Leak_detection_using_DNN.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Leak detection using DNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpHk6r0WgQn8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "6cbe5c42-0267-4ce8-b640-f340f9b93b41"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QZ6qmKl-IZy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0366f17a-4acc-4235-f6a8-1115ae24296f"
      },
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yih9yRUe-RD4",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "0bced7e4-f30a-4a61-8fa7-b8c7fb95e9a7"
      },
      "source": [
        "from google.colab import files\n",
        "file = files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-913cbf12-5d84-4f2d-ace3-c6bba1451459\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-913cbf12-5d84-4f2d-ace3-c6bba1451459\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving x_test.xlsx to x_test.xlsx\n",
            "Saving x_train.xlsx to x_train.xlsx\n",
            "Saving y_test.xlsx to y_test.xlsx\n",
            "Saving y_train.xlsx to y_train.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrG_Bp88-S8F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = pd.read_excel('x_train.xlsx')\n",
        "y_train = pd.read_excel('y_train.xlsx')\n",
        "x_test = pd.read_excel('x_test.xlsx')\n",
        "y_test = pd.read_excel('y_test.xlsx')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nF2VPkhn-Y0C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "957fa52f-2a3a-456a-daf2-b5de07224d18"
      },
      "source": [
        "x_train"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>Junc 10</th>\n",
              "      <th>Junc 11</th>\n",
              "      <th>Junc 12</th>\n",
              "      <th>Junc 13</th>\n",
              "      <th>Junc 21</th>\n",
              "      <th>Junc 22</th>\n",
              "      <th>Junc 23</th>\n",
              "      <th>Junc 31</th>\n",
              "      <th>Junc 32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111.07</td>\n",
              "      <td>110.90</td>\n",
              "      <td>115.38</td>\n",
              "      <td>116.56</td>\n",
              "      <td>113.70</td>\n",
              "      <td>115.94</td>\n",
              "      <td>118.04</td>\n",
              "      <td>112.15</td>\n",
              "      <td>107.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>130.28</td>\n",
              "      <td>122.37</td>\n",
              "      <td>121.40</td>\n",
              "      <td>122.23</td>\n",
              "      <td>120.13</td>\n",
              "      <td>121.88</td>\n",
              "      <td>123.82</td>\n",
              "      <td>117.13</td>\n",
              "      <td>111.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>119.99</td>\n",
              "      <td>110.67</td>\n",
              "      <td>108.43</td>\n",
              "      <td>109.34</td>\n",
              "      <td>107.60</td>\n",
              "      <td>109.12</td>\n",
              "      <td>111.03</td>\n",
              "      <td>104.55</td>\n",
              "      <td>99.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130.28</td>\n",
              "      <td>122.37</td>\n",
              "      <td>121.40</td>\n",
              "      <td>122.23</td>\n",
              "      <td>120.13</td>\n",
              "      <td>121.88</td>\n",
              "      <td>123.82</td>\n",
              "      <td>117.13</td>\n",
              "      <td>111.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>128.59</td>\n",
              "      <td>120.45</td>\n",
              "      <td>118.35</td>\n",
              "      <td>119.99</td>\n",
              "      <td>118.94</td>\n",
              "      <td>120.07</td>\n",
              "      <td>122.05</td>\n",
              "      <td>117.15</td>\n",
              "      <td>112.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>127.54</td>\n",
              "      <td>119.26</td>\n",
              "      <td>117.02</td>\n",
              "      <td>118.67</td>\n",
              "      <td>117.66</td>\n",
              "      <td>118.76</td>\n",
              "      <td>120.74</td>\n",
              "      <td>115.86</td>\n",
              "      <td>110.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>513</th>\n",
              "      <td>123.80</td>\n",
              "      <td>115.01</td>\n",
              "      <td>114.84</td>\n",
              "      <td>113.58</td>\n",
              "      <td>107.14</td>\n",
              "      <td>109.52</td>\n",
              "      <td>111.69</td>\n",
              "      <td>82.28</td>\n",
              "      <td>17.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>514</th>\n",
              "      <td>129.25</td>\n",
              "      <td>121.20</td>\n",
              "      <td>119.63</td>\n",
              "      <td>120.91</td>\n",
              "      <td>119.35</td>\n",
              "      <td>120.82</td>\n",
              "      <td>122.78</td>\n",
              "      <td>117.01</td>\n",
              "      <td>111.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>515</th>\n",
              "      <td>124.13</td>\n",
              "      <td>115.38</td>\n",
