Dyslexia (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dff43242",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "126e3efc",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'labelled_dysx.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-ed8cf10c964a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'labelled_dysx.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Read file'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\7265\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\7265\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\7265\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\7265\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1048\u001b[0m             )\n\u001b[0;32m   1049\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1052\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\7265\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1866\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1868\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1869\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\7265\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m   1360\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1361\u001b[0m         \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m   1363\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1364\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\7265\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    640\u001b[0m                 \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    641\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    643\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'labelled_dysx.csv'"
     ]
    }
   ],
   "source": [
    "file = pd.read_csv('labelled_dysx.csv')\n",
    "print('Read file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4495f966",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Language_vocab</th>\n",
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       "      <th>Visual_discrimination</th>\n",
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      "text/plain": [
       "   Language_vocab  Memory  Speed  Visual_discrimination  Audio_Discrimination  \\\n",
       "0             0.5     0.6    0.5                    0.8                   0.6   \n",
       "1             0.6     0.7    0.8                    0.9                   0.5   \n",
       "2             0.6     0.4    0.3                    0.3                   0.4   \n",
       "3             0.3     0.5    0.2                    0.1                   0.3   \n",
       "4             0.7     0.6    0.7                    0.8                   0.9   \n",
       "\n",
       "   Survey_Score  Label  \n",
       "0           0.7      1  \n",
       "1           0.8      2  \n",
       "2           0.6      1  \n",
       "3           0.5      0  \n",
       "4           0.5      2  "
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60d41800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2787e92b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Speed</th>\n",
       "      <th>Visual_discrimination</th>\n",
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       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.57800</td>\n",
       "      <td>0.572000</td>\n",
       "      <td>0.576800</td>\n",
       "      <td>0.58260</td>\n",
       "      <td>0.580200</td>\n",
       "      <td>0.581000</td>\n",
       "      <td>1.206000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.20019</td>\n",
       "      <td>0.212382</td>\n",
       "      <td>0.215487</td>\n",
       "      <td>0.21719</td>\n",
       "      <td>0.202163</td>\n",
       "      <td>0.210684</td>\n",
       "      <td>0.642175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.60000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.70000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Language_vocab      Memory       Speed  Visual_discrimination  \\\n",
       "count       500.00000  500.000000  500.000000              500.00000   \n",
       "mean          0.57800    0.572000    0.576800                0.58260   \n",
       "std           0.20019    0.212382    0.215487                0.21719   \n",
       "min           0.00000    0.000000    0.000000                0.00000   \n",
       "25%           0.50000    0.400000    0.500000                0.50000   \n",
       "50%           0.60000    0.600000    0.600000                0.60000   \n",
       "75%           0.70000    0.700000    0.700000                0.70000   \n",
       "max           1.00000    1.000000    1.000000                1.00000   \n",
       "\n",
       "       Audio_Discrimination  Survey_Score       Label  \n",
       "count            500.000000    500.000000  500.000000  \n",
       "mean               0.580200      0.581000    1.206000  \n",
       "std                0.202163      0.210684    0.642175  \n",
       "min                0.000000      0.000000    0.000000  \n",
       "25%                0.500000      0.500000    1.000000  \n",
       "50%                0.600000      0.600000    1.000000  \n",
       "75%                0.700000      0.700000    2.000000  \n",
       "max                1.000000      1.000000    2.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d10d3cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# label 0 - low\n",
    "# label 1 - Moderate\n",
    "# label 2 - High\n",
    "features = ['Language_vocab','Memory','Speed','Visual_discrimination','Audio_Discrimination','Survey_Score']\n",
    "X = file[features]\n",
    "y = file['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "225e0c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test,y_train, y_test = train_test_split(X,y, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4316b411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(375, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02d2d85b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy =(TP+TN)/(TP+TN+FP+FN)\n",
    "# Precision = TP/(TP+FP)\n",
    "# Recall = TP/(TP+FN)\n",
    "# F1 Score=2*((precision * recall)/(precision +recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "72ee31e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(12, input_shape=(6,), activation='relu'))\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cd8335b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = 'mean_squared_error',\n",
    "             optimizer = tf.keras.optimizers.Adam(0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b1a34aa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "12/12 [==============================] - 5s 4ms/step - loss: 0.5774\n",
