Sensors.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sensors.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNh6JZp3qR4j8ZsX1m3vuEN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/priya212000/Food-spoilage-detection/blob/main/Sensors.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRCcA3ty4Lex"
      },
      "source": [
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulmSgUMp4MIy",
        "outputId": "44afcaff-846c-47b3-97f6-c56b1907bfb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yLgOJl24ROU"
      },
      "source": [
        "df=pd.read_excel(\"/content/drive/My Drive/datasets_paper/data2.xlsx\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INxHMJi94dBx"
      },
      "source": [
        "data= [df['MQ8A'], df['MQ135A'], df['MQ9A'], df['MQ4A'], df['MQ2A'], df['MQ3A'], df['output']]\n",
        "data2= [df['MQ8D'], df['MQ135D'], df['MQ9D'], df['MQ4D'], df['MQ2D'], df['MQ3D'], df['output']]\n",
        "data3=[df['MQ4D'], df['MQ3D'], df['output']]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdr6UMEVN4f9"
      },
      "source": [
        "headers = [\"MQ8A\", \"MQ135A\", \"MQ9A\", \"MQ4A\", \"MQ2A\", \"MQ3A\", 'output']\n",
        "df1 = pd. concat(data, axis=1, keys=headers)\n",
        "headers2 = [\"MQ8D\", \"MQ135D\", \"MQ9D\", \"MQ4D\", \"MQ2D\", \"MQ3D\", 'output']\n",
        "df3 = pd. concat(data2, axis=1, keys=headers)\n",
        "headers3 = [\"MQ8D\",  \"MQ4D\",'output']\n",
        "df4 = pd. concat(data3, axis=1, keys=headers3)\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQUF-UH14o8q"
      },
      "source": [
        "df= df.fillna(method='ffill')\n"
      ],
      "execution_count": 646,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgq8Me0k5XN_"
      },
      "source": [
        "df=df.astype(int)"
      ],
      "execution_count": 647,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oj52UB8m96Zp"
      },
      "source": [
        "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n"
      ],
      "execution_count": 231,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydxW-4WsjNMQ"
      },
      "source": [
        "df2=df1[25:161]\n",
        "f=[df1[0:25], df1[161:186]]\n",
        "df5=pd.concat(f)"
      ],
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_0gscXU5Y5P",
        "outputId": "e7c6efcb-918b-41bf-d1b3-d3fe76ee4dcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "\n",
        "X = df3.iloc[:, 5:6].values\n",
        "\n",
        "y = df3.iloc[:, 6].values\n",
        "p= df2.iloc[:, 4:5].values\n",
        "q= df2.iloc[:, 6].values\n",
        "df.head()\n"
      ],
      "execution_count": 648,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MQ8A</th>\n",
              "      <th>MQ8D</th>\n",
              "      <th>MQ135A</th>\n",
              "      <th>MQ135D</th>\n",
              "      <th>MQ9A</th>\n",
              "      <th>MQ9D</th>\n",
              "      <th>MQ4A</th>\n",
              "      <th>MQ4D</th>\n",
              "      <th>MQ2A</th>\n",
              "      <th>MQ2D</th>\n",
              "      <th>MQ3A</th>\n",
              "      <th>MQ3D</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>583</td>\n",
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              "      <td>1</td>\n",
              "      <td>462</td>\n",
              "      <td>1</td>\n",
              "      <td>247</td>\n",
              "      <td>1</td>\n",
              "      <td>659</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>594</td>\n",
              "      <td>0</td>\n",
              "      <td>275</td>\n",
              "      <td>1</td>\n",
              "      <td>211</td>\n",
              "      <td>1</td>\n",
              "      <td>464</td>\n",
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              "      <td>1</td>\n",
              "      <td>659</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>598</td>\n",
              "      <td>0</td>\n",
              "      <td>277</td>\n",
              "      <td>1</td>\n",
              "      <td>215</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>603</td>\n",
              "      <td>0</td>\n",
              "      <td>279</td>\n",
              "      <td>1</td>\n",
              "      <td>219</td>\n",
              "      <td>1</td>\n",
              "      <td>473</td>\n",
              "      <td>1</td>\n",
              "      <td>256</td>\n",
              "      <td>1</td>\n",
              "      <td>658</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MQ8A  MQ8D  MQ135A  MQ135D  MQ9A  MQ9D  MQ4A  MQ4D  MQ2A  MQ2D  MQ3A  MQ3D  \\\n",