              "      <td>115.77</td>\n",
              "      <td>114.57</td>\n",
              "      <td>104.34</td>\n",
              "      <td>109.86</td>\n",
              "      <td>112.08</td>\n",
              "      <td>38.80</td>\n",
              "      <td>57.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>516</th>\n",
              "      <td>129.96</td>\n",
              "      <td>122.01</td>\n",
              "      <td>120.53</td>\n",
              "      <td>121.80</td>\n",
              "      <td>120.21</td>\n",
              "      <td>121.71</td>\n",
              "      <td>123.67</td>\n",
              "      <td>117.88</td>\n",
              "      <td>112.76</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>517 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Junc 10  Junc 11  Junc 12  Junc 13  ...  Junc 22  Junc 23  Junc 31  Junc 32\n",
              "0     111.07   110.90   115.38   116.56  ...   115.94   118.04   112.15   107.41\n",
              "1     130.28   122.37   121.40   122.23  ...   121.88   123.82   117.13   111.89\n",
              "2     119.99   110.67   108.43   109.34  ...   109.12   111.03   104.55    99.24\n",
              "3     130.28   122.37   121.40   122.23  ...   121.88   123.82   117.13   111.89\n",
              "4     128.59   120.45   118.35   119.99  ...   120.07   122.05   117.15   112.09\n",
              "..       ...      ...      ...      ...  ...      ...      ...      ...      ...\n",
              "512   127.54   119.26   117.02   118.67  ...   118.76   120.74   115.86   110.79\n",
              "513   123.80   115.01   114.84   113.58  ...   109.52   111.69    82.28    17.35\n",
              "514   129.25   121.20   119.63   120.91  ...   120.82   122.78   117.01   111.89\n",
              "515   124.13   115.38   115.77   114.57  ...   109.86   112.08    38.80    57.30\n",
              "516   129.96   122.01   120.53   121.80  ...   121.71   123.67   117.88   112.76\n",
              "\n",
              "[517 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrxlG-gk-ay7",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "y_train"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>516</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>517 rows × 1 columns</p>\n",
              "</div>"
            ],
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              "     condition\n",
              "0            1\n",
              "1            0\n",
              "2            1\n",
              "3            0\n",
              "4            0\n",
              "..         ...\n",
              "512          0\n",
              "513          0\n",
              "514          0\n",
              "515          1\n",
              "516          0\n",
              "\n",
              "[517 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tC_Z9NHY-b_r",
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          "base_uri": "https://localhost:8080/",
          "height": 419
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        "outputId": "cce6c4cc-c332-4fad-dbc7-c9f2ad20c8d0"
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      "source": [
        "x_test"
      ],
      "execution_count": 7,
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Junc 10</th>\n",
              "      <th>Junc 11</th>\n",
              "      <th>Junc 12</th>\n",
              "      <th>Junc 13</th>\n",
              "      <th>Junc 21</th>\n",
              "      <th>Junc 22</th>\n",
              "      <th>Junc 23</th>\n",
              "      <th>Junc 31</th>\n",
              "      <th>Junc 32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>121.26</td>\n",
              "      <td>112.11</td>\n",
              "      <td>116.94</td>\n",
              "      <td>118.08</td>\n",
              "      <td>115.10</td>\n",
              "      <td>117.40</td>\n",
              "      <td>119.51</td>\n",
              "      <td>113.57</td>\n",
              "      <td>108.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>121.17</td>\n",
              "      <td>112.02</td>\n",
              "      <td>115.50</td>\n",
              "      <td>116.39</td>\n",
              "      <td>113.54</td>\n",
              "      <td>115.71</td>\n",
              "      <td>117.77</td>\n",
              "      <td>111.35</td>\n",
              "      <td>106.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>123.80</td>\n",
              "      <td>115.01</td>\n",
              "      <td>114.84</td>\n",
              "      <td>113.58</td>\n",
              "      <td>107.14</td>\n",
              "      <td>109.52</td>\n",
              "      <td>111.69</td>\n",
              "      <td>82.28</td>\n",
              "      <td>17.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>123.80</td>\n",
              "      <td>115.01</td>\n",
              "      <td>114.84</td>\n",
              "      <td>113.58</td>\n",
              "      <td>107.14</td>\n",
              "      <td>109.52</td>\n",
              "      <td>111.69</td>\n",
              "      <td>82.28</td>\n",
              "      <td>17.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>128.59</td>\n",
              "      <td>120.45</td>\n",
