nn_cath_final.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Model_Build.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BfgLsSfIH8W0",
        "colab_type": "text"
      },
      "source": [
        "This notebook allows you to train and run the Cath model based on NN presented here (https://ecs193-final-project.herokuapp.com/). In order to run this code, you will need have the cleaned data downloaded. This data is located here https://drive.google.com/file/d/1Kb0CZlWMFVKCJ3hSZiMY6tLucX7bxq1w/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpUg5rCmpDZk",
        "colab_type": "code",
        "outputId": "c4aec3ec-ece0-4c30-f26b-f19ad480ad64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "!unzip input.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  input.zip\n",
            "   creating: data/\n",
            "  inflating: __MACOSX/._data         \n",
            "  inflating: data/CleanData.csv      \n",
            "  inflating: __MACOSX/data/._CleanData.csv  \n",
            "  inflating: data/.DS_Store          \n",
            "  inflating: __MACOSX/data/._.DS_Store  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x7hFWmBHqs60",
        "colab_type": "text"
      },
      "source": [
        "**READ IN THE FILES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29wo-FpBqsPM",
        "colab_type": "code",
        "outputId": "c017294e-74c1-4f25-ed77-8118becfbf26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import pandas as pd\n",
        "import sklearn\n",
        "import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "import cv2\n",
        "import itertools\n",
        "from sklearn.model_selection import train_test_split,KFold\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from scipy.signal import resample\n",
        "from collections import Counter\n",
        "from datetime import datetime\n",
        "from datetime import date\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from collections import Counter\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79SFl6CVrr11",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"data/CleanData.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yYzkuEnsHsR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop(['Unnamed: 0',\"UCDID\"],axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85W6aVnFtbud",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[\"Age\"][df[\"Age\"] < 20] += 100 #this is entirely old patients"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnPRJvtDr23f",
        "colab_type": "code",
        "outputId": "e3efc322-75cd-496b-ba62-596447e37a56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(781, 41)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBFjOpPvr6_u",
        "colab_type": "code",
        "outputId": "a0c51896-931a-4eba-8dcc-aa15b25d4143",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        }
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RiskRecentSmoker</th>\n",
              "      <th>ACCHxHypertension</th>\n",
              "      <th>ACCHxHypercholesterol</th>\n",
              "      <th>ACCFamilyHxCAD</th>\n",
              "      <th>RiskPriorMI</th>\n",
              "      <th>ACCCHF</th>\n",
              "      <th>ACCValvularSurgery</th>\n",
              "      <th>ACCPrevCoroIntervention</th>\n",
              "      <th>HeightCM</th>\n",
              "      <th>WeightKG</th>\n",
              "      <th>ACCHxKidneyFailure</th>\n",
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              "      <th>ACCPeripheralVascDisease</th>\n",
              "      <th>ACCHxChronicLungDisease</th>\n",
              "      <th>DiabetesHistory</th>\n",
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              "      <th>StudyStressSPECTMRIResult</th>\n",
              "      <th>StudyStressSPECTMRIIschemia</th>\n",
              "      <th>ProcedureDiagnostic</th>\n",
              "      <th>PCIProcedure</th>\n",
              "      <th>Scheduling_Type</th>\n",
              "      <th>PostDiagRxRecommendation</th>\n",
              "      <th>CoronaryDominance</th>\n",
              "      <th>LeftMainStenosisNA</th>\n",
              "      <th>ProcOutcomesLMStenosis</th>\n",
              "      <th>ProxLADStenosisNA</th>\n",
              "      <th>ProcOutcomesLADStenosis</th>\n",
              "      <th>MidDistLADStenosisNA</th>\n",
              "      <th>ProcOutcomesLADDistStenosis</th>\n",
              "      <th>CircStenosisNA</th>\n",
              "      <th>ProcOutcomesCIRCStenosis</th>\n",
              "      <th>RCAStenosisNA</th>\n",
              "      <th>ProcOutcomesRCAStenosis</th>\n",
              "      <th>RamusStenosisNA</th>\n",
              "      <th>ProxLADGraftStenosisNA</th>\n",
              "      <th>MidDistLADGraftStenosisNA</th>\n",
              "      <th>CircGraftStenosisNA</th>\n",
              "      <th>RCAGraftStenosisNA</th>\n",
              "      <th>RamusGraftStenosisNA</th>\n",
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              "      <td>64</td>\n",
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              "      <td>81.0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>71.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>106.5</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.20</td>\n",
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              "      <td>0.2</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163.1</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>68.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>69</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>157.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>160.0</td>\n",
              "      <td>73.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>180.1</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  RiskRecentSmoker  ...  RamusGraftStenosisNA  Cath_result\n",
              "0   64                 1  ...                     1            0\n",
              "1   64                 1  ...                     1            1\n",
              "2   66                 1  ...                     1            1\n",
              "3   63                 0  ...                     1            0\n",
              "4   63                 0  ...                     1            0\n",
              "5   66                 0  ...                     1            1\n",
              "6   81                 0  ...                     1            0\n",
              "7   69                 0  ...                     1            1\n",
              "8   69                 0  ...                     0            1\n",
              "9   61                 1  ...                     0            1\n",
              "\n",
              "[10 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wb3b9jxvGW2J",
        "colab_type": "code",
        "outputId": "787fff20-1721-4a78-929d-e93e43457185",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "list(df.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Age',\n",
              " 'RiskRecentSmoker',\n",
              " 'ACCHxHypertension',\n",
              " 'ACCHxHypercholesterol',\n",
              " 'ACCFamilyHxCAD',\n",
              " 'RiskPriorMI',\n",
              " 'ACCCHF',\n",
              " 'ACCValvularSurgery',\n",
              " 'ACCPrevCoroIntervention',\n",
              " 'HeightCM',\n",
              " 'WeightKG',\n",
              " 'ACCHxKidneyFailure',\n",
              " 'ACCCerebroVascDisease',\n",
              " 'ACCPeripheralVascDisease',\n",
              " 'ACCHxChronicLungDisease',\n",
              " 'DiabetesHistory',\n",
              " 'DiabetesTherapy',\n",
              " 'StudyStressSPECTMRIResult',\n",
              " 'StudyStressSPECTMRIIschemia',\n",
              " 'ProcedureDiagnostic',\n",
              " 'PCIProcedure',\n",
              " 'Scheduling_Type',\n",
              " 'PostDiagRxRecommendation',\n",
              " 'CoronaryDominance',\n",
              " 'LeftMainStenosisNA',\n",
              " 'ProcOutcomesLMStenosis',\n",
              " 'ProxLADStenosisNA',\n",
              " 'ProcOutcomesLADStenosis',\n",
              " 'MidDistLADStenosisNA',\n",
