1surNfjqItv79FHQEymUzIzMxEYmKi9ZhUKkViYiLS09NbPCc9Pb1ZewBISkq6YnsA0Ov1kEgk8PDwaPH5+vp6GAyGZg+ijthysGnxQ17+IrJPapUTZsaHQimX4myZEZv2F3C16B5G1ABUVlYGs9kMf3//Zsf9/f2h0+laPEen07WpfV1dHZ5++mlMnz79imlw2bJl0Gg01kdwcHA7ekPUxFDXgJ9ySgHw8heRPdNqVLgnrjekEiA7v9J6WZt6BtHnAHWlhoYG3HXXXRAEAW+88cYV2y1YsAB6vd76yM/P78YqqafZflgHk9mCcD83RGrdxS6HiDog3M8NyTFBAIC04yU4VsQrBD2FqAHIx8cHMpkMxcXFzY4XFxdDq9W2eI5Wq21V+0vhJzc3F6mpqVe9FqhUKqFWq5s9iNrrq+ymu7+SYwJ5+YuoBxgR6oX4ixsZ/y8jH6VV9SJXRJ1B1L3AFAoFhg8fjrS0NCQnJwNomgSdlpaGRx99tMVz4uPjkZaWhscee8x6LDU1FfHx8dY/Xwo/J0+exI4dO+DtzR24qXuUGOqw+3QZAOC26CCRqyHqGWxh4v9NUQEo0tfi3IUafLwnF/+4IRwKeY++iNLjif7upaSk4K233sIHH3yAY8eO4eGHH4bRaMSsWbMAADNmzMCCBQus7efOnYtt27ZhxYoVOH78OJYuXYqMjAxrYGpoaMCdd96JjIwMfPLJJzCbzdDpdNDpdDCZuJ4Dda0tB4tgEYChIR4I8XYRuxwi6iQyqQTTR4bAXSVHaVU9vj7AteXsnei7wU+dOhWlpaVYvHgxdDodYmJisG3bNutE57y8PEilv+e0hIQErF+/Hs888wwWLlyIiIgIbN68GYMHDwYAFBQU4OuvvwYAxMTENPtaO3bswA033NAt/SLH9NWBS5e/OPpD1NO4q5wwNTYY7/x8Fll5Fejj64phIdzk2F6Jvg6QLeI6QNQeZ8uMGPfyTsikEvy2YDx83ZVd8nVs4XIAkSP78XgxfjhWAieZBHPGRcCnnZ91rgPU+exmHSCinuTri5OfR4f7dFn4ISLx3dDfD318XNFgFvC/zHyYLRxHsEcMQESdQBAEfHWgAEDT3V9E1HNJJRLcObwXVE5SnK+oxc4crg9kjxiAiDrBkUIDzpQaoZRLMWFQy0s4EFHP4eGisN7puSOnBOcruHO8vWEAIuoEm/c3jf4kDvSHm1L0ewuIqBtE99IgKkgDiwB8nnkejWaL2CVRGzAAEXWQ2SLgm4O8+4vI0UgkEtwWHQhXhQwlVfXYeaJU7JKoDRiAiDpoz9kLKDbUQ+PshLH9fMUuh4i6katSjlujm+b97cwpgU5fJ3JF1FoMQEQd9NX+ptGfm6K0XBmWyAFFBWkwIEANiwBs2n8eFq4uYxf405qoA2pMjdhy8fLX7UN7iVwNEYlBIpFgcnQglPKmu8IyzlWIXRK1AgMQUQdsPaSD0WRGqLcLYkO5IiyRo1I7OyFxQNMOBtuP6FBd3yhyRXQtDEBEHbAxIx8A8NcRwdz5ncjBjerjjQCNCrUNZmw7XCR2OXQNDEBE7ZR7wYg9Z8shlQBThvHuLyJHJ5NKMPninaBZeZXIL+faQLaMAYionT7PPA8AGBPhiwCNs8jVEJEtCPFywbAQDwDA1kNF4HabtosrthG1g9ki4IuLAeivw3txg1IisvrLQC0OFeiRW16Dw4UGRAVpxC6JWsARIKJ2+PVUGQr1ddA4O+EvA/3FLoeIbIjG2QnXRzStCbbtcBEauEK0TWIAImqHjRdHfybHBELlJBO5GiKyNddF+EKtkqOipgHppy+IXQ61gAGIqI30NQ3YfkQHAPjr8GCRqyEiW6SQSzFhYNPGyDtySnhbvA1iACJqo68PFMDUaEGk1h2Dg9Ril0NENiomxAOBHirUN1qQdqxY7HLoTxiAiNro0uUvrv1DRFcjlUhwU1QAAGDv2XIUG7hPmC1hACJqg2NFBhw8r4dcKkFyTKDY5RCRjevj44aBAWoIAL6/eOmcbAMDEFEbfPxbLgBgwiB/eLspRa6GiOzBhEH+kAA4pqvi4og2hAGIqJWq6xuxeX8BAOBvcb1FroaI7IWfuwpDQ5r2CkzlXCCbwQBE1Epf7i+A0WRGH19XxPf1FrscIrIj4yP9IJNIcKqkGmfKqsUuh8AARNQqgiDgk4uXv+6J683Jz0TUJp6uCowIvTgKdKSYW2TYAAYgolbIzK3AcV0VVE5S3Dmsl9jlEJEdGtffD3KpBLnlNThRzFEgsTEAEbXC+7vPAQBuHRIIjYuTuMUQkV1SOzthVJ+my+epx3QcBRIZAxDRNRTpa/Hd4abbV+8bHSpuMURk167v5wuFXIrCyjrrivIkDgYgomv4KD0XZouAuDAvDArkrs5E1H5uSjlGX7yJYsX3J2C2cBRILAxARFdRazLj0715AIBZo8NEroaIeoIx4b5QOUlxsqQaWw4Wil2Ow2IAIrqKzdkFqKhpQC9PZ/xloL/Y5RBRD+CskGFMuC8A4LW0kxwFEgkDENEVWCwC3vnlLABgZnwoZFLe+k5EnSOhrzc0zk44XWrkKJBIGICIruCHY8U4VVINd5Uc00YGi10OEfUgKicZHryu6bL6Ko4CiYIBiOgK3tx1BgDwt1G94a7ire9E1LlmJoTCw8UJZ0qN+OYAR4G6GwMQUQv2nStHZm4FFDIpZiWEil0OEfVA7ionPHhdHwBNc4EazRaRK3IsDEBELVi78zQA4I7hQfBTq0Suhoh6qpkJofB0ccKZMiO+5ihQt2IAIvqTwwV6pB0vgUQC67/OiIi6gptSjgevb/o58/qPpzgK1I0YgIj+5LW0kwCatr3o4+smcjVE1NPNiG8aBTpbZsRX2RwF6i4MQER/cKRQj++PFkMiAf45PlzscojIAbgp5fi/6/sCAF7/kXOBugsDENEfXBr9uWVIIML93EWuhogcxYz43vByVeDchRps5ihQtxA9AK1ZswahoaFQqVSIi4vD3r17r9p+48aNiIyMhEqlQlRUFLZu3drs+U2bNmHChAnw9vaGRCJBdnZ2F1ZPPcnhAj22H7k4+nMjR3+IqPu4KuV4yDoXiKNA3UHUALRhwwakpKRgyZIlyMrKQnR0NJKSklBSUtJi+927d2P69OmYPXs29u/fj+TkZCQnJ+Pw4cPWNkajEWPGjMHy5cu7qxvUQ7y4PQdA09yfCH+O/hBR97o3vje8XRXIvVCDTfsLxC6nx5MIgiDa8pNxcXGIjY3F6tWrAQAWiwXBwcGYM2cO5s+ff1n7qVOnwmg0YsuWLdZjo0aNQkxMDNauXdus7blz5xAWFob9+/cjJiamTXUZDAZoNBro9Xqo1eq2d4zszu5TZbj77T1wkkmQlnIDQrxd2nT++j15XVQZEfVUd8eFXHZs3a7T+H9bjyPEywVpT4yFk0z0CzV2pS2/v0X7mzWZTMjMzERiYuLvxUilSExMRHp6eovnpKenN2sPAElJSVds31r19fUwGAzNHuQ4BEHAC9uOAwDuHhnS5vBDRNRZ/jaqN3zcFMgrr8GXWRwF6kqiBaCysjKYzWb4+zffYdvf3x86na7Fc3Q6XZvat9ayZcug0Wisj+Bg7vvkSL49VISD5/VwVcgwZ3yE2OUQkQNzUcjx0KU7wnacRAPnAnUZjq0BWLBgAfR6vfWRn58vdknUTWpNZizb2jT68+D1feDjphS5IiJydE2jQErkl9diU9Z5scvpsUQLQD4+PpDJZCguLm52vLi4GFqttsVztFptm9q3llKphFqtbvYgx/DmrtMoqKxFoEZl/VcXEZGYnBUy/H3s76tDmxo5CtQVRAtACoUCw4cPR1pamvWYxWJBWloa4uPjWzwnPj6+WXsASE1NvWJ7oqspqKzF2p+a9vxaePMAOCtkIldERNTknrimUaDzFRwF6iqiXgJLSUnBW2+9hQ8++ADHjh3Dww8/DKPRiFmzZgEAZsyYgQULFljbz507F9