              "      <td>118.35</td>\n",
              "      <td>119.99</td>\n",
              "      <td>118.94</td>\n",
              "      <td>120.07</td>\n",
              "      <td>122.05</td>\n",
              "      <td>117.15</td>\n",
              "      <td>112.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>130.28</td>\n",
              "      <td>122.37</td>\n",
              "      <td>121.40</td>\n",
              "      <td>122.23</td>\n",
              "      <td>120.13</td>\n",
              "      <td>121.88</td>\n",
              "      <td>123.82</td>\n",
              "      <td>117.13</td>\n",
              "      <td>111.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>129.96</td>\n",
              "      <td>122.01</td>\n",
              "      <td>120.53</td>\n",
              "      <td>121.80</td>\n",
              "      <td>120.21</td>\n",
              "      <td>121.71</td>\n",
              "      <td>123.67</td>\n",
              "      <td>117.88</td>\n",
              "      <td>112.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>125.52</td>\n",
              "      <td>116.96</td>\n",
              "      <td>116.93</td>\n",
              "      <td>108.58</td>\n",
              "      <td>107.46</td>\n",
              "      <td>104.74</td>\n",
              "      <td>87.75</td>\n",
              "      <td>104.71</td>\n",
              "      <td>97.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>130.28</td>\n",
              "      <td>122.37</td>\n",
              "      <td>121.40</td>\n",
              "      <td>122.23</td>\n",
              "      <td>120.13</td>\n",
              "      <td>121.88</td>\n",
              "      <td>123.82</td>\n",
              "      <td>117.13</td>\n",
              "      <td>111.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>106.26</td>\n",
              "      <td>105.42</td>\n",
              "      <td>109.75</td>\n",
              "      <td>110.10</td>\n",
              "      <td>106.72</td>\n",
              "      <td>109.00</td>\n",
              "      <td>111.05</td>\n",
              "      <td>103.84</td>\n",
              "      <td>98.74</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Junc 10  Junc 11  Junc 12  Junc 13  ...  Junc 22  Junc 23  Junc 31  Junc 32\n",
              "0     121.26   112.11   116.94   118.08  ...   117.40   119.51   113.57   108.85\n",
              "1     121.17   112.02   115.50   116.39  ...   115.71   117.77   111.35   106.43\n",
              "2     123.80   115.01   114.84   113.58  ...   109.52   111.69    82.28    17.35\n",
              "3     123.80   115.01   114.84   113.58  ...   109.52   111.69    82.28    17.35\n",
              "4     128.59   120.45   118.35   119.99  ...   120.07   122.05   117.15   112.09\n",
              "..       ...      ...      ...      ...  ...      ...      ...      ...      ...\n",
              "126   130.28   122.37   121.40   122.23  ...   121.88   123.82   117.13   111.89\n",
              "127   129.96   122.01   120.53   121.80  ...   121.71   123.67   117.88   112.76\n",
              "128   125.52   116.96   116.93   108.58  ...   104.74    87.75   104.71    97.99\n",
              "129   130.28   122.37   121.40   122.23  ...   121.88   123.82   117.13   111.89\n",
              "130   106.26   105.42   109.75   110.10  ...   109.00   111.05   103.84    98.74\n",
              "\n",
              "[131 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vr37h7kh-drd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "c492072c-aa62-4d07-c0ef-697d8fd5532c"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
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              "      <th>129</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>131 rows × 1 columns</p>\n",
              "</div>"
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            "text/plain": [
              "     condition\n",
              "0            1\n",
              "1            1\n",
              "2            0\n",
              "3            0\n",
              "4            0\n",
              "..         ...\n",
              "126          0\n",
              "127          0\n",
              "128          1\n",
              "129          0\n",
              "130          1\n",
              "\n",
              "[131 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KzZTxFD3-fQA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(64, activation='relu', input_shape=(9,)))\n",
        "\n",
        "model.add(keras.layers.Dense(128, activation='relu'))\n",
        "\n",
        "model.add(keras.layers.Dense(256, activation='relu'))\n",
        "\n",
        "model.add(keras.layers.Dense(128, activation='relu'))\n",
        "\n",
        "model.add(keras.layers.Dense(1, activation = 'sigmoid'))\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfCgKsz1-jRe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c7ee21e1-5f59-47b7-b679-46514121127f"
      },
      "source": [
        "model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=40)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 517 samples, validate on 131 samples\n",
            "Epoch 1/40\n",