              " 'ProcOutcomesLADDistStenosis',\n",
              " 'CircStenosisNA',\n",
              " 'ProcOutcomesCIRCStenosis',\n",
              " 'RCAStenosisNA',\n",
              " 'ProcOutcomesRCAStenosis',\n",
              " 'RamusStenosisNA',\n",
              " 'ProxLADGraftStenosisNA',\n",
              " 'MidDistLADGraftStenosisNA',\n",
              " 'CircGraftStenosisNA',\n",
              " 'RCAGraftStenosisNA',\n",
              " 'RamusGraftStenosisNA',\n",
              " 'Cath_result']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3aX5PXPTtLv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_no_norm = df.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4P_esqhsQPu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_var(var,col):\n",
        "  plt.figure()\n",
        "  plt.title(\"Frequency plot for the \" + str(col))\n",
        "  plt.xlabel(col)\n",
        "  plt.ylabel(\"Frequency distribution\")\n",
        "  plt.hist(var,bins = 50)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKiRlc-zspKV",
        "colab_type": "code",
        "outputId": "afd70c39-b67b-493f-ef36-2d0edf97cb7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plot_var(df[\"Age\"],\"Age\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GwczMOl+RxP+liHhK0luAt5O6UfYzcs3MulSRh62/mF8PAKZFxDWSvlZiTGalm3DCNX2Wzz3lgBZHYtZ6Rc7450v6AfB+4OeS1iq4npmZdaAiCbz2xK13RcRSYCTw/0qNyszMStOsHf/6EbEMGE5+epakkcDzwKyWRGdmZkOuWR3/+cCBpDb8tUcw1gSwVYlxmZlZSZq14z8wv27ZunDMzKxszap6mrbV96MXzcy6U7OqntojF4cDPcAdpOqeN5Hq+PcoNzQzMytDw1Y9EbF3ROwNLAAmRkRPROwC7AzMb1WAZmY2tIo059w2Iu6qTUTE3cDrywvJzMzKVOTO3Tsl/Qg4L08fBdxZXkhmZlamImf8xwD3AMfm4d5c1pSk4ZJukXSHpHskfSWXbynpZkkPSJohac2V+QPMzGxgijx68e/AaXkYiOeBfSLiaUlrADdJ+gXwGeC0iLhQ0veBKbjTNzOzlimtz51Ins6Ta+QhgH2AS3L5dGBSWTGYmdmrFanjHzRJw0h3/m4NfBf4C7A0IpbnRebR4DGOkqYCUwHGjx9fZpjWgdx7Zufye9P9ijxsfYfBbjwiXoyInYBxwK7AdgNYd1puQtozatSowYZgZma9FKnq+V6+SPtvkjYYzE5yr57Xk2762lBS7ZfGOHxPgJlZS/Wb+CPiraQmnJsDsyWdL+kd/a0naZSkDfP42sA7gDmkL4BD82KTgSsGGbuZmQ1CoTr+iLhf0omkrhq+A+wsScAXI+LSBquNAabnev7VgIsi4mpJ9wIX5qd4/ZH0KEczM2uRfhO/pDeR2u0fAFwHHBQRt0naDPg90Gfij4g7Sd079C7/K6m+38zM2qDIGf8ZwI9IZ/fP1Qoj4tH8K8DMzLpIkcR/APBcRLwIIGk1YHhEPBsR55YanZmZDbkirXp+DaxdNz0il5mZWRcqkviH192BSx4fUV5IZmZWpiKJ/5n6p3FJ2gV4rsnyZmbWwYrU8R8HXCzpUdITuF4DvL/UqMwqplE3CGZlKNI7562StgO2zUX3RcQ/yg3LzMzKUrSTtn8CJuTlJ0oiIn5SWlRmZlaaIjdwnQu8FrgdeDEXB+DEb2bWhYqc8fcA20dElB2MmZmVr0irnrtJF3TNzGwVUOSMfxPgXkm3kB6nCEBEHFxaVGZmVpoiif+ksoMw63RubmmrkiLNOW+UtAWwTUT8WtIIYFj5oZmZWRmKPHrxo6SHo/8gF40FLi8zKDMzK0+Rqp5PkPrPvxlefijLpqVGZTZAfgB4+w3Ve9CsWs3v59Ao0qrn+Yh4oTaRn5frpp1mZl2qSOK/UdIXgbXzs3YvBq4qNywzMytLkaqeE4ApwF3Ax4Cfk57IZWarsKFqyeRquM5TpFXPS8AP82BmZl2uSF89D9JHnX5EbFVKRGZmVqqiffXUDAcOA0aWE46ZmZWtSFXPkl5F35Y0G/hyOSGZdT/f6WudrEhVz8S6ydVIvwCK9uNvZmYdpkgCP7VufDkwFzi8lGjMzKx0Rap69m5FIGadoJuqaLopVussRap6PtNsfkR8q8F6m5Oe0jWa1CpoWkScLmkkMIP0KMe5wOER8cTAwjYzs8EqcuduD/CvpM7ZxgIfByYC6+WhkeXAZyNie2B34BOStifdEDYzIrYBZuZpMzNrkSJ1/OOAiRHxFICkk4BrIuLoZitFxAJgQR5/StIc0hfHIcBeebHpwA3A5wcRu5mZDUKRM/7RwAt10y/kssIkTQB2JvXwOTp/KQAsbLQtSVMlzZI0a/HixQPZnZmZNVHkjP8nwC2SLsvTk0hn6oVIWhf4GXBcRCyT9PK8iAhJffb0GRHTgGkAPT097g3UzGyIFGnVc7KkXwBvzUXHRMQfi2xc0hqkpP/TiLg0Fz8maUxELJA0Blg0mMDNzGxwit6INQJYFhHnSBolacuIeLDZCkqn9mcBc3q1/LkSmAyckl+vGETcZjZAbv5pNUWac/4nqWXPtsA5wBrAecCe/ay6J/BB4C5Jt+eyL5IS/kWSpgAP4ZvBzMxaqsgZ/3tIF2ZvA4iIRyU1a8ZJXu4mQA1m71s4QjMzG1JFWvW8EBFB7ppZ0jrlhmRmZmUqkvgvkvQDYENJHwV+jR/KYmbWtZpW9eQLtDOA7YBlpHr+L0fEdS2IzczMStA08ed29j+PiB0AJ3szs1VAkYu7t0n6p4i4tfRorCv44dlm3a1I4t8NOFrSXOAZUkudiIg3lRmYmZmVo2HilzQ+Ih4G3tXCeMzMrGTNzvgvJ/XK+ZCkn0XE+1oVlJmZladZ4q+/+WqrsgMxK4O7KTB7tWbt+KPBuJmZdbFmZ/w7SlpGOvNfO4/DKxd31y89OjMzG3INE39EDGtlIGZWLa6Ga58iXTaYmdkqxInfzKxinPjNzCrGid/MrGKc+M3MKsaJ38ysYpz4zcwqxonfzKxinPjNzCrGid/MrGKc+M3MKsaJ38ysYpz4zcwqpsgzdwdF0tnAgcCiiHhjLhsJzAAmAHOBwyPiibJisM7nHhptKDT6HM095YAWR9Idyjzj/zGwX6+yE4CZEbENMDNPm5lZC5WW+CPif4G/9So+BJiex6cDk8rav5mZ9a20qp4GRkfEgjy+EBjdaEFJU4GpAOPHj29BaGbW6Vw1ODTadnE3IoImz/KNiGkR0RMRPaNGjWphZGZmq7ZWJ/7HJI0ByK+LWrx/M7PKa3XivxKYnMcnA1e0eP9mZpVXZnPOC4C9gE0kzQP+EzgFuEjSFOAh4PCy9m+rJtfx2kAM9PNSleafpSX+iDiywax9y9qnmZn1z3fumplVTKubc1oFuXrGrLP4jN/MrGKc+M3MKsaJ38ysYlzHb2bWj1Wt90+f8ZuZVYwTv5lZxTjxm5lVjBO/mVnFOPGbmVWME7+ZWcU48ZuZVYwTv5lZxTjxm5lVjO/cXQUN9C7Dobor0b1wmnUHn/GbmVWME7+ZWcW4qqeLlV214qobs1WTz/jNzCrGid/MrGKc+M3MKsZ1/AWtCg9icJ29WWu0q0l1UT7jNzOrGCd+M7OKWeWretpVRTOYapWB/gw0s+7SKf/LbTnjl7SfpPskPSDphHbEYGZWVS1P/JKGAd8F9ge2B46UtH2r4zAzq6p2nPHvCjwQEX+NiBeAC4FD2hCHmVklKSJau0PpUGC/iPhInv4gsFtE/Huv5aYCU/PktsB9LQpxE+DxFu1rZXRDnI5xaDjGoVHFGLeIiFG9Czv24m5ETAOmtXq/kmZFRE+r9ztQ3RCnYxwajnFoOMZXtKOqZz6wed30uFxmZmYt0I7EfyuwjaQtJa0JHAFc2YY4zMwqqeVVPRGxXNK/A78ChgFnR8Q9rY6jiZZXLw1SN8TpGIeGYxwajjFr+cVdMzNrL3fZYGZWMU78ZmYVU+nEL2m4pFsk3SHpHklfyeVbSro5dykxI1+EbneswyT9UdLVnRijpLmS7pJ0u6RZuWykpOsk3Z9fN2pzjBtKukTSnyTNkbRHJ8Uoadt8/GrDMknHdVKMOc5P5/+XuyVdkP+POu3zeGyO7x5Jx+Wyth9HSWdLWiTp7rqyPuNS8p18TO+UNHGo4qh04geeB/aJiB2BnYD9JO0OfB04LSK2Bp4AprQxxppjgTl1050Y494RsVNdO+QTgJkRsQ0wM0+30+nALyNiO2BH0vHsmBgj4r58/HYCdgGeBS7rpBgljQU+BfRExBtJDTSOoIM+j5LeCHyU1EvAjsCBkramM47jj4H9epU1imt/YJs8TAXOHLIoIsJDusA9ArgN2I1059zquXwP4Fdtjm1c/kDsA1wNqANjnAts0qvsPmBMHh8D3NfG+DYAHiQ3aOjEGHvF9U7gt50WIzAWeAQYSWoVeDXwrk76PAKHAWfVTX8JOL5TjiMwAbi7brrPuIAfAEf2tdzKDlU/469VodwOLAKuA/4CLI2I5XmReaQPezt9m/TBfSlPb0znxRjAtZJm5+42AEZHxII8vhAY3Z7QANgSWAyck6vMfiRpHTorxnpHABfk8Y6JMSLmA98EHgYWAE8Cs+msz+PdwFslbSxpBPBu0k2jHXMce2kUV+1LtmbIjmvlE39EvBjpp/U40k/D7doc0gokHQgsiojZ7Y6lH2+JiImkn6efkPS2+pmRTlna2XZ4dWAicGZE7Aw8Q6+f+h0QIwC5fvxg4OLe89odY65/PoT0RboZsA6vrrpoq4iYQ6p6uhb4JXA78GKvZTrive6tVXFVPvHXRMRS4HrSz9QNJdVubmt3lxJ7AgdLmkvqyXQfUl11J8VYOxMkIhaR6qV3BR6TNAYgvy5qX4TMA+ZFxM15+hLSF0EnxVizP3BbRDyWpzspxrcDD0bE4oj4B3Ap6TPaaZ/HsyJil4h4G+maw5/prONYr1FcpXVvU+nEL2mUpA3z+NrAO0gX/K4HDs2LTQauaE+EEBFfiIhxETGB9PP/NxFxFB0Uo6R1JK1XGyfVT99N6opjcl6s3cdxIfCIpG1z0b7AvXRQjHWO5JVqHuisGB8Gdpc0QpJ45Th2zOcRQNKm+XU88F7gfDrrONZrFNeVwIdy657dgSfrqoRWTrsuwHTCALwJ+CNwJylRfTmXbwXcAjxA+rm9VrtjzXHtBVzdaTHmWO7Iwz3Af+TyjUkXpe8Hfg2MbPPx2wmYld/vy4GNOjDGdYAlwAZ1ZZ0W41eAP+X/mXOBtTrp85hj/D/SF9IdwL6dchxJX+gLgH+QfoVOaRQXqRHHd0nXHe8itaQakjjcZYOZWcVUuqrHzKyKnPjNzCrGid/MrGKc+M3MKsaJ38ysYpz4zfohaZKkkNRRd3WbDZYTv1n/jgRuyq9mXc+J36wJSesCbyHdaHNELltN0vdyv/7XSfq5pEPzvF0k3Zg7q/tV7VZ8s07ixG/W3CGkPvz/DCyRtAupC4AJwPbAB0n9OyFpDeAM4NCI2AU4Gzi5HUGbNbN6/4uYVdqRpE7xIHWSdyTp/+biiHgJWCjp+jx/W+CNwHWpGxuGkW7PN+soTvxmDUgaSeoNdQdJQUrkQep9tM9VgHsiYo8WhWg2KK7qMWvsUODciNgiIiZExOakp3j9DXhfrusfTeo8D9ITkkZJernqR9Ib2hG4WTNO/GaNHcmrz+5/BryG1LPivcB5pEd2PhkRL5C+LL4u6Q7SA0De3LpwzYpx75xmgyBp3Yh4WtLGpO6I94zU579Zx3Mdv9ngXJ0f4rMm8FUnfesmPuM3M6sY1/GbmVWME7+ZWcU48ZuZVYwTv5lZxTjxm5lVzP8H7pypnVTPfI8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McXVSmrduLKG",
        "colab_type": "code",
        "outputId": "4c82cb57-638b-4cd6-84c6-3a3062e1eb1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plot_var(df['HeightCM'],\"Height\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_XXlOMS_uTnM",
        "colab_type": "code",
        "outputId": "8aaa11eb-913e-4e64-dc15-6497c7c70921",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plot_var(df['WeightKG'],\"Weight\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIVY7qYatrEP",
        "colab_type": "text"
      },
      "source": [
        "**NORMALIZE WEIGHTS AND HEIGHTS AND USE AGE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYGNkx5lswkN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler1 = sklearn.preprocessing.MinMaxScaler()\n",
        "scaler2 = sklearn.preprocessing.MinMaxScaler()\n",
        "scaler3 = sklearn.preprocessing.MinMaxScaler()\n",
        "df[\"Age\"] = scaler1.fit_transform(np.array(df[\"Age\"]).reshape((-1,1)))\n",
        "df['HeightCM'] = scaler2.fit_transform(np.array(df[\"HeightCM\"]).reshape((-1,1)))\n",
        "df['WeightKG'] = scaler3.fit_transform(np.array(df[\"WeightKG\"]).reshape((-1,1)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "66JPAz7BK_pf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "with open(\"age_scaler.pkl\",\"wb\") as f:\n",
        "  pickle.dump(scaler1,f)\n",
        "\n",
        "with open(\"ht_scaler.pkl\",\"wb\") as g:\n",
        "  pickle.dump(scaler2,g)\n",
        "\n",
        "with open(\"wt_scaler.pkl\",\"wb\") as h:\n",
        "  pickle.dump(scaler1,h)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "II16nW6tvpkW",
        "colab_type": "code",