              "0   583     0     268       1   203     1   462     1   241     1   660     0   \n",
              "1   589     0     272       1   207     1   462     1   247     1   659     0   \n",
              "2   594     0     275       1   211     1   464     1   250     1   659     0   \n",
              "3   598     0     277       1   215     1   468     1   253     1   659     0   \n",
              "4   603     0     279       1   219     1   473     1   256     1   658     0   \n",
              "\n",
              "   output  \n",
              "0       1  \n",
              "1       1  \n",
              "2       1  \n",
              "3       1  \n",
              "4       1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 648
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBSBhtiqE-j7"
      },
      "source": [
        "a=X[0:26]\n",
        "b=y[0:26]\n",
        "c=X[40:145]\n",
        "d=y[40:145]\n",
        "\n",
        "\n",
        "\n",
        "e,f,g,h=train_test_split(p, q, test_size=0.9, random_state=0)\n",
        "\n"
      ],
      "execution_count": 618,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWnJu3oj55gq",
        "outputId": "9b5201ae-825a-4373-8e4c-8d117b6bf329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
        "#train_test_split(X, y, test_size=0.9, random_state=0)\n",
        "#e, a, g, b\n",
        "#train_test_split(p, q, test_size=0.2, random_state=0)\n",
        "\n",
        "X_test.shape\n"
      ],
      "execution_count": 637,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 637
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDuprTHR7Hyu",
        "outputId": "16acd4ea-ae36-4eca-8cf9-8e0292e1acab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "\n",
        "from sklearn.preprocessing import StandardScaler \n",
        "sc_x = StandardScaler() \n",
        "\n",
        "X_train = sc_x.fit_transform(X_train) \n",
        "X_test = sc_x.transform(X_test) \n",
        "\n",
        "print (X_test[0:3, :]) \n"
      ],
      "execution_count": 638,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.]\n",
            " [0.]\n",
            " [0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UbZ-mYNZMmnd",
        "outputId": "ce1b42d9-bc29-41c6-fb60-5721fd191cd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "\n",
        "from sklearn.linear_model import LogisticRegression \n",
        "classifier = LogisticRegression(random_state = 0) \n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 639,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 639
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CDH0PwydpqE"
      },
      "source": [
        "#X_test"
      ],
      "execution_count": 640,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5O-uF2w17ZQV"
      },
      "source": [
        "y_pred = classifier.predict(X_test)\n"
      ],
      "execution_count": 641,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhksw6ArDUEC",
        "outputId": "17f7001a-37f2-464a-d06b-b21cf929c990",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "from sklearn.metrics import accuracy_score \n",
        "#a1=accuracy_score(y_test, y_pred)\n",
        "#a2=accuracy_score(y_test, y_pred)\n",
        "#a3=accuracy_score(y_test, y_pred)\n",
        "#a4=accuracy_score(y_test, y_pred)\n",
        "#a1=accuracy_score(y_test, y_pred)\n",
        "a1=accuracy_score(y_test, y_pred)\n",
        "a1\n",
        "#a2 135\n",
        "#a3 135, 2\n",
        "#a4 135, 3\n",
        "#a5 9 2\n",
        "#a6 9 3\n",
        "#a7 4 3\n"
      ],
      "execution_count": 642,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42105263157894735"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 642
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "heAV4DL_D02L"
      },
      "source": [
        "v\n",
        "v1=[a1,a2,a3,a4,a5,a6]"
      ],
      "execution_count": 643,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcO2eqrzMSR8",
        "outputId": "aa5e0383-6e39-4fda-b74a-35b4b224cadf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[0],70:v1[1],60:v1[2],50:v1[3], 30:v1[4], 10:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"DIGITAL VALUE OF MQ4 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "print(v1)\n",
        "plt.show()"
      ],
      "execution_count": 602,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.6842105263157895, 0.625, 0.6133333333333333, 0.43010752688172044, 0.5801526717557252, 0.4880952380952381]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "BDRzSCmMM6y8",
