Binary_Machine_Maintenance_ML_CW.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Introduction"
      ],
      "metadata": {
        "id": "pvYxcUXo8e2U"
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    {
      "cell_type": "markdown",
      "source": [
        "This synthetic dataset is modeled after an existing milling machine and consists of 10 000 data points from a stored as rows with 14 features in columns. \n",
        "- UID: unique identifier ranging from 1 to 10000\n",
        "\n",
        "- product ID: consisting of a letter L, M, or H for low (50% of all products), medium (30%) and high (20%) as product quality variants and a variant-specific serial number\n",
        "\n",
        "- type: just the product type L, M or H from product ID column\n",
        "\n",
        "- air temperature [K]: generated using a random walk process later normalized to a standard deviation of 2 K around 300 K\n",
        "\n",
        "- process temperature [K]: generated using a random walk process normalized to a standard deviation of 1 K, added to the air temperature plus 10 K.\n",
        "\n",
        "- rotational speed [rpm]: calculated from a power of 2860 W, overlaid with a normally distributed noise\n",
        "\n",
        "- torque [Nm]: torque values are normally distributed around 40 Nm with a SD = 10 Nm and no negative values.\n",
        "\n",
        "- tool wear [min]: The quality variants H/M/L add 5/3/2 minutes of tool wear to the used tool in the process.\n",
        "\n",
        "- machine failure iindicates whether the machine has failed (1) in this particular datapoint for any of the following failure modes are true.\n",
        "\n",
        "- tool wear failure (TWF): the tool will be replaced of fail at a randomly selected tool wear time between 200 - 240 mins.\n",
        "\n",
        "- heat dissipation failure (HDF): heat dissipation causes a process failure, if the difference between air- and process temperature is below 8.6 K and the tools rotational speed is below 1380 rpm. \n",
        "\n",
        "- power failure (PWF): the product of torque and rotational speed (in rad/s) equals the power required for the process. If this power is below 3500 W or above 9000 W, the process fails.\n",
        "\n",
        "- overstrain failure (OSF): if the product of tool wear and torque exceeds 11,000 minNm for the L product variant (12,000 M, 13,000 H), the process fails due to overstrain.\n",
        "\n",
        "- random failures (RNF): each process has a chance of 0,1 % to fail regardless of its process parameters. \n",
        "\n",
        "\n",
        "This dataset is part of the following publication: S. Matzka, \"Explainable Artificial Intelligence for Predictive Maintenance Applications,\" 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), 2020, pp. 69-74, doi: 10.1109/AI4I49448.2020.00023."
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
      "metadata": {
        "id": "s0IulFDA_12x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "#!pip install matplotlib --upgrade\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rcParams\n",
        "\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "BHVubtDi8mfW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data = pd.read_csv('ai4i2020.csv')"
      ],
      "metadata": {
        "id": "zcseTFEH86-_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Exploration "
      ],
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        "id": "5Lgt3M6GDI3A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data.head()"
      ],
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        "id": "J_SRZ_I79MVm",
        "outputId": "9c547899-1f7b-4e79-8fed-22ca53888b0a"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   UDI Product ID Type  Air temperature [K]  Process temperature [K]  \\\n",
              "0    1     M14860    M                298.1                    308.6   \n",
              "1    2     L47181    L                298.2                    308.7   \n",
              "2    3     L47182    L                298.1                    308.5   \n",
              "3    4     L47183    L                298.2                    308.6   \n",
              "4    5     L47184    L                298.2                    308.7   \n",
              "\n",
              "   Rotational speed [rpm]  Torque [Nm]  Tool wear [min]  Machine failure  TWF  \\\n",
              "0                    1551         42.8                0                0    0   \n",
              "1                    1408         46.3                3                0    0   \n",
              "2                    1498         49.4                5                0    0   \n",
              "3                    1433         39.5                7                0    0   \n",
              "4                    1408         40.0                9                0    0   \n",
              "\n",
              "   HDF  PWF  OSF  RNF  \n",
              "0    0    0    0    0  \n",
              "1    0    0    0    0  \n",
              "2    0    0    0    0  \n",
              "3    0    0    0    0  \n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Determine dataframe dimensions"
      ],
      "metadata": {
        "id": "8lR-KksBE1Gt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data.shape"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "pGwW4yx8EuI-",
        "outputId": "040ef10d-5b52-430c-90aa-426170130839"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Determine the unique values in each column, including the ID columns. This will help identify if there are duplicate values which may affect prediction. "
      ],
      "metadata": {
        "id": "AWCcsk4-DPXs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SVo5vMtDN_o",
        "outputId": "2be9cfd8-ece0-495a-b2f8-684eb794f7c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UDI                        10000\n",
              "Product ID                 10000\n",
              "Type                           3\n",
              "Air temperature [K]           93\n",
              "Process temperature [K]       82\n",
              "Rotational speed [rpm]       941\n",
              "Torque [Nm]                  577\n",
              "Tool wear [min]              246\n",
              "Machine failure                2\n",
              "TWF                            2\n",
              "HDF                            2\n",
              "PWF                            2\n",
              "OSF                            2\n",
              "RNF                            2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since the UDI and Product ID have 10000 unique values, which is equivalent to the total number of rows or observations in the dataset, there are no duplicate values in the dataset. To further ensure that there are no duplicated values, their sum can be detemined. "
      ],
      "metadata": {
        "id": "XG2P3d6qMhWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(raw_data.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9Dj-3xPNg9u",
        "outputId": "5e643259-e28f-4951-f474-44acd4019288"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To begin the data preparation process, missing values need to be handled. Therefore, it is important to determine their sum."
      ],
      "metadata": {
        "id": "XoWFJbgeFI0f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fw4u7bbZFym7",
        "outputId": "1248b147-8ade-4fcf-8ebe-3f60a9d6345c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UDI                        0\n",
              "Product ID                 0\n",
              "Type                       0\n",
              "Air temperature [K]        0\n",
              "Process temperature [K]    0\n",
              "Rotational speed [rpm]     0\n",
              "Torque [Nm]                0\n",
              "Tool wear [min]            0\n",
              "Machine failure            0\n",
              "TWF                        0\n",
              "HDF                        0\n",
              "PWF                        0\n",
              "OSF                        0\n",
              "RNF                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To further ensure that there are no missing values, the dataframe can be searched for ? values which are missing values that are labeled with a question mark. These can be replaced with nan."
      ],
      "metadata": {
        "id": "Fl2sfVNXJUDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data.replace(\"?\",np.nan,inplace=True)\n",
        "raw_data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "It9APH8AJm3-",
        "outputId": "50fe551b-b475-4554-842d-c4a3572982b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UDI                        0\n",
              "Product ID                 0\n",
              "Type                       0\n",
              "Air temperature [K]        0\n",
              "Process temperature [K]    0\n",
              "Rotational speed [rpm]     0\n",
              "Torque [Nm]                0\n",
              "Tool wear [min]            0\n",
              "Machine failure            0\n",
              "TWF                        0\n",
              "HDF                        0\n",
              "PWF                        0\n",
              "OSF                        0\n",
              "RNF                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As seen, the sum of the missing values for all the columns is 0. Therefore, no imputations or row dropping are needed. Next, some statistics and relationships for the values in the dataframe can be determined."
      ],
      "metadata": {
        "id": "PhPQZ06gGM3t"
      }
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      "cell_type": "code",
      "source": [
        "raw_data.info()"
      ],
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        "colab": {
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        },
        "id": "g-B5CjJVF7V7",
        "outputId": "59e7e504-2001-459f-a950-b8a0d739ce3e"
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      "execution_count": null,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   UDI                      10000 non-null  int64  \n",
            " 1   Product ID               10000 non-null  object \n",
            " 2   Type                     10000 non-null  object \n",
            " 3   Air temperature [K]      10000 non-null  float64\n",
            " 4   Process temperature [K]  10000 non-null  float64\n",
            " 5   Rotational speed [rpm]   10000 non-null  int64  \n",
            " 6   Torque [Nm]              10000 non-null  float64\n",
            " 7   Tool wear [min]          10000 non-null  int64  \n",
            " 8   Machine failure          10000 non-null  int64  \n",
            " 9   TWF                      10000 non-null  int64  \n",
            " 10  HDF                      10000 non-null  int64  \n",
            " 11  PWF                      10000 non-null  int64  \n",
            " 12  OSF                      10000 non-null  int64  \n",
            " 13  RNF                      10000 non-null  int64  \n",
            "dtypes: float64(3), int64(9), object(2)\n",
            "memory usage: 1.1+ MB\n"
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      "cell_type": "markdown",
      "source": [
        "It can be seen that the feature types for the dataframe enteries all numerical (float64 and int64) with the exception of the Product ID and Type. These two values along with the UDI will have no significance on the model as they are unique identifier or related to the unique identifiers for each machine. Therefore, the UDI, Product ID, and Type columns are dropped."
      ],
      "metadata": {
        "id": "5yfHhDiIHjYW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data = raw_data.copy()"
      ],
      "metadata": {
        "id": "AfDRU8BEN3Am"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data.drop(['UDI','Product ID','Type'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "F3pA3PtSHfXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(processed_data.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6TxM13BHah9",
        "outputId": "ac4a0750-a168-4fc8-f3a6-cff8cda8a1ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Air temperature [K]', 'Process temperature [K]',\n",
            "       'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]',\n",
            "       'Machine failure', 'TWF', 'HDF', 'PWF', 'OSF', 'RNF'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that the irrelevant features are dropped, it is important to check for skew within the data. To do that, the describe function is used."
      ],
      "metadata": {
        "id": "vpgmrTXVkDSu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "IucaVaPQC6hy",
        "outputId": "54947651-edca-4ded-c5fd-b82288623c93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Air temperature [K]  Process temperature [K]  Rotational speed [rpm]  \\\n",
              "count         10000.000000             10000.000000            10000.000000   \n",
              "mean            300.004930               310.005560             1538.776100   \n",
              "std               2.000259                 1.483734              179.284096   \n",
              "min             295.300000               305.700000             1168.000000   \n",
              "25%             298.300000               308.800000             1423.000000   \n",
              "50%             300.100000               310.100000             1503.000000   \n",
              "75%             301.500000               311.100000             1612.000000   \n",
              "max             304.500000               313.800000             2886.000000   \n",
              "\n",
              "        Torque [Nm]  Tool wear [min]  Machine failure           TWF  \\\n",
              "count  10000.000000     10000.000000     10000.000000  10000.000000   \n",
              "mean      39.986910       107.951000         0.033900      0.004600   \n",
              "std        9.968934        63.654147         0.180981      0.067671   \n",
              "min        3.800000         0.000000         0.000000      0.000000   \n",
              "25%       33.200000        53.000000         0.000000      0.000000   \n",
              "50%       40.100000       108.000000         0.000000      0.000000   \n",
              "75%       46.800000       162.000000         0.000000      0.000000   \n",
              "max       76.600000       253.000000         1.000000      1.000000   \n",
              "\n",
              "                HDF           PWF           OSF          RNF  \n",
              "count  10000.000000  10000.000000  10000.000000  10000.00000  \n",
              "mean       0.011500      0.009500      0.009800      0.00190  \n",
              "std        0.106625      0.097009      0.098514      0.04355  \n",
              "min        0.000000      0.000000      0.000000      0.00000  \n",
              "25%        0.000000      0.000000      0.000000      0.00000  \n",
              "50%        0.000000      0.000000      0.000000      0.00000  \n",
              "75%        0.000000      0.000000      0.000000      0.00000  \n",
              "max        1.000000      1.000000      1.000000      1.00000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aec67567-0448-44a6-b191-2c7de086b018\">\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",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Air temperature [K]</th>\n",
              "      <th>Process temperature [K]</th>\n",
              "      <th>Rotational speed [rpm]</th>\n",
              "      <th>Torque [Nm]</th>\n",
              "      <th>Tool wear [min]</th>\n",
              "      <th>Machine failure</th>\n",
              "      <th>TWF</th>\n",
              "      <th>HDF</th>\n",
              "      <th>PWF</th>\n",
              "      <th>OSF</th>\n",
              "      <th>RNF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>300.004930</td>\n",
              "      <td>310.005560</td>\n",
              "      <td>1538.776100</td>\n",
              "      <td>39.986910</td>\n",
              "      <td>107.951000</td>\n",
              "      <td>0.033900</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.011500</td>\n",
              "      <td>0.009500</td>\n",
              "      <td>0.009800</td>\n",
              "      <td>0.00190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.000259</td>\n",
              "      <td>1.483734</td>\n",
              "      <td>179.284096</td>\n",
              "      <td>9.968934</td>\n",
              "      <td>63.654147</td>\n",
              "      <td>0.180981</td>\n",
              "      <td>0.067671</td>\n",
              "      <td>0.106625</td>\n",
              "      <td>0.097009</td>\n",
              "      <td>0.098514</td>\n",
              "      <td>0.04355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>295.300000</td>\n",
              "      <td>305.700000</td>\n",
              "      <td>1168.000000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>298.300000</td>\n",
              "      <td>308.800000</td>\n",
              "      <td>1423.000000</td>\n",
              "      <td>33.200000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>300.100000</td>\n",
              "      <td>310.100000</td>\n",
              "      <td>1503.000000</td>\n",
              "      <td>40.100000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>301.500000</td>\n",
              "      <td>311.100000</td>\n",
              "      <td>1612.000000</td>\n",
              "      <td>46.800000</td>\n",
              "      <td>162.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>304.500000</td>\n",
              "      <td>313.800000</td>\n",
              "      <td>2886.000000</td>\n",
              "      <td>76.600000</td>\n",
              "      <td>253.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aec67567-0448-44a6-b191-2c7de086b018')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aec67567-0448-44a6-b191-2c7de086b018 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-aec67567-0448-44a6-b191-2c7de086b018');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be seen that the data lies closely to one another especially for Air temperature and Process temperature which perhaps points to a balanced distribution around the mean. This is indicated by their lower standard deviation when compared to rotational speed for example. The data balance can be further explored."
