Predicting_Failure.ipynb

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        "\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import pandas_profiling\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression, ElasticNet, Ridge, Lasso, LogisticRegression\n",
        "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "from sklearn.tree import ExtraTreeRegressor, DecisionTreeClassifier\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n",
        "from sklearn.metrics import confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('failure.csv')"
      ],
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      "execution_count": 512,
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        "df.head(10)"
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              "         date    device  failure  attribute1  attribute2  attribute3  \\\n",
              "0  2015-01-01  S1F01085        0   215630672          56           0   \n",
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              "9  2015-01-01  S1F02DZ2        0   116440096           0         323   \n",
              "\n",
              "   attribute4  attribute5  attribute6  attribute7  attribute8  attribute9  \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 124494 entries, 0 to 124493\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count   Dtype \n",
            "---  ------      --------------   ----- \n",
            " 0   date        124494 non-null  object\n",
            " 1   device      124494 non-null  object\n",
            " 2   failure     124494 non-null  int64 \n",
            " 3   attribute1  124494 non-null  int64 \n",
            " 4   attribute2  124494 non-null  int64 \n",
            " 5   attribute3  124494 non-null  int64 \n",
            " 6   attribute4  124494 non-null  int64 \n",
            " 7   attribute5  124494 non-null  int64 \n",
            " 8   attribute6  124494 non-null  int64 \n",
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            " 10  attribute8  124494 non-null  int64 \n",
            " 11  attribute9  124494 non-null  int64 \n",
            "dtypes: int64(10), object(2)\n",
            "memory usage: 11.4+ MB\n"
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              "      <td>-0.000550</td>\n",
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              "      <td>0.119055</td>\n",
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              "      <td>0.000151</td>\n",
              "      <td>0.000151</td>\n",
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              "      <td>-0.002617</td>\n",
              "      <td>0.146593</td>\n",
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              "      <td>-0.026350</td>\n",
              "      <td>0.141367</td>\n",
              "      <td>0.141367</td>\n",
              "      <td>-0.002736</td>\n",
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              "      <td>0.003702</td>\n",
              "      <td>-0.002617</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.097452</td>\n",
              "      <td>-0.006696</td>\n",
              "      <td>0.009027</td>\n",
              "      <td>-0.001884</td>\n",
              "      <td>-0.001884</td>\n",
              "      <td>0.532366</td>\n",
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              "    <tr>\n",
              "      <th>attribute4</th>\n",
              "      <td>0.067398</td>\n",
              "      <td>0.001837</td>\n",
              "      <td>0.146593</td>\n",
              "      <td>0.097452</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.009773</td>\n",
              "      <td>0.024870</td>\n",
              "      <td>0.045631</td>\n",
              "      <td>0.045631</td>\n",
              "      <td>0.036069</td>\n",
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              "    <tr>\n",
              "      <th>attribute5</th>\n",
              "      <td>0.002270</td>\n",
              "      <td>-0.003370</td>\n",
              "      <td>-0.013999</td>\n",
              "      <td>-0.006696</td>\n",
              "      <td>-0.009773</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.017051</td>\n",
              "      <td>-0.009384</td>\n",
              "      <td>-0.009384</td>\n",
              "      <td>0.005949</td>\n",
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              "    <tr>\n",
              "      <th>attribute6</th>\n",
              "      <td>-0.000550</td>\n",
              "      <td>-0.001516</td>\n",
              "      <td>-0.026350</td>\n",
              "      <td>0.009027</td>\n",
              "      <td>0.024870</td>\n",
              "      <td>-0.017051</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.012207</td>\n",
              "      <td>-0.012207</td>\n",
              "      <td>0.021152</td>\n",
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              "    <tr>\n",
              "      <th>attribute7</th>\n",
              "      <td>0.119055</td>\n",
              "      <td>0.000151</td>\n",
              "      <td>0.141367</td>\n",
              "      <td>-0.001884</td>\n",
              "      <td>0.045631</td>\n",
              "      <td>-0.009384</td>\n",
              "      <td>-0.012207</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.006861</td>\n",
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              "    <tr>\n",
              "      <th>attribute8</th>\n",
              "      <td>0.119055</td>\n",
              "      <td>0.000151</td>\n",
              "      <td>0.141367</td>\n",
              "      <td>-0.001884</td>\n",
              "      <td>0.045631</td>\n",
              "      <td>-0.009384</td>\n",
              "      <td>-0.012207</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.006861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>attribute9</th>\n",
              "      <td>0.001622</td>\n",
              "      <td>0.001122</td>\n",
              "      <td>-0.002736</td>\n",
              "      <td>0.532366</td>\n",
              "      <td>0.036069</td>\n",
              "      <td>0.005949</td>\n",
              "      <td>0.021152</td>\n",
              "      <td>0.006861</td>\n",
              "      <td>0.006861</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed529d42-b230-44c5-9d29-cc3edb3b82c7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed529d42-b230-44c5-9d29-cc3edb3b82c7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ed529d42-b230-44c5-9d29-cc3edb3b82c7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 516
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "nRFiWH6y6fg1",
        "outputId": "3c814b37-b819-461c-e5f7-d6f231c01bfa"
      },
      "execution_count": 517,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             failure    attribute1     attribute2     attribute3  \\\n",
              "count  124494.000000  1.244940e+05  124494.000000  124494.000000   \n",
              "mean        0.000851  1.223868e+08     159.484762       9.940455   \n",
              "std         0.029167  7.045960e+07    2179.657730     185.747321   \n",
              "min         0.000000  0.000000e+00       0.000000       0.000000   \n",
              "25%         0.000000  6.127675e+07       0.000000       0.000000   \n",
              "50%         0.000000  1.227957e+08       0.000000       0.000000   \n",
              "75%         0.000000  1.833084e+08       0.000000       0.000000   \n",
              "max         1.000000  2.441405e+08   64968.000000   24929.000000   \n",
              "\n",
              "          attribute4     attribute5     attribute6     attribute7  \\\n",
              "count  124494.000000  124494.000000  124494.000000  124494.000000   \n",
              "mean        1.741120      14.222693  260172.858025       0.292528   \n",
              "std        22.908507      15.943021   99151.009852       7.436924   \n",
