Admission Predict by Descriptive_Bivariate_Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>330</td>\n",
       "      <td>115</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.34</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>321</td>\n",
       "      <td>109</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>308</td>\n",
       "      <td>101</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>302</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>323</td>\n",
       "      <td>108</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>325</td>\n",
       "      <td>106</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>327</td>\n",
       "      <td>111</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>328</td>\n",
       "      <td>112</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>307</td>\n",
       "      <td>109</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>311</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>314</td>\n",
       "      <td>105</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>317</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>319</td>\n",
       "      <td>106</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>318</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>303</td>\n",
       "      <td>102</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  \\\n",
       "0         337          118                  4  4.5   4.5  9.65         1   \n",
       "1         324          107                  4  4.0   4.5  8.87         1   \n",
       "2         316          104                  3  3.0   3.5  8.00         1   \n",
       "3         322          110                  3  3.5   2.5  8.67         1   \n",
       "4         314          103                  2  2.0   3.0  8.21         0   \n",
       "5         330          115                  5  4.5   3.0  9.34         1   \n",
       "6         321          109                  3  3.0   4.0  8.20         1   \n",
       "7         308          101                  2  3.0   4.0  7.90         0   \n",
       "8         302          102                  1  2.0   1.5  8.00         0   \n",
       "9         323          108                  3  3.5   3.0  8.60         0   \n",
       "10        325          106                  3  3.5   4.0  8.40         1   \n",
       "11        327          111                  4  4.0   4.5  9.00         1   \n",
       "12        328          112                  4  4.0   4.5  9.10         1   \n",
       "13        307          109                  3  4.0   3.0  8.00         1   \n",
       "14        311          104                  3  3.5   2.0  8.20         1   \n",
       "15        314          105                  3  3.5   2.5  8.30         0   \n",
       "16        317          107                  3  4.0   3.0  8.70         0   \n",
       "17        319          106                  3  4.0   3.0  8.00         1   \n",
       "18        318          110                  3  4.0   3.0  8.80         0   \n",
       "19        303          102                  3  3.5   3.0  8.50         0   \n",
       "\n",
       "    Chance of Admit   \n",
       "0               0.92  \n",
       "1               0.76  \n",
       "2               0.72  \n",
       "3               0.80  \n",
       "4               0.65  \n",
       "5               0.90  \n",