            "517/517 [==============================] - 0s 98us/step - loss: 0.3022 - accuracy: 0.9110 - val_loss: 0.6598 - val_accuracy: 0.5420\n",
            "Epoch 2/40\n",
            "517/517 [==============================] - 0s 78us/step - loss: 0.3074 - accuracy: 0.8762 - val_loss: 0.2790 - val_accuracy: 0.9160\n",
            "Epoch 3/40\n",
            "517/517 [==============================] - 0s 80us/step - loss: 0.2281 - accuracy: 0.9091 - val_loss: 0.3236 - val_accuracy: 0.8550\n",
            "Epoch 4/40\n",
            "517/517 [==============================] - 0s 82us/step - loss: 0.2376 - accuracy: 0.9207 - val_loss: 0.2318 - val_accuracy: 0.9237\n",
            "Epoch 5/40\n",
            "517/517 [==============================] - 0s 82us/step - loss: 0.2147 - accuracy: 0.9381 - val_loss: 0.1916 - val_accuracy: 0.9618\n",
            "Epoch 6/40\n",
            "517/517 [==============================] - 0s 80us/step - loss: 0.2619 - accuracy: 0.9072 - val_loss: 0.2374 - val_accuracy: 0.8855\n",
            "Epoch 7/40\n",
            "517/517 [==============================] - 0s 80us/step - loss: 0.2408 - accuracy: 0.9149 - val_loss: 0.1756 - val_accuracy: 0.9466\n",
            "Epoch 8/40\n",
            "517/517 [==============================] - 0s 81us/step - loss: 0.2317 - accuracy: 0.9265 - val_loss: 0.2446 - val_accuracy: 0.8626\n",
            "Epoch 9/40\n",
            "517/517 [==============================] - 0s 82us/step - loss: 0.2038 - accuracy: 0.9420 - val_loss: 0.1588 - val_accuracy: 0.9542\n",
            "Epoch 10/40\n",
            "517/517 [==============================] - 0s 81us/step - loss: 0.2036 - accuracy: 0.9323 - val_loss: 0.2095 - val_accuracy: 0.9313\n",
            "Epoch 11/40\n",
            "517/517 [==============================] - 0s 81us/step - loss: 0.2642 - accuracy: 0.9033 - val_loss: 0.2196 - val_accuracy: 0.9160\n",
            "Epoch 12/40\n",
            "517/517 [==============================] - 0s 81us/step - loss: 0.2473 - accuracy: 0.9130 - val_loss: 0.2606 - val_accuracy: 0.8779\n",
            "Epoch 13/40\n",
            "517/517 [==============================] - 0s 84us/step - loss: 0.3941 - accuracy: 0.8917 - val_loss: 0.3052 - val_accuracy: 0.8321\n",
            "Epoch 14/40\n",
            "517/517 [==============================] - 0s 83us/step - loss: 0.3632 - accuracy: 0.8743 - val_loss: 0.5668 - val_accuracy: 0.6870\n",
            "Epoch 15/40\n",
            "517/517 [==============================] - 0s 84us/step - loss: 0.3369 - accuracy: 0.8433 - val_loss: 0.1984 - val_accuracy: 0.9389\n",
            "Epoch 16/40\n",
            "517/517 [==============================] - 0s 82us/step - loss: 0.2004 - accuracy: 0.9381 - val_loss: 0.6792 - val_accuracy: 0.5420\n",
            "Epoch 17/40\n",
            "517/517 [==============================] - 0s 91us/step - loss: 0.3542 - accuracy: 0.8665 - val_loss: 0.2686 - val_accuracy: 0.8931\n",
            "Epoch 18/40\n",
            "517/517 [==============================] - 0s 78us/step - loss: 0.2262 - accuracy: 0.9284 - val_loss: 0.1829 - val_accuracy: 0.9542\n",
            "Epoch 19/40\n",
            "517/517 [==============================] - 0s 77us/step - loss: 0.2567 - accuracy: 0.9130 - val_loss: 0.2185 - val_accuracy: 0.9389\n",
            "Epoch 20/40\n",
            "517/517 [==============================] - 0s 81us/step - loss: 0.2603 - accuracy: 0.9130 - val_loss: 0.3255 - val_accuracy: 0.9466\n",
            "Epoch 21/40\n",
            "517/517 [==============================] - 0s 79us/step - loss: 0.2316 - accuracy: 0.9284 - val_loss: 0.2837 - val_accuracy: 0.8702\n",
            "Epoch 22/40\n",
            "517/517 [==============================] - 0s 85us/step - loss: 0.2191 - accuracy: 0.9304 - val_loss: 0.2648 - val_accuracy: 0.9695\n",
            "Epoch 23/40\n",
            "517/517 [==============================] - 0s 81us/step - loss: 0.1811 - accuracy: 0.9458 - val_loss: 0.1496 - val_accuracy: 0.9695\n",
            "Epoch 24/40\n",
            "517/517 [==============================] - 0s 79us/step - loss: 0.2047 - accuracy: 0.9439 - val_loss: 0.1974 - val_accuracy: 0.9160\n",
            "Epoch 25/40\n",
            "517/517 [==============================] - 0s 99us/step - loss: 0.1927 - accuracy: 0.9342 - val_loss: 0.2561 - val_accuracy: 0.9313\n",
            "Epoch 26/40\n",
            "517/517 [==============================] - 0s 84us/step - loss: 0.2691 - accuracy: 0.9033 - val_loss: 0.3162 - val_accuracy: 0.9237\n",
            "Epoch 27/40\n",
            "517/517 [==============================] - 0s 80us/step - loss: 0.2517 - accuracy: 0.9265 - val_loss: 0.2547 - val_accuracy: 0.9237\n",
            "Epoch 28/40\n",
            "517/517 [==============================] - 0s 80us/step - loss: 0.2028 - accuracy: 0.9381 - val_loss: 0.3772 - val_accuracy: 0.8321\n",
            "Epoch 29/40\n",
            "517/517 [==============================] - 0s 79us/step - loss: 0.2857 - accuracy: 0.9091 - val_loss: 0.2073 - val_accuracy: 0.9237\n",
            "Epoch 30/40\n",
            "517/517 [==============================] - 0s 86us/step - loss: 0.2307 - accuracy: 0.9265 - val_loss: 0.1623 - val_accuracy: 0.9542\n",
            "Epoch 31/40\n",