        "outputId": "77d2678e-0010-4be8-de43-89df45ff31c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[0],70:v1[1],60:v1[2],50:v1[3], 30:v1[4], 10:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"DIGITAL VALUE OF MQ3 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "print(v1)\n",
        "plt.show()"
      ],
      "execution_count": 644,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.42105263157894735, 0.44642857142857145, 0.4533333333333333, 0.43010752688172044, 0.48854961832061067, 0.4880952380952381]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "emBFd79fK2i-",
        "outputId": "e75f9267-ec9f-4e7d-843a-94d316e2a4d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[0],70:v1[1],60:v1[2],50:v1[3], 30:v1[4], 10:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"DIGITAL VALUE OF MQ8 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "print(v1)\n",
        "plt.show()"
      ],
      "execution_count": 563,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.9736842105263158, 0.9821428571428571, 0.9733333333333334, 0.978494623655914, 0.9770992366412213, 0.9761904761904762]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "MIs0tNy0KWj7",
        "outputId": "bdff7a58-64fc-4198-e8da-49a7d162e5d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[0],70:v1[1],60:v1[2],50:v1[3], 30:v1[4], 10:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"DIGITAL VALUE OF MQ3 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "print(v1)\n",
        "plt.show()"
      ],
      "execution_count": 523,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.42105263157894735, 0.44642857142857145, 0.4533333333333333, 0.43010752688172044, 0.48854961832061067, 0.4880952380952381]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2YDrZIkDSvsCrgJGSAIiIE2qsl5mZDZBOTij7Nmm8oSNJXUPvBbauuV5mZjZAOtlZvEdEHAwsj4jjgd2BV9RbLTMzGyidBMFT+e8Tkl5KOgt4y/qqZGZmA6mTfQSXSBoNnAjcQLpmwHdqrZWZmQ2YHoMgX5Dmyoh4GPihpJ8CIyPikQGpnZmZ1a7HrqGIeI50ucnG9NMOATOzoaWTfQRXStpPjeNGzcxsSOkkCD5EGmTuaUmPSvqHpEd7e5KZmXWHTs4sXiuXpDQzs3VTr0Eg6U2t5jdfqMbMzLpTJ4ePfqpyfyQwGZgPvLWWGpmZ2YDqpGvondVpSVsBJ9dWIzMzG1Cd7CxuthR45dquiJmZDY5O9hGcQjqbGFJw7Ew6w9jMzIaATvYRzKvcXwGcFxHX1FQfMzMbYJ0EwcXAUxGxEkDSMEkbRcQT9VbNzMwGQkdnFgMbVqY3BH5ZT3XMzGygdRIEI6uXp8z3N6qvSmZmNpA6CYLHJe3SmJD0OuDJ+qpkZmYDqZN9BB8DLpL0N9KlKv+JdOlKMzMbAjo5oWyupB2BHfKsRRHxbL3VMjOzgdLJxes/AmwcEQsiYgEwStKH66+amZkNhE72EXwwX6EMgIhYDnywviqZmdlA6iQIhlUvSiNpGDCiviqZmdlA6mRn8WXABZJOy9MfAn5eX5XMzGwgdRIERwOzgCPy9M2kI4fMzGwI6LVrKF/A/npgCelaBG8Fbq23WmZmNlDabhFIegVwYL49AFwAEBFvGZiqmZnZQOhpi+DPpF//74iIN0bEKcDKvixc0jRJiyQtlnRMi8f/VdKfJN0s6UpJW/et+mZmtqZ6CoL3APcCv5b0HUl7kc4s7kg+uuhUYB9gInCgpIlNxW4EJkXEa0ijnH6tL5U3M7M11zYIIuInETET2BH4NWmoiZdI+pakvTtY9mRgcUTcERHPAOcDM5rW8evKcNbXAeP70wgzM+u/TnYWPx4R5+ZrF48n/Yo/uoNljwPurkwvzfPaOYw2h6VKmiVpnqR5y5Yt62DVZmbWqT5dszgilkfE6RGx19qshKQPAJOAE9us9/SImBQRk8aOHbs2V21mVrxOziPor3uArSrT4/O8F5A0Ffgs8OaIeLrG+piZWQt92iLoo7nA9pK2kTQCmAnMqRaQ9FrgNGB6RNxfY13MzKyN2oIgIlYAs4HLSSegXRgRCyWdIGl6LnYiMIp0vYObJM1pszgzM6tJnV1DRMSlwKVN846t3J9a5/rNzKx3dXYNmZlZF3AQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4GZWeEcBGZmhas1CCRNk7RI0mJJx7R4/E2SbpC0QtL+ddbFzMxaqy0IJA0DTgX2ASYCB0qa2FTsLuBQ4Ny66mFmZj0bXuOyJwOLI+IOAEnnAzOAPzUKRMSS/NhzNdbDzMx6UGfX0Djg7sr00jyvzyTNkjRP0rxly5atlcqZmVnSFTuLI+L0iJgUEZPGjh072NUxMxtS6gyCe4CtKtPj8zwzM1uH1BkEc4HtJW0jaQQwE5hT4/rMzKwfaguCiFgBzAYuB24FLoyIhZJOkDQdQNLrJS0F3gucJmlhXfUxM7PW6jxqiIi4FLi0ad6xlftzSV1GZmY2SLpiZ7GZmdXHQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVrhag0DSNEmLJC2WdEyLxzeQdEF+/HpJE+qsj5mZra62IJA0DDgV2AeYCBwoaWJTscOA5RGxHXAS8NW66mNmZq3VuUUwGVgcEXdExDPA+cCMpjIzgLPy/YuBvSSpxjqZmVkTRUQ9C5b2B6ZFxOF5+iBg14iYXSmzIJdZmqdvz2UeaFrWLGBWntwBWFRLpftnDPBAr6W6y1Br01BrDwy9Ng219sC616atI2JsqweGD3RN+iMiTgdOH+x6tCJpXkRMGux6rE1DrU1DrT0w9No01NoD3dWmOruG7gG2qkyPz/NalpE0HNgUeLDGOpmZWZM6g2AusL2kbSSNAGYCc5rKzAEOyff3B34VdfVVmZlZS7V1DUXECkmzgcuBYcAZEbFQ0gnAvIiYA3wP+L6kxcBDpLDoNutkl9UaGmptGmrtgaHXpqHWHuiiNtW2s9jMzLqDzyw2Myucg8DMrHAOgj6S9HFJCyUtkHSepJF5h/j1eaiMC/LO8XWepB0k3VS5PSrpY5I2l3SFpNvy380Gu659IWm0pIsl/VnSrZJ27+Y2SVoi6Zb8Hs3L87q5PSMl/UHSH/Nn6fg8v1s/R2dIuj+fF9WY11Xvj4OgDySNA44CJkXEq0k7wWeShsY4KQ+VsZw0dMY6LyIWRcTOEbEz8DrgCeDHwDHAlRGxPXBlnu4m3wQui4gdgZ2AW+n+Nr0lv1eN49K7uT1PA2+NiJ2AnYFpknajSz9HwJnAtKZ53fX+RIRvHd6AccDdwOakI65+CryddPbg8Fxmd+Dywa5rP9q2N3BNvr8I2DLf3xJYNNj160M7NgXuJB8IUZnfzW1aAowZKu1pasdGwA3Art38OQImAAu69f3xFkEfRMQ9wP8D7gLuBR4B5gMPR8SKXGwpKTC6zUzgvHx/i4i4N9+/D9hicKrUL9sAy4D/lnSjpO9K2pjublMAv5A0Pw+3At3dHiQNk3QTcD9wBXA7Q+Nz1NBV74+DoA9yP98M0pfNS4GNWX2TsOvkvtjpwEXNj0X6SdNNxxgPB3YBvhURrwUep2mzvAvb9MaI2IU0ku9HJL2p+mAXtoeIWBmpS3I8aYDKHQe5SrXphvfHQdA3U4E7I2JZRDwL/Ah4AzA6D5EBrYfSWNftA9wQEX/P03+XtCVA/nv/oNWs75YCSyPi+jx9MSkYurZNeUuUiLiftA9nMl3cnqqIeBj4NakrqNs/R1Vd9f44CPrmLmA3SRvl4bL3Av5E+kfeP5c5BPifQapffx3I891C8MKhP7qqPRFxH3C3pB3yrMZ71JVtkrSxpE0a90n7chbQpe0BkDRW0uh8f0PgbaQd+t3+OarqqvfHZxb3UT7U7X3ACuBG4HBSX+b5pJ3INwIfiIinB62SfZC/XO4CXh4Rj+R5LwYuBF4G/BU4ICIeGrxa9o2knYHvAiOAO4D/TfrR03VtkvRy0lYApG6vcyPii938Hkl6Dek6JMPI70tEnJDb2nWfI0nnAVNIw07/Hfh34Cd00fvjIDAzK5y7hszMCucgMDMrnIPAzKxwDgIzs8I5CMzMCucgsJYkrcyjXS6QdJGkjQahDlMk7THQ662sf888OuZN+Xj3xvzRkj7cz2Ve2jiGvocyJ0ia2p/lrwlJ75I0cQDXN0XSTwdqfdaeg8DaeTLSaJevBp4BjujkSZUzQ9eGKcCgBQHwfuDL+XV4sjJ/NNAyCHprf0T8Sz6btqcyx0bEL/tc2zX3LmDAgsDWHQ4C68TvgO3yWa5n5LHkb5Q0A0DSoZLmSPoVcKWkUZL+O4+hf7Ok/XK5vSVdK+mGvJUxKs9fIun4PP8WSTtKmkAKn4/nX+R7SnpnHq/+Rkm/lLRFfv7YPOb7wjzI3F8ljcmPfSDX9yZJp0ka1tw4SXvlZd6S27eBpMOBA4DPSzqn6SlfAbbNyzwx/7L9naQ5pLOYkfSTPEjcwspAcY22jpE0QelaCd/JZX7R2OqQdKak/du9Nr21ubKuYXlZC/JzP57nbyvpsly/3+XXew/SeFMn5nZt27SsVXXK04/lv1tK+m1l63HPXt7raUrXibgBeE9v/3g2QAZ7+FPf1s0b8Fj+O5x0evz/Ab5EOtsT0q/iv5AG3juUNMbP5vmxrwInV5a1Gemsy98CG+d5RwPH5vtLgCPz/Q8D3833jwM+2bScxkmQhwNfz/f/E/h0vj+NNMDXGOCVwCXA+vmx/wIObmrnSNLQ4q/I02cDH8v3zwT2b/HaTOCFQw5PIQ1ut01lXuO12JA0JMSLK20dk5exAtg5z7+w8tquWm8Pr03LNjfV83XAFZXp0fnvlcD2+f6uwK96am+rx3j+/+MTwGfz/WHAJu3e68prvT2g3OafDvb/um/B2tyMt6FlQ6VhgiFtEXwP+D0wXdIn8/yRpFPoIX3hNE6hn0oa1hqAiFgu6R2kbodrJEEa/uHayvp+lP/Op/0vxfHABUqDeI0gXXcA4I3Au/O6LpO0PM/fi/RlODevc0NWH/xrB9JAgn/J02cBHwFOblOHdv4QEXdWpo+S9O58fyvSl9+DTc+5MyIar/F8Uji00uq1adfmqjuAl0s6BfgZaSjrUaTutovyawKwQS9t68lc4AxJ6wM/iYibJL2Z1u/1jqQ23wYg6QfArNaLtYHkILB2now0TPAqSp/q/SJiUdP8XUm/iHsiUlgc2ObxxpgyK2n/f3kK8I2ImCNpCmmLobd1nhURn+6l3Nqwqv25blOB3SPiCUlXkUKzWXUcnZWkoGqlk9dmNTmAdyJdPOkIUlfXx0jj/u/c45NXt4LclSxpPdKXOxHxW6VhsfcFzpT0DdLVxVZ7r5XGgLJ1kPcRWF9cDhyZAwFJr21T7grSr2pyuc2A64A3SNouz9tY0it6Wd8/SF0NDZvy/NDEh1TmX0P6kkPS3qQuJEhdIPtLekl+bHNJWzetYxEwoVEv4CDgN32sV7NNgeU5BHYEdutlef3Rrs2r5H0G60XED4HPAbtExKPAnZLem8sohwX03K4lpK0rSPsS1s/P3xr4e0R8hzTQ3y60f6//THqtG/sf2v0osAHmILC++DzpC+BmSQvzdCtfADbLOw//SLre7jLSvoTzJN3M810FPbkEeHfeEbknaQvgIknzSZc1bDge2Fvp4uHvJV0R6h8R8SfSF+Av8jqvIF02cJWIeIo0OulFkm4BngO+3VOlIuJBUrfHAkkntihyGTBc0q2kHcvX9dLO/mjZ5qYy44CrchffD4DGltH7gcPye7OQdLElSCN/fkppx/m2Tcv6DvDm/JzdeX4LaArwR0k3kkbl/Wa79zq/1rOAn+Wdxev0GP0l8eij1vUkbQCsjIgVknYnXZ1sSHdDlNhmq4/3EdhQ8DLgwtx3/QzwwUGuz0Aosc1WE28RmJkVzvsIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK9/8B9sqoxzUs3U4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQxohOJ9JTe5",
        "outputId": "8c8ee15f-8702-4d06-808e-7fb860d29ee6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[0],70:v1[1],60:v1[2],50:v1[3], 30:v1[4], 10:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ3 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "print(v1)\n",
        "plt.show()"
      ],
      "execution_count": 457,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.0, 1.0, 1.0, 1.0, 0.9847328244274809, 0.9940476190476191]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "aKgVm_KXIx4S",
        "outputId": "16a722f9-b866-4f4d-dd16-0c9fe409fbd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[0],70:v1[1],60:v1[2],50:v1[3], 30:v1[4], 10:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ4 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "print(v1)\n",
        "plt.show()"
      ],
      "execution_count": 416,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.0, 1.0, 1.0, 1.0, 1.0, 0.9702380952380952]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHAOAjOzwjkIzMwK5yAwMyucg8DMrHC1BoGkKZIWS1oi6dQm6z8l6U5Jt0u6VtKOddbHzMzWVFsQSBoGnA0cCkwApkua0FBsATAxIt4GXAZ8ta76mJlZc3UeEewFLImIeyPiReBiYFq1QERcFxHP5tm5wOga62NmZk3UGQTbAw9U5pflZa0cD/yy2QpJMyTNlzR/+fLl67GKZma2QXQWSzoGmAic1Wx9RJwTERMjYmJPT09nK2dmNsQNr3HbDwJjKvOj87LXkHQQ8HngXRHxQo31MTOzJuo8IpgHjJO0k6RNgKOBWdUCkt4OfA+YGhGP1FgXMzNrobYgiIhVwEnAHOAuYGZELJJ0pqSpudhZwAjgUkm3SZrVYnNmZlaTOk8NERGzgdkNy06rTB9U5/7NzKx/G0RnsZmZDR4HgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVjgHgZlZ4RwEZmaFcxCYmRXOQWBmVrhag0DSFEmLJS2RdGqT9a+TdElef7OksXXWx8zM1lRbEEgaBpwNHApMAKZLmtBQ7HhgZUS8Gfg68JW66mNmZs3VeUSwF7AkIu6NiBeBi4FpDWWmARfk6cuAyZJUY53MzKyBIqKeDUtHAFMi4oQ8/3fA3hFxUqXMwlxmWZ6/J5dZ0bCtGcCMPLsrsLiWSq+dUcCKfkt1l6HWpqHWHhh6bRpq7YENr007RkRPsxXDO12TtRER5wDnDHY9mpE0PyImDnY91qeh1qah1h4Yem0aau2B7mpTnaeGHgTGVOZH52VNy0gaDmwFPFpjnczMrEGdQTAPGCdpJ0mbAEcDsxrKzAI+nKePAH4ddZ2rMjOzpmo7NRQRqySdBMwBhgHnRcQiSWcC8yNiFvDvwIWSlgCPkcKi22yQp6zW0VBr01BrDwy9Ng219kAXtam2zmIzM+sO/maxmVnhHARmZoVzEAyQpE9KWiRpoaSLJG2aO8RvzkNlXJI7xzd4knaVdFvl8aSkT0jaRtI1kv6Qf2492HUdCEkjJV0m6W5Jd0nat5vbJGmppDvyezQ/L+vm9mwq6beSfpf/ls7Iy7v17+g8SY/k70X1Luuq98dBMACStgc+DkyMiLeQOsGPJg2N8fU8VMZK0tAZG7yIWBwRe0TEHsA7gGeBnwGnAtdGxDjg2jzfTb4JXBUR44Hdgbvo/ja9O79Xvdeld3N7XgDeExG7A3sAUyTtQ5f+HQHnA1MalnXX+xMRfrT5ALYHHgC2IV1xdQXwXtK3B4fnMvsCcwa7rmvRtkOAG/P0YmDbPL0tsHiw6zeAdmwF3Ee+EKKyvJvbtBQYNVTa09COzYFbgb27+e8IGAss7Nb3x0cEAxARDwL/D7gfeAh4ArgFeDwiVuViy0iB0W2OBi7K02+MiIfy9MPAGwenSmtlJ2A58ANJCyR9X9IWdHebArha0i15uBXo7vYgaZik24BHgGuAexgaf0e9uur9cRAMQD7PN430z2Y7YAvWPCTsOvlc7FTg0sZ1kT7SdNM1xsOBPYHvRMTbgWdoOCzvwjYdEBF7kkby/Zikd1ZXdmF7iIjVkU5JjiYNUDl+kKtUm254fxwEA3MQcF9ELI+Il4CfAvsDI/MQGdB8KI0N3aHArRHx5zz/Z0nbAuSfjwxazQZuGbAsIm7O85eRgqFr25SPRImIR0h9OHvRxe2piojHgetIp4K6/e+oqqveHwfBwNwP7CNp8zxc9mTgTtIv8hG5zIeBnw9S/dbWdF49LQSvHfqjq9oTEQ8DD0jaNS/qfY+6sk2StpC0Ze80qS9nIV3aHgBJPZJG5unNgINJHfrd/ndU1VXvj79ZPED5Ure/BlYBC4ATSOcyLyZ1Ii8AjomIFwatkgOQ/7ncD7wpIp7Iy/4CmAnsAPwROCoiHhu8Wg6MpD2A7wObAPcCf0/60NN1bZL0JtJRAKTTXj+OiH/q5vdI0ttI9yEZRn5fIuLM3Nau+zuSdBEwiTTs9J+BLwKX00Xvj4PAzKxwPjVkZlY4B4GZWeEcBGZmhXMQmJkVzkFgZlY4B4E1JWl1Hu1yoaRLJW0+CHWYJGm/Tu+3sv8D8+iYt+Xr3XuXj5T00bXc5uzea+j7KHOmpIPWZvvrQtIHJE3o4P4mSbqiU/uz1hwE1spzkUa7fAvwIvCRdp5U+Wbo+jAJGLQgAP4W+L/5dXiusnwk0DQI+mt/RByWv03bV5nTIuJXA67tuvsA0LEgsA2Hg8Da8RvgzflbruflseQXSJoGIOk4SbMk/Rq4VtIIST/IY+jfLunwXO4QSTdJujUfZYzIy5dKOiMvv0PSeEljSeHzyfyJ/EBJ78/j1S+Q9CtJb8zP78ljvi/Kg8z9UdKovO6YXN/bJH1P0rDGxkmanLd5R27f6ySdABwFfEnSfzQ85cvAznmbZ+VPtr+RNIv0LWYkXZ4HiVtUGSiut62jJI1VulfCubnM1b1HHZLOl3REq9emvzZX9jUsb2thfu4n8/KdJV2V6/eb/HrvRxpv6qzcrp0btvVKnfL80/nntpJuqBw9HtjPez1F6T4RtwIf6u8XzzpksIc/9WPDfABP55/DSV+P/1/AP5O+7QnpU/HvSQPvHUca42ebvO4rwDcq29qa9K3LG4At8rLPAqfl6aXAyXn6o8D38/TpwKcbttP7JcgTgH/J0/8GfC5PTyEN8DUK2A34BbBxXvdt4NiGdm5KGlp8lzz/Q+ATefp84Igmr81YXjvk8CTS4HY7VZb1vhabkYaE+ItKW0flbawC9sjLZ1Ze21f228dr07TNDfV8B3BNZX5k/nktMC5P7w38uq/2NlvHq78fpwCfz9PDgC1bvdeV13ocoNzmKwb7d92PYH0extvQspnSMMGQjgj+HfhvYKqkT+flm5K+Qg/pH07vV+gPIg1rDUBErJT0V6TTDjdKgjT8w02V/f00/7yF1p8URwOXKA3itQnpvgMABwAfzPu6StLKvHwy6Z/hvLzPzVhz8K9dSQMJ/j7PXwB8DPhGizq08tuIuK8y/3FJH8zTY0j//B5teM59EdH7Gt9CCodmmr02rdpcdS/wJknfAq4kDWU9gnS67dL8mgC8rp+29WUecJ6kjYHLI+I2Se+i+Xs9ntTmPwBI+hEwo/lmrZMcBNbKc5GGCX6F0l/14RGxuGH53qRPxH0RKSymt1jfO6bMalr/Xn4L+FpEzJI0iXTE0N8+L4iIz/VTbn14pf25bgcB+0bEs5KuJ4Vmo+o4OqtJQdVMO6/NGnIA7066edJHSKe6PkEa93+PPp+8plXkU8mSNiL9cyciblAaFvt9wPmSvka6u9ga77XSGFC2AXIfgQ3EHODkHAhIenuLcteQPlWTy20NzAX2l/TmvGwLSbv0s7+nSKcaem3Fq0MTf7iy/EbSPzkkHUI6hQTpFMgRkt6Q120jaceGfSwGxvbWC/g74D8HWK9GWwErcwiMB/bpZ3tro1WbX5H7DDaKiJ8AXwD2jIgngfskHZnLKIcF9N2upaSjK0h9CRvn5+8I/DkiziUN9Lcnrd/ru0mvdW//Q6sPBdZhDgIbiC+R/gHcLmlRnm/m/wBb587D35Hut7uc1JdwkaTbefVUQV9+AXwwd0QeSDoCuFTSLaTbGvY6AzhE6ebhR5LuCPVURNxJ+gd4dd7nNaTbBr4iIp4njU56qaQ7gJeB7/ZVqYh4lHTaY6Gks5oUuQoYLukuUsfy3H7auTaatrmhzPbA9fkU34+A3iOjvwWOz+/NItLNliCN/PkZpY7znRu2dS7wrvycfXn1CGgS8DtJC0ij8n6z1XudX+sZwJW5s3iDHqO/JB591LqepNcBqyNilaR9SXcnG9KnIUpss9XHfQQ2FOwAzMznrl8EThzk+nRCiW22mviIwMyscO4jMDMrnIPAzKxwDgIzs8I5CMzMCucgMDMr3P8Hkl9A1t9v8f0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxR1fxCGG5xl",
        "outputId": "a46eb882-1e1c-4e61-bb03-b0ec46c2d3c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[0],70:v1[1],60:v1[2],50:v1[3], 30:v1[4], 10:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ135 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "print(v1)\n",
        "plt.show()"
      ],
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.9473684210526315, 0.9107142857142857, 0.92, 0.9354838709677419, 0.9236641221374046, 0.9523809523809523]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "ukEUxfXuF0hD",
        "outputId": "02e8288f-7bed-4f0e-a279-eea7632cd250",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[0],70:v1[1],60:v1[2],50:v1[3], 30:v1[4], 10:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ8 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "print(v1)\n",
        "plt.show()"
      ],
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.9736842105263158, 0.9821428571428571, 0.9733333333333334, 0.978494623655914, 0.9770992366412213, 0.9761904761904762]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "ofG8cZkoGVO2",
        "outputId": "034ed732-6dac-4daf-e9f8-411a357cd0a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[2],70:v2[2],60:v3[2],50:v4[2], 30:v5[2], 10:v6[2]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ9 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1540,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AxoQMYVaGcGA",
        "outputId": "5745c8c8-0a15-4c92-a952-57ed77087289",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[4],70:v2[4],60:v3[],50:v4[2], 30:v5[2], 10:v6[2]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ2 SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1038,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cekeXNz7-cZ5",