      "Epoch 2/20\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.2222\n",
      "Epoch 3/20\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.1307\n",
      "Epoch 4/20\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.1002\n",
      "Epoch 5/20\n",
      "12/12 [==============================] - 0s 1ms/step - loss: 0.0915\n",
      "Epoch 6/20\n",
      "12/12 [==============================] - 0s 1ms/step - loss: 0.0954\n",
      "Epoch 7/20\n",
      "12/12 [==============================] - 0s 1ms/step - loss: 0.0892\n",
      "Epoch 8/20\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0907\n",
      "Epoch 9/20\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0945\n",
      "Epoch 10/20\n",
      "12/12 [==============================] - 0s 7ms/step - loss: 0.0957\n",
      "Epoch 11/20\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 0.0843\n",
      "Epoch 12/20\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 0.0927\n",
      "Epoch 13/20\n",
      "12/12 [==============================] - 0s 6ms/step - loss: 0.0851\n",
      "Epoch 14/20\n",
      "12/12 [==============================] - 0s 5ms/step - loss: 0.1012\n",
      "Epoch 15/20\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 0.0953\n",
      "Epoch 16/20\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0873\n",
      "Epoch 17/20\n",
      "12/12 [==============================] - 0s 6ms/step - loss: 0.0858\n",
      "Epoch 18/20\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 0.0898\n",
      "Epoch 19/20\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0930\n",
      "Epoch 20/20\n",
      "12/12 [==============================] - 0s 4ms/step - loss: 0.1040\n"
     ]
    }
   ],
   "source": [
    "training = model.fit(x_train, y_train, epochs = 20, verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "997cdedd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2554e1e49d0>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WLlyoefPmafr06SosLNTSpUvV1NSk+fPnS5Lmzp2r4cOHa8mSJYqLi9NJJ53UY//U1FRJOmJ9qIuJsio9IVYHm9yqrHcpIyn0WpcAAAhHpoeb2bNnq6qqSvfdd5+cTqemTp2q1atX+wYZ79u3T1ZrWA0N6rfMZFtHuGlo1STZzS4HAICIYDEMwzC7iGCqr69XSkqK6urqZLebGyjmrdygD76p0iNXT9G108OnSw0AgGAbyOd3ZDaJhImuK6aquBwcAAC/IdyYqOuKqQom8gMAwG8INybizuAAAPgf4cZE3feXouUGAAB/IdyYyHd/KcbcAADgN4QbEx1+f6khdtEaAAABQ7gxUWZnt5S73av6lnaTqwEAIDIQbkwUFxMle1zHPIqMuwEAwD8INyZz2Lu7pgAAwPEj3Jise1AxLTcAAPgD4cZkzHUDAIB/EW5M1jXXTQXhBgAAvyDcmCyTifwAAPArwo3JshhQDACAXxFuTMadwQEA8C/Cjcl895fizuAAAPgF4cZkXd1STW6PmlzMUgwAwPEi3JgsyRatxNgoSYy7AQDAHwg3IcA3qJiuKQAAjhvhJgR0Xw5Oyw0AAMeLcBMCsgg3AAD4DeEmBHTfgoFuKQAAjhfhJgR03zyTlhsAAI4X4SYEZHELBgAA/IZwEwK4MzgAAP5DuAkBdEsBAOA/hJsQ0NUtVdfSptY2j8nVAAAQ3gg3ISAlPkax0R2nghtoAgBwfAg3IcBisTDXDQAAfjKocFNWVqb9+/f7Hm/YsEF33nmnnn76ab8VNtR0hZsqrpgCAOC4DCrc/PSnP9X7778vSXI6nbrgggu0YcMG3XPPPXrwwQf9WuBQ0XXFVAVXTAEAcFwGFW62bt2qwsJCSdIrr7yik046SR9//LH+8pe/6Pnnn/dnfUNG9xVTtNwAAHA8BhVu2traZLN1fBivXbtWl112mSRpwoQJKi8v9191Q4hvzA0tNwAAHJdBhZvJkydrxYoV+te//qX33ntPF110kSTpwIEDGjZsmF8LHCp8E/kxoBgAgOMyqHDzhz/8QU899ZTOOeccXX/99SooKJAk/e1vf/N1V2FgMpnIDwAAv4gezE7nnHOOqqurVV9fr7S0NN/6m2++WQkJCX4rbijhaikAAPxjUC03LS0tcrlcvmBTWlqqpUuXaufOncrKyvJrgUOFw97RLXWwya12j9fkagAACF+DCjeXX365XnzxRUlSbW2tioqK9O///u+aNWuWli9f7tcCh4r0hFhFWy0yDKm60W12OQAAhK1BhZtNmzbprLPOkiS99