              "min         0.000000       1.000000       8.000000       0.000000   \n",
              "25%         0.000000       8.000000  221452.000000       0.000000   \n",
              "50%         0.000000      10.000000  249799.500000       0.000000   \n",
              "75%         0.000000      12.000000  310266.000000       0.000000   \n",
              "max      1666.000000      98.000000  689161.000000     832.000000   \n",
              "\n",
              "          attribute8     attribute9  \n",
              "count  124494.000000  124494.000000  \n",
              "mean        0.292528      12.451524  \n",
              "std         7.436924     191.425623  \n",
              "min         0.000000       0.000000  \n",
              "25%         0.000000       0.000000  \n",
              "50%         0.000000       0.000000  \n",
              "75%         0.000000       0.000000  \n",
              "max       832.000000   18701.000000  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>failure</th>\n",
              "      <th>attribute1</th>\n",
              "      <th>attribute2</th>\n",
              "      <th>attribute3</th>\n",
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              "      <th>attribute9</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>124494.000000</td>\n",
              "      <td>1.244940e+05</td>\n",
              "      <td>124494.000000</td>\n",
              "      <td>124494.000000</td>\n",
              "      <td>124494.000000</td>\n",
              "      <td>124494.000000</td>\n",
              "      <td>124494.000000</td>\n",
              "      <td>124494.000000</td>\n",
              "      <td>124494.000000</td>\n",
              "      <td>124494.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.000851</td>\n",
              "      <td>1.223868e+08</td>\n",
              "      <td>159.484762</td>\n",
              "      <td>9.940455</td>\n",
              "      <td>1.741120</td>\n",
              "      <td>14.222693</td>\n",
              "      <td>260172.858025</td>\n",
              "      <td>0.292528</td>\n",
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              "      <td>7.045960e+07</td>\n",
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              "      <td>185.747321</td>\n",
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              "      <td>99151.009852</td>\n",
              "      <td>7.436924</td>\n",
              "      <td>7.436924</td>\n",
              "      <td>191.425623</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.127675e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>221452.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.227957e+08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>249799.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.833084e+08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>310266.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>98.000000</td>\n",
              "      <td>689161.000000</td>\n",
              "      <td>832.000000</td>\n",
              "      <td>832.000000</td>\n",
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              "          const element = document.querySelector('#df-f33610f8-217c-4087-94d1-bcd2ff5bba0f');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 517
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "abs(df.corr()[\"failure\"].sort_values(ascending=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P10CVkUG6fjk",
        "outputId": "594a168c-8bf7-4c43-83ef-3429b600add2"
      },
      "execution_count": 518,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "failure       1.000000\n",
              "attribute7    0.119055\n",
              "attribute8    0.119055\n",
              "attribute4    0.067398\n",
              "attribute2    0.052902\n",
              "attribute5    0.002270\n",
              "attribute1    0.001984\n",
              "attribute9    0.001622\n",
              "attribute6    0.000550\n",
              "attribute3    0.000948\n",
              "Name: failure, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 518
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "sns.heatmap(df.corr(),annot=True,linewidths=0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "ZmoDcoeqi2HW",
        "outputId": "d0d57d9b-d5c8-4007-d033-1316c847850c"
      },
      "execution_count": 519,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdab3d89220>"
            ]
          },
          "metadata": {},
          "execution_count": 519
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.device.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GeYoF9mYcxom",
        "outputId": "a68b6b61-cb00-45df-d52c-7f5bcef3371a"
      },
      "execution_count": 520,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S1F0FGBQ    304\n",
              "W1F0SJJ2    304\n",
              "Z1F0GE1M    304\n",
              "Z1F0KJDS    304\n",
              "Z1F0KKN4    304\n",
              "           ... \n",
              "Z1F0LT6P      5\n",
              "Z1F0LT6Z      5\n",
              "W1F1CJKT      5\n",
              "S1F04KSC      4\n",
              "W1F0WJFT      3\n",
              "Name: device, Length: 1168, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 520
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.attribute7.value_counts()"
      ],
      "metadata": {
        "id": "2cwEeFNvw4dU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee385fce-43e9-409b-c31e-fdb3f02dc07c"
      },
      "execution_count": 521,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      123036\n",
              "8         793\n",
              "16        397\n",
              "24         65\n",
              "48         36\n",
              "32         35\n",
              "128        23\n",
              "40         20\n",
              "176        20\n",
              "6          13\n",
              "72         11\n",
              "96          7\n",
              "56          6\n",
              "312         5\n",
              "736         4\n",
              "80          4\n",
              "104         3\n",
              "152         2\n",
              "832         2\n",
              "272         2\n",
              "112         2\n",
              "22          2\n",
              "744         1\n",
              "216         1\n",
              "496         1\n",
              "240         1\n",
              "136         1\n",
              "424         1\n",
              "Name: attribute7, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 521
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.attribute8.value_counts()"
      ],
      "metadata": {
        "id": "ysn_kxxuw4kh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08cf7c74-23f7-49cb-aee7-816111cbe9b0"
      },
      "execution_count": 522,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      123036\n",
              "8         793\n",
              "16        397\n",
              "24         65\n",
              "48         36\n",
              "32         35\n",
              "128        23\n",
              "40         20\n",
              "176        20\n",
              "6          13\n",
              "72         11\n",
              "96          7\n",
              "56          6\n",
              "312         5\n",
              "736         4\n",
              "80          4\n",
              "104         3\n",
              "152         2\n",
              "832         2\n",
              "272         2\n",
              "112         2\n",
              "22          2\n",
              "744         1\n",
              "216         1\n",
              "496         1\n",
              "240         1\n",
              "136         1\n",
              "424         1\n",
              "Name: attribute8, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 522
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"attribute8\",axis=1,inplace=True)  #attribute7 and attribute 8 attribute are duplicated."