       "6               0.75  \n",
       "7               0.68  \n",
       "8               0.50  \n",
       "9               0.45  \n",
       "10              0.52  \n",
       "11              0.84  \n",
       "12              0.78  \n",
       "13              0.62  \n",
       "14              0.61  \n",
       "15              0.54  \n",
       "16              0.66  \n",
       "17              0.65  \n",
       "18              0.63  \n",
       "19              0.62  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admission_data = pd.read_csv('C:/Users/tp040576/OneDrive - Cerner Corporation/desktop/Maching Learning/Excel_Data Analysis/Admission_Predict1.csv')\n",
    "admission_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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vVV/HlP0kS5oLykXKEkBrkG5bvFR9HVP2kyxpLgAFGYDkSLctXqq+jin7SZY0F4CCDEBypNsWL1Vfx5T9JEuaC0BBBiA50m2Ll6qvY8p+kiXNBWg0ZWlmz5T0VklLJL3L3S+a9f0Vki6VtKw653x3v6rJMQHIT50kYe5Spe/q9HWMmSSs87gfnrpzxs78J684fOjahPQirXPN0OuFziXkPKCOxlKWZrZE0lclPU3SLklbJa1395v6zrlY0nZ3/2szO0nSVe5+/HzXJWUJIFdtSN+lShLW6S8aem7s84Am5JCyPFXSTne/3d2/L+kKSWfOOscl/Wj158Ml3d3geACgUW1I36VKEtbpLxp6buzzgJSaLMgmJPU/23dVx/r9saRzzGyXpKsk/eagC5nZeWY2ZWZTe/bsaWKsALBobUjfpUoS1ukvGnpu7POAlJosyAY1jZv97F8v6b3ufoykZ0t6n5nNGZO7X+zua9197fLlyxsYKgAsXhvSd6mShHX6i4aeG/s8IKUmC7Jdkvobxx2juW9J/oakD0mSu18n6VBJRzY4JgBoTBvSd6mShHX6i4aeG/s8IKUmU5ZbJa0ysxMk7ZZ0tqQXzjrnTklPlfReM/tp9Qoy3pMEArWhn17sMeY85zak70JTlnXm8qJ3Xjcj6bhu5RFzelRunFytLbffO6eX5aAP1W+cXK2v7fn2nGvOPjf2eUBKjRVk7v6Amb1K0tXqbWlxibvfaGZvlDTl7psl/Y6kd5rZb6v3duaLvW3NNYFEZqfgdu/brwuu3CFJ2RQAscfYhjlPrllco+6mbdq+Wx/dtvvg56cOuOuj23Zr7XFHDCzKRs1ldjEmSdfetlcveud1M4qyDZt2zCjGJOnWe+7Xhk075hRGm7bv1hfuvG/GsS/ceZ82bd89Jwka8zwgpUY3hnX3q9z9se6+0t3fVB17fVWMyd1vcvd17v5z7v54d/9kk+MBSlJSoi/V9boo9hrOLsaGHa+TdIydBOV5gzZgp36gpUpK9KW6XhelWsM6ScfYSVCeN2gDCjKgpUpK9KW6XhelWsM6ScfYSVCeN2gDCjKgpUpK9KW6XhfFXsN1K48IOl4n6Rg7CcrzBm3QaC9LAM1pS6JPijfGNsw5d7HX8LJzzwhOWUoa2U+yzhjpPYmSUJABaJXcU4x1hG7hEXurj9A1DCm0JA08Nsja447Qp2/eo7v37dejDz9Ua48b/OpanTHGfj6ENCEvTc5byXRJY83Fm0JzcaCnpEbWXZSqyXeoQdtZSMOLslFSPhdCH7uLTcj5O9q8HJqLA2hQG6L8bRhjKrlv2RC6nUWolM+F0MfuYhNy/o7mg4IMaKk2RPnbMMZUurZlQ8p5hD52F5uQl/L8KgEFGdBSbYjyt2GMqXRty4aU8wh97C42IS/l+VUCCjKgpdoQ5W/DGFPJfcuG0O0sQqV8LoQ+dhebkPN3NB+kLIEhck8e1Ynyp5pLE2NMNZfY40u5ZUPIGEO3swjV1NYTIXMJfew6W3OUgi1B8kHKEhigpORRG+aSe+Iw9/HV0YYxhippLigXKUtgEUpKHrVhLrknDnMfXx1tGGOokuYCUJABA5SUPGrDXHJPHOY+vjraMMZQJc0FoCADBigpedSGueSeOMx9fHW0YYyhSpoLQEEGDFBS8qgNc8k9cZj7+OpowxhDlTQXgJQlMEBJyaM2JB0n10xo6o69M9JtZ50yt0fh5JoJfXjqzhnJv5NXHN74fUndxDp0vUP6MKZM59bpExkzPQm0ASlLAJLSJgnpNThcqrWJfZ/rjI/0JEpCyhJALSmThPQaHC7V2sS+z3XGR3oSXURBBkBS2iQhvQaHS7U2se9znfGRnkQXUZABkJQ2SUivweFSrU3s+1xnfKQn0UUUZAAkpU0S0mtwuFRrE/s+1xkf6Ul0ESlLoANiJtaaSLaFpizr9BqMnRhNmUCV4vZhTJFgrDO+0OcDUBJSlkDh2pBYiz3G2IlRelmOVxvGCIQiZQlAUjsSa7HHGDsxSi/L8WrDGIHYKMiAwrUhsRZ7jLETo/SyHK82jBGIjYIMKFwbEmuxxxg7MUovy/FqwxiB2CjIgMK1IbEWe4yxE6P0shyvNowRiI2UJVC4NvT7i52qq9MbUwpLlsa83rTYyc3QXpa5Jxib6LdZp48mkAIpSwDJpUpZprpenWvG7mVZUoKRHqhoA1KWAFojVcoy1fXqXDN2L8uSEoz0QEVJKMgAJJcqZZnqenWuGbuXZUkJRnqgoiQUZACSS5WyTHW9OteM3cuypAQjPVBREgoyAMmlSlmmul6da8buZVlSgpEeqCgJKUsgQ6n6JsYeX+i5dZJ/sa8Xok7qLzTNFzvhGdorsonUbRMJxpj9Nuv00QRSIWUJZCb3vol1HrdrPSWbSPPlnors4pyBOkhZAi2Ve9/EOo/btZ6STaT5ck9FdnHOQBMoyIDM5N43sc7jdq2nZBNpvtxTkV2cM9AECjIgM7n3TazzuF3rKdlEmi/3VGQX5ww0gYIMyEzufRPrPG7Xeko2kebLPRXZxTkDTSBlCWSmqb6JIWInGJvoFRnzPCk8Ifiid16na2/be/DrdSuP0GXnnjHjnCbSfLETqLHVmXPo+NrQfzVVwhnlImUJZCb39GQb+jrG7nE4uxibNqgoiy33ZGmo3MdXR0lzQfNIWQItlXt6sg19HWP3OBxUjM13PKbck6Whch9fHSXNBfmgIAMyk3t6sg19HUvqcZh7sjRU7uOro6S5IB8jCzIz+z8hxwDEkXt6sg19HUvqcZh7sjRU7uOro6S5IB8hr5A9bcCxZ8UeCICe3NOTbejrGLvH4bqVRww8b9jxmHJPlobKfXx1lDQX5GNoytLMXi7pFZJ+wsxu6PvWYZKubXpgQFelSpjVSUTGHl/sx47d4/Cyc88ISlk2IeV9iSn38dVR0lyQj/m2vfiApE9IulDS+X3H/9Pdm/8kK1ApKV5ep/F0zDnGXsM642ui8XSI0DFunFwdNJ4XrF2hr9+7/+AavmDtikWPMXaRV9LzJraS5oIyDd32wsx+1N3/w8wGviafqihj24tuKSle3kQT5hApt00InXPuWzs08bihW2mUNOdUSpoL2ifGthcfqH7fJmmq+n1b39dA40qKlzfRhDlEym0TQuec+9YOTTxu6FYaJc05lZLmgnINfcvS3Z9T/X7C+IYDzFRSvDzVFgspt00InXPuWzukfB52cc6xlTQXlCtoHzIz+1kze66ZPW/6V9MDA6Sy4uWptlhIuW1C6Jxz39oh5fOwi3OOraS5oFwh+5BdIukSSWdJ+qXq13MaHhcgqax4eRNNmEOk3DYhdM65b+3QxOOGbqVR0pxTKWkuKFdIc/HT3f2khVzczJ4p6a2Slkh6l7tfNOv7fy7pydWXD5d0lLsvW8hjoUwlxcubaDwdYnLNhD48deeMzyadvOLwRW2bEJpY2zi5Wl/b8+05ScLZc25ia4fQMcZuqB4qdCuNJuYcook510ncxp6LVMa/IyjXyObiZvZuSX/m7jfVurDZEklfVW9j2V2StkpaP+w6Zvabkta4+0vnuy4pS6Ce2OnOOom13BOCuac764g9xtjXq/M8bMN6A6FiNhe/VNJ1ZnaLmd1gZjtmbRQ7zKmSdrr77e7+fUlXSDpznvPXS7o84LoAaoid7qyTWMs9IZh7urOO2GOMfb06z8M2rDcQW8hblpdI+jVJOyT9sMa1JyT1/03bJem0QSea2XGSTpB0zZDvnyfpPElasWLxmzECXRI73VknsZZ7QjD3dGcdsccY+3p1nodtWG8gtpBXyO50983u/jV3v2P6V8DPDYpXDfsvwNmSPuLuBwZ9090vdve17r52+fLlAQ8NYFrsdGedxFruCcHc0511xB5j7OvVeR62Yb2B2EIKspvN7ANmtr7mthe7JPVHqY6RdPeQc88Wb1cCjYid7qyTWMs9IZh7urOO2GOMfb06z8M2rDcQW8hblkslfU/S0/uOuaQrR/zcVkmrzOwESbvVK7peOPskMztR0o9Jui5kwAiTsv9jSb0nU4m5hrHTnXUSa3XOjdnzsolm5bETh7HFTmPGTibWeR6SikQXjUxZLuriZs+W9BfqbXtxibu/yczeKGnK3TdX5/yxpEPd/fzhV3oQKcvRUiaUSEctXhfXMFWfz1Al3ZOS5gK0QWjKcr7m4m/T8M98yd1fvfDhLRwF2WjrLrpGuwd8+HVi2VJde/5Tin3sUnRxDVdecNXAD3cvMdNtFz47wYhmKumelDQXoA1ibHsx3VD8UEknS7q1+vV4SQM/fI88dLHvXkm6uIap+nyGKumelDQXoCRDCzJ3v9TdL5W0StKT3f1t7v42SU9VryhDprrYd68kXVzDVH0+Q5V0T0qaC1CSkJTl0ZIO6/v6kdUxZCplQol01OJ1cQ1T9fkMVdI9KWkuQElCUpYXSdpuZp+uvn6ipDc0NyQsVsqEUqq+e1LclF4dsefRxP3LPXUb2vMylabSoinuCwlGIE9BKUsze7Qe3GV/i7t/s9FRzYMP9ZchZZ+8mNqQWGtD6rYN6xiCfo0AZovZy1Lu/k13/3tJX5b0MjP78mIHiG5L2Scvpjb03Es5xpJ6RYagXyOAhRpZkJnZY8zsNWb2eUk3qren2PrGR4aipeyTF1MbEmttSN22YR1D0K8RwEINLcjM7Fwzu0bSv0g6UtLLJH3D3d/g7jvGNUCUKWWfvJjakFhrQ+q2DesYgn6NABZqvlfI3q7eq2EvdPcN7n6D5tkoFqgjZZ+8mNqQWGtD6rYN6xiCfo0AFmq+lOXRkl4g6S1m9ihJH5J0yFhGheI10Sdvy+336tZ77j94bNVRj2g8pVe3x2HXUnVt6BWZqm9oG/pjAhif0JTlMeo1B18v6eGS/s7d/6DhsQ1EyhKDtCFlSapuuFRr04YEKoB2i52y3OXuf+rup0ialPS9xQ4QiKkNKUtSdcOlWps2JFABdEPIxrAzuPstYmNYZKYNKUtSdcOlWps2JFABdEPQK2RA7tqQsiRVN1yqtWlDAhVAN8y37cW66vcfGd9wgIVpQ8qSVN1wqdamDQlUAN0w31uWfynpFEnXSTp5PMMBFqZOui2mOgnGUpKEUvy+oamSoE08bujapOz7CiA/Q1OWZna9pK9IerakD87+vru/utmhDUbKEm1VSpIwVaK1DZpYG9KYQLvFSFk+R9LVkr4raduAXwBqKCVJmCrR2gZNrA1pTKAbhr5l6e7fknSFmX3F3b80xjEBRSolSZgq0doGTawNaUygG0JSlvea2d+Z2T1m9m9m9tFqo1gANZSSJEyVaG2DJtaGNCbQDSEF2XskbVavldKEpI9VxwDUUEqSMFWitQ2aWBvSmEA3hGwMe5S79xdg7zWz1zQ1IGC22AmzVIm1lEnC0HRnyNrUTbSmWu8Uj7txcrW+tufbuva2vQePrVt5xKITqKWkcwEMN7KXpZn9k6T3Srq8OrRe0kvc/anNDm0wUpbdEjth1sXEWuicm1ibUpKlKR+3pLkAXRSzl+VLJf2KpG9K+oak51fHgMbFTph1MbEWOucm1qaUZGnKxy1pLgCGG/mWpbvfKem5YxgLMEfshFkXE2uhc25ibUpJlqZ83JLmAmA4elkia7ETZl1MrIXOuYm1KSVZmvJxS5oLgOEoyJC12AmzLibWQufcxNqUkixN+bglzQXAcCEpSyCZ2MnEVEnHlELn3MTalNSjMtXjljQXAMOFpCwfJel/Szra3Z9lZidJOsPd3z2OAc5Wasqyi/Hy0DnHPi/V+JoYY0me9pbP6NZ77j/49aqjHqFPvfZJc84LXcPYDdCbuHelbOkCYLjQlGVIQfYJ9TaC/UN3/zkze6ik7e6epItwiQVZF+PlsbdiSLU9Rp3H7eJ9DjW7GJs2uygLXcPYTb7bsJ0Fzy8gTzG3vTjS3T8k6YeS5O4PSDow/4+gji7Gy2NvxZBqe