      ],
      "metadata": {
        "id": "QbS5qSjPkBFh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,40), dpi= 100)\n",
        "\n",
        "for i, column in enumerate(processed_data.columns, 1):\n",
        "    plt.subplot(9,3,i)\n",
        "    sns.distplot(processed_data[column])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RFGO2kWnj8Vb",
        "outputId": "bff5d7c1-7824-4b02-9553-627d6417d8dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x4000 with 11 Axes>"
            ],
            "image/png": 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vm6q1vmGPjtVJc/2Q5sDkc3fOTI73dgsadwYA26MWAQDapBYBgFtb6yFNkovpLcZr/s4m5tdCmut30iTRSQMA26cWAQDapBYBgFtY6yHNxlZebb03NiekAYChU4sAAG1SiwDAre25c7NaVEqZKaUc3PD980spP1hK+dY2z9UVC0u9AOb6O2mMOwOAnVKLAABtUosAwK2nUyFNkl9M8r1JUko5nuSjSf52kl8spXxfmwfrghvvpNFJAwBDoBYBANqkFgGAW0zXQppXJPmt/t//YpLTSZ6fXoHyt9o6VFfYSQMAu04tAgC0SS0CALeYroU0B5Nc7v/9W5O8t9a6muTD6RUlt7Qb76Qx7gwAhkAtAgC0SS0CALeYroU0n0/ynaWU5yX5tiS/3H/9ziSXWjtVR8zfcCdN71e5rJMGAHZCLQIAtEktAgC3mK6FND+S5MeSfCHJR2qtD/Zf/9Ykf9DWobpifgs7aRaFNACwE2oRAKBNahEAuMVMtH2AjWqt/76U8ttJ7k7y8Q1v/WqSn2/nVN3RhDTX76TpjTtbNu4MAAamFgEA2qQW2Zn/9MSl/NbnnsnUxFimJ5577wQAuqhTIU2S1FpPp7cYb+NrH23pOJ0yt3ijnTQ6aQBgGNQiAECb1CKD+4f/4VP56BfO5dTR6fzXr3lBbjs01faRAOCmOhXSlFIOJfnhJK9Lb97qs+Z61Vq/pI1zdcX88s1DGjtpAGBwahEAoE1qkZ35/DOzSZKnLi3knb/2+fzNb/nSnDgoqAGg2zoV0iT56STflORfJ3kyidldG8wt9gKY64c0vXFni8adAcBOqEUAgDapRQZ0eX4p564sJkluPzyVM7OL+dQXL+YbX3RHyycDgBvrWkjzZ5P8uVrr77R9kC5qOmlmJsee855OGgAYCrUIANAmtciAHjl7NUly8tBUXnb30fzm587k4txSy6cCgJt77t3+dp1Pcq7tQ3TVvJ00ALDb1CIAQJvUIgN69FwvpLn/5MEcm5lMklwQ0gAwAroW0vwPSX6klHKw7YN00VonzdTm486WjTsDgJ1QiwAAbVKLDKjppHn+bQdzvL+H5sJVIQ0A3de1cWd/O8kLkzxVSvlCkmf917TW+oo2DtUVc/1OmumJzTtplnTSAMBOqEUAgDapRQb0yNkrSZL7Tx7K6mrvAVadNACMgq6FNL/Q9gG6bH6pF8Bcr5NmQkgDAMOgFgEA2qQWGVDTSfOCkwdzbnYxSXJlYTlLK6trD7YCQBd1KqSptf6jts/QZfNL/Z00E88tLqbWQhrjzgBgUGoRAKBNapHBNTtpnn/yYOYWVzI5XrK0UnNpbiknD0+3fDoA2FznHiUopRwvpfz1UsrbSym39V97RSnl3rbP1rYmpLl+J01vJ41OGgDYGbUIANAmtcj2LSyv5ImLc0mS+287lFJKjs/099IYeQZAx3Wqk6aU8hVJPpjkYpIXJPlXSc4leUOS+5N8b2uH64C5ppNm8rkhzZRxZwCwY2qR4VhdrZlfXmn7GAAwctQig3n8/FxqTQ5Ojef2w71w5tjByTwzu5CLV4U0AHRb1zpp3pHkXbXWFyWZ3/D6LyV5bTtH6o5mJ82BiRt10tTUauQZAAxILTIEP/If/1O+6kd+ZW2BLwCwZWqRATx6thl11uuiSZLjM5NJkgtzi62dCwC2omshzauS/IvrvP7FJHft8Vk6pda63kkztflOmiRZXhXSAMCA1CI7VGvN+z7+RBaXV/Ohh55u+zgAMGrUIgP4Qv/BkOffdnDttWMH+yGNThoAOq5rIc1CkqPXef3Lkjyzx2fplIXl9TFm1xt3NrEhpDHyDAAGphbZoT85cyVnr/SeWP3c07N5sj8fHgDYErXIAB5Z66RZD2maTpqLdtIA0HFdC2n+Q5K/X0qZ7H9fSyn3J/mnSd7T3rHaN7+0Ptd95johzfhYyXhZH3kGAAxELbJDv/fwuWd9/zufP9PSSQBgJKlFBvDouV5Ic/+GkObYTG83zQUhDQAd17WQ5m8nOZze0yEzSX4jyeeTXE7yd1s8V+uafTTjYyWT49f/ta3vpdFJAwADUovs0Ee/0AtpXvtldyRJPv7YRU+wAsDWqUUG8Fg/pHn+bYfWXjveH3d28eqS3b0AdNpE2wfYqNZ6McnrSynfkOQr0ytMfr/W+sF2T9a+ppPmel00janxsSwsrwppAGBAapGd+71+SPPmb3wgj5y9kkfOXs2nnriYr3/h7S2fDAC6Ty0ymGbU6h1HptdeO9Yfd7a4spq5pZUcnOrULTAAWNOZ/0KVUsaSvDHJG5K8IElN8nCS06WUUm/xxx7m+iHNgcnNm5/WO2lu6f+rAGAgapGdO31xPo+dm8tYSV5x//Hcf9vBPHL2as71b5wAAJtTiwxmZbXmwtVerXHi0OTa65PjYzk0NZ4riyu5OLckpAGgszox7qyUUtKbu/rTSe5N8skkn0ry/CTvSvLzrR2uI+bXQprNO2maMWg6aQBge9Qiw9GMOnvZPUdz5MBkThzszYI/f9W4MwC4EbXI4C7NLWW1H181tUfjeP/7C2oRADqsK48RvDHJa5O8rtb6axvfKKV8S5JfKKV8b63159o4XBfMCWkAYDe9MWqRHfu9h3shzatecFuS5ER/FnzzdCsAsKk3Ri0ykHP9OuPIgYnn7PA9NjOZL16YywX78QDosE500iT5r5L8k2sLkSSptX4oyY8m+St7fqoOWVjqBS832kmzHtLogAaAbVKLDMEfPHY+ycaQpvf06rkrixb2AsCNqUUGdL4/VvW2Q1PPee/oTO/Z5MvzQhoAuqsrIc1XJPl/b/D++9NbmHfL2spOmsm1nTQ6aQBgm9QiQ/DF83NJki+541CS9REjC8urmV9SnwDADahFBtTsvrt21FmStT00VxdX9vRMALAdXQlpbkvy1A3efyrJiT06SyfZSQMAu0otskPzSytru2fuOnogSTI1MZZD072bI+eNPAOAG1GLDKipMa7XSXNwqncPRUgDQJd1JaQZT7J8g/dX0p39Oa3Y2k6appPGOBEA2Ca1yA49dWk+Sa/r99jM5NrrzV6a5ilXAOC61CIDOnel95DI9TtpmpDmRv/XAkC7uvIf+JLkXaWUhU3en97Lw3TR/LZ20uikAYBtUovs0JMXeyHNXUcPpJSy9vqJg1N5/PxcLuikAYAbUYsM6MJaJ83kc95rxp3N6aQBoMO6EtK8ews/83O7fooOm9/SThohDQAMSC2yQ00nzV3HDjzr9eap1mYUGgBwXWqRAa3tpDHuDIAR1YmQptb6prbP0HVNSLOlTpplIQ0AbMewa5FSytuSvCHJS5LMJfndJH+n1vqZDT9zIMn/kuS703s69gNJvr/WeqN59J11ekMnzUYn+k+12kkDAJtzX2RwaztprjvurHfby7gzALqsKztphqqU8rZSyu+VUi6XUp4upfxCKeXF1/zMgVLKO0spZ0sps6WU95RSTrV15ptpWnO3tJNm1U4aAGjZNyV5Z5LXJHl9kskkv1xKObThZ348yXck+a7+z9+T5L17fM6hWRt3dmzmWa+vd9IIaQBgr+zH+yKbaTppjt9gJ83SSjV1BIDO2pchTfbhjZH55a2ENDppAKALaq3fXmt9V631U7XWjyd5Y5L7k7wySUopx5K8Oclba60fqrV+LMmbknx9KeU1bZ17J9bGnR199sj8jePOavUgCQDskX13X2QzzUjV264z7mx6Yixj/VV5Rp4B0FWdGHc2bLXWb9/4fSnljUmeTu/GyG9uuDHyPbXWD/V/5k1JPl1KeU2t9cN7fOSbmlvsBS86aQBgJB3r/3mu/+cr07tZ8sHmB2qtD5VSHk3ydUmeU4uUUqbz7KXBR3bnqIPZrJPm+MHeuLPF5dXMLa7k4PS+LD8BoFN2475IV2uRppPmtv6I1Y1KKZmZmsiVheVcXVzOsZnn/gwAtG2/dtJca0s3RpI0N0aeo5QyXUo52nxlj4uRppNmZnLzX5lOGgDonlLKWJKfSPI7tdY/6r98V5LFWuuFa378qf571/O2JBc3fD2+C8cd2FonzbFn76SZHB/LkX4wc87IMwBoy47vi6SDtcjyymouzvU6aU5cZ9xZsj7yTCcNAF2170Oa/XJjZH5LO2n6Ic2qkAYAOuSdSb48yXfv8DpvT+8GS/N13w6vNzQrqzVPX15Iktx9TUiTrHfTNONIAIC9M8T7Ip2rRS70A5pSsmmXjJAGgK67FeZNNDdGvnGH13l7knds+P5I9jCoWeukmbKTBgBGRSnlnyX5z5O8tta6sW44nWSqlHL8mpsjp/rvPUetdSHJwoZr78KJB3NmdiErqzXjYyW3H55+zvsnDk3lsfNzOX9FJw0AtGAo90W6WIs0tcWxmclMjF//OeSDU71bX1cXl/fsXACwHfu6k2bDjZFv3uzGyDX/5IY3Rmqtl5qvJJd35dCbmOs/8TE9sYWdNCt20gBAm0rPP0vyF5J8S6314Wt+5GNJlpK8bsO/eXGS+5M8uGcHHZLT/X00dx6ZzvjYc2/YNE+2Xp7XSQMAe2mY90W6aG0fzSajzhKdNAB0374MafbjjZH5pV53zJY6aVZ00gBAy96Z5K8m+Z4kl0spd/W/ZpKk1noxyc8keUcp5ZtLKa9M8rNJHrzeot6ue7If0pw6+txRZ0lyuL+TZnbBE6wAsBf2432R6znf33d34tAWQhp1CAAdtV/Hnb0zvZsifz79GyP91y/WWudqrRdLKc2NkXNJLiX5yXT4xsj8Un8nzcTmuZqQBgA64/v6f/76Na+/Kcm7+n//oSSrSd6TZDrJB5J8/x6cbeieutQLaa63jyZZD2muLHiCFQD2yL67L3I95670unRP3LCTphl3pg4BoJv2a0iz726MNCHNjTtpjDsDgC6otd50SHutdT7JW/pfI+30JZ00ANAx++6+yPU0nTS3HZrc9GeMOwOg6/ZlSLMfb4zMNZ00k8adAQDd0uyk2ayT5pCQBgD21H68L3I9zU6aLY07W1SHANBN+3InzX60tpPmRiFNfxTa8mrNatVNAwDsjSakuWuzcWcHmnFny2oUAGBozvdDmtuMOwNghAlpRkCtda2TZnryBjtpxtYflFk28gwA2CPNuLO7Nhl3dqh/c6TGDRIAYHiacWc33klj3BkA3SakGQELy+vjy27USTMxvv7rXDbyDADYI89cXkiS3LlJSDM+VtZqGCPPAIBhOXd1KcnWxp3NL63o6AWgk4Q0I2B+af1pjxvtpBkfKxkvvW6aRSENALAH5pdW1oKXk4c3v0GyceQZAMAwrI07OzS56c/M9EOammReNw0AHSSkGQHNPpqJsZLJ8Rv/yiYneiGNcWcAwF5oFvZOjY/lyPTEpj93uP/e7LyQBgAYjiakOX6DcWcTY2OZ7u/wNfIMgC4S0oyAZh/NjbpoGpNjvV+pThoAYC+cnW2eYJ1KKWXTn1sLaXTSAABDsLyymsv9uuJGO2mSjXtp1CEAdI+QZgTMr4U0N/91TfafDrGTBgDYC2eu9PbR3GjUWZIcmjbuDAAYnksbunOPHti8mzdJDk713tdJA0AXCWlGwPw2OmkmxpqdNMadAQC7r+mkOXl4+oY/p5MGABimC1d7NciR6YlM3GQ0/HonjZAGgO4R0oyA7Yw7m9JJAwDsoXP9TprbD924k0ZIAwAM04W5pSTJsYOTN/3ZGePOAOgwIc0IWFjqBS4zW+qksZMGANg7G3fS3IiQBgAYpotNSDNz85DGuDMAukxIMwLmtrGTZmqiN+5s2bgzAGAPnNnyuLPewyZ20gAAw3Dxai+kOb6FThrjzgDoMiHNCNjeThqdNADA3mnGnZ08fJNOmgO9GyizC8up1cMkAMDONDtpjs/cuAZJ1kOa5iFYAOgSIc0IsJMGAOiqs1f6nTQ3GXd2qN9Js7RSPUwCAOzYxbled+7RLYw7a8bHz+mkAaCDhDQjYH5bO2l6484WjTsDAPbA2S2OO5ueGM/keK9OmZ038gwA2JkLc/1Omu2MO1tSgwDQPUKaETC/jZ00kzppAIA9UmvNmdn+uLObdNIkyeHp3tJee2kAgJ1a20mzlU6aqV4NopMGgC4S0oyAJqTZSifNZH8nzZKQBgDYZVcXV7Kw3Ks5braTJlkPaWaFNADADl2c64U0x7YQ0hzs30+5KqQBoIOENCOgedJjKztpJid6Y0SWjDsDAHZZM+psZnI8B/tPqN7IobWQxg0SAGBnLvRDmq2MOzvQH3e2sLyalVX3SwDoFiHNCJhf3kZIo5MGANgjZ670R51toYsm0UkDAAzPhau9h0WOzdy8Dtk4maSZVgIAXSGkGQFzi73AZWudNEIaAGBvNJ00Jw9Pb+nnhTQAwLBc3EYnzfhYyXT/fom9NAB0jZBmBDSdNDOTN/91TY4ZdwYA7I1zTSfNoa110jTjzq4IaQCAHai1bmsnTZIc7I88u6qTBoCOEdKMgPlt7aTRSQMA7I0zTSeNkAYA2ENXF1fWHk7dSidNksz0Q5q5RXUIAN0ipBkBa500U3bSAADdMei4sytujgAAO3Ch30UzNT72rH0zN3JwsleHXDXuDICOEdKMgGZe6vTEVjppjDsDAPbG9sed9WqZKwtujgAAg7t4tRfSHJ2ZTCllS//mQNNJY9wZAB0jpBkB80u9rhidNABAl5y90nTSbDGkmWqeYF3O6qoHSgCAwVyY69UgWx11liQHJ5txZ0IaALpFSDMC5vtPeRyYuPmvy04aAGCvnNnmuLOD/U6a1Zq1Zb8AANvVdNIcn9l6SNM8+HpVJw0AHSOkGQFNSLO1ThrjzgCAvXF2dnvjzibGxnJgsld+Nl04AADb1TzscWwbIc3BKZ00AHSTkGYENPNSD2xhGZ5OGgBgL9Rac26b486S9ZFnTcADALBdF5qQZhvjzmaMOwOgo4Q0I2BtJ81WQprx3q90ebVmteqmAQB2x6W55Sz398rctsVOmiQ5NN0Lac7ppAEABnRhbdzZ1muQppPm6uLyrpwJAAYlpOm4WutaJ8305BZ20oyXtb8vG3kGAOySM1d6nTBHDkxkeuLmD5I0DvdDGuPOAIBBDTLubKbfzTu3ZPIIAN0ipOm4heX14mE7nTSJkWcAwO45O9sfdbaNLpokOTTdq2d00gAAg7o416sjjg807kwnDQDdIqTpuPml9VmpW9lJM1ZKxsd63TRCGgBgt5zrd9KcPDy9rX9nJw0AsFNr4862E9L0x53NLa2kGg8PQIcIaTqu2UczMVae1SVzI83IsyXjzgCAXXJm4E4a484AgJ1pxp0d3ca4s2YnzWpNZhd00wDQHUKajmv20Wyli6bRhDk6aQCA3bI27my7nTT9kMa4MwBgUGudNNsIaSbHxzLRnzzS/HsA6AIhTcfNC1u7+psAACAASURBVGkAgA5aG3c24E6aJuQBANiuppPm+MHt1SFNN03z7wGgC4Q0Hbce0mz9V2XcGQCw285caTppthnSTBl3BgAMbmlldW1c2bFtdNIk63tpdNIA0CVCmo5rxp3NbKOTZqrfSbO4rJMGANgdZ2f7nTTbHHd2uD/u7PzVxayueqAEANie81d7D3qUMkBIM6mTBoDuEdJ03CDjzqb7P7uwvLIrZwIAWNtJs81xZwf7485WVqsbJADAtp2/0qsfThycynh/x8xWzfQ7ei/M6egFoDuENB03u9ALWpr57VsxPdH7tS7opAEAdsm5AcedTYyNrY1xNfIMANiupgY5cXB7XTRJcnDSuDMAukdI03Gz8705q4ent158TE/0O2mWdNIAAMO3slpz7mrTSbO9cWfJ+l6ac0IaAGCbmnFnt22zmzdZ30mjmxeALhHSdNzsQq9wOHJgYsv/ZnpSJw0AsHvOX11M7a+TGeQp1kPTTUizMMxjAQC3gLNrnTTbD2kO9kOa8x4UAaBDhDQdt95Js/WQ5kB/3Nm8kAYA2AUbx4xMjG+/nGxCmjOzbpAAANtzfsCRq8l6J815484A6BAhTcddXuiHNNvppDHuDADYRWdmex0wJw9vf9RZkhzq3yAx7gwA2K5zO+ikWR+5qpsXgO4Q0nTcIJ00xp0BALvp7Gyzj2b7N0eS9bpGSAMAbNdOdtIcUoMA0EFCmo6b7XfSHB2kk2ZZJw0AtKGU8tpSyvtKKU+UUmop5Tuveb+UUn6klPJkKWWulPLBUsqL2jrvdp1d66QZLKRZH3fmKVYA2A37uRbZUSfNdO9+yVkhDQAdsi9Dmv1UjMwOMO6s2UmjkwYAWnMoyceTvGWT9//7JH8ryd9I8uokV5J8oJRyYG+OtzPNzZGThwYbd9bUNc9cFtIAwC7Zt7VIU4fcNsDDIof7484uzy9n0T0TADpiX4Y02UfFyOW1cWeTW/4305O9J0PmlxQcANCGWuv7a61/r9b689e+V0opSX4wyT+utf5irfUTSb43yT1JvvPan++iMztY2JskR/qdNM/opAGAXbGfa5HzTUgzQCfNganxjJXe3408A6Ar9mVIsxvFSCllupRytPlKcmS3zr/RWifNdnbSrHXSGHcGAB30QJK7knyweaHWejHJR5J83Wb/qK1a5HrWxp0NuJPmyIHewyfPXBLSAEALRroWObeDnTRjpeRgv5vm7BV1CADdsC9DmpsYqBhJ8rYkFzd8Pb6LZ1wz2++kObKtnTT9kEYnDQB00V39P5+65vWnNrx3Pa3UItdzdrbppBls3FlT11xeWM7coodKAGCPjWwtMre4sjY15MSAD4s0e2l00gDQFbdiSDNoMfL2JMc2fN03/KM910CdNP1xZ4srq1lZrbtyLgBgz7VSi1zP+k6awW6OTE+M5cBkrwy1lwYARkbrtUjT/TI1PpZDU+MDXeNQv5NGSANAV9yKIc1Aaq0LtdZLzVeSy7v9maurdT2k2UYnzYGJ9V/rlcXloZ8LANiR0/0/T13z+qkN7z1HG7XIZs40484G7KQppeTOI71VgE9fnh/auQCALRnZWuT8laUkvVFnvWn223eo/xBs0xkMAG27FUOagYqRNmwMWLbTSTMxPpbx/ia8ZlwaANAZD6dXc7yueaE/1/3VSR5s61Bbtbi8mkv9+mLQTpokufNIL+B5WicNAOy1ka1Fmn00g446SzaENHbSANARW7/zv39sLEb+MHlWMfLPWzzXczRdNJPjZW3PzFZNT4zl6uLK2jUAgL1TSjmc5Es3vPRAKeWrkpyrtT5aSvmJJH+vlPK59GqT/zHJE0l+Ye9Puz1NF83EWMmxmcmBr3NHE9Jc0kkDAMO2X2uR8/0RZbcdGrwGsZMGgK7ZlyHNfilGmi6Yw9MT227jFdIAQKu+Jsmvbfj+Hf0/353kjUn+pySHkvzLJMeT/HaSb6+1dj6xeKofqpw6eiBjY4ONGUnWO2memfUUKwDsgn1Zi5ztBysnDu6gk2bKuDMAumVfhjTZJ8XI5QH20TQOTI4nWTLuDABaUGv99SSbJhi11prk7/e/RkoT0tx5dLB9NI07j/Z30lwS0gDAsO3XWqTppNnJyNVmnLxOGgC6Yl+GNPulGLm81kmz/TbeZjyaThoAYJhOX+yFNHf1Q5ZB3XHYThoAYHuGuZNGSANAV2xv0Ql7qumCOTK9/SxtemL8WdcAABiG0/3Ol1M7DWn6nTjPCGkAgC1a30mzk3FnvfslZ4xcBaAjhDQdNruwlGSwcWfTk71f7WWdNADAED3dH3d217GdhTTNThqdNADAVp0bxk6a/oOwl+aXs7SyOpRzAcBOCGk6bH3c2eCdNFeENADAEJ3uhzSndriT5o5+SHP2ykKW3SABALbg3BA6aWamxjPWH5B/3sgzADpASNNhzT6ZQTppDthJAwDsgvWQZmedNCcPTWesJLWaCQ8AbM35qzsPacZKWfv3Z9UgAHSAkKbDdrSTphl3ZicNADBET13sjzvbYUgzPlZy+2EjzwCArVldrTl/tTcWfichzcZ/70ERALpASNNha500Oxh3ppMGABiW2YXlXFlcSbLzTppkfeTZ05fnd3wtAGB/uzS/lJXVmiQ5fnByR9dqQpozsx4UAaB9QpoOu7yTcWf9Tho7aQCAYTnd76I5Mj2xtnR3J+7shzTP6KQBAG6i6bw9emBi7cHUQZ081KtBdNIA0AVCmg5rxp0N0kkz1XTSGHcGAAzJU80+mmM776JJkjuP9K7z9CUhDQBwY0/2Hxa55/jMjq918rBxZwB0h5Cmw5pRZUcG6aSZ6O+k0UkDAAzJ6SHto2msjzsT0gAAN3b64lyS5K4hPCzSjDs7K6QBoAOENB3WdMEcObD9WavTk81OmqWhngkAuHU91d8dM4x9NEly51HjzgCArWk6ae4eQkhzsh/SnJsV0gDQPiFNhzWdNIOMO5vud9IYdwYADMtTF5uQZnoo12vGnT3ZH6MGALCZ9Y7enY87a7p5mwdQAKBNQpoOuzzf64I5PMi4s34nzZWFlaGeCQC4dZ3uhynDGDOSJPed6N1kefzc1aFcDwDYv4bZSdPstXniwtyOrwUAOyWk6aha6/pOmh100iyurGZhWVADAOzc6Uu9sWTDGnd2/8mDSXrz4K/YowcA3MBaJ80QQ5qnLy9kcXl1x9cDgJ0Q0nTU3NJKVmvv74N00kxNrP9qjTwDAIbh6aaTZkghzdEDkzk209u999h53TQAwOaevNjrehnWTprpibHUuh7+AEBbhDQd1QQrYyWZ6Y8u246xUtaCmllPpgIAO7SyWvP05eF20iTJ/bf1umkeO2fcCABwfVcWlnOpf59kGJ00pZTc2++m+aKRZwC0TEjTUZf7wcrh6YmUUga6xgEhDQAwJGdnF7KyWjNWktsPTw3tus+7rXeD5FF7