      ],
      "metadata": {
        "id": "_05BfWJJwbAP"
      },
      "execution_count": 523,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.attribute3.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XK6ajAiDocF",
        "outputId": "6565ff8e-ca57-4d5e-abaa-403139a326c9"
      },
      "execution_count": 524,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        115359\n",
              "1          3274\n",
              "2           749\n",
              "7           298\n",
              "34          293\n",
              "5           278\n",
              "21          269\n",
              "4           268\n",
              "9           262\n",
              "8           251\n",
              "61          245\n",
              "100         245\n",
              "10          241\n",
              "1331        240\n",
              "220         240\n",
              "107         228\n",
              "15          225\n",
              "62          224\n",
              "2693        179\n",
              "318         177\n",
              "70          118\n",
              "3           113\n",
              "18          106\n",
              "56           96\n",
              "12           90\n",
              "72           84\n",
              "263          84\n",
              "406          84\n",
              "35           70\n",
              "11           14\n",
              "208          13\n",
              "14           12\n",
              "25            6\n",
              "2112          6\n",
              "323           6\n",
              "266           6\n",
              "377           6\n",
              "16            5\n",
              "382           5\n",
              "36            5\n",
              "53            5\n",
              "1326          5\n",
              "24929         4\n",
              "279           3\n",
              "24            1\n",
              "38            1\n",
              "1162          1\n",
              "Name: attribute3, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 524
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.attribute9.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCTZNmT_DofS",
        "outputId": "7941a950-4b4e-4df4-993e-31a1e67c5236"
      },
      "execution_count": 525,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        97358\n",
              "1         9436\n",
              "2         3722\n",
              "3         2327\n",
              "4         1396\n",
              "         ...  \n",
              "1864         5\n",
              "10137        4\n",
              "30           3\n",
              "38           3\n",
              "2269         1\n",
              "Name: attribute9, Length: 65, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 525
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rk4ZFXc6DoiX"
      },
      "execution_count": 525,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.device.nunique()  #1168 device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TiScEdYAkgDe",
        "outputId": "bb50b960-f3c4-4fee-aeb0-bf931d4058c0"
      },
      "execution_count": 526,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1168"
            ]
          },
          "metadata": {},
          "execution_count": 526
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df.failure == 1]  #106 failure"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "47J_rMbqv2ZQ",
        "outputId": "e647387b-e95d-44c2-ee17-b3d192a43338"
      },
      "execution_count": 527,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              date    device  failure  attribute1  attribute2  attribute3  \\\n",
              "4885    2015-01-05  S1F0RRB1        1    48467332       64776           0   \n",
              "6879    2015-01-07  S1F0CTDN        1   184069720         528           0   \n",
              "8823    2015-01-09  W1F0PNA5        1   136429411       64784           0   \n",
              "11957   2015-01-13  W1F13SRV        1   188251248        2040           0   \n",
              "12668   2015-01-14  W1F1230J        1   220461296           0           0   \n",
              "...            ...       ...      ...         ...         ...         ...   \n",
              "109927  2015-08-04  W1F1CB5E        1    16043296          88           0   \n",
              "114251  2015-08-18  Z1F0MRPJ        1    65654088           0           0   \n",
              "122118  2015-10-05  S1F0JGJV        1    13739704           0           0   \n",
              "122808  2015-10-09  Z1F14BGY        1    85259320           0           0   \n",
              "124329  2015-10-26  W1F0T0B1        1    95073232           0           0   \n",
              "\n",
              "        attribute4  attribute5  attribute6  attribute7  attribute9  \n",
              "4885           841           8       39267          56           1  \n",
              "6879             4           9      387871          32           3  \n",
              "8823           406          30      224801           8           0  \n",
              "11957            0           6       39345          32           1  \n",
              "12668            0          14      325125           0           0  \n",
              "...            ...         ...         ...         ...         ...  \n",
              "109927           0           9          30           0           0  \n",
              "114251           0           9      298592           0          11  \n",
              "122118          18           8      343760           0           0  \n",
              "122808         164           8      262932           0           0  \n",
              "124329           7           9      354861          22           0  \n",
              "\n",
              "[106 rows x 11 columns]"
            ],
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              "      <th>date</th>\n",
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              "      <th>4885</th>\n",
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              "      <th>11957</th>\n",
              "      <td>2015-01-13</td>\n",
              "      <td>W1F13SRV</td>\n",
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              "      <td>188251248</td>\n",
              "      <td>2040</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>12668</th>\n",
              "      <td>2015-01-14</td>\n",
              "      <td>W1F1230J</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>109927</th>\n",
              "      <td>2015-08-04</td>\n",
              "      <td>W1F1CB5E</td>\n",
              "      <td>1</td>\n",
              "      <td>16043296</td>\n",
              "      <td>88</td>\n",
              "      <td>0</td>\n",
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              "      <th>114251</th>\n",
              "      <td>2015-08-18</td>\n",
              "      <td>Z1F0MRPJ</td>\n",
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              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122118</th>\n",
              "      <td>2015-10-05</td>\n",
              "      <td>S1F0JGJV</td>\n",
              "      <td>1</td>\n",
              "      <td>13739704</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>122808</th>\n",
              "      <td>2015-10-09</td>\n",
              "      <td>Z1F14BGY</td>\n",
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              "<p>106 rows × 11 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 527
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fI-uS5Ek4glg",
        "outputId": "7e4844eb-0ff9-406e-99ed-e7196337d571"
      },
      "execution_count": 528,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 124494 entries, 0 to 124493\n",
