speech-emotion-recognition-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> Speech Emotion Recognition - Part 2 </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents:\n",
    "* ## [Introduction](#introduction)\n",
    "\n",
    "* ## [Preparing the Data for Modelling:](#prep)\n",
    "\n",
    "* ## [Modeling:](#model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction: <a class=\"anchor\" id=\"introduction\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> This is part 2 of the notebook for the speech emotion recognition project, this part was ran on Google Colab. Part 1 can be found [here](https://github.com/MeshalAlamr/speech-emotion-recognition/blob/main/speech-emotion-recognition-1.ipynb) on the GitHub repo for the project. </b>\n",
    "\n",
    "<b> At the end of part 1, we obtained the final dataframe which has 36,486 rows and 5 columns, including the extracted features which are on the fifth column, having 162 features for all audio files of the augmented data. </b>\n",
    "    \n",
    "<b> If you'd like to replicate the results from part 1 then the Pickle file for the final dataframe can be downloaded [here](https://drive.google.com/file/d/1iKRgJXRL2roULKN_BzrotG-bRqtflznK/view?usp=sharing) on Google Drive. </b>\n",
    "\n",
    "### Part 2 will include:\n",
    "* <b> Preparing the Data for Modelling </b>\n",
    "* <b> Modelling </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessarry libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16329,
     "status": "ok",
     "timestamp": 1637882842871,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "cqekwyEeW1wd",
    "outputId": "6c312e41-ef2f-481a-be5c-439ba758c738"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount(\"/content/gdrive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 4576,
     "status": "ok",
     "timestamp": 1637882851169,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "V0S0KENuWw7Q"
   },
   "outputs": [],
   "source": [
    "# data preperation for modelling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# modelling\n",
    "import tensorflow as tf\n",
    "import keras as keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D\n",
    "from keras.layers import Dense, Embedding\n",
    "from tensorflow.keras import optimizers\n",
    "from keras.layers import Input, Flatten, Dropout, Activation\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "\n",
    "# results / visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\", UserWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data obtained from Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the pickle file was saved with protocol 5 using Python 3.8, it need to be loaded into Python 3.6 which only supports protocol 4 (the same goes when using Google Colab which this notebook was ran on). If you were using Python 3.8 then the pickle file can be read through pandas just as the next cell shows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4661,
     "status": "ok",
     "timestamp": 1637882862654,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "pWdyvxhOakMy",
    "outputId": "c2628597-1dca-4c60-dfd8-33ccfd9f1a88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pickle5\n",
      "  Downloading pickle5-0.0.12-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (256 kB)\n",
      "\u001b[K     |████████████████████████████████| 256 kB 5.3 MB/s \n",
      "\u001b[?25hInstalling collected packages: pickle5\n",
      "Successfully installed pickle5-0.0.12\n"
     ]
    }
   ],
   "source": [
    "!pip install pickle5\n",
    "\n",
    "import pickle5 as pickle\n",
    "with open(\"/content/gdrive/MyDrive/Colab_Notebooks/features.pkl\", \"rb\") as fh:\n",
    "    features_df = pickle.load(fh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1637882942807,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "7KF69w1NXA31",
    "outputId": "276fce40-02b9-49f1-81b5-31f851612f3a"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Emotion</th>\n",
       "      <th>Path</th>\n",
       "      <th>Signal</th>\n",
       "      <th>Sampling Rate</th>\n",
       "      <th>Features</th>\n",
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       "      <td>[0.0060994737, 0.008426055, 0.00677084, 0.0068...</td>\n",
       "      <td>22050</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>36482</th>\n",
       "      <td>surprise</td>\n",
       "      <td>data/Savee/KL_su12.wav</td>\n",
       "      <td>[-0.005803811, -0.006557814, -0.003097208, -0....</td>\n",
       "      <td>22050</td>\n",
       "      <td>[0.04212782118055555, 0.5079319477081299, 0.60...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36483</th>\n",
       "      <td>surprise</td>\n",
       "      <td>data/Savee/KL_su13.wav</td>\n",
       "      <td>[0.0007858629, 0.0010214511, 0.00036459867, 7....</td>\n",
       "      <td>22050</td>\n",
       "      <td>[0.03166142216435185, 0.4451121687889099, 0.47...</td>\n",
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       "      <td>surprise</td>\n",
       "      <td>data/Savee/KL_su14.wav</td>\n",
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       "<p>36486 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "        Emotion  ...                                           Features\n",
       "0       neutral  ...  [0.30078125, 0.7064923048019409, 0.75685280561...\n",
       "1       neutral  ...  [0.2712718822337963, 0.6749655604362488, 0.723...\n",
       "2       neutral  ...  [0.3073685257523148, 0.7161778211593628, 0.761...\n",
       "3       neutral  ...  [0.29731354890046297, 0.7431291937828064, 0.75...\n",
       "4          calm  ...  [0.24427625868055555, 0.611600935459137, 0.698...\n",
       "...         ...  ...                                                ...\n",
       "36481  surprise  ...  [0.0181304431352459, 0.46761587262153625, 0.52...\n",
       "36482  surprise  ...  [0.04212782118055555, 0.5079319477081299, 0.60...\n",
       "36483  surprise  ...  [0.03166142216435185, 0.4451121687889099, 0.47...\n",
       "36484  surprise  ...  [0.051423249421296294, 0.4757654070854187, 0.5...\n",
       "36485  surprise  ...  [0.04493091724537037, 0.5061090588569641, 0.57...\n",
       "\n",
       "[36486 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features_df = pd.read_pickle(\"features.pkl\")\n",
    "features_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preperation for Modelling <a class=\"anchor\" id=\"prep\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "executionInfo": {
     "elapsed": 2508,
     "status": "ok",
     "timestamp": 1637882945609,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "vmKT_3kOXCCd",
    "outputId": "b99270ea-2356-4cff-9834-ff9986a56b37"
   },
   "outputs": [
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       "      <td>6.674835e-06</td>\n",
       "      <td>4.080636e-06</td>\n",
       "      <td>2.743351e-06</td>\n",
       "      <td>3.518657e-06</td>\n",
       "      <td>3.818161e-06</td>\n",
       "      <td>4.319263e-06</td>\n",
       "      <td>3.298869e-06</td>\n",
       "      <td>2.153285e-06</td>\n",
       "      <td>2.286677e-06</td>\n",
       "      <td>5.131694e-06</td>\n",
       "      <td>8.065748e-06</td>\n",
       "      <td>5.375269e-06</td>\n",
       "      <td>2.270858e-06</td>\n",
       "      <td>1.642365e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.271272</td>\n",
       "      <td>0.674966</td>\n",
       "      <td>0.723259</td>\n",
       "      <td>0.724594</td>\n",
       "      <td>0.681301</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.674574</td>\n",
       "      <td>0.630036</td>\n",
       "      <td>0.680146</td>\n",
       "      <td>0.708276</td>\n",
       "      <td>0.735747</td>\n",
       "      <td>0.725877</td>\n",
       "      <td>0.703225</td>\n",
       "      <td>-634.959839</td>\n",
       "      <td>72.811478</td>\n",
       "      <td>-3.487027</td>\n",
       "      <td>20.697269</td>\n",
       "      <td>10.188319</td>\n",
       "      <td>-0.667841</td>\n",
       "      <td>-3.293634</td>\n",
       "      <td>-7.447817</td>\n",
       "      <td>-16.703850</td>\n",
       "      <td>-2.161060</td>\n",
       "      <td>-0.081074</td>\n",
       "      <td>-3.714514</td>\n",
       "      <td>0.273591</td>\n",
       "      <td>-4.517450</td>\n",
       "      <td>-1.117245</td>\n",
       "      <td>0.534380</td>\n",
       "      <td>-6.885534</td>\n",
       "      <td>-1.295200</td>\n",
       "      <td>-3.289555</td>\n",
       "      <td>0.756877</td>\n",
       "      <td>0.003227</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.004554</td>\n",
       "      <td>0.025026</td>\n",
       "      <td>0.068590</td>\n",
       "      <td>0.038717</td>\n",
       "      <td>...</td>\n",
       "      <td>6.318376e-05</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>3.218146e-05</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>6.080316e-06</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>6.328713e-06</td>\n",
       "      <td>5.261341e-06</td>\n",
       "      <td>5.756651e-06</td>\n",
       "      <td>5.887367e-06</td>\n",
       "      <td>7.139188e-06</td>\n",
       "      <td>6.998011e-06</td>\n",
       "      <td>7.050108e-06</td>\n",
       "      <td>6.670963e-06</td>\n",
       "      <td>6.999257e-06</td>\n",
       "      <td>1.217880e-05</td>\n",
       "      <td>9.449916e-06</td>\n",
       "      <td>8.465686e-06</td>\n",
       "      <td>2.638513e-06</td>\n",
       "      <td>1.788902e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.307369</td>\n",
       "      <td>0.716178</td>\n",
       "      <td>0.761089</td>\n",
       "      <td>0.758008</td>\n",
       "      <td>0.758388</td>\n",
       "      <td>0.734855</td>\n",
       "      <td>0.649470</td>\n",
       "      <td>0.630941</td>\n",
       "      <td>0.680735</td>\n",
       "      <td>0.738601</td>\n",
       "      <td>0.729218</td>\n",
       "      <td>0.645748</td>\n",
       "      <td>0.654602</td>\n",
       "      <td>-643.539185</td>\n",
       "      <td>74.687790</td>\n",
       "      <td>-1.654426</td>\n",
       "      <td>16.667934</td>\n",
       "      <td>5.185095</td>\n",
       "      <td>0.672246</td>\n",
       "      <td>-4.452597</td>\n",
       "      <td>-7.804122</td>\n",
       "      <td>-15.013509</td>\n",
       "      <td>-5.399968</td>\n",
       "      <td>-0.586950</td>\n",
       "      <td>-4.113428</td>\n",
       "      <td>1.270109</td>\n",
       "      <td>-4.779651</td>\n",
       "      <td>-3.539862</td>\n",
       "      <td>1.090911</td>\n",
       "      <td>-7.247006</td>\n",
       "      <td>-4.063982</td>\n",
       "      <td>-0.514474</td>\n",
