Regression.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "2798e9d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: seaborn in ./anaconda/lib/python3.9/site-packages (0.11.2)\n",
      "Requirement already satisfied: matplotlib>=2.2 in ./anaconda/lib/python3.9/site-packages (from seaborn) (3.4.3)\n",
      "Requirement already satisfied: numpy>=1.15 in ./anaconda/lib/python3.9/site-packages (from seaborn) (1.20.3)\n",
      "Requirement already satisfied: scipy>=1.0 in ./anaconda/lib/python3.9/site-packages (from seaborn) (1.7.1)\n",
      "Requirement already satisfied: pandas>=0.23 in ./anaconda/lib/python3.9/site-packages (from seaborn) (1.3.4)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in ./anaconda/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in ./anaconda/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (3.0.4)\n",
      "Requirement already satisfied: cycler>=0.10 in ./anaconda/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in ./anaconda/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in ./anaconda/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
      "Requirement already satisfied: six in ./anaconda/lib/python3.9/site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in ./anaconda/lib/python3.9/site-packages (from pandas>=0.23->seaborn) (2021.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a55d703",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Make NumPy printouts easier to read.\n",
    "np.set_printoptions(precision=3, suppress=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "908984d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-12 16:11:50.884297: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-02-12 16:11:50.884342: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc0a726b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('./College/Admission_Predict.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9d1c885",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <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",
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       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0           1        337          118                  4  4.5   4.5  9.65   \n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit   \n",
       "0         1              0.92  \n",
       "1         1              0.76  \n",
       "2         1              0.72  \n",
       "3         1              0.80  \n",
       "4         0              0.65  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8d88f44",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dataset = dataset.drop('Serial No.',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b8cab5e3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b5782bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ac942a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
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       "      <th>90</th>\n",
       "      <td>318</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0.64</td>\n",
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       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>321</td>\n",
       "      <td>114</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.85</td>\n",
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       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>321</td>\n",
       "      <td>111</td>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.90</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
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       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>328</td>\n",
       "      <td>116</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.08</td>\n",
       "      <td>1</td>\n",
       "      <td>0.91</td>\n",
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       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>301</td>\n",
       "      <td>102</td>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  \\\n",
       "90         318          106                  2  4.0   4.0  7.92         1   \n",
       "254        321          114                  4  4.0   5.0  9.12         0   \n",
       "283        321          111                  3  2.5   3.0  8.90         1   \n",
       "445        328          116                  5  4.5   5.0  9.08         1   \n",
       "461        301          102                  3  2.5   2.0  8.13         1   \n",
       "\n",
       "     Chance of Admit   \n",
       "90               0.64  \n",
       "254              0.85  \n",
       "283              0.80  \n",
       "445              0.91  \n",
       "461              0.68  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecd220f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04a5aae9",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
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       "      <th>9</th>\n",
       "      <td>323</td>\n",
       "      <td>108</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>8.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>340</td>\n",
       "      <td>120</td>\n",
       "      <td>5</td>\n",
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       "      <td>0.94</td>\n",
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       "      <th>28</th>\n",
       "      <td>295</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>327</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>338</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  \\\n",
       "9         323          108                  3  3.5   3.0   8.6         0   \n",
       "25        340          120                  5  4.5   4.5   9.6         1   \n",
       "28        295           93                  1  2.0   2.0   7.2         0   \n",
       "31        327          103                  3  4.0   4.0   8.3         1   \n",
       "32        338          118                  4  3.0   4.5   9.4         1   \n",
       "\n",
       "    Chance of Admit   \n",
       "9               0.45  \n",
       "25              0.94  \n",
       "28              0.46  \n",
       "31              0.74  \n",
       "32              0.91  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a56a17d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('Chance of Admit ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05b9ba1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_labels = test_dataset.pop('Chance of Admit ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8ac185cc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90     0.64\n",
       "254    0.85\n",
       "283    0.80\n",
       "445    0.91\n",
       "461    0.68\n",
       "Name: Chance of Admit , dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "18fd9762",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats = train_stats.transpose()\n",
    "\n",
    "type(train_stats)\n",
    "train_stats.to_csv('stats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "71c67b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def norm(x):\n",
    "    return (x-train_stats['mean'])/train_stats['std']\n",
    "norm_train_data = norm(train_dataset)\n",
    "norm_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cd7e738d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
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       "      <th>90</th>\n",
       "      <td>0.158788</td>\n",
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       "      <td>-1.087169</td>\n",
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       "      <th>254</th>\n",
       "      <td>0.426033</td>\n",
       "      <td>1.148913</td>\n",
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       "      <th>283</th>\n",
       "      <td>0.426033</td>\n",
       "      <td>0.650649</td>\n",
       "      <td>-0.057702</td>\n",
       "      <td>-0.860612</td>\n",
       "      <td>-0.509796</td>\n",
       "      <td>0.569985</td>\n",
       "      <td>0.880814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>1.049605</td>\n",
       "      <td>1.481089</td>\n",
       "      <td>1.717747</td>\n",
       "      <td>1.173330</td>\n",
       "      <td>1.706707</td>\n",
       "      <td>0.874360</td>\n",
       "      <td>0.880814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>-1.355601</td>\n",
       "      <td>-0.844142</td>\n",
       "      <td>-0.057702</td>\n",
       "      <td>-0.860612</td>\n",
       "      <td>-1.618047</td>\n",
       "      <td>-0.732064</td>\n",
       "      <td>0.880814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     GRE Score  TOEFL Score  University Rating       SOP      LOR       CGPA  \\\n",
       "90    0.158788    -0.179790          -0.945427  0.664845  0.598456 -1.087169   \n",
       "254   0.426033     1.148913           0.830022  0.664845  1.706707  0.941999   \n",
       "283   0.426033     0.650649          -0.057702 -0.860612 -0.509796  0.569985   \n",
       "445   1.049605     1.481089           1.717747  1.173330  1.706707  0.874360   \n",
       "461  -1.355601    -0.844142          -0.057702 -0.860612 -1.618047 -0.732064   \n",
       "\n",
       "     Research  \n",
       "90   0.880814  \n",
       "254 -1.132475  \n",
       "283  0.880814  \n",
       "445  0.880814  \n",
       "461  0.880814  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e1aa59",
   "metadata": {},
   "source": [
    "# Now we will be building the sequential model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b250c787",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(64,activation=tf.nn.relu,input_shape=[len(train_dataset.keys())]),\n",
    "        layers.Dense(64,activation=tf.nn.relu),\n",
    "        layers.Dense(1)\n",
    "    ]\n",
    "    )\n",
    "    optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "    model.compile(loss='mse',\n",
    "                 optimizer=optimizer,\n",
    "                 metrics = ['mae','mse'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "449b48e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-12 16:56:45.190623: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-02-12 16:56:45.190646: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-02-12 16:56:45.190661: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ansh-IdeaPad-Flex-5-14ITL05): /proc/driver/nvidia/version does not exist\n",
      "2022-02-12 16:56:45.190815: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b13cbe59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 64)                512       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,737\n",
      "Trainable params: 4,737\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf4043f",
   "metadata": {},
   "source": [
    "## Now let us try some examples from the train data only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "56ed676a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.091],\n",
       "       [ 0.245],\n",
       "       [ 0.285],\n",
       "       [ 0.698],\n",
       "       [ 0.441],\n",
       "       [-0.163],\n",
       "       [-0.159],\n",
       "       [ 0.078],\n",
       "       [-0.057],\n",
       "       [-0.076]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = norm_train_data[:10]\n",
    "example_result = model.predict(example_batch)\n",
    "example_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "216236d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "...................................................................................................."