4w6j9vF+xxqUDE26HjoGsZu8t2G7Sx4fgHtFlKQ3W9mPy7JJcnMTpd0X6Oj6pguxstjb8WQanuMOo/bxfscW+gaxm7y3YbtLHh+Ae0WUpC9Vr1elivN7FpJfyvpNxsdVcd0MV4eeyuGVNtj1HncLt7n2ELXMHZM8pdtAAAbIklEQVST7zZsZ8HzC2i3kQWZu39B0hMlPUHS/5D0M+5+Q9MD65Iuxstjb8WQanuMOo/bxfscatVRjwg6HrqGsZt8t2E7C55fQLuNLMjM7JWSHunuN7r7lyU90sxe0fzQumNyzYQufN5qTSxbKpM0sWxp8R/EnVwzobNOmTj4isWwhsl1zou5hqHXq/O4dc7dtH231l10jU44/x+07qJrtGn77gXNoy0+9donzSm+BqUsQ9dw4+RqrVt5xIxjw5p8h6x1E39HQ5/bda7XtX9HgJKEpCy/6O6Pn3Vsu7uvaXRkQ5SYsuyilA2vc9fFOcfWhucX9xnohpgpy4eYPfjBCzNbIulhixkckLLhde66OOfY2vD84j4D6BeyU//Vkj5kZu9QL2n5PyX9Y6OjQvFSNrzOXRfnHFsbnl/cZwD9Ql4h+31J10h6uaRXSvpnSb/X5KBQvpQNr3PXxTnH1obnF/cZQL+QlOUP3f2v3f357n6Wu/+Nu7MxLBYlZcPr3HVxzrG14fnFfQbQb+Rblma2TtIfSzquOt8kubv/RLNDQ8nqNLyeumPvjJ6Ew5JobegLGHLNOk2d2zDn2EJ6VIY+b1I20KZ5N4B+ISnLmyX9tqRt6muZ5O73Nju0wUhZdkvuvSxTXjP36zUhtEdlG+YCoBtipizvc/dPuPs97n7v9K8IYwRGyr2XZcpr5n69JoT2qGzDXACgX0jK8tNm9mZJV0r63vTBagd/oFG597JMec3cr9eE0B6VbZgLAPQLKchOq37vf7nNJT0l/nCAmY5etlS7B/xHdFBaLuS82I+b8pq5X68JS8wGFmWze1S2YS4A0C8kZfnkAb8oxjAWufeyTHnN3K/XhNAelW2YCwD0C3mFTGb23yT9jKRDp4+5+xubGhTGJ/dUXZ00Zsh5sR+37jVjJkZjpzGbSv3FfI5Nf3A/JGUZey65/13B/Lh/yF1IyvIdkh4u6cmS3iXp+ZI+7+6/0fzw5iJlGQ9JtPEqKTHahseOqZR5dBX3DynFTFk+wd1/XdK/u/sbJJ0hafD7BmgVkmjjVVJitA2PHVMp8+gq7h/aIKQgm/5k7HfM7GhJP5B0QnNDwriQRBuvkhKjbXjsmEqZR1dx/9AGIQXZx81smaQ3S/qCpK9LuqLJQWE86KU3Xqn6K9KvcfFKmUdXcf/QBiEpyz9x933u/lH12if9lLv/r+aHhqaRRBuvkhKjbXjsmEqZR1dx/9AGoSnLJ0g6fvp8M5O7/23Azz1T0lslLZH0Lne/aMA5v6Jer0yX9CV3f2Ho4LE4dVJ/WLySEqNteOyYSplHV3H/0AYhKcv3SVop6Yt6sJelu/urR/zcEklflfQ0SbskbZW03t1v6jtnlaQPSXqKu/+7mR3l7vfMd11SlvGQPAIAoFmhKcuQV8jWSjrJR1Vuc50qaae7314N6ApJZ0q6qe+ccyW93d3/XZJGFWOIa77kEQUZAADjE/Kh/i9LevQCrj0hqb/j767qWL/HSnqsmV1rZtdXb3HOYWbnmdmUmU3t2bNnAUPBICSPAADIw9BXyMzsY+p9ruswSTeZ2ec1s7n4c0dc2wYcm/0q20MlrZL0JEnHSPqcmT3O3ffN+CH3iyVdLPXeshzxuAhEvz8AAPIw31uWf7rIa+/SzA1kj5F094Bzrnf3H0j6mpndol6BtnWRj40Ar3vGiQM/Q0byCACA8ZqvINst6VHufm3/QTP7hep7o2yVtMrMTqjOP1vS7ATlJknrJb3XzI5U7y3M2wPHjkWK3Quxri72lmPO3ZgzANQ1X0H2F5L+YMDx71Tf+6X5LuzuD5jZqyRdrd62F5e4+41m9kZJU+6+ufre083sJvUSnK9z93sXMA8s0OSa0dtczE5j7t63XxdcuePgzy9EE9fMHXPuxpwBYCHm+1D/8e5+w+yD7j6l3p5kI7n7Ve7+WHdf6e5vqo69virG5D2vdfeT3H21u9MBIENN9IHrYm855txT+pwBYCHmK8gOned7fOq7Q5pIY3Yx4cmcRx8HgK6aryDbambnzj5oZr8haVtzQ0JumugD18Xecsx59HEA6Kr5CrLXSHqJmX3GzP6s+vUvkl4m6bfGMzzkoIk+cF3sLcece0qfMwAsxNAP9bv7v0l6gpk9WdLjqsP/4O7XjGVkyEYTfeDqXHPDph0z+m2uP+1YbZxcveDHTqWJdQxNMKZKOrahhyApUAA5GNnLMjf0suyWDZt26P3X3znn+Dmnr2hlURZTaC9SepYOx9oAaFpoL8uQ1klAMpdvuavW8S4JTTCSdByOtQGQCwoyZO3AkFdwhx3vktAEI0nH4VgbALmgIEPWltiglqjDj3dJaIKRpONwrA2AXFCQIWvrTzu21vEuCU0wknQcjrUBkIv5WicByU1/cL+ElGVsoQnGNiQdU2FtAOSCgqxQJUX5N06uDirAct8CogkhvUjrnBeqzhqWst5dfH4BGB8KsgJ1saFz6Jy7uDax1VnD3Nc79vMm9/kCyBefIStQF6P8bAExPnXWMPf1jv28yX2+APJFQVagLkb52QJifOqsYe7rHft5k/t8AeSLgqxAXYzyswXE+NRZw9zXO/bzJvf5AsgXBVmBuhjlZwuI8amzhrmvd+znTe7zBZAvPtRfoLZE+WOm1upsATF1x94Z22icdcrgBCJpucHqPL/qnJtivUOfD2wxAqBpNBdHEqkaY9OQO0+p1pv7DKBpNBdH1lKl1kjL5SnVenOfAeSCggxJpEqtkZbLU6r15j4DyAUFGZJIlVojLZenVOvNfQaQCwoyJJEqtUZaLk+p1pv7DCAXpCyRRKrUGmm5PKVab+5zvkg5o2tIWQIAskL6FSUhZQkAaCXSr+giCjIAQFZIv6KLKMgAAFkh/YouoiADAGSF9Cu6iJQloovZo7INSplHHW2YcxvGiMFIv6KLSFkiqq71iixlHnW0Yc5tGCOAbiBliSS61iuylHnU0YY5t2GMANCPggxRda1XZCnzqKMNc27DGAGgHwUZoupar8hS5lFHG+bchjECQD8KMkTVtV6RpcyjjjbMuQ1jBIB+pCwR1eSaCU3dsVeXb7lLB9y1xExnnTJRbK/I1PNIkSRsw5xTjxEA6iJliahIt41PF9e6i3MG0G6kLJEE6bbx6eJad3HOALqBggxRkW4bny6udRfnDKAbKMgQFem28eniWndxzgC6gYIMUZFuG58urnUX5wygG0hZIkhomq+L6bZUPRNLW2vSk+i3YdOOGWnt9acdq42Tq1MPC2gMKUuMRLJtONYmDtYR/TZs2qH3X3/nnOPnnL6CogytQ8oS0ZBsG461iYN1RL/Lt9xV6zhQAgoyjESybTjWJg7WEf0ODHnnZthxoAQUZBiJZNtwrE0crCP6LTGrdRwoAQUZRiLZNhxrEwfriH7rTzu21nGgBKQsMVLdZFuq1GEKpP7iYB3Rb/qD+6Qs0SWkLBEVaTkAAB5EyhJJkJYDAKA+CjJERVoOAID6KMgQFWk5AADqoyBDVKTlAACor9GCzMyeaWa3mNlOMzt/wPdfbGZ7zOyL1a+XNTkeNG9yzYQufN5qTSxbKpM0sWwpH+gHAGCExra9MLMlkt4u6WmSdknaamab3f2mWad+0N1f1dQ4MH6TayayLsC6tC1Hm3BfAHRZk/uQnSppp7vfLklmdoWkMyXNLsiAsZm9Lcfufft1wZU7JIn/+CfEfQHQdU2+ZTkhqb8T7K7q2GxnmdkNZvYRM2MbZjSKbTnyxH0B0HVNFmSDmo7N3oX2Y5KOd/eflfRPki4deCGz88xsysym9uzZE3mY6BK25cgT9wVA1zVZkO2S1P+K1zGS7u4/wd3vdffvVV++U9Ipgy7k7he7+1p3X7t8+fJGBotuYFuOPHFfAHRdkwXZVkmrzOwEM3uYpLMlbe4/wcwe0/flcyV9pcHxAGzLkSnuC4Cua+xD/e7+gJm9StLVkpZIusTdbzSzN0qacvfNkl5tZs+V9ICkvZJe3NR4ShE7ida1ZBtNrPPUhvuyYdMOml0DaAzNxVskduNuGoEDYTZs2qH3X3/nnOPnnL6CogzAvGguXqDYSTSSbUCYy7fcVes4ANRFQdYisZNoJNuAMAeGvJMw7DgA1EVB1iKxk2gk24AwS2zQLj7DjwNAXRRkLRI7iUayDQiz/rTBe1YPOw4AdTXZOgmRxU6itSHZ1lWh6VdSt+Mx/cF9UpYAmkLKEshMaPqV1C0A5I+UJdBSoelXUrcAUA4KMiAzoelXUrcAUA4KMiAzoelXUrcAUA4KMiAzoelXUrcAUA5Sln1KSpilSul1Uew1DE2/Tq6Z0NQde2ck/846ZYLULQC0ECnLSkkJs1QpvS5KuYbcPwDIHynLmkpKmKVK6XVRyjXk/gFAOSjIKiUlzFKl9Loo5Rpy/wCgHBRklZISZqlSel2Ucg25fwBQDgqySkkJs1QpvS5KuYbcPwAoBynLSlsSZiGJvtD0XVNzzj25GXN8KZ83dR4793sCAF1HyrJF2pCezD35l/v4mtDFOQNALkhZFqgN6cnck3+5j68JXZwzALQNBVmLtCE9mXvyL/fxNaGLcwaAtqEga5E2pCdzT/7lPr4mdHHOANA2FGQt0ob0ZO7Jv9zH14QuzhkA2oaUZZ/ck2h1ehyGnJdyjKnkPr4mdHHOGK/c/+0E2oCUZYUkGgDUx7+dwPxIWdZEEg0A6uPfTiAOCrIKSTQAqI9/O4E4KMgqJNEAoD7+7QTioCCrkEQDgPr4txOIg5RlpatJtCbSUakSVyS9gPHr6r+dQGykLDusiXRUqsQVSS8AQI5IWWKkJtJRqRJXJL0AAG1GQdZhTaSjUiWuSHoBANqMgqzDmkhHpUpckfQCALQZBVmHNZGOSpW4IukFAGgzUpYd1kQ6KlXiiqQXAKDNSFkiCFtKAABQX2jKklfIMNLsLSV279uvC67cIUkUZQAARMBnyDASW0oAANAsCjKMxJYSAAA0i4IMI7GlBAAAzaIgw0hsKQEAQLP4UD9GampLiVKSm6XMoymsDwCMRkGGIJNrJqL+R7SU5GYp82gK6wMAYXjLEkmUktwsZR5NYX0AIAwFGZIoJblZyjyawvoAQBgKMiRRSnKzlHk0hfUBgDAUZEiilORmKfNoCusDAGH4UD+SKKUZeCnzaArrAwBhaC4OAADQkNDm4rxlCQAAkBgFGQAAQGIUZAAAAIlRkAEAACTWaEFmZs80s1vMbKeZnT/Pec83MzezkR96AwAAKE1jBZmZLZH0dknPknSSpPVmdtKA8w6T9GpJW5oaCwAAQM6afIXsVEk73f12d/++pCsknTngvD+R9H8lfbfBsQAAAGSryYJsQtJdfV/vqo4dZGZrJB3r7h+f70Jmdp6ZTZnZ1J49e+KPFAAAIKEmCzIbcOzgLrRm9hBJfy7pd0ZdyN0vdve17r52+fLlEYcIAACQXpMF2S5Jx/Z9fYyku/u+PkzS4yR9xsy+Lul0SZv5YD8AAOiaJntZbpW0ysxOkLRb0tmSXjj9TXe/T9KR01+b2Wck/a670xcJwNhs2r6bXpsAkmusIHP3B8zsVZKulrRE0iXufqOZvVHSlLtvbuqxASDEpu27dcGVO7T/BwckSbv37dcFV+6QJIoyAGPV5CtkcverJF0169jrh5z7pCbHAgCzvfnqWw4WY9P2/+CA3nz1LRRkAMaKnfoBdNbd+/bXOg4ATaEgA9BZRy9bWus4ADSFggxAZ73uGSdq6SFLZhxbesgSve4ZJyYaEYCuavQzZMB8Skm3lTKPLpq+T9w/AKlRkCGJUtJtpcyjyybXTHCvACTHW5ZIYr50W5uUMg8AQFoUZEiilHRbKfMAAKRFQYYkSkm3lTIPAEBaFGRIopR0WynzAACkxYf6kUQp6bZS5gEASMvcPfUYalm7dq1PTdF/HAAA5M/Mtrn72lHn8ZYlAABAYhRkAAAAiVGQAQAAJEZBBgAAkBgFGQAAQGJse4HspWreTdNwAMC4UJAha6mad9M0HAAwTrxliaylat5N03AAwDhRkCFrqZp30zQcADBOFGTIWqrm3TQNBwCMEwUZspaqeTdNwwEA48SH+pG1VM27aRoOABgnmosDAAA0hObiAAAALUFBBgAAkBgFGQAAQGIUZAAAAImRskSQOn0d6QEJAEA9FGQYqU5fR3pAAgBQH29ZYqQ6fR3pAQkAQH0UZBipTl9HekACAFAfBRlGqtPXkR6QAADUR0GGker0daQHJAAA9fGhfoxUp68jPSABAKiPXpYAAAANoZclAABAS1CQAQAAJEZBBgAAkBgFGQAAQGIUZAAAAIlRkAEAACRGQQYAAJAYBRkAAEBiFGQAAACJUZABAAAkRkEGAACQGAUZAABAYhRkAAAAiVGQAQAAJEZBBgAAkBgFGQAAQGIUZAAAAIlRkAEAACRGQQYAAJCYuXvqMdRiZnsk3dHwwxwp6VsNPwbq477kh3uSJ+5LfrgneRrHfTnO3ZePOql1Bdk4mNmUu69NPQ7MxH3JD/ckT9yX/HBP8pTTfeEtSwAAgMQoyAAAABKjIBvs4tQDwEDcl/xwT/LEfckP9yRP2dwXPkMGAACQGK+QAQAAJEZBBgAAkFgnCzIzO9bMPm1mXzGzG83st6rjP2dm15nZDjP7mJn9aN/PXGBmO83sFjN7RrrRl8nMDjWzz5vZl6p78obq+AlmtsXMbjWzD5rZw6rjP1J9vbP6/vEpx1+qee7Lq6q1dzM7su98M7O/rL53g5mdnG70ZZrnnlxW/fv0ZTO7xMwOqY5zT8Zgnvvy7urYDWb2ETN7ZHWcf8MaNuye9H3/bWb27b6v094Td+/cL0mPkXRy9efDJH1V0kmStkp6YnX8pZL+pPrzSZK+JOlHJJ0g6TZJS1LPo6RfkkzSI6s/HyJpi6TTJX1I0tnV8XdIenn151dIekf157MlfTD1HEr8Nc99WSPpeElfl3Rk3/nPlvSJ6udOl7Ql9RxK+zXPPXl29T2TdHnf3xXuSdr78qN957xF0vnVn/k3LNE9qb5eK+l9kr7dd37Se9LJV8jc/Rvu/oXqz/8p6SuSJiSdKOmz1WmfknRW9eczJV3h7t9z969J2inp1PGOumzeM/1/KodUv1zSUyR9pDp+qaTJ6s9nVl+r+v5TzczGNNzOGHZf3H27u399wI+cKelvq5+7XtIyM3vMmIbbCfPck6uq77mkz0s6pjqHezIG89yX/5B6r1RKWqrev2sS/4Y1btg9MbMlkt4s6fdm/UjSe9LJgqxf9ZLkGvUq5y9Lem71rRdIOrb684Sku/p+bFd1DBGZ2RIz+6Kke9QriG+TtM/dH6hO6V/3g/ek+v59kn58vCPuhtn3xd23zHM6f1fGYL57Ur1V+WuS/rE6xD0Zk2H3xczeI+mbkn5K0tuq0/k3bAyG3JNXSdrs7t+YdXrSe9Lpgqx6L/+jkl5T/V/MSyW90sy2qfdW5venTx3w4+wXEpm7H3D3x6v3f/anSvrpQadVv3NPxmT2fTGzx81zOvdlDEbck7+S9Fl3/1z1NfdkTIbdF3d/iaSj1Xs35ler07kvYzDgnvyCei+4vG3A6UnvSWcLsur/Ij8q6TJ3v1KS3P1md3+6u5+i3mcwbqtO36UHXy2Tejf27nGOt0vcfZ+kz6j3+YtlZvbQ6lv9637wnlTfP1zS3vGOtFv67ssz5zmNvytjNPuemNkfSVou6bV9p3FPxmzQ3xV3PyDpg3rwozD8GzZGfffkyZJ+UtJOM/u6pIeb2c7qtKT3pJMFWfWe8LslfcXd39J3/Kjq94dI2qDeh8glabOks6sExgmSVqn3GQ1EYmbLzWxZ9eelkn5Rvf+b/LSk51en/XdJf1/9eXP1tarvX1N9dgYRDbkvN8/zI5sl/XqV7Dtd0n0D3hbAIgy7J2b2MknPkLTe3X/Y9yPckzEYcl9uMbOfrI6ZpF/Sg39/+DesYUPuyTZ3f7S7H+/ux0v6jrv/ZPUjSe/JQ0efUqR16n3GYkf13rIk/YGkVWb2yurrKyW9R5Lc/UYz+5CkmyQ9IOmV1f/tIJ7HSLq0+rDlQyR9yN0/bmY3SbrCzDZK2q5eIa3q9/dV/2ezV71EDOIbdl9erd4HYh8t6QYzu8rdXybpKvVSfTslfUfSSxKNu2TD7skDku6QdF31OeQr3f2N4p6My5z7IukfJH3OelsomXpp/ZdX5/NvWPMG/l2Z5/yk94TWSQAAAIl18i1LAACAnFCQAQAAJEZBBgAAkBgFGQAAQGIUZAAAAIlRkAHInpk9ysw+YGa3m9k2M7vOzH65+t6TzOw+M9tuZjeb2Z/2/dyLzWyPmX2x79dJA67/h2Z2o5ndUJ1z2jjnBwBd3YcMQEtUG2puknSpu7+wOnacHuw7K0mfc/fnVJs/bjezv3P3a6vvfdDdXzXP9c+Q9BxJJ7v798zsSEkPW+SYH9rXgxUARuIVMgC5e4qk77v7dOcMufsd7j6nF52775f0RdVrnv0YSd9y9+9V1/iWu98tSWb282b2r2b2JTP7vJkdZmaHmtl7zGxH9arck6tzX2xmHzazj0n6ZHXsdWa2tXrl7Q0LXQAA5eMVMgC5+xlJXwg50cx+TL3WZp/tO/yrZvZf+r4+oyrcpn1S0uvN7KuS/km9V9T+xcwepl7vwV91963Vbuv7Jf2WJLn7ajP7KUmfNLPHTl9b0s+6+14ze3o1llPV26V9s5n9grv3jw0AJPEKGYCWMbO3V69Ybe07/F/N7AZJ35T0cXf/Zt/3Pujuj+/71V+Myd2/LekUSedJ2iPpg2b2YkknSvqGu2+tzvuP6m3I/yLpfdWxm9VrVzRdkH3K3aebET+9+rVdvYLyp9Qr0ABgDl4hA5C7GyWdNf2Fu7+y+pzXVN85058he6yk/1d9huyLsy80TNWb9jOSPmNmO9RrMPwFSYN6y9k8l7p/1nkXuvvfhI4DQHfxChmA3F0j6VAze3nfsYcPOtHdvyrpQkm/H3pxMzvRzPpfuXq8eq963SzpaDP7+eq8w8zsoeq9Hfqi6thjJa2QdMuAS18t6aVm9sjq3AkzOyp0XAC6hVfIAGTN3d3MJiX9uZn9nnpvK96v4UXXOyT9rpmdUH09+zNkr3D3f+37+pGS3mZmyyQ9IGmnpPPc/ftm9qvV95aq9/mxX5T0V5LeUb2S9oCkF1fpzNnj/qSZ/bSk66rvfVvSOZLuWdhKACiZuQ96RR4AAADjwluWAAAAiVGQAQAAJEZBBgAAkBgFGQAAQGIUZAAAAIlRkAEAACRGQQYAAJDY/we/eZVXqqzsTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(admission_data['GRE Score'], admission_data['Chance of Admit '])\n",
    "plt.xlabel('GRE Score')\n",
    "plt.ylabel('Chance of Admit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(admission_data['TOEFL Score'],admission_data['Chance of Admit '])\n",
    "plt.xlabel('TOEFL Score')\n",
    "plt.ylabel('Chance of Admit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(admission_data['CGPA'],admission_data['Chance of Admit '])\n",
    "plt.xlabel('CGPA')\n",
    "plt.ylabel('Chance of Admit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(admission_data['GRE Score'],admission_data['University Rating'])\n",
    "plt.xlabel('GRE Score')\n",
