WQI_PNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "WQI_PNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EBnqoWzLMiL",
        "colab_type": "text"
      },
      "source": [
        "**Packages** **used**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ds9NxgKhgjiR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b5bf5bb2-825a-4f10-d06c-7819cd0deb88"
      },
      "source": [
        "import pandas\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.layers import Dense\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "..."
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ellipsis"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UigRZsJXLgNy",
        "colab_type": "text"
      },
      "source": [
        "# Uploading File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-hi5yxRjd3f",
        "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": 73
        },
        "outputId": "b47233a3-f9d7-44b4-d8ec-2fc2877e4566"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded= files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d3cd3b4d-7443-4267-bb29-05d4abc464be\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d3cd3b4d-7443-4267-bb29-05d4abc464be\">\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 WQI_Parameter_Scores_1994-2013.csv to WQI_Parameter_Scores_1994-2013.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRUzAV0Og_CT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "c331d63d-fc63-41fd-d068-3633c1435639"
      },
      "source": [
        "# load dataset\n",
        "dataframe = pandas.read_csv(\"WQI_Parameter_Scores_1994-2013.csv\")\n",
        "dataset = dataframe.values\n",
        "dataframe.head()"
      ],
      "execution_count": 3,
      "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>Station</th>\n",
              "      <th>Station Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Overall WQI</th>\n",
              "      <th>WQI FC</th>\n",
              "      <th>WQI Oxy</th>\n",
              "      <th>WQI pH</th>\n",
              "      <th>WQI TSS</th>\n",
              "      <th>WQI Temp</th>\n",
              "      <th>WQI TPN</th>\n",
              "      <th>WQI TP</th>\n",
              "      <th>WQI Turb</th>\n",
              "      <th>Location 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>87</td>\n",
              "      <td>91</td>\n",
              "      <td>85</td>\n",
              "      <td>95</td>\n",
              "      <td>78</td>\n",
              "      <td>81</td>\n",
              "      <td>99</td>\n",
              "      <td>84</td>\n",
              "      <td>69</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>86</td>\n",
              "      <td>78</td>\n",
              "      <td>84</td>\n",
              "      <td>77</td>\n",
              "      <td>85</td>\n",
              "      <td>90</td>\n",
              "      <td>99</td>\n",
              "      <td>72</td>\n",
              "      <td>80</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>75</td>\n",
              "      <td>90</td>\n",
              "      <td>79</td>\n",
              "      <td>88</td>\n",
              "      <td>61</td>\n",
              "      <td>90</td>\n",
              "      <td>98</td>\n",
              "      <td>71</td>\n",
              "      <td>49</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>87</td>\n",
              "      <td>88</td>\n",
              "      <td>80</td>\n",
              "      <td>88</td>\n",
              "      <td>75</td>\n",
              "      <td>95</td>\n",
              "      <td>100</td>\n",
              "      <td>79</td>\n",
              "      <td>71</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>95</td>\n",
              "      <td>93</td>\n",
              "      <td>86</td>\n",
              "      <td>96</td>\n",
              "      <td>84</td>\n",
              "      <td>90</td>\n",
              "      <td>100</td>\n",
              "      <td>84</td>\n",
              "      <td>87</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Station             Station Name  ...  WQI Turb                 Location 1\n",
              "0  16A070  Skokomish R nr Potlatch  ...        69  POINT (-123.1771 47.3098)\n",
              "1  16A070  Skokomish R nr Potlatch  ...        80  POINT (-123.1771 47.3098)\n",
              "2  16A070  Skokomish R nr Potlatch  ...        49  POINT (-123.1771 47.3098)\n",
              "3  16A070  Skokomish R nr Potlatch  ...        71  POINT (-123.1771 47.3098)\n",
              "4  16A070  Skokomish R nr Potlatch  ...        87  POINT (-123.1771 47.3098)\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAd670YTLpcp",
        "colab_type": "text"
      },
      "source": [
        "**This is a multiple class problem and can be solved using Artificial Neural Networks.**\n",
        "**Based on designations set by CCME(2005) to classify water based on WQI.**\n",
        "```\n",
        "Excellent /1    95-100     All measurements are within objectives virtually all of the time\n",
        "```\n",
        "```\n",
        "Good /2          80-94      Conditions rarely depart from natural or desirable levels\n",
        "```\n",
        "```\n",
        "Fair /3          65-79      Conditions sometimes depart from natural or desirable levels\n",
        "```\n",
        "```\n",
        "Marginal /4     45-64      Conditions often depart from natural or desirable levels\n",
        "```\n",
        "```\n",
        "Poor /5          0-44      Conditions usually depart from natural or desirable levels**\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBXpJSoKjc-J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def quality_check(num):\n",
        "    s=0\n",
        "    if num <=100 and num >=80 :\n",
        "        s=1\n",