        "outputId": "13a462ff-9ff1-4ad1-ebae-16be52b8f848",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {80:v1[0],70:v1[1],60:v1[2],50:v1[3], 20:v1[4], 5:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"DIGITAL VALUE OF SENSOR\")\n",
        "plt.xlabel(\"Percentage of training set used\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ugLLi16eUzC"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler \n",
        "sc_x = StandardScaler() \n",
        "\n",
        "X= sc_x.fit_transform(X) \n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o31Ob1kMhuPZ",
        "outputId": "93e44b6b-b368-4b12-ff37-67f06bb98e6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import sklearn.linear_model\n",
        "# Fit the data to a logistic regression model.\n",
        "clf = sklearn.linear_model.LogisticRegression(random_state=0)\n",
        "clf.fit(X, y)\n",
        "\n",
        "# Retrieve the model parameters.\n",
        "print(clf.coef_)\n",
        "print(clf.intercept_)\n",
        "b = clf.intercept_[0]\n",
        "w1, w2 = clf.coef_.T\n",
        "\n",
        "# Calculate the intercept and gradient of the decision boundary.\n",
        "c = -b/w2\n",
        "m = -w1/w2\n",
        "\n",
        "# Plot the data and the classification with the decision boundary.\n",
        "xmin, xmax = -10, 10\n",
        "ymin, ymax = -10, 10\n",
        "xd = np.array([xmin, xmax])\n",
        "yd = m*xd + c\n",
        "plt.plot(xd, yd, 'k', lw=1, ls='--')\n",
        "plt.fill_between(xd, yd, ymin, color='tab:red', alpha=0.2)\n",
        "plt.fill_between(xd, yd, ymax, color='tab:pink', alpha=0.2)\n",
        "\n",
        "plt.scatter(*X[y==0].T,s=8 , alpha=0.5)\n",
        "plt.scatter(*X[y==1].T,s=8 , alpha=0.5)\n",
        "plt.xlim(xmin, xmax)\n",
        "plt.ylim(ymin, ymax)\n",
        "plt.ylabel(r'MQ135')\n",
        "plt.xlabel(r'MQ8')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1636,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-4.98757052 -0.05390635]]\n",
            "[2.86534429]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "o7bswbYnkNS8",
        "outputId": "ac57d22d-f0d0-43e9-a3de-b7251d74ac40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "#v1=[a1,a2,a3,a4,a5,a6]\n",
        "#v2=[a1,a2,a3,a4,a5,a6]\n",
        "#v3=[a1,a2,a3,a4,a5,a6]\n",
        "#v4=[a1,a2,a3,a4,a5,a6]\n",
        "#v1=[a1,a2,a3,a4,a5,a6,a7]\n",
        "plt.scatter(df1['MQ8A'], df1['MQ135A'], alpha=0.2, c=df1['output'], cmap='viridis')\n",
        "plt.xlabel('MQ8 ANALOG')\n",
        "plt.ylabel('MQ135 ANALOG')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'MQ135 ANALOG')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "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": "bvujpEeppUfi",
        "outputId": "228dbf2d-1a93-4273-89e7-766981e029af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "plt.scatter(df1['MQ8A'], df1['MQ4A'], alpha=0.2, c=df1['output'], cmap='viridis')\n",
        "plt.xlabel('MQ8 ANALOG')\n",
        "plt.ylabel('MQ4 ANALOG');"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vr7UwyXsqfM2",
        "outputId": "6b363173-795b-4945-f9fd-96e803213a19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.scatter(df1['MQ4A'], df1['MQ9A'], alpha=0.2, c=df1['output'], cmap='viridis')\n",
        "plt.xlabel('MQ4 ANALOG')\n",
        "plt.ylabel('MQ9 ANALOG');"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNqHCnyxvjsV",
        "outputId": "13e2a2f1-daf3-4389-ef07-a9ede02c151d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.scatter(df1['MQ4A'], df1['MQ2A'], alpha=0.2, c=df1['output'], cmap='viridis')\n",
        "plt.xlabel('MQ4 ANALOG')\n",
        "plt.ylabel('MQ2 ANALOG')"
      ],
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4TWGZqNvsv3",
        "outputId": "93d6844c-7f41-4caf-ece5-e5f83038689f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "plt.scatter(df1['MQ4A'], df1['MQ135A'], alpha=0.2, c=df1['output'], cmap='viridis')\n",
        "plt.xlabel('MQ4 ANALOG')\n",
        "plt.ylabel('MQ135 ANALOG')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'MQ135 ANALOG')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "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": "W7120_WZCaZq",
        "outputId": "34502178-df27-4f80-f805-0e8ec2b5d2ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import sklearn.linear_model\n",
        "# Fit the data to a logistic regression model.\n",
        "clf = sklearn.linear_model.LogisticRegression(random_state=0)\n",
        "clf.fit(X, y)\n",
        "\n",
        "# Retrieve the model parameters.\n",