    "plt.ylabel('University Rating')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GRE Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.835977</td>\n",
       "      <td>0.668976</td>\n",
       "      <td>0.612831</td>\n",
       "      <td>0.557555</td>\n",
       "      <td>0.833060</td>\n",
       "      <td>0.580391</td>\n",
       "      <td>0.802610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOEFL Score</th>\n",
       "      <td>0.835977</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.695590</td>\n",
       "      <td>0.657981</td>\n",
       "      <td>0.567721</td>\n",
       "      <td>0.828417</td>\n",
       "      <td>0.489858</td>\n",
       "      <td>0.791594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University Rating</th>\n",
       "      <td>0.668976</td>\n",
       "      <td>0.695590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.734523</td>\n",
       "      <td>0.660123</td>\n",
       "      <td>0.746479</td>\n",
       "      <td>0.447783</td>\n",
       "      <td>0.711250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOP</th>\n",
       "      <td>0.612831</td>\n",
       "      <td>0.657981</td>\n",
       "      <td>0.734523</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.729593</td>\n",
       "      <td>0.718144</td>\n",
       "      <td>0.444029</td>\n",
       "      <td>0.675732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOR</th>\n",
       "      <td>0.557555</td>\n",
       "      <td>0.567721</td>\n",
       "      <td>0.660123</td>\n",
       "      <td>0.729593</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.670211</td>\n",
       "      <td>0.396859</td>\n",
       "      <td>0.669889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGPA</th>\n",
       "      <td>0.833060</td>\n",
       "      <td>0.828417</td>\n",
       "      <td>0.746479</td>\n",
       "      <td>0.718144</td>\n",
       "      <td>0.670211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.521654</td>\n",
       "      <td>0.873289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research</th>\n",
       "      <td>0.580391</td>\n",
       "      <td>0.489858</td>\n",
       "      <td>0.447783</td>\n",
       "      <td>0.444029</td>\n",
       "      <td>0.396859</td>\n",
       "      <td>0.521654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.553202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chance of Admit</th>\n",
       "      <td>0.802610</td>\n",
       "      <td>0.791594</td>\n",
       "      <td>0.711250</td>\n",
       "      <td>0.675732</td>\n",
       "      <td>0.669889</td>\n",
       "      <td>0.873289</td>\n",
       "      <td>0.553202</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GRE Score  TOEFL Score  University Rating       SOP  \\\n",
       "GRE Score           1.000000     0.835977           0.668976  0.612831   \n",
       "TOEFL Score         0.835977     1.000000           0.695590  0.657981   \n",
       "University Rating   0.668976     0.695590           1.000000  0.734523   \n",
       "SOP                 0.612831     0.657981           0.734523  1.000000   \n",
       "LOR                 0.557555     0.567721           0.660123  0.729593   \n",
       "CGPA                0.833060     0.828417           0.746479  0.718144   \n",
       "Research            0.580391     0.489858           0.447783  0.444029   \n",