       "      <td>0.192338</td>\n",
       "      <td>0.003666</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.004505</td>\n",
       "      <td>0.028807</td>\n",
       "      <td>0.065525</td>\n",
       "      <td>0.020931</td>\n",
       "      <td>...</td>\n",
       "      <td>1.500411e-04</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000483</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.001011</td>\n",
       "      <td>0.000770</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>1.277339e-04</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>4.260593e-05</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>1.787946e-05</td>\n",
       "      <td>1.151506e-05</td>\n",
       "      <td>8.228151e-06</td>\n",
       "      <td>6.587115e-06</td>\n",
       "      <td>1.097085e-05</td>\n",
       "      <td>1.281951e-05</td>\n",
       "      <td>1.290721e-05</td>\n",
       "      <td>1.525549e-05</td>\n",
       "      <td>1.986458e-05</td>\n",
       "      <td>2.776636e-05</td>\n",
       "      <td>4.780266e-05</td>\n",
       "      <td>2.483219e-05</td>\n",
       "      <td>8.083573e-06</td>\n",
       "      <td>6.468821e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.297314</td>\n",
       "      <td>0.743129</td>\n",
       "      <td>0.754720</td>\n",
       "      <td>0.747343</td>\n",
       "      <td>0.733528</td>\n",
       "      <td>0.726183</td>\n",
       "      <td>0.682260</td>\n",
       "      <td>0.676388</td>\n",
       "      <td>0.701887</td>\n",
       "      <td>0.736036</td>\n",
       "      <td>0.695124</td>\n",
       "      <td>0.684309</td>\n",
       "      <td>0.707974</td>\n",
       "      <td>-644.859009</td>\n",
       "      <td>69.573090</td>\n",
       "      <td>2.603384</td>\n",
       "      <td>16.992235</td>\n",
       "      <td>7.469007</td>\n",
       "      <td>2.995506</td>\n",
       "      <td>-2.625710</td>\n",
       "      <td>-7.836774</td>\n",
       "      <td>-13.316399</td>\n",
       "      <td>-4.526955</td>\n",
       "      <td>-0.048874</td>\n",
       "      <td>-2.389073</td>\n",
       "      <td>-0.242245</td>\n",
       "      <td>-3.906938</td>\n",
       "      <td>-2.942741</td>\n",
       "      <td>1.678184</td>\n",
       "      <td>-6.506072</td>\n",
       "      <td>-2.595646</td>\n",
       "      <td>-0.604484</td>\n",
       "      <td>-0.405804</td>\n",
       "      <td>0.003320</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.007546</td>\n",
       "      <td>0.036165</td>\n",
       "      <td>0.081686</td>\n",
       "      <td>0.017410</td>\n",
       "      <td>...</td>\n",
       "      <td>1.721186e-04</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.000455</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>1.542786e-04</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>1.791678e-05</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>2.694546e-05</td>\n",
       "      <td>3.007091e-05</td>\n",
       "      <td>2.133095e-05</td>\n",
       "      <td>1.577332e-05</td>\n",
       "      <td>1.624861e-05</td>\n",
       "      <td>1.986174e-05</td>\n",
       "      <td>1.858358e-05</td>\n",
       "      <td>2.485619e-05</td>\n",
       "      <td>8.028382e-05</td>\n",
       "      <td>4.014647e-05</td>\n",
       "      <td>3.653998e-05</td>\n",
       "      <td>3.107788e-05</td>\n",
       "      <td>1.029439e-05</td>\n",
       "      <td>6.636481e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.244276</td>\n",
       "      <td>0.611601</td>\n",
       "      <td>0.698656</td>\n",
       "      <td>0.730102</td>\n",
       "      <td>0.686579</td>\n",
       "      <td>0.656195</td>\n",
       "      <td>0.657558</td>\n",
       "      <td>0.631722</td>\n",
       "      <td>0.669574</td>\n",
       "      <td>0.674671</td>\n",
       "      <td>0.699672</td>\n",
       "      <td>0.729654</td>\n",
       "      <td>0.628885</td>\n",
       "      <td>-652.754456</td>\n",
       "      <td>87.646660</td>\n",
       "      <td>2.713349</td>\n",
       "      <td>20.903515</td>\n",
       "      <td>10.069142</td>\n",
       "      <td>2.383482</td>\n",
       "      <td>-5.915830</td>\n",
       "      <td>-5.536599</td>\n",
       "      <td>-15.037767</td>\n",
       "      <td>-6.882812</td>\n",
       "      <td>0.394059</td>\n",
       "      <td>-4.395927</td>\n",
       "      <td>-0.156529</td>\n",
       "      <td>-4.558533</td>\n",
       "      <td>-1.396951</td>\n",
       "      <td>2.404289</td>\n",
       "      <td>-8.463616</td>\n",
       "      <td>-0.791653</td>\n",
       "      <td>-2.339244</td>\n",
       "      <td>-1.943409</td>\n",
       "      <td>0.002348</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.001818</td>\n",
       "      <td>0.016928</td>\n",
       "      <td>0.031705</td>\n",
       "      <td>0.034757</td>\n",
       "      <td>...</td>\n",
       "      <td>3.190938e-05</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>4.565445e-05</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>8.068723e-06</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>8.630908e-06</td>\n",
       "      <td>9.327180e-06</td>\n",
       "      <td>4.340145e-06</td>\n",
       "      <td>3.813238e-06</td>\n",
       "      <td>6.417456e-06</td>\n",
       "      <td>5.811672e-06</td>\n",
       "      <td>4.467301e-06</td>\n",
       "      <td>1.176222e-05</td>\n",
       "      <td>6.051253e-06</td>\n",
       "      <td>3.856389e-06</td>\n",
       "      <td>4.652284e-06</td>\n",
       "      <td>4.236149e-06</td>\n",
       "      <td>1.549818e-06</td>\n",
       "      <td>1.118311e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36481</th>\n",
       "      <td>0.018130</td>\n",
       "      <td>0.467616</td>\n",
       "      <td>0.521791</td>\n",
       "      <td>0.525407</td>\n",
       "      <td>0.492337</td>\n",
       "      <td>0.496407</td>\n",
       "      <td>0.556227</td>\n",
       "      <td>0.515508</td>\n",
       "      <td>0.575827</td>\n",
       "      <td>0.665251</td>\n",
       "      <td>0.638509</td>\n",
       "      <td>0.534309</td>\n",
       "      <td>0.463501</td>\n",
       "      <td>-574.355042</td>\n",
       "      <td>101.977959</td>\n",
       "      <td>29.466824</td>\n",
       "      <td>49.124184</td>\n",
       "      <td>12.636480</td>\n",
       "      <td>1.829832</td>\n",
       "      <td>-12.536716</td>\n",
       "      <td>-6.304803</td>\n",
       "      <td>-10.387691</td>\n",
       "      <td>-13.003959</td>\n",
       "      <td>-3.756698</td>\n",
       "      <td>-4.816181</td>\n",
       "      <td>-5.519053</td>\n",
       "      <td>-4.804385</td>\n",
       "      <td>2.077319</td>\n",
       "      <td>6.945365</td>\n",
       "      <td>2.054493</td>\n",
       "      <td>5.276241</td>\n",
       "      <td>9.561365</td>\n",
       "      <td>2.030005</td>\n",
       "      <td>0.008041</td>\n",
       "      <td>0.088320</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>0.001108</td>\n",
       "      <td>0.005661</td>\n",
       "      <td>0.069106</td>\n",
       "      <td>0.052140</td>\n",
       "      <td>...</td>\n",
       "      <td>1.338173e-05</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>3.380981e-05</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2.652315e-06</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>2.431463e-06</td>\n",
       "      <td>1.221784e-06</td>\n",
       "      <td>9.984878e-07</td>\n",
       "      <td>9.032217e-07</td>\n",
       "      <td>5.292033e-07</td>\n",
       "      <td>1.559122e-07</td>\n",
       "      <td>5.702100e-08</td>\n",
       "      <td>2.746430e-08</td>\n",
       "      <td>2.026328e-08</td>\n",
       "      <td>2.353200e-08</td>\n",
       "      <td>2.710068e-08</td>\n",
       "      <td>4.160622e-08</td>\n",
       "      <td>5.963533e-08</td>\n",
       "      <td>6.259223e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36482</th>\n",
       "      <td>0.042128</td>\n",
       "      <td>0.507932</td>\n",
       "      <td>0.601484</td>\n",
       "      <td>0.555101</td>\n",
       "      <td>0.479290</td>\n",
       "      <td>0.428419</td>\n",
       "      <td>0.509465</td>\n",
       "      <td>0.568993</td>\n",
       "      <td>0.564511</td>\n",
       "      <td>0.536956</td>\n",
       "      <td>0.495615</td>\n",
       "      <td>0.501036</td>\n",
       "      <td>0.509616</td>\n",
       "      <td>-555.093079</td>\n",
       "      <td>147.536530</td>\n",
       "      <td>15.699092</td>\n",
       "      <td>44.126373</td>\n",
       "      <td>9.595186</td>\n",
       "      <td>-6.863729</td>\n",
       "      <td>-22.881706</td>\n",
       "      <td>-9.794035</td>\n",
       "      <td>-14.396991</td>\n",
       "      <td>-14.203674</td>\n",
       "      <td>-7.264202</td>\n",
       "      <td>-8.749352</td>\n",
       "      <td>-2.146052</td>\n",
       "      <td>-3.914860</td>\n",
       "      <td>-5.024640</td>\n",
       "      <td>2.531824</td>\n",
       "      <td>0.950305</td>\n",
       "      <td>-2.316981</td>\n",
       "      <td>2.871389</td>\n",
       "      <td>3.228227</td>\n",
       "      <td>0.007228</td>\n",
       "      <td>0.049839</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.006313</td>\n",
       "      <td>0.043649</td>\n",
       "      <td>0.052570</td>\n",
       "      <td>...</td>\n",
       "      <td>1.047658e-05</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>2.559343e-05</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>2.116118e-06</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.333897e-06</td>\n",
       "      <td>7.728303e-07</td>\n",
       "      <td>3.956997e-07</td>\n",
       "      <td>3.497793e-07</td>\n",
       "      <td>2.022981e-07</td>\n",
       "      <td>5.720078e-08</td>\n",
       "      <td>2.862853e-08</td>\n",
       "      <td>1.437673e-08</td>\n",
       "      <td>9.489965e-09</td>\n",
       "      <td>9.819932e-09</td>\n",
       "      <td>1.176858e-08</td>\n",
       "      <td>1.827400e-08</td>\n",
       "      <td>2.628869e-08</td>\n",
       "      <td>2.876826e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36483</th>\n",
       "      <td>0.031661</td>\n",
       "      <td>0.445112</td>\n",
       "      <td>0.471160</td>\n",
       "      <td>0.491193</td>\n",
       "      <td>0.478887</td>\n",
       "      <td>0.451148</td>\n",
       "      <td>0.525682</td>\n",
       "      <td>0.632410</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.500421</td>\n",
       "      <td>0.434006</td>\n",
       "      <td>0.385177</td>\n",
       "      <td>0.437438</td>\n",
       "      <td>-565.890747</td>\n",
       "      <td>143.126999</td>\n",