            "Data columns (total 11 columns):\n",
            " #   Column      Non-Null Count   Dtype \n",
            "---  ------      --------------   ----- \n",
            " 0   date        124494 non-null  object\n",
            " 1   device      124494 non-null  object\n",
            " 2   failure     124494 non-null  int64 \n",
            " 3   attribute1  124494 non-null  int64 \n",
            " 4   attribute2  124494 non-null  int64 \n",
            " 5   attribute3  124494 non-null  int64 \n",
            " 6   attribute4  124494 non-null  int64 \n",
            " 7   attribute5  124494 non-null  int64 \n",
            " 8   attribute6  124494 non-null  int64 \n",
            " 9   attribute7  124494 non-null  int64 \n",
            " 10  attribute9  124494 non-null  int64 \n",
            "dtypes: int64(9), object(2)\n",
            "memory usage: 10.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"date\"]=pd.to_datetime(df[\"date\"])\n",
        "df[\"year\"]=df[\"date\"].dt.year\n",
        "df[\"month\"]=df[\"date\"].dt.month\n",
        "df[\"day\"]=df[\"date\"].dt.day"
      ],
      "metadata": {
        "id": "A9QJ1cmocPfC"
      },
      "execution_count": 529,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df.year.value_counts()  #all years are 2015.\n",
        "#df.month.value_counts()\n",
        "#df.day.value_counts()"
      ],
      "metadata": {
        "id": "_rnmSw_acU1T"
      },
      "execution_count": 530,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df.groupby(\"month\")[\"failure\"].mean());"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "c0vRA9ircU4U",
        "outputId": "92c79ebc-fa0d-4db1-df3b-41d8bbe558f1"
      },
      "execution_count": 531,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.DataFrame()\n",
        "#df1[\"workday\"]= df.groupby(\"device\")[\"date\"].max()-df.groupby(\"device\")[\"date\"].min()\n",
        "#df1[\"workday\"]=df1[\"workday\"].dt.days\n",
        "#df1[\"workday\"]"
      ],
      "metadata": {
        "id": "MvChUA6CcU7O"
      },
      "execution_count": 532,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df.columns :\n",
        "\n",
        "  df1[i] = df.groupby(\"device\")[i].max()"
      ],
      "metadata": {
        "id": "mmexDNZycU-u"
      },
      "execution_count": 533,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape,df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQ6fs2e_ez9Y",
        "outputId": "9e50e0a2-b912-4160-fd16-d6ab4a862ac0"
      },
      "execution_count": 534,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1168, 14), (124494, 14))"
            ]
          },
          "metadata": {},
          "execution_count": 534
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1[\"workday\"]= df.groupby(\"device\")[\"date\"].max()-df.groupby(\"device\")[\"date\"].min()\n",
        "df1[\"workday\"]=df1[\"workday\"].dt.days\n",
        "df1[\"workday\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcGADPoUfYb0",
        "outputId": "26760c67-3716-42a2-cef3-a2b9a48bfef7"
      },
      "execution_count": 535,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device\n",
              "S1F01085      5\n",
              "S1F013BB      5\n",
              "S1F0166B      5\n",
              "S1F01E6Y     47\n",
              "S1F01JE0      5\n",
              "           ... \n",
              "Z1F1VMZB    291\n",
              "Z1F1VQFY    180\n",
              "Z1F26YZB     83\n",
              "Z1F282ZV     83\n",
              "Z1F2PBHX     95\n",
              "Name: workday, Length: 1168, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 535
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8-X-VkEftIH",
        "outputId": "d3d15a00-f75e-4398-aa99-6be77c97ae2b"
      },
      "execution_count": 536,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1168 entries, S1F01085 to Z1F2PBHX\n",
            "Data columns (total 15 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   date        1168 non-null   datetime64[ns]\n",
            " 1   device      1168 non-null   object        \n",
            " 2   failure     1168 non-null   int64         \n",
            " 3   attribute1  1168 non-null   int64         \n",
            " 4   attribute2  1168 non-null   int64         \n",
            " 5   attribute3  1168 non-null   int64         \n",
            " 6   attribute4  1168 non-null   int64         \n",
            " 7   attribute5  1168 non-null   int64         \n",
            " 8   attribute6  1168 non-null   int64         \n",
            " 9   attribute7  1168 non-null   int64         \n",
            " 10  attribute9  1168 non-null   int64         \n",
            " 11  year        1168 non-null   int64         \n",
            " 12  month       1168 non-null   int64         \n",
            " 13  day         1168 non-null   int64         \n",
            " 14  workday     1168 non-null   int64         \n",
            "dtypes: datetime64[ns](1), int64(13), object(1)\n",
            "memory usage: 178.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.drop([\"date\",\"year\",\"day\"],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "nN0Wou8Cfw1P"
      },
      "execution_count": 537,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ai90e44f_q-",
        "outputId": "b570b680-f47c-454c-885d-55fbd6f24c97"
      },
      "execution_count": 538,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1168, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 538
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaUe4rJXhLh5",
        "outputId": "d71ede9d-194a-45d4-f435-f9103f547074"
      },
      "execution_count": 539,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1168 entries, S1F01085 to Z1F2PBHX\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   device      1168 non-null   object\n",
            " 1   failure     1168 non-null   int64 \n",
            " 2   attribute1  1168 non-null   int64 \n",
            " 3   attribute2  1168 non-null   int64 \n",
            " 4   attribute3  1168 non-null   int64 \n",
            " 5   attribute4  1168 non-null   int64 \n",
            " 6   attribute5  1168 non-null   int64 \n",
            " 7   attribute6  1168 non-null   int64 \n",
            " 8   attribute7  1168 non-null   int64 \n",
            " 9   attribute9  1168 non-null   int64 \n",
            " 10  month       1168 non-null   int64 \n",
            " 11  workday     1168 non-null   int64 \n",
            "dtypes: int64(11), object(1)\n",
            "memory usage: 150.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtVq-qtTiXMF",
        "outputId": "4300888f-2fee-4622-9e5a-c741f39b488c"
      },
      "execution_count": 540,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['device', 'failure', 'attribute1', 'attribute2', 'attribute3',\n",
              "       'attribute4', 'attribute5', 'attribute6', 'attribute7', 'attribute9',\n",
              "       'month', 'workday'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 540
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "3cIOUz8lhrCF"
      },
      "execution_count": 541,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1[['attribute1', 'attribute2', 'attribute3',\n",
        "       'attribute4', 'attribute5', 'attribute6', 'attribute7', 'attribute9',\n",
        "       'month', 'workday']]=scaler.fit_transform(df1[['attribute1', 'attribute2', 'attribute3',\n",
        "       'attribute4', 'attribute5', 'attribute6', 'attribute7', 'attribute9',\n",
        "       'month', 'workday']])"
      ],
      "metadata": {
        "id": "Fm5PrcC6h0Y0"
      },
      "execution_count": 542,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U0pxKLL5i5Cx",
        "outputId": "e9b33705-18ae-44f4-f591-e26549021f58"
      },
      "execution_count": 543,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1168, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 543