tprcjgcKi0t1Ysvvqg//vGPfi1wqLBaLcpI4nJwAACO16DCTXNzs5KTkyVJf//733XllVfKarXq9NNPV2lpqV8LHEp8c91wOTgAAIM2qHAzbtw4vfnmmyorK9OaNWt04YUXSpIqKytlt9v9WuBQ0jWouIKWGwAABm1Q4ea+++7TXXfdpfz8fBUWFmrGjBmSOlpxTjnlFL8WOJRkds11Q8sNAACDNqhLwa+++mr96Ec/Unl5uW+OG0k6//zzdcUVV/ituKGGO4MDAHD8BhVuJCk7O1vZ2dm+u4OPGDGCCfyOU9eYG+a6AQBg8AbVLeX1evXggw8qJSVFo0aN0qhRo5Samqrf/e538nqZo2WwuAUDAADHb1AtN/fcc4+effZZPfzwwzrzzDMlSR9++KHuv/9+tba26qGHHvJrkUOFg6ulAAA4boMKNy+88IL+9Kc/+e4GLklTpkzR8OHDdeuttxJuBqmr5aa60SWv15DVajG5IgAAws+guqVqamo0YcKEI9ZPmDBBNTU1x13UUJWRFCuLRWr3GqppZpZiAAAGY1DhpqCgQE8++eQR65988klNmTLluIsaqqKjrBqWGCuJrikAAAZrUN1SjzzyiC699FKtXbvWN8fN+vXrVVZWpnfeecevBQ41mclxqm50q6KhVZPEhIgAAAzUoFpuzj77bH3zzTe64oorVFtbq9raWl155ZXatm2b/vznP/u7xiGla66bKlpuAAAYlEHPc5Obm3vEwOEvv/xSzz77rJ5++unjLmyo6p7Ij7luAAAYjEG13CBwfDfPZK4bAAAGhXATYrK4vxQAAMeFcBNi6JYCAOD4DGjMzZVXXtnn87W1tcdTCyRl2bkFAwAAx2NA4SYlJeWYz8+dO/e4ChrqDr8zuGEYsliYpRgAgIEYULh57rnnAlLEsmXL9Oijj8rpdKqgoEBPPPHEUe8w/vrrr+v3v/+9du3apba2Np1wwgn6zW9+oxtuuCEgtQVbZme4cbd7Vd/SrpSEGJMrAgAgvJg+5mbVqlVauHChFi9erE2bNqmgoEAzZ85UZWVlr9unp6frnnvu0fr16/XVV19p/vz5mj9/vtasWRPkygMjLiZKKfEdgYZxNwAADJzp4ebxxx/XTTfdpPnz52vSpElasWKFEhIStHLlyl63P+ecc3TFFVdo4sSJGjt2rO644w5NmTJFH374YZArD5yurqkKrpgCAGDATA03brdbGzduVHFxsW+d1WpVcXGx1q9ff8z9DcNQSUmJdu7cqR//+Me9buNyuVRfX99jCXXdc93QcgMAwECZGm6qq6vl8XjkcDh6rHc4HHI6nUfdr66uTklJSYqNjdWll16qJ554QhdccEGv2y5ZskQpKSm+JS8vz6/HEAi+uW64YgoAgAEzvVtqMJKTk7V582Z99tlneuihh7Rw4UKtW7eu120XLVqkuro631JWVhbcYgfBd8UU3VIAAAzYoO8t5Q8ZGRmKiopSRUVFj/UVFRXKzs4+6n5Wq1Xjxo2TJE2dOlXbt2/XkiVLdM455xyxrc1mk81m82vdgZbJRH4AAAyaqS03sbGxmjZtmkpKSnzrvF6vSkpKNGPGjH6/jtfrlcsVOa0cDibyAwBg0ExtuZGkhQsXat68eZo+fboKCwu1dOlSNTU1af78+ZKkuXPnavjw4VqyZImkjjE006dP19ixY+VyufTOO+/oz3/+s5YvX27mYfhVV7dUFeEGAIABMz3czJ49W1VVVbrvvvvkdDo1depUrV692jfIeN++fbJauxuYmpqadOutt2r//v2Kj4/XhAkT9NJLL2n27NlmHYLf+W7BUE+3FAAAA2UxDMMwu4hgqq+vV0pKiurq6mS3280up1dNrnZNXtwxKeHWB2YqyWZ6BgUAwFQD+fwOy6ulIl2iLVqJsVGSaL0BAGCgCDchiruDAwAwOISbEJV52N3BAQBA/xFuQlT3RH50SwEAMBCEmxDVdQsGLgcHAGBgCDchymGnWwoAgMEg3IQo7gwOAMDgEG5ClO/O4Nw8EwCAASHchKgsrpYCAGBQCDchqqvlpq6lTa1tHpOrAQAgfBBuQpQ9Plqx0R2nhyumAADoP8JNiLJYLId1TTGoGACA/iLchLDuifxouQEAoL8INyHMd8UU3VIAAPQb4SaEMdcNAAADR7gJYQ47c90AADBQhJsQxp3BAQAYOMJNCGMiPwAABo5wE8K67wzOmBsAAPqLcBPCugYUVze61ebxmlwNAADhgXATwtITYhVttUiSqhvpmgIAoD8INyHMarUoI4mJ/AAAGAjCTYjrnuuGcAMAQH8QbkIc95cCAGBgCDchLouJ/AAAGBDCTYhjrhsAAAaGcBPimOsGAICBIdyEOFpuAAAYGMJNiPNdLcWYGwAA+oVwE+J83VKNLnm8hsnVAAAQ+gg3IS4jKVYWi+TxGqppcptdDgAAIY9wE+Kio6walhgribluAADoD8JNGMjs7JpiUDEAAMdGuAkDjs5BxVUMKgYA4JgIN2GAWzAAANB/hJswkEW3FAAA/Ua4CQPMdQMAQP8RbsIA3VIAAPQf4SYMdF0tVUHLDQAAx0S4CQNdLTdVDS4ZBrMUAwDQF8JNGMjsDDduj1d1LW0mVwMAQGgj3ISBuJgopcTHSOKKKQAAjoVwEyZ8g4oZdwMAQJ8IN2HCYe+a64YrpgAA6AvhJkx0Xw5Oyw0AAH0h3ISJzM6J/MprW0yuBACA0Ea4CRMnD0+RJK3dXimvl8vBAQA4GsJNmCie6JA9Llrf17bo490HzS4HAICQRbgJE3ExUbp86nBJ0iufl5lcDQAAoYtwE0aumT5CkrRmm5PJ/AAAOArCTRg5eXiKJmQny9Xu1X9/ecDscgAACEmEmzBisVh09bSO1ptX6ZoCAKBXhJswc8UpwxVttejL/XXa6WwwuxwAAEIO4SbMDEuy6fyJWZJovQEAoDeEmzB0zbQ8SdKbm79Xm8drcjUAAIQWwk0YOmd8pjKSbKpudOv9HZVmlwMAQEgh3ISh6Cirrjq1a86b/SZXAwBAaCHchKmuOW/e31nJncIBADhMSISbZcuWKT8/X3FxcSoqKtKGDRuOuu0zzzyjs846S2lpaUpLS1NxcXGf20eqcVnJOmVkqjxeQ29+8b3Z5QAAEDJMDzerVq3SwoULtXjxYm3atEkFBQWaOXOmKit7H0uybt06XX/99Xr//fe1fv165eXl6cILL9T33w+9D/iugcWvfr5fhsHNNAEAkCSLYfKnYlFRkU477TQ9+eSTkiSv16u8vDzdfvvt+u1vf3vM/T0ej9LS0vTkk09q7ty5Rzzvcrnkcrl8j+vr65WXl6