        "    #elif num <=89 and num >=80:\n",
        "        #s= 2\n",
        "    elif num <=79 and num >=65:\n",
        "        s=2\n",
        "    elif num <=64 : #and num >=50\n",
        "        s=3\n",
        "    #elif num <=49:\n",
        "     #   s=5\n",
        "    return s"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adiUYyXVjaoZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataframe['quality']=dataframe['Overall WQI'].apply(quality_check)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_yHA-5r8HU6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "6113a1b9-83db-4457-c650-5342cc1c0e6a"
      },
      "source": [
        "dataframe"
      ],
      "execution_count": 6,
      "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>Station</th>\n",
              "      <th>Station Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Overall WQI</th>\n",
              "      <th>WQI FC</th>\n",
              "      <th>WQI Oxy</th>\n",
              "      <th>WQI pH</th>\n",
              "      <th>WQI TSS</th>\n",
              "      <th>WQI Temp</th>\n",
              "      <th>WQI TPN</th>\n",
              "      <th>WQI TP</th>\n",
              "      <th>WQI Turb</th>\n",
              "      <th>Location 1</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>87</td>\n",
              "      <td>91</td>\n",
              "      <td>85</td>\n",
              "      <td>95</td>\n",
              "      <td>78</td>\n",
              "      <td>81</td>\n",
              "      <td>99</td>\n",
              "      <td>84</td>\n",
              "      <td>69</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>86</td>\n",
              "      <td>78</td>\n",
              "      <td>84</td>\n",
              "      <td>77</td>\n",
              "      <td>85</td>\n",
              "      <td>90</td>\n",
              "      <td>99</td>\n",
              "      <td>72</td>\n",
              "      <td>80</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>75</td>\n",
              "      <td>90</td>\n",
              "      <td>79</td>\n",
              "      <td>88</td>\n",
              "      <td>61</td>\n",
              "      <td>90</td>\n",
              "      <td>98</td>\n",
              "      <td>71</td>\n",
              "      <td>49</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>87</td>\n",
              "      <td>88</td>\n",
              "      <td>80</td>\n",
              "      <td>88</td>\n",
              "      <td>75</td>\n",
              "      <td>95</td>\n",
              "      <td>100</td>\n",
              "      <td>79</td>\n",
              "      <td>71</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>95</td>\n",
              "      <td>93</td>\n",
              "      <td>86</td>\n",
              "      <td>96</td>\n",
              "      <td>84</td>\n",
              "      <td>90</td>\n",
              "      <td>100</td>\n",
              "      <td>84</td>\n",
              "      <td>87</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>966</th>\n",
              "      <td>23A070</td>\n",
              "      <td>Chehalis R @ Porter</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>62</td>\n",
              "      <td>85</td>\n",
              "      <td>89</td>\n",
              "      <td>92</td>\n",
              "      <td>68</td>\n",
              "      <td>68</td>\n",
              "      <td>52</td>\n",
              "      <td>58</td>\n",
              "      <td>75</td>\n",
              "      <td>POINT (-123.3138 46.9393)</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>967</th>\n",
              "      <td>23A160</td>\n",
              "      <td>Chehalis R @ Dryad</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>65</td>\n",
              "      <td>76</td>\n",
              "      <td>78</td>\n",
              "      <td>96</td>\n",
              "      <td>78</td>\n",
              "      <td>65</td>\n",
              "      <td>77</td>\n",
              "      <td>73</td>\n",
              "      <td>81</td>\n",
              "      <td>POINT (-123.2501 46.6309)</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>968</th>\n",
              "      <td>24B090</td>\n",
              "      <td>Willapa R nr Willapa</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>80</td>\n",
              "      <td>78</td>\n",
              "      <td>87</td>\n",
              "      <td>91</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>57</td>\n",
              "      <td>69</td>\n",
              "      <td>82</td>\n",
              "      <td>POINT (-123.6535 46.6501)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>969</th>\n",
              "      <td>24F070</td>\n",
              "      <td>Naselle R nr Naselle</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>79</td>\n",
              "      <td>67</td>\n",
              "      <td>88</td>\n",
              "      <td>94</td>\n",
              "      <td>87</td>\n",
              "      <td>77</td>\n",
              "      <td>67</td>\n",
              "      <td>88</td>\n",
              "      <td>93</td>\n",
              "      <td>POINT (-123.7468 46.3729)</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>970</th>\n",
              "      <td>16A070</td>\n",
              "      <td>Skokomish R nr Potlatch</td>\n",
              "      <td>1995.0</td>\n",
              "      <td>93</td>\n",
              "      <td>84</td>\n",
              "      <td>85</td>\n",
              "      <td>96</td>\n",
              "      <td>93</td>\n",
              "      <td>84</td>\n",
              "      <td>98</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>POINT (-123.1771 47.3098)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>971 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Station             Station Name  ...                 Location 1  quality\n",