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdGivfSXkoKl",
        "outputId": "6380b3e0-ff08-451c-ea3b-ac15c1980811"
      },
      "execution_count": 544,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1168 entries, S1F01085 to Z1F2PBHX\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   device      1168 non-null   object \n",
            " 1   failure     1168 non-null   int64  \n",
            " 2   attribute1  1168 non-null   float64\n",
            " 3   attribute2  1168 non-null   float64\n",
            " 4   attribute3  1168 non-null   float64\n",
            " 5   attribute4  1168 non-null   float64\n",
            " 6   attribute5  1168 non-null   float64\n",
            " 7   attribute6  1168 non-null   float64\n",
            " 8   attribute7  1168 non-null   float64\n",
            " 9   attribute9  1168 non-null   float64\n",
            " 10  month       1168 non-null   float64\n",
            " 11  workday     1168 non-null   float64\n",
            "dtypes: float64(10), int64(1), object(1)\n",
            "memory usage: 150.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns =['failure', 'attribute1','attribute6','attribute2', 'attribute3',\n",
        "       'attribute4', 'attribute5', 'attribute7', 'attribute9',\n",
        "       'month', 'workday']\n",
        "for i in columns:\n",
        "  df1[i] = df1[i].astype(int)\n",
        "  "
      ],
      "metadata": {
        "id": "2hGTzrbNM0L6"
      },
      "execution_count": 545,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5bG7OSHnOxy",
        "outputId": "5b3a8f86-d3b0-4775-9502-9056404647cb"
      },
      "execution_count": 546,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1168 entries, S1F01085 to Z1F2PBHX\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   device      1168 non-null   object\n",
            " 1   failure     1168 non-null   int64 \n",
            " 2   attribute1  1168 non-null   int64 \n",
            " 3   attribute2  1168 non-null   int64 \n",
            " 4   attribute3  1168 non-null   int64 \n",
            " 5   attribute4  1168 non-null   int64 \n",
            " 6   attribute5  1168 non-null   int64 \n",
            " 7   attribute6  1168 non-null   int64 \n",
            " 8   attribute7  1168 non-null   int64 \n",
            " 9   attribute9  1168 non-null   int64 \n",
            " 10  month       1168 non-null   int64 \n",
            " 11  workday     1168 non-null   int64 \n",
            "dtypes: int64(11), object(1)\n",
            "memory usage: 150.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "liste=list()\n",
        "\n",
        "for i in df.groupby(\"device\")[\"device\"]:\n",
        "  liste.append(i[0][0:3])\n",
        "\n",
        "df1[\"device_type\"]=liste\n",
        "df1[\"device_type\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3SGd_qTJBM6",
        "outputId": "5ba7d099-7ac6-452b-fc19-5c495cb6824b"
      },
      "execution_count": 547,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device\n",
              "S1F01085    S1F\n",
              "S1F013BB    S1F\n",
              "S1F0166B    S1F\n",
              "S1F01E6Y    S1F\n",
              "S1F01JE0    S1F\n",
              "           ... \n",
              "Z1F1VMZB    Z1F\n",
              "Z1F1VQFY    Z1F\n",
              "Z1F26YZB    Z1F\n",
              "Z1F282ZV    Z1F\n",
              "Z1F2PBHX    Z1F\n",
              "Name: device_type, Length: 1168, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 547
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "l-h3E1JvJIOi",
        "outputId": "a600d82f-97fc-440a-c19c-db9f282d3f7c"
      },
      "execution_count": 548,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            device  failure  attribute1  attribute2  attribute3  attribute4  \\\n",
              "device                                                                        \n",
              "S1F01085  S1F01085        0           0           0           0           0   \n",
              "S1F013BB  S1F013BB        0           0           0           0           0   \n",
              "S1F0166B  S1F0166B        0           0           0           0           0   \n",
              "S1F01E6Y  S1F01E6Y        0           0           0           0           0   \n",
              "S1F01JE0  S1F01JE0        0           0           0           0           0   \n",
              "\n",
              "          attribute5  attribute6  attribute7  attribute9  month  workday  \\\n",
              "device                                                                     \n",
              "S1F01085           0           1           0           0      0       -1   \n",
              "S1F013BB           0           4           0           0      0       -1   \n",
              "S1F0166B           0           1           0           0      0       -1   \n",
              "S1F01E6Y           0           0           0           0      0        0   \n",
              "S1F01JE0           0           1           0           0      0       -1   \n",
              "\n",
              "         device_type  \n",
              "device                \n",
              "S1F01085         S1F  \n",
              "S1F013BB         S1F  \n",
              "S1F0166B         S1F  \n",
              "S1F01E6Y         S1F  \n",
              "S1F01JE0         S1F  "
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-14f2cff8-74d5-437c-8c33-38f43f88361f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-14f2cff8-74d5-437c-8c33-38f43f88361f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 548
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"device\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "12klVJdZJWss"
      },
      "execution_count": 549,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.get_dummies(df1,drop_first=True)"
      ],
      "metadata": {
        "id": "Nq940iLM5sQh"
      },
      "execution_count": 550,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BG6CKUQY5sqn",
        "outputId": "88c1b7c9-38f5-4566-a9e6-cb96d4b19b56"
      },
      "execution_count": 551,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1168, 1180)"
            ]
          },
          "metadata": {},
          "execution_count": 551
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df1.drop(\"failure\",axis=1)\n",
        "y = df1[\"failure\"]"
      ],
      "metadata": {
        "id": "qNP5nIV55stO"
      },
      "execution_count": 552,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#x=pd.get_dummies(x,drop_first=True)"
      ],
      "metadata": {
        "id": "O8Gep-v5vPbQ"
      },
      "execution_count": 553,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape,y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98rbOCkl-u4u",
        "outputId": "4ae474ac-eafd-4153-8021-6e1d98653480"
      },
      "execution_count": 554,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1168, 1179), (1168,))"
            ]
          },
          "metadata": {},
          "execution_count": 554
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "AI1XU29_Blmk"
      },
      "execution_count": 555,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "SM= SMOTE(random_state=42)\n",
        "x_train_new,y_train_new = SM.fit_resample(x_train,y_train)"
      ],
      "metadata": {
        "id": "l8cbEHPkjXEu"
      },
      "execution_count": 556,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_new.shape ,y_train_new.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6GDeubpbJhCa",
        "outputId": "60c554cd-fbdc-4c5e-fb10-f88e95f06aa9"
      },
      "execution_count": 557,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1692, 1179), (1692,))"
            ]
          },
          "metadata": {},
          "execution_count": 557