     ]
    }
   ],
   "source": [
    "class PrintDot(keras.callbacks.Callback):\n",
    "    def on_epoch_end(self,epochs,logs):\n",
    "        if epochs%100==0 : print('')\n",
    "        print('.',end='')\n",
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(norm_train_data,\n",
    "                   train_labels,\n",
    "                   epochs=EPOCHS,\n",
    "                   validation_split=0.2,\n",
    "                   verbose=0,\n",
    "                   callbacks=[PrintDot()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "73a76fd4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.tail of          loss       mae       mse  val_loss   val_mae   val_mse  epoch\n",
       "0    0.043000  0.175671  0.043000  0.036127  0.149968  0.036127      0\n",
       "1    0.022483  0.124880  0.022483  0.024328  0.115390  0.024328      1\n",
       "2    0.014222  0.097524  0.014222  0.018197  0.099164  0.018197      2\n",
       "3    0.010800  0.082471  0.010800  0.016462  0.097389  0.016462      3\n",
       "4    0.011449  0.086380  0.011449  0.014299  0.094001  0.014299      4\n",
       "..        ...       ...       ...       ...       ...       ...    ...\n",
       "995  0.000297  0.014492  0.000297  0.007340  0.062666  0.007340    995\n",
       "996  0.000146  0.009571  0.000146  0.007958  0.066013  0.007958    996\n",
       "997  0.000308  0.014678  0.000308  0.007515  0.064967  0.007515    997\n",
       "998  0.000354  0.015787  0.000354  0.007788  0.065699  0.007788    998\n",
       "999  0.000143  0.009660  0.000143  0.006794  0.060827  0.006794    999\n",
       "\n",
       "[1000 rows x 7 columns]>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8a590a",
   "metadata": {},
   "source": [
    "### Now we will plot and look why validation loss is increasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f4bb5a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plot_history(history):\n",
    "    hist = pd.DataFrame(history.history)\n",
    "    hist['epoch'] = history.epoch\n",
    "    plt.figure()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean absolute error (Chance of Admit)')\n",
    "    plt.plot(hist['epoch'],hist['mae'],label='Train Error')\n",
    "    plt.plot(hist['epoch'],hist['val_mae'],label='Val Error')\n",
    "    plt.legend()\n",
    "    plt.ylim([0,0.5])\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean Squared error (Chance of Admit)')\n",
    "    plt.plot(hist['epoch'],hist['mse'],label='Train Error')\n",
    "    plt.plot(hist['epoch'],hist['val_mse'],label='Val Error')\n",
    "    plt.legend()\n",
    "    plt.ylim([0,0.05])\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0c98000a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BesDAYG8kIjcBNwE0adLE48cbY4zxwmuvoV4i8pOIrBaR9f5HQbsFWZfril9Vv1DVtsDFwL+DvZGqjlHVeFWNr1u3rpeQjTHGeOS1jeBt4E5gAVmDzhUkAVeF5BcHbM1jW1R1poi0FJE6qrrb42cYY4w5Rl4TQaKqfl/I954HtBKR5sAW4ArgysANROREYJ2vsbgbEAXsKeTnGGOMOQb5JoKA6Smnich/gIlkH2toYV77qmqaiNwGTAbCgbGqulxERvhefx3X4HytiKTipr4cYiOcGmNMyZL8zrsiMi2ffVVVzyz+kPIXHx+v8+fPL+mPNcaYMk1EFqhqfLDX8i0RqGq/0IRkjDHmeJFvryERGSkiNwRZ/3cRuSNkURljjCkxBXUfHQ68H2T9GN9rxhhjyriCEoH67grOuTKF4PcJGGOMKWMKvKFMRE7wss4YY0zZVFAi+A/wrYj0EZGqvkdf4Gvg2VAHZ4wxJvQK6jX0nojsAh4DTsINEbEceKQIN5gZY4w5DhV4Z7HvhG8nfWOMKacK6j76oIjUyuf1M0XkguIPyxhjTEkpqESwDPhaRJKBhcAuIAZoBXQBpgBPhDJAY4wxoVVQG8FXwFci0groBTQADgAfADep6pHQh2iMMSaUvE5MswY3cb0xxphyxvPk9cYYY8onSwTGGFPBebmzOFxE7iyJYIwxxpS8AhOBqqYDg0ogFmOMMaXA61SVv4jIy8DHwCH/yvxmKDPGGFM2eE0EPX0/HwtYp0CJz1BmjDGmeHntPmozlRljTDnlqdeQiFQXkedFZL7v8ZyIVA91cMYYY0LPa/fRsUAS8Bff4wAwLlRBGWOMKTle2whaquplAcujRGRxCOIxxhhTwryWCI6ISG//goj0AmycIWOMKQe8lghGAO8FtAvsA64LTUjGGGNKUoGJQETCgatVtbOIVANQ1QMhj8wYY0yJ8DJDWbqInOx7bgnAGGPKGa9VQ4tEZBLwKdnvLJ4YkqiMMcaUGK+JoBawh+x3EitgicAYY8o4r20Eu1X17hKIxxhjTAnzOvpotxKIxRhjTCnwWjW02NoIjDGmfLI2AmOMqeC8jj56fagDMcYYUzq8jj7aWkSmisjvvuVOIvJgaEMzxhhTEryONfQmcB+QCqCqS4ErQhWUMcaYkuM1EVRW1bk51qUVtJOInCsiq0RkrYjcG+T1q0Rkqe8xW0Q6e4zHGGNMMfGaCHaLSEtcAzEiMhjYlt8OvvsPXgHOA9oDQ0WkfY7NNgB9VLUT8G9gTCFiN8YYUwy89hq6FXeSbisiW3An8KsK2Kc7sFZV1wOIyARgELDCv4Gqzg7Y/lcgzmM8xhhjionXXkPrgbNEJBYIU9UkD7s1AjYHLCcAp+az/Q3A98FeEJGbgJsAmjRp4iVkY4wxHnmtGgJAVQ95TAIAEuwtgm4o0g+XCO7J43PHqGq8qsbXrVvX48cbY4zxwmvVUFEkAI0DluOArTk3EpFOwFvAeaq6J4TxGGOMCaJQJYJCmge0EpHmIhKF6246KXADEWmCuzv5GlVdHcJYjDHG5MFTiUBEKgN3AU1U9a8i0gpoo6rf5LWPqqaJyG3AZCAcGKuqy0VkhO/114GHgdrAqyICkKaq8cd0RMYYYwpFVINW22ffSORjYAFwraqeJCKVgDmq2iXE8eUSHx+v8+fPL+mPNcaYMk1EFuR1oe21aqilqj5D1p3FRwjeGGyMMaaM8ZoIjvpKAf4byloCKSGLyhhjTInx2mvoUeAHoLGIjAd6ATYiqTHGlANebyj7UUQWAD1wVUK3q+rukEZWzLbsP8L8jXvp17Ye1WIiSzscY4w5bngdhnqqqu5R1W9V9RtV3S0iU0MdXHFatGkft09YzPbE5NIOxRhjjiv5lghEJAaoDNQRkZpkNRBXAxqGOLZiFRHmQk/PKLiXlDHGVCQFVQ3dDNyBO+kvDFh/ADeyaJkRJpYIjDEmmHwTgaq+ALwgIn9X1ZdKKKaQiAi3RGCMMcF47TWUKCLX5lypqu8Vczwh4y8RpFkiMMaYbLwmglMCnscA/XFVRWUmEUSEuXbxDA93UhtjTEXitfvo3wOXRaQ68H5IIgqRcF9jcVq6JQJjjAlU1NFHDwOtijOQUAu3XkPGGBOU19FHvyZrUpkw3BzEn4QqqFDITARWNWSMMdl4bSN4NuB5GvCnqiaEIJ6QySoRZJRyJMYYc3zx2kYwI9SBhFpU2kFay2Y0tWNph2KMMceVgu4sTiL4PMMCqKpWC0lUIVAtYTo/Rt/DrKQOQJPSDscYY44bBd1QVrWkAgm1sIgo9yQttXQDMcaY44znyetFpDNwum9xpqouDU1IIRLuRhzNSD9ayoEYY8zxxevoo7cD44F6vsd4Efl7/nsdX8LCfSWCDCsRGGNMIK8lghuAU1X1EICIPA3MAcrM+ENhEb4SgVUNGWNMNl5vKBMgPWA5nTI2Z7E/EZBuicAYYwJ5LRGMA34TkS9wCWAQ8HbIogqBzMZiSwTGGJON1/sInheR6UBvXCK4XlUXhTKw4iYR1kZgjDHBeB1ioiWwXFUXikhf4HQR2aCq+0MYW7EK91UNqZUIjDEmG69tBJ8D6SJyIvAW0Bz4MGRRhYBVDRljTHBeE0GGqqYBlwIvqOqdQIPQhVX8/CUCMtJKNxBjjDnOeE0EqSIyFLgW+Ma3LjI0IYVGVonAbigzxphAXhPB9cBpwOOqukFEmgMfhC6s4hcZGe2eWNWQMcZk4ykRqOoK4J/AchHpCGxR1adCGlkx85cI7IYyY4zJzmuvoYHA68A6XPfR5iJys6p+H8rgilWYO1TrNWSMMdl5vaHsOaCfqq6FzO6k3wJlJxH4xhoSayMwxphsvLYR7PQnAZ/1wM4QxBM6EdFkIISlJ5d2JMYYc1wpaGKaS31Pl4vId7h5ihW4HJgX4tiKlwjJRBOedqS0IzHGmONKQVVDFwY83wH08T3fBdQMSUQhlCwxhKcHJAJVWDAOOv4FoquUXmDGGFOKCpqh7PpjeXMRORd4AQgH3srZ00hE2uIGtOsGPKCqzx7L5xUkWWKIDEwE66fDN3fCloUw6OWs9R8Oga2L4J+rQxmOMcYcF7z2GorBzUnQAYjxr1fV4fnsEw68AgwAEoB5IjLJ1xXVby/wD+DiQkdeBCkSQ2RGQCJIPex+HtqdfcPVP5REOMYYc1zw2lj8PlAfOAeYAcQBSQXs0x1Yq6rrVfUoMAE3fHUmVd2pqvOAEunTeTRniaBsTalgjDEh4TURnKiqDwGHVPVdYCDQsYB9GgGbA5YTfOsKTURuEpH5IjJ/165dRXkLAJLDKlM1fX+QV7TI72mMMWWd57GGfD/3i8hJQHWgWQH7BLvcLtIZV1XHqGq8qsbXrVu3KG8BwKpKXWmWth4ObPVFaCUCY4zxmgjGiEhN4EFgErACeLqAfRKAxgHLccDWQkdYjDZU6eqebFmY/QW1EoExpuLyOtbQW6q6T1VnqmoLVa2nqm8UsNs8oJWINBeRKOAKXBIpNYdifTVTSdt8awpRIjiwFR6tDgkLij0uY4wpTV5LBIXmm7/gNmAysBL4RFWXi8gIERkBICL1RSQBGAk8KCIJIlItVDFFRFd1T44eyhltwTuvn+5+zh1TnCEZY0yp8zrWUJGo6nfAdznWvR7wfDuuyqhERMZUJkOFo4cPuD6wOdsIkrZDwvys5b3rIboaxNYhs/SwdAJcWlBhyBhjyo6QlQiOR79vS+Iw0cTMfg5e6ZG7beClePj4qqzlF7vCs63dcy8Ny+mpkJaSe/3qyaGZByEjHTb+XPzva4ypUDwnAhHpKSJXisi1/kcoAwuFI0fTqSK+Qed2rQRNd8/9CeFokFsj/Nt4aU94sx/8X73s6zbMhA//AtOeKFLM+Zr1PLwzEDbMKp73O7ANnmsLu+yOamMqEk+JQETeB54FegOn+B7xIYwrJJ69vHP2FRP8V/8e2ggCSwQTbw6+zfZl7ud3/4LEBPf88B73c+86z3F6ttN3k/byiW5IjGO14ivXkG7tIMZUKF7bCOKB9qplu59li7o5B5YrzOEEJIJg7QSBVTRz34Ddq2DoBEj13cmsGYUJ1Rv/e84f6x6PJgbfbtdqqFQTqhRwD8be9e6n3V9RPmz/HWq3hMhKpR2JOc55rRr6HTfERJn3kNxWtB1znhxz5sR3BmZfTj3i2hi+vMUtr/y6aJ+bH6/J5ZVT4IXOwV9LT3MlozmvugQGrkSwa1XWNhnp8NPDkLjFe2x/zoZF471v70VGOqydEvy+D1XYtqR4Py8/25e57sSBv6fjSXIivN4LvvxbaUeSW0a6zR1+nPGaCOoAK0RksohM8j9CGViopEdUzr3SS0Fn6SfZl/0D1uX5QUcD7lfwr0sr+HO82LMOpj9duFJG6qGshuyjh+CXF9wXcsE4+OMbmHxf9u3f7J/1PGGe2/6rW93yn3NgcwHTUYw7D77ynYQWvOMSw7Fa+C58cBks+yz3a/PHwhtnwNqpee+v6pJTru7DReCP4Y9v3c/da4r+XolbYPID7u+Rl/Q0+PV1SPM4w57/GP0l1YwM9yguiVvg3Qvh8N7C7zv2HPh3nazllIP5H3swqrB3Q+E/Oy8pSXC0gO90OeY1ETyKGyH0Cdy0lf5HmZMQc2KQtQUkgl2rYM3k7OsO7c7/ixXspJ+0DVb9AJt+c8vJifBaL/jlRVjzU/Ztd67Mu9H2g0th+hNwMMgkcduXZVVH5eRvyJ72hLvCX/4FpBwIvm1gw7n/6s2fSMadC2+fFXy/YL6+3SWGY+X//I0z3YnoyD63nJIE3450z/3VW8H8+YtLTpMfcMtJ272fyLYthTmv5PG+s+HleFjwLiz9tPAnx8+Gw5yXYcfvWetSDrreZuMGwtPNYd6b8MM98GseMeSU+T/g+98eezb8p0XwbZMPZCW2pO3w3d0FJ5zZL7qOEEs+ylq3bWn27td5SQi4iMhIhycbwff/Kni/QEs/hhe7wML3C7dfXp6MgycawOIPXSeMrYvy/18qDvs3Ffy/cvSwK81PGRXSULzeWTwj2COkkYXI/MTq3J16U/aVmgFvn533Tv7qnUBf3AyP1cw6qeS0Y1nudYkJ8NEQ96XctgT2rHVf/p8egvGD4bl2Wb2LXu3hqnR2LM/9PikH3c/0HF1VH60Or/eGx+vD5zf6ji1HkktNhrlvuudHD0JkbPD4wW23b2PW1aXk+HcJ9kVJT3UnksDlnPv8/jk83sBd4frtWAHvXADTfVNWLPvMXfGpui+Maua806Qegf+2h1dPcyWVMf2y3idpe/arS9Ws30Gqr8fYgnGw7n/wXBt476Lcx5CRDos/yl6d98bpMPn+4Mnfn5DnvAwTb4SJN7mksG4arJ/h/i5PNcn6u+W0x1eamHC1+/0c2uOO78O/wJ8/w5G9WSWPYCeOz25wJcSUg+7iArKXRlOPuJPvkX3B/1+/vAU+v8ENvfLd3a5q0H8DZV4k3P0M/F2/cTq81R9+e8MlZ7+8qoFUs45nwTt5f1bygawEs366O/l/4euwMek2+H2iez77ZXd8Sdvzjx3c7zjY3/LLW2DqKBjT11XtHos1U9xFUHpa8BEJRneEZ5q7559en/17Ay4pPdHAfQd/fv7YYimA1/kIegAvAe2AKNxEM4dUNWR3AYfKDb2bs29Gjn/MI/vyr1/eEuSPuGmO+znnZWh9rrcPn/1i1vMF70DbHO0KSVthxtPQ7/6sdUsmwNn/ds8/HALNesNh3/wJeZ1YAJZ9Cpe84aqoAu3bmJVANMNdaeblu39mX87ZTvLl32B4jrkbVnyVvdeRv/eU35tnZl3J/3AP9Bjhnr92mvu5cZa78v/5eYioBJe/45JnXPesz1/2qfuZtM2VbALNehYy0mCA7wpqwlWwbircvjR7Vdr7l7if25fBko8hLh6qNnBzUXwWMB/T2Y9Dx8uzlrctgvnjsv5fRLLed/8m93Pzb7A2RwkvORE+vhqu+cKdAFd9B016uBOav2dZ4iZ3IZCSlHVC9/Nvsydg6vAdK1zJ0J+wpj/hTtCP7M3eZvV4QPPenJfhnMfd/1WLvhBb11UNgiv5rvTV+H54eVbng02/wuyX3P/rzpVw1igI810UaJAqne//5R5/XwgvdXPrhk+G+p3c/n6jamQ9D4vMep6e6n5HSdtcJ4ePr3KljzuWwXvZRrJ3Prve/b/4e+3NeRnaXeiq6271lb5TDrrvbKsBLnE/2wp6j4Qz7oaoINXFBVk03v0tznok723GX+Z+VmsE03z/R91vcif4Dpdkbbdloev5B1C9MfT6B+zfnPsC9MMh8Jf3ISKq8PEWQLx0BBKR+bixgj7F9SC6Fmilqvfnu2MIxMfH6/z5HoqfeUhOTeeNUTdye8TEYoyqCMKjcp+k/R5NdFeRfnVaw0UvubrVQLH14FCQ6iG//o9Awy5ZJz2Afg+4f0pwX5bCNmKP+MU1Qvo//8afshqiR66E59tl3/6Kj2DCUPe8WhwcSMj9+qbZ7kRTXMIioU4rqNEka5Khlme6L2Kw0h1Ao5Ph4C53Mi6M3iPht9cLbjPKJNDtGlj4HtTvmHXy8mvcAzb/mv9bNIqHLfl8B675IvvfPKfuN2d1DLh5pmtbCcafCN6/1CVTv8Hj3Il5wTi3fNJg6HGLKw3kpWE3aH4G/DI6722u/BSqN3JXxylJ7sIoUMszXUmuMK75wu33Qmd3EXTV51knaL/rf3DVncH8faHreeWn6mJ7yjee5v3bXA/BP2dDq7Pd/x24thl/Mi7M96xSLXdRsubH4K9fPRFOzOf3nA8RWaCqQbv9e04EqhovIktVtZNv3WxV7VmkiI7BsSYCgKvuf4rxUU8WU0QhcN3X8NGVwW9wO571uiP/L7oJraa9XDtIcSnoQqM4hEVCRhnqQVSjSVbJD6Be+6z7eUrC+c9C978Wadf8EoHXxuLDvhFEF4vIMyJyJ5BP5fJxrkVfVmY0Lni70vLuhWUvCUDZSQJnPVrwNkPGQ7PTi+8z216QdxVijSb57xsWpAY3qgpUDuh5M3wyDPsWOl/p2n26Xp31Wp3W8Mh+uC/BXbUG/YzIrDgve9s9L0oS6D0ye7VHnTbBt4usDHGnwMO7oXawDhweNO0FZ/zLVRv6nZPHBV7O3+Ets+G0IF3JG53sfpeXvuVKsDntz1Fi9JoEarVwSaN6Pn/r9kGqvXLaE4IbU/FeImgK7MC1D9yJm5jmVVVdm++OIVAcJYK1Ow9y9vPTqEMic2NuLfwbnP5P1yC1+IPC79u4hyv+rpiU/Urogv+695xR0DQPQVQ5AWKqw25fL6PW58Hq7/PefugE1zMiYa5bvms1fDkie7H7xqkQEZNVDZSfZqe7uv1A922BtGT4j69Y3biHq2P+6SG3fM4TrvEV3Beu/SDoeZtr1Pwkx+glF4yGmc/CSZe69gF/Q+jJw6BybZj1nKv2SU50Ma+cBCdf7z5/yUfu/eu1hRMHQNX60OY814OqWiPodXv2HithkXBCe1dlkprsqn2mBNQDV4tz7Rb12rk49qx1J9qJN7n2gkvfhJrNXLvB5rlZVQ6P7M9q4/j2Lpj3VtZ73rUaDmyBiGg3XEinv7i68cN7XTXe2f+XvR47cQuER7pk8ESD3FeJqlmftf13F5+/Xjk9DRI3u0beyrVcHI1OhoZd3d+w9bkujtkvwY8Pun1iasB5T7sG69g6rgPDWaNctePKb1yng9bnur+F/3N+fd0dQ+chrgF3xST3O+l6tTu+nNUtv3/ubnzbstAlq8TN0Kibe88F77h2g92rIX44LP8S+t7jkklEtDuWT69zJ9sBj8GMZ9z6ky6DnX9Ag06uLST9qGv3an1uVvvcmimwczl0uBRq5Lg4PLzX/X2r1nefv3GW6933xzfQ5nz3P/vFza6t5YqPYMZTsPZ/cMlrWb2vGnZx38fOQ9x77t0AG2a4WE84yR1341NdjOCGeZn7BhzZ7/7Gfe9znTWiq7oqqar13foiOOaqId+bVAKaqGqp3kFTHIngQHIqnR51dXCnhS3no6jHs17sfCUs+dA1bMUPJ6PWiYS9d4H7B46tCz//F/o/DKfflVWPf8kbWb0YAK78xDWwBbb0Xz3RnTyqNXTL6Wkw8a/un2jNj+5kV6Wuq0v8OOBqLn64SxK7VsEr3d0VV/U4112yfic4+Tr3j7ZvI7xzPtwyx53IUo+4k2biFjjxLKh/Eqz6Hn58CG6b506YiZvdyaqTrzE0Yb6r5w2LdFdqAPv+hE+ugTYDXWNk097Q+BR3NdryTNfIe9Yo98+Zkea+tOc/m3c95sGd7rNjqsEfvgbTyrWyb5Oa7E4aRWnEC5SR7o6xZrPcrx097OIIy1EoVnVf+sAGudRkN0RI3Xa5ty9I6hE3l0Xgic/vwDaIruK+5Mer9DT3d42MKe1Ijj8Z6e57Wac1hId0IOdiURxtBBfixhqKUtXmItIFeExVg/S9C63iSASqSvP7AkfHVi4J+5n/Rr1Gxj2bWTJvBqvS6lO9bhy3jF/Ixzd049QT68P2pa5h7da5ULeNa/0/oQM06Ox6Q0RWcjdadRzsrsjmj3O9fKrWL9yXPSXJFe8Le9IpDgnzXe+Z6kGml04+4K60IqKz1h3Z54bqDgsvuRiNMYVWHIlgAXAmMF1Vu/rWZTYcl6TiSAQAm/ce5vRnpnna9qYzWjD45DgW/rmPK7o3YdwvGxj19QpWPHYOlaOO/ysBY4zJLxF4PYulqWqilKPByOpX917UHTNzPWNmupunTm1Rm1Ffuwaiycu3c2rz2jSsYYN6GWPKLs+DzonIlUC4iLQSkZeAYhg8pvREhofx3yF5DMSWjxHvZ91cdufHS+j51P9odu+3TFyYkM9e2c3dsJcyPpCrMaYc8ZoI/g50AFKAj4ADwB0hiqnEdG1cE4AHB7bjws4NPe2zakfwbp2vz1jHN0u3MnXlDno+OZUDyancOn4hP6/ZnW27qSt38Jc35vD+r38eW/DGGFNMPPcaOl4UVxuB39G0DKIiXD4c9fVyxv2ysdjeGyBMYP2Trqvaks37efvnDUxaspXLusXx+cIELu3aiOeHdCnWzzTGmJyK3EZQ0FDTpdFrqLj5kwBAgxztBqc0q8m8jfuO6f0zFPYfPkq1mEgGvZJ112fCPjckwcRFW3js4pOICBOiI8I4mp7ByE+WkHg4lfeGdycsrPy0yxhjjk8FVQ2dBsQBs3DdR5+jjA9DnZ/2DbLG94mKCOPTEcUzgkaXx36ixf3fZVv324asUSQnLd5K24d+4OkfVnHe6Fl8u3QbP6/dTYv7vyM5NZ1f1+9h4IuzSE7NGuBryoodLNq0jwHPz+CrxVkTxqSk5T2u+7pdB3nq+z+ytU8s3LSP9IyyVSo0xhSvfKuGRCQcGAAMBToB3wIfqWqQsZFLRnFXDeW0LCGRtbuS6Nq4Js3qxNLs3m9D9lleNK5Vic17s88v8Le+LXl1evZbzR+9sD0R4WE8+OXvXH5yHKc0r0X3ZrWYtWYX9arFcE6H+pz53HTW7zpE92a1+GTEaczbuJfLX5/D3ee04dZ+2W/zP5SSRuWocMpTTzFjKrIiVw2pajrwA/CDiETjEsJ0EXlMVYtxuMjjR8e46nSMyyoZ/HDH6XyzZBsvT1vL3888kXrVYnjoy9+z7VOjciT7D4dm4KycSQDIlQQAHv06a8yTTxck8OmC7L2YXr+6G+t3uXkF5m7cS3JqOlNW7ABg5bbsk9PsP3yULo/9xO39WzG0exMiwoU6VaJRVdbuPEhyaka231FhpaVnMHPNLvq1qWeJxpjjQIGNxb4EMBCXBJoBk4CxqlqICWyLT6hLBHmZv3EvJzWqTkxkOJ0encyBZDcDWf+29Xjlqm4s33oAVWXw63Ny7Xv3OW34z+RV9GxZm9nr9pR06J58fFMPhoz5lY/+2oO6VaM46/mZ2V4/q90JREeG8e1SN87PxqcGkpGh/GPCIq7u0ZQWdWLZcSCFjnHV2bD7ELVio6gUGU5URBg3vjuf6at2svaJ8wF4+X9rePbH1Ywc0Jqb+7Rgzro9tD6hqt2PYUwIFfnOYhF5FzgJ+B6YoKq/57lxCSmtRBDoaFoGm/cd5uz/zuS7f5xOm/pu+IjcQ1c4G548n1U7kmhbv1qpVzWFWqXIcI742jKiI8IYc2081411g9sN69mMK09twtuzNvDx/M0AXNylIV8u3kq9qtHMfeAstiUeYfqqXVzQqQFVYyLZvPcwtWKjCA8TlmzezynNauXbgK6qpKRlEBNZ9CEv/N+J4iyt/Lh8O/sOH2XIKQWMNGpMiBxLIsgA/DN9B24ogJbGDGXHQyLIT84T/bvDu9Ondd3M5V/X7+GKMbknHnnr2nhWbjvA/iOpvP1zMU7KXYY8NqgDD3+Vu/mpa5MaVK8UyfRVuwD4R/9WjBzQmm+XbmP9roM0rlWZb5dt481r43ny+5W8MWM9q/7vXKIjsieDQylpDHxxFhd1bsjIs4MPj5yeobS8/zv+1rcl7RtW4+z29bP1LMtJVfl0QQLnnVSfqjF5jwrp/7/Y+NTAPLdJz9DMLsW7DqYQFR5G7SrReW5vTGEUy+ijx4vjPREcSE7lx+U7eOCLZaSkZQT94je791s3w6HvVx+4zaOTlvPO7I0A/OvcNkRHhLPnYAqvTl9Hx0bVeWBgO9buPMiDX+YunNWOjWLPoQImHS/HTqxXhbU7c0/fGVezEi8O7cr9E5fxx3Z3Q+BfT29OhsL957dj3sa9LNq0n2mrdtK/bT2e/P6PzH2jIsJ4YUgXzulQn8e+WUHXJjX4Y3sSdapE06hGJUZ84O40v/zkOO4Y0Jp9h47y8v/WctuZJ3JSo+qZpQt/STG/RDBh7ibunbgsW2eAvLY/kJxKVHgYMZHhbN1/hAbVY9h/OJWEfUcKbL85fDSNRyctZ/WOg7x+9cmFGm7F71BKGrHRNs5WWWKJoBTsTEpm76GjtK2fu9A0d8NeGtaI4bLXZlM1JpIpI/tkvrZ4834ufuUX/ndXH1rUrQLArqQUTnl8CqMu6sB1PZsBcNlrs1nw5z5evrIrt324CIBLuzaiWqXIzETi17B6DD/fc2auLqzBtGtQLVfjcXl2TY+mIbvL+6YzWvD5goRsyfmzEaexaNN+ru3ZFFX475TV9G1dj+S0dK4fNy/Xe7x+dTca1ajM+t0HadegGknJaSz8cx+Pf7eStvWr8szgTlz08i9c3aMJH/zqJk154pKODO3emK2Jydz+0SL+dW5boiPC6Ny4BrPX7ebKN3/L9hnvDu/Oln1HuOKUxpnVbiu3HeDw0XROburuvk884hLPxj2HOJSSxuDX5/Dc5Z257GQ3eUtyajqR4WGEhwnJqem8N2cj1/dqzmpflWh4kOq8xMOpVKsUkasKbuv+I3w8bzNrdx3kkQvbczQtg0Y1KvHr+r30aFHLc5XdtsQjhIlwQrXciS4tPYO9h49Sr2rFGV7bEkE5kJyaTnREWOaX4GBKGruSUmheJzazFHHVqU0Y0P4Eho2bR5/WdWlRN5aUtAzuPrsNNWOjMqsnKkeFc/ho9vsNerasTWp6Bp+O6En8/01h90E3wf05HU5g8vIdQWM6uWlNFvy5jzpVoth98CgXdW7IpCVbg25rjn/dmtSga5OajP1lA4GnhQbVY9iWmExczUok7Mvei615nVi++8fptHv4By7s3JAbezfPduOk3zODO9G3dV32H0nlj+1J/Lh8O98s3Ub1SpE0q12Zr27rzYHkVIaNncvCTftz7X/HWa0YPWUNzwzuRPsG1fhm6TauOa0pdatEExURxleLt3D7hMW0qBvLC0O6smZnEiM/WQK4UtWA52eQocrIAW1odUIVJszdzNhfNvDqVd3YczCF01vVpVmdrEkXdyWlUKdKFMmpGZw9egYPnN+O1idU5ZVp62hRN5YmtSrTp01dpq7cwSVd41i57QC7klI4I6AaOD+qyvjfNtHrxDqMmbmORy7skNmu5f+uH0xJ4/1f/+TmM1oGTaSFZYmgnEtNz+C5H1dzS9+WVImO4D+TV3FD7+bUrZq9fjmwnvqH37dnVmv8dOcZtDoha76EG9+dx5SVOxnWsxkNqsdkVpXMvb8/t364kHkb9/HBDafSrkFVXp62lqtObcJZz8/ko7/24NYPF7I3j+qpZrUrs3FP3pO8P3lpR/73x05+8nVrrV4pkqcv68gXi7bkmYxy6t+2HlP/yD3F4uQ7zuCc0TNzrb+1X0temRaa6f+Mdy3qxLJ+96GCNyyCJrUqs2lv3v93gSLDhdR0b+fE2KhwDh1Np1GNSmzZ7xLkxL/15KPfNrFw0z7W7TrEXQNa8+2ybVzYuSFLE/ZTs7K7aJqyMvv/8xWnNOa8jg24bfxCklJcj8TOcdVZkpDIvwd1oHrlKD5fkMB/h3ShVmxUrli8sERgANjg+6I19135fL1kK2e0qkv1ytkbOfcdOspXi7dwXc9mqMKbs9bTtHYs555Uny37j/DmzPU8OLAdEeG5G1EXbdrHv79ZwcJN+xk37BSqV45k0uKtDOrSkK5NarJi6wHOf3FWrv3AJaiV2w5w3gvu9Z4ta/PhX3sA8M3SrZlVYL1PrEOLurG8N8dV6Wx48nwmL9/OWe1OYPO+I/R7djoAb14bT63YKOpVjaZxrcp0+/dPuZLUxqcGcjAlje+XbePuz5YW5ddaKFee2oQPf9tEfNOajL3+lMyZ8rx68tKO3DdxWYiiM8e7MdeczNkd6hdp3+KYj8CUA80Dir5AniOu1oyNYliv5oCbaO3mPlnTLDaqUYlHL+qQ52d0bVKTiX/LPs9xtyY1M5+3b1iNfm3qMs3XAyindg2qZXZBHX1Fl8z1F3RqyBmt6/LKtLXcNaANURFh1KwcRc+WtRERzj2pQa5j7N+2Xraupu9e350LX/6Zuwa05qY+LTKrP6pER3B5fGOiIsK4fcJiAD76aw9qVI7MTErXntaU9+b8yYD2J2SWWPwGdWnIc5d3Zu2ug5w72m3f68TaNKlVmUMp6azffZDft7h2l/NOqs+t/U6kZuXIzEmNqkZHZF4FBvP4JSdxYeeG/LJmN2d3qE9UeBh3fbqEMdeczIzVuxj/26Y89/Xb+NTAzOqT/Azs2IBvl23LXH7hii58uWhLnn+vwN/B7f1bceZzM/LcpkPDaizfWnHan0JhR1JKSN7XSgSmxKkqqvDLut1UigynRd0qHEl1RWxwDZPpGVrkIvD43/7k0/kJfHlrr1yvLd+aSLv61fK8F0FV+WN7Eu0auEb+4e/MY/Hm/Sx48CxUXW+d73/fzhWnNEZE+HPPIepXjyE6IhxV5d3ZG6leOZILOzXMLDFlZGhmQ/3Xt/XO1qtn1fYkasVGUbdqdLaqu1FfL+fnNbt56rKOnNw0x5zOuDGloiPCSU3PYGdSCidUjWbW2t1cP24edapEM++B/ny5eAtTVuwkvllNrvcldoD1uw5mO2FHhgsZCstHnUNMZDjrdh3krk+WcHLTmjx0QfvM38usNbuJb1aT9g9Pztz34Qva07BGJc49yV2l/rxmN6t2JDG4WxydH8sq7TSsHsPs+/pnHuMv957J27M2cHOfFiz4cx8dGlZj3C8baVgjhie+y+q1BTByQGue/2l1tnXtG1RjRZBODX3b1M3sZnxZtzie+0tnJi/fzs2+eUQevqA9Z7atxzOT/2DJ5kS6Na3J1znatQLvhSmsf5x5IhPmbWZnUgp3n9OGnQeSeXeOt84IF3RqwDdLt+W7zS19W3LPuW2LFJtVDRlTylZtT2Lx5n353lC24M99/Lp+T65xn4pb4I2PzevEMu2ffQu1/+gpqxk9ZQ0Xd2nI6Cu65rldRoby9s8b2Hf4KMN7N6dOlWi+WryFprVj6dK4Rp77Bd6Ls+6J8wkPE96bs5GHv1rOBzecSu9WdQB338UXi7YQFRFGu/pVufOTxYy/sQeR4cJbszYwok9LoiLCOJSSRodHXPLK2R33aFoGU1bu4LyT6ufqjZSUnEr3x6dyJDWdfw/qQN829UhNz8hMokPiG/Px/M2MHRbP8Hfmc+1pTXls0EkAHDmaTqWo8MzfdZXoCA6mpFGnSnRmR4weLWpxabc41u48yP3ntwPcfUZPfLeSiDDJbDQfEt+YYb2a8cva3fRuVSdoT0QvSi0RiMi5wAtAOPCWqj6V43XxvX4+cBgYpqoL83tPSwTGFI/0DEXguBvqfP/ho3yzdBuXdmtUbHOCf7loC6e2qEWD6oUbxmTtziRenb6Opy/rRKSvhJew7zAxkeHU8Xiz36JN+4irWTlb541P52+mX9t6eb5Hcmo6ew8dJSYynGoxEUHb4wqrVBKBb+TS1bjRSxOAecBQVV0RsM35uNnPzgdOBV5Q1VPze19LBMYYU3j5JYJjTzN56w6sVdX1qnoUmAAMyrHNIOA9dX4FaohIgxDGZIwxJodQ9hpqBGwOWE7AXfUXtE0jIFuLiYjcBNzkWzwoIquKGFMdYHeBW5UvdswVgx1zxXAsx9w0rxdCmQiCVTzmrIfysg2qOgYYc8wBiczPq2hUXtkxVwx2zBVDqI45lFVDCUDjgOU4IOf4A162McYYE0KhTATzgFYi0lxEooArcJPaBJoEXCtODyBRVfPvSGuMMaZYhaxqSFXTROQ2YDKu++hYVV0uIiN8r78OfIfrMbQW1330+lDF43PM1UtlkB1zxWDHXDGE5JjL3A1lxhhjilcoq4aMMcaUAZYIjDGmgqswiUBEzhWRVSKyVkTuLe14iouINBaRaSKyUkSWi8jtvvW1ROQnEVnj+1kzYJ/7fL+HVSJyTulFX3QiEi4ii0TkG99yeT/eGiLymYj84ftbn1YBjvlO3//07yLykYjElLdjFpGxIrJTRH4PWFfoYxSRk0Vkme+1F8XrNG5+biTI8v3ANVavA1oAUcASoH1px1VMx9YA6OZ7XhU3rEd74BngXt/6e4Gnfc/b+44/Gmju+72El/ZxFOG4RwIfAt/4lsv78b4L3Oh7HgXUKM/HjLuxdANQybf8CTCsvB0zcAbQDfg9YF2hjxGYC5yGuzfre+C8wsRRUUoEXoa7KJNUdZv6BupT1SRgJe5LNAh38sD382Lf80HABFVNUdUNuB5b3Us06GMkInHAQOCtgNXl+Xir4U4YbwOo6lFV3U85PmafCKCSiEQAlXH3GJWrY1bVmcDeHKsLdYy+YXmqqeocdVnhvYB9PKkoiSCvoSzKFRFpBnQFfgNOUN89Gb6f9XyblYffxWjgX0BGwLryfLwtgF3AOF912FsiEks5PmZV3QI8C2zCDTmTqKo/Uo6POUBhj7GR73nO9Z5VlETgaSiLskxEqgCfA3eoan7TQJXp34WIXADsVNUFXncJsq7MHK9PBK764DVV7QocwlUZ5KXMH7OvXnwQrgqkIRArIlfnt0uQdWXqmD3I6xiP+dgrSiIo10NZiEgkLgmMV9WJvtU7/CO5+n76Z3Qv67+LXsBFIrIRV8V3poh8QPk9XnDHkKCqv/mWP8MlhvJ8zGcBG1R1l6qmAhOBnpTvY/Yr7DEm+J7nXO9ZRUkEXoa7KJN8vQPeBlaq6vMBL00CrvM9vw74KmD9FSISLSLNgVa4hqYyQVXvU9U4VW2G+zv+T1WvppweL4Cqbgc2i0gb36r+wArK8THjqoR6iEhl3/94f1z7V3k+Zr9CHaOv+ihJRHr4flfXBuzjTWm3mpdg6/z5uB4164AHSjueYjyu3rhi4FJgse9xPlAbmAqs8f2sFbDPA77fwyoK2bvgeHoAfcnqNVSujxfoAsz3/Z2/BGpWgGMeBfwB/A68j+stU66OGfgI1waSiruyv6EoxwjE+35P64CX8Y0a4fVhQ0wYY0wFV1GqhowxxuTBEoExxlRwlgiMMaaCs0RgjDEVnCUCY4yp4CwRGJODiKSLyOKAR7GNVisizQJHmjTmeBCyqSqNKcOOqGqX0g7CmJJiJQJjPBKRjSLytIjM9T1O9K1vKiJTRWSp72cT3/oTROQLEVnie/T0vVW4iLzpG2v/RxGpVGoHZQyWCIwJplKOqqEhAa8dUNXuuLs3R/vWvQy8p6qdgPHAi771LwIzVLUzbmyg5b71rYBXVLUDsB+4LKRHY0wB7M5iY3IQkYOqWiXI+o3Amaq63jfQ33ZVrS0iu4EGqprqW79NVeuIyC4gTlVTAt6jGfCTqrbyLd8DRKrq/5XAoRkTlJUIjCkczeN5XtsEkxLwPB1rqzOlzBKBMYUzJODnHN/z2biRUAGuAn72PZ8K3AKZcyxXK6kgjSkMuxIxJrdKIrI4YPkHVfV3IY0Wkd9wF1FDfev+AYwVkbtxM4ld71t/OzBGRG7AXfnfghtp0pjjirURGOORr40gXlV3l3YsxhQnqxoyxpgKzkoExhhTwVmJwBhjKjhLBMYYU8FZIjDGmArOEoExxlRwlgiMMaaC+38VQeqJYax+7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5c6a9b",
   "metadata": {},
   "source": [
    "### In order to avoid overfitting we will stop early"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7da89779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "................................................."
