grad_admission.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## US Graduate Schools Admissions\n",
    "\n",
    "dataset: https://www.kaggle.com/tanmoyie/us-graduate-schools-admission-parameters\n",
    "\n",
    "We have the following columns included: \n",
    "* GRE Score\n",
    "* TOEFL Score\n",
    "* University Rating\n",
    "* SOP (Statement of Purpose)\n",
    "* LOR (Letter of Recommendation)\n",
    "* CGPA (Cummulative GPA)\n",
    "* Research\n",
    "* Chance of Admittance - Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_validate\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import Ridge, RidgeCV, LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to check if data is normally distributed\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (8,6)\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('expand_frame_repr',True)\n",
    "pd.set_option('max_rows',80)\n",
    "pd.set_option('expand_frame_repr',False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'C:\\\\Users\\\\gagrocostea\\\\Box\\\\Gaby\\\\Dev\\datasets\\\\'\n",
    "\n",
    "# read data\n",
    "data = pd.read_csv(data_path + 'US_graduate_schools_admission_parameters_dataset.csv', encoding = 'utf-8')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick data recap\n",
    "A quick look at the data tells us that this is a rather small dataset of 400 rows and 9 columns. There is a serial number that we can drop and there are no missing values. Almost all features are numeric - except for Research which is boolean. Universities, letters of recommendation and statements of purpose are all rated from 1 to 5. The target variable is <em>Chance of Admittance</em> which is the probability of getting admitted into graduate school. This what we will try and predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  Chance of Admit \n",
      "0           1        337          118                  4  4.5   4.5  9.65         1              0.92\n",
      "1           2        324          107                  4  4.0   4.5  8.87         1              0.76\n",
      "2           3        316          104                  3  3.0   3.5  8.00         1              0.72\n",
      "3           4        322          110                  3  3.5   2.5  8.67         1              0.80\n",
      "4           5        314          103                  2  2.0   3.0  8.21         0              0.65\n"
     ]
    }
   ],
   "source": [
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "serial_no.           400 non-null int64\n",
      "gre_score            400 non-null int64\n",
      "toefl_score          400 non-null int64\n",
      "university_rating    400 non-null int64\n",
      "sop                  400 non-null float64\n",
      "lor                  400 non-null float64\n",
      "cgpa                 400 non-null float64\n",
      "research             400 non-null int64\n",
      "chance_of_admit      400 non-null float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 28.2 KB\n"
     ]
    }
   ],
   "source": [
    "# clean up column names\n",
    "data.columns = [c.strip().lower().replace(\" \",\"_\") for c in data.columns]\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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         lor        cgpa    research  chance_of_admit\n",
       "count  400.000000  400.000000   400.000000         400.000000  400.000000  400.000000  400.000000  400.000000       400.000000\n",
       "mean   200.500000  316.807500   107.410000           3.087500    3.400000    3.452500    8.598925    0.547500         0.724350\n",
       "std    115.614301   11.473646     6.069514           1.143728    1.006869    0.898478    0.596317    0.498362         0.142609\n",
       "min      1.000000  290.000000    92.000000           1.000000    1.000000    1.000000    6.800000    0.000000         0.340000\n",
       "25%    100.750000  308.000000   103.000000           2.000000    2.500000    3.000000    8.170000    0.000000         0.640000\n",
       "50%    200.500000  317.000000   107.000000           3.000000    3.500000    3.500000    8.610000    1.000000         0.730000\n",
       "75%    300.250000  325.000000   112.000000           4.000000    4.000000    4.000000    9.062500    1.000000         0.830000\n",
       "max    400.000000  340.000000   120.000000           5.000000    5.000000    5.000000    9.920000    1.000000         0.970000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Data Exploration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations between GRE, TOEFL Scores and Chance Of Admittance\n",
    "\n",
    "From the pairplot we can see that there is a positive correlation between GRE Scores and chance of being admitted, and the same can be said about TOEFL Scores. GRE scores & TOEFL scores are positively correlated, and chance of being admitted seems evenly distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x13904b70>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data[['gre_score','toefl_score','chance_of_admit']], kind='scatter', palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   gre_score  toefl_score  university_rating       sop       lor      cgpa  research  chance_of_admit\n",
      "gre_score           1.000000     0.835977           0.668976  0.612831  0.557555  0.833060  0.580391         0.802610\n",
      "toefl_score         0.835977     1.000000           0.695590  0.657981  0.567721  0.828417  0.489858         0.791594\n",
      "university_rating   0.668976     0.695590           1.000000  0.734523  0.660123  0.746479  0.447783         0.711250\n",
      "sop                 0.612831     0.657981           0.734523  1.000000  0.729593  0.718144  0.444029         0.675732\n",
      "lor                 0.557555     0.567721           0.660123  0.729593  1.000000  0.670211  0.396859         0.669889\n",
      "cgpa                0.833060     0.828417           0.746479  0.718144  0.670211  1.000000  0.521654         0.873289\n",
      "research            0.580391     0.489858           0.447783  0.444029  0.396859  0.521654  1.000000         0.553202\n",
      "chance_of_admit     0.802610     0.791594           0.711250  0.675732  0.669889  0.873289  0.553202         1.000000\n"
     ]
    }
   ],
   "source": [
    "# correlations\n",
    "corrmat = data.drop('serial_no.', axis=1).corr()\n",
    "print(corrmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chance_of_admit      1.000000\n",
       "cgpa                 0.873289\n",
       "gre_score            0.802610\n",
       "toefl_score          0.791594\n",
       "university_rating    0.711250\n",
       "sop                  0.675732\n",
       "lor                  0.669889\n",
       "research             0.553202\n",
       "Name: chance_of_admit, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cumulative GPA has highest correlation\n",
    "corrmat.sort_values(by = 'chance_of_admit', ascending=False).chance_of_admit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Highest Correlations found with target Chance of Admit:</b>\n",
    "\n",
    "<li>CPGA</li>\n",
    "<li>GRE Score</li>\n",
    "<li>TOEFL score</li>\n",
    "<li>university_rating</li>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FBfIAAAAolu2TLQAAcPVyOSAXotkCAAC2xTQiAAAAikWyBQAAbOu3i5iXZiRbAAAAJiLZAgAAtuWENVs0WwAAwLaccDZi6T8CAAAAGyPZAgAAtuWEacTSfwQAAAA2RrIFAABsi2QLAAAAxSLZAgAAtuWEsxFptgAAgH0xjQgAAIDikGwBAADbYoE8AAAAikWyBQAAbMvlclldwmWj2QIAALbF2Yj4U6qHNba6BFPsy1xhdQmmcB272eoSTLPZ35l/9WNC8q0uwRzu0v+fzYV0jsy1ugRTzHTo3zH8NfxpAAAAtsUCeQAAABSLZAsAANgXC+QBAABM5IA5OAccAgAAgH2RbAEAAPtywDQiyRYAAICJSLYAAIB9kWwBAACgOCRbAADAvhwQC9FsAQAA2zKYRgQAAEBxSLYAAIB9lf5gi2QLAADATCRbAADAvtylP9qi2QIAAPbFAnkAAAAUh2QLAADYV+kPtki2AAAAzESyBQAA7IsF8gAAACZigTwAAACKQ7IFAADsq/QHWyRbAAAAZrJVspWXl6eFCxfq4YcfvuT3FBUVqW/fvjp16pRuuukmRUVFqUuXLiZWCQAArhgHLJC3VbJ15MgRffDBB3/6PdnZ2Zo7d64qVapkUmUAAAB/ja2SrWnTpmn79u1KSUnRxo0blZOTo6KiIj333HO644479L///U8TJ06Uj4+PqlevrqSkJL388svavXu3RowYodDQ0GLH/+KLL/Tmm2/K19dXVatW1dixY5Wdna3BgwfrxIkTMgxDr732mkJCQjRw4MBz9t++fXtdf/318vPz0yuvvKJhw4YpOztbkjR8+HDdeOONV+JjAgDg6lH6gy17NVtPPPGEtm7dqpMnT+rOO+9Uz549lZmZqS5duujLL7/Uyy+/rDlz5qhy5cr617/+pY8++kgjR47UgAEDlJSUpMmTJxc7/uLFi9WrVy+1a9dOH3/8sXJycjR16lS1aNFCXbp00X//+19t2LBBmzdvPmf/6enpOnXqlJ566inVrVtX48aN0+23366uXbtq9+7dGjJkiObOnXuFPikAAK4OhgMu/WCrZus3O3bsUIcOHSRJYWFhCgwMVHZ2trKystS/f39JUm5uru66664/Ne6QIUM0ffp0zZ07V5GRkbrnnnu0a9cuderUSZJ0xx13SDrTlP1x/0ePHpUkRURESJK2bt2qVatW6dNPP5UkHT9+/DKPGgAA2IHH41FiYqK2bNkiPz8/jRo1SjVr1vQ+/9Zbb2nJkiVyuVx64okn1KpVq2LHs1Wz5Xa75fF4FBUVpe+++05169ZVZmamjh8/rooVK+raa6/VlClTVKFCBX311VcqV67cnxo/LS1NzzzzjCpXrqwRI0boyy+/VFRUlDZu3KjatWtrzZo1WrZs2Xn3HxQU5K1RkiIjI3XfffepQ4cO+uWXX/70WjMAAHAJLFggn56ervz8fKWlpWn9+vUaM2aMpk6dKulMuJKamqovvvhCp0+fVseOHUtXs1W5cmUVFBToxIkT2rNnjz7//HPl5uYqKSlJfn5+GjZsmPr27SvDMFS+fHmNHTtWp0+fvuTx69evr969eysoKEjly5dX8+bN1axZMw0dOlQLFy6UJCUnJ6tChQoaOnToWfv39T37o3riiSc0bNgwvf/++8rJyVFCQkKJfhYAAMAaGRkZatKkiSQpJiZGmzZt8j4XEBCg6667TqdPn9bp06fluoRpTls1W/7+/vrkk08u+Hzjxo3VuHHjcx5///33JUnPPPNMseO3aNFCLVq0OOfxadOmnfPYlClTznns66+/9n4fHBx83tcAAIASZMGSrZycHAUGBnq3fXx8VFhY6A1ewsPD1a5dOxUVFalfv34XHc9WzVZJyM/P12OPPXbO4xEREUpKSrKgIgAA8JeZsEA+LS1NaWlp3u24uDjFxcV5twMDA3Xy5Envtsfj8TZay5cvV1ZWlr766itJ0mOPPabY2FjVr1//gvtzXLPl5+en1NRUq8sAAAA29cfm6o9iY2O1dOlStW3bVuvXr1d0dLT3uUqVKqls2bLy8/OTy+VShQoVLnqSnOOaLQAA4CAWLJBv1aqVVq5cqfj4eBmGoeTkZM2aNUs1atRQy5Yt9e2336pz585yu92KjY296NURaLYAAAB+x+12n7P0KCoqyvv9s88+q2efffaSx6PZAgAA9lX6r2lKswUAAGzMAVeQt9WNqAEAAJyGZAsAANgXyRYAAACKQ7IFAADsywGxEM0WAACwL6YRAQAAUBySLQAAYF+lP9gi2QIAADATyRYAALAtw4J7I5Y0ki0AAAATkWwBAAD7csDZiDRbAADAvkp/r8U0IgAAgJlItgAAgH2xQB4AAADFIdm6goIGNbC6BFO4jt1sdQmm2Js01eoSTJPXu6fVJZhijhFodQmmKGhczeoSTHNNgL/VJZgiv1lFq0twDhbIAwAAmKj091pMIwIAAJiJZAsAANgXC+QBAABQHJItAABgXw5Itmi2AACAbRmlv9diGhEAAMBMJFsAAMC+HDCNSLIFAABgIpItAABgXw64gjzJFgAAgIlItgAAgH05YM0WzRYAALAvB8zBOeAQAAAA7ItkCwAA2BcL5AEAAFAcki0AAGBfLJAHAAAwj8E0IgAAAIpDsgUAAOzLAbGQAw4BAADAvki2AACAfbFAHgAAwEQskAcAAEBxTGm2jhw5osTExBIf9/nnn1d+fr4OHjyor7/+usTHl6S0tDQVFBRo8+bNSklJMWUfAADgErldJf91pQ/BjEFDQ0NNabYmTpwoPz8/rVq1SmvXri3x8SVp+vTp8ng8qlOnjhISEkzZBwAAuHr8qTVbCxYs0M6dO/Xiiy8qLy9Pbdq0UdWqVVW7dm1t27ZNOTk5ev3112UYhgYMGKCkpCQlJyfrnXfekST169dPzz33nHJycjRx4kT5+PioevXqSkpK0qJFizR//nx5PB49++yz+uSTT7R3717l5eXpscceU9u2bdWiRQstXrxYM2bMUG5urmJiYjRmzBh9/vnn8vHx0bhx41SvXj21adPmnNr379+vJ598UkFBQWratKkaNGjgTa5yc3P12muv6bvvvtORI0f0/PPPq2fPnpo3b54mTpyoe++9V7Gxsdq1a5cqV66syZMnq6CgQIMGDVJWVpbCw8O1Zs0arVixogR+JAAAwKv0L9kqmWSrfv36evvtt3XXXXdpyZIl3sdr166tvLw8HThwQFlZWcrOzladOnX08ssvKyUlRe+++67CwsL00UcfSZIqVqyouXPn6uabb9bq1auVkpKiN998U0VFRd4xfXx81LdvX7Vv31733HOPGjZsqBUrVqioqEjLly9Xy5YtL1jnkSNH9NZbb+nxxx/Xtm3bNG7cOL3zzjtq0aKFPvvsMz388MMKDQ3VxIkTz3rfvn379NxzzyktLU1Hjx7Vxo0blZaWpmrVqmnevHlKSEjQL7/8UhIfJQAAcJi/fDaiYRje7+vWrStJuvbaa/Xzzz+f9bpOnTrp448/lp+fnx588EEdPXpUWVlZ6t+/v6QzqdJdd92lGjVqKCIiQpIUGBiol19+WS+//LJycnJ03333XbCOhx9+WKmpqfJ4PLrzzjvl5+d3wddWq1bN+3xYWJhGjx6tcuXKKTMzU7GxsRd8X3BwsMLDwyVJ4eHhysvL044dO9S0aVNJUlRUlEJCQi74fgAA8NcYV9ulH/z9/XXkyBFJ0g8//HBJ72nbtq169eoll8ulmTNnqly5crr22ms1ZcoUVahQQV999ZXKlSunQ4cOye0+E7RlZWXphx9+0BtvvKG8vDw1a9ZM999/v3dMt9stj8cjSWrUqJGSk5P14Ycfehu4C/ltfEkaPny40tPTFRgYqJdeesnbPLpcLu/Yv3Gd57TT6OhorVu3Tvfcc4/27t2r7OzsS/o8AADAn+CAZutPTSM2adJEBw4cUJcuXfTpp5+qfPnyF31P+fLlVbt2bUVFRSkwMFBut1vDhg1T3759FR8frzlz5ig6Ovqs94SGhurIkSPq2LGjevfurUcffVS+vv/XF0ZHR+urr77yTll26NBBP//8s2rVqnXJx3L//ferc+fOio+P18mTJ5WVlSXpTPPWt2/fs5K78+nUqZMOHDigbt26afLkyfL397/kfQMAgKuHy7hYV1EKvPnmmwoODlanTp2u2D7Xrl2rU6dOqXHjxtq9e7f69Omj9PT0Yt/ztw+cuYD+2DHPxV9UCu1Nmmp1CaYJ693T6hJMUS020OoSTHFg5a9Wl2Ca70c78xfVelMvvKSltNv7bLMrur/rh39a4mPuHnXuiXRmKvVXkB88eLCys7M1efJkSWeuk7V48eJzXjdgwADdcsstJbbf6tWra8CAAUpJSVFhYaFGjBhRYmMDAADnKPXN1pgxY87ajouLU1xcnOn7DQ0NVWpqqun7AQDgquaAe92U+mYLAAA4GPdGBAAAQHFItgAAgH1dbZd+AAAAwJ9DsgUAAOzLAckWzRYAALAtgwXyAAAAKA7JFgAAsC8HxEIOOAQAAAD7ItkCAAD2xZotAAAAFIdkCwAA2BeXfgAAADCRA5otphEBAABMRLIFAADsq/QHWyRbAAAAZiLZAgAAtmU4YM0WzRYAALAvrrMFAACA4pBsAQAA+2IaEX9GTHih1SWYYrO/M/8Y5fXuaXUJpsmcNdvqEszh29vqCkxhXFPO6hJM5LG6AFOEhjJxhP/jzP8lAQCAM5T+YItmCwAA2JfbASGhAw4BAADAvki2AACAbTngyg8kWwAAAGYi2QIAALblhGSLZgsAAOB3PB6PEhMTtWXLFvn5+WnUqFGqWbOmJGnz5s1KTk72vnb9+vV644031LRp0wuOR7MFAABsy2VBtJWenq78/HylpaVp/fr1GjNmjKZOnSpJqlOnjlJTUyVJn376qa655ppiGy2JZgsAANiYFdOIGRkZatKkiSQpJiZGmzZtOuc1p06d0uTJk/Xuu+9edDyaLQAAcFVJS0tTWlqadzsuLk5xcXHe7ZycHAUGBnq3fXx8VFhYKF/f/2ubPvzwQ7Vu3VohISEX3R/NFgAAsC0zkq0/Nld/FBgYqJMnT3q3PR7PWY2WJC1atEiTJk26pP1x6QcAAIDfiY2N1fLlyyWdWQAfHR191vMnTpxQfn6+wsPDL2k8ki0AAGBbLgtioVatWmnlypWKj4+XYRhKTk7WrFmzVKNGDbVs2VK7du1S1apVL3k8mi0AAGBbViyQd7vdSkpKOuuxqKgo7/f169fXlClTLn28EqsMAAAA5yDZAgAAtuV2wBXkSbYAAABMRLIFAABsi3sjAgAAmMgJzRbTiAAAACYi2QIAALZlxY2oSxrJFgAAgIlItgAAgG1ZcQX5kuaAQwAAALAvki0AAGBbDliyRbP1R7t27dKQIUPk6+srHx8fjR07VrNmzVJGRoYkqX379urZs6cGDx4swzB06NAhnTp1Sq+99tpZ900CAACXj2bLgb799lvddNNNGjx4sL777jt9+eWX2r9/v95//30VFhaqa9euuv322yVJ1atX12uvvaZvvvlG48aN07Rp0yyuHgAA2A1rtv6gU6dOCg4OVp8+ffTee+/p2LFjatSokVwul8qUKaMGDRpox44dkuRtum655Rbt2rXLyrIBAHAkl6vkv640mq0/+Oqrr9SwYUPNnj1brVu31vz5871TiAUFBVq3bp1q1qwpSfrhhx8kSWvXrlWtWrUsqxkAANgX04h/UK9ePQ0cOFCTJ0+W2+3W5MmTtXjxYsXFxamgoECtW7fWTTfdJElavny5vvrqK3k8Hr366qsWVw4AgPO4WbPlPDVq1FBaWtpZj/3WXP1Rz5491bRp0ytRFgAAVyUnLJBnGhEAAMBEJFt/0ZgxY6wuAQAAxyPZAgAAQLFItgAAgG25HLBCnmYLAADYFtOIAAAAKBbJFgAAsC2SLQAAABSLZAsAANgWyRYAAACKRbIFAABsywFXfqDZAgAA9sU0IgAAAIpFsgUAAGzL5YBYyAGHAAAAYF8kWwAAwLacsGaLZgsAANiWywHdFtOIAAAAJiLZAgAAtuWAYItkCwAAwEwkWwAAwLackGzRbAEAANui2cKfcldYntUlmCImJN9y09NhAAAgAElEQVTqEkwxxwi0ugTz+Pa2ugJTZL45y+oSTFFv/NNWl2CaimWusboEU/j4ZlldAmyEZgsAANiWE25EzQJ5AAAAE5FsAQAA2yLZAgAAQLFItgAAgG25XYbVJVw2mi0AAGBbTCMCAACgWCRbAADAtpyQCjnhGAAAAGyLZAsAANgWC+QBAABMxAJ5AAAAFItkCwAA2JYTUiEnHAMAAIBtkWwBAADbcsKaLZotAABgWy4HnI3INCIAAICJSLYAAIBtOWEakWQLAADARCRbAADAtpyQCtFsAQAA23LC7Xqc0DACAADYFskWAACwLRbIXyUWLFig8ePHW10GAAAohUi2AACAbTkhFaLZ+hNmzpypJUuWyNfXV40aNdLAgQM1efJkrVu3TqdOndLo0aMVFRVldZkAAMBGaLYu0Z49e7R69WrNmzdPvr6+euaZZ7R06VJJUmRkpIYPH25xhQAAOI8T1mzRbF2izZs3q3nz5ipTpowkqVGjRtq2bZskKSIiwsrSAABwLC79cBWpU6eONmzYoMLCQhmGoTVr1nibLLebjxEAAJwfydYlqlmzpmJjY9WlSxd5PB41bNhQ99xzj3766SerSwMAwLGYRrxKPPjgg97ve/fufdZzzzzzzJUuBwAAlCI0WwAAwLacsFCHZgsAANgWC+QBAABQLJItAABgW05YIE+yBQAAYCKSLQAAYFtOSLZotgAAgG05YQqOZgsAAOB3PB6PEhMTtWXLFvn5+WnUqFGqWbOm9/lvvvlGb7zxhiSpbt26GjlypFyuC0dwTmgYAQCAQ7ldRol/XUx6erry8/OVlpamF154QWPGjPE+l5OTo3HjxmnatGl6//33VbVqVWVnZxd/DJf9KQAAADhIRkaGmjRpIkmKiYnRpk2bvM+tW7dO0dHReu2119S1a1dVqVJFISEhxY7HNCIAALAtMxbIp6WlKS0tzbsdFxenuLg473ZOTo4CAwO92z4+PiosLJSvr6+ys7O1evVqffzxxypXrpy6deummJgYRUREXHB/NFsAAOCq8sfm6o8CAwN18uRJ77bH45Gv75mWKSgoSDfffLNCQ0MlSY0aNdLmzZuLbbaYRgQAALblNuHrYmJjY7V8+XJJ0vr16xUdHe19rl69etq6dauOHj2qwsJCff/997rhhhuKHY9kCwAA2JYV19lq1aqVVq5cqfj4eBmGoeTkZM2aNUs1atRQy5Yt9cILL6hPnz6SpNatW5/VjJ0PzRYAAMDvuN1uJSUlnfVYVFSU9/t27dqpXbt2lzwezRYAALAt1yVcqsHuWLMFAABgIpItAABgW9wbEQAAwEROmIJzwjEAAADYFskWAACwrUu5l6HdkWwBAACYiGTrChrdY4fVJZjD7cyevaBxNatLMI1xTTmrSzBFvfFPW12CKTa9+IbVJZhmdfPuVpdgil3D060uwTwPN76iu2OBPAAAgImc0Gw5M5IAAACwCZItAABgWz5WF1ACSLYAAABMRLIFAABsi0s/AAAAoFgkWwAAwLaccDYizRYAALAtJzRbTCMCAACYiGQLAADYlg/JFgAAAIpDsgUAAGzLCWu2aLYAAIBtcZ0tAAAAFItkCwAA2JYTphFJtgAAAExEsgUAAGzLx+oCSgDNFgAAsC2mEQEAAFAski0AAGBbXPoBAAAAxSLZAgAAtsW9EQEAAFAski0AAGBbTjgbkWYLAADYlhOaLaYRAQAATHRVJ1u5ubkaMmSIDh48qIKCAr388suaNWuWsrKyFB4erjVr1mjFihXq0aOHIiIitGvXLhmGoYkTJyokJEQjRozQ4cOHlZ2draZNm6p///5WHxIAAI5CslXKzZs3T1WrVlVaWprGjBmjlStXqlq1apo3b54SEhL0yy+/eF8bGxur1NRUtWnTRtOnT9ehQ4cUExOjt956S3PnztXcuXMtPBIAAGBXV3WytXPnTjVt2lSSFB0drXfffde7HRUVpZCQEO9rb7/9dklnmq6vv/5aQUFB2rhxo1atWqXAwEDl5+df+QMAAMDhfLioaekWFRWljRs3SpL27dunzz77TOvWrZMk7d27V9nZ2d7Xbtq0SZK0du1a3XDDDVqwYIEqVKigCRMm6NFHH1Vubq4Mo/T/gQAAwE7cJnxdaVd1shUfH6+hQ4eqe/fuKioq0pQpU/Tuu++qW7duuu666+Tv7+997UcffaS3335bAQEBGjt2rH7++WcNGDBAGRkZCggIUM2aNZWVlaWwsDALjwgAANjNVd1s+fv7a8KECd7ttWvXqlOnTmrcuLF2797tTbkkacCAAYqKivJuBwcHa9GiRVe0XgAArjZOWCB/VTdbf1S9enUNGDBAKSkpKiws1IgRI6wuCQAAlHI0W78TGhqq1NTUcx4/32MAAMB8JFsAAAAm4mxEAAAAFItkCwAA2JYTphFJtgAAAExEsgUAAGyLZAsAAADFItkCAAC25YRki2YLAADYlo8Dmi2mEQEAAExEsgUAAGzLzUVNAQAAUBySLQAAYFtOSIVotgAAgG054WxEJzSMAAAAtkWyBQAAbItLPwAAAKBYJFsAAMC2nHDpB5otAABgWyyQBwAAQLFItgAAgG05Idmi2bqCek2rZXUJpugcmWt1Caa4JsDf6hJM5LG6AFNULHON1SWYYnXz7laXYJrmjd61ugRTvLW0p9UlwEZotgAAgG05Yb2TE44BAADAtki2AACAbblYswUAAGAeB/RaTCMCAACYiWQLAADYlhOmEUm2AAAATESyBQAAbMsJqRDNFgAAsC2XA25E7YSGEQAAwLZItgAAgG05YH08yRYAAICZSLYAAIBtOeHSDzRbAADAthzQazGNCAAAYCaSLQAAYFtuC6Itj8ejxMREbdmyRX5+fho1apRq1qzpfX7UqFFau3atypcvL0maMmWKKlSocMHxaLYAAAB+Jz09Xfn5+UpLS9P69es1ZswYTZ061fv8Dz/8oH//+98KCQm5pPGYRgQAALblMuHrYjIyMtSkSRNJUkxMjDZt2uR9zuPxaM+ePRoxYoTi4+P14YcfXnQ8ki0AAIDfycnJUWBgoHfbx8dHhYWF8vX11alTp9S9e3f17t1bRUVFeuSRR1SvXj3Vrl37guPRbAEAANsy49IPaWlpSktL827HxcUpLi7Oux0YGKiTJ096tz0ej3x9z7RMAQEBeuSRRxQQECBJuv322/XTTz/RbAEAgNLJjPXxf2yu/ig2NlZLly5V27ZttX79ekVHR3uf2717t55//nl99NFH8ng8Wrt2rR544IFi90ezBQAA8DutWrXSypUrFR8fL8MwlJycrFmzZqlGjRpq2bKlOnTooM6dO6tMmTK6//77VatWrWLHo9kCAAC2ZcVFTd1ut5KSks56LCoqyvv9448/rscff/zSxyuxyq5CLVq0UF5entVlAAAAGyPZAgAAtmXFRU1LWqltthYsWKD58+fL4/GoR48emj17ttxutxo2bKgXX3xRGRkZeu211+Tr66uKFStq/Pjx8vf318iRI7Vnzx55PB71799ft912mz777DO999573rFff/11bdu2TePHj1eZMmXUuXNnVapUSSkpKZKkunXr6pVXXpEkJSYmav/+/ZKklJQUVapU6cp/GAAAOJQDeq3S22xJUsWKFfXqq6+qa9eumj9/vgICAjRw4ECtXLlSK1asUKtWrfTYY4/p66+/1vHjx7Vs2TIFBwcrOTlZ2dnZ6t69u5YsWaLdu3drxowZCggI0IgRI7RixQqFhYUpLy9PH3zwgQoLC3Xvvffqgw8+UOXKlZWSkqLDhw9Lkh566CE1atRIgwcP1sqVK9W2bVuLPxUAAGAnpbrZioiI0N69e3X06FH17dtXknTy5Ent27dPTzzxhKZNm6aePXsqLCxM9evX19atW5WRkaENGzZIkgoLC5Wdna3KlSvrpZdeUvny5bVz507FxMR4x5ek7OxsVaxYUZUrV5YkJSQkeGuoV6+eJKlKlSrKzc29YscOAMDVwOUyrC7hspXqZsvtdqtatWoKDw/XzJkzVaZMGS1YsEB16tTRokWL9MADD+ill17S9OnT9f777ysyMlLXXnutnnjiCeXm5mrq1Kny9fXVpEmTtGzZMklS7969ZRiGd3xJqly5so4fP65ff/1VQUFBGjVqlO677z5JksuMq60BAADHKNXNliSFhISoV69e6tGjh4qKilS1alW1adNG+fn5Gjx4sMqVK6cyZcooKSlJYWFhGj58uLp3766cnBx17dpVgYGBio2N1QMPPKBy5cqpYsWKysrKUrVq1bz7cLvdGjlypPr16ye32626devq5ptvtvCoAQC4Ojgh0nAZv8U4MF3y+i+tLsEUnSOdOX16TUBZq0swkRP++TpXxTI1rC7BFKuztlldgmmaN3rX6hJM8dbSnlaXYJquUa2v6P52nlhU4mNGVuhQ4mMWh+tsAQAAmKjUTyMCAADnckIq5IRjAAAAsC2SLQAAYFtOOOmfZAsAAMBEJFsAAMC2HBBs0WwBAAD7YhoRAAAAxSLZAgAAtuWAYItkCwAAwEwkWwAAwLbcDoi2aLYAAIBtOaDXYhoRAADATCRbAADAtlwuw+oSLhvJFgAAgIlItgAAgG05Yc0WzRYAALAtriAPAACAYpFsAQAA23JAsEWyBQAAYCaSLQAAYFtOSIVotgAAgG05YYE8zdYVNHP4AatLMMVMf2f+McpvVtHqEkwTGuqE3xXP5eObZXUJptg1PN3qEkzz1tKeVpdgisfunm11Cabpure11SWUOs78XxIAADhE6Y+2nPnrLQAAgE2QbAEAANtykWwBAACgOCRbAADAtlyu0p8L0WwBAAAbYxoRAAAAxSDZAgAAtsUCeQAAABSLZAsAANhY6U+2aLYAAIBtOeFsxNJ/BAAAADZGsgUAAGys9E8jkmwBAACYiGQLAADYlhMu/UCzBQAAbMsJzRbTiAAAACYi2QIAADZW+nOh0n8EAAAANkayBQAAbMvlYs0WAAAAikGyBQAAbKz0J1s0WwAAwLa49AMAAACK9aebrQULFmj8+PFm1HLZ0tPT1b59e73zzjt/eYy5c+dq8uTJl/Ta0aNH6+DBg/r111+1aNGiv7xPAABwIW4Tvq4sRyVbS5cu1YABA/TII49ckf0NGzZM1113nbZs2aKvv/76iuwTAACULhdds5Wbm6shQ4bo4MGDKigo0N///nd9//33evTRR3X06FF16dJFcXFx+uyzz/Tee+953/f6669r27ZtevPNN1WmTBnt379fbdu21ZNPPqndu3dr+PDhKigoUNmyZTVx4kTl5eXp5ZdfVl5envz9/fWPf/xD4eHh563p+PHjGjhwoHJyclRUVKTnnntOp06d0rJly7RhwwYFBwfrlltuOed9OTk5GjZsmE6cOKHs7Gw9/PDD6tq1q7777jslJyerUqVKcrvdiomJ0f79+/X8888rPDxc+/fvV7t27bRt2zb9+OOPat68uQYMGKAePXooMTFR06ZN008//aS0tDTFxcVdxo8DAAD8nhPWbF202Zo3b56qVq2qiRMnauvWrfr222/l6+urt956SwcOHFDfvn0VFxen3bt3a8aMGQoICNCIESO0YsUKhYWF6eDBg1q4cKHy8/PVpEkTPfnkk3rttdfUt29fNW3aVP/5z3/0448/6sMPP1SPHj3UrFkz/fe//9X48eM1YcKE89Y0depU3XnnnerZs6cyMzPVpUsXpaenq0mTJmrbtu15Gy1J2rNnj9q1a6d7771XmZmZ6tGjh7p27apXX31VEyZMUEREhEaOHOl9/b59+zRz5kzl5uaqZcuWWr58uQICAnT33XdrwIAB3tc98cQTmjdvHo0WAAAlzAnX2bpos7Vz5041bdpUkhQdHa1Nmzapbt26crlcCg0NVW5uriSpcuXKeumll1S+fHnt3LlTMTEx3vf4+vrK19dXZcuWlSTt2rXL2xC1bdtWkpScnKzp06fr3//+twzDUJkyZS5Y044dO9ShQwdJUlhYmAIDA3X06NGLHmyVKlU0e/ZsffHFFwoMDFRhYaEkKTMzUxEREZKk2NhY7d27V5JUvXp1VahQQX5+fqpSpYqCgoIkOeMHDwAAroyLNltRUVHauHGj7rnnHu3bt0///Oc/1bFjx7Nec+LECU2aNEnLli2TJPXu3VuGYUg6f2Py25h33nmnFi5cqGPHjikyMlKPPvqoYmNjtWPHDq1Zs6bYmr777jvVrVtXmZmZOn78uLcRKs7MmTMVExOjrl27atWqVfrmm28kSaGhodqxY4e3rkqVKl2w9vNxu93yeDyX9FoAAPBnlP6A46LNVnx8vIYOHaru3burqKhIvXv3VnZ29lmvCQwMVGxsrB544AGVK1dOFStWVFZWlqpVq3beMQcNGqQRI0Zo6tSpKlu2rMaNG6fmzZsrMTFReXl5ys3N1bBhwy5YU79+/TR06FB9/vnnys3NVVJSknx9L37JsLvvvluJiYlatGiRgoKC5OPjo/z8fI0bN86bypUvX97bbF2qGjVqaOvWrXr77bfVq1evP/VeAADgbC7jtwgKpruh/dtWl2AOf2deGze/WXWrSzBNaKijTkT28vEt/b8Bn8+u4fOtLsE0r//n71aXYIrH7p5tdQmmOb137hXdX17R/0p8TH+fv5X4mMWx9f+SCQkJOnbs2FmPBQYGaurUqcW+LzExUTt27Djn8TfffNO7bgwAAJQGpf+XKFs3WykpKX/pfYmJiSVbCAAAwF9k62YLAABc3ZxwBQBnLtwAAACwCZItAABgYyRbAAAAKAbJFgAAsC2XA3Ihmi0AAGBjTCMCAACgGCRbAADAtlwkWwAAACgOyRYAALAtJ1zUlGYLAADYWOmfhCv9RwAAAGBjJFsAAMC2WCAPAACAYtFsAQAAG3OZ8FU8j8ejESNGKC4uTj169NCePXvO+5o+ffpo7ty5Fx2PZgsAANiWy+Uq8a+LSU9PV35+vtLS0vTCCy9ozJgx57zmX//6l44dO3ZJx0CzBQAA8DsZGRlq0qSJJCkmJkabNm066/nPPvtMLpdLTZs2vaTxaLYAAICNuUv8Ky0tTQ8++KD3Ky0t7aw95uTkKDAw0Lvt4+OjwsJCSdLWrVu1ePFiPffcc5d8BJyNCAAAripxcXGKi4u74POBgYE6efKkd9vj8cjX90zL9PHHHyszM1M9e/bUgQMHVKZMGVWtWrXYlItmCwAA2JYVl36IjY3V0qVL1bZtW61fv17R0dHe5wYNGuT9fvLkyapSpcpFpxNdhmEYplULAABQyng8HiUmJmrr1q0yDEPJyclavny5atSooZYtW3pf91uz1aVLl2LHo9kCAAAwEQvkAQAATESzBQAAYCKaLQAAABPRbAEAAJiIZgsAAMBENFsAAAAmotkCgD9h586dVpcAoJSh2XKY3bt365tvvtHhw4flpEuobd26VV27dlWHDh00Y8YMLV261OqSUIxdu3bpySefVJs2bfTss8/qwIEDVpdUYoYNG2Z1CVdEVlaW1SWUiBMnTuizzz7Txx9/7P1yig8++OCs7XfeeceiSnAx3K7HQd599119+eWXOnbsmDp27Ki9e/dqxIgRVpdVIkaPHq1XX31Vw4cPV6dOndSnTx/dfffdVpd12Zo0aaKjR48qODhYv/76q/z8/FSlShWNHDlSd911l9Xl/WUvvfSSnn76acXGxiojI0ODBw9Wamqq1WWViHLlyik5OVkRERFyu8/8vlrcPdZKi9dff11z585VQUGBcnNzdf3112vJkiVWl3XZnn76aVWtWlVVqlSRJLlcV/7WLyVt8eLF+vrrr7V69WqtWrVKklRUVKRt27bpkUcesbg6nA/NloMsWbJEc+bM0SOPPKJevXrpoYcesrqkElWzZk25XC6FhISofPnyVpdTIm699VYlJCQoMjJSe/fuVUpKip5++mkNHDiwVDdbAQEBatasmSSpefPmmjVrlsUVlZxbbrlFkvTLL79YXEnJ+n//7/9p+fLlSk5OVu/evfXKK69YXVKJMAxDr776qtVllKgmTZooNDRUv/76q7fRd7vdql69usWV4UJothzkt2nD335z8/Pzs7KcElWpUiXNmzdPp0+f1pIlS1SxYkWrSyoRhw8fVmRkpCSpRo0aOnTokGrWrCkfHx+LK7s84eHhmjJlim6//Xb98MMP8vPz04oVKyRJjRs3tri6y5OQkKBly5Zp27ZtioiI0D333GN1SSUiKChIfn5+OnnypGrWrKnTp09bXdJlyc/PlyRVr15d69at00033eR9rrT/23j69Gnddtttuuaaa856/NSpUxZVhIuh2XKQ9u3bq1u3bjp48KAef/xxx/wnIEnJycmaNm2agoODtWnTJo0ePdrqkkpEaGioxo8fr1tuuUXr1q1TlSpVtHLlSpUpU8bq0i6Ly+XSvn37tG/fPklSlSpVvFNSpb3ZmjBhgvbs2aPY2Fh9/PHHysjI0EsvvWR1WZft2muv1YcffqiAgACNHz9eOTk5Vpd0WVq3bi2XyyXDMLRq1Srv9y6XS1999ZXV5V2WWbNmaciQIecsE3G5XKzbsiluRO0gO3fulGEY2rp1qyIiIlS7dm2rSyoxL7zwgiZMmGB1GSUuLy9PaWlp2rFjh6Kjo9WpUyf9+OOPql69uneNSWm1detWbd++XREREapTp47V5ZSY+Ph4zZs3T9KZNLlz587nLFQujTwejw4fPqyKFSvqo48+0h133KEbbrjB6rIum2EYOnz4sMLDw7VhwwbVr1/f6pJwFSLZcpBhw4Zp7ty5ioqKsrqUEpefn6+ffvpJERERjpom9fPzU0xMjLcZ2bBhg2699VaLq7p8qampWrx4serXr6+ZM2eqTZs2euyxx6wuq0QUFhbK4/HI7XbL4/E4YsG1JGVnZ2vmzJnavXu3atWqpdDQUKtLKhEjR47Utddeq6eeekoLFy7UokWLHHNG6cSJEzV//vyzHvttuh72QrPlIE49S0o6c0mLp556yrvthKkA6cz6n+zsbIWHh3unOJzQbC1evFjvvfeefH19VVBQoPj4eMc0W+3atVOXLl3UoEEDbdiwQW3btrW6pBLRv39/tWnTRp06dVJGRoYGDRqk6dOnW13WZdu8ebOSkpIkScOHD1e3bt0srqjkLFu2TF9//bUjfvF0OpotB3HqWVKStGjRIhmGoaNHjyooKKjULyD/zS+//OKdknISwzDk63vmn5cyZcqU+jVo0pm1Wr+lWGFhYVq6dKnq1Kmjo0ePWlxZyenataskqXbt2vrss88srqZkGIah7OxsBQcH6/jx4yoqKrK6pBJTt25d5eXl0WyVAjRbDuLUs6QkafXq1Ro6dKgqVKig48eP6x//+EepvjTCbyIiIpSZmamwsDCrSylRDRs21LPPPquGDRsqIyNDsbGxVpd02X47a1Q683NzwnXefi8yMlILFy7Ubbfdph9++EFBQUHatWuXpDPHW1olJCTooYceUlBQkI4fP66RI0daXVKJqVWrlho3bqwqVao4ZvG/U7FA3kF+f5bUd999p+rVqzviLClJ6tKli/71r38pLCxMmZmZSkhIcMSi5HvvvVf79+9XSEiI9zEnrLnIzMxUenq6jh07