        "outputId": "20638d5d-fda2-4a9b-bd73-4de672e83a95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        }
      },
      "source": [
        "plot_var(df[\"Age\"],\"Age\")\n",
        "plot_var(df[\"HeightCM\"],\"Height\")\n",
        "plot_var(df[\"WeightKG\"],\"Weight\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yM7Mi5Un8iySdD3wMuE7SOjm3MzOzNVCeBH4U2RO3PhQRK4DhwH8WGpWZmRWm3nX8G0bESmAoMDuVDQdeAeY0JDozM+t39U7uXgQcQnYNf+cjGDsFsHWeHUgaRPaPYlFEHCJpK+ASYNNU93ER8WovYjczs17osqsnIg5JP7eKiK3Tz85XrqSffBF4qGL+bODciNgGeBY4sTeBm5lZ79Tr6ql7rX6eRy9KGk12NdBZwMlpaOf9gH9Nq0wHTie7PNTMzBqgXldP5yMXhwLtwD1k3T3vIuu62TtH/d8FTgE6R/jcFFgREavS/EK6GPdH0iSyRz8yZsyYHLuygcRDLZgVp15Xz74RsS+wBNg9Itoj4t3AbsCi7iqWdAiwLCLm9iawiJiW9tne1tbWmyrMzKyGPHfubh8R93XORMT9knbMsd0+wARJB5F9a9gQmApsLGlwavWPJsc/ETMz6z95Ev+9kn4K/CrNfxy4t7uNIuKrwFcBJI0DvhIRH5d0GXAk2ZU9E4GrexG3tZB6wym468as8fLcwHUC8ADZ1TlfBB5MZb11KtmJ3kfJ+vwv6ENdZmbWQ3kevfh34Nz06pWImE26CSwiHgf26G1dZmbWNx5zx8ysZJz4zcxKJs/D1t/ZiEDMzKwx8rT4fyTpdkmflbRR4RGZmVmhuk38EfHPZJdwbgnMlXSRpAMKj8zMzAqRq48/IuYBp5FdivkB4HuSHpb0kSKDMzOz/penj/9dks4lG2FzP+DQiNgxTff6Ek8zM2uOPHfufh/4KfC1iHi5szAiFks6rbDIzMysEHkS/8HAyxHxGoCktYChEfFSRFxYaHRmZtbv8vTx/xFYt2J+WCozM7MWlCfxD42IFzpn0vSw4kIyM7Mi5Un8L1Y+jUvSu4GX66xvZmZrsDx9/CcBl0laTPYErn8CPlZoVGZmVpg8o3PeIWkHYPtU9EhE/KPYsMzMrCh5WvwA7wHGpvV3l0RE/LKwqMzMrDDdJn5JFwJvB+4GXkvFATjxm5m1oDwt/nZgp4iIooMxM7Pi5bmq536yE7pmZjYA5GnxjwAelHQ78EpnYURMKCwqMzMrTJ7Ef3pvKpY0FLgJWCft5/KI+IakrYBLyB60Phc4LiJe7c0+zMys5/KMx/9nYD4wJE3fAdyZo+5XgP0iYhdgV+BASXsBZwPnRsQ2wLPAib2M3czMeiHPsMyfBC4Hzk9FWwBXdbddZDqHehiSXkE2nPPlqXw6cHgPYzYzsz7Ic3L3P4B9gJXwxkNZNstTuaRBku4GlgE3AI8BKyJiVVplIdk/klrbTpI0R9Kcjo6OPLszM7Mc8iT+Vyr74CUNJmu5dysiXouIXYHRwB7ADnkDi4hpEdEeEe1tbW15NzMzs27kSfx/lvQ1YN30rN3LgGt6spOIWAHcCOwNbJz+eUD2D2FRT+oyM7O+yZP4JwMdwH3Ap4DryJ6/W5ekNkkbp+l1gQPIHt94I3BkWm0icHXPwzYzs97KM0jb68BP0qsnRgHTJQ0i+wczIyJ+K+lB4BJJZwJ3ARf0sF4zM+uDPGP1PEGNPv2I2LredhFxL7BbjfLHyfr7zcysCfKO1dNpKPBRYHgx4ZiZWdHy3MC1vOK1KCK+S/YAdjMza0F5unp2r5hdi+wbQN5x/M1sDTd28rU1y+dPcftuoMqTwM+pmF5FNnzDUYVEY2ZmhctzVc++jQjEzMwaI09Xz8n1lkfEd/ovHDMzK1req3reA8xM84cCtwPzigrKzMyKkyfxjwZ2j4jnASSdDlwbEccWGZiZmRUjz5ANI4HKB6W8msrMzKwF5Wnx/xK4XdKVaf5wsnH0zcysBeW5qucsSb8D/jkVnRARdxUblpmZFSVPVw/AMGBlREwFFqbn5pqZWQvK8+jFbwCnAl9NRUOAXxUZlJmZFSdPi/9fgAnAiwARsRjYoMigzMysOHkS/6sREaShmSWtV2xIZmZWpDyJf4ak88kemfhJ4I/0/KEsZma2hqh7VY8kAZeSPSR9JbA98N8RcUMDYjMzswLUTfwREZKui4h3Ak72ZmYDQJ6unjslvaenFUvaUtKNkh6U9ICkL6by4ZJukDQv/dykx1GbmVmv5blzd0/gWEnzya7sEdmXgXd1s90q4MsRcaekDYC5km4AjgdmRcQUSZOByWSXi1qL6+qBHgOZj/lNfnBL6+gy8UsaExFPAh/qTcURsQRYkqafl/QQsAVwGDAurTYdmI0Tv5lZw9Rr8V9FNirnAkm/iYgjersTSWOB3YDbgJHpnwLA03jANzOzhqrXx6+K6a17uwNJ6wO/AU6KiJWVyyrvD6ix3SRJcyTN6ejo6O3uzcysSr3EH11M5yZpCFnS/3VEXJGKl0oalZaPApbV3HnEtIhoj4j2tra23uzezMxqqNfVs4uklWQt/3XTNLx5cnfDehWnewAuAB6qejzjTGAiMCX9vLq3wVuxfBLPbGDqMvFHxKA+1r0PcBxwn6S7U9nXyBL+DEknAguAo/q4HzMz64E8l3P2SkTczOrnCSqNL2q/ZmZWX97x+M3MbIBw4jczKxknfjOzknHiNzMrGSd+M7OSceI3MysZJ34zs5Jx4jczKxknfjOzknHiNzMrGSd+M7OSceI3MysZJ34zs5Jx4jczK5nChmU2W5N19ZAZszJwi9/MrGSc+M3MSsZdPWZWqJ52q/mZzsVzi9/MrGQKa/FL+hlwCLAsIt6RyoYDlwJjgfnAURHxbFEx2MDjk7JmfVdki/8XwIFVZZOBWRGxLTArzZuZWQMVlvgj4ibgb1XFhwHT0/R04PCi9m9mZrU1uo9/ZEQsSdNPAyO7WlHSJElzJM3p6OhoTHRmZiXQtJO7ERFA1Fk+LSLaI6K9ra2tgZGZmQ1sjU78SyWNAkg/lzV4/2ZmpdfoxD8TmJimJwJXN3j/ZmalV1jil3QxcAuwvaSFkk4EpgAHSJoH7J/mzcysgQq7jj8ijuli0fii9mlmZt3znbtmZiXjxG9mVjIepM3MWkZXQ3Z4YLeecYvfzKxknPjNzErGXT3mES/NSsYtfjOzknHiNzMrGSd+M7OSceI3MysZJ34zs5LxVT0DkG9yMbN63OI3MysZJ34zs5JxV481lW8eM2s8t/jNzErGLf4W1qzWslvp5eD3eeByi9/MrGSc+M3MSqYpXT2SDgSmAoOAn0ZEYQ9d7+nX1WZe6+6v1tbKyvj57a97Zhp9703DW/ySBgE/BD4M7AQcI2mnRsdhZlZWzejq2QN4NCIej4hXgUuAw5oQh5lZKSkiGrtD6UjgwIj4RJo/DtgzIj5Xtd4kYFKa3R54pJe7HAE808ttW5WPuRx8zANfX4/3bRHRVl24xl7OGRHTgGl9rUfSnIho74eQWoaPuRx8zANfUcfbjK6eRcCWFfOjU5mZmTVAMxL/HcC2kraStDZwNDCzCXGYmZVSw7t6ImKVpM8BfyC7nPNnEfFAgbvsc3dRC/Ixl4OPeeAr5HgbfnLXzMyay3fumpmVjBO/mVnJDJjEL+lASY9IelTS5BrL15F0aVp+m6SxjY+yf+U45pMlPSjpXkmzJL2tGXH2p+6OuWK9IySFpJa+9C/P8Uo6Kr3PD0i6qNEx9rccn+sxkm6UdFf6bB/UjDj7k6SfSVom6f4ulkvS99Lv5F5Ju/dphxHR8i+yk8SPAVsDawP3ADtVrfNZ4Lw0fTRwabPjbsAx7wsMS9OfKcMxp/U2AG4CbgXamx13we/xtsBdwCZpfrNmx92AY54GfCZN7wTMb3bc/XDc7wd2B+7vYvlBwO8AAXsBt/VlfwOlxZ9nGIjDgOlp+nJgvCQ1MMb+1u0xR8SNEfFSmr2V7J6JVpZ3uI9vAmcDf29kcAXIc7yfBH4YEc8CRMSyBsfY3/IccwAbpumNgMUNjK8QEXET8Lc6qxwG/DIytwIbSxrV2/0NlMS/BfBUxfzCVFZznYhYBTwHbNqQ6IqR55grnUjWYmhl3R5z+gq8ZUQMhKEi87zH2wHbSfqLpFvTyLetLM8xnw4cK2khcB3w+caE1lQ9/Xuva40dssH6j6RjgXbgA82OpUiS1gK+Axzf5FAaaTBZd884sm90N0l6Z0SsaGpUxToG+EVEnCNpb+BCSe+IiNebHVirGCgt/jzDQLyxjqTBZF8RlzckumLkGvpC0v7AfwETIuKVBsVWlO6OeQPgHcBsSfPJ+kJntvAJ3jzv8UJgZkT8IyKeAP6P7B9Bq8pzzCcCMwAi4hZgKNlgZgNZvw51M1ASf55hIGYCE9P0kcCfIp01aVHdHrOk3YDzyZJ+q/f9QjfHHBHPRcSIiBgbEWPJzmtMiIg5zQm3z/J8rq8ia+0jaQRZ18/jjQyyn+U55ieB8QCSdiRL/B0NjbLxZgL/lq7u2Qt4LiKW9LayAdHVE10MAyHpDGBORMwELiD7Svgo2UmUo5sXcd/lPOb/BdYHLkvnsZ+MiAlNC7qPch7zgJHzeP8AfFDSg8BrwH9GRMt+k815zF8GfiLpS2Qneo9v8UYcki4m+wc+Ip27+AYwBCAiziM7l3EQ8CjwEnBCn/bX4r8vMzProYHS1WNmZjk58ZuZlYwTv5lZyTjxm5mVjBO/mVnJOPFb6Ul6oWr+eEk/6GabCfVGB03rjJP02y6WnSRpWM+jNes7J36zXoiImRExpQ9VnAQ48VtTOPGb1SGpTdJvJN2RXvuk8je+FUh6exog7T5JZ1Z9g1hf0uWSHpb063Tn5ReAzYEbJd3YhMOykhsQd+6a9dG6ku6umB/Om8METAXOjYibJY0hu6N0x6rtpwJTI+JiSZ+uWrYbsDPZ0MF/AfaJiO9JOhnYNyKe6e+DMeuOE78ZvBwRu3bOSDqebDRTgP2BnSoe3bChpPWrtt8bODxNXwR8u2LZ7RGxMNV7NzAWuLk/gzfrKSd+s/rWAvaKiNUe6tKDZ/hUjoj6Gv6bszWA+/jN6rueigd9SNq1xjq3Akek6byD/z1PNoy0WcM58ZvV9wWgPT3g+kGgug8fsit0TpZ0L7AN2dPdujMN+L1P7lozeHROsz5K1+O/HBEh6WjgmIio9SxgszWC+xvN+u7dwA+UdfyvAP69yfGY1eUWv5lZybiP38ysZJz4zcxKxonfzKxknPjNzErGid/MrGT+Px70+uEGPhYVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AIzwGUSLxXTu",
        "colab_type": "code",
        "outputId": "d7985e8a-8791-4bd0-e9a2-c2326c607eb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        }
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RiskRecentSmoker</th>\n",
              "      <th>ACCHxHypertension</th>\n",
              "      <th>ACCHxHypercholesterol</th>\n",
              "      <th>ACCFamilyHxCAD</th>\n",
              "      <th>RiskPriorMI</th>\n",
              "      <th>ACCCHF</th>\n",
              "      <th>ACCValvularSurgery</th>\n",
              "      <th>ACCPrevCoroIntervention</th>\n",
              "      <th>HeightCM</th>\n",
              "      <th>WeightKG</th>\n",
              "      <th>ACCHxKidneyFailure</th>\n",
              "      <th>ACCCerebroVascDisease</th>\n",
              "      <th>ACCPeripheralVascDisease</th>\n",
              "      <th>ACCHxChronicLungDisease</th>\n",
              "      <th>DiabetesHistory</th>\n",
              "      <th>DiabetesTherapy</th>\n",
              "      <th>StudyStressSPECTMRIResult</th>\n",
              "      <th>StudyStressSPECTMRIIschemia</th>\n",
              "      <th>ProcedureDiagnostic</th>\n",
              "      <th>PCIProcedure</th>\n",
              "      <th>Scheduling_Type</th>\n",
              "      <th>PostDiagRxRecommendation</th>\n",
              "      <th>CoronaryDominance</th>\n",
              "      <th>LeftMainStenosisNA</th>\n",
              "      <th>ProcOutcomesLMStenosis</th>\n",
              "      <th>ProxLADStenosisNA</th>\n",
              "      <th>ProcOutcomesLADStenosis</th>\n",
              "      <th>MidDistLADStenosisNA</th>\n",
              "      <th>ProcOutcomesLADDistStenosis</th>\n",
              "      <th>CircStenosisNA</th>\n",
              "      <th>ProcOutcomesCIRCStenosis</th>\n",
              "      <th>RCAStenosisNA</th>\n",
              "      <th>ProcOutcomesRCAStenosis</th>\n",
              "      <th>RamusStenosisNA</th>\n",
              "      <th>ProxLADGraftStenosisNA</th>\n",
              "      <th>MidDistLADGraftStenosisNA</th>\n",
              "      <th>CircGraftStenosisNA</th>\n",
              "      <th>RCAGraftStenosisNA</th>\n",
              "      <th>RamusGraftStenosisNA</th>\n",
              "      <th>Cath_result</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.346535</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.289958</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.527027</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.336634</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.486486</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.716049</td>\n",