u2bcOKFStw/PhxLF26FBkZGXj00UetbcrLy5GdnY2jR48CAHJycpCdnd3heULU8zz/7VHUNVgQF+aFm6MCxC6HiMjKWSHDwzdcWh2ao0BdQdQANHXqVLz88stYvHgxYmJikJ2djW3btlknOufl5aGoqMjaPiEhAevXr8e6desQHR2Nzz//HJs3b8bgwYOtbb7++msMHToUN998MwBg2rRpGDp06GW3yZNj++FoMbYe0kEmlWDpbYMgkUjELomIqJl74kLg665EQWUtPs/kKFBnE3UdIFvFdYB6tur6Rvxl5U8o0tfhobF9sGDSgA6/JtcBIqK2amkdoD9795ez+M+WowjycMaOJ2+AQs57l67GLtYBIhLLy9tzUKSvQ4iXCx4b30/scoiIrujuuBD4XRwF2pjJO5Q7EwMQOZTfzlzAB+nnAADP3z6YE5+JyKapnGR45OJcoDU/nkJ9o1nkinoOBiByGFV1DXjifwcgCMDUEcG4LsJX7JKIiK5p2sgQ+KuVKNTXYWMG5wJ1FgYgchj//uYoCiprEezljEW3DhS7HCKiVmkaBQoHAKzZwVGgzsIARA7hmwOF+DzzPKQS4JW7YuCmlItdEhFRq02NDYZWrUKRvg7/28e5QJ2BAYh6vNOl1Zj/xUEAwCM3hGNEqJfIFRERtY3KSYZ/jGuaC7Qq7SSq6hpErsj+MQBRj1ZrMuORj7NgNJkxqo8XHkvkZqdEZJ+mjQxBHx9XlFWb8MbO02KXY/cYgKjHEgQB8zcdRE5xFXzclHht2lDIZfyWJyL75CSTYuFNTeuWvf3LWeSX14hckX3jbwPqsdbsOIWvsgshl0rw+vSh8FOrxC6JiKhDxg/wQ0Jfb5gaLXhxe47Y5dg1BiDqkb47VISXvz8BAPj35EGI7+stckVERB0nkUjwr5sHQCJpurkjM7dC7JLsFm+FoR5nz5kLmLshGwAQ39cbEki4VQUR9RiDAjW4a3gwNmTk49ktR/HlIwncz7AdOAJEPcqRQj0e+CADpkYLBmjdcdNg7vJORD3PExP6wUUhQ3Z+Jb4+UCh2OXaJAYh6jFMlVZj57j5U1TdiZJgXpo0MgUzKfxURUc/jp1ZZt8hY/t1x1DVwccS2YgCiHuFkcRWmrfsNZdX1GBigxtszR8CJd3wRUQ/2wHV9EKhRoVBfh9d/PCl2OXaHvyHI7h0tNGD6W7+hrNqEgQFqfPJAHNQqJ7HLIiLqUionGRbfOggAsG7XGZwsrhK5IvvCAER2Lf30BUx9Mx1l1SYMClRj/YNx8HRViF0WEVG3SBrkj8QBfmgwC/jX5sMQBEHskuwGAxDZrW8OFGLmu3utc37WPzgKHi4MP0TkOCQSCZbeNgjOTjLsPVuODdwnrNUYgMjuWCwCVqaewJxP98NktiBpkD8+vH8kNM687EVEjqeXpwtS/tIPAPDct8dQUFkrckX2gQGI7IqhrgEPf5KJ19KaJvw9eF0Y/nvPcKicZCJXRkQknvvHhGFYiAeq6xvx9OcHeSmsFRiAyG4cKdTjttd/wfYjxXCSSfDiHUPwr5sH8lZ3InJ4MqkEL/81Gkq5FL+cKsMnXPz1mhiAyOaZLQLe/Ok0bv/vbpy7UIMgD2f876F43BUbLHZpREQ2o4+vG56aGAkAeO7bo7wr7Bq4FQZ1q7ZuSVFWXY/PM88j7+Kux5Fad9w5vBeOFVXhWBE/3EREfzQrIRQ7c0rw88kyPLp+P756dDSnCFwBR4DIJlkEAbtPl+H1H08ir7wGSrkUU4YG4d5RveGiYG4nImqJVCrByrti4OOmRE5xFf6z5ajYJdksBiCyOQWVtVi36wy2HCxCg1lAH19X/HN8BEaEenHDPyKia/B1V+KVqdEAmkbdN+zjfKCW8J/SZDNq6hvx/dFi7DtXDgGAQiZF0mAt4sK8IGXwISJqtesifPF4Yj+88sMJPLP5MML93DC8t5fYZdkUBiASndkiYN+5cqQeLUbtxQ39hvTSYNLgAK7tQ0TUTnNuDMdxnQHfHdbhoY+ysPkfCejl6SJ2WTaDAYhEYxEEHCrQ44ejxbhgNAEAtGoVbokOQB8fN5GrIyKyb9KLt8afLTPiuK4KM97Zi41/j4e3m1Ls0mwC5wBRtxMEATk6A9bsOIUN+/JxwWiCq0KGW4cE4B/jwhl+iIg6iatSjvdmxSLIwxlnyoyY9f4+VNc3il2WTeAIEHUbQRBwrMiAnTklyK9oWqpdKZfiuggfjO7rAyVv1SQi6nQBGmd8OHsk/ro2HQfP63Hfu3vx7qxYqFWOPcWAI0DU5RrNFnyVXYBJq37GR7/lIr+iFnKpBNeF+2DehP64MdKf4YeIqAv19XXD+7NioVbJkZFbgXvf3oPKGpPYZYmKI0DUZarqGvB55nm8v/scci80LWSolEsRF+aN0eHecHfwf30QEXWnIb08sP7BUbj3nT04cF6Pu95MxzszYxHs5ZgToxmAqNOdLTPig93n8Hnmeeu1Zk8XJ9w/OgwuCjmcFRztISISw+AgDTY8FI9739mDE8XVSF7zK9bNGO6Qt8gzAFGnMDVa8MOxYny2Lx8/nyzFpY2I+/q64r7RYbhjWBBcFPI2b4VBRESdq5+/O776xxg88OE+HC4wYNq63/BUUiRmjwmD1IE2l2YAog45WVyFDfvysWl/AcqNv19PHtffF7NGh+G6CB+u3kxEZGO0GhX+91A85m08iG8PFeH5rcfw86kyvDAlCoEezmKX1y0YgKjN8str8M3BQnxzoAjHigzW437uStw5vBfuGhGMUB9XESskIqJrcVHIsfruoRi91wf/2XIEu06UInHlT0j5Sz/MTAiFk6xn3yfFAETXJAgCThRXY0dOCb47rMOB/Errc3KpBOMi/TAtNhhj+/lC3sM/MEREPYlEIsHdcSEYGeaJBZsOYd+5Cjz37TF8/FsuHv9LP9w6JLDHXhaTCMKl2Rp0icFggEajgV6vh1qtFrscUehrG7DvbDl25JRgZ04pCiprrc9JJUBCXx/cMiQASYO08HRVtPp1OQeIiKjJ3XEhYpfQjMUiYGNmPl7clmNdnb+PjyvuGx2KO4b1gqvS9sdM2vL7mwGoBY4WgARBgM5Qh/15ldh7thx7zpbjuM6AP35nKOVSJPT1xo0D/DFxkBa+7u1bSp0BiIioia0FoEuM9Y1479ezeHPXGVTVNd3J66qQYeLgAEyOCcSoPt5QyG1ztN/uAtCaNWvw0ksvQafTITo6Gq+//jpGjhx5xfYbN27EokWLcO7cOURERGD58uW46aabrM8LgoAlS5bgrbfeQmVlJUaPHo033ngDERERraqnJweg6vpGnCsz4nRpNY4WGXC00IAjhYZmE5gvCfV2wXURvhgX6Yv4Pj6dcvs6AxARURNbDUCXVNc34vOMfHyQnouzZUbrcTelHKPDvTEyzBvRvTQYFKixmeVN2vL7W/TxrA0bNiAlJQVr165FXFwcXn31VSQlJSEnJwd+fn6Xtd+9ezemT5+OZcuW4ZZbbsH69euRnJyMrKwsDB48GADw4osv4rXXXsMHH3yAsLAwLFq0CElJSTh69ChUKlV3d7FbNJotMNQ1Ql/bgMoaE4oN9Sg21EFnqEOxvg75FTU4W1aDsur6Fs+XSSWI8HPDyDCvpkeoF/zUPfPvioiIrs1NKcd9o8MwMyEUmbkV2JxdgG2HdSirNmH7kWJsP1IMoOn3Rz9/dwwKVCPEywXBXs4I9nRBsJcLvF0VNjs3VPQRoLi4OMTGxmL16tUAAIvFguDgYMyZMwfz58+/rP3UqVNhNBqxZcsW67FRo0YhJiYGa9euhSAICAwMxBNPPIEnn3wSAKDX6+Hv74/3338f06ZNu2ZNXTUClKOrwpFCPRotAiwWAWZBgNnyp4cgwGz+/TlTowV1DWbUNVhQ12j+/f8bzKi6GHj0tQ1t2tzOx02B3t6uiNS6Y1CgBoMC1eivdYeqG7aj4AgQEVETWx8BaonFIuBwoR67TpQiO1+P7PzKK/7D+hK1Sg4vVwU8XBTwcHGCq1IOV4UMN0b6YeLggE6tz25GgEwmEzIzM7FgwQLrMalUisTERKSnp7d4Tnp6OlJSUpodS0pKwubNmwEAZ8+ehU6nQ2JiovV5jUaDuLg4pKentxiA6uvrUV//+xuo1+sBNP1FdqZvMk7jtbRTnfqaf+aikELjrICvuxL+aiX83FXwdVci0MMZvb1cEOzt0uIGeKZaI0y1LbxgJ6sxVnX9FyEisgOd/Tumu4SqpQgd4Y8ZI/yb5pDq63C4QI/TpUacr6hBQWUtzlfUothQB4sAVNYDlfrLX8dd2oCEkM5dMuXS32lrxnZEDUBlZWUwm83w9/dvdtzf3x/Hjx9v8RydTtdie51OZ33+0rErtfmzZcuW4d///vdlx4ODg1vXESIiojZ6UOwCRLb4VWBxF712VVUVNBrNVduIPgfIFixYsKDZqJLFYkF5eTm8vb2tqxgbDAYEBwcjPz+/x02M/iP2s2dxhH46Qh8B9rOnYT+7hiAIqKqqQmBg4DXbihqAfHx8IJPJUFxc3Ox4cXExtFpti+dotdqrtr/03+LiYgQEBDRrExMT0+JrKpVKKJXNb+v28PBosa1are7R36yXsJ89iyP00xH6CLCfPQ372fmuNfJziahTsxUKBYYPH460tDTrMYvFgrS0NMTHx7d4Tnx8fLP2AJCammptHxYWBq1W26yNwWDAnj17rviaRERE5FhEvwSWkpKCmTNnYsSIERg5ciReffVVGI1GzJo1CwAwY8YMBAUFYdmyZQCAuXPnYuzYsVixYgVuvvlmfPbZZ8jIyMC6desANC3r/dhjj+G5555DRESE9Tb4wMBAJCcni9VNIiIisiGiB6CpU6eitLQUixcvhk6nQ0xMDLZt22adxJyXlwep9PeBqoSEBKxfvx7PPPMMFi5ciIiICGzevNm6BhAAPPXUUzAajfi///s/VFZWYsyYMdi2bVuH1gBSKpVYsmTJZZfKehr2s2dxhH46Qh8B9rOnYT/FJ/o6QERERETdzTaXZyQiIiLqQgxARERE5HAYgIiIiMjhMAARERGRw2EA+gOz2YxFixYhLCwMzs7O6Nu3L5599tlme4rcd999kEgkzR4TJ04User2qaqqwmOPPYbevXvD2dkZCQkJ2Ldvn/V5QRCwePFiBAQEwNnZGYmJiTh58qSIFbfPtfppj+/nrl27cOuttyIwMBASicS6D94lrXnvysvLcc8990CtVsPDwwOzZ89GdXV1N/bi2jqjn6GhoZe9vy+88EI39uLartXPTZs2YcKECdaV6bOzsy97jbq6OvzjH/+At7c33NzccMcdd1y2YKyYOqOPN9xww2Xv5d///vfu6UArXa2fDQ0NePrppxEVFQVXV1cEBgZixowZKCwsbPYa9v7ZbG0/beGzyQD0B8uXL8cbb7yB1atX49ixY1i+fDlefPFFvP76683aTZw4EUVFRdbHp59+KlLF7ffAAw8gNTUVH330EQ4dOoQJEyYgMTERBQUFAIAXX3wRr732GtauXYs9e/bA1dUVSUlJqKurE7nytrlWPwH7ez+NRiOio6OxZs2aFp9vzXt3zz334MiRI0hNTcWWLVuwa9cu/N///V93daFVOqOfAPCf//yn2fs7Z86c7ii/1a7VT6PRiDFjxmD58uVXfI3HH38c33zzDTZu3IiffvoJhYWFmDJlSleV3Gad0UcAePDBB5u9ly+++GJXlNtuV+tnTU0NsrKysGjRImRlZWHTpk3IycnBbbfd1qydvX82W9tPwAY+mwJZ3XzzzcL999/f7NiUKVOEe+65x/rnmTNnCpMnT+7myjpXTU2NIJPJhC1btjQ7PmzYMOFf//qXYLFYBK1WK7z00kvW5yorKwWlUil8+umn3V1uu12rn4Jg/+8nAOHLL7+0/rk1793Ro0cFAMK+ffusbb777jtBIpEIBQUF3VZ7W7Snn4IgCL179xZeeeWVbqy0Y/7czz86e/asAEDYv39/s+OVlZWCk5OTsHHjRuuxY8eOCQCE9PT0Lqy2fdrTR0EQhLFjxwpz587t0to609X6ecnevXsFAEJubq4gCD3js9mSP/dTEGzjs8kRoD9ISEhAWloaTpw4AQA4cOAAfvnlF0yaNKlZu507d8LPzw/9+/fHww8/jAsXLohRbrs1NjbCbDZftjCks7MzfvnlF5w9exY6nQ6JiYnW5zQaDeLi4pCent7d5bbbtfp5ib2/n3/UmvcuPT0dHh4eGDFihLVNYmIipFIp9uzZ0+01t0dbvkdfeOEFeHt7Y+jQoXjppZfQ2NjY3eV2qczMTDQ0NDT7u4iMjERISIhdfV5b45NPPoGPjw8GDx6MBQsWoKamRuySOkSv10MikVj3nuwJn82W/Lmfl4j92RR9JWhbMn/+fBgMBkRGRkImk8FsNuP555/HPffcY20zceJETJkyBWFhYTh9+jQWLlyISZMmIT09HTKZTMTqW8/d3R3x8fF49tlnMWDAAPj7++PTTz9Feno6wsPDodPpAMC6Gvcl/v7+1ufswbX6CfSM9/OPWvPe6XQ6+Pn5NXteLpfDy8vLbt7f1n6P/vOf/8SwYcPg5eWF3bt3Y8GCBSgqKsLKlSu7td6upNPpoFAoLvvlYm+f12u5++670bt3bwQGBuLgwYN4+umnkZOTg02bNoldWrvU1dXh6aefxvTp062bhPaEz+aftdRPwDY+mwxAf/C///0Pn3zyCdavX49BgwYhOzsbjz32GAIDAzFz5kwAwLRp06zto6KiMGTIEPTt2xc7d+7E+PHjxSq9zT766CPcf//9CAoKgkwmw7BhwzB9+nRkZmaKXVqnulY/e8r7SS1LSUmx/v+QIUOgUCjw0EMPYdmyZTa5ND9d2R/nwURFRSEgIADjx4/H6dOn0bdvXxEra7uGhgbcddddEAQBb7zxhtjldJmr9dMWPpu8BPYH8+bNw/z58zFt2jRERUXh3nvvxeOPP27diLUlffr0gY+PD06dOtWNlXZc37598dNPP6G6uhr5+fnYu3cvGhoa0KdPH2i1WgC47C6S4uJi63P24mr9bIm9vp+XtOa902q1KCkpafZ8Y2MjysvL7eb9be/3aFxcHBobG3Hu3LmuLK9babVamEwmVFZWNjtuj5/XtoiLiwMAu/usXgoFubm5SE1NbTYq0hM+m5dcrZ8tEeOzyQD0BzU1Nc02XgUAmUwGi8VyxXPOnz+PCxcuICAgoKvL6xKurq4ICAhARUUFtm/fjsmTJyMsLAxarRZpaWnWdgaDAXv27EF8fLyI1bZfS/1sib2/n6157+Lj41FZWdlstO/HH3+ExWKx/lKxde39Hs3OzoZUKr3sMoM9Gz58OJycnJr9XeTk5CAvL89uP6+tcelWeXv6rF4KBSdPnsQPP/wAb2/vZs/3hM8mcO1+tkSMzyYvgf3Brbfeiueffx4hISEYNGgQ9u/fj5UrV+L+++8HAFRXV+Pf//437rjjDmi1Wpw+fRpPPfUUwsPDkZSUJHL1bbN9+3YIgoD+/fvj1KlTmDdvHiIjIzFr1ixIJBI89thjeO655xAREYGwsDAsWrQIgYGBSE5OFrv0NrlaP+31/ayurm72r96zZ88iOzsbXl5eCAkJueZ7N2DAAEycOBEPPvgg1q5di4aGBjz66KOYNm0aAgMDRerV5Traz/T0dOzZswfjxo2Du7s70tPT8fjjj+Nvf/sbPD09RerV5a7Vz/LycuTl5VnXUcnJyQHQNFqg1Wqh0Wgwe/ZspKSkwMvLC2q1GnPmzEF8fDxGjRolSp/+rKN9PH36NNavX4+bbroJ3t7eOHjwIB5//HFcf/31GDJkiCh9asnV+hkQEIA777wTWVlZ2LJlC8xms3Vej5eXFxQKRY/4bLamnzbz2RT1HjQbYzAYhLlz5wohISGCSqUS+vTpI/zrX/8S6uvrBUFouq16woQJgq+vr+Dk5CT07t1bePDBBwWdTidy5W