pvnz5yslJcVRi+SdqEePHud9vLSf4bZw4UK1a9dO2dnZqly5smPW2ElSp06dNG3atLMuhUPKZU8kWw6yZs0a75RUz5491blzZ4srKjk+Pj7e9CcsLEz+/v4WV1QyvvjiC6tLMMXgwYPVr18/zZkzRwMGDFBycrJjriDvVE79+bz//vu67777Sv3Zvedz3XXXKSAggAarFKDZcpDfnyX1W6TsFIGBgUpNTdWtt96qNWvWqFKlSlaXdFmmTJmip556SgMGDDjn5+SES1wUFhbq1ltv1fTp09WuXTvNmTPH6pJwEU69dVR+fr46dux41olDTvg7Jp25KHKrVq28V453uVyOXAPqBDRbDtK2bVtHniUlSePGjdOUKVM0ceJERUVFKTk52eqSLkuLFi0knblmkxMVFBTo1VdfVcOGDbVq1SpHLUp2KqfeOurFF1+0ugTTTJw40eoScIlothzk0UcfVePGjbVz50499NBDuvHGG60uqcRkZ2frpptu0ksvvaTx48frxIkTpTrdqlWrlvLz8/XOO+9o4sSJMgxDHo9Hffv2LdXrY34zZswYrVy5Ug8//LDS09M1btw4q0vCRTj11lHR0dFasWKFCgsLZRiGsrKy9Le//c3qsi5LSkrKBZ9LSEi4gpXgUtFsOcj777+v7du3a+jQoXr00Ud13333qWPHjlaXVSIGDRqk559/XpLUrFkzDRs2TLNnz7a4qr9u/vz5mjZtmn7++We1bt1ahmHIx8dHDRs2tLq0EnH99dfr+uuvlyRHJaxO9vtbR61fv94xt4569tlndf3112vr1q3y9/dXQECA1SVdtt/Wn6Wnp6tatWqKjY3Vxo0bdejQIYsrwwUZcIyOHTsaubm5hvH/27vXoCjLNwzg18thVwUNDXDkYMFi0sFTJqYojmCTY6GpCCpKpI5S0pgkgZogmIIRSBlK2CCTEio1VjOijqhpWh5SUjOVNBMHRTeWg5xh2f8Hh51Q0Pqz9Oz7ev1m+MC7X64dx8fb572f5zYYDA0NDYbAwEDBiUwnKCio1e+zZs0SlMS0cnNzRUcgMhgMBkNBQYFh/fr1hpiYGIOXl5fh559/Npw+fdqg1WpFR+uQ2bNnGwwGgyE6Otqg1+sN06dPF5zIdObMmdPq99DQUEFJ6GG4s6UgFhYWxlN61tbWimqQ79GjB7Zv347Bgwfj7NmzsLGxER3JJFqayBsbGwEAt2/fNt52TfRf+uCDD5CYmAgPDw/MmzcP0dHRyM7OFh3LJOrr61FbWwtJklBTUyM6jsmUlZWhqKgIffv2xR9//CH74eFKxmJLQfz8/DBz5kwMHIms15QAAA4/SURBVDgQ58+fNzZhK0FiYiI2btyIffv2wcPDQ/YN8i2ioqIwduxYnD59Go6Ojor6h4DkxcrKyjh42tXV1XhyT+6Cg4ORlZUFb29vjBkzRjGv6gFg2bJliIiIwK1bt+Dg4MDeSDPGS00V5sKFC7h69Src3d3h6ekpOo5J3blzB5IkIT8/H2PHjpV1g3yL0NBQZGVlYenSpUhISMDMmTN5TQIJERERARcXF+Pu8fXr1xVzRQIAVFRUwNLSEra2tqKjdJrGxkbZ99gpFXe2FOT3339HXV0dnJyckJCQgLCwMIwYMUJ0LJN477334O3tjYKCAjQ3N2Pfvn1IS0sTHavDDAYDtFotampqUFNTg4qKCtGR6BGVkJCAnJwcHDp0CBqNptXgdzk7efIk4uLioNfrMX78eDg5OWHatGmiY5nEtm3bsHnzZuNJSysrK8VelCx3ytgnJgBAbGwsVCoV0tPTsXjx4gceD5ab4uJiTJo0CVeuXEF8fLxiehPCw8ORn5+PiRMnws/PDz4+PqIj0SNKrVYjNDQUcXFxCAkJUcyt5Kmpqdi6dSvs7e0RFhaGnJwc0ZFMZseOHdiyZQt8fHyQkJBgfA1M5oc7WwpiZWWFfv36obGxEYMHD1bURZKNjY3Iy8uDh4cHdDodysvLRUcyibNnz2Lu3LkA7vbcEZFpWVhYwM7ODpIkQa1WK+ZwDQD07NkTjo6OqK6uxvDhw/HJJ5+IjkTt4M6WgkiShHfffRc+Pj7Iy8tTxH0yLebNm4e9e/diwYIF2LJlC9555x3RkUzi0KFDiiqKicxN3759kZycjPLycmRkZMDJyUl0JJPp3r078vPzjWN6dDqd6EjUDjbIK4hOp8O5c+fg4+OD48ePw9PTE3Z2diguLoazs7PoeJ0iNjYWcXFxomP83/z9/VFaWgoXFxdIksTZZkQm1tTUhNzcXBQWFkKj0SAwMFAxr0irqqpQVFQEe3t7ZGZmYuzYsRg+fDgaGhoU8x2VgsXWIyAkJEQRI2DaIvfvVlxcfN8zZ2dnnDlzBoMGDRKQiEhZampqUFlZCUtLS+zYsQOvvfaaYv/z2ULu66IS8TXiI4D1tPlydna+7weAoo7cE4m0ZMkSnD9/HklJSbC2tkZMTIzoSJ2Oa775YbH1CFDSTfKPCi6WRKZRWVkJX19flJSUYP78+WhoaBAdqdNxzTc/LLaIzBAXSyLTaGxsRGZmJp599llcvnwZ1dXVoiPRI4jF1iNAybskSv5uRNRxUVFRKC0txZtvvonjx49j5cqVoiN1Oq6L5of3bClQRUVFq1E2L774osA0HbN9+/Z2PwsKCkJmZuZ/mOa/w8WSyDSef/551NXVYc+ePRg6dCjc3NxER+qwrVu3YtasWe0epOHlpuaHxZaCnDhxAvHx8feNpVi4cKHoaP+3y5cvP3AGotzngO3duxd+fn6wsmr9V9Hf319QIiJlSUlJQUlJCa5cuQJra2tkZGQgJSVFdKwO2b59O1xcXLBu3TpERka2+mzUqFGIjY0VlIzaw2JLQT7++GNs3boVb7/9NsLCwjBjxgzZzwD77bffkJ2dLfv7tNpz7tw5pKWlwdvbGwEBAdBoNACAwMBAwcmIlOHUqVPIzs7G7NmzMXnyZEWM61m0aBHy8/NRWlqKXbt2tfps1KhRglLRg7DYUhAljqXo0qULpk6dimvXruHSpUutPlPC5Z9LlixBREQEDh8+jNTUVGi1WgQGBmLixIn37XYR0b+n1+tRX18PSZKg1+thYSH/VuVx48Zh3LhxOHDgAHx9faHT6WBnZ6eI76ZUXM0VRIljKTZt2oTbt28jJiYGK1euVFwvk8FgwJEjR/DNN9+guLgYEydOhE6nQ3h4ONLT00XHI5K9kJAQTJkyBTqdDtOmTUNoaKjoSCbTrVs3+Pn5oXv37qisrMSqVavg7e0tOha1gTfIK0hDQwO+/vprFBYWwt3dHUFBQYoZ2dDU1ISdO3fi5s2bGD58OPr164devXqJjtVhL730El544QUEBARg6NChxudLly5FQkKCwGREyhAcHIyNGzfizz//hIuLiyLWjRYzZsxAamoqevfujVu3biE8PBy5ubmiY1EbuLOlIGFhYYo9nRcbGwtHR0f8+OOPeO655xAVFYVNmzaJjtVh0dHR8PPzM/6el5eHCRMmsNAiMhFJkrB06VK4ubkZX7NFREQITmUalpaW6N27NwCgd+/eUKvVghNRe1hsKUjLBPi/LypKOOYMAEVFRVi9ejVOnToFX19fZGRkiI7UIQcPHsTp06exa9cunDlzBsDd3pIDBw5gwoQJgtMRKcfUqVNFR+g0tra22LJlC4YNG4aTJ08+8OQ2icViS0F0Ot19w0eVMoxUr9dDp9MBuDvpXu6NoJ6enigvL4darTYWxJIk4dVXXxWcjEhZJk+eLDpCp0lKSsKGDRuwbt06aDQarFmzRnQkagd7thRk9OjRKC0tRa9evVBWVgaVSgUHBwfExsbKvmnyxIkTWLFiBbRaLfr06YNly5bJ+js1NTXBysoKdXV19xWOSumzIyIxFi5ciLS0NNEx6G+4s6Ugw4YNQ3h4ONzd3VFUVIS0tDS89dZbiIyMlHVhAgBeXl7YvXs3ysrK8Nhjj8n+WoSoqCgkJydjwoQJxjmIBoMBkiRh//79gtMRkZxVVlaKjkD3kPe/WNRKSUkJ3N3dAdy9BuLGjRt44oknYGlpKThZxx07dgzLly9XzBHn5ORkAMCBAweMz/R6vSL+rIhILA6yNz8sthTEwcEBH330EYYMGYKCggLY29vj6NGjsh9pA9y9Hf/LL79sdcRZzsVWi927d6O5uRkNDQ1ISkrC3LlzMXfuXNGxiIjIhOTdZUytfPjhh3B0dMThw4fRp08fJCYmolu3brKfAwYo94hzZmYmRo4cie+++w7ff/89Dh48KDoSERGZGHe2FEStViMkJKTVsyFDhghKY1r3HnG2s7MTHckkWprhbWxsoFKpUF1dLTgREckdr4AwPyy2SBYGDBiAmzdvIjU1Fe7u7oq5BdrV1RVTp07FihUr8Omnn2LgwIGiIxGRTNy6dQtJSUkoKyvDyy+/jP79+2PQoEFYv3696Gh0D179QGYtNzcXX331Fa5cuQKNRgMAaG5uNo7vkbtjx45hwIABsLGxgVarhYODg+hIRCQT8+fPxxtvvIENGzYgLi4O0dHR2LFjh+hY1AbubJFZmzRpEkaMGIHPPvsMYWFhAAALCws8/vjjgpOZxvr165GdnQ0ALLSI6F+pr6/HiBEjsHHjRri7uyuml1WJWGyRWVOpVHBxccGqVatER+kUkiRh4cKFipzbRkSdS6VS4YcffkBzczN++eUXXohsxvgakUigtl6FKnm8CBGZTklJCdauXYvCwkJoNBpERkbC1dVVdCxqA69+IBLI398fTU1NuH79OpycnDBmzBjRkYhIJtRqNQICArBr1y54eXnxFKIZY7FFJFBsbCxu3LiBo0ePorq6GlFRUaIjEZFMRERE4M6dOwDuXvcQGRkpOBG1h8UWkUBFRUVYtGgRVCoVfH19jQsnEdHD1NbWYvz48QDu7pLX1tYKTkTtYbFFJJBer4dOp4MkSaiqqjI2yRMRPYy1tTWOHj2Kqqoq/PTTT1w/zBgb5IkEOnnyJN5//31otVr06dMHy5cvx8iRI0XHIiIZuHbtGtauXYurV6/Cw8MDkZGR6Nu3r+hY1AYWW0QCXbx4EZ6entDpdOjZsyckSRIdiYiITIzFFpFAYWFhKC8vx5QpU/DKK6/AxsZGdCQikon09HR8/vnn6NKli/HZkSNHBCai9rDYIhJMq9Xi22+/RX5+PjQaDVavXi06EhHJwKRJk7Bt2zZ07dpVdBR6CHbTEQnW1NSEhoYGNDc3w9LSUnQcIpIJZ2fnVrtaZL44rodIoNdffx319fUICAhAVlYWunXrJjoSEclEY2Mj/P398dRTTwG4O/4rOTlZcCpqC18jEgl06dIl9O/fX3QMIpKhEydO3PfMy8tLQBJ6GBZbRALEx8cjJiYGQUFBxhOIBoMBkiRh27ZtgtMRkRyUl5fjyJEjaGpqgsFgwO3bt7FgwQLRsagNLLaIBPjrr79gb2+P4uLi+z5zdnYWkIiI5CYkJARPPvkkCgsLoVar0bVrV6Snp4uORW1ggzyRAPb29gAACwsL5OXlYefOncYfIqJ/Kj4+Hm5ubti8eTMqKipEx6F2sNgiEmjRokWoqqqCvb298YeI6J+qr69HbW0tJElCTU2N6DjUDp5GJBLIxsYGixcvFh2DiGQoODgYWVlZ8Pb2xpgxYzB06FDRkagd7NkiEmjNmjUYNGgQnn76aWOjvJubm+BURCQ3VVVVsLW1FR2D2sGdLSKBLly4gIsXL7Z69sUXXwhKQ0Rykpubi6ysLNTV1Rmf7d+/X2Aiag+LLSKB7j2N2L17d0FJiEhucnJykJGRAQcHB9FR6CFYbBEJtGfPHgB379j69ddfsXfvXsGJiEguevbsyatiZII9W0RmJDg4GNnZ2aJjEJEZS0lJAQAUFBRApVLhmWeeMfZ8RkREiIxG7eDOFpFAycnJxkVSq9XCwoK3sRDRg7UcorG1tYVarUaPHj2QkpKCOXPmCE5G7eHOFpFAf7/EVK1WY/To0ezbIqJ/JCAgAImJifDw8MD169cRHR3NnXEzxZ0tIoEmT54sOgIRyZSVlRU8PDwAAK6urtwZN2MstoiIiGTIyckJKSkpGDx4MM6ePQtHR0fRkagdfI1IREQkQ/X19cjJycHVq1eh0Wgwffp0qFQq0bGoDSy2iIiIiDoRX/ASERERdSIWW0RERESdiMUWERERUSdisUVERETUiVhsEREREXWi/wHXA4KVkod9PgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,8))\n",
    "_= sns.heatmap(corrmat, vmax=.9,cmap=\"YlGnBu\", square=True, ax=ax)\n",
    "ax.set_title('Feature Correlations')\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "_= sns.heatmap(corrmat[['chance_of_admit','gre_score','toefl_score','cgpa','university_rating']], vmax=1.0,\n",
    "            cmap=\"YlGnBu\", square=False)\n",
    "ax.set_title('Features with highest correlations')\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What if a student with 50% or more chance of admittance got admitted into grad school?\n",
    "\n",
    "<em>Spoiler Alert</em> - this is most likely <b>NOT</b> how students are admitted into graduate school. The admittance rate would be greater than 90% in this dataset, so almost anyone would get in..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['admit'] = [1 if x>=.5 else 0 for x in data.chance_of_admit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
       "      <th>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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   serial_no.  gre_score  toefl_score  university_rating  sop  lor  cgpa  research  chance_of_admit  admit\n",
       "0           1        337          118                  4  4.5  4.5  9.65         1             0.92      1\n",
       "1           2        324          107                  4  4.0  4.5  8.87         1             0.76      1\n",
       "2           3        316          104                  3  3.0  3.5  8.00         1             0.72      1\n",
       "3           4        322          110                  3  3.5  2.5  8.67         1             0.80      1\n",
       "4           5        314          103                  2  2.0  3.0  8.21         0             0.65      1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = data.admit.value_counts().reset_index()\n",
    "temp['threshold'] =.5\n",
    "#temp = pd.pivot_table(temp, columns=['index'], index='threshold',values='admit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>index</th>\n",
       "      <th>admit</th>\n",
       "      <th>threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>367</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  admit  threshold\n",
       "0      1    367        0.5\n",
       "1      0     33        0.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.barplot(ax=ax, data=temp, y='admit', x='index')\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('admit', axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative GPA and University Rating\n",
    "\n",
    "1) Plotting the data using distplot\n",
    "\n",
    "The first plot shows the distribution of the cumulative gpa feature - these scores look normally distributed.\n",
    "We that as university rating increases so does the cumulative GPA from the scatterplot. There are some students attending universityes with ratings 4 with higher cumulative GPA than those attending the highest rated schools. But in general there is a linear relationship that can be seen as the university rating increases.\n",
    "\n",
    "2) Plotting the data using qqplot \n",
    "\n",
    "This plot generates its own sample of the idealized distribution that we are comparing with, in this case the Gaussian distribution. The idealized normal samples are divided into 5 quantiles, and each data point is paired with a similar sample from the idealized distribution. The X-axis in the plot shows the idealized samples and the Y-axis has the data samples.\n",
    "Our samples closely fit the expected diagonal pattern for a sample from a Gaussian distribution.\n",
    "\n",
    "3) Statistical Normality tests\n",
    "\n",
    "Taking a closer look at this - our null hypothesis H0 states that our sample is normally distributed. <br>We set our alpha to 0.05 and <br>\n",
    "* if p<= alpha: we reject H0 - not normal\n",
    "* if p> alpha: fail to reject - normal\n",
    "\n",
    "a) Shapiro-Wilk test <br>\n",
    "Evaluates a data sample and quantifies how likely it is to be drawn from a Gaussian distribution.\n",
    "\n",
    "b) D'Agostino test (stats.normaltest) <br>\n",
    "This calculates summary statistics on the data: <em>skewness</em> - a measure of asymmetry of the data which quantifies how far left/right the data is pushed. <em>Kurtosis</em> quantifies how much of the data is in the tail of the distribution.\n",
    "\n",
    "In both cases, from  the given evidence that we have with this dataset, it looks like our null hypothesis is very likely true - our Cumulative GPA scores are <b>normally distributed</b>. We can not reject the null hypothese that the data is normall distributed because the p-values are greater than the chosen alpha<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    400.000000\n",
       "mean       8.598925\n",
       "std        0.596317\n",
       "min        6.800000\n",
       "25%        8.170000\n",
       "50%        8.610000\n",
       "75%        9.062500\n",
       "max        9.920000\n",
       "Name: cgpa, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cgpa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\gagrocostea\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1) general plot of data distribution\n",