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss',patience=10)\n",
    "history = model.fit(norm_train_data,\n",
    "                   train_labels,\n",
    "                   epochs=EPOCHS,\n",
    "                   validation_split=0.2,\n",
    "                   verbose=0,\n",
    "                   callbacks=[early_stop,PrintDot()])\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a6a8abd",
   "metadata": {},
   "source": [
    "## Test Data loss\n",
    "Now let us look at various losses on our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b2376c87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error is  0.06 percent\n"
     ]
    }
   ],
   "source": [
    "loss,mae,mse = model.evaluate(norm_test_data,test_labels,verbose=0)\n",
    "print(\"Mean absolute error is {:5.2f} percent\".format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2460ff6e",
   "metadata": {},
   "source": [
    "### Now we will be making some predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8a363cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(norm_test_data).flatten()\n",
    "plt.scatter(test_labels,test_predictions)\n",
    "plt.xlabel('True percentage')\n",
    "plt.ylabel('Predicted percentage')\n",
    "_=plt.plot([0,1],[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f19ab213",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = test_predictions-test_labels\n",
    "plt.hist(error,bins=25)\n",
    "plt.xlabel('Error')\n",
    "_=plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b7f086",
   "metadata": {},
   "source": [
    "Now let us predict for a random person with given scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "faf863fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.42603321287418094,\n",
       " 1.148913263254646,\n",
       " -0.05770209107828859,\n",
       " 1.1733301116013737,\n",
       " 1.1525815601982128,\n",
       " 0.8743598299191816,\n",
       " 0.8808140174482542]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op = norm([321,114,3,4.5,4.5,9.08,1])\n",
    "a=[]\n",
    "for index,value in op.items():\n",
    "    a.append(value)\n",
    "a\n",
    "# chances = model.predict([a])\n",
    "# print('Chance of getting into the university is:',chances)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb1e261",
   "metadata": {},
   "source": [
    "### Now we will be saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "bef2a59b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Predictor/assets\n"
     ]
    }
   ],
   "source": [
    "model.save('Predictor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "78418cb1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting tensorflowjs\n",
      "  Downloading tensorflowjs-3.13.0-py3-none-any.whl (77 kB)\n",
      "\u001b[K     |████████████████████████████████| 77 kB 2.0 MB/s eta 0:00:011\n",
      "\u001b[?25hRequirement already satisfied: tensorflow<3,>=2.1.0 in ./.local/lib/python3.9/site-packages (from tensorflowjs) (2.7.0)\n",
      "Requirement already satisfied: six<2,>=1.12.0 in ./anaconda/lib/python3.9/site-packages (from tensorflowjs) (1.16.0)\n",
      "Collecting tensorflow-hub<0.13,>=0.7.0\n",
      "  Downloading tensorflow_hub-0.12.0-py2.py3-none-any.whl (108 kB)\n",
      "\u001b[K     |████████████████████████████████| 108 kB 3.1 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: astunparse>=1.6.0 in ./.local/lib/python3.9/site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.6.3)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in ./.local/lib/python3.9/site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.0)\n",
      "Requirement already satisfied: numpy>=1.14.5 in ./anaconda/lib/python3.9/site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.20.3)\n",
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      "Requirement already satisfied: google-pasta>=0.1.1 in ./.local/lib/python3.9/site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.2.0)\n",
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      "Requirement already satisfied: tensorflow-estimator<2.8,~=2.7.0rc0 in ./.local/lib/python3.9/site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.7.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.32.0 in ./anaconda/lib/python3.9/site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.37.0)\n",
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      "Requirement already satisfied: google-auth<3,>=1.6.3 in ./.local/lib/python3.9/site-packages (from tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (2.5.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in ./.local/lib/python3.9/site-packages (from tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (0.6.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in ./.local/lib/python3.9/site-packages (from tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (1.8.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in ./anaconda/lib/python3.9/site-packages (from tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (2.26.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in ./.local/lib/python3.9/site-packages (from tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (0.4.6)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in ./anaconda/lib/python3.9/site-packages (from tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (58.0.4)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in ./anaconda/lib/python3.9/site-packages (from tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (2.0.2)\n",
      "Requirement already satisfied: markdown>=2.6.8 in ./.local/lib/python3.9/site-packages (from tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (3.3.6)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in ./.local/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (4.8)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in ./.local/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (0.2.8)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in ./.local/lib/python3.9/site-packages (from google-auth<3,>=1.6.3->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (5.0.0)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in ./.local/lib/python3.9/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in ./anaconda/lib/python3.9/site-packages (from markdown>=2.6.8->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (4.8.1)\n",
      "Requirement already satisfied: zipp>=0.5 in ./anaconda/lib/python3.9/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (3.6.0)\n",
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      "Requirement already satisfied: charset-normalizer~=2.0.0 in ./anaconda/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in ./anaconda/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in ./anaconda/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (3.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./anaconda/lib/python3.9/site-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (1.26.7)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in ./.local/lib/python3.9/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow<3,>=2.1.0->tensorflowjs) (3.1.1)\n",
      "Installing collected packages: tensorflow-hub, tensorflowjs\n",
      "Successfully installed tensorflow-hub-0.12.0 tensorflowjs-3.13.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflowjs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "5d501898",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflowjs as tfjs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "52d3ad3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfjs.converters.save_keras_model(model, 'Javascript_Models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "f38ad82d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ram://ffe8485b-bb6d-4f21-b022-503ac9b72742/assets\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "with open('College_Prediction_Web.pkl', 'wb') as file:\n",
    "    pickle.dump(model, file)"
   ]
  }
 ],
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