23YcMGoU+fPoJCoRC0Wq3wj3/8Q6isrLQ+b7FYhEWLFgn+/v6CUqkUxo8fL+Tk5IhYcftcrZ/2+n7u2LFDAHDZY+bMmYIgtO69u3DhgjB9+nTBzc1NUKvVwqxZs4SqqioRenNlHe1nZmamEBcXJ2g0GkGlUgkDBgwQ/t//+39CXV2dSD1q2bX6+d5777X4/JIlS6yvUVtbKzzyyCOCp6en4OLiItx+++1CUVGROB1qQUf7mJeXJ1x//fWCl5eXoFQqhfDwcGHevHmCXq8Xr1MtuFo/L93i39Jjx44d1tew989ma/ppK59NiSD8YZljIiIiIgfAOUBERETkcBiAiIiIyOEwABEREZHDYQAiIiIih8MARERERA6HAYiIiIgcDgMQERERORwGICIiInI4DEBERETkcBiAiKjDJBLJVR9Lly61tv3ggw8QGxsLFxcXuLu7Y+zYsdiyZUuz19u5c+cVX+vSvkJLly5t8fkffvjB+nxMTEyr+2A2m/HCCy8gMjISzs7O8PLyQlxcHN5+++0O//0Qke3hZqhE1GFFRUXW/9+wYQMWL15s3dASANzc3AAATz75JFavXo3nnnsOycnJaGhowMcff4zJkydj1apVePTRR5u9bk5ODtRqdbNjf9wtetCgQdbAc4mXl1e7+vDvf/8bb775JlavXo0RI0bAYDAgIyMDFRUV7Xq91jCZTFAoFF32+kR0ZQxARNRhWq3W+v8ajQYSiaTZMQD47bffsGLFCrz22muYM2eO9fjzzz+Puro6pKSkYPLkyQgODrY+5+fnBw8Pjyt+XblcftnXaa+vv/4ajzzyCP76179aj0VHRzdrY7FY8PLLL2PdunXIz8+Hv78/HnroIfzrX/8CABw6dAhz585Feno6XFxccMcdd2DlypXWAHjfffehsrISsbGxWLNmDZRKJc6ePYv8/Hw88cQT+P777yGVSnHddddh1apVCA0N7ZS+EdHleAmMiLrFp59+Cjc3Nzz00EOXPffEE0+goaEBX3zxhQiVNdFqtfjxxx9RWlp6xTYLFizACy+8gEWLFuHo0aNYv349/P39AQBGoxFJSUnw9PTEvn37sHHjRvzwww+XjWqlpaUhJycHqamp2LJlCxoaGpCUlAR3d3f8/PPP+PXXX+Hm5oaJEyfCZDJ1aZ+JHBlHgIioW5w4cQJ9+/Zt8ZJPYGAg1Go1Tpw40ex4r169mv25d+/eOHLkiPXPhw4dso6uAMDAgQOxd+/edtW3cuVK3HnnndBqtRg0aBASEhIwefJkTJo0CQBQVVWFVatWYfXq1Zg5cyYAoG/fvhgzZgwAYP369airq8OHH34IV1dXAMDq1atx6623Yvny5dag5Orqirffftv69/Dxxx/DYrHg7bffhkQiAQC899578PDwwM6dOzFhwoR29YeIro4BiIi6jSAIbWr/888/w93d3fpnJyenZs/3798fX3/9tfXPSqWy3bUNHDgQhw8fRmZmJn799Vfs2rULt956K+677z68/fbbOHbsGOrr6zF+/PgWzz927Biio6Ot4QcARo8eDYvFgpycHGsAioqKahYCDxw4gFOnTjXrJwDU1dXh9OnT7e4PEV0dAxARdYt+/frhl19+aXHib2FhIQwGA/r169fseFhY2FXnACkUCoSHh3dajVKpFLGxsYiNjcVjjz2Gjz/+GPfeey/+9a9/wdnZuVO+xh8DEgBUV1dj+PDh+OSTTy5r6+vr2ylfk4guxzlARNQtpk2bhurqarz55puXPffyyy/DyckJd9xxhwiVXdnAgQMBNM3viYiIgLOzM9LS0lpsO2DAABw4cABGo9F67Ndff4VUKkX//v2v+DWGDRuGkydPws/PD+Hh4c0eGo2mcztERFYcASKibhEfH4+5c+di3rx5MJlMzW6DX7VqFV599dVmd4ABQElJCerq6pod8/b2vuxS2JXU1tYiOzu72TF3d3f07dv3srZ33nknRo8ejYSEBGi1Wpw9exYLFixAv379EBkZCblcjqeffhpPPfUUFAoFRo8ejdLSUhw5cgSzZ8/GPffcgyVLlmDmzJlYunQpSktLMWfOHNx7773Wy18tueeee/DSSy9h8uTJ+M9//oNevXohNzcXmzZtwlNPPXXZPCgi6hwMQETUbV599VUMGTIE//3vf/HMM89AJpNh2LBh2Lx5M2699dbL2rc0cpKeno5Ro0a16uudOHECQ4cObXZs/Pjxl60dBABJSUn49NNPsWzZMuj1emi1Wtx4441YunQp5PKmH5WLFi2CXC7H4sWLUVhYiICAAPz9738HALi4uGD79u2YO3eudaHHS7fBX42Liwt27dqFp59+GlOmTEFVVRWCgoIwfvz4y9ZAIqLOIxHaOiuRiIiIyM5xDhARERE5HAYgIiIicjgMQERERORwGICIiIjI4TAAERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HAYgIiIicjgMQERERORw/j81DrVrQbOtegAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['TOEFL Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='GRE Score', ylabel='Chance of Admit '>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='GRE Score',y='Chance of Admit ',data=data,ci=None,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows 400\n",
      "Number of Columns 9\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Rows\",data.shape[0])\n",
    "print(\"Number of Columns\",data.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Get Information About Our Dataset Like Total Number Rows, Total Number of Columns, Datatypes of Each Column And Memory Requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Serial No.         400 non-null    int64  \n",
      " 1   GRE Score          400 non-null    int64  \n",
      " 2   TOEFL Score        400 non-null    int64  \n",
      " 3   University Rating  400 non-null    int64  \n",
      " 4   SOP                400 non-null    float64\n",
      " 5   LOR                400 non-null    float64\n",
      " 6   CGPA               400 non-null    float64\n",
      " 7   Research           400 non-null    int64  \n",
      " 8   Chance of Admit    400 non-null    float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 28.3 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.Check Null Values In The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serial No.           0\n",
       "GRE Score            0\n",