              "      <td>0.219236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.486486</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.493827</td>\n",
              "      <td>0.470297</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.527027</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.408642</td>\n",
              "      <td>0.183876</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.729730</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.654321</td>\n",
              "      <td>0.204385</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.567568</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.226308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.567568</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.370370</td>\n",
              "      <td>0.236917</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.459459</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.618519</td>\n",
              "      <td>0.247525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Age  RiskRecentSmoker  ...  RamusGraftStenosisNA  Cath_result\n",
              "0  0.500000                 1  ...                     1            0\n",
              "1  0.500000                 1  ...                     1            1\n",
              "2  0.527027                 1  ...                     1            1\n",
              "3  0.486486                 0  ...                     1            0\n",
              "4  0.486486                 0  ...                     1            0\n",
              "5  0.527027                 0  ...                     1            1\n",
              "6  0.729730                 0  ...                     1            0\n",
              "7  0.567568                 0  ...                     1            1\n",
              "8  0.567568                 0  ...                     0            1\n",
              "9  0.459459                 1  ...                     0            1\n",
              "\n",
              "[10 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLSD1gDOcyFM",
        "colab_type": "code",
        "outputId": "b29a4adf-bde0-43c9-abab-67c780041de4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        }
      },
      "source": [
        "df_no_norm.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RiskRecentSmoker</th>\n",
              "      <th>ACCHxHypertension</th>\n",
              "      <th>ACCHxHypercholesterol</th>\n",
              "      <th>ACCFamilyHxCAD</th>\n",
              "      <th>RiskPriorMI</th>\n",
              "      <th>ACCCHF</th>\n",
              "      <th>ACCValvularSurgery</th>\n",
              "      <th>ACCPrevCoroIntervention</th>\n",
              "      <th>HeightCM</th>\n",
              "      <th>WeightKG</th>\n",
              "      <th>ACCHxKidneyFailure</th>\n",
              "      <th>ACCCerebroVascDisease</th>\n",
              "      <th>ACCPeripheralVascDisease</th>\n",
              "      <th>ACCHxChronicLungDisease</th>\n",
              "      <th>DiabetesHistory</th>\n",
              "      <th>DiabetesTherapy</th>\n",
              "      <th>StudyStressSPECTMRIResult</th>\n",
              "      <th>StudyStressSPECTMRIIschemia</th>\n",
              "      <th>ProcedureDiagnostic</th>\n",
              "      <th>PCIProcedure</th>\n",
              "      <th>Scheduling_Type</th>\n",
              "      <th>PostDiagRxRecommendation</th>\n",
              "      <th>CoronaryDominance</th>\n",
              "      <th>LeftMainStenosisNA</th>\n",
              "      <th>ProcOutcomesLMStenosis</th>\n",
              "      <th>ProxLADStenosisNA</th>\n",
              "      <th>ProcOutcomesLADStenosis</th>\n",
              "      <th>MidDistLADStenosisNA</th>\n",
              "      <th>ProcOutcomesLADDistStenosis</th>\n",
              "      <th>CircStenosisNA</th>\n",
              "      <th>ProcOutcomesCIRCStenosis</th>\n",
              "      <th>RCAStenosisNA</th>\n",
              "      <th>ProcOutcomesRCAStenosis</th>\n",
              "      <th>RamusStenosisNA</th>\n",
              "      <th>ProxLADGraftStenosisNA</th>\n",
              "      <th>MidDistLADGraftStenosisNA</th>\n",
              "      <th>CircGraftStenosisNA</th>\n",
              "      <th>RCAGraftStenosisNA</th>\n",
              "      <th>RamusGraftStenosisNA</th>\n",
              "      <th>Cath_result</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>87.6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>106.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163.1</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>68.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>157.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>160.0</td>\n",
              "      <td>73.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>180.1</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  RiskRecentSmoker  ...  RamusGraftStenosisNA  Cath_result\n",
              "0   64                 1  ...                     1            0\n",
              "1   64                 1  ...                     1            1\n",
              "2   66                 1  ...                     1            1\n",
              "3   63                 0  ...                     1            0\n",
              "4   63                 0  ...                     1            0\n",
              "5   66                 0  ...                     1            1\n",
              "6   81                 0  ...                     1            0\n",
              "7   69                 0  ...                     1            1\n",
              "8   69                 0  ...                     0            1\n",
              "9   61                 1  ...                     0            1\n",
              "\n",
              "[10 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tO_7vLwEx63H",
        "colab_type": "text"
      },
      "source": [
        "**Normalize and Non normalize**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbLhh5uExeGa",
        "colab_type": "code",
        "outputId": "c90f4a71-7c89-45a9-ddb0-f23c7d4f7236",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df['Cath_result'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    480\n",
              "0    301\n",
              "Name: Cath_result, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4iYDophkyBqA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "values = df.values\n",
        "values_no_norm = df_no_norm.values\n",
        "X_norm,y_norm = values[:,:-1],values[:,-1].astype(int) #remove age\n",
        "X_no_norm,y_no_norm = values_no_norm[:,:-1],values_no_norm[:,-1].astype(int) #remove age\n",
        "CHOICE = 300"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_Miw1mkUDot",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_even(X,y):\n",
        "  C0 = np.argwhere(y == 0).flatten()\n",
        "  C1 = np.argwhere(y == 1).flatten()\n",
        "\n",
        "  subC0 = np.random.choice(C0,CHOICE)\n",
        "  subC1 = np.random.choice(C1,CHOICE)\n",
        "\n",
        "  X_final = np.vstack([X[subC0], X[subC1]])\n",
        "  y_final = np.hstack([y[subC0], y[subC1]])\n",
        "  return X_final,y_final"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuj5w9yqXhG_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_no_norm = X_no_norm.astype(float)\n",
        "y_no_norm = y_no_norm.astype(float)\n",
        "\n",
        "X_norm = X_norm.astype(float)\n",
        "y_norm = y_norm.astype(float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WfSBq7XYDQu",
        "colab_type": "code",
        "outputId": "121ef34d-f681-4b67-9be3-d1ff18f1d165",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_norm.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(781, 40)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qu9F-f8QYG5X",
        "colab_type": "code",
        "outputId": "be1ab504-9030-4bdd-e762-f7ec0bec88d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_norm.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(781,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTLn52ipYIHb",
        "colab_type": "code",