       "      <td>39.953476</td>\n",
       "      <td>26.066145</td>\n",
       "      <td>4.497488</td>\n",
       "      <td>-18.054771</td>\n",
       "      <td>-14.239442</td>\n",
       "      <td>-0.162105</td>\n",
       "      <td>-18.337458</td>\n",
       "      <td>-24.493874</td>\n",
       "      <td>-11.776628</td>\n",
       "      <td>-3.797245</td>\n",
       "      <td>-2.104504</td>\n",
       "      <td>-7.409347</td>\n",
       "      <td>-6.799134</td>\n",
       "      <td>1.291445</td>\n",
       "      <td>2.337642</td>\n",
       "      <td>-1.921818</td>\n",
       "      <td>2.581617</td>\n",
       "      <td>3.139871</td>\n",
       "      <td>0.007864</td>\n",
       "      <td>0.008635</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000425</td>\n",
       "      <td>0.003645</td>\n",
       "      <td>0.031695</td>\n",
       "      <td>0.063324</td>\n",
       "      <td>...</td>\n",
       "      <td>9.694958e-07</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>9.973880e-07</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>9.540544e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>9.169803e-07</td>\n",
       "      <td>4.246268e-07</td>\n",
       "      <td>2.685889e-07</td>\n",
       "      <td>1.955869e-07</td>\n",
       "      <td>1.182934e-07</td>\n",
       "      <td>3.707684e-08</td>\n",
       "      <td>1.689016e-08</td>\n",
       "      <td>8.011726e-09</td>\n",
       "      <td>4.354761e-09</td>\n",
       "      <td>3.618042e-09</td>\n",
       "      <td>3.395549e-09</td>\n",
       "      <td>3.005059e-09</td>\n",
       "      <td>1.443821e-09</td>\n",
       "      <td>7.009023e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36484</th>\n",
       "      <td>0.051423</td>\n",
       "      <td>0.475765</td>\n",
       "      <td>0.537150</td>\n",
       "      <td>0.527179</td>\n",
       "      <td>0.535551</td>\n",
       "      <td>0.494147</td>\n",
       "      <td>0.528551</td>\n",
       "      <td>0.536035</td>\n",
       "      <td>0.541296</td>\n",
       "      <td>0.569161</td>\n",
       "      <td>0.497057</td>\n",
       "      <td>0.536830</td>\n",
       "      <td>0.513340</td>\n",
       "      <td>-546.170532</td>\n",
       "      <td>108.574280</td>\n",
       "      <td>17.612986</td>\n",
       "      <td>16.843660</td>\n",
       "      <td>-10.062366</td>\n",
       "      <td>-6.319212</td>\n",
       "      <td>-17.721697</td>\n",
       "      <td>-6.000184</td>\n",
       "      <td>-16.911253</td>\n",
       "      <td>-15.893400</td>\n",
       "      <td>0.768639</td>\n",
       "      <td>-2.322278</td>\n",
       "      <td>0.572429</td>\n",
       "      <td>-2.709535</td>\n",
       "      <td>-0.554532</td>\n",
       "      <td>2.845199</td>\n",
       "      <td>-1.852203</td>\n",
       "      <td>1.145466</td>\n",
       "      <td>3.652693</td>\n",
       "      <td>-0.661899</td>\n",
       "      <td>0.008778</td>\n",
       "      <td>0.002529</td>\n",
       "      <td>0.001177</td>\n",
       "      <td>0.001288</td>\n",
       "      <td>0.002182</td>\n",
       "      <td>0.015727</td>\n",
       "      <td>0.028996</td>\n",
       "      <td>...</td>\n",
       "      <td>1.946760e-05</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>5.640672e-05</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>3.712007e-06</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>4.228702e-06</td>\n",
       "      <td>1.597890e-06</td>\n",
       "      <td>2.044650e-06</td>\n",
       "      <td>1.025002e-06</td>\n",
       "      <td>6.001300e-07</td>\n",
       "      <td>2.072579e-07</td>\n",
       "      <td>5.972032e-08</td>\n",
       "      <td>2.550167e-08</td>\n",
       "      <td>9.190079e-09</td>\n",
       "      <td>1.000525e-08</td>\n",
       "      <td>1.420251e-08</td>\n",
       "      <td>2.871202e-08</td>\n",
       "      <td>5.326412e-08</td>\n",
       "      <td>6.452172e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36485</th>\n",
       "      <td>0.044931</td>\n",
       "      <td>0.506109</td>\n",
       "      <td>0.570872</td>\n",
       "      <td>0.546673</td>\n",
       "      <td>0.545030</td>\n",
       "      <td>0.507918</td>\n",
       "      <td>0.533202</td>\n",
       "      <td>0.570223</td>\n",
       "      <td>0.618037</td>\n",
       "      <td>0.579717</td>\n",
       "      <td>0.530486</td>\n",
       "      <td>0.500991</td>\n",
       "      <td>0.488237</td>\n",
       "      <td>-559.986816</td>\n",
       "      <td>127.469772</td>\n",
       "      <td>27.333570</td>\n",
       "      <td>19.555998</td>\n",
       "      <td>-22.901579</td>\n",
       "      <td>-16.705935</td>\n",
       "      <td>-6.737510</td>\n",
       "      <td>3.487469</td>\n",
       "      <td>-19.805525</td>\n",
       "      <td>-15.736290</td>\n",
       "      <td>-0.022896</td>\n",
       "      <td>-5.643213</td>\n",
       "      <td>1.651553</td>\n",
       "      <td>-1.569747</td>\n",
       "      <td>-4.365533</td>\n",
       "      <td>-0.139080</td>\n",
       "      <td>-0.320745</td>\n",
       "      <td>1.530366</td>\n",
       "      <td>3.395892</td>\n",
       "      <td>-1.987674</td>\n",
       "      <td>0.009386</td>\n",
       "      <td>0.008593</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.001223</td>\n",
       "      <td>0.038296</td>\n",
       "      <td>0.035438</td>\n",
       "      <td>...</td>\n",
       "      <td>1.450385e-05</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>5.109646e-05</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>4.696274e-06</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2.889683e-06</td>\n",
       "      <td>1.694889e-06</td>\n",
       "      <td>1.510396e-06</td>\n",
       "      <td>7.593150e-07</td>\n",
       "      <td>3.546786e-07</td>\n",
       "      <td>1.957675e-07</td>\n",
       "      <td>8.058839e-08</td>\n",
       "      <td>2.042028e-08</td>\n",
       "      <td>6.462758e-09</td>\n",
       "      <td>4.753970e-09</td>\n",
       "      <td>4.367233e-09</td>\n",
       "      <td>3.952898e-09</td>\n",
       "      <td>2.079545e-09</td>\n",
       "      <td>1.237944e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36486 rows × 162 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2    ...           159           160           161\n",
       "0      0.300781  0.706492  0.756853  ...  5.375269e-06  2.270858e-06  1.642365e-07\n",
       "1      0.271272  0.674966  0.723259  ...  8.465686e-06  2.638513e-06  1.788902e-07\n",
       "2      0.307369  0.716178  0.761089  ...  2.483219e-05  8.083573e-06  6.468821e-07\n",
       "3      0.297314  0.743129  0.754720  ...  3.107788e-05  1.029439e-05  6.636481e-07\n",
       "4      0.244276  0.611601  0.698656  ...  4.236149e-06  1.549818e-06  1.118311e-07\n",
       "...         ...       ...       ...  ...           ...           ...           ...\n",
       "36481  0.018130  0.467616  0.521791  ...  4.160622e-08  5.963533e-08  6.259223e-08\n",
       "36482  0.042128  0.507932  0.601484  ...  1.827400e-08  2.628869e-08  2.876826e-08\n",
       "36483  0.031661  0.445112  0.471160  ...  3.005059e-09  1.443821e-09  7.009023e-10\n",
       "36484  0.051423  0.475765  0.537150  ...  2.871202e-08  5.326412e-08  6.452172e-08\n",
       "36485  0.044931  0.506109  0.570872  ...  3.952898e-09  2.079545e-09  1.237944e-09\n",
       "\n",
       "[36486 rows x 162 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = pd.DataFrame(features_df['Features'].to_list())\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1637882945609,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "a54P5ZZRXFsB"
   },
   "outputs": [],
   "source": [
    "X = np.array(features)\n",
    "y = features_df[\"Emotion\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OneHotEncoding the labels and splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1637882945610,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "hj79w0A6XF2x"
   },
   "outputs": [],
   "source": [
    "encoder = OneHotEncoder()\n",
    "y = encoder.fit_transform(np.array(y).reshape(-1,1)).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1637882945610,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "a5B96kvdXF5i",
    "outputId": "cb724c5b-5449-438e-aada-c16e19ea437d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((36486, 162), (36486, 8))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 389,
     "status": "ok",
     "timestamp": 1637882945996,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "HnYsye87XF8b",
    "outputId": "5fc92097-223a-4078-d11b-86743a4ff57b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((27364, 162), (27364, 8), (9122, 162), (9122, 8))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=42, shuffle=True)\n",
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1637882945996,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "g3crZUTkXF-6",
    "outputId": "63caf291-f8df-4b63-acd9-0034da3bb4bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((27364, 162), (27364, 8), (9122, 162), (9122, 8))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)\n",
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1637882945997,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "1sCOuDcXXGBq",
    "outputId": "af256cbf-57c2-47a7-bd73-80ef39b58386"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((27364, 162, 1), (27364, 8), (9122, 162, 1), (9122, 8))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = np.expand_dims(x_train, axis=2)\n",
    "x_test = np.expand_dims(x_test, axis=2)\n",
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k3_UL_CCXKt1"
   },
   "source": [
    "## Modelling <a class=\"anchor\" id=\"model\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3294,
     "status": "ok",
     "timestamp": 1637882949289,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "EzM2Vo5eXGEC",
    "outputId": "da27d44b-6b38-4dd3-a526-4cac533ae57a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 162, 256)          1536      \n",
      "                                                                 \n",
      " max_pooling1d (MaxPooling1D  (None, 81, 256)          0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 81, 256)           327936    \n",
      "                                                                 \n",
      " max_pooling1d_1 (MaxPooling  (None, 41, 256)          0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " conv1d_2 (Conv1D)           (None, 41, 128)           163968    \n",
      "                                                                 \n",
      " max_pooling1d_2 (MaxPooling  (None, 21, 128)          0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 21, 128)           0         \n",