aQCATTR78Q5PT+TIgcmhXPPeE0IaALpBSNNRTSfNToqP6YnxZ10LAGBQj53v3cC46+iBTIwPr4R83lonjZAGALi+Ye6jadxzrBfSPCGkAaBlQpqOmt3QSTOo6UmdNADAcDx85kqS5IE7Dg31us87IaQBAG7syX5IM4x9NI21TprzQhoA2iWk6ajL/e6Xwwd2ENL0x51d1kkDAOzQnzwzmyR54PbhhjRrO2nOC2kAgOs7fXG9o3dY7unvpHniopAGgHYJaTrq0vxSkp110jT/9pn+kl8AgEE1nTRfcvvhoV53LaQ5N5da61CvDQDsD7vSSXNcJw0A3SCk6ain+gXInUemB77GsZnePpummAEAGNRujTu75/hMSknmllZyZnZxqNcGAPaHJ9d20swM7ZprIc0FD4oA0C4hTUc90TwlcnzwAuToWkjjqRAAYHCrq3VDJ81wQ5qpibG1xb2P2ksDAFzHbnTS3HXsQEpJFpZXc/aKB0UAaI+QpqOaYOWeHRQgx3XSAABD8OSl+Swsr2ZyvKw9dTpM9/UX9z5uLw0AcB1rO2mGGNJMTYytTS954oKHWwFoj5Cmo568MLxOmtNCGgBgBx5+ptdFc/9tBzMxPvzysdlL8+hZIQ0A8GzzSys5f7W3t3eYnTRJb+xqYi8NAO0S0nTUE0PopGl20jx9eT7LK6tDORcAcOv5kzOzSZIHbj+8K9d/Xj+keUwnDQBwjWY6yIHJsbX7HMOycS8NALRFSNNBswvLuTy/nGRnnTSHpicyOV6yWpOnLy8M63gAwC3mT/qdNF9yx3D30TTWOmnspAEArvHHT/ceFnn+bYdSShnqtYU0AHSBkKaDnuwXB0cOTOTw9MTA1xkrJaeO9jpx7KUBAAb18JleSPPA7bsT0jz/ZC+k+fzTV1Jr3ZXPAABG00OnLyVJXnr3kaFf+97+Xjw7aQBok5Cmg57oByr3HNv5Yt5mXqu9NADAoJqQ5kt2KaR56d1HMz5WcmZ2wYMlAMCzfPr05STJS+4+OvRrN/ddHj0npAGgPUKaDmo6ae45vvOFeHf1C44nLyo4AIDtW1heyeP9XTEP7NK4swOT4/myU72nYz/x+MVd+QwAYDQ99GSvk+Yldw2/k+bF/Wt+/unLWVheGfr1AWArhDQd1HTS7GQfTaPppPFUKgAwiEfPXs1qTQ5PT+SOw9O79jlfed+xJMknHr+wa58BAIyW+aWVtY7el+5CJ819J2Zy/OBkllZqPnt6dujXB4CtENJ0UDML9Z5jQ+ikOWrcGQAwuKaz5UWnDg99We9GL++HNJ/8ok4aAKDn80/PZrUmJw5O5s4jw39YpJSSl9/bf1Dkix4UAaAdQpoOakaT3T2EnTTNyLQnjDsDAAbwkYfPJkm+9oHbdvVzvvK+40l6oVCtdVc/CwAYDZ9eG3V2dNceFmlCmj/yoAgALRHSdNCTF5pxZ8PbSaOTBgAYxEcfPpckefUuhzRfdupIpibGcnFuKY+cvbqrnwUAjIaHTl9Okrzk7uHvo2msddLYiwdAS4Q0HVNrXet6uWcInTTNTpqnLy9keWV1x9cDAG4dT12azxfOXk0pyde8YHdDmqmJsbVZ85/wJCsAkOSh071OmpfeNfx9NI1m5Opnn7qc+aWVXfscANiMkKZjLlxdyvxSL0y5awg7aW4/PJ2JsZKV1ZpnZhd2fD0A4NbxkX4XzcvuPpqjByZ3/fO+sn+T5BOPmQkPALe6Wms+/eTud9Lce3wmJw5OZmml5jP9zh0A2EtCmo5pumhOHprKgcnxHV9vfKzk1NFe2POkkWcAwDZ8tL+P5tUPnNyTzzNuBABoPDO7kHNXFjNWkhfduXshTSklL+/vxvukbl4AWiCk6Zhh7qNpNB059tIAANvR7KP52l3eR9P46vt7N0j+8PELuTS/tCefCQB00+8/cj5J8sDthzIztfOHWG/k5ff2xql90oMiALRASNMxTSfN3UPYR9No9tI8fObK0K4JAOxv564s5rNPzSbZu5DmhXcczpfeeTiLy6t5/yef3JPPBAC66X2f6NUCf+alp3b9s15+b+9BkY8/buQqAHtPSNMxD/5xb6zIS+4aXivvq/qLfn/jM88M7ZoAwP72aw89nST5slOHc9uhqT35zFJK3vCKe5Mk7/n9L+7JZwIA3XNlYTm/+umnkiTf8ZX37PrnvfL5JzI+VvLQ6cv53FP20gCwt4Q0HTK/tJJf7wcp3/qyu4Z23de99M4kyf/3yLmcv7I4tOsCAPtTrTU//dsPJ0n+iz24MbLRd37VvSmlN2rtsXNX9/SzAYBu+OCnn8r80moeuP1Q/tQ9R3f98+44Mp1veUnv3sm/++hju/55ALCRkKZDfutzZzK3tJJ7j8/ky+8dXhFy34mDeendR7Nak1/7zNNDuy4AsD/9zufP5tNPXsrM5Hj+6muev6effc/xmXz9C08mSX7+D3TTAMCt6H0ffyJJ8h1fcXdKKXvymd/ztfcnSd77B49nfmllTz4TABIhTad84FOnkySvf9mpoRchr+9303yw3y4MALCZf/lbf5Ik+cuvel6OH9ybUWcbveGr70uS/PuPPZ6FZTdJAOBWcuHqYn7js70pI3sx6qzx2i+7I/ccO5ALV5fW7s8AwF4Q0nTE8srq2rzVb/tTwxt11vgzL+st2vuNzzzjZgcAsKmPP3Yhv/nZZzJWkr/2DQ+0coZv//K7cuLgZB49dzU/+v6HWjkDANCOH/+Vz2ZppeYldx3Ji04Nb1/vzYyPlfzlV/W6aX7uwUeyulr37LMBuLXd0iFNKeUtpZQvlFLmSykfKaV8bVtn+egXzuX81aWcODiZV73gxNCv/+X3HMupo9O5sriS3/3js0O/PgCwfV2qRZLkj5+ZzZvf/XtJkj/3Fffk/pMHWznHoemJ/M9/8SuTJD/7O1/IL3uaFQB2Rddqkf/w8Sfy7gcfSZL8nW9/yZ5//l961X2ZGh/Lxx45n3/yS59OrYIaAHbfRNsHaEsp5S8neUeSv5HkI0l+MMkHSikvrrXu+eKWX/5Ur4vmdS89lYnx4WdnY2Mlr3/ZqfybDz+at73nk3n3X/vavPiuvXsiBYDdc3VxObPzy1lYXs3M1HgOT0/kwOR428fiJrpWizx27mr+yr/6SM7MLuZldx/NP/7zX77XR3iWP/OyU/nr3/hAfvq3H84P/p9/mB943Yvypm94IFMTt/QzRgB74t98+JFcnl/O2SsLOTe7mIvzS5mdX87c0kqWV2ruOzGTQ9MTOXJgIvccn8l9J2Zy34mDuff4TG4/PLVnO0TYma7VIp9/+nJ++D2fSJK85ZtfmG9+yZ17fYTcfWwmP/pfvjxv/b8+nv+fvTsPk7WuDn3/XVXdu/fAHgBlAwLOEhPNIDGCSQSDBo2aRG9IuJzzJHCISdCcJJh7cyTexCEDmhjQq2Q40eN0ne49cDAmGhKEOG5QUWM0ohIHNsPeIHueelz3j/et3rVrV3V3DV1D9/fzPPVU1fu+9fbql97di1q11u9tn/o2GybGuPKCx5tbS9KAvO+Oe5jL5ODkDHsOTbP38DR7Dk9zcHKGuUx+4PTNPOKENTxy4wSnbFzLKZsmeNSWdSP3e3vVFmmAVwB/m5nvAIiI3wBeAPwX4PX9Dua8x5/M/XsO84IfPG3ZvsZvXfhE7vjWLr754AEu/uvP8J/PfTTff/omtqxbQ7USjFWDsUpQMaGWNAJafaat2afdmh3b/ENxzc/a7Niln7O7mJJkcmaOfYeni9uRGR7aP8l9ew5z7+7D3Lv7EPuPzBz3ui3rxzl101pO27yW07as47RNazl181o2rh1nYqzCmtqtWuEHz9jsmymDMVS5yOv/8S527DvCE045gfdc8WNsXj/e7xCO83vP+z7u2rGfT939Pa756F288zPf4bzHncxTHrWZR2ycYNPaMcYqFSoVqEZQrQRRux908JLUgVoqUMsdkvr8oNyWjccee/yxxxw9uLZvLpMj03Mcmprh0NQsBydn+N6BKXbuO8KOvUfYue8I9+w6xMwCo57+/YF9LfetHa/wqC3reFRZtDl5wxo2rRtj09pxNq0bZ+14hbFKhbFqMF6tUK0EP3TGFqoVf3MPwFDlIu/Z9l0OTc1y3uNO5qrnPKnfX37eS552Bg8fmOJPPvI13vyxb/Ke27/LC556Gk845QRO3bx2PodeM1ZhrMw9VrtOOo467VHqvLmpgxg7/Fr9/N467fbqa4ydfrU+vqzz/9YdXv++fm/9/Rnp5IVJcnByln1HiiLM3sPTPLR/kvv3HOYbOw+w9/A0sy3ykk9+83tNt5+ycYIzT1rPmSeu45EbJ9iyfg2b142zZf0468arjFUrjFeC8fJ3+Q+cvnmgHwiM1di6GRFrgEPAL2TmTXXb3wVsycyfa/KaCWCibtNG4N7t27ezadOm5Q65pf/389sX3P+LP3rmMc/3HJriv77vi3xx+57lDEuS1GcBrBmvMDk919brKgFffs1FPYtj3759nHnmmQCbM7P1Ozir3DDmIvuPTPOn//A1rnrukzhl09quzwcL5ymNOUorc3PJh750H2+65Rs8fHC6J3FJkhZXCdi8bpyTNqxh09pxTlg7xvrxKtVKcM5jTuTw1Cx7Dk3zwN7D3L/nCPfvPcyD+yc7euPps6+6kPVrevMZUnORpRnGXGR2LnnXZ77Ni37odB65sbtcpN33Spr5wOfu4W2f+BY79k12FYskqXsBnLC2yuZ1RV6yce0YlQged8oGdh2Y4qH9k3zvwCQP7j/Coan23hcB+Ngrzmfr5t78f3AnuchqLdKcDtwHPDMzt9Vt/zPg/Mx8RpPXvAZ4dd+ClCRptJ2RmfcNOohhZS4iSdKyMxdZgLmIJEnLbsm5yGoed9auayhmtdY7Cdg1gFhqNgL3AmcA+wcYx6jxunXG69YZr1tnvG7tG6ZrthG4f8AxrETDmIssZJh+Joed12rpvFZL57Vqj9dr6UbhWpmLLI9RyUVG4Wd0mHi92uP1ao/Xqz1er/YM8/VqKxdZrUWa7wGzwNaG7VuBHc1ekJmTQGOP60Bbp+vmnu63jXvpvG6d8bp1xuvWGa9b+4bsmg3664+CFZGLLGTIfiaHmtdq6bxWS+e1ao/Xa+lG5FoNa1zDZMXmIiPyMzo0vF7t8Xq1x+vVHq9Xe4b8erUVz+BWwxmgzJwC7gQurG2LiEr5fFur10mSJPWCuYgkSRokcxFJkobHau2kgaJF910R8Xngs8DvABuAdww0KkmStFqYi0iSpEEyF5EkaQis2iJNZn4wIh4JvA44FfgS8LzM3DnYyNoyCbyW49uNtTCvW2e8bp3xunXG69Y+r9mIWSG5yEL8mVw6r9XSea2WzmvVHq/X0nmtVogVnIv4M9oer1d7vF7t8Xq1x+vVnhVzvSIzBx2DJEmSJEmSJEnSqrMq16SRJEmSJEmSJEkaNIs0kiRJkiRJkiRJA2CRRpIkSZIkSZIkaQAs0kiSJEmSJEmSJA2ARZohFxEvj4jvRMSRiLgjIn5skeMvjoi7yuP/LSJ+pl+xDpN2rltEvDQiPhkRu8vbLYtd55Wq3Z+3utddEhEZETctd4zDqIN/p1si4vqIeCAiJiPiG6vt32oH1+x3IuLrEXE4IrZHxHURsbZf8Q6DiHhWRHw4Iu4v/739/BJec0FEfKH8Obs7Ii7rQ6haRcxTls7cZOnMR5bOHKQ95h