              "0    16A070  Skokomish R nr Potlatch  ...  POINT (-123.1771 47.3098)        1\n",
              "1    16A070  Skokomish R nr Potlatch  ...  POINT (-123.1771 47.3098)        1\n",
              "2    16A070  Skokomish R nr Potlatch  ...  POINT (-123.1771 47.3098)        2\n",
              "3    16A070  Skokomish R nr Potlatch  ...  POINT (-123.1771 47.3098)        1\n",
              "4    16A070  Skokomish R nr Potlatch  ...  POINT (-123.1771 47.3098)        1\n",
              "..      ...                      ...  ...                        ...      ...\n",
              "966  23A070      Chehalis R @ Porter  ...  POINT (-123.3138 46.9393)        3\n",
              "967  23A160       Chehalis R @ Dryad  ...  POINT (-123.2501 46.6309)        2\n",
              "968  24B090     Willapa R nr Willapa  ...  POINT (-123.6535 46.6501)        1\n",
              "969  24F070     Naselle R nr Naselle  ...  POINT (-123.7468 46.3729)        2\n",
              "970  16A070  Skokomish R nr Potlatch  ...  POINT (-123.1771 47.3098)        1\n",
              "\n",
              "[971 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "joFqmlgeN-P8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "f9937d71-755d-4275-f148-8333815831a7"
      },
      "source": [
        "print(dataframe.loc[dataframe['quality']==4])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Empty DataFrame\n",
            "Columns: [Station, Station Name, Year, Overall WQI, WQI FC, WQI Oxy, WQI pH, WQI TSS, WQI Temp, WQI TPN, WQI TP, WQI Turb, Location 1, quality]\n",
            "Index: []\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ovx_2OamOsND",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = dataframe.values"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MEpbc2T8Teh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = dataset[:,4:11].astype(float)\n",
        "Y = dataset[:,13]\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvDBgmHOONXt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# encode class values as integers\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(Y)\n",
        "encoded_Y = encoder.transform(Y)\n",
        "# convert integers to dummy variables (i.e. one hot encoded)\n",
        "dummy_y = np_utils.to_categorical(encoded_Y)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zn_Au5bTMEh3",
        "colab_type": "text"
      },
      "source": [
        "## 1 - hot encoding *since multi class problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qthq7z8eSOXh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "5af85785-e581-41e5-87d5-f58864ce02f0"
      },
      "source": [
        "dummy_y"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       ...,\n",
              "       [1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [1., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SyYLimsIgqTK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BB1VTJxiLEz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split( X, dummy_y, test_size=0.2)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEMqgai1ioxY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "45cd5c84-1380-41ce-b0e3-7ebf5d5d00e4"
      },
      "source": [
        "dummy_y.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(971, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmwaESz2MPUm",
        "colab_type": "text"
      },
      "source": [
        "## **Creating an artificial neural network with two hidden layers**\n",
        "Softmax function has been used to calculate the probability in the output layer.\n",
        "THe two hidden layers consists 16 hidden units each.The input layer consists of 7 features that are available from the data set -- as discussed earlier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ae-xC0DdjuZi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def baseline_model():\n",
        "  classifier2= Sequential()\n",
        "  classifier2.add(Dense(16,input_dim=7, activation='relu'))\n",
        "  classifier2.add(Dense(16, activation='relu'))\n",
        "  classifier2.add(Dense(5, activation='softmax'))\n",
        "  classifier2.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "  return classifier2  "
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93FzUuV1n2Eg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "estimator = KerasClassifier(build_fn=baseline_model, epochs=1000, batch_size=10, verbose=0)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rujWCqBM6p_",
        "colab_type": "text"
      },
      "source": [
        "## K- fold Cross - Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWzHVtT2rk2F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "kfold = KFold(n_splits=10, shuffle=True)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqYpUKr1rqj1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#results = cross_val_score(estimator, X, dummy_y, cv=kfold)\n",
        "#print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1W8waUKievD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "17a3d5f2-1961-4f42-9dc8-03bc0ee3e14c"
      },
      "source": [
        "classifier1= Sequential()\n",
        "classifier1.add(Dense(14,input_dim=7, activation='relu'))\n",
        "classifier1.add(Dense(10, activation='relu'))\n",
        "classifier1.add(Dense(10, activation='relu'))\n",
        "classifier1.add(Dense(3, activation='softmax'))\n",
        "classifier1.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "classifier1.fit(x_train,y_train, epochs=400, batch_size=4, verbose=1)\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 3.0808 - accuracy: 0.5052\n",
            "Epoch 2/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.9736 - accuracy: 0.5606\n",