      "                                                                 \n",
      " conv1d_3 (Conv1D)           (None, 21, 64)            41024     \n",
      "                                                                 \n",
      " max_pooling1d_3 (MaxPooling  (None, 11, 64)           0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 704)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                22560     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 8)                 264       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 557,288\n",
      "Trainable params: 557,288\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu', input_shape=(x_train.shape[1], 1)))\n",
    "model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
    "\n",
    "model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
    "\n",
    "model.add(Conv1D(128, kernel_size=5, strides=1, padding='same', activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv1D(64, kernel_size=5, strides=1, padding='same', activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(units=32, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Dense(units=8, activation='softmax'))\n",
    "model.compile(optimizer = 'adam' , loss = 'categorical_crossentropy' , metrics = ['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 503699,
     "status": "ok",
     "timestamp": 1637883462280,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "pg8TDkPVXGGi",
    "outputId": "4d846b21-9ff5-4e42-ed2a-1db566d1d088"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "428/428 [==============================] - 20s 23ms/step - loss: 1.6447 - accuracy: 0.3369 - val_loss: 1.3526 - val_accuracy: 0.4612 - lr: 0.0010\n",
      "Epoch 2/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.3607 - accuracy: 0.4525 - val_loss: 1.2403 - val_accuracy: 0.5001 - lr: 0.0010\n",
      "Epoch 3/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.2693 - accuracy: 0.4905 - val_loss: 1.1763 - val_accuracy: 0.5318 - lr: 0.0010\n",
      "Epoch 4/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.2150 - accuracy: 0.5133 - val_loss: 1.1498 - val_accuracy: 0.5364 - lr: 0.0010\n",
      "Epoch 5/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.1734 - accuracy: 0.5308 - val_loss: 1.1097 - val_accuracy: 0.5561 - lr: 0.0010\n",
      "Epoch 6/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.1439 - accuracy: 0.5456 - val_loss: 1.1125 - val_accuracy: 0.5597 - lr: 0.0010\n",
      "Epoch 7/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.1191 - accuracy: 0.5520 - val_loss: 1.0686 - val_accuracy: 0.5807 - lr: 0.0010\n",
      "Epoch 8/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.0922 - accuracy: 0.5633 - val_loss: 1.0906 - val_accuracy: 0.5683 - lr: 0.0010\n",
      "Epoch 9/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.0706 - accuracy: 0.5765 - val_loss: 1.0898 - val_accuracy: 0.5737 - lr: 0.0010\n",
      "Epoch 10/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.0548 - accuracy: 0.5814 - val_loss: 1.0274 - val_accuracy: 0.5896 - lr: 0.0010\n",
      "Epoch 11/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.0341 - accuracy: 0.5907 - val_loss: 1.0203 - val_accuracy: 0.5989 - lr: 0.0010\n",
      "Epoch 12/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 1.0163 - accuracy: 0.5974 - val_loss: 1.0255 - val_accuracy: 0.5950 - lr: 0.0010\n",
      "Epoch 13/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.9912 - accuracy: 0.6063 - val_loss: 0.9912 - val_accuracy: 0.6055 - lr: 0.0010\n",
      "Epoch 14/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.9791 - accuracy: 0.6115 - val_loss: 0.9740 - val_accuracy: 0.6130 - lr: 0.0010\n",
      "Epoch 15/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.9556 - accuracy: 0.6184 - val_loss: 0.9558 - val_accuracy: 0.6245 - lr: 0.0010\n",
      "Epoch 16/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.9445 - accuracy: 0.6279 - val_loss: 0.9710 - val_accuracy: 0.6115 - lr: 0.0010\n",
      "Epoch 17/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.9226 - accuracy: 0.6400 - val_loss: 0.9274 - val_accuracy: 0.6367 - lr: 0.0010\n",
      "Epoch 18/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.9082 - accuracy: 0.6426 - val_loss: 0.9714 - val_accuracy: 0.6173 - lr: 0.0010\n",
      "Epoch 19/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.8930 - accuracy: 0.6506 - val_loss: 0.9243 - val_accuracy: 0.6319 - lr: 0.0010\n",
      "Epoch 20/50\n",
      "428/428 [==============================] - 9s 20ms/step - loss: 0.8705 - accuracy: 0.6589 - val_loss: 0.9072 - val_accuracy: 0.6435 - lr: 0.0010\n",
      "Epoch 21/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.8660 - accuracy: 0.6631 - val_loss: 0.8972 - val_accuracy: 0.6476 - lr: 0.0010\n",
      "Epoch 22/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.8411 - accuracy: 0.6717 - val_loss: 0.8934 - val_accuracy: 0.6551 - lr: 0.0010\n",
      "Epoch 23/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.8388 - accuracy: 0.6707 - val_loss: 0.9076 - val_accuracy: 0.6474 - lr: 0.0010\n",
      "Epoch 24/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.8134 - accuracy: 0.6822 - val_loss: 0.8948 - val_accuracy: 0.6501 - lr: 0.0010\n",
      "Epoch 25/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.8030 - accuracy: 0.6854 - val_loss: 0.8876 - val_accuracy: 0.6533 - lr: 0.0010\n",
      "Epoch 26/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.7826 - accuracy: 0.6933 - val_loss: 0.9017 - val_accuracy: 0.6560 - lr: 0.0010\n",
      "Epoch 27/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.7792 - accuracy: 0.6986 - val_loss: 0.8776 - val_accuracy: 0.6627 - lr: 0.0010\n",
      "Epoch 28/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.7632 - accuracy: 0.7042 - val_loss: 0.8931 - val_accuracy: 0.6549 - lr: 0.0010\n",
      "Epoch 29/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.7483 - accuracy: 0.7079 - val_loss: 0.8723 - val_accuracy: 0.6642 - lr: 0.0010\n",
      "Epoch 30/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.7373 - accuracy: 0.7129 - val_loss: 0.8762 - val_accuracy: 0.6647 - lr: 0.0010\n",
      "Epoch 31/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.7293 - accuracy: 0.7195 - val_loss: 0.8583 - val_accuracy: 0.6774 - lr: 0.0010\n",
      "Epoch 32/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.7079 - accuracy: 0.7291 - val_loss: 0.8819 - val_accuracy: 0.6744 - lr: 0.0010\n",
      "Epoch 33/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.7095 - accuracy: 0.7273 - val_loss: 0.8343 - val_accuracy: 0.6860 - lr: 0.0010\n",
      "Epoch 34/50\n",
      "428/428 [==============================] - 9s 22ms/step - loss: 0.6794 - accuracy: 0.7373 - val_loss: 0.8414 - val_accuracy: 0.6812 - lr: 0.0010\n",
      "Epoch 35/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.6850 - accuracy: 0.7368 - val_loss: 0.8390 - val_accuracy: 0.6892 - lr: 0.0010\n",
      "Epoch 36/50\n",
      "428/428 [==============================] - 9s 22ms/step - loss: 0.6649 - accuracy: 0.7444 - val_loss: 0.8310 - val_accuracy: 0.6936 - lr: 0.0010\n",
      "Epoch 37/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.6586 - accuracy: 0.7450 - val_loss: 0.8379 - val_accuracy: 0.6997 - lr: 0.0010\n",
      "Epoch 38/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.6435 - accuracy: 0.7514 - val_loss: 0.8471 - val_accuracy: 0.6986 - lr: 0.0010\n",
      "Epoch 39/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.6284 - accuracy: 0.7583 - val_loss: 0.8441 - val_accuracy: 0.7037 - lr: 0.0010\n",
      "Epoch 40/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.6302 - accuracy: 0.7595 - val_loss: 0.8255 - val_accuracy: 0.7080 - lr: 0.0010\n",
      "Epoch 41/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.6155 - accuracy: 0.7634 - val_loss: 0.8350 - val_accuracy: 0.6960 - lr: 0.0010\n",
      "Epoch 42/50\n",
      "428/428 [==============================] - 9s 22ms/step - loss: 0.6059 - accuracy: 0.7701 - val_loss: 0.8398 - val_accuracy: 0.6991 - lr: 0.0010\n",
      "Epoch 43/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.5983 - accuracy: 0.7715 - val_loss: 0.8560 - val_accuracy: 0.7013 - lr: 0.0010\n",
      "Epoch 44/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.5719 - accuracy: 0.7803 - val_loss: 0.8097 - val_accuracy: 0.7184 - lr: 0.0010\n",
      "Epoch 45/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.5768 - accuracy: 0.7837 - val_loss: 0.8339 - val_accuracy: 0.7060 - lr: 0.0010\n",
      "Epoch 46/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.5701 - accuracy: 0.7849 - val_loss: 0.8148 - val_accuracy: 0.7142 - lr: 0.0010\n",
      "Epoch 47/50\n",
      "428/428 [==============================] - 9s 22ms/step - loss: 0.5579 - accuracy: 0.7887 - val_loss: 0.8264 - val_accuracy: 0.7146 - lr: 0.0010\n",
      "Epoch 48/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.5525 - accuracy: 0.7886 - val_loss: 0.8145 - val_accuracy: 0.7156 - lr: 0.0010\n",
      "Epoch 49/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.5498 - accuracy: 0.7909 - val_loss: 0.8225 - val_accuracy: 0.7191 - lr: 0.0010\n",
      "Epoch 50/50\n",
      "428/428 [==============================] - 9s 21ms/step - loss: 0.5405 - accuracy: 0.7973 - val_loss: 0.8225 - val_accuracy: 0.7214 - lr: 0.0010\n"
     ]
    }
   ],
   "source": [
    "rlrp = ReduceLROnPlateau(monitor='loss', factor=0.4, verbose=0, patience=2, min_lr=0.0000001)\n",
    "history=model.fit(x_train, y_train, batch_size=64, epochs=50, validation_data=(x_test, y_test), callbacks=[rlrp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "executionInfo": {
     "elapsed": 3211,
     "status": "ok",
     "timestamp": 1637884147390,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "XcSrr57DXGI7",
    "outputId": "4aa9ac71-b7a3-47b4-dd87-f4c1253c3f41"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "286/286 [==============================] - 2s 8ms/step - loss: 0.8225 - accuracy: 0.7214\n",
      "Accuracy of our model on test data :  72.14426398277283 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Accuracy of our model on test data : \" , model.evaluate(x_test,y_test)[1]*100 , \"%\")\n",
    "\n",
    "epochs = [i for i in range(50)]\n",
    "fig , ax = plt.subplots(1,2)\n",
    "train_acc = history.history['accuracy']\n",
    "train_loss = history.history['loss']\n",
    "test_acc = history.history['val_accuracy']\n",
    "test_loss = history.history['val_loss']\n",
    "\n",
    "fig.set_size_inches(20,6)\n",
    "ax[0].plot(epochs , train_loss , label = 'Training Loss')\n",
    "ax[0].plot(epochs , test_loss , label = 'Testing Loss')\n",
    "ax[0].set_title('Training & Testing Loss')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlabel(\"Epochs\")\n",