        "outputId": "3d91e6c1-52c4-4fc8-babf-f92641856a07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_no_norm.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(781, 40)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUdJUS4uYJB0",
        "colab_type": "code",
        "outputId": "e8a2bf9c-f09b-43c5-b370-02616bf1e6e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_no_norm.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(781,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDbTXXhlYq3h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_norm,y_norm = shuffle(X_norm,y_norm,random_state = 42)\n",
        "X_no_norm,y_no_norm = shuffle(X_no_norm,y_no_norm,random_state = 99)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GSXoOhSj26Qq",
        "colab_type": "text"
      },
      "source": [
        "**BUILD MODELS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsluFbWiHwUQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def graph_nn(loss, accuracy, epochs):\n",
        "    \n",
        "    plt.figure(1)\n",
        "    plt.plot(epochs, loss, label='train')\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.xlabel(\"Epoch\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    plt.figure(2)\n",
        "    plt.plot(epochs, accuracy, label='train')\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.xlabel(\"Epoch\")\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJsuYDOfM2F8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \n",
        "    plt.figure()\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwMLvgNg0jh0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def ANN(input_dim,num_outputs,num_neurons = 500,activation = \"relu\",hidden_layers = 3,loss = \"binary_crossentropy\",optimizer = \"Adam\"):\n",
        "    model = None\n",
        "    model = keras.models.Sequential()\n",
        "    model.add(keras.layers.Dense(num_neurons,input_dim = input_dim,activation = activation))\n",
        "    for i in range(hidden_layers):\n",
        "        model.add(keras.layers.Dense(num_neurons,activation = activation))\n",
        "    \n",
        "    model.add(keras.layers.Dense(num_outputs,activation = \"sigmoid\"))\n",
        "    print(\"Let's now compile the model\")\n",
        "    model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odaSYRKd0AVx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def KF(X,y,num_splits = 10,epochs = 5,batch_size = 20,normalized = False):\n",
        "    kf = KFold(n_splits=num_splits, random_state = None)\n",
        "    foldno = 1\n",
        "    cross_val_scores = []\n",
        "    title = str()\n",
        "    models = []\n",
        "    if normalized:\n",
        "      title = 'Confusion matrix with normalization'\n",
        "    else:\n",
        "      title = 'Confusion matrix with no normalization'\n",
        "\n",
        "    for train_index, test_index in kf.split(X):\n",
        "        print(\"FOLD NUMBER %d\"%foldno)\n",
        "        \n",
        "        model = None\n",
        "        model = ANN(X.shape[1],y.shape[1],num_neurons=300,hidden_layers=3,activation='relu',loss = \"binary_crossentropy\")\n",
        "        \n",
        "        print(\"TRAIN:\", len(train_index), \"TEST:\", len(test_index))\n",
        "        \n",
        "        X_train, X_test = X[train_index], X[test_index]\n",
        "        y_train, y_test = y[train_index], y[test_index]\n",
        "        \n",
        "        print(Counter(y_train.ravel()),\"is the train counts\")\n",
        "        print(Counter(y_test.ravel()),\"is the test counts\")\n",
        "        \n",
        "        history = model.fit(X_train,y_train,batch_size = batch_size,epochs = epochs,verbose = 0)          \n",
        "        pred = model.predict(X_test)\n",
        "        scores = model.evaluate(X_test,y_test)\n",
        "        acc = scores[1] * 100.00\n",
        "        cross_val_scores.append(acc)\n",
        "        \n",
        "        \n",
        "        print(\"Test accuracy : %.2f\"%acc)\n",
        "        \n",
        "        \n",
        "        foldno += 1\n",
        "        y_pred = (pred.ravel() > 0.5).astype(int)\n",
        "        y_test = y_test.ravel().astype(int)\n",
        "        \n",
        "        cnf_matrix = confusion_matrix(y_test, y_pred)\n",
        "        np.set_printoptions(precision=2)\n",
        "        plot_confusion_matrix(cnf_matrix, classes=[\"Neg\",\"Pos\"],\n",
        "                      title= title)\n",
        "        \n",
        "        loss = history.history['loss']\n",
        "        accuracy = history.history['accuracy']\n",
        "        models.append(model)\n",
        "        graph_nn(loss,accuracy,range(1,epochs + 1))\n",
        "        print('\\n')\n",
        "    \n",
        "    max_acc = np.argmax(cross_val_scores)\n",
        "    best_model = models[max_acc]\n",
        "    print(f\"{max_acc + 1}th is the best model\")\n",
        "    # serialize model to JSON\n",
        "    model_json = best_model.to_json()\n",
        "    with open(\"model.json\", \"w\") as json_file:\n",
        "        json_file.write(model_json)\n",
        "    # serialize weights to HDF5\n",
        "    best_model.save_weights(\"model.h5\")\n",
        "    print(\"Saved model to disk\")\n",
        "\n",
        "\n",
        "    return np.mean(cross_val_scores)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_0JFD8308XU",
        "colab_type": "text"
      },
      "source": [
        "Build with all the features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtNgndik06HK",
        "colab_type": "code",
        "outputId": "160f7ec5-eb59-4b17-d34f-423eac82597f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "KF(X_norm,y_norm.reshape((-1,1)),epochs=100,num_splits=10,batch_size=20,normalized=True)  #with all normalization"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FOLD NUMBER 1\n",
            "Let's now compile the model\n",
            "TRAIN: 702 TEST: 79\n",
            "Counter({1.0: 433, 0.0: 269}) is the train counts\n",
            "Counter({1.0: 47, 0.0: 32}) is the test counts\n",
            "79/79 [==============================] - 0s 346us/step\n",
            "Test accuracy : 94.94\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 2\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 431, 0.0: 272}) is the train counts\n",
            "Counter({1.0: 49, 0.0: 29}) is the test counts\n",
            "78/78 [==============================] - 0s 344us/step\n",
            "Test accuracy : 97.44\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 3\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 429, 0.0: 274}) is the train counts\n",
            "Counter({1.0: 51, 0.0: 27}) is the test counts\n",
            "78/78 [==============================] - 0s 349us/step\n",
            "Test accuracy : 89.74\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 4\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 436, 0.0: 267}) is the train counts\n",
            "Counter({1.0: 44, 0.0: 34}) is the test counts\n",
            "78/78 [==============================] - 0s 306us/step\n",
            "Test accuracy : 93.59\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 5\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 430, 0.0: 273}) is the train counts\n",
            "Counter({1.0: 50, 0.0: 28}) is the test counts\n",
            "78/78 [==============================] - 0s 340us/step\n",