            "517/517 [==============================] - 0s 77us/step - loss: 0.2055 - accuracy: 0.9342 - val_loss: 0.2216 - val_accuracy: 0.9084\n",
            "Epoch 32/40\n",
            "517/517 [==============================] - 0s 80us/step - loss: 0.2106 - accuracy: 0.9342 - val_loss: 0.2869 - val_accuracy: 0.8855\n",
            "Epoch 33/40\n",
            "517/517 [==============================] - 0s 82us/step - loss: 0.2485 - accuracy: 0.9072 - val_loss: 0.2320 - val_accuracy: 0.9237\n",
            "Epoch 34/40\n",
            "517/517 [==============================] - 0s 91us/step - loss: 0.3195 - accuracy: 0.8801 - val_loss: 0.3993 - val_accuracy: 0.8702\n",
            "Epoch 35/40\n",
            "517/517 [==============================] - 0s 85us/step - loss: 0.3147 - accuracy: 0.8743 - val_loss: 0.3431 - val_accuracy: 0.8702\n",
            "Epoch 36/40\n",
            "517/517 [==============================] - 0s 81us/step - loss: 0.3057 - accuracy: 0.9072 - val_loss: 0.2178 - val_accuracy: 0.9466\n",
            "Epoch 37/40\n",
            "517/517 [==============================] - 0s 80us/step - loss: 0.2260 - accuracy: 0.9362 - val_loss: 0.1874 - val_accuracy: 0.9466\n",
            "Epoch 38/40\n",
            "517/517 [==============================] - 0s 79us/step - loss: 0.1954 - accuracy: 0.9400 - val_loss: 0.1755 - val_accuracy: 0.9466\n",
            "Epoch 39/40\n",
            "517/517 [==============================] - 0s 80us/step - loss: 0.1750 - accuracy: 0.9478 - val_loss: 0.2109 - val_accuracy: 0.9313\n",
            "Epoch 40/40\n",
            "517/517 [==============================] - 0s 81us/step - loss: 0.1983 - accuracy: 0.9362 - val_loss: 0.3350 - val_accuracy: 0.9389\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f455871de80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efCZGgqJIj0x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "e8dd4db3-dcc5-488f-ad1d-fa16e22ebfae"
      },
      "source": [
        "print(model.evaluate(x=x_train, y=y_train))\n",
        "model.metrics_names"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "517/517 [==============================] - 0s 33us/step\n",
            "[0.2567514478295638, 0.9535783529281616]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['loss', 'accuracy']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sKorNSetItwk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "71c19f47-c6cd-48f8-9b86-baa5de5d2486"
      },
      "source": [
        "print(model.evaluate(x=x_test, y=y_test))\n",
        "model.metrics_names"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "131/131 [==============================] - 0s 56us/step\n",
            "[0.33497709320246716, 0.9389312863349915]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['loss', 'accuracy']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1fOcD_nIwg-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "2472496a-95f3-47c2-ad72-86955622121e"
      },
      "source": [
        "Y_pred = model.predict(x_test)\n",
        "Y_pred = [ 1 if y>=0.5 else 0 for y in Y_pred ]\n",
        "print(Y_pred)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-4qEKKqJGla",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "1d241538-6083-4d32-a876-050eaa9409d0"
      },
      "source": [
        "combinedDF = pd.concat([x_train, y_train], axis=1)\n",
        "combinedDF"
      ],
      "execution_count": 16,
      "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>Junc 10</th>\n",
              "      <th>Junc 11</th>\n",
              "      <th>Junc 12</th>\n",
              "      <th>Junc 13</th>\n",
              "      <th>Junc 21</th>\n",
              "      <th>Junc 22</th>\n",
              "      <th>Junc 23</th>\n",
              "      <th>Junc 31</th>\n",
              "      <th>Junc 32</th>\n",
              "      <th>condition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111.07</td>\n",
              "      <td>110.90</td>\n",
              "      <td>115.38</td>\n",
              "      <td>116.56</td>\n",
              "      <td>113.70</td>\n",
              "      <td>115.94</td>\n",
              "      <td>118.04</td>\n",
              "      <td>112.15</td>\n",
              "      <td>107.41</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>130.28</td>\n",
              "      <td>122.37</td>\n",
              "      <td>121.40</td>\n",
              "      <td>122.23</td>\n",
              "      <td>120.13</td>\n",
              "      <td>121.88</td>\n",
              "      <td>123.82</td>\n",
              "      <td>117.13</td>\n",
              "      <td>111.89</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>119.99</td>\n",
              "      <td>110.67</td>\n",
              "      <td>108.43</td>\n",
              "      <td>109.34</td>\n",
              "      <td>107.60</td>\n",
              "      <td>109.12</td>\n",
              "      <td>111.03</td>\n",
              "      <td>104.55</td>\n",
              "      <td>99.24</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>130.28</td>\n",
              "      <td>122.37</td>\n",
              "      <td>121.40</td>\n",
              "      <td>122.23</td>\n",
              "      <td>120.13</td>\n",
              "      <td>121.88</td>\n",
              "      <td>123.82</td>\n",