    "\n",
    "ax[1].plot(epochs , train_acc , label = 'Training Accuracy')\n",
    "ax[1].plot(epochs , test_acc , label = 'Testing Accuracy')\n",
    "ax[1].set_title('Training & Testing Accuracy')\n",
    "ax[1].legend()\n",
    "ax[1].set_xlabel(\"Epochs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 1674,
     "status": "ok",
     "timestamp": 1637884193186,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "ecjUx-ZeXGLT"
   },
   "outputs": [],
   "source": [
    "pred_test = model.predict(x_test)\n",
    "y_pred = encoder.inverse_transform(pred_test)\n",
    "\n",
    "y_test_labels = encoder.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 630
    },
    "executionInfo": {
     "elapsed": 757,
     "status": "ok",
     "timestamp": 1637884195162,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "7CGdoLMlXP7m",
    "outputId": "3de9629c-014f-4e4c-9f5f-b3c396800cbf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sWQCcc2zblgqxe6Ln39LglNMot8vfxeefTaV5qzZA0GidOX1q9nPvjB7B+Y0v5PD4hEhzRi0xsRLHHnc8AGXKlKVWrTqsXp3sOZV/ZsahZcoAkJ6eTnp6ekyfBV3QMtIz2LYtlfT0dFJTUklMrOQ7ksSAqK9TOj23KcoMB0rHZqcw+sO52Y/7/as1T74xma0p2z2mKnirk5OpXKVy9uNKSUkkJ+s/nCx16h3NrBlTyUhPZ+Ufy/l54QJWJ6/yHavADBk8iJtvvYNiu3QH9e97Dy2anMfSxYvpcmk3T+mit379OipUTAQgoUJF1q9fB8Ca1cnMnDaZNh26+IwXuT9WLGfhwh854cQGAIwe+SZdOlzCA33vYdMuQxuKgoyMDDq3b0Oj887mzLPOpn79Br4jeZGUlMQVV11D8wsb0bTRuZQtV5azzznXdyy/ilm0U4yK+mdGN5vZpl2m383sXTOrncvyPcxsrpnNHTo0dsYunn7CkWxNTWPBrysBqH9UNWrVSOS9qfM9JxPfWrRuR2KlJG64+lKeG/IIx5/YgGJxsTt+5++Y8dlU4uMTsqtiOfXtP5AJn0ynZq3afPLRJA/p/DOz7ELYM48/Qo+bbqNYscL5t5CbrVu38O9ePbnjzrspW7YsHbtcxvgJn/DWmHFUrJjIkMce8R0xcnFxcYx+ZzwfT5nO99/N55dffvYdyYtNGzcybepkJnw0mY+nzCAlJYUJ74/3HUtiQNSXhHoCWA6MIOjUuxSoA2T97GjDnAs754YCWa1Rd8vzN0UWdE86XXTqTlXSMxrU4tTjjmDhhAcoHleMxIRyfPTSLVx03ZMeUxaMSklJrFq5o/K3OjmZpKQkj4liS1zx4vzr1t7Zj2++rhvVj6jpL1ABmj/vW2ZMn8qsmZ+xbft2tmz5i7733En/gcHvYMTFxdG0eUveeO0VLm7b3nPaaCQkVGDd2jVUqJjIurVriI+vAMBPPy6g/73BZZg3bviTL2bNJC4ujvMaNvEZt8CkpaXx7149adHqYhpf2AyAChUqZj/frkMnbr3pBl/xvCtfvjyn/+MMZs2cQb16R/mOE7k5c2ZRrVp1EhKCoSxNmjRj3rxvaXVxG8/JPIrhk4+iFPVeuMQ596JzbrNzblPY6LzIOTeK4CSomGdmdGh2CmM++jp73ktjZlK7WR+OadWPxlcP4ZelqwtlgxTg+BNOZNmyJSxf/jtp27fz4cQJXNCose9YMSM1NYWUlGCM8twvZhEXF0fNWoXzV3Rv7NmLDz6exvhJkxnw8H847fQzeGDAI/y+LLjqm3OOGdOn7nQSVGF39vkN+XBCUPH5cMJ4zjm/EQAjx3/IqPEfMWr8R1zQuCm33tmn0DZInXM82O9eatWqQ7crrs6ev2bNjnH6U6d8Sp169XzE82b9+vVs2rQJgNTUVObMnlWkjo2cqlSpyvz5/yMlJQXnHF98MZvatQvn56Tsm6grpVvNrDMwNnzcEUgN77uIswDw+qCrOO/UelQ8vCyLPnyQB1+YyJ8bt/B4705UjC/LO09dz/yfVnBJeIb+uafUZfmqP1myYp2PuN4VL16cu/v05YYe3cnMzKBtuw7UrVu4/3N56L47+d83X7Fxwwa6XNyEK6+7kfLlD+Pp/wxk44Y/uafXv6h71DE88uSLbFi/nt63Xk8xMyomVuLufoN8x4+Uc44H7rubLVv+wjlHvaOOoXeffr5jFYj+997JvK+Dv4uOrZtw9XU30vWKa3ngnjuY+N67JFWuwv0DvZ7H6cW8b79hwgfjqVvvKC7r1BaAG3vexkeTJvDTwh8xM6pWrcY9fR/wnDRaa9es5t577iIzM4PMTEezi5pzQcNGvmN5cWL9BlzY9CIu69yOuLjiHHPMsXToVLTGW++mCJ/0lpMFP0Uf0caCcaNPAmcRNELnALcBK4BTnXMz97C6K31ybHTf+5Ty7TMApKZ7DhIDSoVfqZb/WbhPLsuP6vElAdiYUrgvP5Ufh5UOOoBWbtTfRZXDgr+Lv7Z5+c4fU8oeEvynr8/OHZ+dKWl+c8SC0iWAGLhGSOkmAyM9SFMm3+P9Pecm0kqpc+434OI8nt5Tg1RERESkcNKYUiDiRqmZJQLXATVzbts5d02UOUREREQktkQ9pnQ8MAP4FMiIeNsiIiIisUdjSoHoG6WHOud6730xERERESlKom6UfmBmLZ1zEyPeroiIiEhs0phSIPrrlN5C0DBNCX/NabOZbYo4g4iIiIjEmKjPvi9nZglAPaBUlNsWERERiUkaUwpEf/Z9d4JqaXVgHnAmMAsonD9tIiIiIiL54qP7/nRgqXOuEXAysDHiDCIiIiKxw4pFO8WoqJOlOudSAczsEOfcQuDoiDOIiIiISIyJ+uz75WZ2ODAO+MTM/gSWRpxBRERERGJM1Cc6tQvv3m9mU4HDgA+jzCAiIiISU3SiExB9pTSbc266r22LiIiISGzx1igVEREREWL65KMoaS+IiIiIiHeqlIqIiIj4pDGlgCqlIiIiIhIDVCkVERER8UljSgFVSkVEREQkBqhSKiIiIuKTKqWAKqUiIiIiEgNUKRURERHxSWffA6qUioiIiEgMUKVURERExCeNKQVUKRURERGRGKBKqYiIiIhPGlMKqFIqIiIiIjFAjVIRERER8e6g6r5P+fYZ3xFiRqmD6l+uYFWPL+k7Qsw4rLS+Z2apcpj+LrKUPURdg1n02blD6RK+E0g2negEqFIqIiIiIjHgoPrOmJruO4F/Wd/yF69N9RskBtSqWArQ3wXs+LvQvtC+yClrX6Sk+c0RC7KqgtoXO/aFjpEYqpzrRCdAlVIRERERiQGx8h1BREREpEgyVUoBVUpFREREJAaoUioiIiLikSqlAVVKRURERMQ7VUpFREREfFKhFFClVERERERigCqlIiIiIh5pTGlAlVIRERERyWZmw8xstZl9n2Negpl9Yma/hLfx4Xwzs6fMbJGZzTezU3Ksc2W4/C9mduXetqtGqYiIiIhHZhbplA+vAc13mXcXMNk5Vw+YHD4GaAHUC6cewPPhe0oA+gFnAP8A+mU1ZPOiRqmIiIiIZHPOfQas32V2G+D18P7rQNsc84e7wBzgcDOrAlwEfOKcW++c+xP4hN0bujvRmFIRERERj6IeU2pmPQiqmlmGOueG7mW1JOfcyvD+KiApvF8N+D3HcsvDeXnNz5MapSIiIiJFSNgA3VsjdE/rOzNzBzASoO57EREREdm75LBbnvB2dTh/BVAjx3LVw3l5zc+TGqUiIiIiHsXgiU65eQ/IOoP+SmB8jvlXhGfhnwlsDLv5PwKamVl8eIJTs3BentR9LyIiIiLZzOwtoCFQ0cyWE5xF/zAw2syuBZYCncPFJwItgUXAVuBqAOfcejN7EPgqXK6/c27Xk6d2okapiIiIiE8xdu1859xleTzVJJdlHXBjHq8zDBiW3+2q+15EREREvFOlVERERMQj/cxoQJVSEREREfFOlVIRERERj1QpDahSKiIiIiLeqVIqIiIi4pEqpYHIG6VmVh+omXPbzrl3os4hIiIiIrEj0kapmQ0D6gM/AJnhbAeoUSoiIiJFkiqlgagrpWc6546LeJsiIiIiEuOiPtFptpmpUSoiIiKSxSKeYlTUldLhBA3TVcA2gl3jnHP1I84hIiIiIjEk6krpK8A/gebAxUDr8DbmfT7jMy5pdRGtmzfllZeG+o5T4B4f2JcurRryf93a7/bc22+9TvNzGrBxw58AbPlrM/3uvJkbruxEj8vb8fGEcVHH9WLbtm107dKRTu0uod0lrXjumad8R/KqqB0je6J9scOmTZu447aetL24Oe0ubsH/5n3rO5I32hc76BjZmZlFOsWqqCula5xz70W8zb8tIyODgQP68+JLr5KUlETXLh1p2KgxderW9R2twDRt2YaLO1zGYw/22Wn+muRVfP3lbColVcme9/7boziiZm0eePRpNvy5nu6XtaFRs1aUKFEi6tiRKlmyJC8Pe51Dy5QhLS2Nq/7ZlXPPO5/6DU7yHS1yRfEYyYv2xc4efXgAZ59zHo8NeYq0tO2kpKT6juSN9kVAx4jkJepK6bdmNsLMLjOz9llTxBn22fffzadGjSOpXqMGJUqWpHnLVkybOtl3rAJ14kmnUq58+d3mv/jUYLr/6zbI+U3LjJStW3HOkZqylXLlDyMuLi7CtH6YGYeWKQNAeno66enpO++XIqQoHiN50b7YYfPmzXzz9Ve069ARgBIlSlI+l8+VokD7YgcdI7tTpTQQdaO0NMFY0mYE3fZZXfgxbXVyMpWrVM5+XCkpieTkZI+J/Jg9YyoVEitRu97RO82/pMOlLFvyG13bXMj1V3Tk+lvvpFixovFjYRkZGXRu34ZG553NmWedTf36DXxH8kLHyA7aFzusWLGc+PgE+t57N106tuWBvn1I2brVdywvtC920DEieYm05eCcuzqX6Zq8ljezHmY218zmDh2qMSc+paamMHL4y1zR/V+7Pff1l7OoU+8YRoz/lOdeG81zjw9iy5a/PKSMXlxcHKPfGc/HU6bz/Xfz+eWXn31HEokZGenpLPxxAZ27XMaoseMoVbo0w14pmp/l2hciexfJmFIze5rgIvm5cs71zGP+UCDrqHWp6QUQLh8qJSWxauWq7Merk5NJSkryE8aTlSuWs+qPFdxwZWcA1q5J5qZrLuXJl97k4wnj6dLtGsyMqtWPoHKVaixfupijjzvRc+rolC9fntP/cQazZs6gXr2jfMeJnI6RHbQvdkiqXJlKSZU5MexBaNqsOcNeLpoNMe2LHXSM7C6Wu9SjFFWldC7w9R6mmHb8CSeybNkSli//nbTt2/lw4gQuaNTYd6xI1apTj1ETpjH87UkMf3sSFROTeGbYSBIqVKRSUmW+/foLAP5cv47ly5ZQuWp1z4kL3vr169m0aRMAqampzJk9i5q1antO5YeOkR20L3aoWDGRypUrs2TxbwB8MWc2tevU8ZzKD+2LHXSMSF4iqZQ6516PYjsFpXjx4tzdpy839OhOZmYGbdt1oG7der5jFahB/Xoz/9u5bNqwgW5tm9Lt2htofnHu56R1vaoH/xlwH9f/swPOOa75160cdnh8xImjt3bNau695y4yMzPIzHQ0u6g5FzRs5DuWF0XxGMmL9sXOet9zH/f0voO0tDSq1ahB/wcH+Y7kjfZFQMdILlQoBcCcy7NX/cBvzCwR6A0cB5TKmu+cy89XJG/d97GkVPg1YvHaonkpkZxqVQz+hPR3sePvQvtC+yKnrH2RkuY3RywoHV6hTvtix77QMZJ9jHhvEla6dnR0jTFg9Sudvb/n3ER9ivSbwI9ALeABYAnwVcQZRERERGKGLgkViLpRWsE59wqQ5pybHp55r4EkIiIiIkVc1L/olNVxstLMWgF/AAkRZxARERGJGbFcvYxS1I3Sh8zsMOB24GmgPHBrxBlEREREJMZE3X3fieDkqu+dc42ApkC7iDOIiIiIxAyNKQ1E3Sit75zbkPXAObceODniDCIiIiISY6Luvi9mZvHOuT8BzCzBQwYRERGRmBHL1csoRd0g/A8w28zGhI87AQMiziAiIiIiMSbSRqlzbriZzWXHZaDaO+cWRJlBREREJKaoUAp46DoPG6FqiIqIiIhINo3nFBEREfFIY0oDUZ99LyIiIiKyGzVKRURERMQ7dd+LiIiIeKTu+4AqpSIiIiLinSqlIiIiIh6pUhpQpVREREREvFOlVERERMQnFUoBVUpFREREJAaoUioiIiLikcaUBlQpFRERERHvVCkVERER8UiV0oAqpSIiIiLinSqlIiIiIh6pUhpQpVREREREvFOlVERERMQjVUoDqpSKiIiIiHeqlIqIiIj4pEIpcJA1SksdVGkLVq2KpXxHiBn6u9hB+2IH7YsdSpfwnSB2aF/soGNEYo2670VERETEu4Pqe9KfWzN8R/Au/tA4AFLSPAeJAVkVj2o3vOs3SAxY8Xw7ANZv0TGSUCY4RtZsTvecxL/EcsFH/JJ1qZ6T+FezQtC7tHit9kVWT1uqDpGYqRbrRKeAKqUiIiIi4l2MfEcQERERKZpUKQ2oUioiIiIi3qlSKiIiIuKRCqUBVUpFRERExDtVSm2Y5PAAACAASURBVEVEREQ80pjSgCqlIiIiIuKdKqUiIiIiHqlQGlClVERERES8U6VURERExCONKQ2oUioiIiIi3qlSKiIiIuKRCqUBVUpFRERExDtVSkVEREQ8KlZMpVJQpVREREREYoAapSIiIiLiXaSNUjOrlZ95IiIiIkWFWbRTrIq6Uvp2LvPGRpxBRERERGJMJCc6mdkxwPHAYWbWPsdT5YFSUWQQERERiUW6eH4gqrPvjwZaA4cDF+eYvxm4LqIMIiIiIhKjImmUOufGA+PN7Czn3OwotikiIiJyMFChNBD1mNJ2ZlbezEqY2WQzW2Nm3SLOICIiIiIxJupGaTPn3CaCrvwlQF3g3xFnEBEREYkZZhbpFKuibpSWCG9bAWOccxsj3r6IiIiIxKCof2b0fTNbCKQAN5hZIpAacQYRERGRmBHL1csoRVopdc7dBZwNnOacSwO2AG2izCAiIiIisSfSSqmZXZHjfs6nhkeZQ0RERCRWqFAaiLr7/vQc90sBTYBvUKNUREREpEiLtFHqnLs552MzOxwYGWWGfdG25YWUKVOGYsWKERdXnNdGjMl+7s3hr/L0kMF8OOVzDo+P95gyei2aNc7eL8Xj4hgx+h3fkQrctY3q0PXcmhgw4vMlvDzlV1qfUpVerY6lXuVytHpkGvOXbQCgRJzxSNeTqX/k4TgHfUfPZ/Yva/2+gQKyefMmBvXvy6+//oJh9On3EEfUrMl9d93Oyj9WUKVqNR565HHKlz/Md9QCN+rN13l//NsYRu269bin3wA+GDeW0W+9wYrlv/PBpzM5/PDC+VnxnwF9+eLzzzg8PoGhb+78eTB2xOu89MzjjJ44jcMOj8c5x/NDHuHL2TMpVaoUt9/7IPWOPtZT8gPv8YE79sWL/915X7z9VrAvRk0I9sWYN19j6scTAcjISOf3pYsZNWEa5YrA8QKQkZHBZZ07UCkpiWeee9F3HK80pjQQ9dn3u9oC1PKcYY+eHfoab4x6d6cGafKqlXw5ZxaVK1fxmMyvl4a9zui3xxeJBunRVcvR9dyatHp4Gk0HTOHCEytTM7EMC//YzHVDv2DOop0bnF3PrQnAhQ9N4dKnZtK34wmFtmtmyOBBnHn2uYx6ZwJvjHqHmrVr88arL3PaP85kzPgPOe0fZ/LGqy/7jlng1qxOZuyoN3ll+GjeGD2ezMxMJn88kRMbnMITz71C5SpVfUcsUM1atmHAkOd3m786eRXffDmbSkk7Piu/mj2TFcuX8ero97mld1+eHvxQlFELXNOWbXjo8d33xZrkVXy9y77odPlVPPf6aJ57fTRXX9+TE086tcg0SAHefGM4tWvX8R1DYkikjVIze9/M3gunD4CfgHejzHAgPPHYI9x0y+0aBFJE1Ktcjm8Xryc1LYOMTMecn9fS4qSqLFq1mV+T/9pt+aOqlOfzn9YAsG7zdjZtTaPBEYWvQvbX5s3M+2YuF7ftAECJEiUpV648M6ZPoWXrtgC0bN2Wz6ZN9hkzMhkZGWzblkp6ejrbUlOpmFiJo445lipVq/mOVuBOPPlUypUvv9v8F58czLU33rZTFWj2jKlc2PxizIxjT6jPlr82s27tmijjFqigYZnLvnhqMN3/dVue/29M+/RDGjZtUdDxYkbyqlXM+Gwa7Tp09B0lJphFO8WqqCuljwH/CadBwPnhGfkxyczo+a/uXNm1I+PeHg3AZ1Mnk1ipEvWOPsZzOn/M4IYe13JZ5/aMHTPKd5wCt/CPzZxRtyLxZUpSqkQcjU+oTNX40nkuv2D5RprVr0JcMaNGhUM58YjDqZqQ9/IHqz/+WM7h8Qk8dH8frrisPQP730dKylbWr1tHxcREACpUrMj6des8Jy14iZWSuLTbVXRofSFtmzekTNmy/OPMc3zH8mrWZ1OpmFiJOvWO3mn+2jWrSUxKyn5cMTGJdWtWRx0vUrNnTKVCYiVq77IvsqSmpjB3zuec2/DCiJP58+jDA7nt9n9TrJjvDluJJVGPKZ2+L8ubWQ+gB8CLL75Ip27XFkiuvLz46n+pVCmJ9evX0fP67hxZszavDRvKU88V/u7IPXl1+FskJSWxft06rr/uamrVqs2pp52+9xUPUotWbebZj39mRM+z2botgx+WbyDTuTyXHzlrKfUql2PSXQ1Zvj6Fub+tJyMz7+UPVhkZGfy8cAG333kPx5/YgCGDBzJ8l676WP/1kANl06aNzJw+hdHvfUy5cuW4r3cvPpr4Phe1vNh3NC9SU1MYOfxlBj3xgu8o3mXti4FD8t4XX8yczvH1TyoyXffTp00lISGB444/ga++/MJ3HMmFmd0GdAcc8B1wNVCF4DygCsDXwD+dc9vN7BCCE9ZPBdYBXZxzS/Znu1F33282s027TL+b2btmVnvX5Z1zQ51zpznnTuvRo0eUUQGoVCn4Np+QUIELGjfh26+/YuWKFXTr0o62LS9kzepkruzaoVB1PeVHUljlSKhQgUZNmvL9d/M9Jyp4I2ctpcWgaXR4fAYbt6bxWy7d9lkyMh33j/2OZgOncs0LczisdIk9Ln+wqlQpicRKSRx/YgMAGjVpxs8LF5BQoQJr1wTHxNo1a4hPSPAZMxJzv5xDlarViY9PoHjxEpzf6EK+m/+t71jerFyxnFV/rOCGKzpzRfsWrFmTzI1XX8r6dWupmFiJNcnJ2cuuXZNMhcRKHtMWrOx9cWVnrujQgrVrkrnpmmBfZJk++UMaXlh0uu7nffsN06ZNoUXTxvS+oxdffTGHu3vf4TuWV7H0M6NmVg3oSXBN+ROAOOBS4BFgiHOuLvAnkFUpvBb4M5w/JFxuv0RdN3+C4LfuqwHVgTuAEQQt72ERZ9mjlJStbNmyJfv+l7NncezxJzBpykzGTfyUcRM/JbFSEq+PeJsKFRM9p41OytatbNnyV/b92bM+p269ep5TFbwK5UoCUDW+NC1Oqsq7Xy3Pc9lSJeIoXTIOgPOOSSQ90/HLqs2R5IxShYqJJCVVZumSxUDQMKtZqw7nnt+IiR+MA2DiB+M474LGPmNGIqlyFX74/n+kpqbgnOPrr+ZQs2bRPYGjVp16jJ44jeHvTGL4O5NITEzi2VdHklChImee25BPP3wf5xw/fj+fQ8uULdSfobXq1GPUhGkMf3sSw9+eRMXEJJ4ZFuwLgC1/bWb+t19z1nkN/QaN0C233c4nUz5j0idTeOSxxzn9jDMZ9MhjvmPJzooDpc2sOHAosBJoDIwNn38daBvebxM+Jny+ie1nF1nU1ym9xDnXIMfjoWY2zznX28zuiTjLHq1ft47evXoCwaU6mrVoxVnnnOc5lX/r1q2j1y03ApCekUGLlq0559zzPacqeC/1OIP4MiVJz3D0Gfk/NqWk0bxBFR7q0oCEsiUZfuNZ/LB8I5c/PYuK5Q5hRM+zycyEVRtT6PnaXN/xC0yv3n24v8+dpKWlUa16dfrcPwCX6ejT+zbeH/c2latU5aFHHvcds8Adf0J9GjVpxjWXdyIuLo6jjj6WS9p3YszI/zJi+DDWr1vLlZe246xzzueu+/r7jnvADerbm/nfzmXjhg1c3qYp/+x+A80vbp/rsv84+zy+mj2Tqzu15pBSpbi9T+HaH4P6Bfti04YNdGvblG7X5r0vAD6fPoVT/3EWpUofGmFKiTVRj3LKOTwyNNQ5NxTAObfCzB4DlhH8LPzHBN31G5xz6eHyywkKjIS3v4frppvZRoIu/n2+FqK5PYyNO9DMbDZBaTerpd