e6ujrZ7Xb/HYgJ6lvbVPjQWrW2efXmgjM1NS/V7JIAAAiI+vp6paSk9Ovz29SWG7fbrY0bN6q4uNi3zmq1qri4WOvXr+/XazQ3N6utrU3p6em9Pr9kyRKlpKT4lry8PL/UHgrscTG6+KQcSdxMEwCALqaGm+rqank8Hjkcjh7rHQ6HnE5nv17j7rvvVm5ubo+AdLhFixaprq7Ot5SVRVYIuKbzdgz/vfmAWtwek6sBAMB80WYXcDwefvhhvfzyy1q3bp3i4uJ63cZms8lmswW5suA5fcwwjUiL1/5DLVqzzalZpww3uyQAAExlastNRkaGoqKiVFFR0WN9RUWFsrOz+9z3scce08MPP6y///3vmjJlSiDLDGlW62E309wYWa1SAAAMhqnhJjY2VtOmTVNJSYlvndfrVUlJiWbMmHHU/R555BH97ne/0+rVqzV9+vRglBrSrjq1I9x8vPugymqaTa4GAABzmX4p+MKFC/XMM8/ohRde0Pbt23XLLbeoqalJ8+fPlyTNnTtXixYt8m3/hz/8Qffee69Wrlyp/Px8OZ1OOZ1ONTY2mnUIpstLT9CZ44bJMKT/2sSMxQCAoc30cDN79mw99thjuu+++zR16lRt3rxZq1ev9g0y3rdvn8rLy33bL1++XG63W1dffbVycnJ8y2OPPWbWIYSErjlvXtu4X14vc94AAIYu0+e5CbaBXCcfTlrcHhU+tFYNrnb95y+KdMa4DLNLAgDAb8Jmnhv4T3xslH4yNVeS9OpGuqYAAEMX4SaCdM158+7WctW3tplcDQAA5iDcRJCpeak6IStJrW1e/c+X5cfeAQCACES4iSAWi0XXTGfOGwDA0Ea4iTCzThmuKKtFX+yr1a7KBrPLAQAg6Ag3ESYrOU7njs+SJL36OQOLAQBDD+EmAnV1Tf3Xpu/V5vGaXA0AAMFFuIlA503I0rDEWFU3uvTBziqzywEAIKgINxEoJsqqKzrvDs7AYgDAUEO4iVDXTO+4HUPJ9kodbHSZXA0AAMFDuIlQ47OTVTAiRe1eQ2988b3Z5QAAEDSEmwh29fTum2kOsVuIAQCGMMJNBLtsSq5io63a4WzQlu/rzC4HAICgINxEsJSEGF00OVsSc94AAIYOwk2E65rz5q3N36u1zWNyNQAABB7hJsKdMTZDw1PjVd/arr9/XWF2OQAABBzhJsJFWS266tTOOW8+Z84bAEDkI9wMAVdP67hq6sNd1fq+tsXkagAACCzCzRAwcliCTh+TLsOQXt/IwGIAQGQj3AwR13S23ry6cb+8Xua8AQBELsLNEHHxydlKskVrX02zNuytMbscAAAChnAzRCTERut/TcmRxJw3AIDIRrgZQrrmvHlnS7kaXe0mVwMAQGAQboaQU0emaUxmolraPHr7qwNmlwMAQEAQboYQi8XSPbCYrikAQIQi3AwxV546XFaL9HnpIe2uajS7HAAA/I5wM8Q47HE6Z3yWJOk15rwBAEQgws0QdM20joHFr2/ar3aP1+RqAADwL8LNEHT+RIfSEmJUUe/Sv76tNrscAAD8inAzBMVGWzXrlM6baW7kZpoAgMhCuBmiuq6aeu/rCtU0uU2uBgAA/yHcDFGTcu06abhdbR5Db23+3uxyAADwG8LNEMacNwCASES4GcIun5qr2Cirvi6v19bv68wuBwAAvyDcDGGpCbG6YLJDEnPeAAAiB+FmiOua8+bNzd/L1e4xuRoAAI4f4WaIO+uETGXb41Tb3Ka1X1eaXQ4AAMeNcDPERVktumoac94AACIH4Qa6uvOqqX9+U6U91U0mVwMAwPEh3ECjMxI1Y8wweQ1p9lPruXIKABDWCDeQJP37tQUa70hWZYNL1z61Xu/vZPwNACA8EW4gScpNjdert8zQmeOGqdnt0S9e+Fz/+ek+s8sCAGDACDfwscfF6LkbC3XVqSPk8Rr6/97YokfX7JBhGGaXBgBAvxFu0ENstFWPXTNFd5x/giRp2fu7deeqzcyBAwAIG4QbHMFisejXF5yoR66eomirRW9tPqC5z25QXXOb2aUBAHBMhBsc1bXT8/Tc/NOUZIvWp3tqdNWKj1VW02x2WQAA9Ilwgz6ddUKmXv3VDGXb47SrslFX/L+PtWU/l4oDAEIX4QbHNDHHrjcWnKEJ2cmqbuy4VPwfOyrMLgsAgF4RbtAvOSnxevVXM3TWCRlqaeu4VPylT0rNLgsAgCMQbtBvyXExWnnjabpm2gh5Denf3tyqh9/dIa+XS8UBAKGDcIMBiYmy6pGrp2jhBSdKklZ8sFt3cKk4ACCEEG4wYBaLRf/n/BP079cUKNpq0X9/eUA3/GmDapvdZpcGAADhBoN31bQReuFnhUq2RWvD3hpduZxLxQEA5iPc4LicOS5Dr91yhnJT4vRdVZOu+H8f6cuyWrPLAgAMYYQbHLfx2cl6Y8GZmpRjV3WjW9c9/Yne+5pLxQEA5iDcwC8c9ji98qsZOvvETLW0efTLP3+uF9fvNbssAMAQRLiB3yTZovWnedN13Wl58hrSfW9t0+/f2c6l4gAwBHi8hj797qAe/O+vtfLDPabWYnq4WbZsmfLz8xUXF6eioiJt2LDhqNtu27ZNV111lfLz82WxWLR06dLgFYp+iYmyasmVJ+v/zhwvSXr6n9/p9r9+odY2LhUHgEjT2ubRP3ZU6O7XvlLhQ2s1++lPtPKjPfrPDftMrSvazB++atUqLVy4UCtWrFBRUZGWLl2qmTNnaufOncrKyjpi++bmZo0ZM0bXXHONfv3rX5tQMfrDYrFowbnjNDw1Xv/3tS/19pZyVdS36pm505WWGGt2eQCOoc3j1TtbyvX53kPKS4/XiY5knehIVk5KnCwWi9nlwWQNrW16f2eV1mxzat2OSjW5u/94tcdFq3iiQxdOzpZhGKb9e7EYhmFan0FRUZFOO+00Pfnkk5Ikr9ervLw83X777frtb3/b5775+fm68847deedd/a5ncvlksvl8j2ur69XXl6e6urqZLfbj/sY0LePd1frl3/eqIbWdqUlxOiMcRk6fcwwzRiTrrGZSfxHCYSQZne7Vn1Wpj/9a4++r2054vlkW7ROzE7WiY4kX+A50ZGsjKRY3ssRrqrBpbXbK7Rmm1Mf7zoot8fre85ht+nCSdmaOTlbRWPSFRMVmE6h+vp6paSk9Ovz27SWG7fbrY0bN2rRokW+dVarVcXFxVq/fr3ffs6SJUv0wAMP+O31MDBnjM3Q67ecoZ+98JnKalr09lflevurcklSRlKsikYP0+lj0nX6mGEal0XYAcxQ3ejSix/v1YuflKq2uU2SNCwxVj8pyFVVg0s7Kxq