+LM9/QqDAHaY95SHvMRdpjPtIe85GlWW05iUWaIRYRvwRcC7wWeBrwr8DNEXFKi+OfCbwfeDvwI8BNwE0R8ZT+RDwc2r1uwAUU1+3ZwHnAduCfIuJRyx/t8OjgutVe9xjgjcAnlznEodTBv9M1wD8DjwF+ATgbeClwXz/iHQYdXLNLgdeXxz8ZuAL4JeBP+xLw8NhAca1evpSDI+KxwD8AtwE/DLwJeFtEXLRsEWpVMU9ZOnOTpTMfWTpzkPaYfyyZ+YaGnjlIe8xD2mMu0h7zkfaYj7RlVeUkkZmDjkEtRMQdwOcy8zfL5xWKP45vyczXNzn+g8CGzHxh3bbbgS9l5m/0KeyBa/e6NXl9FdgN/GZmvntZgx0inVy38lp9AvgfwE8CWzJz0cr2StLBv9PfAP5P4Psyc7qvwQ6JDq7ZW4EnZ+aFddv+AnhGZv5En8IeKhGRwIszs+UntCLiDcALMvMpdds+QPHv9Hl9CFMrnHnK0pmbLJ35yNKZg7TH/KN95hsaVuYg7TEPaY+5SHvMR9pjPtKZ1ZCT2EkzpMrK8jnALbVtmTlXPj+vxcvOqz++dPMCx684HV63RuuBcWBXzwMcUl1ctz8EHszMty9vhMOpw+v2s8A24PqI2BkRX4mI3y+TuhWvw2v2GeCcKFuAI+JxwM8AH1neaEfeqv+boOVjnrJ05iZLZz6ydOYg7TH/WFar8ne7BsccpD3mIe0xF2mP+Uh7zEeW3Uj/rh8bdABq6RFAFdjZsH0n8H0tXnNqi+NP7W1oQ62T69boDcD9HP8PeyVr+7pFxE9QtFn+8PKGNtQ6+Xl7HPBTwHsp/rA+AfhLiqT3tcsT5lBp+5pl5vsi4hHApyIiKP52/XVmrob23m60+puwKSLWZebhAcSklcM8ZenMTZbOfGTpzEHaY/6xfMw31G/mIO0xD2mPuUh7zEfaYz6yvEY6J7GTRqoTEa8ELqFooTsy6HiGVURsBN4DvDQzvzfoeEZMBXgQ+LXMvDMzPwj8CbDi2+w7FREXAL8PvIxiZutLgBdExB8MMi5J6gdzk9bMR9pmDtIG8w9JMg9ZjLlIR8xH2mA+snrYSTO8vgfMAlsbtm8FdrR4zY42j1+JOrluAETE/wG8EnhOZn55ecIbWu1et8dTLPL24aKQD5RF34iYAc7OzP9YlkiHSyc/bw8A05k5W7fta8CpEbEmM6d6H+ZQ6eSa/RHwnsx8W/n83yJiA/DfI+JPyvZgHa/V34R9w/4JEo0E85SlMzdZOvORpTMHaY/5x/Ix31C/mYO0xzykPeYi7TEfaY/5yPIa6ZzETpohVf5SuhOoXxiqUj7f1uJl2+qPLz13geNXnA6vGxHxe8AfAM/LzM8vd5zDpoPrdhfwVIp23trt74DbysfblznkodDhz9ungSeUx9U8CXhghScjQMfXbD3QmHjUErpAraz6vwlaPuYpS2dusnTmI0tnDtIe849ltSp/t2twzEHaYx7SHnOR9piPtMd8ZNmN9u/6zPQ2pDfgl4AjwK8ATwb+BtgNbC33vxu4pu74ZwLTwO9SzDJ8DTAFPGXQ38uQX7f/BkwC/xvF/MLa7YRBfy/DfN2avP6dwE2D/j6G/boBZwL7gLdQJCIvoJiR+apBfy9DfM1eU16zS4DHUvyRvRv44KC/lz5ftxM4mvgncFX5+Kxy/zXAu+uOfyxwEPiz8m/Cy4AZ4KJBfy/eVsbNPGVZr9WqzU3MR5b152pV5yDmH0u+TuYb3ob+Zg6y7Ndr1eYhnVyvJq9fNblIhz9f5iPmI0u9VR++kwAAIABJREFUVqsqJ3Hc2RDLzA9GxCOB11H8QfwSxacYaosgnUVdNTUzPxMRlwJ/DPwp8E3g5zPzK/2NfLDavW7AlcAa4H82nOq1FL8MV4UOrpvo6N/p9oi4CLgO+DJwH/BmisUYV4UOftb+mOIP8h8DjwIeAj4MvKpvQQ+HH6X4RFbNteX9u4DLgNMorh0AmfntiHgBxc/abwP3Ar+amTf3JVqteOYpS2dusnTmI0tnDtIe848lM9/Q0DMHaY95SHvMRdpjPtIe85G2rKqcJMpKkyRJkiRJkiRJkvrINWkkSZIkSZIkSZIGwCKNJEmSJEmSJEnSAFikkSRJkiRJkiRJGgCLNJIkSZIkSZIkSQNgkUaSJEmSJEmSJGkALNJIkiRJkiRJkiQNgEUaSZIkSZIkSZKkAbBII0mSJEmSJEmSNAAWaSRJkiRJkiRJkgbAIo2koRERucjtNRHxQES8suF1ry/3X9Cw/V8i4j3l48tanPNX+/gtSpKkERAR74yIm5psv6DMH7bUPc6ImIuIvRHxxYj4s4g4reF1r2mRhzynf9+VJEkaBWUeUssVpiLi7oj4w4g4ISKmI+KShuM/UB77mIbt34mIPyofm4tIQ2xs0AFIUp36NzR+CXgdcHbdtgPl8wuA19dtfzawvdz+LwARsRY4F3hX3XH7Gs4HsLfrqCVJ0mp2NkWOsQl4GvB7wBURcUFm/lvdcV8FGt8I2dWfECVJ0oj5R+ByYAL4GeB6YBr4PMV7Hx+oO/YCjr4n8k6AiHgs8Gjg1rrjzEWkIWWRRtLQyMwdtccRsbfYdHRbuf024C8iYiwzZyJiI/AjwFXAxXWHnkeRzNx27Jc49nySJEldejAz9wA7gG9ExIeALwJ/BfxE3XEz5iGSJGmJJuvyhr+KiBcDP0vxHsdLagdFxJOBtcCbqSvSlI8ngW115zQXkYaU484kjZrbgBOAp5fPfxL4BnAD8IyygwaK7prvZOZ3+h6hJElatTLzMPDXwI9HxCmDjkeSJK0Ih4E1FO+JnF03WvXZwKcoOmYuqDv+2cC2zDzSzyAldcYijaSRkpnfBO7jaPJxAfDx8tMg91B00NS239bw8s0RcaDu5idIJElSKy9syBsOAB9d4mvvKu8fU7ftqQ3n+2xPo5UkSStOFJ4DXERRiPk0MEXDeyLAncAjyjFnAOdz/Hsi5iLSkHLcmaRR9C8Uicg15f2fl9s/DlwQEbcDzwD+tuF1+ylmxdfMLWeQkiRppN0GXNmw7RnA/7OE10Z5n3Xbvk4xpqRmsvPQJEnSCvfC8gMi4xQfsn8f8JrMPBQRn6N4L+T9FMWYPy/HwX+G4j2RAM7i+CKNuYg0pCzSSBpFtwFvjoiTKdaj+Xi5/ePArwOfoGgDvrXhdXOZeXffopQkSaPsYGPeEBFnLPG1Ty7vv1O3bco8RJIkLVHtwyJTwP2ZOdOw75ci4geAdcAXyu0fpxhzVgEOAXc0nNNcRBpSjjuTNIpuAzYArwC+mZkPlts/AfwY8Pxy+30Dik+SJK1SEbEO+DXgE5n50KDjkSRJI+lgZt6dmfc0FGigeE/kicClwKcyc7bc/gmKzpoLgE9n5lTfopXUFYs0kkZOZn6LYv2Z/8rRLhoycztwP8UbI41tvZIkScvhlIg4NSKeGBGXUMyKfwTHj0qTJEnqhc9QjCo75j0R4LPAKcDP4Xsi0kixSCNpVN0GbKRYn6bex8vtJiSSJKkfvk7xIZE7gVcCtwBPycx/H2hUkiRpRcrMI8DtNLwnkpmTddt9T0QaIZGZix8lSZIkSZIkSZKknrKTRpIkSZIkSZIkaQAs0kiSJEmSJEmSJA2ARRpJkiRJkiRJkqQBsEgjSZIkSZIkSZI0ABZpJEmSJEmSJEmSBsAijSRJkiRJkiRJ0gBYpJEkSZIkSZIkSRoAizSSJEmSJEmSJEkDYJFGkiRJkiRJkiRpACzSSJIkSZIkSZIkDYBFGkmSJEmSJEmSpAGwSCNJkiRJkiRJkjQAFmkkSZIkSZIkSZIGwCKNJEmSJEmSJEnSAIwNOoBRFREBnA7sH3QskiQNmY3A/ZmZgw5kUCLiUcAbgOcD64G7gcsz8/Pl/gBeC7wU2AJ8GrgyM7/ZxtcwF5EkqblVn4tIkqTRYZGmc6cD9w46CEmShtQZwH2DDmIQIuJEiqLLbRRFmoeAJwK76w77PeC3gF8Bvg38EXBzRHx/Zh5Z4pcyF5EkqbVVm4tIkqTREn6wpDMRsQnYu337djZt2jTocCRJGgr79u3jzDPPBNicmfsGHc8gRMTrgR/PzJ9ssT+A+4G/yMw3lts2AzuByzLzAy1eNwFM1G3aCNxrLiJJ0lHmIpIkadTYSdOlTZs2+caIJEmq97MUXTH/H3A+xad4/zIz/7bc/1jgVOCW2gsyc29E3AGcBzQt0gBXA69u3GguIkmSJEnS6KoMOgBJkqQV5nHAlcA3gYuAvwL+74j4lXL/qeX9zobX7azb18w1wOa62xm9CliSJEmSJA2GnTSSJEm9VQE+n5m/Xz7/YkQ8BfgN4F2dnjQzJ4HJ2vNiapokSZIkSRpldtJIkiT11gPAvzds+xpwVvl4R3m/teGYrXX7JEmSJEnSKmCRRpIkqbc+DZzdsO1JwHfLx9+mKMZcWNsZEZuAZwDb+hGgJEmSJEkaDiuySBMRr4mIbLjdVbd/bURcHxEPR8SBiLghIho/zSpJktSJ64BzI+L3I+IJEXEp8GvA9QCZmcCbgP8rIn42Ip4KvBu4H7hpUEFLkiRJkqT+W8lr0nwVeE7d85m6x9cBLwAuBvYCbwVuBH68b9FJkqQVKTM/FxEvBq4B/pCic+Z3MvO9dYf9GbAB+O/AFuBTwPMy80i/45UkSZIkSYOzkos0M5l53Fz3iNgMXAFcmpm3ltsuB74WEedm5u19jlOSJK0wmfn3wN8vsD8pCjh/2LegJEmSJEnS0FmR485KT4yI+yPiWxHx3oioLdZ7DjAO3FI7MDPvAu4Bzmt1soiYiIhNtRuwcTmDlyRJkiRJkiRJK9tKLdLcAVwGPA+4Engs8MmI2AicCkxl5p6G1+ws97VyNcVotNrt3h7HLEmSJEmSJEmSVpEVOe4sMz9a9/TLEXEH8F3gF4HDHZ72GuDauucbsVAjSZJWketvu5vHnLyBF/zgaYMORZIkSZKkFWFFFmkaZeaeiPgG8ATgn4E1EbGloZtmK3DcGjZ155gEJmvPI2K5wu3a++64Z8H9lz7jrAX3S5IkNfruwwf585u/ziNOWGORRpIkSZKkHlmp486OEREnAI8HHgDuBKaBC+v2nw2cBWwbSICSJElDbv+RGQAOTM4MOBJJkiRJklaOFdlJExFvBD5MMeLsdOC1wCzw/szcGxFvB66NiF3APuAtwLbMvH1QMUuSJA2zyZnZ8n6OzBzqrmJJkiRJkkbFiizSAGcA7wdOBh4CPgWcm5kPlfuvAuaAG4AJ4GbgZQOIU5IkaSQcmZ4DIBNm5pLxqkUaSZIkSZK6tSKLNJl5ySL7jwAvL2+SJElaRK2TBmBqZo7x6qqYmitJkiRJ0rLy/64lSZK0qFonDRQjzyRJkiRJUvcs0kiSJGlRjZ00kiRJkiSpexZpJEmStKj6ThqLNJIkSZIk9YZFGkmSJC1qcrquk2Z2doEjJUmSJEnSUlmkkSRJ0qKO1HXP1HfVSJIkSZKkzlmkkSRJ0qIm68edzVqkkSRJkiSpFyzSSJIkaVFHZurGnbkmjSRJkiRJPWGRRpIkSYuq76SZtEgjSZIkSVJPWKSRJEnSouykkSRJkiSp9yzSSJIkaVHHrEljkUaSJEmSpJ6wSCNJkqRF1XfSTNY9liRJkiRJnbNII0mSpEXZSSNJkiRJUu9ZpJEkSdKi6rtnpmYt0kiSJEmS1AsWaSRJkrSo+k6a+