0R6OWcOzNsnJ60h9Xdn1szCjxjrIs/NKjApaR5DhIDSpcIbqvdcNBdwOGAW/F8OwDWb9ExklAmOEbWbE7fy5KFX2K5oO6wZF2q5yT+1axQCoDFa7UvalUM9kWqDhFKBYeI94Hvpz00NdITD+be2yjP92xm8cDbQBdgAzCGoN12f9hFj5nVACY5504ws++B5s655eFzvwJnOOf2uVEadaX0cuBJ4Lnw8Wygm5mVBm6KOIuIiIiIdzF2QuiFwGLn3BoAM3sHOAc43MyKh9XS6sCKcPkVQA1gedjdfxjBCU/7LOqz738D8joddWaUWURERERkN8uAM83sUILu+ybAXGAqQQ/3SOBKYHy4/Hvh49nh81PcfnbDR332/aNmVt7MSpjZZDNbY2bdoswgIiIiEkti6eL5zrkvCLrrvyG4HFQxYCjQG+hlZosIxoy+Eq7yClAhnN8L2O/rz0fdfd/MOXenmbUDlgDtgc+A/0acQ0RERERy4ZzrB/TbZfZvwD9yWTYV6HQgtht1ozRre62AMc65jTE2jkJEREQkUmoLBaJulH5gZgsJxijcYGaJgE6FFBERESnioj7R6S4zexTY6JzLMLMtBBddFRERESmSVCgNRNIoNbPGzrkpZtY+x7yci7wTRQ4RERERiU1RVUrPB6YQXA7KEVyoNuetGqUiIiJSJGlMaSCqRulmM+sFfM+OxijhfREREREp4qJqlJYNb48GTie44KoRVE6/jCiDiIiISMxRoTQQSaPUOfcAgJl9BpzinNscPr4fmBBFBhERERGJXZH+ohOQBGzP8Xh7OE9EREREirCor1M6HPjSzN4NH7cFXos4g4iIiEjM0IlOgaivUzrAzCYB54WzrnbOfRtlBhERERGJPVFXSnHOfQN8E/V2RURERGKRCqWBqMeUioiIiIjsJvJKqYiIiIjsoDGlAVVKRURERMQ7VUpFREREPFKlNKBKqYiIiIh4p0qpiIiIiEcqlAZUKRURERER71QpFREREfFIY0oDqpSKiIiIiHeqlIqIiIh4pEJpQJVSEREREfFOlVIRERERjzSmNHBQNUrjD43zHSFmlC7hO0HsWPF8O98RYkZCGR0jWRLLHVQfbwWqZoVSviPEjFoVtS+ylNIhIjFG3fciIiIi4t1B9T1pY0qm7wjeHVY6+B6Rmu45SAzI+pa/fkuG3yAxIKtCWqXH256T+LdyaAcAkjeleU7iX1L5oEtF+2LHvvhhxRbPSfw7vloZAFL0ZxEzvY7qvQ+oUioiIiIi3h1UlVIRERGRwqaYSqWAKqUiIiIiEgNUKRURERHxSIXSgCqlIiIiIuKdKqUiIiIiHuni+QFVSkVERETEO1VKRURERDwqpkIpoEqpiIiIiMQAVUpFREREPNKY0oAqpSIiIiLinSqlIiIiIh6pUBpQpVREREREvFOlVERERMQjQ6VSUKVURERERGKAGqUiIiIi4p2670VEREQ80sXzA6qUioiIiIh3qpSKiIiIeKSL5wdUKRURERER71QpFREREfFIhdLAfldKzayumZU6kGFEREREpGjKV6PUzAaa2ZXhfTOzT4CfgZVmdkZBBhQREREpzIqZRTrFqvxWSi8HfgrvtwBOAs4EhgMPF0AuERERESlC8jumNAlYHt5vCYx2zn1pZuuBuQWSTERERKQIiOHiZaTyWyldBxwZ3m8GTA7vF4e9/2CrmcWZ2cJ9jyciIiIiRUF+K6VvAyPM7GcgAfgonH8SsGhvKzvnMszsJzM7wjm3bP+iioiIiBQ+uk5pIL+N0l7AUuAI4E7n3JZwfhXg+Xy+Rjzwg5l9CWStj3PuknyuLyIiIiKFVL4apc65dOA/ucwfsg/bum8flo0ZGRkZXNm1E4mVKjHk6Re47+5/8+OC7ylevDjHn1Cfu++9n+IlSviOGanPZ3zGIw8PIDMjk3YdOnHtdT18R4rU5s2bGNS/L7/++guG0affQ8z6/DNmTJtCsWJGfEIF7n1gIImJlXxHLRDdG9fl8vNqYma8OWMxL01exJ2XHMdFJ1Ul0znWbd7GLa/OJXljKmcdVZHXbjybZWuD76ETv1nBkAmFcyTP6BHD+WDc25gZtevW466+D/H9/Hk89+RjpKelcdSxx9H73v4UL174Lw9dlPfF2tWreOrhvmz4cx2G0bR1e1p36MqsaZ8w6vUXWb5sMY889wZ1jz4ue523Rwxj8sRxFCsWx7U3/5uTTz/b4zuIxqZNm+jf714WLfoZw7j/wYE0OOlk37G8UaE0kOcngpmdkt8Xcc59k49lpuf39WLJyBFvULNWbbZs+QuA5i1b03/gowDcd/cdjHt3LB07X+YzYqQyMjIYOKA/L770KklJSXTt0pGGjRpTp25d39EiM2TwIM48+1wGDn6CtLTtpKamUrtOXf7vXz0BGP3WGwwb+hy9+9zvN2gBOLpqeS4/ryYtB01le3omI245l0/mr+S5j3/m0fcWAHBt4zr0an0svd/8FoAvflnLFc/M8hm7wK1ZnczYUW/yxqjxHFKqFP3uvp1PP5rAsBef5YnnXqHGkTV55YVn+HDCeFq36eA7boEq6vuiWFwcV15/G3WOOpaUrVu44/rLaXDqmRxRqw53PvAYLwwZsNPyvy/5jZlTPuLJYWNZv24N999xA88Mf5e4uDhP7yAajz48gLPPOY/HhjxFWtp2UlJSfUeSGLCnE53mAl+Ft3uavsrPhszsTDP7ysz+MrPtZpZhZpv+VvoClpy8is9nTKdN+47Z88457wLMDDPjuONPZHVysseE0fv+u/nUqHEk1WvUoETJkjRv2YppUyfvfcVC4q/Nm5n3zVwubhv8Z1qiREnKlStPmbJls5dJSUkptOOD6lUpxzeL15OyPYOMTMecn9fQ8pRq/JWanr3MoSWL45zHkJ5kpKezbds20tPTSU1NoVSp0pQoUYIaR9YE4LQzzmL6lE/9hoxIUd4XCRUSqXPUsQCUPrQM1Y+oxbq1q6l+ZG2qHVFzt+W/nDWNcxtfRImSJUmqUo0q1aqzaOH30YaO2ObNm/nm669o1yH4v7VEiZKUL1/ecyq/dJ3SwJ4apbWA2uHtnqba+dzWM8BlwC9AaaA78Ox+pY7IkMGDuPnWOyhmu++m9LQ0Jk14j7POOddDMn9WJydTuUrl7MeVkpJILkIN8z/+WM7h8Qk8dH8frrisPQP730dKylYAXnjmCdq0aMzHkz7guhtu9py0YPy0YhNn1KtIfJmSlC4ZR+MTKlM1vjQAd7U9nrkPt6D9GTUY/N4P2eucWjuBT+9rwps9z+GoKuV8RS9QiZWSuLTbVXS6+ELatWhEmTLlaNy0ORkZGSxcEDQwpk3+mNXJqzwnLXjaFzusXvUHixf9xFHHnpDnMuvXrKZiYlL24wqJSaxbuyaKeN6sWLGc+PgE+t57N106tuWBvn1I2brVdyyJAXk2Sp1zS/M75XdjzrlFQJxzLsM59yrQfE/Lm1kPM5trZnOHDh2a/3d1AMz4bCrx8Qkce9zxuT7/yMD+nHzKaZx8ymmR5hK/MjIy+HnhAtp37MLwt96hdOnSDH/1ZQCuv+lWxk+aQrMWrRk78k3PSQvGL6s28+yHPzPy1nMZ0fMcfvh9I5mZQVn04XE/cNpdk3jni9+5ulEdAL5btoHT757EhQ9O5pUpv/LqvwrnWLnNmzYy87OpjBr/Ee9OmkJqagqfTPqAfgMG88yQR+lx5aUcWqYMccX2+5edDxraF4GUlK082u8OrvnX7RxapuzeVyhCMtLTWfjjAjp3uYxRY8dRqnRphr0S7f/xEpvy/algZiea2TNmNsnMqoTz2ppZfkcmbzWzksA8M3vUzG7b2/adc0Odc6c5507r0SPak2nmz/uWGdOn0qZFE/rcdTtzv/qCvvfcCcBLLzzLn3/+ya133BVpplhQKSmJVSt3VDhWJyeTlJS0hzUKl0qVkkislMTxJzYAoFGTZvy8cMFOy1zUojXTpnziI14k3vp8CRcNmEK7xz5j49bt/Jr8107Pv/PlMlqdUg2Av1LT2botA4Ap36+iRJyRULZk5JkL2twv51ClajUOj0+gePESnN+oCd/Pn8cJ9U/imZeGM/T1kTQ4+dTs7uvCTPsC0tPTGNzvDs6/sCVnnt9kj8smJFZi7ZodvU3r1iRToWJiQUf0KqlyZSolVebE+sHnaNNmzflxwYK9rFW4WcRTrMpXo9TMmhGMHa0GNCbofgeoA/TL57b+GW7vJoJLQtUAYnaU+409e/HBx9MYP2kyAx7+D6edfgb9Bz7KuHfGMGfWTB56+DGKFfJv+rk5/oQTWbZsCcuX/07a9u18OHECFzRq7DtWZCpUTCQpqTJLlywGgv+Aa9aqw+/LlmQvM2P6FI6smd9RLQefCuUOAaBaQmlanlKNd7/8nVqVdlSCLmpQlUWrNgOQWP6Q7Pkn1YynWDFj/V/bow0cgaTKVVjw3XxSU1NwzvH1V19wZK3a/Ll+HQDbt29nxOvDuKR9Z89JC15R3xfOOZ4d3J9qR9Tikk7d9rr86WddwMwpH5G2fTvJK1ewcsXv1D0m7+7+wqBixUQqV67MksW/AfDFnNnUrlPHcyqJBfm9HseDQC/n3HNmtjnH/GnA7fl5AefcUjMrDVRxzj2wbzFjxyMDHqBylapce0Vwxn2jJhfS/f9u9JwqOsWLF+fuPn25oUd3MjMzaNuuA3Xr1vMdK1K9evfh/j53kpaWRrXq1elz/wAG9e/LsqWLMStG5SpVubNPfr+rHXxeuf5M4suUJC0jk7tHfMumlDQev/JU6iSVJdPB8nVb6f1mcEGO1qdW58oLapOekUlqWgbXD/3Cc/qCcdwJ9WnYpCndu3UmLi6Oekcfw8XtOvHy808xa+Z0XKajTYcunHr6Gb6jFriivi8Wfj+P6Z9M4Mjadel13aUAXH7tTaSlbeflpx9l08Y/GXBPT2rVOYq+jz7HEbXqcE7DpvS8uiNxcXFc1/OuQn/mPUDve+7jnt53BJ+jNWrQ/8FBviN5VVhPjt1X5vJxmqyZbQGOd84tCRulDZxzv5lZLeBH51ypfLzGxcBjQEnnXC0zOwnovw8Xz3cbUzLzuWjhdVjpoDqb42TnIqtU+JVq/ZYMv0FiQEKZ4D+xKj3e9pzEv5VDgw6Y5E1pnpP4l1Q+uIay9sWOffHDii17WbLwO75aGQBS9GdB6eDPwnuL8LLh8yK9ZslbV5zk/T3nJr/9z+sJuu53dQqwPJ+vcT/wD2ADgHNuHsHZ+yIiIiJFVjGLdopV+W2UjgAGm1l1wAHFzewCgsrn8Hy+RppzbuMu84rg1QxFREREZFf5HVN6L/AasJSgzL0gvB0BDMh7tZ38YGZdgTgzqwf0BAr3z7yIiIiI7IXGlAbyVSl1zqU55y4HjgI6A12BY5xz/3TO7XFAn5m9Ed79FTge2Aa8BWwCbt3f4CIiIiJSeOS3UgqAc+5XM0sO7/+1t+VDp5pZVaAL0Aj4T47nDgX0g7ciIiJSZKlQGtiXi+ffambLgI3ARjP73cxus73XnF8AJgPHAHNzTF+HtyIiIiJSxOWrUmpmjwI9gMHA7HD2WUBfoApwZ17rOueeAp4ys+edczf8vbgiIiIihYvGlAby233fHejunBubY94UM/sJeJE9NEqzqEEqIiIiInnZlzGl8/OYV/R+a1NERETkAInla4dGKb8NyuFAbr+leQPwRi7zRURERETyLc9KqZk9tcty3czsImBOOO8MoCrwZsHFExERESncNKY0sKfu+xN3efx1eHtkeLsqnI450KFEREREpGjJs1HqnGsUZRARERERKbr26eL5IiIiInJgqfM+kO9GqZk1Ai4DjgBK5nzOOdf4AOcSERERkSIkX2ffm9lVwCSgHNAQWAPEA6cACwoom4iIiEihV8ws0ilW5feSUHcANznnLgPSgLudcycD/wX+KqhwIiIiIlI05LdRWhv4NLy/DSgb3n8GuOoAZxIREREpMsyinWJVfhul6wi67gFWACeE9ysApQ90KBEREREpWvJ7otMMoBnwHTAaeMrMmgJNgE8KKJuIiIhIoaeL5wfyWym9CXgrvD8IGExQJR0NXFsAuURERETEAzM73MzGmtlCM/vRzM4yswQz+8TMfglv48NlzcyeMrNFZjbfzE7Z3+3mq1HqnFvvnPsjvJ/pnHvEOXcJ8ABwzv5uXERERKSoi8ExpU8CHzrnjgEaAD8CdwGTnXP1gMnhY4AWQL1w6gE8v7/7Ib+V0rzUBt7/m68hIiIiIjHAzA4DzgdeAXDObXfObQDaAK+Hi70OtA3vtwGGu8Ac4HAzq7I/29YvOomIiIh4FPW1Q82sB0FVM8tQ59zQ8H4tguvRv2pmDYCvgVuAJOfcynCZVUBSeL8a8HuO11oezlvJPlKjVERERKQICRugQ/N4ujjBjyPd7Jz7wsyeZEdXfdb6zszcgc71d7vvRURERORviLExpcuB5c65L8LHYwkaqclZ3fLh7erw+RVAjRzrVw/n7bM9VkrNrP1e1q+5PxsVERERkdjjnFtlZr+b2dHOuZ8ILv+5IJyuBB4Ob8eHq7wH3GRmI4EzgI05uvn3yd6678fmJ//+bFhEREREYvI6pTcDb5pZSeA34GqC3vXRZnYtsBToHC47EWgJLAK2hsvuF3PuoGlTHjRBRURE5KDhvUV447s/RtrGebbdsd7fc250opOIiIiIRzrBJ3BQNUpXbUrzHcG7yuVLAJCa7jlIDCgV/vUuXpvqN0gMqFWxFKBjBHYcI4lXj/KcxL81r3YBIPn/2bvv8Ciqto/j3zsJobfQLKgQQFEp+gh2rGBDRQWxo74+YkFQei9W7L1iF30QVBTEilIVkaYCihQpCtKkEwiknPePmYSgAQKSOZvk9+HaK7uzszv3DDO7995zzhntF1QL94uNqZmeI/GvXIkgBdqSphOQpYrFZMGwyFJyLiIiIiLeFahKqYiIiEhhE4MdnbxQpVREREREvFOlVERERMSjOBVKgd0kpWY2izwOw+Sca7DfIhIRERGRImd3ldK8DJwvIiIiIv+CKqWBXSalzrm7owxERERERIoutSkVERER8Ui97wN5TkrN7EbgKuBQIDHnc8655P0cl4iIiIgUIXkaEsrMugKPAdOBGsBHwGwgCXgtv4ITERERKeziLNpbrMrrOKU3A22dcz2BNOBZ59zFBInqYfkVyr0lBAAAIABJREFUnIiIiIgUDXlNSqsDU8L7W4Fy4f0hQMv9HZSIiIhIUWEW7S1W5TUpXQFUDu8vAU4K79cmj2OZioiIiIjsSl47Oo0BLgZmAK8CT5hZa+A/wLB8ik1ERESk0IuL5fJlhPKalLYlrKo65140s3XAKcAHwEv5FJuIiIiIFBF5Skqdc5lAZo7HQ4Gh+RWUiIiISFGR17aUhV2eklIz+8/unnfOzdg/4YiIiIhIUZTX0/fTCDo05Wz0kLODU/x+i0hEREREipy8JqU1//a4GHAs0BvouV8jEhERESlC1M8pkNc2pUtymbzAzDYA/YHP8vI+ZtYeeNs5ty7vIYqIiIhIYZfXSumuLAKO2Yv5qwFTzWwGweVJv3DOaZxTERERKbI0JFQgTx2+zCzpb7dKZlYPGAjMzevCnHN9gDoEY53eAMw3swfMrNY+xC4iIiIihUReK6V/8c8rNxnwB3DF3izQOefMbAXBVaLSgYrA+2Y22jnXbW/eS0RERKSgU6E0kNek9My/Pc4EVgMLnHPpeV2Ymd0JtCFIcl8Bujrn0swsDpgPKCkVERERKYLympQuAv7Irf2nmR3qnPs9j++TBFz2945TzrlMM7swj+8hIiIiUmjEqVIK7F1SeiCwKudEM6sUPpencUqdc/3N7D9m1oKgOcC3WQPvO+fm5DlqERERESlU8nplK+OfbUoBygCpeV2YmfUF3gQqAZWB182sT15fLyIiIlLYxJlFeotVu62UmtnT4V0HDDSzLTmejgeOB37ci+VdCzR0zqWG7/9g+Pr79uI9RERERKSQ2dPp+/rhXwOOBLbneG47MAN4dC+W9ydQgh3V1eLAsr14faTeHzKYUR99gHOOCy9pxeVXX8cLTz3KpInjSSiWwEHVD6FHv/soW7ac71Ajs2L5cnr37MbaNWvAjFaXt+aa6673HVa+evyBfnz/7QQqVEzipbeHAzD41Rf4fOQHlK+QBMANt7Tn+JObAPDuW6/yxagPiYuL47aO3Wl0wineYs9vuR0jr77wDN9MGEOcxVEhKYme/e+ncpWqvkPNF22b1eHa02phBm+PX8hLo+fRtcXRXHd6Mms2bQPg/g9m8dXM5RxSqRTfPnA+v63YBMC039bQ9a3pPsPPN+/9bb9offV1jP3qC14f9DxLFi/kpTeGUPeoer7DjExGRgZtrrqcqlWr8sSzLzJsyDsMeectlv7xO6PHTaJCxYq+Q8x3A/r0YsKEcSQlVeL9jz4GYPQXn/Pi88+yaOFvDB4yjKPr1d/DuxReMVy8jNRuk1Ln3JkAZvY6cKdzbuO/XN4G4GczG01QfW0GTMmqyDrnOvzL999vFi6Yz6iPPuDFN4eQkFCMbh1u5aQmp9PohJO4ud1dJCQk8OIzj/POG69wa/tOvsONTHxCPF269eDIo44mJWUzV17ekhNPOoVatWv7Di3fNLugBRe1vIpH7+290/RLr7iOVlfvnJAvWfQb47/+nJfeHs7av1bR885beOXdkcTH56nZdYGyq2Pkyutu5Kbb2gPw/rtv8+YrL9C5Z3/P0e5/dQ8uz7Wn1eLce0ezPT2ToZ1O48uf/gTgxS/n8fzn/xzCefGqFM7s/2XUoUYqa794Kdwvuna4lZObnE7NWrW57+EneXTg3b5DjNy77wymZnIyKZs3A9DwmGM59bQzuPW/bTxHFp2LLrmUK66+hr69emRPq1W7Do89+TT33V34Ph9k3+S1TWlP4B/lQDOrbmbV9mJ5HwK9gLHAOKA3MAKYHt5ixpLFCzmyXn1KlChJQkICDf/TiAljv6LxiaeQkBDk8kfVa8DqlSs9RxqtKlWqcuRRRwNQunQZkpOTWbWqcG+D+sccR9lyeauGfzdxHKeffR6JiYkccFB1Dqx+CHPnzM7nCP3Y1TFSukyZ7HlSt24ttCWAww8sy4yFa9i6PYOMTMekuatpflx132F59/f94phwv6hRsxaH1qjpO7zIrVy5gm8mjqfFpa2ypx1x5FEcdPDBHqOK3nGNGlO+fPmdpiXXqkWNmsmeIootcRbtLVblNSl9Gzg/l+nnAoPzujDn3JvAEOAHglP/Q5xzb2bd8vo+UahZqzYzf5zBhvXrSU3dyuRJE1m1csVO83w68kNOOPlUTxH6t2zZUn6dM4f6DRr6DsWLkR+8y61tWvH4A/3YtDE4ibBm9UqqVNvxO61y1WqsWb1qV29RoO3uGHn5+ado1fxsvvr8E2665Q7PkeaPOcs2cOLhVahYOpGSifE0bXAgByeVAuCms+sw7p5zeer/GlO+VLHs1xxapTRjBpzDiO5ncmKdyr5Cz1d5+ewsSh5/eCAdOnYhLi6vX7ciRVdej5JGwIRcpk8Mn8sTM7sA+A14GngWWGBmuSW7WfO3NbNpZjZt0KBBeV3MflGjZi2ubvN/dGnflq4dbqX24UcQn+NDZfBrLxGfEE+z84vm8KpbUlLofFcHuvboRZkclbGi4sJLW/P6sFE8/8YwkipV4eVn96ZpdeGwu2Pk5tvv5P1Pvqbpec0ZPux/niPNH/OXb+KZT+fwXpfTGdrpNGb/vp6MTMcbYxfQuNsnnNn/C1auT+WeK48BYOWGVI7t/DFnDfiSvu/+yIu3nkSZEnkdla/gyNovOrdvS5dwvyiqCdnE8WOpmJSUfXZJZFcs4n+xKq+fiAkEnZL+rsQupu/K48CZzrkFAOE17z8BPsttZufcICArG3UrNqbtxaL+veYtWtK8RUsABj33JFWqHgDAZx9/xKRvJvDE869ghfTU5O6kpaXR6a4OXND8Ipo2O8d3OF5UTKqUff+8iy+jf9egDWWlKtV2atLx16qVVCqknXxg18dIlmbnX0j3O2/j/wpptfSdiYt4Z+IiAHq3rM+fa7eyeuO27OcHj/+Nd+46DYDt6ZlsTw/6is5cso7FqzZT64Cy/LR4XfSB57MLW7Tkwt3sF0XFTz/+wMRxY5n0zQS2bdtOSspm+vbsxr0DH/YdmkhMyuvP1++B23KZ3g6YuhfL25SVkIYWApv24vWRWrd2DQArVyxn4tivaXreBXw/6RuGDH6NgY89Q4kSJT1HGD3nHAP69SY5OZk2N9zoOxxv1vy1Ovv+pPFjqJEcdPQ68dTTGf/152zfvp0Vfy7lz6W/c8SRhbeXcW7HyNLfd1yw7ZvxYwp1O8LKZYPf5AcnlaL5cdX5YPISqpUvkf38BcdV59dlGwCoVLZ49viAh1UpTXK1MixZnRJ90BHIuV9MCPeLouiOOzvxyehxjPzsax546DEaNz5BCanIbuS1UtobGGNmDYAx4bSzgGOBpnuxvGlm9ikwjKD3/eXAVDO7DMA5N3wv3ivf9e3ekY0b1pOQkMBd3XpTtmw5nnrkfrZv307ndjcDcFT9BoWyZ/Gu/DBjOqNGjqDO4YfT+rIWALS/qxNNTjvdc2T5Z2D/7sz8YRob16/n2kuace1NtzHzh2ksnD8XzKh2wEF06NYXgBrJtTntrHO45ZpLiYuPp12nXoWy532W3I6Rh+/txx9LFmNxwbbp3LOf7zDzzet3nELF0omkZTi6D57Oxq1pDLz2P9Q7tALOwR9/pdDlzWkAnHR4FbpfWo/0jEwyHXR5czrrU7bvYQkFU9/uHdkQ7hcdw/1iwtiveOrRgaxft5buHW+n9uF