    "fig, ax = plt.subplots()\n",
    "_ = sns.distplot(data.cgpa, kde=True, kde_kws={'color':'red', 'shade':True, 'lw':1}, ax=ax)\n",
    "ax.set_xlabel('')\n",
    "ax.set_title(\"Distribution of CGPA Scores\")\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2) qqplot\n",
    "from statsmodels.graphics.gofplots import qqplot\n",
    "fig, ax = plt.subplots()\n",
    "_ = qqplot(data.cgpa, line = 's', ax=ax)\n",
    "ax.set_title(\"QQplot of CGPA\")\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=0.993, p=0.072\n",
      "Our data sample looks Gaussian (fail to reject H0)\n"
     ]
    }
   ],
   "source": [
    "# 3) a) Shapiro normality test\n",
    "statistic, p = stats.shapiro(x=data.cgpa)\n",
    "print('Statistics=%.3f, p=%.3f' % (statistic, p))\n",
    "# interpret\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "    print('Our data sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "    print('Our data sample does not look Gaussian (reject H0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen alpha is: 0.0500\n",
      "p value is 0.052520729609327604\n"
     ]
    }
   ],
   "source": [
    "#3) b) D’Agostino’s K^2 Test - test for skewness & kurtosis\n",
    "alpha = 5e-2\n",
    "print(\"Chosen alpha is: %.4f\" %alpha)\n",
    "k2, p = stats.normaltest(data.cgpa)\n",
    "print(\"p value is\", p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The null hypothesis cannot be rejected - our data is likely normal\n"
     ]
    }
   ],
   "source": [
    "# null hypothesis: scores come from a normal distribution\n",
    "if p < alpha:  \n",
    "    print(\"The null hypothesis can be rejected - our data is likely NOT normal\")\n",
    "else:\n",
    "    print(\"The null hypothesis cannot be rejected - our data is likely normal\")\n",
    "#The null hypothesis can be rejected\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### University Ratings - A quick look\n",
    "Univeristy ratings of 2 and 3 make up about 50% of the data. We can also see that university ratings and cumulative gpa increase together - which makes sense. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>university_rating</th>\n",
       "      <th>row_count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>133</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>107</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   university_rating  row_count  percent\n",
       "0                  3        133     26.6\n",
       "1                  2        107     21.4\n",
       "2                  4         74     14.8\n",
       "3                  5         60     12.0\n",
       "4                  1         26      5.2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.DataFrame(data.university_rating.value_counts())\n",
    "temp.columns = ['row_count']\n",
    "temp.index.name = 'university_rating'\n",
    "temp.reset_index(inplace=True)\n",
    "temp['percent'] = temp.row_count/temp.shape[0]\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = pd.DataFrame(data.university_rating.value_counts())\n",
    "temp.columns = ['count']\n",
    "temp.index.name = 'university_rating'\n",
    "temp.reset_index(inplace=True)\n",
    "temp\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "_ =sns.barplot(data = temp, y='count', x = 'university_rating', ax = ax)\n",
    "ax.set_title(\"University Ratings\")\n",
    "ax.set_xlabel(\"\")\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\gagrocostea\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "_ = sns.distplot(data.university_rating, kde=True, kde_kws={'color':'green', 'shade':True, 'lw':1, 'bw':.75}, ax=ax)\n",
    "ax.set_xlabel('')\n",
    "ax.set_title(\"Distribution of University Ratings\")\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "_ = sns.boxplot(ax=ax, data=data, x= 'university_rating', y= 'cgpa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "_ = sns.scatterplot(data = data, x ='cgpa', y='chance_of_admit', hue = 'university_rating',\n",
    "                palette='prism',x_jitter=True, y_jitter=True, ax = ax)\n",
    "ax.set_title(\"Chance of Admittance vs. CPGA colored \\nby University Rating\")\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRE & TOEFL Scores and Research \n",
    "\n",
    "Overall GRE score percentiles are higher for those students with research than that of students who do not have research. \n",
    "From the boxplot we can see that the highest GRE scores belong to students with research experience and they also are highest in chance of getting admitted. We see a similar situation with TOEFL scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>research</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>181.0</td>\n",
       "      <td>309.491713</td>\n",
       "      <td>8.774723</td>\n",
       "      <td>290.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>219.0</td>\n",
       "      <td>322.853881</td>\n",
       "      <td>9.808563</td>\n",
       "      <td>293.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean       std    min    25%    50%    75%    max\n",
       "research                                                                \n",
       "0         181.0  309.491713  8.774723  290.0  303.0  310.0  315.0  339.0\n",
       "1         219.0  322.853881  9.808563  293.0  317.0  324.0  329.0  340.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['research']).gre_score.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n6j//+Y9+8YtfSJIGDx6su+++W5mZmbrmmmv0q1/9SjabTZmZmXr44YcTcplYAEB8sRnNx3wCAAAAAABEGFMwAAAAAACA6WhAAAAAAAAA05nWgFi9erUuu+yyg65/7733dOGFF2rixIl6+eWXzbp7AAAAAAAQR0wJoXziiSf0+uuvh9b8blBbW6s5c+bolVdeUUZGhn7xi18oJydHvXv3NqMMAAAAAAAQJ0wZATFgwAA99NBDB13/3//+VwMGDFDPnj2VlpamE088Uf/85z/NKAEAAAAAAMQRU0ZAnH322dq8efNB11dWViorKyt0+ZBDDgmtTd2WU045RUcccUREawQAAAAAAF3zzTffaMWKFWHd1pQGRGsyMzO1Z8+e0OU9e/Y0aUi05ogjjtCSJUvMLA0AAAAAAHTQBRdcEPZto7oKxqBBg1RRUaGdO3eqpqZG//znP/WTn/wkmiUAAAAAAIAYiMoIiDfeeENVVVWaOHGipk2bpquuukqGYejCCy9U3759o1ECAAAAAACIIdMaEP379w8tszl+/PjQ9aNHj9bo0aPNulsAAAAAABCHopoBEUm1tbXavHmz9u7dG+tSOqRbt27q37+/UlNTY10KAAAAAABRY9kGxObNm5WVlaUf/vCHstlssS4nLIZh6LvvvtPmzZt11FFHxbocAAAAAACiJqohlJG0d+9e9erVyzLNB0my2Wzq1auX5UZtAAAAAADQVZZtQEiyVPOhgRVrBgAAAACgqyzdgIhHo0eP1r59+2JdBgAAAAAAcYUGBAAAAAAAMJ1lQyhbsmTJEr366qsKBoO67LLL9Oyzz8put+vEE0/ULbfcopUrV+ree++Vw+FQjx49dN999yk9PV0zZsxQRUWFgsGgbrjhBp1yyil655139MILL4T2/cADD+iLL77Qfffdp9TUVF1yySXq2bOnHn74YUnS0KFD9Yc//EGSNHPmTG3evFmS9PDDD6tnz57RPxgAAAAAAMSRhGpASFKPHj00Z84cud1uvfrqq8rIyNCtt96q5cuX66OPPlJeXp6uuuoqvffee9q1a5fef/99OZ1OzZ49W36/X5deeqneeustffXVV3r88ceVkZGhu+66Sx999JH69u2rffv2afHixQoEAhozZowWL16sXr166eGHH9bWrVslSRdeeKFOOukkTZs2TcuXL9e4ceNifFQAAAAAAIithGtAHHXUUfr666/l8/l07bXXSpL27NmjTZs2KT8/X8XFxbr88svVt29fnXDCCfr888+1cuVKrVmzRpIUCATk9/vVq1cv3XbbbTrkkEPk9Xo1fPjw0P4lye/3q0ePHurVq5ckaerUqaEajjvuOEnSD37wA1a8AAAAAJBQysrKVFpaKr/fL0lyOp0aM2aMcnNzY1zZAVao0QoifRwTrgFht9vVv39/HXbYYXrqqaeUmpqqJUuW6Ec/+pHeeOMNTZgwQbfddpsee+wxvfzyy8rOzla/fv2Un5+vvXv3asGCBXI4HHrwwQf1/vvvS5KuvPJKGYYR2r8k9erVS7t27dLOnTt16KGHqrCwUOedd54kVroAAAAAkPgan5TGKyvUaAWROo4J14CQJJfLpSuuuEKXXXaZ6urqdMQRR+icc85RTU2Npk2bpu7duys1NVWzZs1S37599fvf/16XXnqpKisr5Xa7lZmZqREjRmjChAnq3r27evTooe3bt6t///6h+7Db7ZoxY4amTJkiu92uoUOH6vjjj4/howYAAAAA8+Xm5io3N1cFBQWSpKKiohhXdDAr1GgFkT6OCdWAuOCCC0I/n3/++Tr//PObbB82bJiWLFly0O+1dBAfeOCBFu/jlFNOCf185pln6swzz2yy/b333gv9fMstt4RXOAAAAAAACS6hGhAAAAAAAKBriouL5fV6Q5cbfm4YCSFJ2dnZys/P79B+aUAAAAAAQAKzQiBjpGo068Q53pWVlam4uFj79u1TIBBoss3hcCg9PV35+flhH0+v16s1/9ogZbjqrwikSJLWfLm9/nK1r1N10oAAAAAAgCRghUDGrtZo1olzUspwScee0/K2DW93apc0IAAAAAAggVkhkDGiNZpw4mwmn8+nOXPmaPr06XK5XJ3aR8Pxi3f2WBcAAAAAAECyKikp0fr161VSUhLrUkzHCAgAAAAASEDJmodgJT6fTx6PR4ZhyOPxyO12d3oUhBUkZAPirptu1vfbt0dsfz379NGs+X9sdXswGNTMmTO1ceNGpaWlqbCwUAMHDozY/QMAAABARyVjHoLf75eqfK1Ptajyye9PjW5RbSgpKVEwGJRUf15ZUlKiqVOnxrgq845jQjYgvt++XZdt2Bix/S1sZ3tZWZlqamq0aNEirVq1SnPnztWCBQsidv8AAAAA0CkWy0NINuXl5aFVKwKBgMrLy+OiAWGWhGxARNvKlSs1atQoSdLw4cO1bt26GFcEAAAAxEYkAvUQn8yY0hHpfTqdTm3aWdtm0yWeVgHJycnRsmXLFAgE5HA4lJOTE+uSJJl3HGlAREBlZaUyMzNDl1NSUkIvIAAAACCZNA7US+RvcpORGVM6knGaSGNut1sej0eSZLfb5Xa7Y1yRuThDjoDMzEzt2bMndDkYDNJ8AAAAQNJJtkC9pGTGlI4knibicrmUl5enpUuXKi8vL+HfL5wlR8CIESNUXl6ucePGadWqVRoyZEisSwIAAACiLl4D9cxUVlam0tLS+tA+1Q9dHzNmjHJzczu0n9tvv12ff/65amtrQ5kADRwOh1JTUzVkyBDNnj07YrUnrOpG4Ym11fX/pmYc2KY+MSmrNW63WxUVFfE3+qHaJ61dcuAYNkjNkALV6sxxpAERAXl5eVq+fLkmTZokwzD4UAAAAEBSskqgnhk5FY0bEJ2xY8cOVe6pkmx2yTCabKurrdO+mlrt2LGjy3Umuuzs7CaXGzIlso9qOFnuc9BtYs3lcmnevHmxLqOJhmPk9/vl99c12eZ09pDTObBTxzEhGxA9+/Rpd+WKju6vLXa7XbNmzYrgPQIAAADWE6+Bes1FMqciNzdXubm5odDEoqKiTu3HjNA/KyxJGekam4dVdvV5SVYdCRLtiIRsQMya/8dYlwAAAAAkHSsE6kUqp8KMFSGARJeQDQgAAAB0TqTmsyM5WSFQL1I5FStXrtSmzd9I9v2nVEb9Ptes31B/ORgIvY9iJdKjKswYUeF0OrVpy7b6nIGWsgZsnZ/WgvhDAwIAAAAH6ep8diSvuA3U2y+iORV2h9S9lSZLVWIvHxkpZmUNID7RgAAAAEBIpOazI3nFY6BeY5HKqTAjsyHemfGYmaKSXOyxLgAAAAAAosXtdsturz8NitecCiBR0YAAAAAAkDQacipsNlvc5lQAiSohp2DcMu332uGLXOBLb5dT980tbPd2q1ev1n333aeFCyO5CCgAAEDLrBAYaYUakXziPacCXdfw2dN4dZLOfvaUlZWpuLhY+/btC+WHNHA4HEpPT1d+fn7Y+25YQaU+96LpeavT6ZTT6UzYFVQSsgGxw+fXtr5nRG6H2z5o9yZPPPGEXn/9dWVkZETufgEAAMJghcBIK9SI5BHvORWInHj8zPF6vVrzrw2SI0OqrWmyrTKwq35VkASVkA2IWBgwYIAeeuihJuv+AgAAmMkKgZFWqBHotOpGS1I2LCGZmnFgm/rEpCwc+OyJt32FZLjaDPNMVDQgIuTss8/W5s2bY10GAAAAgChovjRkw1D/7KMamg59WD4SaIYGBAAAAAB0UPP5+XE7yodRGogjNCAAAACAOEeYZ9cka+ifKaM0aGh0md/vlyq3SysXSkaw6UabXTKC8vtTY1OcyWhAAAAAABZBmGfnJGvoX6RHaTDtJDJ69+4tv9+v2tpaBQJGk20OR4pSU7upd+/eMaquZT6fT3PmzNH06dO7tHStKQ2IYDComTNnauPGjUpLS1NhYaEGDhwY2v7444/rrbfeUmZmpq6++mrl5ORE9P57u5xhrVzRof2FoX///nr55Zcjdr8AAACARJhnV/n9fslQ/Tf1qS2sWldbfdDICBzMMtNO4tzs2bNjXUKHlZSUaP369SopKdHUqVM7vR9TGhBlZWWqqanRokWLtGrVKs2dO1cLFiyQJG3cuFFvvvmmFi9eLEmaNGmS/u///i+iy1feN7cwYvsCAAAAACBZ+Xw+eTweGYYhj8cjt9vd6VEQpjQgVq5cqVGjRkmShg8frnXr1oW2/fe//9XJJ5+s9PR0SdLAgQO1ceNGDR8+3IxSAAAAgDaRrxC/IvXcOJ3O+mkWtdUHcgsapGZIts5Pa2mosWE6QkFBQedfP9U+ae2SlmsMVIt8BcRCSUmJgsH6rIpgMNilURD2SBbWoLKyUpmZmaHLKSkpCgQCkqRjjjlG//znP1VZWSm/36/PPvtM1dXVre0KAAAAiIqWAgoRH7r63GRnZ+uEocfqyN49lJmR1uS/I3v30AlDj+1ydkFDmGU81wh0Rnl5eeh8PhAIqLy8vNP7MmUERGZmpvbs2RO6HAwG5XDU39WgQYM0efJkXXPNNRo4cKCGDRvW6TeqYRiy2WwRqTlaDMNo/0YAAACIGvIV4leknhszV7doqLGrEm0FDiSOnJwcLVu2TIFAQA6Ho0sZjqaMgBgxYoQ++KA+BHLVqlUaMmRIaJvP55Pf79eLL76oO+64Q1u2bNHRRx/d4fvo1q2bvvvuO0ud0BuGoe+++07dunWLdSkAAAAAALTL7XbLbq9vHdjtdrnd7k7vy5QREHl5eVq+fLkmTZokwzA0e/ZsPf300xowYIBGjx6tzZs368ILL1RqaqoKCgqUkpLS4fvo37+/Nm/erB07dpjwCMzTrVs39e/fP9ZlAAAAAADQLpfLpby8PC1dulR5eXnxtwyn3W7XrFmzmlw3aNCg0M/Nt3VGamqqjjrqqC7vBwAAANZCaGRkxPNxLC4uDoU6SmoS8NggOzubaQthimhQpgn7Q/vKyspUXFysffv2hfIYGjgcDqWnpys/P9+058DtdquioqJLox8kkxoQAAAAgNkanzij8+LxOHq9Xq351wYpY/83rYH6EdNrvtxef7naF6PKrC3Sz3E8vWZgLpfLpXnz5nV5PzQgAAAAYCmERkZG3B/HDJd07Dktb9vwdnRrsbhIBWWatT+0L1GOuSkhlAAAAAAAAI3RgAAAAAAAAKajAQEAAAAAAExHAwIAAAAAAJiOBgQAAAAAADAdDQgAAAAAAGA6GhAAAAAAAMB0jlgXAAAAgMRTXFwsr9cbutzwc0FBQei67Oxs5efnR702IBbKyspUXFysffv2KRAINNnmcDiUnp6u/Px85ebmxqhCwHyMgAAAAEDEeb1erfnXBq35crvWfLldlYEUVQZSQpfX/GtDkwYFEE0+n0+33nqrfD5fXO8TSDSMgAAAAIA5MlzSsee0vG3D29GtBWikpKRE69evV0lJiaZOnRqVfebm5jK6AUmPERAAAAAAkobP55PH45FhGPJ4PBEZsWDGPoFExAgIAACAOFVWVqbS0lL5/X5JktPp1JgxY/gWFQnP7/dLVb7WR8pU+eT3p3Zq3yUlJQoGg5KkYDAYkVEQZuwTSEQ0IAAAAOJc4waERMAj0BXl5eWhEMhAIKDy8vIuNwvM2CeQiGhAAAAAxKmGOeMNjYWioiJJUmlpqdb8a0N9xoIkBVIkSWu+3F5/uZrh37A2p9OpTTtr28wQaWjIdVROTo6WLVumQCAgh8OhnJycLlRq3j6BREQDAgAAwIoiHPBohVEVVqgR8c/tdsvj8UiS7Ha73G53XO4TSEQ0IAAAABBaNjOeR1VYoUbEP5fLpby8PC1dulR5eXlyuVxxuU8gEdGAAAAASCKtBVtKiuioCtNCBC2ytCcBovHN7XaroqIioiMVzNgnkGhoQAAAACSh5sGWMAfHOT65XC7Nmzcv7vcJJBoaEAAAAEmkrWDLSDIzRNAKWjvOAJDM7LEuAAAAAAAAJD4aEAAAAAAAwHRMwQAAIMEka/hdsj7ucLW3hKXX65VsmTGpLVassKynFWpsEPH3YHWjENPa6vp/UzMObFOfrhUMIOpoQAAAkKCSNfwuWR93e7xer75YvVr9AnWSpAy7TZK0e+WnkqTgYKXWAAAgAElEQVSqVIeUlVwNCCss62mFGpuLxHswOzu7yeWGxkv2UQ1Nhz4H3QZA/KMBAQBAgknW8Ltkfdwd0S9Qp6u+39XitsJeTu2Ncj1xwQrLesZ5jc1HaTRWWlqq0tLSDo/SaH5b3tdAYqABAQAAAKDTrDhKA0Bs0IAAACBBWGmuOKLP7/drhyNFT/bs0eL2GpvtwDx7JITWMhn+85//RP6zIs5HaQCIDzQgAABIEHwLCaAlzTMZ+KwAECs0IAAASCR8C4lWOJ1OObxftp0B0bDCABJCa7kopaWlfFYAiAl7rAsAAAAAAACJjxEQAAAAQISQxQIAraMBAQCAxbQWLOf3+6UqX+vDp6t88vtTo1hp8mntucnNzY1xZfW2NgqhrLTbJEmZQUPS/hBKdFmk8xV4XwNIJDQgAACwqObBcogf8fjcZGdnN7m8Y/8384ftv76716vKqFeVoMhXAIAW0YAAAMBi2gqW27Szts0Tn3g6IU5ErT038aD5kP/mNRYUFBz4lh5xw+l0xv37mlEaAMJFCCUAAAAAADAdIyAAAAAAdJoVRmkAiA80IAAA6IB4DxlMRqw60DENr+HGx2nMmDExrio2mDoQ/1p7vfKZC1gTDQgAADohHkMGk1WkVx1IFrx2YSW8XoHEQAMCAIAOiOeQwaTGqgNha3gNN1daWhqDamKLqQPxr7XXKwBrMiWEMhgM6q677tLEiRN12WWXqaKiosn2J598UhdccIEuvPBCeTweM0oAAAAAAABxxJQREGVlZaqpqdGiRYu0atUqzZ07VwsWLJAk7dq1SwsXLlRpaamqq6v185//XHl5eWaUAQAAgDBZIQ/BCjV2VFlZmYqLi7Vv3z4FAoEm2xwOh4LBoJSaGaPqACCyTBkBsXLlSo0aNUqSNHz4cK1bty60LSMjQ4cffriqq6tVXV0tm81mRgkAAAAAACCOmDICorKyUpmZBzq1KSkpCgQCcjjq7+6www7Tueeeq7q6Ok2ZMsWMEgAAANABVshDsEKNHdVexkFBQcGBQFUAsDhTGhCZmZnas2dP6HIwGAw1Hz744ANt375d7777riTpqquu0ogRI3TCCSeYUQoAAAAQNVaYJmKFGgEkJlMaECNGjFB5ebnGjRunVatWaciQIaFtPXv2VLdu3ZSWliabzaasrCzt2rXLjDIAAAAA66tu1Cyora7/NzXjwDb1iUlZANBRpjQg8vLytHz5ck2aNEmGYWj27Nl6+umnNWDAAJ111ln6+9//rksuuUR2u10jRozQ6aefbkYZABDXysrKVFpaWv9NlOqHFo8ZM4blxiIsHo5zPNQAWElr75l42V9bIj1NJDs7u8llr9dbf/1RDU2HPgfdJto1AkC4TGlA2O12zZo1q8l1gwYNCv38u9/9Tr/73e/MuGsAsJzGfxDDPPFwnOOhBsBKIv2eseJ7MD8/v8nlgoICSVJRUVEsymkdozQAhMGUBgQAoH0NwWNx+8dkgoiH4xwPNaB1Pp9Pc+bM0fTp0+VyuWJdDtT6e6a0tDQu9oemzBilAWvg8xMdRQMCAAAktZKSEq1fv14lJSWaOnVqrMsJCyGCiCeWGaWBiLPi5ydiiwYEAABIWj6fTx6PR4ZhyOPxyO128y1eAikuLg59Gy8d+Ga+4QTZ6/VKtswWfxdA2/j8RGfQgACAOEVwIWC+kpISBYNBSfXLhpv9LV6k3tfxFCLYZsBjjHMBvF6vvli9Wv0CdZKkDLtNkrR75aeSpKpUh+SoIbsgQhpeC40bPfx/K3FF+/MTiYEGBADEOSuGpgFWUV5erkAgIEkKBAIqLy+Pyh/Qifi+bv6Y4iUXoF+gTld93/KS70WuQxXslhaqieyCyEik1zVaF6vPT1gbDQgAiFMEF6JTkiCJvnnoWVfyEHJycrRs2TIFAgE5HA7l5OR0qqb2hvpL9Sfk+fn5Cfe+bu2zqvm33hF7zB14jfv9fu1wpOjJnj1a3FWNzabeTmeopkR5XmI19aThtYDkEKnPTyQXGhAAACSIePnG2WyRDD1zu93yeDyS6pcRd7vdndpPe0P9tzpSulQn6iXLa7yrwpp6kkX2BbomUp+fSC40IAAASBDJkETfUuhZV/IQXC6X8vLytHTpUuXl5XUpQK2tof6tfQOPjunoa9zpdMrh/bLN5yUrQacLtPV6LOzl1N4o14PEE8nPTyQPGhAAOoxwxPjE8xIZsTiOifjcmfWYWgo96yq3262Kigq+veugjkw7AZCY+PxER9GAANBpiRiilgh4XiIjFscxEZ+7SD+mlkLPBg0a1KV9ulwuzZs3LxLlJRWrTDvZ2igDonJ/jZlBI7QtK2aVAdbH5yc6igYEgA4jHDE+8bxERjSPY/NvkBsrLS1VaWmpZb9BNus4thR69vXXX0dk3+i4eJ920jwPYsf+99th+6/PauE2AADz0IAAACBGvF6v1vxrg5Sxf95soP4b4zVfbq+/XO2LUWXxq6XQs7lz58a4KsSrZMhFAQAroQEBAGhVpOfxJ2LWQVf4/X7JaHRFw1KCDYwDUxhQj9Azk0VwicstjhQF4uT12/DZ0zinIpk/ewAgVmhAAADaFel5/ImYdYDoIfTMHMmwxCWfOQAQWzQgAACtivQ8fnIqmurK8pHJjNAzcyTyEpcNnz0AgNiiAQEASEgsEQgra+/16/V6pb01YU+XAAAgHtCAAAAkJAIeYWXtLXG5N9WhzMzM0PSIRJwuYWkdyNIAgGRCAwIAkkRSBkBmuNqc3oADkvL1EefaW+IyKzs7ND2CaU3xwzJZGjRJAMQADQgASDIEQKItvD6ArrHC0p9phiFHtzRG0ACIOhoQAJAkCIBEWyz/+uDbXCBsveqCjKABEBM0IAAAgKVZZsg7AABJjgYEAACwNCsMeQcAADQgACDusHwkkoEVQi87UqPf79cOR4qe7NmjxX1tSnUoZePGpstoquvv64YaG+8v3o4jIoPnGkAioAEBAHGG5SMTR3vNJK/XK9kyY1JbvLBC6GUkagxKCtTWHXgfR/h9Hc/HD5HFcw3AymhAAEA8YvnIqDNj5InX69UXq1erX6BOkpRht0mSdq/8VJJUleqQshKrAdGR4xjvoZcdCeZ0Op1yeL9sddnMwl5O7T3kBxF/XzfUiMTHcw0gEdCAAABA7TcLtjpSOrXffoG6tk9KO7XX+GXWcQQAANZHAwIAwmCF+erouraaBUWuQ+X1eiM+h78jwh1dMHjw4JjOFW/rOLaWkRCO9t6H8TxHvk6Sqnytj3So8snvT41mSRGxtVHuReX+ZlNm0AhtyzLpfuP5uW5ghRoBINrCakAsXrxYF198cejyc889p1/+8pemFQUA8coK89VhjhqbTXv31pg2hz8c4Y4uGDx4sKTEfZ229z5M1Mcdb5ovbbpj/4n2Yfuvz2rhNpFmhefaCjUCQLS02YB488039d5772nFihX6xz/+IUmqq6vTF198QQMCQFLpyFxwHCxhVvaIg2yOcEYXJOpc8fbeh/H8uFMk1XZv+/VjtRPVWC5/Gs/PdQMr1GglPp9Pc+bM0fTp0+VyuWJdDoBOarMBMWrUKPXu3Vs7d+7UxIkTJUl2u11HHnlkVIoDAHRdPJz8kwsAAOiKkpISrV+/XiUlJZo6dWqsywHQSW02IKqrq3XKKaeoT58+Ta6vqqoytSgAQOTEy7KeZuUCAAASm8/nk8fjkWEY8ng8crvdjIIALKrNBsTTTz+t6dOn66677mpyvc1m03PPPWdqYQBgBWVlZSouLta+ffsUCASabHM4HEpPTw8tNxhTcTB1ANYWbhCr3+/XjkbBhM1tcaQosH8fAMyRaO/DkpISBYNBSVIwGGQUBGBhbTYgpk+fLklauHBhVIoBAMSHeJi2gfiUDEGsvP6B+FJeXh5q8gcCAZWXl9OAACwqrFUw7r//fr366qtNrvvoo49MKQiwIoKRkleihoyR2RAZ7X0LWWOzSbXVUa6qc8INYnU6nXJ4v2xzOVO/3x/T5UzbEy/TloDOau99+GTPHsqyUBMxJydHy5YtUyAQkMPhUE5OTqxLAtBJYTUg3n//fb333ntKS0szux7AkghGQiIiswFmiIflTMPCtCUgbrjdbnk8Hkn1gfhutzvGFQHorLAaEEOHDtW+fftoQAAtIBgJQGva+xaysJdTe1MzolxVHODkHkAHuFwu5eXlaenSpcrLy+PvLMDCwmpAHH300Ro5cqR+8IMfyDAM2Ww2vfvuu2bXBlgCwUjxKdzAPESHmYForT3Xfr9fqvK1fkJb5ZPfn9qp+4S13X777fr8889VW1vbYnhsIBCQUjMjf8fVPmntkoOn3aRmSIFqSQdWHUu0EEHEp4bPz8ZToeL1/5Vut1sVFRWMfgAsLqwGxNKlS/Xuu++qRw+G3ALNEYwU35IhMA/1eK4Rrh07dqiqslIpkoxm24J1dQpKUoR7U2mGIUe3NDmdPeT31zXZ5nT2kNM5UNnZ2ZG9UyBMVvjcdLlcmjdvXqzLANBFYTUgDj/8cGVkZDAFA2gBwUjxKdzAPESHmYForT3XpaWl2rSzts2h/lb4o9tsybjig1lTY7Y2GrFQuT+4NTNY3+Kotdl0dHZ22J9FiRYiGK5kfD3GUqIGKQOIX2E1ILZu3aq8vDwdeeSRkiSbzaaXXnqp1dsHg0HNnDlTGzduVFpamgoLCzVw4EBJ0r///W/Nnj07dNtVq1bpkUce0RlnnNGVxwHEDMFIiLX2/mD3er2SzYTh5EgIrHgSGc1HL+zY/z48bP/1WS3cBgfj9QgAiS3sZTg7oqysTDU1NVq0aJFWrVqluXPnasGCBZKkH/3oR1q4cKEk6e2331afPn1oPsDSCEZCrLX3B3tVqkPKogGB1rHiSdc1/0ae0Vedx+sRABJXmw2Ihx9+uNVtbc1xX7lypUaNGiVJGj58uNatW3fQbaqqqvTQQw/p+eefD7dWIG4RjIRI6kx4Ylt/sP+hl1PBBAxjjPTIj/ZC/2pstoPDAwFYKsiwNZ15DIQdA0DHtdmA+MEPfiCp/gO2f//+GjFihNauXastW7a0udPKykplZh74oy8lJSU0P77BK6+8orFjx/JtMdrl8/k0Z84cTZ8+PW5fLwQjAdHHyA8gviRCpkpnHgMBuAAQvjYbEJMmTZIkeTwezZw5U5J03nnn6corr2xzp5mZmdqzZ0/ocjAYbNJ8kKQ33nhDDz74YGdqRpIpKSnR+vXrWd4SScPpdEY0PDFFUm13V0KGMbY18qOwl1N7O7Avs4IJYT11Eku4dkAiBBl25jEQdgwAHRdWBoTf79fXX3+tAQMGyOv1qrKyss3bjxgxQuXl5Ro3bpxWrVqlIUOGNNm+e/du1dTU6LDDDut85UgKPp9PHo9HhmHI4/HI7XbH7SgIIFyERgIdw8oIAAAkhrAaELfffrtuuukmbdu2Tb179253qHleXp6WL1+uSZMmyTAMzZ49W08//bQGDBigs846S19++aWOOOKIiDyAZJGs8wxLSkoUDAYl1Y+kYRQEos2MEx+mDiCZdWZ0QbRXRkjkUUPhSIRMBwBAfAqrAXHSSSfplVdeCV2ura1t8/Z2u12zZs1qct2gQYNCP59wwgl69NFHO1In9ku2eYbl5eUKBAKSpEAgoPLychoQiCqzTnwiOXUASAasjBB9yfK3BgAgesJqQLz00kt6+umnFQgEZBiGHA6HSktLza4NjSTrPMOcnBwtW7YsFGKak5MT65KQhDjxASIn2UcXWEEiZDoAAOJTWA2Il19+WQsXLtSCBQs0duxYPfvss2bXBUiqX97S4/FIqh9ZwzKXAIBksbXRsrCV+0dfZQaN0LasmFUGAEDnhNWAcDqd6tOnj/bs2aNTTjmF1SsQNS6XS3l5eVq6dKny8vIIoAQAJIXs7Owml3fsz2M4bP/1WS3cBgCAeBdWAyIrK0tlZWWy2Wx66aWX5PP5zK4LCHG73aqoqIjI6Acrh3k2hCH6/f5Q/Q2cTqecTqdlU+Aj+byQlh8Zfr9fOxp9+9rcFkeKAs1eh0hM36XYtcPrbbpKi3hPma358Uy2KZgAgMQUVgOisLBQX3/9tW6++WY99dRTmjlzpiSppqZGaWlpZtYHyOVytbvySkdZMczT6/Vqzb82SI4MqbamybbKwC5t2rItRpVFTiSel2in5QOJrsZm0969NVrz5fb6KwL176HQ5Wq+lAAAAOEJqwGRmZmpoUOHSpKmTZsWuv7qq6/Wc889Z05lgAksH+aZ0XZwm1VF+nkhNLLrnE6nHN4v2zyOWRZq4IWrrTn3NTZbzOqKuQT97AEAANEVVgOiNYZhRKoOAABiqr059929XlVGvSoAAIDE0aUGhC2Zvw0CACSU9ubcFxQUHJh2AAAAgA7rUgMCQOS1Fsjo9/ulKl/rw52rfPL7U6NYKeJFe4GRNTabVFsd5aqQzCI9lcWMUNR2a6xu9Hnb8P5Jzaj/t9onqU/HHgQAAGAKBhCvrBiUCQBWmMrSXo2ub7+VJB1+eH2ToWHlj+yjGpoOfVgCEwCATmizAfH888/r0ksv1erVqzVs2LCDtg8ePNi0woBk1VogY2lpqTbtrG0zCI5mRXJqLzCysJdTexu+uQVMZsZUlkiHonZ0iUvLBhcjqbU1ymerI0VZMasMQDJrswGxaNEi9e/fX/fff79uvfXWJttGjhypGTNmmFocAAAAgI5pb5RPVgu3AYBoaLMBcf3116usrEzfffed3nrrrSbbRo4caWphAJDUkmz+eaXdpq/Xr9f48eMVCASabHM4HEpPT1d+fr5yc3NjVCEQnoYcn4ZpGwUFBRozZgyvXURVR0f5AEC0tNmAaBgK/t5772n06NHy+Xw69NBDZbfbo1UfACSdNMOQo1taaL45888B62FKHAAABwsrhLJ79+4666yzlJWVpV27dunuu+/W6aefbnZtAOJMcXFx6GRYUpNv+BpkZ2cf9M0LOqZXXVBZ2dlN5sxLif3NVWbQ0GE//nFCP0Z0jVXmszd8edMen8+nOXPmaPr06XK5XFGoDACA2AurAfHAAw+opKREffv21bZt2zR16lQaEECEtXdy7/V6JVtmTGpr4PV69cXq1eoXqJMkZew/Cdi98lNJ9ScBHUFDA0A4EnE+e0lJidavX6+SkhJNnTo11uUAABAVYTUgUlJS1LdvX0lS3759lZ6ebmpRQDJq7+S+KtUhZcW2ASFJ/QJ1bSbRd0SkGxoAElOizWf3+XzyeDwyDEMej0dut5tREACApBBWAyIzM1MLFy7UT3/6U33yySfq2bOn2XUBCaEhjMzv90uqnxPcVhhZWyf3hb2c2huFGqItkg0NSfL7/drRaKh2c1scKQrsPxawtvae6xqb7UCAZ4Iw4/VtpeOYKAGPJSUlCgaDkqRgMJjwoyAS5XkDAHRdWGmS8+bN07fffqv7779fW7Zs0ezZs82uC0gofr8/1ABI5hoAIBKcTqelQx7Ly8tDq70EAgGVl5fHuKLosPrzBgDourBGQGRlZem222476PrrrrtOjzzySMSLAqyuebZBY6WlpSotLY1atkFDIJrVhyyHy+l0yuH9ss1RFVn8AZwQ2nuuC3s5tTdQHf/LmXZgyVUzXt9hHceGemIs3IDHeJeTk6Nly5YpEAjI4XAoJycn1iWZKlGeNwBA14XVgGjNrl0t/7ECJDuv16s1/9ogZeyf0xuozzJY8+X2+svVvhhVBiQPKyxnaoUaEXlut1sej0eSZLfb5Xa7Y1wRAADR0aUGhM1mi1QdQOLJcEnHntPytoZvOwGYxgrLmVqhRkSey+VSXl6eli5dqry8PAIoAQBJo0sNiERXVlam4uJi7du3LzRXs4HD4VB6erry8/MZVoiD+P1+qcrXeqOhyie/P/Wg37FKEFxrKu02fb1+vcaPH897Jsq2NnrtVO5fTSQzaEja/9pB2FoLzOvM+zoR1EmdetytHUcrMTM80e12q6Kiot3RDwQ4AgASCQ0IALC45kP0d+w/UTls//XdvV5VRr0q6yMsLzIS4Tia8RhcLpfmzZsX0xoAAIi2LjUgEn05TkKT0FlOp1Obdta2OQWj+R+TVgqCa01m0NBhP/4xw8ejrHmYafNh/AUFBQfyR9Cu1j77S0tLO/y+ltoenbLVkaKsDtYX6f21J0VSbfe2p5S19LjbOo5WEQ9/B8RDDQAAREpYDYht27Zp3rx58vv9Ovvss3XMMcdo2LBheuihh8yuD4iI5qtSNB7K2iBaq1IASB7tjU7JauE20dwfAABANIXVgLjzzjt15ZVX6tFHH9VJJ52kadOm6eWXXza7NiBivF6vvli9Wv0CdZKkjP3fGu5e+amk+m8NO6Nhbq7f75dUP4rBanOc0bb2sjm2OFIU2P/8A821Nzol1vtLduQrAAAQXWE1IPbt26dTTz1VCxYsUHZ2ttLT082uC4i4foG6Vqc3tHZyGa7GDQgAgLXw2Q0AQHSE1YBIS0vThx9+qGAwqFWrViktLc3suoC41nxKR2Ohb9NsmVGuCmZoL5vjyZ49lJWgJy/RzhoAoo18BQAAoiusBsTdd9+te++9V36/X0899ZRmzpxpcllAfGtvSkdVqkPKogEB6yJrAABaRq4UAHReWA2I9PR0XXTRRTr99NP1/PPPJ/zqF0A42prSUdjLqb1RrgeIJLIGAKBlZuVKAUAyCKsBcdNNN2nixImS6pfevPXWW/XYY4+ZWhiAFlT7pLVLpNrqptenZkiBakl9YlIWzBEPAXnxUEMi4DgC4bFK8K+ZuVIAkMjCakBUV1dr7NixkqTx48dr8eLFphYF4GBphiFHtzQ5nT3k99c12eZ09pDTOZAh8fslWnZBPATkxUMNiYDjCAAAkllYDYjU1FQtX75cw4YN09q1a2W3282uC0AzveqCysrOZgh8OxIpuyAeAvLioYZEwHEEwpPMwb8AkAzCakAUFhbq3nvvVWFhoQYPHqxZs2aZXRcAdArZBQAAAEB8CqsBMXDgQD366KNm1wIAAAAAABJUWA2I4uJi/fnPf1a3bt1C13300UemFQXzNQSi+fcHOTmdTgLRgFZsdaToAWdP7W42/SwrGFSl3W65TAkAQHIgABdAvAmrAfH222/rww8/VEZGhtn1IMoaNyAAHKwhL8Lv96u6WfJ6N6dThzmdlsmUAAAkJ/7OAxAvwmpAHHHEEU1GP8D6GgLRmB8fXxJt9YZE0DxTAgAAqyAAF0C8CasBUVtbq/Hjx2vIkCGSJJvNpj/+8Y+t3j4YDGrmzJnauHGj0tLSVFhYqIEDB4a2/+1vf9MjjzwiSRo6dKhmzJghm83WlceBMBUXF4eG4UlqMiSvQXZ2NiddMZBIqzcALaHBBgAAkNzCakBcc801HdppWVmZampqtGjRIq1atUpz587VggULJEmVlZWaN2+ennvuOblcLj3xxBPy+/1yuVwdrx4d5vV69cXq1eoXqJMkZew/Cdi98lNJ9ScBiA1Wb0Aio8EGAACAsBoQQ4YM0UcffaRAICDDMLR9+3adfPLJrd5+5cqVGjVqlCRp+PDhWrduXWjbZ599piFDhujee+/Vpk2bdPHFF9N8aIMZIxb6BeraXF8b7fP7/drR6Nvc5mpsNqm2OspVAfGLBpvFVfukDW/X/9zw2ZaacWCb+sSkLAAAYC1hNSB+97vf6Yc//KE+//xzpaentxtGWVlZqczMzNDllJQUBQIBORwO+f1+rVixQq+99pq6d++uyZMna/jw4TrqqKO69kgSFCMWAACxlGYYcnRLU/ZR9U2GhkZ4w2WpD6NXAABAWMJqQEjSrFmzNH36dN1zzz2aPHlym7fNzMzUnj17QpeDwaAcjvq7OvTQQ3X88cerd+/ekqSTTjpJ//73v2lAtIERC/HH6XTK4f2y1eelsJdTe1NZNQaA9fWqCyorOzs0WoXRKwAAoLPs7d+k3r59+1RdXS2bzaaqqqo2bztixAh98MEHkqRVq1aFwisl6bjjjtPnn38un8+nQCCg1atXa/DgwZ0sHwAAAAAAWEFYIyAmT56sZ555RqeffrrOPPNMnXjiiW3ePi8vT8uXL9ekSZNkGIZmz56tp59+WgMGDNBZZ52lm2++WVdffbUkaezYsU0aFLCusrIylZaWyu/3S6ofJTBmzBiWf0og7WVfbHGkKLD/+Y+1htdj49wUXo/JiddCHCJTAgCApBRWA+Lss88O/XzOOec0yXdoid1u16xZs5pcN2jQoNDP5557rs4999yO1AkLadyAAGKN1yEa8FqID83zIsiUAAAgeYTVgFi8eLGeeeYZ7d27N3Tdu+++a1pRsKbc3Fzl5uYyPziBtZd98WTPHsqKk5O8htcjwGshvrAiCgAAySusBsSLL76oxx9/PBQcCQAAAAAA0BFhNSCcTqeOOOIIs2sBAAAAAAAJqs0GxPz58yVJNTU1uuqqqzR06FDZbDZJ0k033WR+dUAc29oojLHSXv++yAwakqSa/e8TIJpaC1uUROgfAAAAYq7NBsRRRx0lScrMzFR6erp69Oih+fPn61e/+lVUigPiVfOAtB37T/gO2399d69XlVGvCqjXPGyR0D+0p62G6lZHirJiVhkAAEgkbTYgJkyYIEm66KKLNHfuXA0ePFgnnXSSpk2bpiuvvDIqBQKREOnlI9sLUSsoKNCaL7d3slqgc1oLW2x+HaF/aKy9hmpWC7eJNZ/Ppzlz5mj69OlyuVyxLgcAAIQprAwIh8OhwYMHS5KOPPJI2e12U4sCAADRYcVVKUpKSrR+/XqVlJRo6tSpsS4HAACEKawGxOGHH6758+dr+PDhWrNmjfr0Ya4wrMVKy0cCAFrn8/nk8XhkGIY8Ho/cbjejIAAAsIiwGhBz5szRiy++qL/97W8aNGiQfvOb35hdF5C0WgsSbGlofVuKi4tD+5DUZH8NsrOzD/r2E7ASsguST0lJiYLBoCQpGAwyCgIAAKVAjowAACAASURBVAsJqwGRnp6uK664wuRSADTWPEiwo7xer75YvVr9AnWSpIz9J2e7V34qqf7kDLAyK2YXoOvKy8sVCAQkSYFAQOXl5TQgAACwiLAaEACip7Ugwc7oF6hrc9oJYGVWzC5A1+Xk5GjZsmUKBAJyOBzKycmJdUkAACBMpEkCAADLcLvdoTBsu90ut9sd44oAAEC4GAEBdEFreQ3xINJLjwKQVO2TNrxd/3Ntdf2/qRkHtilxQ5ojlU/TVS6XS3l5eVq6dKny8vIIoAQAwEJoQAAR0NW8BgDxr3meRMOJePZRDU2HPkmROREPn3dut1sVFRWMfgAAwGJoQABd0FpeQ2lpaQyqaYqlR4HISvbMiUjm03SVy+XSvHnzYl0GAADoIBoQABBlTI8BAABAMiKEEgAAAAAAmI4REAAQZUyPAQDrYhQbAHQeIyAAAAAAAIDpGAGRZOjaAwAAdB6j2ACg8xgBAQAAAAAATMcIiCRD