              "      <td>117.13</td>\n",
              "      <td>111.89</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>128.59</td>\n",
              "      <td>120.45</td>\n",
              "      <td>118.35</td>\n",
              "      <td>119.99</td>\n",
              "      <td>118.94</td>\n",
              "      <td>120.07</td>\n",
              "      <td>122.05</td>\n",
              "      <td>117.15</td>\n",
              "      <td>112.09</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>127.54</td>\n",
              "      <td>119.26</td>\n",
              "      <td>117.02</td>\n",
              "      <td>118.67</td>\n",
              "      <td>117.66</td>\n",
              "      <td>118.76</td>\n",
              "      <td>120.74</td>\n",
              "      <td>115.86</td>\n",
              "      <td>110.79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>513</th>\n",
              "      <td>123.80</td>\n",
              "      <td>115.01</td>\n",
              "      <td>114.84</td>\n",
              "      <td>113.58</td>\n",
              "      <td>107.14</td>\n",
              "      <td>109.52</td>\n",
              "      <td>111.69</td>\n",
              "      <td>82.28</td>\n",
              "      <td>17.35</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>514</th>\n",
              "      <td>129.25</td>\n",
              "      <td>121.20</td>\n",
              "      <td>119.63</td>\n",
              "      <td>120.91</td>\n",
              "      <td>119.35</td>\n",
              "      <td>120.82</td>\n",
              "      <td>122.78</td>\n",
              "      <td>117.01</td>\n",
              "      <td>111.89</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>515</th>\n",
              "      <td>124.13</td>\n",
              "      <td>115.38</td>\n",
              "      <td>115.77</td>\n",
              "      <td>114.57</td>\n",
              "      <td>104.34</td>\n",
              "      <td>109.86</td>\n",
              "      <td>112.08</td>\n",
              "      <td>38.80</td>\n",
              "      <td>57.30</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>516</th>\n",
              "      <td>129.96</td>\n",
              "      <td>122.01</td>\n",
              "      <td>120.53</td>\n",
              "      <td>121.80</td>\n",
              "      <td>120.21</td>\n",
              "      <td>121.71</td>\n",
              "      <td>123.67</td>\n",
              "      <td>117.88</td>\n",
              "      <td>112.76</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>517 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Junc 10  Junc 11  Junc 12  Junc 13  ...  Junc 23  Junc 31  Junc 32  condition\n",
              "0     111.07   110.90   115.38   116.56  ...   118.04   112.15   107.41          1\n",
              "1     130.28   122.37   121.40   122.23  ...   123.82   117.13   111.89          0\n",
              "2     119.99   110.67   108.43   109.34  ...   111.03   104.55    99.24          1\n",
              "3     130.28   122.37   121.40   122.23  ...   123.82   117.13   111.89          0\n",
              "4     128.59   120.45   118.35   119.99  ...   122.05   117.15   112.09          0\n",
              "..       ...      ...      ...      ...  ...      ...      ...      ...        ...\n",
              "512   127.54   119.26   117.02   118.67  ...   120.74   115.86   110.79          0\n",
              "513   123.80   115.01   114.84   113.58  ...   111.69    82.28    17.35          0\n",
              "514   129.25   121.20   119.63   120.91  ...   122.78   117.01   111.89          0\n",
              "515   124.13   115.38   115.77   114.57  ...   112.08    38.80    57.30          1\n",
              "516   129.96   122.01   120.53   121.80  ...   123.67   117.88   112.76          0\n",
              "\n",
              "[517 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKZHAiQXJMgZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "d9d56541-26b4-4b60-f118-a8b4a7f244c1"
      },
      "source": [
        "total = 0\n",
        "correct = 0\n",
        "wrong = 0\n",
        "wrong_prediction_df = pd.DataFrame(columns = ['Junc 10', 'Junc 11', 'Junc 12', 'Junc 13', 'Junc 21', 'Junc 22', 'Junc 23', 'Junc 31', 'Junc 32', 'condition']) \n",
        "for i in range(len(Y_pred)):\n",
        "  total=total+1\n",
        "  if(y_test['condition'][i] == Y_pred[i]):\n",
        "    correct=correct+1\n",
        "  else:\n",
        "    wrong=wrong+1\n",
        "    currentPressureArray = x_test.loc[i].to_list()\n",
        "    currentPressureArray.append(y_test['condition'][i])\n",
        "    wrong_prediction_df = wrong_prediction_df.append(pd.Series(currentPressureArray, index = combinedDF.columns), ignore_index=True)\n",
        "\n",
        "\n",
        "print(\"Total \" + str(total))\n",
        "print(\"Correct \" + str(correct))\n",
        "print(\"Wrong \" + str(wrong))\n",
        "print(wrong_prediction_df)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total 131\n",
            "Correct 123\n",
            "Wrong 8\n",
            "   Junc 10  Junc 11  Junc 12  Junc 13  ...  Junc 23  Junc 31  Junc 32  condition\n",
            "0   125.10   116.49   114.40   115.71  ...   117.61   111.93   106.78        1.0\n",