1eeyZaJtlxYp33xnM4DdeZc2av7jq8haccupp9BlQuIfr7tG1E9OnTmX9+nWce/bp3Hp7e8qXL89DA+9j3dq1dLj9Vo6oW5fnB73qO1QvYrnzUZQsl8vZ5z6jWUOgK0EiCkFnpUeccz/leWHB0FK74pxz/7e756M+fR+LDigXdJpITfccSAzIao636K88X1Ss0KpZOajO6RjZcYxUuXGo50j8W/36FQCs1H5BtXC/2Jia6TkS/8qVCE6SbknL2/d/YVaqmAH+G1k+OOa3SP8zepxVy/s65ybPrezD5PPav083s6bOua/y+B5F93yviIiISC5UKQ3sU9dPMzsYuBH4P+AwgkuO5uV1JYCbgKMJOkkBsIcKqYiIiIgUcnkep8PM4s3ssrBN6GLgUuBFYG8u5TMYOIBgfNPxQHViuKOTiIiISH4zs0hvsWqPlVIzOwL4L9AGSAH+R3B50Oucc7/s5fJqO+cuN7MWzrk3zex/BGOdioiIiEgRtttKqZlNBCYDFYHWzrlk51yff7G8rNb2682sHlAeKLyDOIqIiIjsgS4zGthTpfQk4DlgkHPu5/2wvEFmVhHoA4wEygB998P7ioiIiEgBtqektDHBqftvzGwx8BbBtev31WCgJVADyLrWfbVdzi0iIiJSyMVwM89I7fb0vXPuB+dcO+BAgkuEXgz8Eb6ueVj13BsjgBZAOrA5vBXOS5qIiIiISJ7laUgo51wqQZVzsJnVJqiedgTuM7Mxzrnz87i86s658/YtVBEREZHCJ06lUmAvhoTK4pxb4JzrARwCtAb25jp5k8ys/t4uU0REREQKt30aPB/AOZdBcDp+xJ7mNbNZBNe6TwBuNLOFwDaCS3s551yDfY1DREREpCCL5R7xUdrnpHQvXRjRckRERESkAIokKXXOLYliOSIiIiIFjZqUBva6TamIiIiIyP6mpFREREREvIuqTamIiIiI5CIOnb8HVUpFREREJAaoUioiIiLikTo6BVQpFRERERHvVCkVERER8UiD5wdUKRURERER71QpFREREfEoTo1KAVVKRURERCQGqFIqIiIi4pEKpQFVSkVERETEO1VKRURERDxSm9KAKqUiIiIi4p0qpSIiIiIeqVAaMOec7xjyqsAEKiIiIgWG95Twtam/R5rj/F/jQ72vc25UKRURERHxSG0pAwUqKU1N9x2BfyXC/7Et21U4LpUY/NBbtn6750j8O7hCIgCbUjM9R+Jf2RLBx/u6LRmeI/GvYql4AA5tP9JzJP79/szFACxcneo5Ev+Sq5QA9J0KO75TJTYoORcRERER7/QbQURERMQjU08nQJVSEREREYkBqpSKiIiIeKQ6aUCVUhERERHxTpVSEREREY90mdGAKqUiIiIi4p0qpSIiIiIeqU4aUKVURERERLxTpVRERETEIzUpDahSKiIiIiLeqVIqIiIi4pGu6BRQpVREREREvFOlVERERMQjVQgD2g4iIiIishMzizezH8xsVPi4ppl9b2YLzGyomSWG04uHjxeEz9fY12UqKRURERHxyMwiveXRncCcHI8fAp5wztUG1gE3hdNvAtaF058I59snSkpFREREJJuZVQeaA6+Ejw04C3g/nOVN4JLwfovwMeHzZ9s+9txSUioiIiJShJhZWzObluPW9m+zPAl0AzLDx5WA9c659PDxUuDg8P7BwB8A4fMbwvn3WiQdncxsE+ByewpwzrlyUcQhIiIiEmuiHhDKOTcIGJRrLGYXAqucc9PN7Iwo44okKXXOlY1iOSIiIiLyr5wCXGxmFwAlgHLAU0AFM0sIq6HVgWXh/MuAQ4ClZpYAlAfW7MuCvZy+N7OqZnZo1s1HDCIiIiKxIJY6OjnnejrnqjvnagBXAmOcc9cAY4FW4WzXAyPC+yPDx4TPj3HO5XZ2fI8iTUrN7GIzmw8sAsYDi4HPooxBRERERPZad6CTmS0gaDP6ajj9VaBSOL0T0GNfFxD14Pn3AicCXznnjjWzM4FrI45BREREJGbEaq9z59w4YFx4fyFwfC7zpAKX74/lRb0d0pxza4A4M4tzzo0FGkUcg4iIiIjEmKgrpevNrAwwAXjHzFYBKRHHICIiIhIz9nFYz0In6kppC2AL0BH4HPgNuCjiGEREREQkxkRWKTWzeGCUc+5MgsFY39zDS0REREQKPdVJA5FVSp1zGUCmmZWPapkiIiIiUjBE3aZ0MzDLzEaToy2pc65DxHHslW3btnFjm2tI276d9IwMmp1zLrffEdMh71cD+vZiwoRxJCVV4v0PPwZg7q9zuP/eAWzbto34+Hh69elPvfoNPEeaPx6+ty+Tv51AhYpJvDbkQwDGff0Fb778Ar8vXsjzrw/hiCOPBmDDhvXc3aMTv86ZzbnNW3Bn194+Q89X27Zt4+YbryMtbTsZ6emc3excbrm9Pc45nn/2Kb7+8nPi4uNpdfmVXHnNdb7DzXeXXNCU0qVLExcXR3x8Am/87z2eeeIRvpkwjoRixahe/RD63H0/ZcsWzgvY/d/pNbnq5MMwgyGTfufVcQspX6oYz9/YiOpJJVm6diu3vzaNDVvTqFWtDI9ecwz1qpfnkVG/MmjMb77D328ef6AfUyYFnxcvDh4OwNuvvsDnH39A+QpJAFx/S3uOP6kJM6Z+x+svPEV6ehoJCcW4qV1HjjnuBJ/hR2LF8uX07tmNtWvWgBmtLm/NNdddv+cXFmJqUhqIOikdHt5y2qcBVqOUmJjIK6+9SanSpUlLS+OG667m1Can0aDhMb5Di8RFLS7liquuoW/vHUOPPfn4I7S9tR2nNjmNiRPG8+Tjj/DK64M9Rpl/zr2wBZdcfhUP3r0jwayZXIe7H3qCJx68Z6d5ExMTufGWO1i0cAGLfpsfdaiRSkxM5MVXXqdUqdKkp6Vx0w3XcvKpTVi0cCErVyzn/RGfEhcXF3zxFBHPDXqDChUrZj8+/sSTua19RxISEnj2qcd487WXuePOzh4jzB+HH1iWq04+jIsenUhaRiaDbz+Rr2av5JpTDuPbeat5fvQCbm9Wm9ub1WbgyDmsT9lO//dnc26DA3yHvt81u6AFF7e8ikfv2/kH6SWtr6PV1TsnXuXKV2DAw09TqXJVFi+cT59Ot/H2R19FGa4X8QnxdOnWgyOPOpqUlM1ceXlLTjzpFGrVru07NPEs6o5OFZxzb+a8ARX3+CrPzIxSpUsDkJ6eTnp6epH6WXNco8aUL79zqwszIyVlMwCbN2+iSpWqPkKLRMNjG1Gu3M7rf1jNZA49rOY/5i1ZshT1j/kPiYmJUYXnjZlRqlTO4yINw3h/2LvcfMvtxMUFHy9JlSr5DNOrE046hYSE4Ld/vfoNWbVyheeI8kedamX4Yck6UtMyyMh0TJ6/hvMbHkiz+gfw/vd/APD+939wToMDAVizeTszf19PekbM1yT2Wv1jjqNsubxVw2sffiSVKgefnYfVrM22bdvYvn17foYXE6pUqcqRRwVnl0qXLkNycjKrVq30HJVfcVikt1gVdVKaW33+hohj2CcZGRm0vqwFZzY5mRNPOpkGDRr6DsmrLt178eRjj3Be0zN44rGHaX9XJ98hiQcZGRlc3fpSmp15KieceDL1GjRk2dLf+fKLz7juqlZ0uL0tvy9Z7DvMSJgZHW7/L9df3YqPPhj2j+c/HjGck05p4iGy/Dd3+SaOr1WJCqWKUaJYPGceXZUDK5agctnirNq4DYBVG7dRuWxxz5H68/Hwd7nt+lY8/kA/Nm3c+I/nvxn3FbUPP7JI/KDNadmypfw6Zw71i/h3qgQiSUrN7Coz+xioaWYjc9zGAmt387q2ZjbNzKYNGjQoilB3KT4+nmHDR/DlmPHMnjWT+fPneY3Ht/eGDqFztx58/tU4unTtyd39+vgOSTyIj4/nf8M+5NMvx/Lz7FksmD+P7dvTKJ5YnMFD3ueSy1pxT/+isW+89PrbvDXkA5549iXeHzqEH6ZPy37u9VdeJCE+nvMuKJwj4C1YuZkXRi/gnXYnMfj2E/ll6UYyM3Orgha+ymheNL+0Na8NHcVzrw8jqVIVXn720Z2eX7JwAa+98CTtu/X1FKEfW1JS6HxXB7r26EWZMmV8h+OVWbS3WBVVpXQS8Bjwa/g369YZOHdXL3LODXLONXLONWrbtm0kge5JuXLlaHz8CUz6ZqLvULwaNfIjzm56DgDNzj2Pn2fP9ByR+FS2XDkaNT6e7yZ9Q9Vq1Tjz7GYAnHl2M+bPn+s5umhUrVoNgKSkSpx+1tn88nNwTIwa+SHfThjP3fc/XKgHyB46+XeaPzKBy5/6lg1bt7NwdQp/bdpG1XJBdbRqueL8tanwn5rOTcWkSsTHxxMXF8f5F1/GvDmzs59bvWol9/bqSJc+93HQwYd4jDJaaWlpdLqrAxc0v4imzc7xHY7EiEiSUufcEufcOOfcSc658TluM5xz6VHE8G+sXbuWjeHpltTUVCZ/N4kaNZM9R+VXlSpVmT5tCgBTvp/MoYce5jkiidq6tWuzT0Ompqby/eTvqFGjJmeceTbTpn4PwPRpUznssBoeo4zG1q1bSElJyb4/5btJJNeqw3ffTuTtN17lkSefo0TJkp6jzF+VygSnnQ+qWJLzGh7IiGlLGT1rBa1OCBKtViccwuhZhbNN7Z6s/Wt19v1JE8ZwWHLQoWfzpo3073oHN952J0c3ONZXeJFzzjGgX2+Sk5Npc8ONvsORGGLORXc6xcw2seP8TSJQDEhxzuWlVbhL9ZS+zpv7K3169SAzM4PMTMc5557Hrbff4SWWEuF4CVu2R/f/1qNbJ6ZPncr69etISqrEre3aU6NGTR558H7SMzIoXrw4PXv346ij60UWE0CpxKDqtGx9/lZf7u3TjZ9mTGXD+vVUTErihrbtKFuuPM88+gAb1q+jTJmy1Dq8Lg8//RIAV11yLltSNpOWlkaZMmV5+OlB1Eiula8xHlwhSAg2pWbm63Jymj9vLv379AyPi0yanXMeN9/ajk0bN9KnV1dWLF9OqVKl6NlnAIcfUTeyuMqWCH5rr9uSEdkyly39g+6dgmHiMjLSOef85tz431tpdfG5bN+elt1RsF79hnTvMyCyuCqWigfg0PYj831Z7991ChVLJZKWmcm9w3/m23l/UaFUMV74v0YcVLEky9Zt5bbXprFhSxpVyhZnVNfTKFMigUwHW7alc/YDY9mcjx/yvz9zMQALV6fm2zIAHuzfnZk/TmPj+vVUSEriuptuY+YP01g4fy6YUe2Ag+jQtS9Jlasw5I1BDH37VQ6uvuNH/f1PvECFivnbOTC5SgkAfH2nzpg+jRvbXEOdww8nzoLjtf1dnWhy2umRxxJ+p3o/hfHJ7FWRtm1pXq+q93XOTaRJ6U4LDs5jtQBOdM712NP8eExKY4mPpDRWRZWUFgQ+ktJY5SMpjVVRJqWxLqqktCDwnZTGEiWlsSXq3vfZXOAjdtOmVERERKSwU0enQKSD55vZZTkexgGNAP1sFRERESnior6iU87xUNKBxQSn8EVERESKpFge0D5KkSalzjl1sxMRERGRf4i0TamZHW5mX5vZ7PBxAzMrGiNri4iIiORCbUoDUXd0ehnoCaQBOOdmAldGHIOIiIiIxJio25SWcs5N+dtVTTQohYiIiBRZsVy9jFLUldK/zKwW4QD6ZtYKWB5xDCIiIiISY6KulLYDBgF1zWwZsAi4JuIYRERERGKGqfc9EH1Sugx4HRgLJAEbgeuBeyKOQ0RERERiSNRJ6QhgPTAD+DPiZYuIiIjEnDgVSoHok9LqzrnzIl6miIiIiMS4qJPSSWZW3zk3K+LlioiIiMQktSkNRJ2UngrcYGaLgG2AAc451yDiOEREREQkhkSdlJ4f8fJEREREpACINCl1zi2JcnkiIiIisU6D5weiHjxfREREROQfoj59LyIiIiI5qKNTQJVSEREREfFOlVIRERERjzR4fkCVUhERERHxTpVSEREREY/UpjSgSqmIiIiIeKdKqYiIiIhHGqc0oEqpiIiIiHinSqmIiIiIRyqUBlQpFRERERHvVCkVERER8ShOjUoBMOec7xjyqsAEKiIiIgWG94zwuwXrI81xTqpdwfs650aVUhERERGPYjJD9KBAJaWp6b4j8K9E+D+2ZbsKx6USg8N49SbtGFXKBjvG2pQMz5H4l1Q6HoBN2zI9R+Jf2eJBt4FFf6V6jsS/mpVLAFDylN6eI/Fv67f3A/pOhR3fqRIb1NFJRERERLzTbwQRERERn3T+HlClVERERERigCqlIiIiIh6ZSqWAKqUiIiIiEgNUKRURERHxSGPnB1QpFRERERHvVCkVERER8UiF0oAqpSIiIiLinSqlIiIiIj6pVAqoUioiIiIiMUCVUhERERGPNE5pQJVSEREREfFOlVIRERERjzROaUCVUhERERHxTpVSEREREY9UKA2oUioiIiIi3ikpFRERERHvdPpeRERExCedvwdUKRURERGRGKBKqYiIiIhHGjw/oEqpiIiIiHinSqmIiIiIRxo8P6BKqYiIiIh4p0qpiIiIiEcqlAZUKRURERER71QpFREREfFJpVIgoqTUzGYBblfPO+caRBGHiIiIiMSmqCqlF4Z/24V/B4d/r4lo+f9avz49mTB+HElJlRg+YpTvcCI1oG8vJkwI1v39Dz/Onj7kncEMe/d/xMXH0+S007mrU1ePUUZn6Dtv8vGIDzCM5Np16NX/ftb8tZr+vbqwccN6jjjyaPreM5BixRJ9h5rvNm3ayMB7+vHbb/MxjN7972PVqhW8+tJzLF60kFcHD+XIo+r5DjPfrVixnP69e7B2zRrM4NKWrbnq2jbM/XUOA+8dwPbt24mPj6d7737Uq1/4foM//kA/vv92AhUqJvHS28MBGPzqC3w+8gPKV0gC4IZb2nP8yU3YuGE99/XuzLxff6bZ+RfTrnMvn6HvFy/2vIzzTzmC1etSaHTd0wBcdmY9et90FnUPq0KTm19kxq/LADircS3uvfVcEovFsz0tg17Pfc74GQsB+OKZmzigclm2bksH4KK7Xmf1+hQ/KxWBjIwMrmrdkqrVqvHs8y/5DscrjVMaiCQpdc4tATCzZs65Y3M81cPMZgA9oojj32hxyWVcdfW19O7Z3XcokbuoxaVccdU19O29479p6pTJjBs7hqEfjCAxMZG1a9Z4jDA6q1et5P2h7/D2sJEUL1GCvj068fWXn/LdtxO54uo2ND33Ah554G5GjRjOpa2u9B1uvnvikYGcePKpPPDIk6SlbSc1NZWyZcsy8NGneegZeVTbAAAgAElEQVT+Ab7Di0xCfDwdO3ej7lFHk5KSwnVXtuSEk07m6Sce5eZb23FKk9P4ZuJ4nn7iUQa99pbvcPe7Zhe04KKWV/Hovb13mn7pFdfR6urrd5qWmJhIm5vbsWThAhYvXBBlmPlm8KczePGDybzSt1X2tJ8XruTKXv/j2a4tdpp3zfottOo+mOV/beKomlX5+IkbqXXJQ9nP33j3e9kJbGH3zuC3SE6uxeaUzb5DkRgRdUcnM7NTcjw42UMM++S4Ro0pV7687zC8OK5RY8r/bd3fG/ouN950M4mJQTUwqVIlH6F5kZGRwbZtqaSnp7MtNZVKlaswY+r3nHH2OQCcf2ELJo772nOU+W/zpk38OGMaF13SEoBixRIpW7YcNZJrcViNmp6ji1blKlWpe9TRAJQuXZoaNWuxatVKzIyU8At386bNVKlS1WeY+ab+McdRtly5PM1bomQp6jX8D8USi+dzVNH59qfFrN24Zadpc5esZv7vf/1j3p/mL2f5X5sA+GXRKkoUTyCxWHwkccaSlStWMHHCOC5t2WrPMxcBZtHeYlXUHZ1uAl4zs/IEzXrXAf8XcQyyHyxZspgfZkzjuWeeJDExkU5dunN0vfq+w8p3VapW48prb6DlhU0pXrwEjU88mSOOPJoyZcuSkJCQPc/qVas8R5r//vxzKRUqJnHfgN7Mn/crdY88mo5de1KyZCnfoXn157JlzP11DvXqN6Rzt57ccevNPPXYI2S6TF5763++w4vUyA/e5avPP+bwukdx8x1d8py4FhWXnnE0P879k+1pGdnTXup1GRmZjo/G/cyDb4z1GF3+evjBB+jYuSspKYW3eYLsvUirlM656c65hkBDoIFz7hjn3IxdzW9mbc1smplNGzRoUHSByh5lZGSwYcMG3npnKB07d6Nbl7twbpd92QqNjRs38M34MQwb+SUffT6W1K1b+X7SN77D8iIjI4N5v/7CZa2u4K0hwylZsiRvvf6K77C82rIlhW6dOtC5Ww/KlCnD+8PepVPXHnwyeiyduvbg3v59fIcYmQsvbc3rw0bx/BvDSKpUhZeffdR3SDHlyJpVue/2c7njkRHZ0268+z0at3mGprcP4pSGh3H1ecd4jDD/jB83lqSkJI46uvC3N88ri/gWqyI/dW5mzYFbgDvNrJ+Z9dvVvM65Qc65Rs65Rm3bto0uSNmjatWqcXbTZpgZ9eo3IM7iWLdune+w8t20KZM58KDqVKyYREJCMU47symzfvqBzZs2kZ4edE5YvWolVaoWztO0OVWtWo0qVatxdP2GAJx59jnM+/UXz1H5k56WRrdOd3Je84s4q2nQlGPUyI84q2kzAJqecx4/z57lM8RIVUyqRHx8PHFxcZx38WXM/WW275BixsFVyjH0gWv4773vs2jZ2uzpf/61EYDNW7YzdPRPND7qEF8h5qsff5jBuHFjOL/ZWXTv0omp30+mZ/cuvsOSGBBpUmpmLwJXAO0JkvXLgcOijEH2jzPOasrUKVMAWLJ4EWlpaVSsWNFzVPmv2gEH8vPsn0hN3YpzjulTJ1OjZi2ObXQ8477+EoDPRo3g1NPP8hxp/qtUuQrVqh3AksWLgCBhr1Gzlueo/HDOcU//PtSsmcy1bW7Inl6lSlWmT5sKwNTvJ3PIoUXn427NX6uz708aP4YaybU9RhM7ypcpwfBH2tD3xS/4btbv2dPj4+OoVD5o+pIQH8cFJ9fl54UrfYWZr+7s2JnRYybw2egxPPTo4zQ+4UQGPlTEK+kqlQJgUZ5yNbOZzrkGOf6WAT5zzjXJw8tdanp+R7hr3bt0YtrUKaxfv46kSpW4rV17Lmt5eeRxlAhbAW/ZHt3/W49unZg+dWqw7kmVuLVdey686GIG9O3N3Lm/UqxYMTp27sbxJ5wYWUwApRKDI2v1pmh3jFdfepavv/yc+Ph4Dj/iSLr3vYfVq1YyoFcXNm7cQJ0jjqTfvQ9ldwKLQpWywY6xNiVjD3PuX/PmzmHgPf1IS0vj4OrV6T3gfmZMm8rjD9/P+nVrKVO2HIcfXpcnn385spiSSgedRjZty4xsmT/OmM5/b7iW2nUOJy4u+K1/e4e7KFO6DI8+9AAZGRkkJhanR59+HBl2iIpC2eJBLIv+Ss3X5Qzs352ZP0xj4/r1VExK4tqbbmPmD9NYOH8umFHtgIPo0K0vlSpXAaBNy/PZkrKZ9PQ0ypQpy/1PvMhh+fyDpmblEgCUPKX3Hubce28OaE2TY5OpXKEUq9Zu5t5Xv2bdxq083vFCKlcozfrNqcycv5yLO71B9+vPoOt1p7Ng6Y4RSy6663VSUrcz+rmbKZYQT3y8MXbqb3R75lMyM/f/Z/3Wb+8HwOd3apapU77nzTde8zYkVPid6j1Nm71sc6Tt3+odXMb7Oucm6qR0inPueDObDFwGrAVmO+fy8hPaa1IaK3wkpbHKV1Iai3wlpbHIR1Iaq6JKSguC/ExKC5pYSkp9U1IaW6Luff+xmVUAHgFmEFzlKboSioiIiEiM0eD5gag7Ov0KZDjnPgCeAyYDH0Ucg4iIiIjkwswOMbOxZvaLmf1sZneG05PMbLSZzQ//Vgynm5k9bWYLzGymmf1nX5cddVLa1zm3ycxOBc4CXgFeiDgGERERkZgRY4PnpwOdnXNHAScC7czsKIKrb37tnKsDfM2Oq3GeD9QJb235F3ld1ElpVoO35sDLzrlPgMJ/gXARERGRAsA5tzxrDHnn3CZgDnAw0AJ4M5ztTeCS8H4L4C0XmAxUMLMD92XZUSely8zsJYJhoT41s+IeYhARERGJGVGPCJXz4kThLdfB4M2sBnAs8D1QzTm3PHxqBVAtvH8w8EeOly0Np+21qDs6tQbOAx51zq0PM+muEccgIiIiUmQ55wYBu71UZjhs5wfAXc65jZbjvL9zzpnZfh8xINKk1Dm3BRie4/FyYPmuXyEiIiJSyMVY53szK0aQkL7jnMvK21aa2YHOueVhUXFVOH0ZkPPyY9XDaXtNp85FREREBAh60wOvAnOcc4/neGokcH14/3pgRI7pbcJe+CcCG3Kc5t8rUZ++FxEREZEcYmyc0lOA64BZZvZjOK0X8CAwzMxuApYQNMkE+BS4AFgAbAFu3NcFKykVEREREQCcc9+w6wYFZ+cyvwPa7Y9lKykVERER8SgPY4cWCWpTKiIiIiLeqVIqIiIi4pEKpQFVSkVERETEO1VKRURERHxSqRRQpVREREREYoCSUhERERHxTqfvRURERDyKscHzvVGlVERERES8U6VURERExCMNnh9QpVREREREvFOlVERERMQjFUoDqpSKiIiIiHeqlIqIiIj4pFIpoEqpiIiIiMQAVUpFREREPNI4pQFVSkVERETEO1VKRURERDzSOKUBc875jiGvCkygIiIiUmB4TwkX/ZUaaY5Ts3IJ7+ucG1VKRURERDyKyQzRgwKVlKam+47AvxLh/5i2xY5tsXmbiuhligcfadovduwXW9P8xhELShYL/mq/0GdnTlnb4u3pS/0GEgOuPa667xAkhwKVlIqIiIgUOiqVAup9LyIiIiIxQEmpiIiIiHin0/ciIiIiHmnw/IAqpSIiIiLinSqlIiIiIh5p8PyAKqUiIiIi4p0qpSIiIiIeqVAaUKVURERERLxTpVRERETEI7UpDahSKiIiIiLeqVIqIiIi4pVKpaBKqYiIiIjEAFVKRURERDxSm9KAKqUiIiIi4l2kSamZVTOzV83ss/DxUWZ2U5QxiIiIiMQSi/gWq6KulL4BfAEcFD6eB9wVcQwiIiIiEmOiTkorO+eGAZkAzrl0ICPiGERERERihlm0t1gVdVKaYmaVAAdgZicCGyKOQURERERiTNS97zsBI4FaZvYtUAVoFXEMIiIiIhJjIk1KnXMzzOx04AiCtrZznXNpUcYgIiIiEkssprsfRSfq3veXAyWdcz8DlwBDzew/UcYgIiIiIrEn6jalfZ1zm8zsVOBs4FXghYhjEBEREYkdGhMKiD4pzepp3xx42Tn3CZAYcQwiIiIiEmOiTkqXmdlLwBXAp2ZW3EMMIiIiIjFDhdJA1Alha4LB8891zq0HkoCuEccgIiIiIjEmkt73ZlbOObcRKAGMC6clAduAaVHEICIiIhKLYnlA+yhFNSTU/4ALgekEA+fn3PwOSI4oDhERERGJQZEkpc65C83MgNOdc79Hscz9adu2bdzY5hrStm8nPSODZuecy+13dPAdljffTpzAQw/eT2ZGJpe2vJybbm7rO6TIrFixnH69u7N2zRrMjEtbtubqa9vw/LNPMX7s18TFxVExKYm77x1IlarVfIcbuYyMDK5q3ZKq1arx7PMv+Q7Hm40bN3JP/z4sWDAPwxhw7wM0POZY32FFbsXy5fTu2Y21a9aAGa0ub801113vOywv+vXpyYTx40hKqsTwEaN8hxOZzMwMXul9O+WSKnFl1wf48NkH+HPRXOLjEzioVl2a39SR+IQdqcifv/3Ka/3bc1n7Phx1wukeI4+WxikNRNam1DnngE+iWt7+lJiYyCuvvcl7H45k2Acf8e03E5n504++w/IiIyODB+6/h+dffIUPR37C55+O4rcFC3yHFZn4+Hg6du7O+x99whtvv8t7Q99h4W8LaHPDTQz9YCRD3vuIJqedwcsvPe87VC/eGfwWycm1fIfh3cMP3s/JpzTho48/Z9jwEdQsotskPiGeLt168OHHn/L2kKG8O+R/RerzIqcWl1zGCy+94juMyE35bDiVDz40+3G9U87m9kff4JaHXiF9+zZ+GPtp9nOZmRl8PeRlatVv5CNUiQFRd3SaYWaNI17mv2ZmlCpdGoD09HTS09OLbAOQ2bNmcsghh1H9kEMolpjIeRc0Z9zYr32HFZkqVapy5FFHA1C6dBlq1qzFqlUrKVOmTPY8W7duJbb7N+aPlStWMHHCOC5tWbSvHLxp0yZmTJ+avR2KFUukXLlynqPy4+/HS3JyMqtWrfQclR/HNWpMufLlfYcRqY1rVjP/x+859swLsqfVOfYEzAwz46Baddm4dnX2c1O/+Ii6xzehVPkKPsL1S93vgeiT0hOA78zsNzObaWazzGxmxDHsk4yMDFpf1oIzm5zMiSedTIMGDX2H5MWqlSs54MADsh9XrVaNlSuL5pfMn8uW8uuvc6hXP9gXnnv6CS5odgaffzKK29oVveYdDz/4AB07dyUurmiP8rZs2VIqVkyiX5+eXNHqEu7u15utW7b4Dsu7ZcuW8uucOdQvop+dRdEXg5+j6VVtsVyKOBnp6cz6ZjS1GwZ1qo1rV/Pr1G9o1PTiqMOUGBL1t8e5QC3gLOAigs5PF+1qZjNra2bTzGzaoEGDIgoxd/Hx8QwbPoIvx4xn9qyZzJ8/z2s84teWLSl07dSBLt16ZldJ23XoyKejx3Fe8wsZOuRtzxFGa/y4sSQlJXHU0fV8h+JdRno6v875hdZXXMXQ9z+iRMmSvPaq388v37akpND5rg507dFrp7MKUnjNm/EdpctV5MDkw3N9/rPXn+LQug04tG4DAL5863nOvupmrIj+qFWhNBBV73sAnHNLwmvdn0rQ6/5b59yM3cw/CMj6NHep6REEuQflypWj8fEnMOmbidSpk/vBVphVrVaNFctXZD9etXIl1aoVrQ49aWlpdO3UgfObX8RZTc/5x/PnN7+IO2+/hVuLULX0xx9mMG7cGL6ZOIFt27aRkrKZnt27MPChR32HFrlqBxxA1WoHZFcEm51zHq+9UnST0rS0NDrd1YELml9E02b/PF6kcPpj3s/MmzGJBT9+T3radrZt3cKHzz3Ape16Mf6Dt0jZuJ7WHe/Onn/5onkMf+Y+ALZs2sCCH6cQFxdP3can+loF8SDSpNTM+gGXA8PDSa+b2XvOufuijGNvrV27loSEBMqVK0dqaiqTv5vEjTfd7DssL46uV5/ff1/M0qV/UK1qNT7/9BMGPvKY77Ai45zj3v59qFmzFte2uTF7+u9LFnPoYTUAGD/2a2rUrOkpQj/u7NiZOzt2BmDqlO95843XimRCClC5chUOOOAAFi9aSI2ayXw/+TuSaxXNjk7OOQb0601ycjJtbrhxzy+QQuPsK//L2Vf+F4DFv/zI5E+GcWm7Xvww9hMWzpzKtb0f3akq2v6pd7Lvj3jxIeoce2KRSkiLaDeVf4g0KQWuARo651IBzOxB4EcgppPSv1avok+vHmRmZpCZ6Tjn3PM4/YwzfYflRUJCAj179+O2tv8lMzODSy5tSe3adXyHFZkff5jBJ6NGULvO4Vx1+SVAcNp+xPD3WbJ4MRZnHHjgQfTqe/ce3kkKs+69+tKrexfS0tI4+JBDuOfegb5D8uKHGdMZNXIEdQ4/nNaXtQCg/V2daHJa0RnqJ0v3Lp2YNnUK69evo9lZp3Fbu/Zc1vJy32FF7pNXn6RC5Wq83r89AHUbn8ppl7XxHJXECgtGaopoYWZjgUvDS4xiZhWA4c65s/Lw8pg4fe9bifBnhLbFjm2xeVt0+3CsKlM8+Jmt/WLHfrE1zW8csaBkseCv9gt9duaUtS3enr7UbyAx4NrjqkMMNLNck5Ie6RdZpdIJ3tc5N1FXSjcAP5vZaII2pc2AKWb2NIBzrug0whMRERFBg+dniTop/TC8ZRkX8fJFREREJAZFlpSaWTxwjnPumqiWKSIiIhLr1NEpEOVlRjOAw8wsMaplioiIiEjBEPXp+4XAt2Y2EkjJmuicezziOEREREQkhkSdlP4W3uKAshEvW0RERERiVNRXdNLgjSIiIiI5qE1pIOorOo0lGApqJ3kcp1RERERECqmoT993yXG/BNAS0FDGIiIiUmRpnNJA1Kfvp/9t0rdmNiXKGEREREQk9kR9+j4px8M4oBFQPsoYRERERGKJ2pQGoj59P52gTakBacBi4KaIYxARERGRGBPZ4Pmh7sAxzrmawGCCsUq3RByDiIiISMywiG+xKuqktI9zbqOZnQqcBbwCvBBxDCIiIiISY6JOSjPCv82Bl51znwC67KiIiIgUXSqVAtEnpcvM7CXgCuBTMyvuIQYRERERiTFRJ4StgS+Ac51z64EkoGvEMYiIiIhIjIl6nNItwPAcj5cDy6OMQURERCSWaPD8gE6di4iIiIh3UY9TKiIiIiI5aPD8gCqlIiIiIuKdKqUiIiIiHqlQGlClVERERES8U6VURERExCeVSgFVSkVEREQkBqhSKiIiIuKRxikNqFIqIiIiItnM7Dwzm2tmC8ysR1TLVaVURERExKNYGqfUzOKB54BmwFJgqpmNdM79ku/Lds7l9zL2lwITqIiIiBQY3lPC1PRoc5wSCbteZzM7CRjgnDs3fNwTwDk3ML/jKkin7y0WbmZ2i+8YYuWmbaFtoW2hbaHtoG1RCLaFdyUSsChvZtbWzKbluLXNEc7BwB85Hi8Np+W7gpSUxoq2e56lyNC22EHbYgdtix20LQLaDjtoW+ygbeGJc26Qc65Rjtsg3zGBklIRERER2WEZcEiOx9XDaflOSamIiIiIZJkK1DGzmmaWCFwJjIxiwep9v/diosQdI7QtdtC22EHbYgdti4C2ww7aFjtoW8Qg51y6md0BfAHEA685536OYtkFqfe9iIiIiBRSOn0vIiIiIt4pKRURERER75SUyj4xsxvM7FnfcexvZjbAzLqY2T1m1jSC5V1iZkfl93L2FzPrYGZzzOwd37FExcxqmNls33EUdOF2vHofX7t5f8cTa7Sf7czMPjWzCr7jkGgpKd2PzEwdxwoJ51w/59xXESzqEqDAJKXA7UAz59w1+/oGOk6KrBpArkmp9onCL6//xxaIc85d4Jxbn99xSWwp0kmpmX1kZtPN7OesqxmY2WYzu9/MfjKzyWZWLZxeK3w8y8zuy/rlbmZnmNlEMxsJ/BJW2O7KsYz7zexOLyu4D8ysjZnNDNd/sJldZGbfm9kPZvZV1vb422veMLMXwu2zMNwmr4UVtTc8rMZeMbPeZjbPzL4BjginvWFmrcL7D5rZL+F2eTSctrv9YVSO937WzG7I7X3M7GTgYuARM/vRzGpFu+Z7x8xeBJKBz8Jt9pqZTQn3jRbhPDXC42FGeDs5nL7TceJxNfZVvJm9HH5WfGlmJc3sZjObGh4rH5hZKcjed14Mr5Iyz8wuDKffYGYjzGycmc03s/7h9Jj+zAj/T+fksv61zOzz8DN0opnVDefPPnbCx1lVzgeBJuG+3jHcHiPNbAzwtZmVMbOvw/1mVtY+VdCYWWkz+yTcL2ab2RVm1i/cV2ab2SCz4ErnZnZcON9PQDvPoefJLtZvsZlVDp9vZGbjwvsDwu+Rb4HBuzkGapjZXDN7C5gNHJL1nrktL3zNcWY2Ptz/vjCzA/1sEdmvnHNF9gYkhX9LEhwIlQAHXBROfxjoE94fBVwV3r8V2BzePwNIAWqGj2sAM8L7ccBvQCXf65rH7XE0MA+onLV9gIrsGKXhv8Bj4f0bgGfD+28A7xJcrq0FsBGoH67/dOAY3+u2m3U+DpgFlALKAQuALuE6tQr3ibk5tkGFPOwPo3K8/7PhttrV+7wBtPK9HfZiey0GKgMPANdmrUu435QOt2OJcHodYFqO7ZJ9nBSkW3hMp2ftx8Aw4NqcxzVwH9A+x//p5+H+X4fgEn0lwv1gebgvZH3mNIr1z4zdrP/XQJ1w2gnAmBzr3yrH63d1bNwQbpusz+EEoFx4v3J4LFrO9ygIN6Al8HKOx+Wz1jF8PJgd3zEzgdPC+48As33Hv4/rt5gd3xuNgHHh/QEE3wElc/yf7+oYyAROzPG+i8P9ILflFQMmAVXCaVcQDFvkffvo9u9uRbpSCnQIf6FOJrh6QR1gO0HCAcHBVCO8fxLwXnj/f397nynOuUUAzrnFwBozOxY4B/jBObcmv1ZgPzsLeM859xeAc24twZUcvjCzWUBXgsQ1Nx+74NNhFrDSOTfLOZcJ/MyObRiLmgAfOue2OOc28s8BgjcAqcCrZnYZsCWcvrv9ITe7ep+C6hygh5n9CIwjSLoOJfiyeDncX95j56YJ2cdJAbTIOfdjeD/rc6FeWCGcBVzDzsfGMOdcpnNuPrAQqBtOH+2cW+Oc2woMB04tIJ8Zua3/ycB74T7wErAvlarR4ecMBD9qHzCzmcBXBNfa/seZmQJgFtDMzB4ysybOuQ3AmRaccZpF8Dl7tAXtJSs45yaErxvsK+C9lNv67c7IcH/P8o9jIJy+xDk3OY/LOwKoB4wO978+BN9VUsAV2XY8ZnYG0BQ4yTm3JTzdUAJIC5MrgAzyto1S/vb4FYJfhAcAr+2PeD16BnjcOTcy3GYDdjHftvBvZo77WY8L7H7mgkGEjwfOJqic3kHwpbIr6ezcLKbEPr5PrDOgpXNu7k4TzQYAK4GGBNshNcfTfz9OCpKc+3QGQZXnDeAS59xPFjTROCPHPH8fANrtYXqsf2b8ff2rAeudc8fkMm/2MWBmcUDibt435z5xDVAFOM45l2ZmiwmPn4LEOTfPzP4DXADcZ2ZfE5yab+Sc+yM8RgrcemXZxfrl/Nz7+7r9/bjf1TGQ6+fDLpb3IfCzc+6kfVwNiVFFuVJaHlgXJqR1gRP3MP9kgtMIEFxya3c+BM4DGhNcEaGgGANcbmaVAMwsiWA7ZV3z9npfgeWjCcAlYRu5ssBFOZ80szJAeefcp0BHgmQLdr0/LAGOMrPiYSXk7D28zyag7P5frXz3BdA+R9u4Y8Pp5YHlYZX8OoKrgRRWZYHlZlaMIKHK6XIzi7OgnXAyQdMNCCo+SWZWkqCT27fh9IL2mbERWGRml0N255SsfXoxQbMYCNpMFwvv72lfLw+sChPSM4HD9nvUETCzg4Atzrm3CU7J/yd86q/wc6AVgAs68aw3s6xK4T53HozSLtZvMTv+z1vu4qVZdnUM7M3y5gJVzOykcJ5iZrars3hSgBTYCtZ+8Dlwq5nNIdjBczttkNNdwNtm1jt87S5PWTjntpvZWIJKQsb+Cji/Oed+NrP7gfFmlgH8QFAZfc/M1hEkrTU9hrjfOedmmNlQ4CdgFcE1f3MqC4wwsxIE1cFO4fRc94ewEjKMoK3UIoJtuLv3eZfgdHcHgnZ4v+XDauaHe4EngZlhNWwRcCHwPPCBmbUh2C4FuTq6J32B74HV4d+cCdfvwBSCdsq3OudSw/x9CvABwanGt51z06DAfmZcA7xgZn0IEs93CY6jlwn29Z/YeR+YCWSE098A1v1/e/ceI2dVxnH8+yuXQKsQowiEoBAUJECAlEsxouAtpYYgTYloqtCaSJpwCUnBaKCpkoaAGAWNtjUQUSCgBVPaQoC2UOQablIbKZeWAgVaWoFQ3Kob+vjHc0befTu7O9N2O9vO75NMdt/3vHPOeU92Zp+cy3tq+d0MzCtD3E8Cy4f8DobGUeTixU1ALzCFDL6WAWvo+x0zCbhBUgD3bu+KbqFm97cnOTXpCnI6z0A2+wxIOqid8srnZQJwnaS9yVjml+R0MduBeZvRFilX1m6MiJB0NrnIpenq0PJP+mngrDKnzHYy7fw9WHdRPnFifkTMqZ0/lxzCPb/Je/ydYTu9gT4DZtDdPaXtGg38ugxXvgtMbnaR8kHo88nFM/7nsvNq6e/BbDD+zjAzS+4pNTMzM7OO6+aFTmZmZmY2TDgoNTMzM7OOc1BqZmZmZh3noNTMhoykCeVxN43jc/XhXujbuy7zy8r4oSwjVNn3fQvz6FgbmZl1koNSsy4j6fcleApJvZJWSrpG0qjtUPxt5MPkWyJplaSpQ1ifalmnlDb5xPYoz8zM+vIjocy600Jyx6XdgJPJbS5HkQ/C7kPSrsAHsQ0e1VH2u9446IVmZtZ13FNq1p3+ExFrIuK1iLiF3E3nm5D710taVoaRV5D7no+StLek2ZLekrRB0hJJx1UzlfQ9Sa9I6pE0n9wjvZq+2dC0pMGxIQMAAASbSURBVHGSHpe0UdI/Jc2TtIekB8itJn/W6NmtvOfzpfweSa9L+q2kvSrpI0uP8PuS1kr68dY2mKTjJd0rab2k9yQ91NjmsGY/SQtK3V6RNLGWzwGSbpX0TnktkPTZAco9UNJcSW+XPJeXDRvMzHYqDkrNDLL3crfK8cHAd4CzgKPJwHQBcAC5neixwIPAYkn7A0g6kdw+cjZwDDAP+OlAhUoaC9wJ3EduSHAqsIT8bhoPrC557F9eSDqK3JLxzlK38aW8GypZXwN8jdyH+yulvl9soz2a+SjwR7Jn+QTgb8Bdkj5eu+4npW7HkG3xh0bwXnYCux/4N/Al4CTgTWBhSWvmN8BIsm2OILe4fXcr78XMbNjx8L1Zl5N0AhmALqqc3h34bkSsLdd8mQyy9ilD8ACXSzqdnAZwNXARsCgiZpT0FyQdD3x/gOIvB+ZExGWVc0vLzx5JHwAbImJNJf0S4LaI+HnlHqYAz0j6JNBTypwcEfeU9ElkgLvFImJx9VjSBWTQexpwUyXpjoiYVX6fIelUMpCcCJwNCJjUmA4h6TzgLTLY/1OToj8N3B4Rz5bjl7fmPszMhisHpWbdaWwZRt+V7CGdC1xQSV/dCEiL0WRv3brcWfX/9gAOKb8fTvaOVj3KwEHpsWTvajtGA5+R9K3KuUalDiGD0t1L2QBExPuS/t5mOX2UgPcKssdyX2AXYE/gU7VLH21y/I1K3Q8GNtTacSQftmPdtcDM0qu8iNyO9KktvA0zs2HLQalZd3oQ+AHQC7wREb219H/VjkcAa8mh67r3tn31BjSCXJj1iyZprwOHDlG5N5LB6MXAKnJKwyIyAG7VCHLYv9mc0LebvSEirpd0DzAO+CrwiKQrI2J6G+WamQ17DkrNulNPRLzUxvVPkwHZpohY2c81zwFjaufqx3XPkHM+f9dP+n/JHsl6XY7or/5lcVZvKXtlOTcKOBJYMUh9BvIF4MKIWFDy3Jcyz7VmDH3nt44h26ZR928D6yOi5XmhEbGanJ86W9IPyakS09u9ATOz4cxBqZm1YiHwMDBX0qXAcmA/YCywMCL+ClxH9uL9CJgDnAKcOUi+M4B5kl4CbiGH4b8OzIqIHrJH8mRJN5FPDFgPXAU8JmkmMAvYAHwOOD0izitD9dcDV0laB7wBTGPz4LY/R0qqB4xLgReAiZIeJx+fdTUZNNeNl/QE8AAwgQy6TyxpNwNTyXacBrwKHAicAcyMiBfrmUm6Fri7lL8X2eb/aPFezMx2GF59b2aDKotyxgGLyV7N58lFOYeRQR8R8Rg5f3QKGcSNZ5DevIi4iwxcTyN7TZeQczY3lUumkUHbCmBdec9SciX9QeX6Z4EryekFDVPJVe5/KT+XkVMWWnF/qUv1NRKYDHwEeAq4lewNXdXk/dPJBVBLybaYFBFPlLr3lLqvBP5MBvc3Ah8D3umnPiOAX5GB6H3lPs9p8V7MzHYY2gbPwzYzMzMz2yruKTUzMzOzjnNQamZmZmYd56DUzMzMzDrOQamZmZmZdZyDUjMzMzPrOAelZmZmZtZxDkrNzMzMrOMclJqZmZlZx/0PcL4Y3VqnUSEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test_labels, y_pred)\n",
    "plt.figure(figsize = (12, 10))\n",
    "cm = pd.DataFrame(cm , index = [i for i in encoder.categories_] , columns = [i for i in encoder.categories_])\n",
    "sns.heatmap(cm, linecolor='white', cmap='Blues', linewidth=1, annot=True, fmt='')\n",
    "plt.title('Confusion Matrix', size=20)\n",
    "plt.xlabel('Predicted Labels', size=14)\n",
    "plt.ylabel('Actual Labels', size=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 290,
     "status": "ok",
     "timestamp": 1637884214983,
     "user": {
      "displayName": "Meshal Alamr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09596551090673860638"
     },
     "user_tz": -180
    },
    "id": "qQHhELNWXGNr",
    "outputId": "75847675-a524-4c32-d997-96231e94299d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       angry       0.86      0.80      0.83      1476\n",
      "        calm       0.78      0.91      0.84       163\n",
      "     disgust       0.63      0.65      0.64      1410\n",
      "        fear       0.76      0.65      0.70      1434\n",
      "       happy       0.73      0.67      0.69      1435\n",
      "     neutral       0.64      0.71      0.67      1275\n",
      "         sad       0.67      0.77      0.71      1466\n",
      "    surprise       0.92      0.92      0.92       463\n",
      "\n",
      "    accuracy                           0.72      9122\n",
      "   macro avg       0.75      0.76      0.75      9122\n",
      "weighted avg       0.73      0.72      0.72      9122\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_labels, y_pred))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyOYhCz2OeS+Ne44czpgERpN",
   "name": "speech-emotion-recognition.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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