1x4AAAAAEAs0IBC3ysrKVFpaKv/+KSFOp1NjxoyJm3XoYb5Ku01fr1+v8ePHKxAINNnmcDiUnp6u/Px8XhMW0vC+9nq9kqSCggLe153AcQQAAFZEAwJdVlxcHPojWFKTP4gbZGdnKz8/v0P78/v98vv9qq6ullSfX7Fo0SKVlpZ2aH8A4o+TkVYRwXEEAABWQgMCXeb1evXF6tXqF6iTJGXYbZKk3Ss/lSRtdaR0en+9Vf8tuCRl7t0n+XfqC8fXkSsecS0zaOiwH/9YRUVFsS4FEZKbm8u39BHAcUQi29ooLDv0N0DQCG3LilllB1ihRgCIRzQgEBH9AnVt5krEen8AACD+ZWdnN7m8Y/+oysP2X5/Vwm2izQo1AkC8ogEBAACAuNB8emXDdM54GglnhRoBIF7RgAAAIMYIlQQAAMnAHusCAABAPafTSbAkkGB8Pp9uvfVW+Xy+WJcCADHHCAgAAGKMUEkgcZWUlGj9+vUqKSnR1KlTY10OAMQUIyAAAAAAE/h8Pnk8HhmGIY/HwygIAEmPERBxzu/3a0ejpZ6a2+JIUcDvj3JV5krGx5yseK4BAFYVTnZLSUmJgsGgJCkYDDIKAkDSYwQEAAAA0EltZbeUl5crEAhIkgKBgMrLy6NZGgDEHUZAxDmn0ymH90td9f2uFrc/2bOHshIssCwZH3Oy4rkGAFhVONktOTk5WrZsmQKBgBwOh3JycqJUHQDEJ0ZAAAAAACZwu92y2+v/3Lbb7XK73TGuCABiiwYEAAAAYAKXy6W8vDzZbDbl5eXJ5XLFuiQAiCmmYCBpbG0Udlhpt0mSMoNGaFtWzCqzFo4jAADhc7vdqqioYPQDAIgGBJJEdnZ2k8s79idWH7b/+qwWboODcRwBAOgYl8ulefPmxboMAIgLNCCQFPLz85tcLigokCQVFRXFohzL4jgCAAAA6CxTGhDBYFAzZ87Uxo0blZaWpsLCQg0cODC0vbCwUJ9++qkOOeQQSdKjjz6qrCwGbgMAAAAAkKhMaUCUlZWppqZGixYt0qpVqzR37lwtWLAgtH39+vX685//TBAPAAAAAABJwpQGxMqVKzVq1ChJ0vDhw7Vu3brQtmAwqIqKCt1111363//+p4suukgXXXSRGWUAsVXtk/5/e/caHVV1sHH8mckkEQVZiWiFFlwJiqIUBVFbFTQlicplIQoCgSKgKHcRNNxFEEKRoi2gy4KIFhpFrZcKoknaKF4QFRSWIKBcgoWqSMIl3JLJ7PcDZt4AZ+DMnByTSf6/L5rbk71P5iHJzj57Nq04/v+lR47/N7bO/79NF7g+BA6MBCpfXl6ecnJytO3nM1AyMzOVnp6u1NTUKh4ZAABA9ebKAkRxcbHq1q0bfDkmJkZ+v18+n0+HDx9Wnz591L9/f5WVlalv375q0aKFLrvsMjeGAlSJkw9iLP9FJTmpfNHhAtcPa+TASMBdCQkJVT2EqFBYWKgZM2Zo3Lhx7HwEAKCWc2UBom7dujp06FDw5UAgIJ/v+KeqU6eO+vbtqzp1jv8l+He/+502bdrEAgRqlOpwWGN1GANQE6WmprLbIQzZ2dnasGGDsrOzNWzYsKoeDgAAqEJeN0Jbt26tlStXSpK+/PJLNWvWLPi2HTt2KCMjQ2VlZSotLdXatWt1xRVXuDEMAKi2ym+PWVj/XP01ob7+mlA/+PL3vpiqHh5QKQoLC5WbmytjjHJzc1VYWFjVQwIAAFXIlR0QaWlp+uijj9SzZ08ZY5SVlaVFixapSZMmat++vTp37qy77rpLsbGx6tKliy655BI3hlFlyu8PLioqknR8my73BwMox+0xqC2ys7MVCAQkHd8NWXEXBGdpAABQ+7iyAOH1ejV16tQTXte0adPg/w8cOFADBw5041NXKxUXIACgHLfHoLbIz8+X3++XJPn9fuXn559yGwbfIwEAqD1cWYCo7crvD+aXCqDmqa0H6tXWecOZlJQUvfvuu8GDqFNSUoJv4ywNAABqH1fOgED1xr3nQOQqHqhXm9TWecOZjIwMeb3Hf9Twer3KyMio4hEBAICqxA6ISvTMM88E72WVdMJ9reWSk5NP2X79S+LecyByJx+ol5GRUSt2A9TWecO5xMREpaWl6e2331ZaWhqPGwAAajkWICrRtm3b9M26dbrQXyZJquP1SJIOrlkrSdVidwH3ngORO92BejVZbZ03KkdGRoYKCgrY/QAAALgFo7Jd6C/TPfsP6J79B/RA0X49ULQ/+HL5wgSA6GR1oF5tUFvnjcqRmJioWbNmsfsBAACwA6IyFRUVac/P5ytY+Z8vRv6fnxmjJqmt80btc7oD9Wqy2jpvAAAAVC52QACATbX1QL3aOm8AAABULnZAVKKEhAT5tm3XPfsPWL59Yf1zVa8GPt+5G/P+vsKOiuKfz9KoGzDBt9VzMF4gUrX1QL3aOm8AAABUrqhbgMjLy1NOTo6Kft7Sn5CQoPT0dJ5LPEzl17HiM3VUl+vIM3WgOqvMA/Wqcw9PxkGCAAAAcCrqFiDKVVyAqE7c+Mu9m7sBqtv1k3imDlRv5QfqVabq2MOTuTFvAAAA1C5RswDxzDPPBP9KeLKcnBzl5OQoOTn5lF9ef0lu/OXerd0Aqamp1fKvrEBtQg8BAABQm0TNAsS2bdv0zbp1waeyrPPzToCDa9ZKOr4ToKq58Zd7dgMAAAAAAGqCqFmAKCoqkqnwcvktCOWM/v+2jKrmxn3d0XSveGWpjXN2A9cR5XgsAAAAoCpFzQJENHLjvu5ouFe8stXGObuB64hyPBYAAABQFaJmASKanuLSjfu6a+O94rVxzm7gOqIcjwUAAABUpahZgJDcfTYIAAAAAADgnqhZgHDr2SAAAAAAAID7omYBovzZIMoPUauIQ9QAAABqDg7NBYCaKWoWIE7GIWoAAAA1Gz/vAUDNEnULEByiBhxXWFioGTNmaNy4cUpMTKzq4QAAUGn4eQ8AaqaoW4AAcFx2drY2bNig7OxsDRs2rKqHEzEOlwUAAABqBxYggChUWFio3NxcGWOUm5urjIyMqNwFweGyAAAAQO3BAgROkJeXp2eeeUbHjh2T3+8/4W0+n0/x8fEaNGhQlW6LjIYxui07O1uBQECSFAgEonYXRPnhsuUyMzMlSY8//nhVDAcAAACAi7xVPQAA4cvPzw8uvvj9fuXn51fxiAAAAADg9NgBgRNEw6FP0TBGt6WkpOjdd9+V3++Xz+dTSkpKVQ8JAAAAAE6LHRBAFMrIyJDXe7y+Xq9XGRkZVTwiAAAAADg9FiCAKJSYmKi0tDR5PB6lpaVF5QGUAAAAAGoXbsEAolRGRoYKCgrY/QAAAAAgKrAAAUSpxMREzZo1q6qHAQAAAAC2cAsGAAAAAABwHQsQAAAAAADAddyCAQCIanl5ecrJydG2bdskSZmZmUpPT6/1T9cLAABQ3bAAAQCoERISEqp6CAAAADgNFiAAAFEtNTWV3Q4AAABRgDMgAAAAAACA61iAAAAAAAAArmMBAgAAAAAAuI4zIFApvvfFaGH9cyVJxV6PJKluwATfVq/KRgYAAAAAqA5YgIBjycnJJ7y85+enwmv48+vrWbwPAAAAAKB2cWUBIhAI6NFHH9XmzZsVFxenadOm6aKLLjrlfe677z61b99evXr1cmMY+IUMGjTohJczMzMlSY8//nhVDAcAAAAAUA25cgZEXl6eSkpKtHTpUo0ePVp/+tOfTnmfv/zlL9q/f78bnx4AAAAAAFQzrixArFmzRm3btpUkXXXVVfrqq69OePs777wjj8ejdu3aufHpAQAAAABANePKAkRxcbHq1q0bfDkmJkZ+v1+StGXLFi1btkwPPPCAG58aAAAAAABUQ66cAVG3bl0dOnQo+HIgEJDPd/xTvfHGG/rhhx909913a9euXYqNjdWvf/1rdkMAAAAAAFCDubIA0bp1a+Xn56tDhw768ssv1axZs+Dbyg8olKS5c+eqQYMGLD4AAAAAAFDDubIAkZaWpo8++kg9e/aUMUZZWVlatGiRmjRpovbt27vxKQEAgAN5eXnKycnRtp+fSjkzM1Pp6elKTU2t4pEBAICawpUFCK/Xq6lTp57wuqZNm57yfsOHD3fj0wMAgAglJCRU9RAAAEAN5coCBAAAiC6pqansdgAAAK5y5VkwAAAAAAAAKmIBAgAAAAAAuI5bMFCrcMha9cTXBQAAAKj5WIBArcQha9UTXxcAAACg5mIBArUKh6xVT3xdAAAAgJqPMyAAAAAAAIDrWIAAAAAAAACuYwECAAAAAAC4jgUIAAAAAADgOhYgAAAAAACA61iAAAAAAAAArmMBAgAAAAAAuM5X1QMAarK8vDzl5ORo27ZtkqTMzEylp6crNTW1ikfmTE2dFwAAAAD3sAAB/AISEhKqegiuqKnzAgAAAFD5WIAAXJSamlojdwXU1HkBAAAAcA9nQAAAAAAAANexAAEAAAAAAFzHAgQAAAAAAHAdCxCo9goLC/Xwww+rsLCwqocCAAAAAIgQCxCo9rKzs7VhwwZlZ2dX9VAAAAAAABFiAQLVWmFhoXJzc2WMUW5uLrsgAAAAACBKsQCBSpOXl6fMzExt27ZN27ZtU2ZmpvLy8hxlZmdnKxAISJICgQC7IAAAAAAgSrEAgUqXkJCghISESsnKz8+X3++XJPn9fuXn51dKLgAAAADgl+Wr6gGg5khNTVVqamqlZqakpOjdd9+V3++Xz+dTSkpKpeYDAAAAAH4Z7IBAtZaRkSGv9/jD1Ov1KiMjo4pHBAAAAACIBAsQqNYSExOVlpYmj8ejtLQ0JSYmVvWQAAAAAAAR4BYMVHsZGRkqKChg9wMAAAAARDEWIFDtJSYmatasWVU9DAAAAACAA9yCAQAAAAAAXMcCBAAAAAAAcB0LEAAAAAAAwHUsQAAAAAAAANexAAEAAAAAAFzHAgQAAAAAAHAdCxAAAAAAAMB1LEAAAAAAAADXsQABAAAAAABc56vqAdixa9cu3XHHHVU9DAAAAAAAUMGuXbtsv6/HGGNcHAsAAAAAAAC3YAAAAAAAAPexAAEAAAAAAFzHAgQAAAAAAHAdCxAAAAAAAMB1LEAAAAAAAADXRcXTcJYrLS3V+PHjtWvXLpWUlGjw4MG68MILNXnyZMXFxal58+aaMGGCvF6v5s2bp/fee08+n0/jx49Xy5YtHWc++eST+vjjj+XxeDRx4kTLzHDyJOnIkSPq2bOnRo8erXbt2jke46BBg7Rv3z7FxsYqPj5ezz77rKO81157TS+++KLKysrUvn17DR061NEYP/zwQy1YsECSZIzRmjVrtGzZMjVt2jTiMc6YMUNr1qyR1+vVmDFjdPXVVzu+jtOmTdPatWt1zjnn6KGHHtKVV155Sl5ZWZkmTpyo7du3KyYmRjNmzJAxRmPHjpXH49Ell1yiyZMnh/V4DCdTkgoKCjR06FAtW7bMcd7MmTO1du1a+f1+9ejRQ3fddZfjTDudCXfOdjoTTqadzoSTZ7czdjPtdiacMdrtTDiZkXamSZMmkqSsrCwlJSWpV69ekqSXX35ZL730knw+nwYPHqyUlBTbYwyVKUmFhYXq2bOn3nrrLcXHxzvKe/7557V8+XJJ0k033aRhw4Y5HuM//vEPvfbaa/J4PBo6dKjlvMOdcyAQ0H333af27duf8PpIMyt+rSXp6aefVr169SLOe//99/XUU09Jki6//HJNnjxZHo8n4jF+/fXXysrKCn7cl19+qaeeeuqUfy/CGePChQu1fPlyeTweDRo0SGlpaY6v4/z587V8+XLVrVtX9957r+2v9aFDh/TYY48pJiZGcXFxmjlzpho0aOCoM6Eypcg6EyrPSWdCZUbamdPNOdLOhMqMtDOh8px0xipzz549EXcm1BiddCZUZqSdKSkp0aRJk2SM0WWXXaZJkyYpJibGUWdCZUqRdSZUnpPOhMqMtDOnm3OknQmVGWlnQuU56YxV5pYtWyLuTKgxOulMqMxIO1P+feutt97SkiVLtHTpUkn2fzY7IxNFXn31VTNt2jRjjDGFhYXmpptuMl27djVr1qwxxhjzxBNPmDfeeMN89dVX5o9//KMJBAJm165d5o477nCcuWHDBtO3b18TCATMd999Zzp37uwor9zYsWNNly5dzPvvv+94jMYYc9ttt5lAIFAp17GgoMB069bNHDlyxJSVlZknn3zSlJSUVMq8jTFmwYIFZvbs2Y7yvv76a9O9e3cTCATM9u3bTdeuXR3P+z//+Y8ZMGCAKSsrM3v37g2ZmZuba8aOHWuMMeaTTz4xgwYNMvfff7/55JNPjDHGTJo0yeTk5IT1eLSbaYwxr7/+uunatau5/vrrHeetWrXKDBkyxBhjzLFjx0xqaqrZt2+fo0y7nQlnzsbY60w4mXY6YzcvnM6EO29jTt8Zu3nhdMZuppPO7N2719xzzz2mffv2Jjs72xhjzI8//mg6depkjh07Zg4cOBD8fyeZxhizcuVK06VLF9OqVStz9OhRR3k7d+40Xbt2NX6/35SVlZkePXqYr7/+2lHm3r17TYcOHUxJSYk5ePCgadeuneVjM5w5G2PM7NmzTbdu3U55faSZPXv2NHv37g2ZFU7ewYMHTceOHYN58+fPD5kd7ryNMebtt982o0aNcpS3f/9+c9NNN5ljx46Zffv2mZtvvtnxvDdt2mQ6d+5sjh49ao4ePWpuv/12c/jwYVt5vXv3Nhs3bjTGGPPiiy+arKwsx52xyjQm8s5Y5TntjFWmk86EmrMxkXcmVGaknbHKc9qZ083bmPA7Y5XntDNWmU46M3jwYPPpp58aY4wZM2aMycnJcdwZq0xjIu+MVZ7TzlhlOulMqDkbE3lnQmVG2hmrPKedOd28jQm/M1Z5TjtjlemkM8YYs3HjRtO3b1/TvXt3Y0x4P5udSVTtgLj11lt1yy23BF+OiYnRDz/8oNatW0uSWrdurX//+9/av3+/brzxRnk8HjVq1EhlZWUqLCxUYmJixJldunTRwoUL5fF4tHv37uAqudO8Vq1ayRhTKfO+4YYbdODAAQ0aNEgHDhzQfffdZ7kyZTfvyJEjatGihcaMGaM9e/Zo0KBBio2NdTxvSfr+++/15ptv6p///KejvLZt2+qss85SSUmJiouL5fOFfkjbzWzcuLHatm0rr9erxMRExcTEaM+ePTr//PNPyEtNTdXNN98sScHHxHvvvadrr71WktSuXTt99NFHSkpKsv14tJuZlpam+vXra8mSJSFXSMPJGzt2rJo3bx78uLKyspDX0m7m5MmTbXUmnDnb7YzdzFatWtnqjN28vXv32u5MOPOWztwZu3nDhw+33Rm7mY0aNYq4M4cOHdLw4cO1cuXK4PutX79erVq1UlxcnOLi4tSkSRNt2rTJcgeN3UxJ8nq9WrRoke68886w5myVd+GFF+rZZ58N/vXH7/db/qUrnMzExES9+eab8vl82rVrl84991zLv9CEM+d33nlHHo8n5G6hcDMDgYAKCgr0yCOP6KefflK3bt3UrVu3iPO++OILNWvWTDNnztR3332n7t27W/7bGO68Jenw4cOaO3eulixZ4iivTp06atSokY4cOaIjR45Yfk3Czdy6dauuvfba4GPmoosu0ubNm3XVVVedMW/KlCm64IILJB3/tzo+Pt5xZ6wypcg7Y5XntDNWmU46E2rOTjpjlemkM1Z5TjsTat5SZJ2xynPaGatMJ5159NFHFRMTo5KSEu3Zs0fnnXee485YZUqRd8Yqz2lnrDKddCbUnJ10xirTSWes8px2JtS8pcg6Y5XntDNWmU46U1RUpD//+c8aP368Jk2aJCm8n83OJKrOgDjnnHNUt25dFRcXa8SIERo5cqQaN26sTz/9VJKUn5+vI0eOqLi4WHXr1j3h4w4ePOgoU5J8Pp+efPJJ3X///erUqZOjvFWrVqmgoCDkNvdIMktLSzVgwAA99dRTmjdvnmbMmKG9e/dGnFdUVKTPP/9c06dP19y5czVt2jQdOHDA8XWUpEWLFqlfv36Ki4tzlOfz+eT1enXbbbepf//+GjBggOPr2Lx5c33wwQcqLS3Vd999p2+//faEsVfk8/k0ZswYPfbYY7rllltkjAn+o1H+uAvn8Wg3U5JSUlJ09tlnh8wJJy8+Pl7169dXaWmpxo4dqx49egS3vTkZo53O2M0LpzN2M+12xm5eOJ0J5zpKZ+6M3bxwOmM300lnGjdufMrtGsXFxSdstTznnHNUXFxse4xWmZJ0ww03KCEh4bTztZsXGxurxMREGWM0c+ZMXX755UpKSnI8Rp/PpyVLlqhHjx4nLJhGkrdlyxYtW7ZMDzzwwBnnbDfz8OHD6tOnj2bNmqVnn31W2dnZ2rRpU8R5RUVFWr16tR566CEtWLBAL7zwgrZv3+5ojOVeffVV3XrrrSF/0Awnr2HDhurYsaO6du2qvn37hsyzm3nppZfq888/V3FxsYqKivTFF1/Y7kz5L2Zr167VkiVL1K9fP8edscqUIu+MVZ7TzoQaY6Sdscpz2hmrTCedscpz2plQ11GKrDOh8px0xirTSWdiYmK0a9cuderUSUVFRUpKSnLcGatMKfLOWOU57UyoMUbaGas8p52xynTSGas8p50JdR2lyDoTKs9JZ6wyI+1Menq6JkyYoPHjx5/wu0C4nTmtiPZNVKHdu3ebrl27mldeecUYY8zWrVvNgAEDzMCBA82cOXPM9OnTzQsvvGDmz58f/JguXbqcdhuPncyKDh48aDp06GAKCgoizhs1apTp0aOH6dOnj7n++utNp06dgtvNIs0sKSkxhw4dCn7MiBEjzGeffRZxXnZ2tpk6dWrwYwYNGmTWrVvn+DqWlZWZ9PR0c+TIkZBZdvNeeOEFM3r0aOP3+83+/ftNx44dzffff+94jE8//bTp1auXmTJliundu7fZv3//acf6448/mptvvtm0adMm+Lrc3FwzZcqUsB+PdjIrOt0tGOHk7du3z/Tt29fMmzfPVl44YzxTZ+zkhdsZO5nhdMZOXridsZNpjP3O2MkLtzN2xxhpZ8qv/5w5c4JbN/Py8szkyZOD7ztkyBCzfv1622O0yqwoJSUl5NbYcPKOHj1qRo0aZSZNmmT8fv8Z88IZ47Fjx0zfvn3NqlWrIs6bOXOm6datm+nTp49JSUkx6enpp711yU6m3+83Bw8eDL7vzJkzzeuvvx5x3vvvv2/uv//+4Ps+9thjZvny5Y7GWK5bt25m9+7dZ8w6U15eXp7p06dPcBtr7969w+p1qDG+/PLLpmfPnmbMmDFm+PDhIbdWW+UtX77cdOrUyezcuTM4RqedOTmzokg6Y5XntDOnG2MknTk5rzI6c3Km086cnFcZnQl1HSPtjNXj0WlnrMbopDMVMzIzMyvt+0zFzIoi/T5zcl5lfJ8JNcZIv89UzKus7zMVMyvj+0zFvMr6PlMxs5yT7zMV8yrr+8zJY4ykM7/97W9NSkqK6dOnj+nevbtp1aqVmTZtWsSdsRJVOyB++uknDRgwQA8//HBwK87777+vrKwszZ8/X/v27dMNN9yg1q1b68MPP1QgENDu3bsVCARCrkzZzVy1apWmTJkiSYqPj5fP57PcHmM3b/bs2XrppZe0ePFitW3bVg8//PAJW+Ajyfz44481cuRISdKhQ4f0zTffKDk52dF1/PTTT3Xs2DEdPnxYW7duDR5KEmmmdPyvcklJSTrrrLMss8LJO/fcc3X22WcrJiZG55xzjuLi4nTo0CFHmdu3b9d5552n7OxsDRw4UB6PR+eee+4peW+88Yb+9re/STq+Rdfj8ahFixZavXq1JGnlypVq06ZNWI9Hu5l22c07evSo+vXrpzvvvDPkoYnhZtrtjN28cDpjN9NuZ8L5WtvtTDhfazudsZsXTmfsZjrpTPnW0opatmypNWvW6NixYzp48KC2bt2qZs2a2R6jVaZddvOMMRoyZIguvfRSTZ069bSf027mtm3bNGzYMBljFBsbq7i4uODhq5HkZWZm6pVXXtHixYvVtWtX9evXL+QWWbuZO3bsUEZGhsrKylRaWqq1a9fqiiuuiDivRYsW2rJliwoLC+X3+7Vu3TpdfPHFjsYoSQcPHlRJSYkaNmxo+fZw8urXr6+zzjpLcXFxio+PV7169ULubLKbWVhYqKKiIr344ouaMGGC/ve//+mSSy6xlZebm6slS5Zo8eLFaty4sSTnnbHKtMtuntPOWGU66YxVntPOWGU66YxVntPOhPpaR9oZqzynnbHKdNKZYcOGaceOHZKO/9XW6/U67oxVpl1285x2xirTSWes8px2xirTSWes8px2JtTXOtLOWOU57YxVZqSdadCggVasWKHFixfriSee0MUXX6wJEyaE1Zkz8Rhzhpupq5Fp06ZpxYoVJ/yC0L9/f/31r39VnTp1dN111+nBBx+UJM2dO1crV65UIBDQIEeomgAABilJREFUuHHjQv7SZjezrKxMU6dO1ebNmxUIBNStWzfLreDhjLHc2LFj1aFDh5CFDSdz+vTpWrdunbxer+69916lpqY6ynv++ef1r3/9S8YY3X333br99tsdj3HFihVau3atJkyYYJkVTl5ZWZmmTJmiLVu2qKysTB06dFD//v0dZR47dkwPPfSQfvjhB8XHx+uRRx6xLOzhw4c1btw4/fTTT/L7/Ro4cKCaNm2qSZMmqbS0VMnJyZo2bZpiYmJsPx7DySx3ww036KOPPnKUt3jxYs2bN++EX+izsrIsfxC1mynJVmcimfOZOhNOpp3OhJNntzPhZNrpTDhfF7udsZvp9/sj7kz59Z47d64aNGhwwrNgLF26VMYY3X///SG3iYaTWe4Pf/iDVqxYYXkvrd283NxcjRo16oR7KUeNGqVWrVo5GuO8efO0cuVKeTwetW3b1vLE80jmHOr1kWQuWLBA77zzjmJjY9WlSxfLzHDyli9froULF0o6flbPfffd53iM69ev1zPPPKOnn37aMivcvDlz5uiDDz6Q1+tV69atlZmZabmgajfTGKPJkydrw4YNio2N1ejRo3XNNdfYyhs/frwaNmwYXOS75pprNGLECEedCZVZLtzOWOU1b97cUWdCjTHSzpxpzpF0JlRmpJ0JleekM6EyI+1MqDwnnbHKHD58eMSdSUxM1OOPP67Y2FjVqVNH06ZN0wUXXOCoM6Eyy4XbGau8devWOepMqDFG2pkzzTmSzoTKjLQzofKcdCZUZqSdCZXnpDNWmeeff37EnSn/vvXf//5Xo0aN0ssvvyzJ/s9mZxJVCxAAAAAAACA6RdUtGAAAAAAAIDqxAAEAAAAAAFzHAgQAAAAAAHAdCxAAAAAAAMB1LEAAAAAAAADXsQABAAAAAABcxwIEAAAAAABwna+qBwAAAKqno0ePKjMzUz/++KMaNmyozz77TElJSUpISNCBAwc0f/58PfrooyooKFAgENDIkSN13XXXWWYVFhZq5MiRMsaotLRUU6ZM0aWXXqqnn35aeXl5KisrU69evdSzZ08999xzWr58uXw+n9q0aaOHH35Yc+fO1RdffKHDhw9r+vTp+vjjj7Vs2TJ5PB516NBBffv2/YWvDgAACBcLEAAAwNLSpUv1m9/8RnPmzNHWrVvVqVMnJSUlqXPnzkpLS1N2drYSEhKUlZWloqIi9enTR8uXL7fMWr9+verVq6fZs2fr22+/VXFxsTZu3KiVK1fqlVdeUUlJiWbPnq3NmzdrxYoVeumll+Tz+TR8+HDl5+dLkpKTkzVx4kR9++23evvtt5WdnS2Px6N+/frpxhtvVHJy8i95eQAAQJhYgAAAAJa2bt2qdu3aSZKaNm2qxMRESVJSUpIkacuWLVqzZo3Wr18vSfL7/SoqKlJCQsIpWe3atdOOHTs0ZMgQ+Xw+DR48WNu3b1fLli0VExOjOnXqaOLEiVqxYoWuvPJKxcbGSpLatGmjb7755pTPu3v3bvXr10+StH//fu3cuZMFCAAAqjnOgAAAAJaaNWumL774QpK0c+dOFRUVSZI8Ho+k4zsSOnbsqMWLF2vBggW69dZbVb9+fcus1atX64ILLtBzzz2nwYMH64knnlBycrI2btyoQCCg0tJS9e/fX0lJSVq/fr38fr+MMcHbPiTJ6/UGP+/FF1+sv//971q8eLHuuOMONWvWzO3LAQAAHGIHBAAAsNStWzeNHTtWvXv3VqNGjRQfH3/C23v27KmJEyeqT58+Ki4uVkZGRnCR4GSXXXaZHnzwQb3wwgvyer0aOnSomjdvrrZt26pXr14KBALq1auXLrvsMt12223B11199dVKTU3Vpk2bTsj6/e9/r169eqmkpEQtW7bUr371K1evBQAAcM5jjDFVPQgAAFD9rF27VocPH9aNN96oHTt26N5771VeXl5VDwsAAEQpFiAAAIClPXv2aNSoUSotLZXf79eIESOCZ0KEMm/ePK1evfqU12dlZalx48ZuDRUAAEQBFiAAAAAAAIDrOIQSAAAAAAC4jgUIAAAAAADgOhYgAAAAAACA61iAAAAAAAAArmMBAgAAAAAAuI4FCAAAAAAA4Lr/A3m4pCVfeooQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize =(18,6))\n",