            "1   125.30   116.71   116.23   115.60  ...   114.29    86.62    22.02        1.0\n",
            "2   124.43   115.72   115.64   114.36  ...   112.43    82.87    17.50        1.0\n",
            "3   127.08   118.73   116.44   118.09  ...   120.16   115.30   110.22        1.0\n",
            "4   125.19   116.58   116.13   115.45  ...   114.02    85.63    18.67        1.0\n",
            "5   125.67   117.13   116.70   116.06  ...   114.73    86.98    22.13        1.0\n",
            "6   126.26   117.80   116.99   116.91  ...   116.07    89.53    23.45        1.0\n",
            "7   126.23   117.77   116.99   116.85  ...   115.91    88.60    19.93        1.0\n",
            "\n",
            "[8 rows x 10 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8lyVtOR8LtEt",
        "colab_type": "text"
      },
      "source": [
        "DATA VISUALISATION\n",
        "\n",
        "THE TRAIN SET PRESSURE DATA HAS BEEN PLOTTED FOR LEAK AND NON-LEAK CASE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lg_MI4PnJURi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d0bd0396-f467-4d6c-ae50-1fe50033beaf"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "for col in combinedDF:  \n",
        "    if col != 'condition':\n",
        "        plt.scatter('condition', col , data=combinedDF)\n",
        "        plt.ylabel(col +  'pressure')\n",
        "        plt.xlabel('0=no leak, 1=leak')\n",
        "        plt.axis([-0.1, 1.1, 0, 140])\n",
        "        plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0ptHaq+qsTV+OJM0c5y9fOSXHFF44Yvmp9GYyvRowFCRpnM5fvvIJdx+tvP9hTvxm710KXQZDP5ePjh+5nmQO8LWBVSRJM8D6nmjuMhTG85KdXwOOM0jSBEzlJ5q/Te9uI4BZwO7AuYMsSpKmu6n8RPPf8dtQeAy4raq6ncZPkqa4KfdEc5Jf0QuDtV8DVEkeAf4N+GBVXTrA+iRpWppyTzRX1VZjbUsyC9gD+ErzU5K0kSbjE83jGWimqlZX1TXA32/ieiRJHRpXKKxRVadtqkIkSd2bUChIkqaXzkIhyawky5P8Y7O+c5Irk6xIck6SJ3dVmyTNVF2eKbwbuGnE+ieAT1XVc4H7gGM6qUqSZrBOQiHJjsAr6b3NjSQBXgac1+xyJrCwi9okaSbr6kzhfwPvBx5v1p8J3F9VjzXrdwKj3qeV5Ngky5IsW7Vq1eArlaQZZOihkORVwD1VddV4vl9Vi6tqQVUtmDt37iauTpJmtn6mudjUDgAOT3IYvam4nw58GpiTZLPmbGFHwKk0JGnIhn6mUFUnVtWOVTUfOBL4p6p6A3AZ8Npmt6OBC4ZdmyTNdJPpOYUPAO9NsoLeGMPpHdcjSTNOF5ePWlV1OXB5s3wLsG+X9UjSTDeZzhQkSR0zFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJraGHQpKdklyW5MYkNyR5d9O+TZJLkvys+bn1sGuTpJmuizOFx4D3VdXuwP7AcUl2B04ALq2qXYBLm3VJ0hANPRSq6u6qurpZ/hVwEzAPOAI4s9ntTGDhsGuTpJmu0zGFJPOBvYErge2q6u5m0y+A7cb4zrFJliVZtmrVqqHUKUkzRWehkGRL4BvAn1fVgyO3VVUBNdr3qmpxVS2oqgVz584dQqWSNHN0EgpJNqcXCF+pqm82zb9Msn2zfXvgni5qk6SZrIu7jwKcDtxUVZ8csWkJcHSzfDRwwbBrk6SZbrMOjnkA8CfAdUl+0rT9JXAycG6SY4DbgNd1UJskzWhDD4Wq+j6QMTYfNMxaJElP5BPNkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJak26UEhySJKbk6xIckLX9UjSTDKpQiHJLOBU4FBgd+CoJLt3W5UkzRyTKhSAfYEVVXVLVf0G+BpwRMc1SdKMsVnXBaxlHnDHiPU7gf1G7pDkWODYZvU/k9w8pNoAtgX+fYjHGzb7N7VN5/5N577B8Pv3O2NtmGyhsEFVtRhY3MWxkyyrqgVdHHsY7N/UNp37N537BpOrf5Pt8tFKYKcR6zs2bZKkIZhsofBjYJckOyd5MnAksKTjmiRpxphUl4+q6rEkfwZcDMwCvlRVN3Rc1kidXLYaIvs3tU3n/k3nvsEk6l+qqusaJEmTxGS7fCRJ6pChIElqGQqj2NBUG0mekuScZvuVSeYPv8rx66N/701yY5Jrk1yaZMx7miebfqdJSfLHSSrJpLgNsF/99C/J65o/vxuSfHXYNU5EH383n53ksiTLm7+fh3VR53gk+VKSe5JcP8b2JPlM0/drk+wz7BoBqCo/Iz70Brj/Dfhd4MnANcDua+3zTuDzzfKRwDld172J+/dS4GnN8jumSv/66Vuz31bAFcBSYEHXdW/iP7tdgOXA1s36s7quexP3bzHwjmZ5d+DWruveiP69BNgHuH6M7YcBFwEB9geu7KJOzxTW1c9UG0cAZzbL5wEHJckQa5yIDfavqi6rqoea1aX0nheZCvqdJuV/Ap8A/muYxW0C/fTvbcCpVXUfQFXdM+QaJ6Kf/hXw9Gb5GcBdQ6xvQqrqCuDe9exyBHBW9SwF5iTZfjjV/ZahsK7RptqYN9Y+VfUY8ADwzKFUN3H99G+kY+j918tUsMG+NafkO1XVhcMsbBPp58/uecDzkvwgydIkhwytuonrp39/BbwxyZ3Ad4Djh1PaUGzs/zcHYlI9p6DJJckbgQXAf++6lk0hyZOATwJv7riUQdqM3iWkA+md4V2RZM+qur/Tqjado4Azqup/JXkR8A9J9qiqx7subLrwTGFd/Uy10e6TZDN6p7H/MZTqJq6vqUSSvBz4IHB4VT0ypNomakN92wrYA7g8ya30rtsumUKDzf382d0JLKmqR6vq58C/0guJqaCf/h0DnAtQVT8EnkpvMrnpYFJM82MorKufqTaWAEc3y68F/qmakaIpYIP9S7I3cBq9QJhK16TX27eqeqCqtq2q+VU1n954yeFVtaybcjdaP383z6d3lkCSbeldTrplmEVOQD/9ux04CCDJ79ELhVVDrXJwlgBvau5C2h94oKruHnYRXj5aS40x1UaSvwaWVdUS4HR6p60r6A0cHdldxRunz/6dAmwJfL0ZP7+9qg7vrOg+9dm3KavP/l0MvCLJjcBqYFFVTYmz2D779z7gC0neQ2/Q+c1T5T/IkpxNL7C3bcZETgI2B6iqz9MbIzkMWAE8BLylkzqnyP+ekqQh8PKRJKllKEiSWoaCJKllKEiSWoaCJKllKGjS63fm0wEc99bmXv+BfzfJnzX9q409ZpL/3PgKJ/5dTU+Ggia1JLOAU4FD6c2KeVSS3butaiB+ALwcuK3rQjSzGQqa7Pqd+XQdSf6qmcP+8iS3JHnXiG3vTXJ98/nzPn7XG5P8KMlPkpzWhBVJPpdkWfPugo+M8r3ZSS5K8rb1/f6qWl5Vt/bTrw3UuSjJj5v5+D8yov38JFc1dR47yve2TfLDJK+caA2a2nyiWZPdaDNH7geQ5FP03v2wtq9V1cnN8m7NPlsBNyf5HLAXvadF96M3d/2VSf65qpaPVkAzncLrgQOq6tEknwXeAJwFfLCq7m1C4tIke1XVtc1Xt6QXYmdV1Vnj6XySlwKfGmXTQ1X1h2vt+wp68xzt2/RrSZKXNFM2v7Wpczbw4yTfWPOkc5Lt6E2x8KGqumQ8dWr6MBQ0ZVXVe/rY7cJmQr9HktwDbAe8GPhWVf0aIMk3gT+i93Ka0RwEvIDeP6YAs4E1c0K9rvkv782A7eld4loTChcAf1tVX9nYvq1RVZcBz+9z91c0nzX92JJeSFwBvCvJq5v2nZr2/6A3zcKlwHFV9c/jrVPTh6GgyW7MmSP7PFMYOcPrasb3dz7AmVV14hMak52BvwBeWFX3JTmD3gRta/wAOCTJV8c7P8/GnCk0dX68qk5b63ccSG+84kVV9VCSy0fU+RhwFXAwYCjIMQVNemPOnFlV76mq54/yOXm9vxH+BViY5GlJtgBe3bSN5VLgtUmeBZBkm/TeW/104NfAA80lmEPX+t6HgfvoDZTTfPfSJH2/OKV5C95ofVw7EKA3kdxbk2zZHGteU/MzgPuaQNiN3pTh7SGAtwK7JflAv3Vp+jIUNKk1b7ZbM3PmTcC5VXXDBH/n1cAZwI+AK4EvjjWe0Ox/I/Ah4HtJrgUuAbavqmvoXar5KfBVemcGa3s3MDvJ36b3kp/nMsorGZO8q5k5c0fg2iRfHEe/vtfU8cMk19F7VexWwHeBzZLcBJxMb8rwkd9bTe/lNS9L8s6NPa6mF2dJlYYkyR70Bnzf23Ut0lgMBUlSy8tHkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJav1/PEW1OSEYc+0AAAAASUVORK5CYII=\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": "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": "display_data",