       "TOEFL Score          0\n",
       "University Rating    0\n",
       "SOP                  0\n",
       "LOR                  0\n",
       "CGPA                 0\n",
       "Research             0\n",
       "Chance of Admit      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Get Overall Statistics About The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>200.500000</td>\n",
       "      <td>316.807500</td>\n",
       "      <td>107.410000</td>\n",
       "      <td>3.087500</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.452500</td>\n",
       "      <td>8.598925</td>\n",
       "      <td>0.547500</td>\n",
       "      <td>0.724350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>115.614301</td>\n",
       "      <td>11.473646</td>\n",
       "      <td>6.069514</td>\n",
       "      <td>1.143728</td>\n",
       "      <td>1.006869</td>\n",
       "      <td>0.898478</td>\n",
       "      <td>0.596317</td>\n",
       "      <td>0.498362</td>\n",
       "      <td>0.142609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>100.750000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>200.500000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>8.610000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>300.250000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.062500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Serial No.   GRE Score  TOEFL Score  University Rating         SOP  \\\n",
       "count  400.000000  400.000000   400.000000         400.000000  400.000000   \n",
       "mean   200.500000  316.807500   107.410000           3.087500    3.400000   \n",
       "std    115.614301   11.473646     6.069514           1.143728    1.006869   \n",
       "min      1.000000  290.000000    92.000000           1.000000    1.000000   \n",
       "25%    100.750000  308.000000   103.000000           2.000000    2.500000   \n",
       "50%    200.500000  317.000000   107.000000           3.000000    3.500000   \n",
       "75%    300.250000  325.000000   112.000000           4.000000    4.000000   \n",
       "max    400.000000  340.000000   120.000000           5.000000    5.000000   \n",
       "\n",
       "             LOR         CGPA    Research  Chance of Admit   \n",
       "count  400.000000  400.000000  400.000000        400.000000  \n",
       "mean     3.452500    8.598925    0.547500          0.724350  \n",
       "std      0.898478    0.596317    0.498362          0.142609  \n",
       "min      1.000000    6.800000    0.000000          0.340000  \n",
       "25%      3.000000    8.170000    0.000000          0.640000  \n",
       "50%      3.500000    8.610000    1.000000          0.730000  \n",
       "75%      4.000000    9.062500    1.000000          0.830000  \n",
       "max      5.000000    9.920000    1.000000          0.970000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Dropping Irrelevant Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
       "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop('Serial No.',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA',\n",
       "       'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Store Feature Matrix In X and Response(Target) In Vector y"
   ]
  },
  {
   "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>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  \\\n",
       "0        337          118                  4  4.5   4.5  9.65         1   \n",
       "\n",
       "   Chance of Admit   \n",
       "0              0.92  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA',\n",
       "       'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Chance of Admit ',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Chance of Admit ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Splitting The Dataset Into The Training Set And Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      0.80\n",
       "18     0.63\n",
       "202    0.97\n",
       "250    0.74\n",