        "print(clf.coef_)\n",
        "print(clf.intercept_)\n",
        "b = clf.intercept_[0]\n",
        "w1, w2 = clf.coef_.T\n",
        "\n",
        "# Calculate the intercept and gradient of the decision boundary.\n",
        "c = -b/w2\n",
        "m = -w1/w2\n",
        "\n",
        "# Plot the data and the classification with the decision boundary.\n",
        "xmin, xmax = -4, 4\n",
        "ymin, ymax = -4, 4\n",
        "xd = np.array([xmin, xmax])\n",
        "yd = m*xd + c\n",
        "plt.plot(xd, yd, 'k', lw=1, ls='--')\n",
        "plt.fill_between(xd, yd, ymin, color='tab:red', alpha=0.2)\n",
        "plt.fill_between(xd, yd, ymax, color='tab:pink', alpha=0.2)\n",
        "\n",
        "plt.scatter(*X[y==0].T,s=8 , alpha=0.5)\n",
        "plt.scatter(*X[y==1].T,s=8 , alpha=0.5)\n",
        "plt.xlim(xmin, xmax)\n",
        "plt.ylim(ymin, ymax)\n",
        "plt.ylabel(r'MQ135')\n",
        "plt.xlabel(r'MQ8')\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 1644,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[2.65051685 2.6845212 ]]\n",
            "[1.392938]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "fWeR5o_fDwkY",
        "outputId": "4dd26a19-f0ad-4496-d948-e6ca5a6db565",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import sklearn.linear_model\n",
        "# Fit the data to a logistic regression model.\n",
        "clf = sklearn.linear_model.LogisticRegression(random_state=0)\n",
        "clf.fit(X, y)\n",
        "\n",
        "# Retrieve the model parameters.\n",
        "print(clf.coef_)\n",
        "print(clf.intercept_)\n",
        "b = clf.intercept_[0]\n",
        "w1, w2 = clf.coef_.T\n",
        "\n",
        "# Calculate the intercept and gradient of the decision boundary.\n",
        "c = -b/w2\n",
        "m = -w1/w2\n",
        "\n",
        "# Plot the data and the classification with the decision boundary.\n",
        "xmin, xmax = -4, 4\n",
        "ymin, ymax = -4, 4\n",
        "xd = np.array([xmin, xmax])\n",
        "yd = m*xd + c\n",
        "plt.plot(xd, yd, 'k', lw=1, ls='--')\n",
        "plt.fill_between(xd, yd, ymin, color='tab:red', alpha=0.2)\n",
        "plt.fill_between(xd, yd, ymax, color='tab:pink', alpha=0.2)\n",
        "\n",
        "plt.scatter(*X[y==0].T,s=8 , alpha=0.5)\n",
        "plt.scatter(*X[y==1].T,s=8 , alpha=0.5)\n",
        "plt.xlim(xmin, xmax)\n",
        "plt.ylim(ymin, ymax)\n",
        "plt.ylabel(r'MQ9')\n",
        "plt.xlabel(r'MQ135')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1593,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[4.34582239 0.76655163]]\n",
            "[1.30723351]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "FuxKYQpQEtcJ",
        "outputId": "0cb5c475-bf88-477c-de69-5f978700812d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import sklearn.linear_model\n",
        "# Fit the data to a logistic regression model.\n",
        "clf = sklearn.linear_model.LogisticRegression(random_state=0)\n",
        "clf.fit(X, y)\n",
        "\n",
        "# Retrieve the model parameters.\n",
        "print(clf.coef_)\n",
        "print(clf.intercept_)\n",
        "b = clf.intercept_[0]\n",
        "w1, w2 = clf.coef_.T\n",
        "\n",
        "# Calculate the intercept and gradient of the decision boundary.\n",
        "c = -b/w2\n",
        "m = -w1/w2\n",
        "\n",
        "# Plot the data and the classification with the decision boundary.\n",
        "xmin, xmax = -4, 4\n",
        "ymin, ymax = -4, 4\n",
        "xd = np.array([xmin, xmax])\n",
        "yd = m*xd + c\n",
        "plt.plot(xd, yd, 'k', lw=1, ls='--')\n",
        "plt.fill_between(xd, yd, ymin, color='tab:red', alpha=0.2)\n",
        "plt.fill_between(xd, yd, ymax, color='tab:pink', alpha=0.2)\n",
        "\n",
        "plt.scatter(*X[y==0].T,s=8 , alpha=0.5)\n",
        "plt.scatter(*X[y==1].T,s=8 , alpha=0.5)\n",
        "plt.xlim(xmin, xmax)\n",
        "plt.ylim(ymin, ymax)\n",
        "plt.ylabel(r'MQ4')\n",
        "plt.xlabel(r'MQ9')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1597,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.57297063 4.51981334]]\n",
            "[0.99520784]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "Nq3GTzz1E-gj",
        "outputId": "ebc5c0d5-369f-4bb1-a894-5df63e8e6062",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import sklearn.linear_model\n",
        "# Fit the data to a logistic regression model.\n",
        "clf = sklearn.linear_model.LogisticRegression(random_state=0)\n",
        "clf.fit(X, y)\n",
        "\n",
        "# Retrieve the model parameters.\n",