       "Chance of Admit     0.802610     0.791594           0.711250  0.675732   \n",
       "\n",
       "                       LOR       CGPA  Research  Chance of Admit   \n",
       "GRE Score          0.557555  0.833060  0.580391          0.802610  \n",
       "TOEFL Score        0.567721  0.828417  0.489858          0.791594  \n",
       "University Rating  0.660123  0.746479  0.447783          0.711250  \n",
       "SOP                0.729593  0.718144  0.444029          0.675732  \n",
       "LOR                1.000000  0.670211  0.396859          0.669889  \n",
       "CGPA               0.670211  1.000000  0.521654          0.873289  \n",
       "Research           0.396859  0.521654  1.000000          0.553202  \n",
       "Chance of Admit    0.669889  0.873289  0.553202          1.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admission_data_correlation = admission_data.corr()\n",
    "admission_data_correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "sns.heatmap(admission_data_correlation,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>head_Size</th>\n",
       "      <th>Brain_Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5623</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5469</td>\n",
       "      <td>1762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4221</td>\n",
       "      <td>1235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4012</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5678</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5679</td>\n",
       "      <td>1864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5680</td>\n",
       "      <td>1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5681</td>\n",
       "      <td>1866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5682</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4512</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   head_Size  Brain_Weight\n",
       "0       5623          1530\n",
       "1       5469          1762\n",
       "2       4221          1235\n",
       "3       4012          1012\n",
       "4       5678          1863\n",
       "5       5679          1864\n",
       "6       5680          1865\n",
       "7       5681          1866\n",
       "8       5682          1867\n",
       "9       4512          1321"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brain_data = pd.read_csv('C:/Users/tp040576/OneDrive - Cerner Corporation/desktop/Maching Learning/Excel_Data Analysis/Brain_Data.csv')\n",
    "brain_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.42908357084414095, intercept=-608.5352062870027, rvalue=0.9575934196661959, pvalue=8.015339837183024e-13, stderr=0.028172731755741327)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope,intercept,r_value,pvalue,stderr = \\\n",
    "stats.linregress(brain_data['head_Size'],brain_data['Brain_Weight'])\n",
    "stats.linregress(brain_data['head_Size'],brain_data['Brain_Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Square Value 0.9169851573879992\n"
     ]
    }
   ],
   "source": [
    "print('R-Square Value',r_value**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42908357084414095"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-608.5352062870027"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intercept"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}