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression, ElasticNet, Ridge, Lasso, LogisticRegression\n",
        "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "from sklearn.tree import ExtraTreeRegressor, DecisionTreeClassifier\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n",
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "uq5DgpBCn5vi"
      },
      "execution_count": 558,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g = GaussianNB() \n",
        "b = BernoulliNB()\n",
        "k = KNeighborsClassifier()\n",
        "gbc = GradientBoostingClassifier()\n",
        "log = LogisticRegression()\n",
        "d = DecisionTreeClassifier()\n",
        "r = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "CbUG_KkDoelB"
      },
      "execution_count": 559,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "algorithms = [g, b, k, log, gbc, r, d]\n",
        "names = [\"GaussianNB\", \"BernoulliNB\", \"K Nearest\", \"Logistic\", \"GradientBoosting\", \"RandomForest\", \"DesicionTree\"]"
      ],
      "metadata": {
        "id": "b_g39s8Sn5yE"
      },
      "execution_count": 560,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def algo_test(x, y, algorithms = algorithms, names = names):\n",
        "    for i in range(len(algorithms)):\n",
        "        algorithms[i] = algorithms[i].fit(x, y)\n",
        "    accuracy = []\n",
        "    precision = []\n",
        "    recall = []\n",
        "    f1 = []\n",
        "    for i in range(len(algorithms)):\n",
        "        accuracy.append(accuracy_score(y, algorithms[i].predict(x)))\n",
        "        precision.append(precision_score(y, algorithms[i].predict(x)))\n",
        "        recall.append(recall_score(y, algorithms[i].predict(x)))\n",
        "        f1.append(f1_score(y, algorithms[i].predict(x)))\n",
        "    metrics = pd.DataFrame(columns = [\"Accuracy\", \"Precision\", \"Recall\", \"F1\"], index = names)\n",
        "    metrics[\"Accuracy\"] = accuracy\n",
        "    metrics[\"Precision\"] = precision\n",
        "    metrics[\"Recall\"] = recall\n",
        "    metrics[\"F1\"] = f1\n",
        "    return metrics.sort_values(\"F1\", ascending = False)"
      ],
      "metadata": {
        "id": "SbaJsPSiohHZ"
      },
      "execution_count": 561,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "algo_test(x_train_new,y_train_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "5roy-EQvohNA",
        "outputId": "d186dde3-e8c8-44ca-cdfc-e717f1ba2da9"
      },
      "execution_count": 562,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Accuracy  Precision    Recall        F1\n",
              "RandomForest      1.000000   1.000000  1.000000  1.000000\n",
              "DesicionTree      1.000000   1.000000  1.000000  1.000000\n",
              "GaussianNB        0.999409   0.998819  1.000000  0.999409\n",
              "GradientBoosting  0.815603   0.734622  0.988180  0.842742\n",
              "Logistic          0.809102   0.732444  0.973995  0.836124\n",
              "BernoulliNB       0.693262   0.623958  0.972813  0.760277\n",
              "K Nearest         0.682624   0.611875  0.998818  0.758868"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ddd291eb-c5cd-4b44-8450-6865ace7fa9a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RandomForest</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DesicionTree</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.999409</td>\n",
              "      <td>0.998819</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoosting</th>\n",
              "      <td>0.815603</td>\n",
              "      <td>0.734622</td>\n",
              "      <td>0.988180</td>\n",
              "      <td>0.842742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Logistic</th>\n",
              "      <td>0.809102</td>\n",
              "      <td>0.732444</td>\n",
              "      <td>0.973995</td>\n",
              "      <td>0.836124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.693262</td>\n",
              "      <td>0.623958</td>\n",
              "      <td>0.972813</td>\n",
              "      <td>0.760277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K Nearest</th>\n",
              "      <td>0.682624</td>\n",
              "      <td>0.611875</td>\n",
              "      <td>0.998818</td>\n",
              "      <td>0.758868</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ddd291eb-c5cd-4b44-8450-6865ace7fa9a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-ddd291eb-c5cd-4b44-8450-6865ace7fa9a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ddd291eb-c5cd-4b44-8450-6865ace7fa9a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 562
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import imblearn\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.metrics import geometric_mean_score, make_index_balanced_accuracy, classification_report_imbalanced\n"
      ],
      "metadata": {
        "id": "RFeKkpieK-qd"
      },
      "execution_count": 563,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RUS=RandomUnderSampler(random_state=42)\n",
        "x_RUS,y_RUS=RUS.fit_resample(x_train,y_train)"
      ],
      "metadata": {
        "id": "AGz5lIzen50W"
      },
      "execution_count": 564,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " for item in algorithms:\n",
        "    predict=item.fit(x_RUS,y_RUS).predict(x_test)"
      ],
      "metadata": {
        "id": "tmJ6HWmAn52Y"
      },
      "execution_count": 565,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "algo_test(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Fu0MmwVfLoYB",
        "outputId": "78dafa91-0ef2-4f4a-b8da-215703fa0b02"
      },
      "execution_count": 566,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Accuracy  Precision    Recall        F1\n",
              "RandomForest      1.000000   1.000000  1.000000  1.000000\n",
              "DesicionTree      1.000000   1.000000  1.000000  1.000000\n",
              "GaussianNB        0.998929   0.988764  1.000000  0.994350\n",
              "K Nearest         0.918630   0.642857  0.306818  0.415385\n",
              "Logistic          0.917559   0.823529  0.159091  0.266667\n",
              "GradientBoosting  0.918630   1.000000  0.136364  0.240000\n",
              "BernoulliNB       0.905782   0.000000  0.000000  0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0597c96-c8ac-4bb4-ba16-53d2ca34eb9d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RandomForest</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DesicionTree</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.998929</td>\n",
              "      <td>0.988764</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.994350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K Nearest</th>\n",
              "      <td>0.918630</td>\n",
              "      <td>0.642857</td>\n",
              "      <td>0.306818</td>\n",
              "      <td>0.415385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Logistic</th>\n",
              "      <td>0.917559</td>\n",
              "      <td>0.823529</td>\n",
              "      <td>0.159091</td>\n",
              "      <td>0.266667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoosting</th>\n",
              "      <td>0.918630</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>0.240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.905782</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "  </tbody>\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c0597c96-c8ac-4bb4-ba16-53d2ca34eb9d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c0597c96-c8ac-4bb4-ba16-53d2ca34eb9d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 566
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "metadata": {
        "id": "2-Jo31tvOptH"
      },
      "execution_count": 567,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "model.add(Dense(12,activation=\"relu\")) \n",