       "274    0.58\n",
       "       ... \n",
       "71     0.96\n",
       "106    0.87\n",
       "270    0.72\n",
       "348    0.57\n",
       "102    0.62\n",
       "Name: Chance of Admit , Length: 320, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  \\\n",
       "0        337          118                  4  4.5   4.5  9.65         1   \n",
       "1        324          107                  4  4.0   4.5  8.87         1   \n",
       "2        316          104                  3  3.0   3.5  8.00         1   \n",
       "3        322          110                  3  3.5   2.5  8.67         1   \n",
       "4        314          103                  2  2.0   3.0  8.21         0   \n",
       "\n",
       "   Chance of Admit   \n",
       "0              0.92  \n",
       "1              0.76  \n",
       "2              0.72  \n",
       "3              0.80  \n",
       "4              0.65  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=sc.fit_transform(X_train)\n",
    "X_test=sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.45711129,  0.42466178, -0.057308  , ..., -1.05965163,\n",
       "         0.13986648,  0.92761259],\n",
       "       [ 0.1022887 ,  0.42466178, -0.057308  , ..., -0.50194025,\n",
       "         0.36110014, -1.07803625],\n",
       "       [ 2.05381293,  2.08593034,  1.6892215 , ...,  1.17119391,\n",
       "         2.25009529,  0.92761259],\n",
       "       ...,\n",
       "       [-0.96217907, -0.40597251, -0.93057275, ..., -0.50194025,\n",
       "        -0.62594237,  0.92761259],\n",
       "       [-1.31700165, -1.40273364, -1.8038375 , ..., -1.61736302,\n",
       "        -2.27668588, -1.07803625],\n",
       "       [-0.25253389, -0.23984565, -0.93057275, ...,  0.05577114,\n",
       "        -0.57488845, -1.07803625]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Import The models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  \\\n",
       "0        337          118                  4  4.5   4.5  9.65         1   \n",
       "1        324          107                  4  4.0   4.5  8.87         1   \n",
       "2        316          104                  3  3.0   3.5  8.00         1   \n",
       "3        322          110                  3  3.5   2.5  8.67         1   \n",
       "4        314          103                  2  2.0   3.0  8.21         0   \n",
       "\n",
       "   Chance of Admit   \n",
       "0              0.92  \n",
       "1              0.76  \n",
       "2              0.72  \n",
       "3              0.80  \n",
       "4              0.65  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr =LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "\n",
    "svm = SVR()\n",
    "svm.fit(X_train,y_train)\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train,y_train)\n",
    "\n",
    "gr = GradientBoostingRegressor()\n",
    "gr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13. Prediction on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1 = lr.predict(X_test)\n",
    "y_pred2 = svm.predict(X_test)\n",
    "y_pred3 = rf.predict(X_test)\n",
    "y_pred4 = gr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14. Evaluating the Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "score1 = metrics.r2_score(y_test,y_pred1)\n",
    "score2 = metrics.r2_score(y_test,y_pred2)\n",
    "score3 = metrics.r2_score(y_test,y_pred3)\n",
    "score4 = metrics.r2_score(y_test,y_pred4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8212082591486991 0.7597814848647667 0.8125547278838504 0.7929200601497781\n"
     ]
    }
   ],
   "source": [
    "print(score1,score2,score3,score4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.DataFrame({'RegressionModels': ['Linear', 'SupportVector', 'DecisionTree', 'RandomForest'], 'R2_SCORE': [score1,score2,score3,score4]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "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>RegressionModels</th>\n",