          "data": {
            "image/png": 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ZmY0CgxloJiK2R8TPga8Mcz1mZi1Bvftg+mkvy6BCoUdEXD5chZiZtZJG48kVjzMPLRTMzGxw2sfW//XbqL0slR1dUpukVZK+n65PlbRC0hpJ10nauarazMyK9oetLw2ovSxVRtKHgYdr1r8AXBwRrwGeBeZUUpWZWQka9RJV3HtUTShImgS8jeRtbkgS8BbgxnSXRcCsKmozM2tlVV0pfAk4D+i5TtoH2BgR29L1dUDduWMlzZXUJamru7u7+ErNzArQ6Jdv1QO9pR9f0tuBDRGxcjCfj4iFEdEZEZ0dHR3DXJ2ZWTmatfsozzQXw+0Y4GRJJ5FMxb0n8GVgvKQx6dXCJMBTaZjZqNWsoVD6lUJEXBARkyJiCnAa8JOIeB9wO/DudLfZwC1l12Zm1uqq7r6q9XHgXElrSMYYrqy4HjOzllNF91EmIu4A7kiX1wJHVFmPmVmra6YrBTOzljGxwWs3G7WXxaFgZlaBecdPo31s28va2se2Me/4aRVVlHAomJlVYNaMiZxy+ETa0mlR2yROOXwis2bUfUSrNA4FM7MK3LxqPd9ZuT57/eb2CL6zcj03r6r2bnyHgplZBRYsXc3mrdtf1rZ563YWLF1dUUUJh4KZWQWe3Lh5QO1lcSiYmVXggAZ3GTVqL4tDwcysAs1691GlD6+ZmbWqnruMFixdzZMbN3PA+HbmHT+t8ruPHApmZhWZNaP6W1B7c/eRmZllHApmZpZxKJiZWcahYGZmGYeCmZllHApmZpZxKJiZWcahYGZmGYeCmZllHApmZpZxKJiZWcahYGZmmdJDQdJkSbdLekjSg5I+nLbvLek2Sb9Iv+9Vdm1mZq2uiiuFbcBHI2I6cBRwtqTpwPnAsog4CFiWrpuZWYlKD4WIeCoi7kmXnwceBiYCM4FF6W6LgFll12Zm1uoqHVOQNAWYAawA9o+Ip9JNTwP7N/jMXEldkrq6u7tLqdPMrFVUFgqSxgHfAf45Ip6r3RYRAUS9z0XEwojojIjOjo6OEio1M2sdlYSCpLEkgXBNRNyUNv9a0oR0+wRgQxW1mZm1siruPhJwJfBwRFxUs2kJMDtdng3cUnZtZmatrop3NB8D/A1wv6R707Z/AS4Erpc0B3gMOLWC2szMWlrpoRAR/wOoweZjy6zFzMxezk80m5lZxqFgZmYZh4KZmWUcCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlnEomJlZxqFgZmaZpgsFSSdIWi1pjaTzq67HzKyVNFUoSGoDLgVOBKYDp0uaXm1VZmato6lCATgCWBMRayPiReDbwMyKazIzaxljqi6gl4nAEzXr64Aja3eQNBeYm67+TtLqkmoD2Bf4TYnHK5vPb2Qbzec3ms8Nyj+/VzXa0Gyh0K+IWAgsrOLYkroiorOKY5fB5zeyjebzG83nBs11fs3WfbQemFyzPiltMzOzEjRbKNwNHCRpqqSdgdOAJRXXZGbWMpqq+ygitkn6J2Ap0AZcFREPVlxWrUq6rUrk8xvZRvP5jeZzgyY6P0VE1TWYmVmTaLbuIzMzq5BDwczMMg6FOvqbakPSLpKuS7evkDSl/CoHL8f5nSvpIUn3SVomqeE9zc0m7zQpkk6RFJKa4jbAvPKcn6RT0z+/ByV9q+wahyLH381XSrpd0qr07+dJVdQ5GJKukrRB0gMNtkvSJem53yfpsLJrBCAi/FXzRTLA/UvgQGBn4OfA9F77nAV8LV0+Dbiu6rqH+fz+CtgtXf7HkXJ+ec4t3W8P4E5gOdBZdd3D/Gd3ELAK2Ctd36/quof5/BYC/5guTwcerbruAZzfG4HDgAcabD8J+BEg4ChgRRV1+kphR3mm2pgJLEqXbwSOlaQSaxyKfs8vIm6PiBfS1eUkz4uMBHmnSfks8AXgD2UWNwzynN/fAZdGxLMAEbGh5BqHIs/5BbBnuvwK4MkS6xuSiLgTeKaPXWYCiyOxHBgvaUI51f2RQ2FH9abamNhon4jYBmwC9imluqHLc3615pD862Uk6Pfc0kvyyRHxgzILGyZ5/uxeC7xW0l2Slks6obTqhi7P+X0KOEPSOuCHwIfKKa0UA/1/sxBN9ZyCNRdJZwCdwJuqrmU4SNoJuAh4f8WlFGkMSRfSm0mu8O6U9KcRsbHSqobP6cDVEfEfko4GvinpkIh4qerCRgtfKewoz1Qb2T6SxpBcxv62lOqGLtdUIpLeCnwCODkitpRU21D1d257AIcAd0h6lKTfdskIGmzO82e3DlgSEVsj4lfA/5GExEiQ5/zmANcDRMRPgV1JJpMbDZpimh+Hwo7yTLWxBJidLr8b+EmkI0UjQL/nJ2kGcDlJIIykPuk+zy0iNkXEvhExJSKmkIyXnBwRXdWUO2B5/m7eTHKVgKR9SbqT1pZZ5BDkOb/HgWMBJL2OJBS6S62yOEuAM9O7kI4CNkXEU2UX4e6jXqLBVBuSPgN0RcQS4EqSy9Y1JANHp1VX8cDkPL8FwDjghnT8/PGIOLmyonPKeW4jVs7zWwocJ+khYDswLyJGxFVszvP7KPB1SR8hGXR+/0j5B5mka0kCe990TGQ+MBYgIr5GMkZyErAGeAH4QCV1jpD/nmZmVgJ3H5mZWcahYGZmGYeCmZllHApmZpZxKJiZWcahYE0v78ynBRz30fRe/8I/K+mf0vOLgR5T0u8GXuHQP2ujk0PBmpqkNuBS4ESSWTFPlzS92qoKcRfwVuCxqgux1uZQsGaXd+bTHUj6VDqH/R2S1ko6p2bbuZIeSL/+OcfPOkPSzyTdK+nyNKyQdJmkrvTdBZ+u87l2ST+S9Hd9/fyIWBURj+Y5r37qnCfp7nQ+/k/XtN8saWVa59w6n9tX0k8lvW2oNdjI5ieardnVmznySABJF5O8+6G3b0fEhenywek+ewCrJV0GHErytOiRJHPXr5D0XxGxql4B6XQK7wGOiYitkr4KvA9YDHwiIp5JQ2KZpEMj4r70o+NIQmxxRCwezMlL+ivg4jqbXoiIN/Ta9ziSeY6OSM9riaQ3plM2fzCtsx24W9J3ep50lrQ/yRQLn4yI2wZTp40eDgUbsSLiIzl2+0E6od8WSRuA/YG/AL4bEb8HkHQT8JckL6ep51jgcJJfpgDtQM+cUKem//IeA0wg6eLqCYVbgC9GxDUDPbceEXE78Pqcux+XfvWcxziSkLgTOEfSO9P2yWn7b0mmWVgGnB0R/zXYOm30cChYs2s4c2TOK4XaGV63M7i/8wIWRcQFL2uUpgIfA/48Ip6VdDXJBG097gJOkPStwc7PM5ArhbTOf4uIy3v9jDeTjFccHREvSLqjps5twErgeMChYB5TsKbXcObMiPhIRLy+zteFff5E+G9glqTdJO0OvDNta2QZ8G5J+wFI2lvJe6v3BH4PbEq7YE7s9bl/BZ4lGSgn/ewySblfnJK+Ba/eOfYOBEgmkvugpHHpsSamNb8CeDYNhINJpgzPDgF8EDhY0sfz1mWjl0PBmlr6ZruemTMfBq6PiAeH+DPvAa4GfgasAK5oNJ6Q7v8Q8Engx5LuA24DJkTEz0m6ah4BvkVyZdDbh4F2SV9U8pKf11DnlYySzklnzpwE3CfpikGc14/TOn4q6X6SV8XuAdwKjJH0MHAhyZThtZ/bTvLymrdIOmugx7XRxbOkmpVE0iEkA77nVl2LWSMOBTMzy7j7yMzMMg4FMzPLOBTMzCzjUDAzs4xDwczMMg4FMzPL/D8u1ZIooduiQgAAAABJRU5ErkJggg==\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": "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": "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": "Inp6HfMvLKln",
        "colab_type": "text"
      },
      "source": [
        "DATA VISUALISATION:\n",
        "\n",
        "THE FOLLOWING GRAPHS SHOW THE PRESSURES WHICH WERE WRONGLY PREDICTED"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvUtnbevJcDW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ab326f1a-e165-4c1b-86d3-4bb848c15a93"
      },
      "source": [
        "for col in wrong_prediction_df:  \n",
        "    if col != 'condition':\n",
        "        plt.scatter('condition', col , data=wrong_prediction_df)\n",
        "        plt.ylabel(col +  'pressure')\n",
        "        plt.xlabel('0=no leak, 1=leak')\n",
        "        plt.axis([-0.1, 1.1, 0, 140])\n",
        "        plt.show()"
      ],
      "execution_count": 24,
      "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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\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": "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": "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": "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkY2pwOlKFss",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}