    "_ = sns.boxplot(data=data, x = 'gre_score', y = 'chance_of_admit', hue='research', ax = ax, palette='Set1')\n",
    "ax.set_title(\"GRE Scores vs. Chance of admittance\")\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>research</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>181.0</td>\n",
       "      <td>104.143646</td>\n",
       "      <td>4.842098</td>\n",
       "      <td>92.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>219.0</td>\n",
       "      <td>110.109589</td>\n",
       "      <td>5.646859</td>\n",
       "      <td>95.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean       std   min    25%    50%    75%    max\n",
       "research                                                               \n",
       "0         181.0  104.143646  4.842098  92.0  101.0  104.0  107.0  120.0\n",
       "1         219.0  110.109589  5.646859  95.0  107.0  110.0  114.0  120.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['research']).toefl_score.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize =(18,6))\n",
    "_ = sns.boxplot(data=data, x = 'toefl_score', y = 'chance_of_admit', hue='research', ax = ax, palette='Set1')\n",
    "ax.set_title(\"TOEFL Scores vs. Chance of admittance\")\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "    Box plots of University Rating & Research vs chance of admittance\n",
    "        - Need to scale data - GRE & TOEFL since these are not on the same scale\n",
    "'''\n",
    "var = ['university_rating','research']\n",
    "fig, axes = plt.subplots(figsize=(10, 6),nrows=2)\n",
    "for i, v in enumerate(var):\n",
    "    sns.boxplot(y = 'chance_of_admit', x =v, ax = axes[i], data=data[[v, 'chance_of_admit']], palette='Set1')\n",
    "    axes[i].set_xlabel('')\n",
    "    axes[i].set_ylabel('')\n",
    "    axes[i].set_title(v)\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "    Box plots of Letter of Recommendation & Statement of Purpose vs chance of admittance\n",
    "'''\n",
    "var = ['lor','sop']\n",
    "fig, axes = plt.subplots(figsize=(10, 6),nrows=2)\n",
    "for i, v in enumerate(var):\n",
    "    sns.boxplot(y = 'chance_of_admit', x =v, ax = axes[i], data=data[[v, 'chance_of_admit']], palette='Set1')\n",
    "    axes[i].set_xlabel('')\n",
    "    axes[i].set_ylabel('')\n",
    "    axes[i].set_title(v)\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "    Box plots of GRE & TOEFL Scores vs chance of admittance\n",
    "        - Need to scale GRE & TOEFL since these are not on the same scale\n",
    "'''\n",
    "var = ['gre_score','toefl_score']\n",
    "fig, axes = plt.subplots(figsize=(18, 8),nrows=2)\n",
    "for i, v in enumerate(var):\n",
    "    sns.boxplot(y = 'chance_of_admit', x =v, ax = axes[i], data=data[[v, 'chance_of_admit']], palette='Set1')\n",
    "    axes[i].set_xlabel('')\n",
    "    axes[i].set_ylabel('')\n",
    "    axes[i].set_title(v)\n",
    "plt.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Pedicting the chance of being admitted to graduate school - Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 1 - Linear Regression with training and test set\n",
    "<br>\n",
    "Using 360 rows of data for training and 40 for testing, we scale the data using the standardscaler (mean = 0 and variance = 1) and then fit a linear regression model to the data. \n",
    "\n",
    "The first model gets an R^2 score is 0.78, which means that about 78% of the variability in Y can be explained using X. <br>\n",
    "When looking at RMSE, we are an average of 6% chance of admittance away from the ground truth when making predictions on our test set. \n",
    "\n",
    "The features with the highest coefficients are <em>Cumulative GPA & TOEFL Score</em> which indicates that they have the most influence on the chance of getting admitted to graduate school in this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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  research  chance_of_admit\n",
       "0           1        337          118                  4  4.5  4.5  9.65         1             0.92\n",
       "1           2        324          107                  4  4.0  4.5  8.87         1             0.76\n",
       "2           3        316          104                  3  3.0  3.5  8.00         1             0.72\n",
       "3           4        322          110                  3  3.5  2.5  8.67         1             0.80\n",
       "4           5        314          103                  2  2.0  3.0  8.21         0             0.65"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle data\n",
    "X, y = shuffle(data.drop(['serial_no.','chance_of_admit'], axis=1), data.chance_of_admit, random_state=23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into training and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 7)\n",
      "360 rows of training data\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print('%d rows of training data' % X_train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suppress conversion errors\n",
    "from sklearn.exceptions import DataConversionWarning\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore', category=DataConversionWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use Linear regression to predict target\n",
    "linreg = LinearRegression()\n",
    "scaler = preprocessing.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit scaler on training data\n",
    "scaler = scaler.fit(X_train)\n",
    "# transform training data using standard scaler\n",
    "X_train_transformed = scaler.transform(X_train)\n",
    "\n",
    "# transform test data fit scaler\n",
    "X_test_transformed = scaler.transform(X_test)\n",
    "\n",
    "# fit model to training data\n",
    "linreg = linreg.fit(X_train_transformed, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression R^2 score on training set 0.8025\n",
      "Linear Regression R^2 score on test set     0.7881\n",
      "Linear Regression MSE score on training 0.0040\n",
      "Linear Regression RMSE 0.0631\n"
     ]
    }
   ],
   "source": [
    "linreg_score = linreg.score(X_test_transformed, y_test)\n",
    "print(\"Linear Regression R^2 score on training set %.4f\" %linreg.score(X_train_transformed,y_train))\n",
    "print(\"Linear Regression R^2 score on test set     %.4f\" %linreg_score)\n",
    "\n",
    "pred = linreg.predict(X_test_transformed)\n",
    "linreg_mse = mean_squared_error(y_pred=pred, y_true=y_test)\n",
    "print(\"Linear Regression MSE score on training %.4f\" %linreg_mse)\n",
    "print(\"Linear Regression RMSE %.4f\" %math.sqrt(linreg_mse))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "        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>feature</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cgpa</td>\n",
       "      <td>0.070619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>toefl_score</td>\n",
       "      <td>0.020241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lor</td>\n",
       "      <td>0.019396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gre_score</td>\n",
       "      <td>0.017442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>research</td>\n",
       "      <td>0.014512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>university_rating</td>\n",
       "      <td>0.004500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sop</td>\n",
       "      <td>-0.001821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature  coefficient\n",
       "5               cgpa     0.070619\n",
       "1        toefl_score     0.020241\n",
       "4                lor     0.019396\n",
       "0          gre_score     0.017442\n",
       "6           research     0.014512\n",
       "2  university_rating     0.004500\n",
       "3                sop    -0.001821"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at coefficients - which variables are most important in model?\n",
    "linreg_coefs = linreg.coef_\n",
    "coef_df = pd.DataFrame(data = list(zip(X_train.columns,linreg_coefs)), columns=['feature','coefficient'])\n",
    "coef_df.sort_values(by = 'coefficient', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 2 - Linear Regression using 10-fold cross validation\n",
    "In the second model I created a pipeline to scale the date, fit it and perform cross validation. <br>The average training and test R^2 score remained quite the same at 80% and 78%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build pipeline to combine standard scaler and Linear Regression\n",
    "scaler = preprocessing.StandardScaler()\n",
    "linreg_pipe = Pipeline(steps=[('standardscaler', scaler ),('linear', LinearRegression())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fit_time': array([0.01304197, 0.00401211, 0.00552583, 0.00451183, 0.00752997,\n",
      "       0.01004672, 0.00551558, 0.00351048, 0.00454402, 0.00451088]), 'score_time': array([0.00250554, 0.00150442, 0.00200438, 0.00150347, 0.00150371,\n",
      "       0.00150442, 0.00300717, 0.00200438, 0.0009706 , 0.00150466]), 'test_score': array([0.72835965, 0.73714592, 0.87183514, 0.80379514, 0.81545097,\n",
      "       0.73894106, 0.7618908 , 0.72425731, 0.80234485, 0.80944669]), 'train_score': array([0.80884704, 0.80773844, 0.79167226, 0.80258291, 0.80200557,\n",
      "       0.80578924, 0.80802737, 0.80802246, 0.80200763, 0.80139747])}\n"
     ]
    }
   ],
   "source": [
    "scores = cross_validate(return_train_score=True, error_score=True,\n",
    "                        estimator=linreg_pipe, X=X ,y=y, cv=10)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average score for train: 0.8038\n",
      "Average score for test:  0.7793\n"
     ]
    }
   ],
   "source": [
    "print(\"Average score for train: %.4f\" %scores['train_score'].mean())\n",
    "print(\"Average score for test:  %.4f\" %scores['test_score'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 3 - Ridge Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this model I performed cross valiation in 2 ways: \n",
    "\n",
    "<em>1) 5-Fold cross validation by looping</em>\n",
    "\n",
    "I looped through an array of alpha values and performed 5-fold cross validation and assigned that alpha to the Ridge regression model. I did this to then plot alpha vs the average CV score for that fold, where we can see that the optimal alpha gives the best score of 0.785.\n",