       "      <th>R2_SCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear</td>\n",
       "      <td>0.821208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SupportVector</td>\n",
       "      <td>0.759781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.812555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.792920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RegressionModels  R2_SCORE\n",
       "0           Linear  0.821208\n",
       "1    SupportVector  0.759781\n",
       "2     DecisionTree  0.812555\n",
       "3     RandomForest  0.792920"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='RegressionModels', ylabel='R2_SCORE'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='RegressionModels',y='R2_SCORE',data=final_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  \\\n",
       "0        337          118                  4  4.5   4.5  9.65         1   \n",
       "1        324          107                  4  4.0   4.5  8.87         1   \n",
       "2        316          104                  3  3.0   3.5  8.00         1   \n",
       "3        322          110                  3  3.5   2.5  8.67         1   \n",
       "4        314          103                  2  2.0   3.0  8.21         0   \n",
       "\n",
       "   Chance of Admit   \n",
       "0              0.92  \n",
       "1              0.76  \n",
       "2              0.72  \n",
       "3              0.80  \n",
       "4              0.65  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = [1 if value>0.8 else 0 for value in y_train]\n",
    "y_test = [1 if value>0.8 else 0 for value in y_test]\n",
    "\n",
    "y_train = np.array(y_train)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15. Import The models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import svm\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16. Model Training & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.925\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "y_pred1= lr.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.925\n"
     ]
    }
   ],
   "source": [
    "svm = svm.SVC()\n",
    "svm.fit(X_train,y_train)\n",
    "y_pred2 = svm.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8875\n"
     ]
    }
   ],
   "source": [
    "knn=KNeighborsClassifier()\n",
    "knn.fit(X_train,y_train)\n",
    "y_pred3 = knn.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)\n",
    "y_pred4 = rf.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.975\n"
     ]
    }
   ],
   "source": [
    "gr = GradientBoostingClassifier()\n",
    "gr.fit(X_train,y_train)\n",
    "y_pred5 = gr.predict(X_test)\n",
    "print(accuracy_score(y_test,y_pred5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.DataFrame({'ClassifierModels':['LogisticRegression','SupportVector','KNeighbors','RandomForest','GradientBoosting'],\n",
    "                           'ACC_SCORE':[accuracy_score(y_test,y_pred1),\n",
    "                                       accuracy_score(y_test,y_pred2),\n",
    "                                       accuracy_score(y_test,y_pred3),\n",
    "                                       accuracy_score(y_test,y_pred4),\n",
    "                                       accuracy_score(y_test,y_pred5)]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>ClassifierModels</th>\n",
       "      <th>ACC_SCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SupportVector</td>\n",
       "      <td>0.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>0.8875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>0.9750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ClassifierModels  ACC_SCORE\n",
       "0  LogisticRegression     0.9250\n",
       "1       SupportVector     0.9250\n",
       "2          KNeighbors     0.8875\n",
       "3        RandomForest     0.9500\n",