            "Test accuracy : 92.31\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ISAZTEASysoyZk4qAeEdxQkVxLgePdvY9fkJEJNMoCJIkmoemlhX0rSsrzOV4dy/tXT3pKktEJFQKgiSzq0uAAVcEiZvKdC+BiGQoBUGSmX1XBCeCoEyPmRCRDKcgSPL22ZX8weW1vP28yr51esyEiGS67HQXcDYpysvmb2+c329d34PnFAQikqF0RXAaiSDQvQQikqkUBKcxIT+b7CxTEIhIxlIQnIaZ6TETIpLRFATDUF6oIBCRzKUgGAY9gVREMpmCYBjKi3I1J4GIZCwFwTDoikBEMpmCYBjKi3Jpbu+iu6c33aWIiKScgmAYyotycYcj7V3pLkVEJOVCDQIzW2pmm81sm5ndM8j2K83sFTPrNrP3hVnLaOjuYhHJZKEFgZnFgPuB64B5wG1mNm/AbruAPwAeDquOVNDzhkQkk4X5rKHFwDZ33w5gZo8ANwEbEzu4+45g21nd+K4rAhHJZGE2DU0BdictNwTrxp2K4ngQvNnckeZKRERSb1x0FpvZMjNbY2Zrmpqaxvz7q4rzqKss4pnNjWP+3SIiYQszCPYAU5OWa4J1I+buD7j7IndfVFVVlZLiRsLMuHb+ZF6sP0jzMY0cEpHMEmYQrAZmm1mdmeUCtwLLQ/y+UF07v5ruXmflpv3pLkVEJKVCCwJ37wbuBJ4AXgcedfcNZnavmd0IYGaXmlkDcAvw72a2Iax6RuvCmlImT8jnF+v3pbsUEZGUCnWGMndfAawYsO7TSa9XE28yOutlZRnXzq/mkdW7OdbZTWGuJncTkcwwLjqLzxbXLpjM8e5entty5h3WTa3HqW9qS2FVcS9tP0hn91k9CldEzlIKghFYXFtOWWHOqJqHPrN8PR9+aHUKq4JdB4/xgQdW8cjqXSn9XBGJBgXBCGTHsrhmbjUrNzUO+tf3zoNH+cX6vTy9uZFV208eYeTurNp+iF2HjnGssztldW3e3wrAS28cStlnikh0qKF7hJYumMwPXm7ghfoDXDVnUt/6xtYObrjv17QdP/EL/h3nVfHt/7u4b7m+6Wjf46y3Nx1lwZSJKakp0dT0ys7DKfk8EYkWXRGM0BWzKikvyuVLv9zc76rgCz/fRGd3L9+946386KOX856LpvBi/UGOJgXDmh0n/mJPZT9BfWP8s/Y2d7DnSHvKPldEokFBMEL5OTE+/57zWb+nha8+uQWAl3ce4kev7OGOt9exZHYlF08r4z0X19DZ08uq7Qf73rt6x2FKC3Mwi18RpMr2A0f7noeUHDYiIsOhIDgD186fzK2XTuXrz9bzQv0BPrN8A5Mn5HPnO2f17XNpXRkFOTGeTRphtHrHId5aV87UssKUXRG4O9sa2/itedUU5sZ4Wc1DIjJCCoIz9De/PY/p5YV8+KHVrN/TwqeufwtFeSe6XPKyY1w+s6IvCPa3dLDr0DEurS1nRlVRyq4IDh3tpLm9i/OqS7hoWilrdoQfBI2tHSz/zZuhf4+IjA0FwRkqysvmn269iO5eZ3FdOTdeeO5J+7xjThU7Dx5jx4GjrA6abC6tLWdmVTHbD7TR2+ujrqM+CJSZk4q5ZFoZm/a19OuwTjV35+4frOPj33+VxlY9jVUkE2jU0CgsnFrK8juvoKa0EDM7afs7zos/IO/ZLU28ceAoBTkx5p07gfVvNtPR1cvelg6mlBaMqoZEE9PMqiIAeh3W7jrCktmVo/rcofxq4/6+G+rqG48yqSQ/lO8RkbGjK4JRmn/uRCYW5gy6bXpFEbUVhTy7pYnVOw5x8fRScmJZzKwqBk6M9hmN+sY28nOyOHdiARdNK8UM1uwMp8O4o6uHex/fyDkT47/8w7hDWkTGnoIgZO84r4oX6g/w+t4WFk0vB+gLgu0p+EVa39RGXWUxWVnGhPwc5lSXhNZh/PVn62k43M6Xb7mQgpxYSkc+iUj6KAhC9o45VXR09dLrsLguHgSVxbmU5Gf3te+PRn3T0b5mIYBLppfx6q4j9KSg/yHZ7kPH+Ldn6vntC87h8lmVzKgq0hWBSIZQEITsshkV5GZnEcsyFk4tBeIT3cysKh71L9KOrh52Hz7Wd4UBsKi2jLbj3Wze1zqqzx7oO6t24g5/ef1cAGakoH4ROTsoCEJWmJvNlbOruGR6Wb/hpakYQrrj4FHc4yOGEhLNT6tTfGPZ2t1HmD9lAucGndszq4rYc6Sdjq6elH6PiIw9BcEYuO+2hTz4B5f2Wzezqph9LR2jGuqZCJLkpqGasgKmlRemdH7lnl5n/Z5mLkh6NtKMqmLc4Y0D6icQGe8UBGOgMDeb4rz+I3UTv7zfGMVVQWLU0YzKE1cEZsbVcyfxfP3BlD3hdHtTG8c6ezi/prRvXaJ+dRiLjH8KgjTpG0I6inb2+qY2ppQWUJAb67f+mrnVdHb38uutB0ZVY8K6hmYALqw5cUVQV1nUV4OIjG8KgjSZVlFIlo1uCGl909F+/QMJl9aWU5KXzcrXU9M89NqeZgpzY8xI6pQuzM1mSmlBSobAikh6KQjSJC87xrTywjMeQuru1De19esfSMjNzuLKOVWs3NSYksdYrGs4woJzJxLL6n/3dHwIqZqGRMY7BUEajWYI6b6WDo519vQbOprsmrmTONB2nHV7mkdTIt09vWx4s4Xza06eRGdmVTHbm9pwT+09CyIythQEaXR+zUQ27289o7b8tbuOADD3nAmDbr/qvElkGTz1+v5R1bi1sY3j3b1cMGgQFHG0s4f9LcdH9R0ikl4KgjRaduUMZlYV86ePrqWpdWS/TH+97QDFedn9OnCTlRXlsmh6OU+Osp9gXUM8cM4fZFrNGSl8VIaIpI+CII0Kc7O5/3cvpqW9i088unZE7fnPbzvAZTPKyY4NfQqvnjuJjXtbeHMU01eua2imJD+b2oqT+yJSMfJJRNJPQZBmcyaX8Jnfmc//bD3Avz+3fVjvaTh8jB0Hj3HFrFM/avrquZMAeGLDvjOu77U9zZw/ZSJZWSc/Zrt6Qh5FuTF1GIuMcwqCs8Bti6dywwXn8OVfbmbDm6fv3H1hW3we5NMFwcyqYi6eVsp9K7eOuOkJ4Hh3D6/vHbyjGOI3r+mZQyLjn4LgLGBm/P3NCygtzOXPH1tHV0/vKff/9bYDVJXkMXuQewgGfu4X33chRzt7+OufvDbi0T1b9rXR1eNcMKV0yH1SOe2miKSHguAsUVqYy+duXsCGN1t44BRNRO7OC/UHuGJmxaCzog00a1Ixn3z3eTyxYf+I5xletyfeUTzYiKGEmVXF7DnSTnunHj4nMl4pCM4iSxdM5oYLzuGfn9zK1v2DP0Z68/5WDrR1nrZZKNkfvn0GF00r5dM/3cDe5uF1HG/Z38o3nttOZXEeNWVDT6eZeLT2yk2jG6YqIumjIDjLfPbG+RTlxbj7sXWD/pWduOdgJEEQyzK+dMuFdHT18LbPP8VV//g0H/veK/xi/d5B93983ZvcfP/ztB3v4WsfvPiUVx5LZlUyvaKQbz2/Y9j1iMjZRUFwlqkszuNzN5/PuoYj3PrAizS2dPTb/kL9QWZUFvXNCzBcM6uK+eEfXc4n330ec8+ZwKu7DvOR777CnzzyKs3tXQBs3tfKJx5dy50Pv8pbJpfw+B8v6ZtVbShZWcbvXTadNTsPs36UdzGLSHpkn34XGWs3XHAOObFLuOuRtdx0//N88/ZFzD93Ip3dvby0/SDvubjmjD53wZSJLAhuDOvu6eX+p+u576mtvPTGIWZNKuZ/th4gPyeLZVfO4O7fmkNu9vD+Trhl0VS+8qstfOuFHXzplgvPqDYRSR8bb8+JWbRoka9ZsybdZYyJ9Xua+cNvr2FfSwdmkDhVX//QxSxdcE5KvuM3u49w9w9+Q3N7F7dfXsvvLp5GWVHuiD/nr3/yGo+uaeDFe95FRXFeSmoTkdQxs5fdfdGg2xQEZ7fGlg4eWb2b7p5esrKM4rxsfv9ttcP+a3243H1Yo5CGsnV/K+/+6nP82bVz+Ng7Zw3rPV09vfzNT9Zz7YLJvHPOpDP+bhE5vVMFgZqGznKTJuTz8atnh/49owkBgNnVJSyZVcl3V+1k2ZUzyDnFoy8SHn5pF4+s3s3j6/by33+8pG+yGxEZW+oslpS5Y0kde5s7+PufvX7afY8c6+SrT27hommlZMeMj33vFTq6dC+CSDooCCRl3vmWSdyxpI5vvbCDh55/45T7/tOTW2lp7+Lz7zmfL99yIRv3tvC5n20co0pFJFmoQWBmS81ss5ltM7N7BtmeZ2b/FWx/ycxqw6xHwveX18/l2vnV3Pv4Rn61cfCbzLY1tvKdVTu5bfE03jJ5AlfPreb/XTmD767axX0rt3Kss3uMqxaJttA6i80sBmwB3g00AKuB29x9Y9I+HwUucPePmNmtwP9x9w+c6nOj1lk8HrV39nDrAy+yeX8rS+dPZuHUUs6vmUhPLxw62slDz7/Bxr0tPHP3VX0jjLp6evno917hVxv3U1aYwx1L6nj3vMlMLMhhYkEO+TlZo+7HEImytIwaMrO3AX/r7tcGy58CcPfPJ+3zRLDPi2aWDewDqvwURSkIxoem1uPc+/hGXtp+kMZBnnz6dzcv4Pcum37S+pd3HuJfn9rG05ub+q03g5xYFnmxLLJjRizLMDMs2GZY8O+Jjm+z+A/Etyd/Vt/rft9hg67vX8iIVp8RBd74MdZn6uNXz+Z3Ljz3jN6brlFDU4DdScsNwFuH2sfdu82sGagA+s3daGbLgGUA06ZNC6teSaGqkjz+5baLcHf2Nnew8c0W8nNilBXlUFWcx6QJ+YO+75Lp5Tz04cVs3tfKlv2ttHR00dzeRXtnD53dvXT29NLd4/S60+vxYa/u4CT+pW+Z4M+J5L8qkv/G6L+eQdfTb5/Bt6T0T6nxNZo70jwNJ2tiQU4onzsuho+6+wPAAxC/IkhzOTICZsa5pQUjfiTGnMklzJlcElJVIpIszM7iPcDUpOWaYN2g+wRNQxOBgyHWJCIiA4QZBKuB2WZWZ2a5wK3A8gH7LAduD16/D3jqVP0DIiKSeqE1DQVt/ncCTwAx4EF332Bm9wJr3H058B/Ad8xsG3CIeFiIiMgYCrWPwN1XACsGrPt00usO4JYwaxARkVPTncUiIhGnIBARiTgFgYhIxCkIREQibtxNTGNmTcDOEbylkgF3KkdEFI87iscM0TzuKB4zjO64p7t71WAbxl0QjJSZrRnq+RqZLIrHHcVjhmgedxSPGcI7bjUNiYhEnIJARCTiohAED6S7gDSJ4nFH8ZghmscdxWOGkI474/sIRETk1KJwRSAiIqegIBARibiMDgIzW2pmm81sm5ndk+56wmBmU83saTPbaGYbzOyuYH25mf3KzLYG/5alu9ZUM7OYmb1qZo8Hy3Vm9lJwvv8rePx5RjGzUjN7zMw2mdnrZva2iJzrPw3+f683s++bWX6mnW8ze9DMGs1sfdK6Qc+txd0XHPs6M7t4NN+dsUFgZjHgfuA6YB5wm5nNS29VoegGPunu84DLgI8Fx3kPsNLdZwMrg+VMcxfwetLyPwBfdfdZwGHgjrRUFa5/Bn7h7m8BLiR+/Bl9rs1sCvBxYJG7LyD+WPtbybzz/S1g6YB1Q53b64DZwc8y4N9G88UZGwTAYmCbu293907gEeCmNNeUcu6+191fCV63Ev/FMIX4sX472O3bwM3pqTAcZlYD3AB8M1g24F3AY8EumXjME4Eric/jgbt3uvsRMvxcB7KBgmAmw0JgLxl2vt39OeLzsiQb6tzeBPynx60CSs3snDP97kwOginA7qTlhmBdxjKzWuAi4CWg2t33Bpv2AdVpKiss/wT8OdAbLFcAR9y9O1jOxPNdBzQBDwVNYt80syIy/Fy7+x7gS8Au4gHQDLxM5p9vGPrcpvT3WyYHQaSYWTHwQ+BP3L0leVsw/WfGjBM2s98GGt395XTXMsaygYuBf3P3i4CjDGgGyrRzDRC0i99EPAjPBYo4uQkl44V5bjM5CPYAU5OWa4J1GcfMcoiHwPfc/UfB6v2JS8Xg38Z01ReCK4AbzWwH8Sa/dxFvOy8Nmg4gM893A9Dg7i8Fy48RD4ZMPtcA1wBvuHuTu3cBPyL+fyDTzzcMfW5T+vstk4NgNTA7GFmQS7xzaf0xpBwAAAKLSURBVHmaa0q5oG38P4DX3f0rSZuWA7cHr28HfjrWtYXF3T/l7jXuXkv8vD7l7h8EngbeF+yWUccM4O77gN1mNidYdTWwkQw+14FdwGVmVhj8f08cd0af78BQ53Y58PvB6KHLgOakJqSRc/eM/QGuB7YA9cBfpbuekI5xCfHLxXXA2uDneuJt5iuBrcCTQHm6aw3p+K8CHg9ezwD+F9gG/ADIS3d9IRzvQmBNcL5/ApRF4VwDnwU2AeuB7wB5mXa+ge8T7wPpIn71d8dQ5xYw4qMi64HXiI+oOuPv1iMmREQiLpObhkREZBgUBCIiEacgEBGJOAWBiEjEKQhERCJOQSAygJn1mNnapJ+UPcTNzGqTny4pcjbIPv0uIpHT7u4L012EyFjRFYHIMJnZDjP7opm9Zmb/a2azgvW1ZvZU8Fz4lWY2LVhfbWY/NrPfBD+XBx8VM7NvBM/X/6WZFaTtoERQEIgMpmBA09AHkrY1u/v5wL8SfwIqwL8A33b3C4DvAfcF6+8DnnX3C4k/E2hDsH42cL+7zweOAO8N+XhETkl3FosMYGZt7l48yPodwLvcfXvwoL997l5hZgeAc9y9K1i/190rzawJqHH340mfUQv8yuMTjWBmfwHkuPvnwj8ykcHpikBkZHyI1yNxPOl1D+qrkzRTEIiMzAeS/n0xeP0C8aegAnwQ+J/g9Urgj6BvfuWJY1WkyEjoLxGRkxWY2dqk5V+4e2IIaZmZrSP+V/1twbo/Jj5r2J8Rn0Hsw8H6u4AHzOwO4n/5/xHxp0uKnFXURyAyTEEfwSJ3P5DuWkRSSU1DIiIRpysCEZGI0xWBiEjEKQhERCJOQSAiEnEKAhGRiFMQiIhE3P8HHR6+lgDNWx0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 6\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 434, 0.0: 269}) is the train counts\n",
            "Counter({1.0: 46, 0.0: 32}) is the test counts\n",
            "78/78 [==============================] - 0s 308us/step\n",
            "Test accuracy : 93.59\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 7\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 436, 0.0: 267}) is the train counts\n",
            "Counter({1.0: 44, 0.0: 34}) is the test counts\n",
            "78/78 [==============================] - 0s 313us/step\n",
            "Test accuracy : 88.46\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 8\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 436, 0.0: 267}) is the train counts\n",
            "Counter({1.0: 44, 0.0: 34}) is the test counts\n",
            "78/78 [==============================] - 0s 336us/step\n",
            "Test accuracy : 93.59\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 9\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 429, 0.0: 274}) is the train counts\n",
            "Counter({1.0: 51, 0.0: 27}) is the test counts\n",
            "78/78 [==============================] - 0s 340us/step\n",
            "Test accuracy : 89.74\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 10\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 426, 0.0: 277}) is the train counts\n",
            "Counter({1.0: 54, 0.0: 24}) is the test counts\n",
            "78/78 [==============================] - 0s 294us/step\n",
            "Test accuracy : 92.31\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "2th is the best model\n",
            "Saved model to disk\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92.57059335708618"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjGogpEoVXQ5",
        "colab_type": "code",