            "Epoch 3/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.7964 - accuracy: 0.6534\n",
            "Epoch 4/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.7239 - accuracy: 0.6856\n",
            "Epoch 5/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.6771 - accuracy: 0.6959\n",
            "Epoch 6/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.6536 - accuracy: 0.7049\n",
            "Epoch 7/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.6114 - accuracy: 0.7281\n",
            "Epoch 8/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.6119 - accuracy: 0.7255\n",
            "Epoch 9/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.5949 - accuracy: 0.7564\n",
            "Epoch 10/400\n",
            "194/194 [==============================] - 0s 2ms/step - loss: 0.5870 - accuracy: 0.7307\n",
            "Epoch 11/400\n",
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            "194/194 [==============================] - 1s 3ms/step - loss: 0.3568 - accuracy: 0.8557\n",
            "Epoch 365/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3302 - accuracy: 0.8660\n",
            "Epoch 366/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.3553 - accuracy: 0.8415\n",
            "Epoch 367/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.3423 - accuracy: 0.8492\n",
            "Epoch 368/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3870 - accuracy: 0.8402\n",
            "Epoch 369/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3724 - accuracy: 0.8531\n",
            "Epoch 370/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.3507 - accuracy: 0.8454\n",
            "Epoch 371/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3394 - accuracy: 0.8621\n",
            "Epoch 372/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3536 - accuracy: 0.8531\n",
            "Epoch 373/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3586 - accuracy: 0.8402\n",
            "Epoch 374/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3555 - accuracy: 0.8544\n",
            "Epoch 375/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3423 - accuracy: 0.8518\n",
            "Epoch 376/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3350 - accuracy: 0.8505\n",
            "Epoch 377/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3615 - accuracy: 0.8479\n",
            "Epoch 378/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3556 - accuracy: 0.8531\n",
            "Epoch 379/400\n",
            "194/194 [==============================] - 0s 2ms/step - loss: 0.3895 - accuracy: 0.8273\n",
            "Epoch 380/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3463 - accuracy: 0.8544\n",
            "Epoch 381/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3413 - accuracy: 0.8647\n",
            "Epoch 382/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.3281 - accuracy: 0.8634\n",
            "Epoch 383/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3393 - accuracy: 0.8441\n",
            "Epoch 384/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3335 - accuracy: 0.8621\n",
            "Epoch 385/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.3438 - accuracy: 0.8518\n",
            "Epoch 386/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3600 - accuracy: 0.8428\n",
            "Epoch 387/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3592 - accuracy: 0.8505\n",
            "Epoch 388/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3540 - accuracy: 0.8479\n",
            "Epoch 389/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.3313 - accuracy: 0.8608\n",
            "Epoch 390/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3345 - accuracy: 0.8582\n",
            "Epoch 391/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3347 - accuracy: 0.8660\n",
            "Epoch 392/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3779 - accuracy: 0.8415\n",
            "Epoch 393/400\n",
            "194/194 [==============================] - 0s 3ms/step - loss: 0.3483 - accuracy: 0.8505\n",
            "Epoch 394/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3417 - accuracy: 0.8492\n",
            "Epoch 395/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3286 - accuracy: 0.8531\n",
            "Epoch 396/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3285 - accuracy: 0.8686\n",
            "Epoch 397/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3490 - accuracy: 0.8621\n",
            "Epoch 398/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3464 - accuracy: 0.8531\n",
            "Epoch 399/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3612 - accuracy: 0.8492\n",
            "Epoch 400/400\n",
            "194/194 [==============================] - 1s 3ms/step - loss: 0.3436 - accuracy: 0.8582\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f7d1d9094e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAsPMtnJrqyC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "8963be23-3878-49b3-d4cb-1926825b6539"
      },
      "source": [
        "score = classifier1.evaluate(x_test, y_test, verbose = 1) \n",
        "\n",
        "print('Test loss:', score[0]) \n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.5209 - accuracy: 0.7846\n",
            "Test loss: 0.5208988785743713\n",
            "Test accuracy: 0.7846153974533081\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j89TB7ARZbKR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qIOfmF63NeSZ",
        "colab_type": "text"
      },
      "source": [
        "**Results : Validation accuracy is 72.8% and test accuracy is 78.4%**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CdKNrrfaZXU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}