        "print(clf.coef_)\n",
        "print(clf.intercept_)\n",
        "b = clf.intercept_[0]\n",
        "w1, w2 = clf.coef_.T\n",
        "\n",
        "# Calculate the intercept and gradient of the decision boundary.\n",
        "c = -b/w2\n",
        "m = -w1/w2\n",
        "\n",
        "# Plot the data and the classification with the decision boundary.\n",
        "xmin, xmax = -4, 4\n",
        "ymin, ymax = -4, 4\n",
        "xd = np.array([xmin, xmax])\n",
        "yd = m*xd + c\n",
        "plt.plot(xd, yd, 'k', lw=1, ls='--')\n",
        "plt.fill_between(xd, yd, ymin, color='tab:red', alpha=0.2)\n",
        "plt.fill_between(xd, yd, ymax, color='tab:pink', alpha=0.2)\n",
        "\n",
        "plt.scatter(*X[y==0].T,s=8 , alpha=0.5)\n",
        "plt.scatter(*X[y==1].T,s=8 , alpha=0.5)\n",
        "plt.xlim(xmin, xmax)\n",
        "plt.ylim(ymin, ymax)\n",
        "plt.ylabel(r'MQ2')\n",
        "plt.xlabel(r'MQ4')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1601,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 4.62023863 -0.52227554]]\n",
            "[0.65454082]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "8auNqgYcFIDA",
        "outputId": "ec8d0cfd-8d39-41b2-98eb-aca0891392e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import sklearn.linear_model\n",
        "# Fit the data to a logistic regression model.\n",
        "clf = sklearn.linear_model.LogisticRegression(random_state=0)\n",
        "clf.fit(X, y)\n",
        "\n",
        "# Retrieve the model parameters.\n",
        "print(clf.coef_)\n",
        "print(clf.intercept_)\n",
        "b = clf.intercept_[0]\n",
        "w1, w2 = clf.coef_.T\n",
        "\n",
        "# Calculate the intercept and gradient of the decision boundary.\n",
        "c = -b/w2\n",
        "m = -w1/w2\n",
        "\n",
        "# Plot the data and the classification with the decision boundary.\n",
        "xmin, xmax = -4, 4\n",
        "ymin, ymax = -4, 4\n",
        "xd = np.array([xmin, xmax])\n",
        "yd = m*xd + c\n",
        "plt.plot(xd, yd, 'k', lw=1, ls='--')\n",
        "plt.fill_between(xd, yd, ymin, color='tab:red', alpha=0.2)\n",
        "plt.fill_between(xd, yd, ymax, color='tab:pink', alpha=0.2)\n",
        "\n",
        "plt.scatter(*X[y==0].T,s=8 , alpha=0.5)\n",
        "plt.scatter(*X[y==1].T,s=8 , alpha=0.5)\n",
        "plt.xlim(xmin, xmax)\n",
        "plt.ylim(ymin, ymax)\n",
        "plt.ylabel(r'MQ3')\n",
        "plt.xlabel(r'MQ2')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1604,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1.98920329 -3.90812521]]\n",
            "[0.72942656]\n"
          ],
          "name": "stdout"
        },
        {
          "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": "q7JRIQMCINnC",
        "outputId": "dfb7b20c-f822-42e2-e790-03279f31eb81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {9:a7, 4:v1[1],2:v1[2],3:v1[3]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ135 SENSOR\")\n",
        "plt.xlabel(\"The feature sensor added with MQ135\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1543,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O53HsAtvOnCW",
        "outputId": "2ed425c9-4181-4275-ad1f-d76b17e40d2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {4:a7,2:v1[4],3:v1[5]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ9 SENSOR\")\n",
        "plt.xlabel(\"The feature sensor added with MQ9\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1606,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fjHroL_9GwT",
        "outputId": "d7fda2f5-7c1b-4173-d57f-7cc30875c868",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "D = {2:a7,3:v1[6]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ4 SENSOR\")\n",
        "plt.xlabel(\"The feature sensor added with MQ4\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1614,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fof46_c9OIh",
        "outputId": "5026403f-5d21-4dce-f4b7-9729ea73bc63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "himport matplotlib.pyplot as plt\n",
        "\n",
        "D = {135:v1[0], 9:v1[0], 4:v1[0], 2:v1[0], 3:v1[0]}\n",
        "\n",
        "plt.bar(range(len(D)), list(D.values()), align='center')\n",
        "plt.xticks(range(len(D)), list(D.keys()))\n",
        "plt.title(\"MQ8 SENSOR\")\n",
        "plt.xlabel(\"The feature sensor added with the rest\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 1519,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0U61Gx3O9tc2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}