        "outputId": "6ca51350-5441-4d31-e62b-95b7015f49c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "KF(X_no_norm,y_no_norm.reshape((-1,1)),epochs=500,num_splits=10,batch_size=20)  #with no normalization"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FOLD NUMBER 1\n",
            "Let's now compile the model\n",
            "TRAIN: 702 TEST: 79\n",
            "Counter({1.0: 430, 0.0: 272}) is the train counts\n",
            "Counter({1.0: 50, 0.0: 29}) is the test counts\n",
            "79/79 [==============================] - 1s 16ms/step\n",
            "Test accuracy : 63.29\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qbl+DNDNrLyJWSfoCMBHoBfw6IqZ2tBwnyOKre7uKNYz3XRNFxM3AzWtShiKi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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D7hk47Drg/c3dTCcCj1XVI8CPgHckWZRkb3oD1K1dTLNTq7O5StKgzgapG+cDVyfZB3gQ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 2\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 431, 0.0: 272}) is the train counts\n",
            "Counter({1.0: 49, 0.0: 29}) is the test counts\n",
            "78/78 [==============================] - 1s 17ms/step\n",
            "Test accuracy : 92.31\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K8GU+ZmbF5LAR0mPSmFlu1HHIhd9KmiVpesGy5SVNkvR4+r/isK9OkGaWC6Ku\nvflMBMb3WHY8cGNErAfcmObLcoI0s9yo15ALEXEb8EqPxXsDF6THFwD7VCrHbZBmlh/Vt0GOkFQ4\nlMCEKsbGXjEiZqbHLwIrVtqJE6SZ5UYNl/nMWZwhFyIiJFUcb8aH2GaWG22qbuqllyStBJD+rzjM\npBOkmeVHY8d9/QtwSHp8CPDnSk9wgjSzXOjuMLdOl/lcAtwJbCDpOUmHA2cCu0h6HNg5zZflNkgz\ny4c6DsgVEQeUWLVTLeU4QZpZbuTsRhonSDPLkZxlSCdIM8sJ0ZazHnOdIM0sF3LYV4UTpJnlSM4y\npBOkmeWGO8w1MyshZ02QTpBmlh85y49OkGaWE3W8ULxenCDNLBeyDnPzlSGdIM0sN/KVHp0gzSxH\nclaBdII0s/zwZT5mZqXkKz86QZpZfuQsPzpBmlk+SLizCjOzkvKVH50gzSw/6pkfJT0NvAF0AQt7\nMwqiE6SZ5UYDjrB3jIg5vX2yE6SZ5UR1A3I1k0c1NLNcyG41rG4CRkiaUjAdWaTIAG6QNLXE+opc\ngzSzvmhOFW2K20TE85I+AEyS9EhE3FbLTlyDNLPcaJOqmqoREc+n/2cBVwCja46n1ieYmTVElYfX\n1eRHSYMlDel+DOwKTK81JB9im1ku1HnQrhWBK1L3aQOA30XEdbUW4gRpZvlRpwwZEU8BmyxuOU6Q\nZpYbebvMxwnSzHIjZ7diO0GaWX44QZqZleBDbDOzIrrvpMkTRUSrY1gskmYDz7Q6jgYaAfT6Zntr\nqSX5s1sjIkbWs0BJ15G9Z9WYExHj67n/Yvp8glzSSZrSm26arPX82fV9vpPGzKwEJ0gzsxKcIPNv\nQqsDsF7zZ9fHuQ3SzKwE1yDNzEpwgjQzK8EJ0sysBCdIswaR3r0vRJLvWuuDnCD7gO4fmqRlC+ct\nvyQp0hlQSQcAuzlJ9j1OkDnX/UOTNAa4TdJH0ryTZI4VJMf/BL4FzIiIha2NymrlBJlzKRmOB44g\n61zkOkkbOUnmmzLrAQcDewBPS9pL0tckrd/i8KxKvg4y5yStBVwHHBYR/5B0InAYsFdEPFh4KGet\nVeyzkPQDYCzwKLAcWecVL0fE91oQotXIbSL59zIwGXgKICJOlbQ2cI2ksWncXyfJFuvR5vgxYDgw\nDbgUeBy4KSKeTAPYf6R1kVotnCBzpqDNcTmAiHhN0lDgU8Av02aXkP3IrpK0XUS82aJw+73uz6sg\nOX4D+Cwwm+yP2x3AxRHxhqTDgSOBQ1sVr9XGCTJnUnL8BHAs8Kqku4DjgUskrQrMI0uWhwFfAgYD\nTpCtMwDoBJD0QeDjwLYRMdMz28kAAAV0SURBVE/Sp4FtgY1Sv6WbkjWV1Dw+s7WGT9LkQI/r5cYC\n3wEOAu4GjoiIR4DPAM+RJcSDyQ7htgYWNT1gA0DSLsCFko5PJ9JeBjqA7QAi4nJgILB3RDwJfD0i\nHmxZwFYzJ8gWkzQS+KKkjrRoaeD7wFbA3sCuafk7EfHLiDgeGAKcA3wuImY3O2aDlBDPAP5B9kfr\nAGAz4HfAaEmj06ZTgXZJ7RGxoCXBWq/5ELv1tgbGAAMlTQTayRLky8BuETE31VSOknRUWv4MsFNE\nLMlDTeSWpOWBa8hqhldJWg34EbAC2RUHnwXOkjQDGJe262pZwNZrrkG2iKT29PAq4FpgA+CgiLgZ\n+BOwPLCSpM8APwF+GxGzI2JRRDzv5Ng6EfEK8AngTElDI+JZYCGwakQ8Dvwc+DJZstwpIma0Llpb\nHL4OsgUkbQB8EbgBuC0iFkjaDdgNeCgifi3pZGAlYBhZcrzel/PkS/rMfgpcD6wMfD4i3m5tVFZP\nTpAtIGl74Gay6+MuA9YGfgjsQtYG+QIwMZ3RHhQR81sWrJUlaWeyP3QfjIhZ/ryWLE6QLSJpG+Bq\nsvbHT5Odlf4k2ZnqdYGTgd8CRITPVOdYqkn+CNgxIma1Oh6rH5+kaZGIuCP18vJH4GPpQuKryS4A\nPxL4pxNj3xAR10pamuw++S2yRa55LAlcg2wxSbsDPwO2TI3/hXfTuM2xD5HU4bualiyuQbZYRFwj\naRHwiKQNIuLV7qTo5Ni3ODkueVyDzAlJewBvRcQtrY7FzDJOkDnjw2qz/HCCNDMrwXfSmJmV4ARp\nZlaCE6SZWQlOkP2IpC5J0yRNl/SH7mFke1nWDunCdtJgVMeX2XaYpP/oxT5OTj10V7W8xzYTJe1b\nw77WlOSObO09nCD7l3kRMSoiNgbeAY4qXJlG4qv5OxERf4mIM8tsMgyoOUGatZoTZP91O7Buqjk9\nKulCYDqwmqRdJd0p6d5U0+yArJNYSY9Iupds2AfS8kMl/Tw9XlHSFZLuT9PHgDOBdVLt9Ydpu29K\nukfSA5JOKSjru5Iek3QHWRdwZUk6IpVzv6TLe9SKd5Y0JZW3Z9q+XdIPC/b9pcV9I23J5QTZD0ka\nQNa1Wnf3/+sBv4yIjYC3gBOAnSNiM2AKcKykQWS9mH8C2Bz4YInifwrcGhGbkPWwPYNsTJ0nU+31\nm5J2TfscDYwCNpe0naTNyTqbHQXsDmxZxcv5U0Rsmfb3MHB4wbo10z72AH6dXsPhwGsRsWUq/whl\nQ+uavY9vNexflpE0LT2+HTiPrB/DZyLirrR8LPBh4O9pqJylgTuBDck60HgcQNJFZJ1q9DSObMwc\nUi/ar0ka3mObXdN0X5rvIEuYQ4AruvtUlPSXKl7TxpJOJzuM7yDrm7HbZanDj8clPZVew67ARwva\nJ5dL+36sin1ZP+ME2b/Mi4hRhQtSEnyrcBEwKSIO6LHde563mAR8PyJ+02MfX+1FWROBfSLifkmH\nAjsUrOt5F0SkfX85IgoTKZLW7MW+bQnnQ2zr6S5ga0nrAkgaLGl94BFgTUnrpO0OKPH8G4Gj03Pb\nlY3v/QZZ7bDb9cAXCto2V5H0AeA2YB9Jy0gaQnY4X8kQYKakpYADe6zbT1Jbinlt4NG076PT9kha\nX9LgKvZj/ZBrkPYeETE71cQukTQwLT4hIh6TdCTwV0lvkx2iDylSxFeACZIOB7qAoyPiTkl/T5fR\nXJvaIT8E3JlqsG+SDVdwr6RLgfuBWcA9VYT8PWAyMDv9XxjTv8iGzh0KHBUR8yWdS9Y2ea+ync8G\n9qnu3bH+xvdim5mV4ENsM7MSnCDNzEpwgjQzK8EJ0sysBCdIM7MSnCDNzEpwgjQzK+H/AY/R4Ims\ndxSDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hORhe4ra7hJPFWiM4+mWlJnHdGSMiggDAPZeO4+bziklP8XP6qFwABmWnkZuV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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9CAS/vmZGj2YEHYvsNE0f1dRQijkpm2dX76OlPRKzRS9Y+8U7nK1uO9MWjnLF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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 3\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 432, 0.0: 271}) is the train counts\n",
            "Counter({1.0: 48, 0.0: 30}) is the test counts\n",
            "78/78 [==============================] - 1s 17ms/step\n",
            "Test accuracy : 38.46\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4BjggIh4ubMpZcxV7LyT9CNgR+A+wKtnkFXMj4ttNCNF6yH0i+TcXGA88BRAR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\ngjQzK8EJ0sysBCdIM7MSnCDNzEr4P/wUk4Pzeo3IAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sA94PfDQiTqrWycxrM3NJZi4BPgM8mJkvR8Q04PPAecBpwMURcVpTbXU3V0kaqckRxKnA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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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whZJ/tx0oR2cFMDFdHXIcMB1Y1uQ6lWkZMCMtzyCbY6iUfyZdGTIVeCnXje4z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\nsJoo0mPzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 4\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 432, 0.0: 271}) is the train counts\n",
            "Counter({1.0: 48, 0.0: 30}) is the test counts\n",
            "78/78 [==============================] - 1s 18ms/step\n",
            "Test accuracy : 92.31\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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55h9mu62yq9hOPOVn7D18nwZH1hy61WAVEfGapMuA7wALChbtAWxa8Jegn6Q+wI7AZ9Jr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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6bzQnDSuirCSPrXbi87nlOxlVmsdvPn8SU46NHdsQjkRZtm0/f3t3KxsrE+ca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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ttTsTNmc/Y+wAWtojzDx6ELMftpZzcM9MLQz6GVCc+utIt6FJsq5SKU4gSB6C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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 5\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 441, 0.0: 262}) is the train counts\n",
            "Counter({0.0: 39, 1.0: 39}) is the test counts\n",
            "78/78 [==============================] - 1s 18ms/step\n",
            "Test accuracy : 50.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5kE2Ym68M6QRpZrmRr/ToBGlmOZKzCqQTpJnlh2fzMTMrJl/50QnSzPIjZ/nR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\nhyW7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 6\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 430, 0.0: 273}) is the train counts\n",
            "Counter({1.0: 50, 0.0: 28}) is the test counts\n",
            "78/78 [==============================] - 2s 19ms/step\n",
            "Test accuracy : 35.90\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pZtZeRKyU9GVgItAD+E1ETOtsOU6Q+Vf1dhWrG++7BoqIW4FbV6cMRUSVwjEz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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Bd5GFjJmZHSNDBoek/yTpU5K2A/9I9s4qRcS7IuKLQ12X8wgwX9I8SVOA5cDGqu8xP7d7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\nZtaQ/w9V/LurN9yfqAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 7\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 432, 0.0: 271}) is the train counts\n",
            "Counter({1.0: 48, 0.0: 30}) is the test counts\n",
            "78/78 [==============================] - 2s 19ms/step\n",
            "Test accuracy : 61.54\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m8LmIWCxpG2DLiPDYCLMeIm/dbHk7k2YIcJWkJmAhcHSD4zGzeqlz7bASuUqQETEJ2LTR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\nVoITpJlZCf8HmrCY03JrN5gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 8\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 425, 0.0: 278}) is the train counts\n",