       "4    GradientBoosting     0.9750"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='ClassifierModels', ylabel='ACC_SCORE'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=final_data['ClassifierModels'],y=final_data['ACC_SCORE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17. Save The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA',\n",
       "       'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Chance of Admit ',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Chance of Admit ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y  = [1 if value>0.8 else 0 for value in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.76210664,  1.74697064,  0.79882862, ...,  1.16732114,\n",
       "         1.76481828,  0.90911166],\n",
       "       [ 0.62765641, -0.06763531,  0.79882862, ...,  1.16732114,\n",
       "         0.45515126,  0.90911166],\n",
       "       [-0.07046681, -0.56252785, -0.07660001, ...,  0.05293342,\n",
       "        -1.00563118,  0.90911166],\n",
       "       ...,\n",
       "       [ 1.15124883,  1.41704229,  0.79882862, ...,  1.16732114,\n",
       "         1.42900622,  0.90911166],\n",
       "       [-0.41952842, -0.72749202, -0.07660001, ...,  0.61012728,\n",
       "         0.30403584, -1.09997489],\n",
       "       [ 1.41304503,  1.58200646,  0.79882862, ...,  0.61012728,\n",
       "         1.78160888,  0.90911166]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr = GradientBoostingClassifier()\n",
    "gr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['admission_model']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(gr,'admission_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load('admission_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA',\n",
       "       'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\karti\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(sc.transform([[337,118,4,4.5,4.5,9.65,1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *\n",
    "import joblib\n",
    "from sklearn.preprocessing import StandardScaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_entry():\n",
    "    \n",
    "    p1 = float(e1.get())\n",
    "    p2 = float(e2.get())\n",
    "    p3 = float(e3.get())\n",
    "    p4 = float(e4.get())\n",
    "    p5 = float(e5.get())\n",
    "    p6 = float(e6.get())\n",
    "    p7 = float(e6.get())\n",
    "\n",
    "    model = joblib.load('admission_model')\n",
    "    result = model.predict(sc.transform([[p1,p2,p3,p4,p5,p6,p7]]))\n",
    "    \n",
    "    if result == 1:\n",
    "        Label(master, text=\"High Chance of getting admission\").grid(row=31)\n",
    "    else:\n",
    "        Label(master, text=\"You may get admission\").grid(row=31)\n",
    "    \n",
    "master =Tk()\n",
    "master.title(\"Graduate Admission Analysis and Prediction\")\n",
    "label = Label(master,text = \"Graduate Admission Analysis and Prediction\",bg = \"blue\",\n",
    "               fg = \"white\").grid(row=0,columnspan=2)\n",
    "\n",
    "Label(master,text = \"Enter Your GRE Score\").grid(row=1)\n",
    "Label(master,text = \"Enter Your TOEFL Score\").grid(row=2)\n",
    "Label(master,text = \"Enter University Rating\").grid(row=3)\n",
    "Label(master,text = \"Enter SOP\").grid(row=4)\n",
    "Label(master,text = \"Enter LOR\").grid(row=5)\n",
    "Label(master,text = \"Enter Your CPGA\").grid(row=6)\n",
    "Label(master,text = \"Research\").grid(row=7)\n",
    "\n",
    "\n",
    "e1 = Entry(master)\n",
    "e2 = Entry(master)\n",
    "e3 = Entry(master)\n",
    "e4 = Entry(master)\n",
    "e5 = Entry(master)\n",
    "e6 = Entry(master)\n",
    "e7 = Entry(master)\n",
    "\n",
    "e1.grid(row=1,column=1)\n",
    "e2.grid(row=2,column=1)\n",
    "e3.grid(row=3,column=1)\n",
    "e4.grid(row=4,column=1)\n",
    "e5.grid(row=5,column=1)\n",
    "e6.grid(row=6,column=1)\n",
    "e7.grid(row=7,column=1)\n",
    "\n",
    "Button(master,text=\"Predict\",command=show_entry).grid()\n",
    "\n",
    "mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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