        "model.add(Dense(8,activation=\"relu\"))\n",
        "model.add(Dense(8,activation=\"relu\"))\n",
        "model.add(Dense(8,activation=\"relu\"))\n",
        "model.add(Dense(4,activation=\"relu\"))\n",
        "model.add(Dense(1,activation=\"sigmoid\"))"
      ],
      "metadata": {
        "id": "V9bszE3aOp2L"
      },
      "execution_count": 568,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"binary_crossentropy\",optimizer=\"adam\",metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "-o-MfkDlOp64"
      },
      "execution_count": 569,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x,y,epochs=150,batch_size=10,verbose=1) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlVutZ2fO5my",
        "outputId": "a518d6c7-13d5-44dc-cb60-9a2c5cae3e58"
      },
      "execution_count": 570,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/150\n",
            "117/117 [==============================] - 1s 2ms/step - loss: 0.5119 - accuracy: 0.9084\n",
            "Epoch 2/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.2781 - accuracy: 0.9092\n",
            "Epoch 3/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.2481 - accuracy: 0.9092\n",
            "Epoch 4/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.2160 - accuracy: 0.9092\n",
            "Epoch 5/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.1608 - accuracy: 0.9092\n",
            "Epoch 6/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.1174 - accuracy: 0.9092\n",
            "Epoch 7/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0955 - accuracy: 0.9092\n",
            "Epoch 8/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0848 - accuracy: 0.9717\n",
            "Epoch 9/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0767 - accuracy: 0.9889\n",
            "Epoch 10/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0719 - accuracy: 0.9914\n",
            "Epoch 11/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0675 - accuracy: 0.9923\n",
            "Epoch 12/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0641 - accuracy: 0.9923\n",
            "Epoch 13/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0601 - accuracy: 0.9932\n",
            "Epoch 14/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0567 - accuracy: 0.9940\n",
            "Epoch 15/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0546 - accuracy: 0.9940\n",
            "Epoch 16/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0528 - accuracy: 0.9940\n",
            "Epoch 17/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0511 - accuracy: 0.9940\n",
            "Epoch 18/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0494 - accuracy: 0.9940\n",
            "Epoch 19/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0478 - accuracy: 0.9940\n",
            "Epoch 20/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0464 - accuracy: 0.9940\n",
            "Epoch 21/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0450 - accuracy: 0.9940\n",
            "Epoch 22/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0436 - accuracy: 0.9940\n",
            "Epoch 23/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0423 - accuracy: 0.9940\n",
            "Epoch 24/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0411 - accuracy: 0.9940\n",
            "Epoch 25/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0399 - accuracy: 0.9940\n",
            "Epoch 26/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0388 - accuracy: 0.9940\n",
            "Epoch 27/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0377 - accuracy: 0.9940\n",
            "Epoch 28/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0367 - accuracy: 0.9940\n",
            "Epoch 29/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0359 - accuracy: 0.9940\n",
            "Epoch 30/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0350 - accuracy: 0.9940\n",
            "Epoch 31/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0341 - accuracy: 0.9940\n",
            "Epoch 32/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0334 - accuracy: 0.9940\n",
            "Epoch 33/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0326 - accuracy: 0.9940\n",
            "Epoch 34/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0341 - accuracy: 0.9932\n",
            "Epoch 35/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0306 - accuracy: 0.9940\n",
            "Epoch 36/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0352 - accuracy: 0.9914\n",
            "Epoch 37/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0316 - accuracy: 0.9932\n",
            "Epoch 38/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0311 - accuracy: 0.9932\n",
            "Epoch 39/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0307 - accuracy: 0.9932\n",
            "Epoch 40/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0302 - accuracy: 0.9932\n",
            "Epoch 41/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0298 - accuracy: 0.9932\n",
            "Epoch 42/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0295 - accuracy: 0.9932\n",
            "Epoch 43/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0291 - accuracy: 0.9932\n",
            "Epoch 44/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0289 - accuracy: 0.9932\n",
            "Epoch 45/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0255 - accuracy: 0.9949\n",
            "Epoch 46/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0251 - accuracy: 0.9949\n",
            "Epoch 47/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0264 - accuracy: 0.9940\n",
            "Epoch 48/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0293 - accuracy: 0.9923\n",
            "Epoch 49/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0292 - accuracy: 0.9923\n",
            "Epoch 50/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0290 - accuracy: 0.9923\n",
            "Epoch 51/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0288 - accuracy: 0.9923\n",
            "Epoch 52/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0287 - accuracy: 0.9923\n",
            "Epoch 53/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0383 - accuracy: 0.9906\n",
            "Epoch 54/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0289 - accuracy: 0.9932\n",
            "Epoch 55/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0252 - accuracy: 0.9940\n",
            "Epoch 56/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0240 - accuracy: 0.9940\n",
            "Epoch 57/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0227 - accuracy: 0.9949\n",
            "Epoch 58/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0225 - accuracy: 0.9949\n",
            "Epoch 59/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0223 - accuracy: 0.9949\n",
            "Epoch 60/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0222 - accuracy: 0.9949\n",
            "Epoch 61/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0220 - accuracy: 0.9949\n",
            "Epoch 62/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0219 - accuracy: 0.9949\n",
            "Epoch 63/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0218 - accuracy: 0.9949\n",
            "Epoch 64/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0217 - accuracy: 0.9949\n",
            "Epoch 65/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0216 - accuracy: 0.9949\n",
            "Epoch 66/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0215 - accuracy: 0.9949\n",
            "Epoch 67/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0214 - accuracy: 0.9949\n",
            "Epoch 68/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0213 - accuracy: 0.9949\n",