            "Counter({1.0: 55, 0.0: 23}) is the test counts\n",
            "78/78 [==============================] - 2s 20ms/step\n",
            "Test accuracy : 70.51\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CzL+at6tY3XjfNVBE3ATctDJlKCJqFI6Z2arFbZBmZmU4QZqZleEEaWZWhhOk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\nmZXhBGlmVoYTpJlZGU6QZmZl/H/5f+iJ52enZwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VfYHtbtvdXV27dstJehwREc12Z6hqZ3qBXtvLis+LqQTJu0g6HlgInGO7em/IRuDIut0m\nF2VbgXGSRjSUlyKT4xERzUoLDtubgQ2SphVFZwKr6veRdBRwN3Cx7WfqNj0KHFNcQXUgcAFwryvX\nx/6Syn0kUFkD/Z6y2lCrZ9nfISJi3zGgp+PuhquARcUv/3XApZKuALA9H/g6lXmLm4oHCW4vhpe2\nS/oSlUe5HwDcavup4pzXAT+W9HfA4xST72WoPasqyRERUVVqcNheAXQ3FM+v2345cPkOjr0PuK9F\n+ToqV12VrvZ03D3x3SIi9g1lznHs8/J03IiIZgmOiIjoSIKjjSwdGxHRLMHRRvUhhxmsioioSXAM\nQHocERE1CY42MjkeEdEswRERER1JcLRRu48jfY6IiKoERxsZqoqIaJbgaCNPx42IaJbgaKN2MW6S\nIyKiKsEREREdSXC0Ub0BMENVERE1CY42spBTRESzBEc7mRyPiGiS4Ggj93FERDRLcEREREcSHG3k\nBsCIiGalBoekcZIWS1ojabWkUxq2HyvpYUlvSLq2rnyapBV1r1ckXVNs+4akjXXbzi6r/lmPIyKi\nWalrjgNzgQdsny/pQOCQhu0vAl8Gzq0vtP00MB1A0gHARmBJ3S432P5uabXur0fla24AjIioKa3H\nIWkscBpwC4DtN21vq9/H9hbbjwJvtTnVmcBztp8vq647kx5HRERNmUNVU4E+4DZJj0taKGn0Lpzn\nAuBHDWVfkvSEpFsljW91kKTZknok9fT19e3Ct61dVRURETVlBscIYAYwz/aJwGvAnE5OUAxvfQq4\ns654HnA0laGsTcD3Wh1re4HtbtvdXV1du1D9TI5HRLRSZnD0Ar22lxWfF1MJkk7MAh6z/UK1wPYL\ntt+2/Q5wMzBzUGrbQn9/I8kREdGvtOCwvRnYIGlaUXQmsKrD01xIwzCVpEl1H88DVu5yJXem+qyq\nJEdERL+yr6q6ClhUDDmtAy6VdAWA7fmS3gv0AIcC7xSX3B5n+5ViPuQTwBcbzvltSdOpdAjWt9ge\nERElKjU4bK8AuhuK59dt3wxM3sGxrwETWpRfPJh1bCf3cURENMud421kcjwiolmCo43aehyJjoiI\nqgRHG7WlYyMioirBERERHUlwtOEs5BQR0STB0UZtqCrJERFRleBow7msKiKiSYJjADJUFRFRk+CI\niIiOJDjayEhVRESzBEcb1fU4cgNgRERNgqON9DgiIpolONrIQw4jIpolOCIioiMJjjZqQ1XpckRE\nVCU42qhNjg9xRSIi9iIJjjbsne8TEbG/KTU4JI2TtFjSGkmrJZ3SsP1YSQ9LekPStQ3b1kt6UtIK\nST115YdJelDS2uLr+DLbUPmeZX+HiIh9R9k9jrnAA7aPBU4AVjdsfxH4MvDdHRx/hu3ptuuXn50D\nPGT7GOCh4nNEROwhpQWHpLHAacAtALbftL2tfh/bW2w/CrzVwanPAW4v3t8OnDsI1W2pfwXATI5H\nRPQrs8cxFegDbpP0uKSFkkZ3cLyBn0laLml2XfnhtjcV7zcDhw9SfZsrkPU4IiKalBkcI4AZwDzb\nJwKv0dmw0qm2ZwCzgCslnda4gytdgpZT2JJmS+qR1NPX19d57cnSsRERrZQZHL1Ar+1lxefFVIJk\nQGxvLL5uAZYAM4tNL0iaBFB83bKD4xfY7rbd3dXVtYtNqMizqiIiakoLDtubgQ2SphVFZwKrBnKs\npNGSxlTfA58EVhab7wUuKd5fAtwzaJVukMtxIyKajSj5/FcBiyQdCKwDLpV0BYDt+ZLeC/QAhwLv\nSLoGOA6YCCwp/tIfAdxh+4HinN8E/knSZcDzwGfKqnz/DYBlfYOIiH1QqcFhewXQ3VA8v277ZmBy\ni0NfoXL5bqtzbqXSeyldJscjIprlzvE2ak/HTXJERFQlOCIioiMJjnYyOx4R0STB0YbJ/EZERKME\nRxt2rqiKiGiU4GjDOBPjERENEhwREdGRBEcbGaqKiGiW4Ggjk+MREc0SHG1UehxJjoiIegmONkzG\nqiIiGiU4IiKiIwmOdtLhiIhokuBoI5PjERHNEhxt2M7keEREg7IXctrnpccRsX9666236O3t5fXX\nXx/qqpRu1KhRTJ48mZEjRw5o/wRHG3k4bsT+q7e3lzFjxjBlypRh/egh22zdupXe3l6mTp06oGMy\nVNWGyeR4xP7q9ddfZ8KECcM6NKCyUN2ECRM66lmVGhySxklaLGmNpNWSTmnYfqykhyW9IenauvIj\nJf1S0ipJT0m6um7bNyRtlLSieJ1dVv3trP4XsT/bX/7/77SdZQ9VzQUesH2+pAOBQxq2vwh8GTi3\noXw78BXbj0kaAyyX9KDtVcX2G2x/t9SaUzwdt+xvEhGxjymtxyFpLHAacAuA7Tdtb6vfx/YW248C\nbzWUb7L9WPH+VWA1cERZdW0ryRERQ2Tbtm3cdNNNHR939tlns23btp3vuIvKHKqaCvQBt0l6XNJC\nSaM7PYmkKcCJwLK64i9JekLSrZLG7+C42ZJ6JPX09fXtQvUzOR4RQ2tHwbF9+/a2x913332MGzeu\nrGqVOlQ1ApgBXGV7maS5wBzgvw70BJLeA9wFXGP7laJ4HnA9lbnr64HvAV9oPNb2AmABQHd39y5H\nQDocEfE3//wUq/71lZ3v2IHj3ncof/0f/qTtPnPmzOG5555j+vTpjBw5klGjRjF+/HjWrFnDM888\nw7nnnsuGDRt4/fXXufrqq5k9ezYAU6ZMoaenh9///vfMmjWLU089laVLl3LEEUdwzz33cPDBB+9W\n3cvscfQCvbarPYXFVIJkQCSNpBIai2zfXS23/YLtt22/A9wMzBzEOr+LnRUAI2LofPOb3+Too49m\nxYoVfOc73+Gxxx5j7ty5PPPMMwDceuutLF++nJ6eHm688Ua2bt3adI61a9dy5ZVX8tRTTzFu3Dju\nuuuu3a5XaT0O25slbZA0zfbTwJnAqp0dB6DKb+tbgNW2v9+wbZLtTcXH84CVg1nvennkSEQAO+0Z\n7CkzZ858170WN954I0uWLAFgw4YNrF27lgkTJrzrmKlTpzJ9+nQAPvKRj7B+/frdrkfZV1VdBSwq\nrqhaB1wq6QoA2/MlvRfoAQ4F3pF0DXAccDxwMfCkpBXFuf7K9n3AtyVNp/J7fT3wxTIbkNyIiL3F\n6NG1aeJf/epX/PznP+fhhx/mkEMO4fTTT295L8ZBBx3U//6AAw7gD3/4w27Xo9TgsL0C6G4onl+3\nfTMwucWh/8IOfmfbvnjQKrgTmRyPiKE0ZswYXn311ZbbXn75ZcaPH88hhxzCmjVreOSRR/ZYvfLI\nkTZM5jgiYuhMmDCBj370o3zoQx/i4IMP5vDDD+/fdtZZZzF//nw++MEPMm3aNE4++eQ9Vq8ERxsf\net9Y3tqebkdEDJ077rijZflBBx3E/fff33JbdR5j4sSJrFxZmwa+9tprW+7fqQRHGxfMPIoLZh41\n1NWIiNir5CGHERHRkQRHRMQOeD+5QqbTdiY4IiJaGDVqFFu3bh324VFdj2PUqFEDPiZzHBERLUye\nPJne3l529Vl3+5LqCoADleCIiGhh5MiRA14Rb3+ToaqIiOhIgiMiIjqS4IiIiI5ouF8xACCpD3h+\nFw+fCPxuEKuzL0ib9w9p8/5hd9r8x7a7Ggv3i+DYHZJ6bDc+qHFYS5v3D2nz/qGMNmeoKiIiOpLg\niIiIjiQ4dm7BUFdgCKTN+4e0ef8w6G3OHEdERHQkPY6IiOhIgiMiIjqS4NgBSWdJelrSs5LmDHV9\nBoukWyVtkbSyruwwSQ9KWlt8HV+US9KNxb/BE5JmDF3Nd52kIyX9UtIqSU9JurooH7btljRK0q8l\n/aZo898U5VMlLSva9o+SDizKDyo+P1tsnzKU9d8dkg6Q9LiknxSfh3WbJa2X9KSkFZJ6irJSf7YT\nHC1IOgD4ATALOA64UNJxQ1urQfND4KyGsjnAQ7aPAR4qPkOl/ccUr9nAvD1Ux8G2HfiK7eOAk4Er\ni/+ew7ndbwAft30CMB04S9LJwLeAG2x/AHgJuKzY/zLgpaL8hmK/fdXVwOq6z/tDm8+wPb3ufo1y\nf7Zt59XwAk4Bflr3+WvA14a6XoPYvinAyrrPTwOTiveTgKeL9/8TuLDVfvvyC7gH+MT+0m7gEOAx\n4CQqdxCPKMr7f86BnwKnFO9HFPtpqOu+C22dXPyi/DjwE0D7QZvXAxMbykr92U6Po7UjgA11n3uL\nsuHqcNubivebgcOL98Pu36EYjjgRWMYwb3cxZLMC2AI8CDwHbLO9vdilvl39bS62vwxM2LM1HhT/\nHfhL4J3i8wSGf5sN/EzSckmzi7JSf7azHke8i21LGpbXaEt6D3AXcI3tVyT1bxuO7bb9NjBd0jhg\nCXDsEFepVJL+PbDF9nJJpw91ffagU21vlPRHwIOS1tRvLONnOz2O1jYCR9Z9nlyUDVcvSJoEUHzd\nUpQPm38HSSOphMYi23cXxcO+3QC2twG/pDJMM05S9Q/G+nb1t7nYPhbYuoerurs+CnxK0nrgx1SG\nq+YyvNuM7Y3F1y1U/kCYSck/2wmO1h4FjimuxjgQuAC4d4jrVKZ7gUuK95dQmQOoln+uuBLjZODl\nuu7vPkOVrsUtwGrb36/bNGzbLamr6Gkg6WAqczqrqQTI+cVujW2u/lucD/zCxSD4vsL212xPtj2F\nyv+zv7B9EcO4zZJGSxpTfQ98ElhJ2T/bQz2xs7e+gLOBZ6iMC/+Xoa7PILbrR8Am4C0q45uXURnX\nfQhYC/wcOKzYV1SuLnsOeBLoHur672KbT6UyDvwEsKJ4nT2c2w0cDzxetHkl8PWi/P3Ar4FngTuB\ng4ryUcXnZ4vt7x/qNuxm+08HfjLc21y07TfF66nq76qyf7bzyJGIiOhIhqoiIqIjCY6IiOhIgiMi\nIjqS4IiIiI4kOCIioiMJjohBIOnt4umk1degPVFZ0hTVPc04YqjlkSMRg+MPtqcPdSUi9oT0OCJK\nVKyV8O1ivYRfS/pAUT5F0i+KNREeknRUUX64pCXFOhq/kfRvi1MdIOnmYm2NnxV3g0cMiQRHxOA4\nuGGo6rN12162/WHgf1B5eivA3wO32z4eWATcWJTfCPwfV9bRmEHlbmCorJ/wA9t/AmwDPl1yeyJ2\nKHeORwwCSb+3/Z4W5eupLKi0rnjQ4mbbEyT9jso6CG8V5ZtsT5TUB0y2/UbdOaYAD7qyKA+SrgNG\n2v678lsW0Sw9jojyeQfvO/FG3fu3yfxkDKEER0T5Plv39eHi/VIqT3AFuAj4f8X7h4A/h/6FmMbu\nqUpGDFT+aokYHAcXq+1VPWC7eknueElPUOk1XFiUXQXcJumrQB9waVF+NbBA0mVUehZ/TuVpxhF7\njcxxRJSomOPotv27oa5LxGDJUFVERHQkPY6IiOhIehwREdGRBEdERHQkwRERER1JcEREREcSHBER\n0ZH/D4OdVyT3xJDQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WHhtNjoyIJ9Pr3wJHptej7r9DusTx74FfMsrbnS4D3Q/sIFtV9RHg6YjYkw7J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\nKmZm1jAOFTMza5j/D3RlfQU2YUu4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 9\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 440, 0.0: 263}) is the train counts\n",
            "Counter({1.0: 40, 0.0: 38}) is the test counts\n",
            "78/78 [==============================] - 2s 21ms/step\n",
            "Test accuracy : 91.03\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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agHcBR6TXTpL0rzLljierndKywVbRS+HX1Mx/LuSd/1W5prHJ4f9Xo2isGvbeZwx77zOm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\nSDOzMv4fzWApwzYFRQQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AJBEZLyJpwJXAIuegMabKGDPUGFNkjCkClgMXG2NWJrFNAGSkxLuG1CJQFMW/JE0IjDFh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\njgqBoiiKz/n/ZNDhofBTihgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KiK52tCNmBfzg23t5ck057+yt5tN/XcWWw/V8+Hdv288LhTVPrT3Ie+V1vLmzkqD5Bwew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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "FOLD NUMBER 10\n",
            "Let's now compile the model\n",
            "TRAIN: 703 TEST: 78\n",
            "Counter({1.0: 427, 0.0: 276}) is the train counts\n",
            "Counter({1.0: 53, 0.0: 25}) is the test counts\n",
            "78/78 [==============================] - 2s 20ms/step\n",
            "Test accuracy : 67.95\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F/hVZTXClDtvdJiKGp0f/iJjfgFjMLKcESJU9yomIb0XEgIgYBBxMdlR6KHAb8Om02uFA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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lU/dxTgAiYimwFKC1tdVPSDczGyT19Di+klveBTweEd172zlnFTBD0nSyX+7nA+/us89N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\ni4PDzMz6xcFhZmb94uAwM7N++f+msDG5DaOODgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "66.32911398460323"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    }
  ]
}