0p7pJDa52bSw9pI2lh3rsn5YQ0x12spM13tERgFITaKkNZ2U1zVqzzak125z6vPSQDm8KGZORqAsnZ2vmZIcKRqTKag2t/7tNCzfV1dXyeDxyOBw91jscDu3YscNvP2fRokVauHCh73FXyw2C5wRHskoWnqMv99fqk90H9cmeg/p87yFVN7r19pZyvb2FsAOYofRgk57513d69fP9crV3/CWePyxBN/14jK46dYTiYqJ827raPdpT3aRvKhr1jbNB31R0LKU1zTrU3KZP99To0z01PV4/K9l2WAtPkk7MTtYJWUlKjosJ6nGifwzD0A5nQ2egqdD28voez588PEUzJzs0c3J2yP//bOqYm2Cw2Wyy2WxmlzHkxUZbdVp+uk7LT9ftOkGudo++2l/XZ9gZlhir08cEN+zUt7bJWdeqA7UtHV/rWuWsa9Gh5jaNSk/QhBy7JmQna1xWUo//+IcqwzBU29ymJne7Wtu8crV7fF9dXV/bvWptO+xrm1et7T/86pWr7cht3e1eWSzSj8Zl6OKTc3RafrqiQuwvxHD01f5aPfXBd3p3a7m6LmYsGJGiX509VhdOzu71d2yLjtKEbLsmZNulgu71LW6Pdlc16puKBu2saNC3FY3a6WzQ97UtqmxwqbLBpQ93Vfd4reGp8TrRkaSJOXZNG5WmU0emMR7PJF6voU37DmnNNqf+/nWFSg92zzJvtUiFo9M1c3K2LpycreGp8SZWOjCmhZuMjAxFRUWpoqLnZG8VFRXKzs42qSoEiy06qs+ws7H0kA42HRl2ijqDzuljhumEAYadhtY2lde1diy1LZ3ft/jWOeta1ehq79drRVktGp2RqAnZyZrYGXjGZydreGp8SP81MxCGYaimyX3Y76elM+x1hr/6jvXudu+xX+w47a5q0gvrS5WRFKsLJ2frkpNydPqYdEUHqG8/EhmGoQ++qdJTH3yn9d8d9K0/d3ymbv7xWJ0+Jn1Q/3bjY6N00vAUnTQ8pcf6Rle7vq3oauFp9LX0VNS79H1ti76vbdH7O6t824/JTNS0kWmaNqpjGZuZFHJdHZGitc2jT/fUdASabRWqbuwelxobbdWPT8jQhZOzVTzRofQwDZ2mDyguLCzUE088IaljQPHIkSN12223+W1A8Q8NZEASzHN42Pl0T40+L61Ra1vPD9HDw07h6HRFWSy+lpYDtZ0fwnUdLTDlAwguKfExykmJ61hS45Vjj1NKQoy+q2rSDme9djgbfOMSfig5LloTspM1Iduu8dnJmpjT0SQfas3whmHoYJPb97vxhbzO0DfQ4GKLtsoWbVVcTJRsMVbZoqMUd4yvP9zOt/8Pvta1tOnvnX9V1rV0/97TEmJ0wSSHLj45R2eOzVBsdGgEnbqWNm0srdGGPYe0qfSQ4mKjNDUvVafkpWpqXmrQWyjaPF79z1cH9NQH32mHs0GSFG216LKpubr5x2M6WmKCqK65Td9UNmins0Fb9tdp475D2lXZeMR29rhonToqrSPw5KepYESqEm0R39kwaO0er2qa3KpscKmq0aWqBpeqO792LV2P61t7/l+YbIvWuROyNHNyts4Znxmyv+eBfH6bGm5WrVqlefPm6amnnlJhYaGWLl2qV155RTt27JDD4dDcuXM1fPhwLVmyRFLHIOSvv/5aknTJJZdozpw5mjNnjpKSkjRu3Lh+/UzCTXhyt3v11f5affLdQX3yXe9hpz/scdHKTY1XdkqcclLiu0NMSrxyUju+T4jt+41tGIYqG1zaXt4RdHY6G7S9vF67qxrV5un97TQiLV4Tsu2amNMdfEZnJA66i8UwDF/XTbPbo5Y2j1rch3/f3mN9daNbzh+0Uh1+tUNfMpJsyk2NU7b9sMCX0vE4NzVeWXabbNGB76Jr83i1fvdBvbvVqb9vc+pgU/dd6JPjonXBxI6gc9YJGUHtMqxqcOmzvTXasKdj2e6sV1//q+YPS9ApI9M6As/IVE3ItgckmDW52vXyZ2V69l/f6UBdqyQpMTZK1xeO1M9+NFq5IdTFcKjJrS/KDvkGK39ZVqeWH8yNFWW1aGJOsqaNTOsIPaPSIqqltDder6HalrYjwklv4aWm2d3nv7sfykiy6YJJDs2c7NAZIfTHQV/CJtxI0pNPPqlHH31UTqdTU6dO1R//+EcVFRVJks455xzl5+fr+eeflyTt3btXo0ePPuI1zj77bK1bt65fP49wExl+GHY2lh5SdJRFuSkdwSU3tSOwZKfE+dblpMQF9C8Sd7tX31U3doadho5WnvIGOetbe93eFm3ViY6O7qwRafFqbesKKx3BpCu49P59u4534meLpeM/uMMDXvbhYS8lTg57XEj+p9fu8WrD3hqt3urUu1udqmroblZPjI3SeRMduvikjr9CjxVWB8IwDO0/1KINe2p8gea76qYjtssflqDC0emanp8uV5tHX5TVavO+2l63jY226uThKb6wMzUv9bg+tKsbXXr+o7368yelvpaujCSb5p+Zr/9dNEopCaHVitibNo9XO8obtLG0Rhv31Wrj3hpfQDucw27zjdmZNipNk3NTQvLfa2+8XkPVTS4dqO1oMf2+s9X0QG2LrwX6YKNb7QN4o1st0rAkmzKSbMpMtimz82tGUmzH48PWpcTHhF0wDKtwE2yEm8hk5mRRx1Lb7NYOZ4N2dLb0dLX2/PAv08GKibIoPiZKCbHRio+N6vw+qsf3qQmxHa0tKR2tLdn20A0uA+X1Gtq475De3eLUu1vLVX7Yh2BcjFXnjs/SRSdl6/yJDiUNMNwahqFdlY3acFjLTPkPPmQtFmm8I1lFo9N12uh0FeanK8se1+vr1Ta7tbmsVpvLavXFvo6vh3e1dclMtmlqZzfWKSNTNWVE6jFr31vdeeXTxv2+7sTRGYm6+cdjdMUpw8N+APyB2hZt2tfRsrOp9JC2Hag/4oPfFm1VwYhUX8vOiLR4JcR2vDcSbR3vh2D9P9HQ2qYDtR1d4wdqW1Re2xVcurvN+9t6mpYQ0x1YOgNKRo/w0vE1PTE2ogfcE276QLhBKPB6De2rafaN4amod/UaSrq/7yW4dD4O1IRZ4cjrNfTl/lqt3urUO1vLVVbTPRFdx0DJTF18UraKJzmUEn9kC0a7x6vt5Q36dM9BbdhTo89LD6nmsO4vqWO8yskjUlSYn97ROjMqfdCtIYZhaO/BZn2x75Av8GwvP/JD22qRTnQkH9a6k6ZxWUmKslr0ZVmtnvrnbr271enrlpial6pfnT1GF0zq/cqnSNDi9uir/bX6vDPsbNx36Khj4bpYLPK9n7rCTqItujMARSkxNloJto7nux7Hx0Yp8bB1XfvGRFlV1eDqCC51rR0tL7UdweVAbYsa+jHGz2KRHMlxyknt+KMjt/OPj66W0yy7TcMSbRHxR4g/EG76QLgBhgbDMLTtQL3e3Vqud7Y4teewLqGYKIvOGJuhS07O1sj0xI4BwHsPaePemh5TyUsdrT+njkzTafnpKhqdrqkjU/3a1fVDrW0ebf2+rkfrTm+zBSfZopWbGqdvKroH454/IUu/PHusTstPC9mWzEAxDEPfVTf5Wna+2Ferg01uXzevGVLiY3qGltQ4De8ML7mpHa2n/HHSf4SbPhBugKHHMAztrGjwdV0dHgh+KDkuWqd1tsoUjk7XSSEwjqOyvrVj3E5Zrb7Yd0hf7a/zfWDHRFl0+dThuvnHY3SiI9nUOkOV12uotd2jJlf3mLZmd3vn487v3R0D8Xtu41GTq10tbR1fu9Y1u9vlavMqI9nmG993eIjpWheqVx2FK8JNHwg3AHZVNmr11nK9u9Wpmia3Th2ZpsLRHfMujc9ODvmuHI/X0DcVDdpd1ahpo9KUkxI6Vz4BgUK46QPhBgCA8DOQz286+wAAQEQh3AAAgIhCuAEAABGFcAMAACIK4QYAAEQUwg0AAIgohBsAABBRCDcAACCiEG4AAEBEIdwAAICIQrgBAAARhXADAAAiCuEGAABEFMINAACIKNFmFxBshmFI6rh1OgAACA9dn9tdn+N9GXLhpqGhQZKUl5dnciUAAGCgGhoalJKS0uc2FqM/ESiCeL1eHThwQMnJybJYLH597fr6euXl5amsrEx2u92vrx1qONbINZSOl2ONXEPpeIfKsRqGoYaGBuXm5spq7XtUzZBrubFarRoxYkRAf4bdbo/of2CH41gj11A6Xo41cg2l4x0Kx3qsFpsuDCgGAAARhXADAAAiCuHGj2w2mxYvXiybzWZ2KQHHsUauoXS8HGvkGkrHO5SOtb+G3IBiAAAQ2Wi5AQAAEYVwAwAAIgrhBgAARBTCDQAAiCiEmwFatmyZ8vPzFRcXp6KiIm3YsKHP7V999VVNmDBBcXFxOvnkk/XOO+8EqdLBW7JkiU477TQlJycrKytLs2bN0s6dO/vc5/nnn5fFYumxxMXFBani43P//fcfUfuECRP63Cccz6sk5efnH3GsFotFCxYs6HX7cDqv//znP/WTn/xEubm5slgsevPNN3s8bxiG7rvvPuXk5Cg+Pl7FxcX69ttvj/m6A33PB0tfx9vW1qa7775bJ598shITE5Wbm6u5c+fqwIEDfb7mYN4LwXCsc3vjjTceUfdFF110zNcNxXN7rGPt7f1rsVj06KOPHvU1Q/W8BhLhZgBWrVqlhQsXavHixdq0aZMKCgo0c+ZMVVZW9rr9xx9/rOuvv14///nP9cUXX2jWrFmaNWuWtm7dGuTKB+aDDz7QggUL9Mknn+i9995TW1ubLrzwQjU1NfW5n91uV3l5uW8pLS0NUsXHb/LkyT1q//DDD4+6bbieV0n67LPPehzne++9J0m65pprjrpPuJzXpqYmFRQUaNmyZb0+/8gjj+iPf/yjVqxYoU8//VSJiYmaOXOmWltbj/qaA33PB1Nfx9vc3KxNmzbp3nvv1aZNm/T6669r586duuyyy475ugN5LwTLsc6tJF100UU96v7rX//a52uG6rk91rEefozl5eVauXKlLBaLrrrqqj5fNxTPa0AZ6LfCwkJjwYIFvscej8fIzc01lixZ0uv21157rXHppZf2WFdUVGT88pe/DGid/lZZWWlIMj744IOjbvPcc88ZKSkpwSvKjxYvXmwUFBT0e/tIOa+GYRh33HGHMXbsWMPr9fb6fLieV0nGG2+84Xvs9XqN7Oxs49FHH/Wtq62tNWw2m/HXv/71qK8z0Pe8WX54vL3ZsGGDIckoLS096jYDfS+YobdjnTdvnnH55ZcP6HXC4dz257xefvnlxnnnndfnNuFwXv2Nlpt+crvd2rhxo4qLi33rrFariouLtX79+l73Wb9+fY/tJWnmzJlH3T5U1dXVSZLS09P73K6xsVGjRo1SXl6eLr/8cm3bti0Y5fnFt99+q9zcXI0ZM0Zz5szRvn37jrptpJxXt9utl156ST/72c/6vIlsOJ/XLnv27JHT6exx3lJSUlRUVHTU8zaY93woq6urk8ViUWpqap/bDeS9EErWrVunrKwsjR8/XrfccosOHjx41G0j5dxWVFTo7bff1s9//vNjbhuu53WwCDf9VF1dLY/HI4fD0WO9w+GQ0+nsdR+n0zmg7UOR1+vVnXfeqTPPPFMnnXTSUbcbP368Vq5cqbfeeksvvfSSvF6vzjjjDO3fvz+I1Q5OUVGRnn/+ea1evVrLly/Xnj17dNZZZ6mhoaHX7SPhvErSm2++qdraWt14441H3Sacz+vhus7NQM7bYN7zoaq1tVV33323rr/++j5vrDjQ90KouOiii/Tiiy+qpKREf/jDH/TBBx/o4osvlsfj6XX7SDm3L7zwgpKTk3XllVf2uV24ntfjMeTuCo6BWbBggbZu3XrM/tkZM2ZoxowZvsdnnHGGJk6cqKeeekq/+93vAl3mcbn44ot930+ZMkVFRUUaNWqUXnnllX79RRSunn32WV188cXKzc096jbhfF7Roa2tTddee60Mw9Dy5cv73DZc3wvXXXed7/uTTz5ZU6ZM0dixY7Vu3Tqdf/75JlYWWCtXrtScOXOOOcg/XM/r8aDlpp8yMjIUFRWlioqKHusrKiqUnZ3d6z7Z2dkD2j7U3Hbbbfqf//kfvf/++xoxYsSA9o2JidEpp5yiXbt2Bai6wElNTdWJJ5541NrD/bxKUmlpqdauXatf/OIXA9ovXM9r17kZyHkbzHs+1HQFm9LSUr333nt9ttr05ljvhVA1ZswYZWRkHLXuSDi3//rXv7Rz584Bv4el8D2vA0G46afY2FhNmzZNJSUlvnVer1clJSU9/rI93IwZM3psL0nvvffeUbcPFYZh6LbbbtMbb7yhf/zjHxo9evSAX8Pj8WjLli3KyckJQIWB1djYqN27dx+19nA9r4d77rnnlJWVpUsvvXRA+4XreR09erSys7N7nLf6+np9+umnRz1vg3nPh5KuYPPtt99q7dq1GjZs2IBf41jvhVC1f/9+HTx48Kh1h/u5lTpaXqdNm6aCgoIB7xuu53VAzB7RHE5efvllw2azGc8//7zx9ddfGzfffLORmppqOJ1OwzAM44YbbjB++9vf+rb/6KOPjOjoaOOxxx4ztm/fbixevNiIiYkxtmzZYtYh9Mstt9xipKSkGOvWrTPKy8t9S3Nzs2+bHx7rAw88YKxZs8bYvXu3sXHjRuO6664z4uLijG3btplxCAPym9/8xli3bp2xZ88e46OPPjKKi4uNjIwMo7Ky0jCMyDmvXTwejzFy5Ejj7rvvPuK5cD6vDQ0NxhdffGF88cUXhiTj8ccfN7744gvf1UEPP/ywkZqaarz11lvGV199ZVx++eXG6NGjjZaWFt9rnHfeecYTTzzhe3ys97yZ+jpet9ttXHbZZcaIESOMzZs393gfu1wu32v88HiP9V4wS1/H2tDQYNx1113G+vXrjT179hhr1641Tj31VOOEE04wWltbfa8RLuf2WP+ODcMw6urqjISEBGP58uW9vka4nNdAItwM0BNPPGGMHDnSiI2NNQoLC41PPvnE99zZZ59tzJs3r8f2r7zyinHiiScasbGxxuTJk4233347yBUPnKRel+eee863zQ+P9c477/T9XhwOh3HJJZcYmzZtCn7xgzB79mwjJyfHiI2NNYYPH27Mnj3b2LVrl+/5SDmvXdasWWNIMnbu3HnEc+F8Xt9///1e/912HY/X6zXuvfdew+FwGDabzTj//POP+B2MGjXKWLx4cY91fb3nzdTX8e7Zs+eo7+P333/f9xo/PN5jvRfM0texNjc3GxdeeKGRmZlpxMTEGKNGjTJuuummI0JKuJzbY/07NgzDeOqpp4z4+Hijtra219cIl/MaSBbDMIyANg0BAAAEEWNuAABARCHcAACAiEK4AQAAEYVwAwAAIgrhBgAARBTCDQAAiCiEGwAAEFEINwAAIKIQbgAMeRaLRW+++abZZQDwE8INAFPdeOONslgsRywXXXSR2aUBCFPRZhcAABdddJGee+65HutsNptJ1QAId7TcADCdzWZTdnZ2jyUtLU1SR5fR8uXLdfHFFys+Pl5jxozRa6+91mP/LVu26LzzzlN8fLyGDRumm2++WY2NjT22WblypSZPniybzaacnBzddtttPZ6vrq7WFVdcoYSEBJ1wwgn629/+FtiDBhAwhBsAIe/ee+/VVVddpS+//FJz5szRddddp+3bt0uSmpqaNHPmTKWlpemzzz7Tq6++qrVr1/YIL8uXL9eCBQt08803a8uWLfrb3/6mcePG9fgZDzzwgK699lp99dVXuuSSSzRnzhzV1NQE9TgB+InZtyUHMLTNmzfPiIqKMhITE3ssDz30kGEYhiHJ+NWvftVjn6KiIuOWW24xDMMwnn76aSMtLc1obGz0Pf/2228bVqvVcDqdhmEYRm5urnHPPfcctQZJxr/927/5Hjc2NhqSjHfffddvxwkgeBhzA8B05557rpYvX95jXXp6uu/7GTNm9HhuxowZ2rx5syRp+/btKigoUGJiou/5M888U16vVzt37pTFYtGBAwd0/vnn91nDlClTfN8nJibKbrersrJysIcEwESEGwCmS0xMPKKbyF/i4+P7tV1MTEyPxxaLRV6vNxAlAQgwxtwACHmffPLJEY8nTpwoSZo4caK+/PJLNTU1+Z7/6KOPZLVaNX78eCUnJys/P18lJSVBrRmAeWi5AWA6l8slp9PZY110dLQyMjIkSa+++qqmT5+uH/3oR/rLX/6iDRs26Nlnn5UkzZkzR4sXL9a8efN0//33q6qqSrfffrtuuOEGORwOSdL999+vX/3qV8rKytLFF1+shoYGffTRR7r99tuDe6AAgoJwA8B0q1evVk5OTo9148eP144dOyR1XMn08ssv69Zbb1VOTo7++te/atKkSZKkhIQErVmzRnfccYdOO+00JSQk6KqrrtLjjz/ue6158+aptbVV//Ef/6G77rpLGRkZuvrqq4N3gACCymIYhmF2EQBwNBaLRW+88YZmzZpldikAwgRjbgAAQEQh3AAAgIjCmBsAIY2ecwADRcsNAACIKIQbAAAQUQg3AAAgohBuAABARCHcAACAiEK4AQAAEYVwAwAAIgrhBgAARJT/Hw3Cr5clB7irAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.plot(training.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bf64c021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict([x_test]).round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "adf6e843",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0b7696f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      1.00      0.95        19\n",
      "           1       0.95      0.97      0.96        62\n",
      "           2       1.00      0.93      0.96        44\n",
      "\n",
      "    accuracy                           0.96       125\n",
      "   macro avg       0.95      0.97      0.96       125\n",
      "weighted avg       0.96      0.96      0.96       125\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00ae9c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#0.3, 0.5, 0.2, 0.1, 0.3, 0.5 and the label will give value 0\n",
    "lang_vocab = 0.3\n",
    "memory_score = 0.5\n",
    "speed_score = 0.2\n",
    "visual_score = 0.1\n",
    "audio_score = 0.3 \n",
    "survey_score = 0.5 \n",
    "array = np.array([[lang_vocab, memory_score, speed_score,visual_score, audio_score, survey_score]])\n",
    "label = int(model.predict(array))\n",
    "print(\"label \", label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee85d04f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}