            "Epoch 69/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0212 - accuracy: 0.9949\n",
            "Epoch 70/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0211 - accuracy: 0.9949\n",
            "Epoch 71/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0211 - accuracy: 0.9949\n",
            "Epoch 72/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0210 - accuracy: 0.9949\n",
            "Epoch 73/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0209 - accuracy: 0.9949\n",
            "Epoch 74/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0209 - accuracy: 0.9949\n",
            "Epoch 75/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0208 - accuracy: 0.9949\n",
            "Epoch 76/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0208 - accuracy: 0.9949\n",
            "Epoch 77/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0207 - accuracy: 0.9949\n",
            "Epoch 78/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0207 - accuracy: 0.9949\n",
            "Epoch 79/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0206 - accuracy: 0.9949\n",
            "Epoch 80/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0206 - accuracy: 0.9949\n",
            "Epoch 81/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0206 - accuracy: 0.9949\n",
            "Epoch 82/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0205 - accuracy: 0.9949\n",
            "Epoch 83/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0205 - accuracy: 0.9949\n",
            "Epoch 84/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0205 - accuracy: 0.9949\n",
            "Epoch 85/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0204 - accuracy: 0.9949\n",
            "Epoch 86/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0204 - accuracy: 0.9949\n",
            "Epoch 87/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0204 - accuracy: 0.9949\n",
            "Epoch 88/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0204 - accuracy: 0.9949\n",
            "Epoch 89/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0204 - accuracy: 0.9949\n",
            "Epoch 90/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9949\n",
            "Epoch 91/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9949\n",
            "Epoch 92/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9949\n",
            "Epoch 93/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9949\n",
            "Epoch 94/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0203 - accuracy: 0.9949\n",
            "Epoch 95/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0203 - accuracy: 0.9949\n",
            "Epoch 96/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0203 - accuracy: 0.9949\n",
            "Epoch 97/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 98/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 99/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 100/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 101/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 102/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 103/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 104/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 105/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 106/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0202 - accuracy: 0.9949\n",
            "Epoch 107/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 108/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 109/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 110/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 111/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 112/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 113/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 114/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 115/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 116/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 117/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 118/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 119/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 120/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 121/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 122/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 123/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 124/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 125/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 126/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 127/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 128/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 129/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 130/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 131/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 132/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 133/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 134/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 135/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 136/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 137/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 138/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 139/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 140/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 141/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 142/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 143/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 144/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 145/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 146/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 147/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9949\n",
            "Epoch 148/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0200 - accuracy: 0.9949\n",
            "Epoch 149/150\n",
            "117/117 [==============================] - 0s 3ms/step - loss: 0.0200 - accuracy: 0.9949\n",
            "Epoch 150/150\n",
            "117/117 [==============================] - 0s 2ms/step - loss: 0.0200 - accuracy: 0.9949\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdab38fdbe0>"
            ]
          },
          "metadata": {},
          "execution_count": 570
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LP8VylRlO6L6",
        "outputId": "2d496295-f1f4-4480-8832-15b39bc95f24"
      },
      "execution_count": 571,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_6 (Dense)             (None, 12)                14160     \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 8)                 104       \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 8)                 72        \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 8)                 72        \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 4)                 36        \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1)                 5         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,449\n",
            "Trainable params: 14,449\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores=model.evaluate(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ztLrWe5O6OS",
        "outputId": "3101cbbf-3ca7-4f58-a683-6175659d6091"
      },
      "execution_count": 572,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "37/37 [==============================] - 0s 2ms/step - loss: 0.0200 - accuracy: 0.9949\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"%s:%.2r%%\" % (model.metrics_names[1],scores[1]*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQkBS_wTPH_q",
        "outputId": "3ba7d307-701b-4496-948c-9f77b2752fe9"
      },
      "execution_count": 573,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy:99%\n"
          ]
        }
      ]
    }
  ]
}