    "\n",
    "<em>2) 5-Fold cross validation using RidgeCV</em>\n",
    "\n",
    "Here the average R^2 score was very close at 0.788. Using the returned coefficients, the cumulative gpa is seemingly the most informative feature followed by TOEFL scores. Since GRE and TOEFL scores are highly correlated, I think the model would perform similiarly with GRE or TOEFL scores... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************\n",
      "CROSS VALIDATION/K-FOLD FOR RIDGE REGRESSION\n",
      "******************************\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"*\"*30)\n",
    "print(\"CROSS VALIDATION/K-FOLD FOR RIDGE REGRESSION\")\n",
    "print(\"*\"*30)\n",
    "\n",
    "alphas = np.logspace(-4, -.5, 30)\n",
    "#alphas=[0.1, 1.0, 20.0]\n",
    "scores = list()\n",
    "scores_std = list()\n",
    "for alpha in alphas:\n",
    "    ridge.alpha = alpha\n",
    "    this_scores = cross_val_score(estimator = ridge, X=X, y=y, n_jobs=1, cv=5)\n",
    "    scores.append(np.mean(this_scores))\n",
    "    scores_std.append(np.std(this_scores))\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.semilogx(alphas, scores)\n",
    "plt.ylabel('CV score')\n",
    "plt.xlabel('alpha')\n",
    "plt.axhline(np.max(scores), linestyle='--', color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([1.00000e-04, 1.32035e-04, 1.74333e-04, 2.30181e-04, 3.03920e-04,\n",
       "       4.01281e-04, 5.29832e-04, 6.99564e-04, 9.23671e-04, 1.21957e-03,\n",
       "       1.61026e-03, 2.12611e-03, 2.80722e-03, 3.70651e-03, 4.89390e-03,\n",
       "       6.46167e-03, 8.53168e-03, 1.12648e-02, 1.48735e-02, 1.96383e-02,\n",
       "       2.59294e-02, 3.42360e-02, 4.52035e-02, 5.96846e-02, 7.88046e-02,\n",
       "       1.04050e-01, 1.37382e-01, 1.81393e-01, 2.39503e-01, 3.16228e-01]),\n",
       "    cv=5, fit_intercept=True, gcv_mode=None, normalize=False, scoring=None,\n",
       "    store_cv_values=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=5\n",
    "ridge_cv = RidgeCV(alphas=alphas, cv=5)\n",
    "\n",
    "# fit Ridge Regression using 5fold cross validation\n",
    "ridge_cv.fit(X_train_transformed, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Regression R^2 score 0.7881\n",
      "Ridge Regression MSE score 0.0040\n",
      "Ridge Regression RMSE 0.0631\n"
     ]
    }
   ],
   "source": [
    "ridge_cv_score = ridge_cv.score(X_test_transformed, y_test)\n",
    "print(\"Ridge Regression R^2 score %.4f\" %ridge_cv_score)\n",
    "\n",
    "ridge_cv_pred = ridge_cv.predict(X_test_transformed)\n",
    "ridge_cv_mse = mean_squared_error(y_pred=ridge_cv_pred, y_true=y_test)\n",
    "\n",
    "print(\"Ridge Regression MSE score %.4f\" %ridge_cv_mse)\n",
    "print(\"Ridge Regression RMSE %.4f\" %math.sqrt(ridge_cv_mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             feature  coefficient\n",
      "5               cgpa     0.070369\n",
      "1        toefl_score     0.020283\n",
      "4                lor     0.019390\n",
      "0          gre_score     0.017534\n",
      "6           research     0.014506\n",
      "2  university_rating     0.004548\n",
      "3                sop    -0.001752\n"
     ]
    }
   ],
   "source": [
    "ridge_cv_coefs = ridge_cv.coef_\n",
    "ridge_coef_df = pd.DataFrame(data = list(zip(X_train.columns, ridge_cv_coefs)), columns=['feature','coefficient'])\n",
    "print(ridge_coef_df.sort_values(by = 'coefficient', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge Regression with best features: CGPA, TOEFL & GRE Scores\n",
    "Lastly, I fit ridge regression models for Cumulative GPA, TOEFL & Score scores and then separately using Cumulative GPA and one of the two scores since these are highly correlative. The highest scoring model in the case of GRE vs TOEFL was GRE at 79% R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit scaler on training data - CPGA, GRE & TOEFL\n",
    "scaler_2 = preprocessing.StandardScaler()\n",
    "scaler_2.fit(X_train[['cgpa','gre_score','toefl_score']])\n",
    "\n",
    "# transform training data using standard scaler\n",
    "X_train_transformed_2 = scaler_2.transform(X_train[['cgpa','gre_score','toefl_score']])\n",
    "X_test_transformed_2 = scaler_2.transform(X_test[['cgpa','gre_score','toefl_score']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([1.00000e-04, 1.32035e-04, 1.74333e-04, 2.30181e-04, 3.03920e-04,\n",
       "       4.01281e-04, 5.29832e-04, 6.99564e-04, 9.23671e-04, 1.21957e-03,\n",
       "       1.61026e-03, 2.12611e-03, 2.80722e-03, 3.70651e-03, 4.89390e-03,\n",
       "       6.46167e-03, 8.53168e-03, 1.12648e-02, 1.48735e-02, 1.96383e-02,\n",
       "       2.59294e-02, 3.42360e-02, 4.52035e-02, 5.96846e-02, 7.88046e-02,\n",
       "       1.04050e-01, 1.37382e-01, 1.81393e-01, 2.39503e-01, 3.16228e-01]),\n",
       "    cv=5, fit_intercept=True, gcv_mode=None, normalize=False, scoring=None,\n",
       "    store_cv_values=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit Ridge Regression using 5-fold cross validation\n",
    "ridge_2 = RidgeCV(alphas=alphas, cv=5)\n",
    "ridge_2.fit(X_train_transformed_2, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Regression R^2 score 0.7894\n",
      "Ridge Regression MSE score 0.0040\n",
      "Ridge Regression RMSE 0.0631\n"
     ]
    }
   ],
   "source": [
    "ridge_2_score = ridge_2.score(X_test_transformed_2, y_test)\n",
    "print(\"Ridge Regression R^2 score %.4f\" % ridge_2_score)\n",
    "\n",
    "ridge_2_pred = ridge_2.predict(X_test_transformed_2)\n",
    "ridge_2_mse = mean_squared_error(y_pred=ridge_2_pred, y_true=y_test)\n",
    "\n",
    "print(\"Ridge Regression MSE score %.4f\" %ridge_cv_mse)\n",
    "print(\"Ridge Regression RMSE %.4f\" %math.sqrt(ridge_cv_mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             feature  coefficient\n",
      "0          gre_score     0.085242\n",
      "1        toefl_score     0.024939\n",
      "2  university_rating     0.021484\n"
     ]
    }
   ],
   "source": [
    "ridge_2_coefs = ridge_2.coef_\n",
    "ridge_2_coef_df = pd.DataFrame(data = list(zip(X_train.columns, ridge_2_coefs)), columns=['feature','coefficient'])\n",
    "print(ridge_2_coef_df.sort_values(by = 'coefficient', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model with GRE Score and with TOEFL Score - which is better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Regression model using gre_score is: R^2 score 0.7978\n",
      "Ridge Regression model using toefl_score is: R^2 score 0.7765\n"
     ]
    }
   ],
   "source": [
    "features = ['gre_score','toefl_score']\n",
    "\n",
    "new_scaler = preprocessing.StandardScaler()\n",
    "\n",
    "\n",
    "for feat in features:\n",
    "    feature_list = ['cgpa']\n",
    "    feature_list.append(feat)\n",
    "    new_scaler.fit(X_train[feature_list])\n",
    "    X_train_scaled = new_scaler.transform(X_train[feature_list])\n",
    "    X_test_scaled = new_scaler.transform(X_test[feature_list])\n",
    "    \n",
    "    # fit Ridge Regression using 5-fold cross validation - CPGA & GRE\n",
    "    ridge_new = RidgeCV(alphas=alphas, cv=5)\n",
    "    ridge_new.fit(X_train_scaled, y_train)\n",
    "    score_new = ridge_new.score(X_test_scaled, y_test)\n",
    "    print(\"Ridge Regression model using %s is: R^2 score %.4f\" %(feat,score_new))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_ridge_new = ridge_new.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\gagrocostea\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "# make predictions on test data\n",
    "X_test['predictions'] = pred_ridge_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>toefl_score</th>\n",
       "      <th>chance_of_admit</th>\n",
       "      <th>predictions</th>\n",
       "      <th>diff_squared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>110</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.639608</td>\n",
       "      <td>0.032259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>105</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.553767</td>\n",
       "      <td>0.021384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>107</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.705240</td>\n",
       "      <td>0.021095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>110</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.744311</td>\n",
       "      <td>0.011170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>107</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.734688</td>\n",
       "      <td>0.011091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     toefl_score  chance_of_admit  predictions  diff_squared\n",
       "40           110             0.46     0.639608      0.032259\n",
       "358          105             0.70     0.553767      0.021384\n",
       "63           107             0.56     0.705240      0.021095\n",
       "360          110             0.85     0.744311      0.011170\n",
       "186          107             0.84     0.734688      0.011091"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge predictions & look at rows with highest squared difference\n",
    "predicted_test = X_test.merge(pd.DataFrame(y_test), right_index = True, left_index=True)\n",
    "predicted_test['diff_squared'] = predicted_test.apply(lambda x: (x['predictions']-x['chance_of_admit'])**2, axis=1)\n",
    "predicted_test.sort_values(by = 'diff_squared', ascending=False)[['toefl_score','chance_of_admit','predictions','diff_squared']].head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>316.583333</td>\n",
       "      <td>107.197222</td>\n",
       "      <td>3.063889</td>\n",
       "      <td>3.397222</td>\n",
       "      <td>3.443056</td>\n",
       "      <td>8.582028</td>\n",
       "      <td>0.538889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.479605</td>\n",
       "      <td>6.073138</td>\n",
       "      <td>1.143628</td>\n",
       "      <td>1.007904</td>\n",
       "      <td>0.884850</td>\n",
       "      <td>0.591979</td>\n",
       "      <td>0.499179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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>8.157500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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>8.565000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gre_score  toefl_score  university_rating         sop         lor        cgpa    research\n",
       "count  360.000000   360.000000         360.000000  360.000000  360.000000  360.000000  360.000000\n",
       "mean   316.583333   107.197222           3.063889    3.397222    3.443056    8.582028    0.538889\n",
       "std     11.479605     6.073138           1.143628    1.007904    0.884850    0.591979    0.499179\n",
       "min    290.000000    92.000000           1.000000    1.000000    1.000000    6.800000    0.000000\n",
       "25%    308.000000   103.000000           2.000000    2.500000    3.000000    8.157500    0.000000\n",
       "50%    317.000000   107.000000           3.000000    3.500000    3.500000    8.565000    1.000000\n",
       "75%    325.000000   111.000000           4.000000    4.000000    4.000000    9.040000    1.000000\n",
       "max    340.000000   120.000000           5.000000    5.000000    5.000000    9.920000    1.000000"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  }
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