seSJEmSJKlzFmkkSZK0qCN20kiSJEmS1HMWaSRJkrSoYzppXJNGkiRJkqSesEgjSZKkRdV30tSvTyNJkiRJkjpnkUaSJEmLqu+kmbKTRpIkSZKknrBII0mSpEUdsyaNRRpJkiRJknrCIo0kSZIW5Zo0kiRJkiT1nkUaSZIkLSgz7aSRJEmSJGkZWKSRJEnSgqZnk8yjzy3SSJIkSZLUGxZpJEmStKD6LhqAyYbnkiRJkiSpMxZpJEmStKD69WgApmbtpJEkSZIkqRcs0kiSJGlBR6aP7Zxx3JkkSZIkSb1hkUaSJEkLmmwoyjQ+lyRJkiRJnbFII0mSpAXZSSNJkiRJ0vKwSCNJkqQFNXbOWKSRJEmSJKk3LNJIkiT1WES8JiKy4XZX3f61EXF9RDwcEQci4oaI2DrImBcy2dBJ47gzSZIkSZJ6wyKNJEnS8vgqcFrd7Sfq9l0HvAi4GDgfOB24sd8BLlWtKDNejWOeS5IkSZKk7owNOgBJkqQVaiYzdzRujIjNwBXApZl5a7ntcuBrEXFuZt7e7GQRMQFM1G3auAwxN1Vbk2bT2nEePjjF1MzsIq+QJEmSJElLYSeNJEnS8nhiRNwfEd+KiPdGxFnl9nOAceCW2oGZeRdwD3DeAue7Gthbd7t3ecI+Xq1zZtO68WOeS5IkSZKk7thJI0mS1Ht3AJcBX6cYdfZq4JMR8RTgVGAqM/c0vGZnua+Va4Br655vpE+FmqOdNEXqODUzx3tv/y4Rcdyxlz7jrOO2SZIkSZKk5izSSJIk9VhmfrTu6Zcj4g7gu8AvAoc7POckMFl73qxAslxqnTMb1xadNAnMJVT7F4IkSZIkSSuS484kSZKWWdk18w3gCcAOYE1EbGk4bGu5b+jMd9KsO/r5npk5R55JkiRJktQtizSSJEnLLCJOAB4PPADcCUwDF9btPxs4C9g2kAAXMb8mTdlJAzAzm4MKR5IkSZKkFcNxZ5IkST0WEW8EPkwx4ux04LXALPD+zNwbEW8Hro2IXcA+4C3Atsy8fVAxL6TWSbN2vEolilFnM3MWaSRJkiRJ6pZFGkmSpN47A3g/cDLwEPAp4NzMfKjcfxUwB9wATAA3Ay8bQJxLUuukWTteZaxaYWpmjlmLNJIkSZIkdc0ijSRJUo9l5iWL7D8CvLy8Db1aJ83EWIWxSjAFTM+6Jo0kSZIkSd1yTRpJkiQt6JhOmkoA2EkjSZIkSVIPWKSRJEnSgo6uSVNhrFqkj65JI0mSJElS9yzSSJIkaUG1TpqJsSrVspNmxnFnkiRJkiR1zSKNJEmSFnRMJ02tSGMnjSRJkiRJXbNII0mSpAXVd9K4Jo0kSZIkSb2z4os0EfHKiMiIeFPdtrURcX1EPBwRByLihojYOsg4JUmShtVkkzVpph13JkmSJElS11Z0kSYing78OvDlhl3XAS8CLgbOB04HbuxvdJIkSaPBThpJkiRJkpbHii3SRMQJwHuBlwK767ZvBq4AXpGZt2bmncDlwDMj4tyBBCtJkjTEXJNGkiRJkqTlsWKLNMD1wD9k5i0N288BxoH57Zl5F3APcF6rk0XERERsqt2AjcsQsyRJ0tCp76SpluPOZhx3JkmSJElS18YGHcByiIhLgKcBT2+y+1RgKjP3NGzfWe5r5Wrg1b2JUJIkaXTYSSNJkiRJ0vJYcZ00EXEm8GbgP2XmkR6e+hpgc93tjB6eW5IkaWi5Jo0kSZIkSctjJXbSnAOcAnwhImrbqsCzIuI3gYuANRGxpaGbZiuwo9VJM3MSmKw9rzu3JEnSipWZx3bSVIscaHrWIo0kSZIkSd1aiUWajwFPbdj2DuAu4A3AdmAauBC4ASAizgbOArb1L0xJkqThNzOX1Jpmik6aohF7ds41aSRJkiRJ6taKK9Jk5n7gK/XbIuIg8HBmfqV8/nbg2ojYBewD3gJsy8zb+x2vJEnSMKt10QBMjFeouiaNJEmSJEk9s+KKNEt0FTBH0UkzAdwMvGygEUmSJA2h2no0ABNjR8edzTjuTJIkSZKkrq2KIk1mXtDw/Ajw8vImSZKkFmqdNBNjFSJiftyZnTSSJEmSJHWvMugAJEmSNLxqnTQTY0XaODY/7sw1aSRJkiRJ6pZFGkmSJLVU66RZO14FcNyZJEmSJEk9ZJFGkiRJLc130owf20kz67gzSZIkSZK6ZpFGkiRJLU2VRZo11SJtrM6vSeO4M0mSJEmSumWRRpIkSS3VOmbGqw1r0jjuTJIkSZKkrlmkkSRJUkvTs0XHTLUszsyvSeO4M0mSJEmSumaRRpIkSS3VOmnG5jtpKsdslyRJkiRJnbNII0mSpJamy7FmtTFntftah40kSZIkSeqcRRpJkiS1NN9J0zDuzE4aSZIkSZK6Z5FGkiRJLc3MFR0zteJMbW0a16SRJEmSJKl7FmkkSZLU0sz8uLNj16SpFW8kSZIkSVLnLNJIkiSppflOmoZxZ7XijSRJkiRJ6pxFGkmSJLVUG2tWK87UijWzc0mmhRpJkiRJkrphkUaSJEkttRp3loDL0kiSJEmS1B2LNJIkSWqpsZOmbKQBYM5OGkmSJEmSumKRRpIkSS3NzBZr0lTL6kylrkozayuNJEmSJEldsUgjSZKklmqdNOPlmLNKHC3S2EkjSZIkSVJ3LNJIkiSppdqaNNUm487spJEkSZIkqTsWaSRJktTS7Fwx7my8rM5ExHyhxhqNJEmSJEndsUgjSZKklqbLSky1cjRtrK1PM2eVRpIkSZKkrlikkSRJWkYR8cqIyIh4U922tRFxfUQ8HBEHIuKGiNg6yDhbqY00G68enXNWW5fGNWkkSZIkSeqORRpJkqRlEhFPB34d+HLDruuAFwEXA+cDpwM39je6pZmeLcadVSvHF2lck0aSJEmSpO5YpJEkSVoGEXEC8F7gpcDuuu2bgSuAV2TmrZl5J3A58MyIOHeB801ExKbaDdi4vN9BoVaIGas2GXdmjUaSJEmSpK5YpJEkSVoe1wP/kJm3NGw/BxgH5rdn5l3APcB5C5zvamBv3e3enkbbwvRsWaQ5ppOmuJ913JkkSZIkSV2xSCNJktRjEXEJ8DSKwkqjU4GpzNzTsH1nua+Va4DNdbczehDqombKcWdjdWvSzHfS2EojSZIkSVJXxgYdgCRJ0koSEWcCbwaem5lHenXezJwEJuu+Tq9OvaD5cWdN1qSZs5NGkiRJkqSu2EkjSZLUW+cApwBfiIiZiJgBzgd+q3y8E1gTEVsaXrcV2NHfUBc3PV+kOZo2VsqCzaydNJIkSZIkdcVOGkmSpN76GPDUhm3vAO4C3gBsB6aBC4EbACLibOAsYFv/wlya2bkm487mO2kGEpIkSZIkSSuGRRpJkqQeysz9wFfqt0XEQeDhzPxK+fztwLURsQvYB7wF2JaZt/c73sVMzzbrpCnu7aSRJEmSJKk7FmkkSZL67ypgjqKTZgK4GXjZQCNqodmaNFXXpJEkSZIkqScs0kiSJC2zzLyg4fkR4OXlbahNzx4/7qwSrkkjSZIkSVIvVBY/RJIkSatVrRBTreukqVTspJEkSZIkqRcs0kiSJKmlmXJNmvHq0bTRcWeSJEmSJPWGRRpJkiS1NDNXjDs7tpOmuC8noUmSJEmSpA5ZpJEkSVJLM3O1Tprj16Sxk0aSJEmSpO5YpJEkSVJLtXFn1UrduLOyq6a2Xo0kSZIkSeqMRRpJkiS1VBt3Nl6xk0aSJEmSpF6zSCNJkqSWauPO6tekqT2es5NGkiRJkqSuWKSRJElSS7VxZ2PVo2ljrZNm1hqNJEmSJEldsUgjSZKklmrrzowdM+6suHfcmSRJkiRJ3bFII0mSpJamZ4s1acaqx487m3XcmSRJkiRJXbFII0mSpJaOdtIcP+7MThpJkiRJkrpjkUaSJEktLdRJM2cnjSRJkiRJXbFII0mSpJaar0lT66QZSEiSJEmSJK0YK7JIExFXRsSXI2JfedsWEc+v2782Iq6PiIcj4kBE3BARWwcZsyRJ0jCarhVpqnXjzsqHrkkjSZIkSVJ3VmSRBrgXeCVwDvCjwK3AhyLiB8r91wEvAi4GzgdOB24cQJySJElDrVknTdU1aSRJkiRJ6omxQQdQLyIel5nf6vY8mfnhhk2viogrgXMj4l7gCuDSzLy1/LqXA1+LiHMz8/YWsU0AE3WbNnYbpyRJGi69ykVWisxsPu6sfGwnjSRJkiRJ3Rm2Tpq7I+K2iPjPEbG2FyeMiGpEXAJsALZRdNeMA7fUjsnMu4B7gPMWONXVwN662729iE+SJA2Vnucio2ymrggzVjmaNtpJI0mSJElSbwxbkeZpwJeBa4EdEfE3EfFjnZwoIp4aEQeASeCvgRdn5r8DpwJTmbmn4SU7y32tXANsrrud0UlckiRpqPUsF1kJZmbrijTVuk6a8qGNNJIkSZIkdWeoijSZ+aXM/G2KNWL+C3Aa8KmI+EpEvCIiHtnG6b4O/DDwDOCvgHdFxPd3EdtkZu6r3YD9nZ5LkiQNpx7nIiNvZm5u/nHVcWeSJEmSJPXcUBVpajJzJjNvBC4G/hvwBOCNwPaIeHdEnLaEc0xl5t2ZeWdmXg38K/DbwA5gTURsaXjJ1nKfJEla5XqRi6wE9Z0049WjaWPFcWeSJEmSJPXEUBZpIuJHI+IvgQeAV1C8KfJ44LkUn2z9UAenrQATwJ3ANHBh3dc7GziLYs0aSZK0yi1TLjJy6tekqWukme+qsZNGkiRJkqTujA06gHoR8QrgcuBs4CPALwMfyczarI1vR8RlwHcWOc81wEeBe4CNwKXABcBFmbk3It4OXBsRu4B9wFuAbZl5e6+/J0mSNDp6lYusFLVxZ+PVIOJolaZqJ40kSZIkST0xVEUa4ErgfwDvzMwHWhzzIHDFIuc5BXg3xRz5vRQLAF+Umf9c7r8KmANuoOiuuRl4WXehS5KkFaBXuciKUBt3Vr8eDUCl7MWuW7JGkiRJkiR1YNiKNM8F7qn7tCoAUXx088zMvCczp4B3LXSSzFzwjZPMPAK8vLxJkiTV9CQXWSlq487GK8dOyK2tSTNrJ40kSZIkSV0ZtjVp/gN4RJPtJwHf7nMskiRp9TEXqTNbtspUqw2dNLVxZ65JI0mSJElSV4atSBMttp8AHOlnIJIkaVUyF6kzXY47G2vopKmNP7OTRpIkSZKk7gzFuLOIuLZ8mMDrIuJQ3e4q8AzgS30PTJIkrQrmIs3NztWKNC06aSzSSJIkSZLUlaEo0gA/Ut4H8FRgqm7fFPCvwBv7HZQkSVo1zEWamJ4txp2NNYw7q3XSzM0d9xJJkiRJktSGoSjSZOazASLiHcBvZ+a+AYckSZJWEXOR5lp20pTTzxx3JkmSJElSd4aiSFOTmZcPOgZJkrR6mYsca35NmmrDmjS1cWdzFmkkSZIkSerGwIs0EXEjcFlm7isft5SZL+lTWJIkaZUwF2nNNWkkSZIkSVpeAy/SAHspFumtPZYkSeonc5EWpuear0lTKYs2s3bSSJIkSZLUlYEXaerHijhiRJIk9Zu5SGuz5bizaqX5uLNZazSSJEmSJHWlsvgh/RMR6yJifd3zR0fE70TETw8yLkmStDqYixxrpuykGT9u3Flx75o0kiRJkiR1Z6iKNMCHgF8GiIgtwGeB3wU+FBFXDjIwSZK0KpiL1JmZq3XSHFukqT13TRpJkiRJkrozbEWapwGfLB//ArADeDTFmyW/NaigJEnSqmEuUmemnGc2Xj02ZaxYpJEkSZIkqSeGrUizHthfPv5p4MbMnANup3iDRJIkaTn1JBeJiCsj4ssRsa+8bYuI59ftXxsR10fEwxFxICJuiIitPf1OeqBVJ02ltiaN484kSZIkSerKsBVp7gZ+PiLOBC4C/qncfgqwb2BRSZKk1aJXuci9wCuBc4AfBW6lGJn2A+X+64AXARcD5wOnAzd2HX2PzcyWa9JUW407g7SbRpIkSZKkjg1bkeZ1wBuB7wB3ZOa2cvtPA18cVFCSJGnV6EkukpkfzsyPZOY3M/Mbmfkq4ABwbkRsBq4AXpGZt2bmncDlwDMj4txefjPdat1Jc/SxzTSSJEmSJHVubNAB1MvM/xkRnwJOA/61btfHgP81mKgkSdJqsRy5SERUKTpmNgDbKLprxoFb6r7uXRFxD3AexWi1ZueZACbqNm3sJJ521DppxhrWpKnG0SrNXCZVji3iSJIkSZKkpRmqIg1AZu6gWKS3fttnBxSOJElaZXqVi0TEUymKMmspumhenJn/HhE/DExl5p6Gl+wETl3glFcDr243jm7UOmnGGjtp6p7PzSVU+xmVJEmSJEkrx1AVaSJiA8X89gspZr8f87HNzHzcIOKSJEmrQ49zka8DPwxsBn4BeFdEnN9FeNcA19Y930ix9s2yOVqkObaTplLXSTPrmjSSJEmSJHVsqIo0wNsoFs99D/AA4P/1d6C2gG+Eo0ckSWpTz3KRzJwC7i6f3hkRTwd+G/ggsCYitjR002yloYOn4XyTwGTteT/+zs+26qRxTRpJkiRJknpi2Io0zwdekJmfHnQgo+gfv/IA7972Xb60fQ/VCF727Cewed34oMOSJGmULGcuUqFYU+ZOYJqiW+cGgIg4GziLYjza0JieX5Pm2CJNRFCJokAza5VGkiRJkqSODVuRZjewa9BBjKpX/91X2blv/gO2fOuhA/zIWScOMCJJkkZOT3KRiLgG+ChwD8VYskuBC4CLMnNvRLwduDYidgH7gLcA2zLz9m6/di+16qQBqFaCudlkznFnkiRJkiR1rLL4IX31B8DrImL9oAMZNYenZucLNI9/5AYAdh2aGmRIkiSNol7lIqcA76ZYl+ZjwNMpCjT/XO6/Cvh7ik6aT1CMOXtJl1+z56ZnyyJN9fiUsbYuzZydNJIkSZIkdWzYOml+F3g8sDMivkMxCmReZj5tEEGNgnt3HwJg48QYj3vkCfzHQwfZdcAijSRJbepJLpKZVyyy/wjw8vI2tGbnynFnTTppakUax51JkiRJktS5YSvS3DToAEbVvbvuTvhyAAAgAElEQVQPA3DGSes5acMawE4aSZI6YC5S52gnTZMiTVm4sUYjSZIkSVLnhqpIk5mvHXQMo2p72Ulz5onrOGl9UaTZfdAijSRJ7TAXOVatS6ZaOX7cWa1uM+uaNJIkSZIkdWzY1qQhIrZExK9GxDURcVK57WkR8ahBxzbMtu8qizQnrefkspNm35EZpmfnBhmWJEkjx1zkqJly3Nl4s3FnFdekkSRJkiSpW0PVSRMRPwjcAuwFHgP8LbCLYiHds4BfHlhwQ277rmLc2ZknrmO8WmFirMLkzBy7D05xyqa1A45OkqTRYC5yrJly3Fm1ybizatTGnVmkkSRJkiSpU8PWSXMt8M7MfCJwpG77R4BnDSak0TA/7uyk9UTE0XVpHHkmSVI7zEXqzJRdMuNNxp3VOmkcdyZJkiRJUueGrUjzdOBvmmy/Dzi1z7GMlPpxZ8DRIs0hizSSJLXBXKTOzPyaNAt00jhZVZIkSZKkjg1bkWYS2NRk+5OAh/ocy8jYe3iafUdmADjjxHUAnLTeThpJkjpgLlJnplzbbrzJuLNac43jziRJkiRJ6tywFWn+DvjDiBgvn2dEnAW8AbhhcGENt1oXzckb1rB+TbHM0ImOO5MkqRPmInWOdtI0GXdWdtLMzlmkkSRJkiSpU8NWpPld4ASKT6quAz4O3A3sB141wLiG2r27DwNwRjnqDIqCDVikkSSpTeYidWqdNGNNOmmqFmkkSZIkSera2KADqJeZe4HnRsSPAz9E8SbJFzLzlsFGNtzu3V2uR1OOOoOja9LsPjRFZhJx/JsrkiTpWOYix6p10ow1WZOmUm5z3JkkSZIkSZ0bmiJNRFSAy4CXAI8BEvg2sCMiItN3AFqpjTs7s66TZvP6cQKYnk32T86wae14i1dLkiQwF2lmZrYs0lSPb76uddJYpJEkSZIkqXNDMe4sijaPvwPeBjwK+Dfgq8CjgXcC/2tgwY2A7eW4szNPPFqkGatU2Ly+KMzsduSZJEkLMhdpbnbBTprifm6unxFJkiRJkrSyDEsnzWXAs4ALM/O2+h0R8VPATRHxy5n57kEEN+yOdtKsO2b7SevXsOfQNLsOTvHokzcMIjRJkkbFZZiLHGe6rMA0LdLU1qSxk0aSJEmSpI4NRScN8L8Df9r4pghAZt4KvB74T32PakTct6fopDmjrpMG4IS1RQ3u4NRs32OSJGnEmIs0Md9JUz2+SFN1TRpJkiRJkro2LEWaHwT+cYH9H6VYvFcNDk/Ncqgswjxy48Qx+zasKYo0hyZn+h6XJEkjxlykienamjSV41PG+U6aOYs0kiRJkiR1aliKNCcBOxfYvxM4sU+xjJTdh4r1ZtZUK2xYUz1m3/qJ4vnBKYs0kiQtwlykidkFxp3Nd9JYpJEkSZIkqWPDUqSpAgtVEmYZnvVzhsqug0WR5sQN40Qc+wZKrZPm4KTjziRJWoS5SBMztU6aarNOmuLeGo0kSZIkSZ0bljcbAnhnREy22D/RYvuqV+ukOXH9muP2bZgox53ZSSNJ0mLMRZqYKSsw1SadNPPjzlyTRpIkSZKkjg1LkeZdSzjm3csexQiqddKctOH4Is36NbVxZ3bSSJK0CHORJmZmi3Fn49UmRRrHnUmSJEmS1LWhKNJk5uWDjmFUHR131qSTZn7cmZ00kiQtxFykuVonzVjl+HFnVTtpJEmSJEnq2rCsSdNTEXF1RHwuIvZHxIMRcVNEnN1wzNqIuD4iHo6IAxFxQ0RsHVTMndpd66RpMu5s/UTRSXN4apY530CRJEltmi/SNOmkqc530vQ1JEmSJEmSVpQVWaQBzgeuB84FnguMA/8UERvqjrkOeBFwcXn86cCNfY6za7sOLd5Jk8ARR55JkqQ21cadjTVdk6a494MgkiRJkiR1bijGnfVaZj6v/nlEXAY8CJwDfCIiNgNXAJdm5q3lMZcDX4uIczPz9j6H3LHdB6cBOGn9+HH7qpVg7XiFI9NzHJyaZf3EivzPLUmSlslC485qa9I47kySJEmSpM6t1E6aRpvL+13l/TkU3TW31A7IzLuAe4Dzmp0gIiYiYlPtBmxcxniXbKE1aQDWl900h6Zcl0aSJLVnoXFnlaiNO7NII0mSJElSp1Z8kSYiKsCbgE9n5lfKzacCU5m5p+HwneW+Zq4G9tbd7l2GcNu2uxx3dlKLIs2GNcW6NAcnLdJIkqT2LDTurLYmzaxFGkmSJEmSOrbiizQUa9M8Bbiky/NcQ9GRU7ud0eX5emK+k2Z9iyJNOeLsoGvSSJKkNszNJbX6y1i1ybizWieNNRpJkiRJkjq2ohcpiYi3Ai8EnpWZ9Z0vO4A1EbGloZtma7nvOJk5CUzWnXsZIm5PZi7aSTM/7sxOGkmS1IaZuupLtVknTblpzjVpJEmSJEnq2IrspInCW4EXAz+Vmd9uOOROYBq4sO41ZwNnAdv6FmiXDkzOMD1bvDHSspOmNu7MThpJktSG+jFm483WpHHcmSRJkiRJXVupnTTXA5cCPwfsj4jaOjN7M/NwZu6NiLcD10bELmAf8BZgW2bePpiQ27f74DQA68arrCuLMY3mx53ZSSNJktowPTc3/7hZJ83RcWcWaSRJkiRJ6tRKLdJcWd7/S8P2y4F3lo+vAuaAG4AJ4GbgZX2IrWd2LTLqDGB9Wbw5ZCeNJElqw+xsXSdN5fjm61rhZs5OGkmSJEmSOrYiizSZueiCMZl5BHh5eRtJuw8WRZoTN4y3PGa+k2bKThpJkrR0tU6aiKOjzerVOmlm7aSRJEmSJKljK3JNmtViV61I02I9GrCTRpIkdaa21kyzLhqAarm5biqaJEmSJElqk0WaEbZ7CePOXJNGkiR1YqYcd9ZsPRqwk0aSJEmSpF6wSDPCltJJs2FNUaSZnJljZtaPukqSpKWZKTtpxqoLF2lck0aSJEmSpM5ZpBlhS+mkmRivUPsArCPPJEnSUtU+3DHWopOm1mEzZyeNJEmSJEkds0gzwuY7aRYo0lQiWF920xyccuSZJElamqOdNM3TRcedSZIkSZLUPYs0I2z3wWkATlpg3BnA+jVVwE4aSZL6JSKujojPRcT+iHgwIm6KiLMbjlkbEddHxMMRcSAiboiIrYOKuVFtTZpFO2mcpipJkiRJUscs0oywXYdqnTTjCx63YaLspJm0k0aSpD45H7geOBd4LjAO/FNEbKg75jrgRcDF5fGnAzf2Oc6WZsrqS+s1aYp7O2kkSZIkSerc2KADUOd2H1x8TRo42kljkUaSpP7IzOfVP4+Iy4AHgXOAT0TEZuAK4NLMvLU85nLgaxFxbmbe3ueQjzM/7qzS/DM9RztpLNJIkiRJktQpO2lG1NxcsrvspFls3NmGck2aQ9OOO5MkaUA2l/e7yvtzKLprbqkdkJl3AfcA5zU7QURMRMSm2g3YuIzxLjrurLYmzZydNJIkSZIkdcwizYjad2Sa2gdXtyxSpFlXdtIcdk0aSZL6LiIqwJuAT2fmV8rNpwJTmbmn4fCd5b5mrgb21t3uXYZw59XGnVVbFWnK7bN20kiSJEmS1DGLNCNqz6FpADasqbJmbOH/jOst0kiSNEjXA08BLunyPNdQdOTUbmd0eb4F1cadjVeb5xm12o01GkmSJEmSOueaNCNqz+GiSLNYFw3AuvGiSHPIIo0kSX0VEW8FXgg8KzPrO192AGsiYktDN83Wct9xMnMSmKw79zJEfFRt3FmrTppq2EkjSZIkSVK37KQZUbX1aDavG1/02PW1NWmmZpY1JkmSVIjCW4EXAz+Vmd9uOOROYBq4sO41ZwNnAdv6FugCZmaLcWfj1RZFmopr0kiSJEmS1C07aUbU3nLc2YkbFi/SzK9JM20njSRJfXI9cCnwc8D+iKitM7M3Mw9n5t6IeDtwbUTsAvYBb/n/27vz6Mju6sDj36tSSS2pu9XdduzuNgYM9hgbkxjiCTEDHDJOwAMZMDkkZJhzmCwDAzFJSCYDYQ9mJzMOhGUSTkIwOQmQDBMzDsN48IRs4DRhM4QlYTGh7V6wu1u9qLXrN3/Ue5KQu6R6r/bS93NOHUlV7726/Wup+6e6de8F7kwp/V13Qv5e81mSZnS4cs7Hh8IkjSRJkiRJzTJJ06emskqaXWObtzvLZ9LY7kySpI55YfbxL9fd/7PA+7LPfwVYBj4MjAK3A7/QgdgaMreQJ2nqzKTJK2mWOxaSJEmSJEkDxyRNn8pn0kyOF6ikmV/y3a6SJHVASmnTgTEppVngxuzWc+YWa2/uGKmXpMn+hEspkVJq+4wcSZIkSZIGkTNp+tRU1u5sVyMzaaq1JE1i9V2xkiRJG5lb3LiSJp9JA7Dse0AkSZIkSSrFJE2fytud7R7fvN3ZcGWIkUrtr9q5NJIkqRGrSZqNZ9KAc2kkSZIkSSrLJE2fKtLuDFZbnp2dX2xbTJIkaXCsJGmqDVTSWEojSZIkSVIpJmn6VJF2ZwDjK0kaK2kkSdLm8pk09dqdfW8lTUdCkiRJkiRp4Jik6VN5u7NdDbQ7AxjL5tLMmKSRJEkNyOfYjdRN0qx+vmS7M0mSJEmSSjFJ06fydme7G2x3tlJJ40waSZLUgM1m0kTESsuzxaXljsUlSZIkSdIgMUnTh5aXEycLz6QZBmDGmTSSJKkBm7U7AxjOkzT2O5MkSZIkqRSTNH3o9OwieVeRSWfSSJKkNphfqaSpv12sVmqPLVhJI0mSJElSKSZp+tCJbB7N+EilbguS9ZxJI0mSilhpd1atv9cYruTtzqykkSRJkiSpDJM0fWh1Hs1Iw+dYSSNJkoqYa6SSZiirpFm2kkaSJEmSpDJM0vShqaySptFWZ7CapJlZMEkjSZI2N5ftGUY2mkljJY0kSZIkSU0xSdOHTmaVNLvGG0/SjI0MA3B2frEtMUmSpMGyWklTv91ZPpNm0Zk0kiRJkiSVYpKmD52YrlXSFEvS2O5MkiQ1br6BdmfDQ7VKmoVlK2kkSZIkSSrDJE0fmlqppCkwkyYb+jszv8SyL6RIkqRNzC3W3tix4UwaK2kkSZIkSWqKSZo+NHU2S9IUmEmTV9Ik4IwtzyRJ0iZW2p1V67c7y2fSLDiTRpIkSZKkUkzS9KEyM2mqlSGq2QspU9MLbYlLkiQNjjxJM1KxkkaSJEmSpHYxSdOHTpzNZtKMNd7uDGB8ZBiAqZn5lsckSZIGy9xC1u6suvlMmkVbqUqSJEmSVIpJmj600u6sQCUNwFjWriQ/X5IkqZ6VdmcbzKRZbXdmJY0kSZIkSWWYpOlDq+3OilXS5HNppmZM0kiSpPpSSswv5Uma+jNpqkN5uzMraSRJkiRJKsMkTR+aytudFaykGc+TNGdtdyZJkupbWEqkLO+yYbuzvJJm2UoaSZIkSZLKMEnTZ5aX02olzVjZJI2VNJIkqb65xaWVz0cq9beL1YqVNJIkSZIkNcMkTZ85PbtIPpt3snAlzTAAx6etpJEkSfXl82hgk5k0Q86kkSRJkiSpGSZp+syJrFXZ+Ehlwx7x55JX0pikkSRJG8mTNCPDQ0RE3eOG80qaZStpJEmSJEkqwyRNnzmWJVj2TIwUPnditFZJc8KZNJIkaQPzWZJmoyoagGo2k8Z2Z5IkSZIklWOSps/kVTDnlUnSZO3Ojp0xSSNJkurLZ9JsVrWbV9LY7kySJEmSpHJM0vSZ49NzQNlKGtudSZKkzc0tNFhJk82ksd2ZJEmSJEnlmKTpM6vtzkYLn5u3Ozs+PU9KvpgiSZLOba7BdmdW0kiSJEmS1ByTNH0mb1V23vby7c7ml5Y5M7fY0rgkSdLgyNudjWyapHEmjSRJkiRJzRjIJE1EPDEibouIQxGRIuKGdY9HRNwUEYcjYiYi7oiIy7oVbxHHVyppiidpRoaHVgb82vJMkiTVM59X0lQ3nklTHcoqaZatpJEkSZIkqYyBTNIAE8BdwI11Hn8J8EvAC4DHAtPA7RGxrTPhlZe3OzuvRJIGVqtpjpmkkSRJdTTe7sxKGkmSJEmSmjHc7QDaIaX0MeBjABHxPY9F7Y4XA69PKX0ku++5wFHgBuCDHQ22oOPTc0C5dmdQm0szNbPA8TMmaSRJ0rnl7c42S9JUnUkjSZIkSVJTBrWSZiOXAHuBO/I7UkongQPAtfVOiojRiNiZ34AdbY/0HPLkyp6J0VLnT4zW2pYcP2uSRpIkndvcQoOVNENZJc2ylTSSJEmSJJWxFZM0e7OPR9fdf3TNY+fyMuDkmts9rQ9tYymllrU7cyaNJEmqZ7Xd2SYzabJKmqXlxHIyUSNJkiRJUlFbMUlT1puAyTW3B3U6gLPzSysvmuwpm6QZNUkjSZI2Nl9wJg04l0aSJEmSpDK2YpLmSPbxwnX3X7jmsQdIKc2llE7lN+B0uwKs51jW6mx0eIjxkY3f2VrPRHbeMWfSSJKkOlZm0lQ3a3e2+viic2kkSZIkSSpsKyZp7qaWjLkuvyObMfNY4M5uBdWIY9NzQK3VWURscvS5rVbSzLUsLkmSNFgabXdWGQqysTQsOJdGkiRJkqTCBjJJExHbI+LqiLg6u+uS7OsHp5QS8DbglRHx9Ih4FPB+4BBwa7dibkTeouy87aOlr2G7M0mS2i8inhgRt0XEoYhIEXHDuscjIm6KiMMRMRMRd0TEZd2Kd708STOySbszWJ1LYyWNJEmSJEnFDWSSBrgG+Hx2A7g5+/ym7Ou3Au8A3gP8PbAduD6lNNvhOAs5liVWys6jgTXtzkzSSJLUThPAXcCNdR5/CfBLwAuoVfNOA7dHxLbOhLexuYWs3VkDSZrhrJTGShpJkiRJkoob7nYA7ZBS+kugbj+wrJrm1dmtb6xU0jSRpBnPKmlOmKSRJKltUkofAz4GPKBFadTueDHw+pTSR7L7ngscBW4APtjRYM9hfilvd9ZoJc2SlTSSJEmSJJUwqJU0A+l4Syppakma6fklZrN3yUqSpI66BNgL3JHfkVI6CRwArq13UkSMRsTO/AbsaFeAcwuNzaQBGK5klTRLVtJIkiRJklSUSZo+cuxMlqTZXj5Js606RDV7McW5NJIkdcXe7OPRdfcfXfPYubwMOLnmdk/rQ6vJZ9KMVp1JI0mSJElSO5mk6SPHpueA5tqdRQS7x2vnm6SRJKmvvAmYXHN7ULueaG6xVm07Uml8Js2iM2kkSZIkSSrMJE0fWW13NtrUdfJ2acdM0kiS1A1Hso8Xrrv/wjWPPUBKaS6ldCq/AafbFWCRSprhLJGzYCWNJEmSJEmFmaTpI3m7s/OaaHe29vzjWWWOJEnqqLupJWOuy+/IZsw8FrizW0GttZKkaWAmTd5GddGZNJIkSZIkFTbc7QDUuLySppl2Z7BaiZMnfSRJUmtFxHbg0jV3XRIRVwPHU0rfiYi3Aa+MiK9TS9q8DjgE3Nr5aB9oNUnTSLuzrJJm2UoaSZIkSZKKMknTJ2bml5hZqPWH39NkkiZP8jiTRpKktrkG+MSar2/OPt4C/AzwVmACeA+wC/hb4PqU0mwHY6xrLttzNFJJM2wljSRJkiRJpZmk6RPHstZkI5Uhto8299e2MpPGShpJktoipfSXQGzweAJend16znyBmTRVZ9JIkiRJklSaM2n6RJ5Q2TMxQkTd13wacuHOWruzI6d64s26kiSpx+TtzkYqjbQ7yypplq2kkSRJkiSpKJM0feLwyVpCZe/ktqavtW9yLLvmTNPXkiRJg2duMWt3ZiWNJEmSJEltZZKmTxyaqiVU9u9qPkmTX+PwlJU0kiTpgfJKGmfSSJIkSZLUXiZp+kRe9bI/q4JpRl5Jc3pukdOzC01fT5IkDZbVJE0j7c6spJEkSZIkqSyTNH3iUNbubN+u5pM0E6PD7Nw2DKy2UZMkSQJIKTFfIElTrTiTRpIkSZKkskzS9ImVdmctmEkDsD9L9uTXlSRJgtUqGoCRRippnEkjSZIkSVJpJmn6RD4/Zn8LKmkA9mXJHitpJEnSWmuTNI3MpKkOOZNGkiRJkqSyTNL0gYWlZY6eztudtaaSJm+bZpJGkiStlbc6i1htZbaRlUqaZStpJEmSJEkqyiRNHzh6apaUYKQyxPkToy25Zt427bDtziRJ0hpzi0tAbR5NxOZJmpWZNFbSSJIkSZJUmEmaPnAoa3W2d3IbQ0Obv1jSiH2TVtJIkqQHytudNdLqDGB4qLadXHQmjSRJkiRJhZmk6QOHT9aqXfa3qNUZrLZNO3TSShpJkrRqbqGWbBkZbmybmFfSLCxbSSNJkiRJUlEmafrAvVlLsv1Z9UsrrFTSTM2Ski+qSJKkmrXtzhqRz6SxkkaSJEmSpOJM0vSBw1m7s/27WpmkqVXSzCwscXJmoWXXlSRJ/W1+pd1ZsUoaZ9JIkiRJklScSZo+kLc729fCdmfbqhX2TIwAqzNvJEmSys6kWVi2kkaSJEmSpKJM0vSBe9tQSQOr1TSHnUsjSZIyK0maqpU0kiRJkiS1m0maPpAnUVo5kwZW59IcOmkljSRJqsln0oxUCs6kWU4sO+dOkiRJkqRCTNL0uLPzi0ydrc2M2d/Cdmdrr3d4ykoaSZJUM7eQV9I01u6sOhQrny8tm6SRJEmSJKkIkzQ9Lp8Xs2N0mB3bqi29dl5Jc9hKGkmSlJnNKmlGh4tV0gAsLDmXRpIkSZKkIkzS9Lh7syqXfS2uogF48J5xAL5135mWX1uSJPWn+0/PA3D+9pGGjq8MBXkxjXNpJEmSJEkqxiRNj/vHI6cAuPSC7S2/9hX7dtSe4+hp25NIkiRgdRbe3p2Nz8IbHqptKa2kkSRJkiSpGJM0Pe4rh2pJmkfun2z5tR9y3gRj1QqzC8vcff90y68vSZL6T94Gdd9k41W8w5VaKc2ClTSSJEmSJBVikqbHfTlL0ly5b2fLr10ZCi7fW6um+erhUy2/viRJ6j9HsiTN3gJJmh3bhgE4NbvQlpgkSZIkSRpUJml62OzCEt/M5sVcub/1SRqAK7Lkj0kaSZIEq+3O9heYh7d7vDa/5sTZ+bbEJEmSJEnSoDJJ08O+duQ0y6k2uPeCHaNteY4rs7k0XzFJI0nSljc9t8ip2UUA9k42PpNmV56kmbaSRpIkSZKkIkzS9LB8Hs0V+3YSEW15DitpJElS7sipWquzHaPDbB8dbvi8PeNVwEoaSZIkSZKKMknTw75y+CQAj9w/2bbneESWpDl6ao7j076wIknSVlZmHg2sVtJMmaSRJEmSJKkQkzQ97MtZJU275tEAbB8d5iHnjQNW00iStNUdLpmk2TNRS9IcP2u7M0mSJEmSijBJ06OWlhNfO3wagCv3tS9JA3DFXlueSZIkOHJyBoB9BZM0u7NKmum5RWbml1oelyRJkiRJg8okTY+6+/5pZhaWGKtWuOT8ibY+Vz6X5ismaSRJ2tJWK2nGCp23rTrE6HBtW3nv1NmWxyVJkiRJ0qAySdOj7jo4BcDle3dQGYq2PtdVF9WSNAe+dZyUUlufS5Ik9a58Jk3RSpqIWGl5dvD4TMvjkiRJkiRpUJmk6VEf/dJhAB5/6fltf67HPfx8xkcq3Ds1w+e+M9X255MkSb2p7EwagF1Zy7N7TlhJI0mSJElSo0zS9KBjZ+b4q3+6D4AbHn1R259vbKTCk6+8EIDb7jrU9ueTJEm96cipcpU0AHvGqwDcc8JKGkmSJEmSGmWSpgfddtchlpYT3/+gSS69YHtHnvMZV9eSQX/+xcMsLi135DklSVLvmF1Y4vj0PAD7dhabSQOrlTQHraSRJEmSJKlhJml60J99oVbNcsPV7a+iyT3+svPZPV7l/jNz3PmtYx17XkmS1BvyeTRj1Qo7x4YLn797pd2ZlTSSJEmSJDXKJE2P+dZ9Z7jr4BSVoeDf/sD+jj1vtTLEUx+1D4CPfMGWZ5IkbTX5PJp9k9uIiMLn756w3ZkkSZIkSUWZpOkhy8uJN3z0qwA84bLz+b4dox19/nz+zZ99/l4OWE0jSdKWcuRULbmyt8Q8GlitpDk+Pc/03GLL4pIkSZIkaZBt6SRNRNwYEd+OiNmIOBARP9TNeH77L77O//vadxkZHuLXnnx5x5//mofs5pmPvoil5cQvfuDz3Hd6ruMxSJK0lfTSXiSvpCmbpNlWrTBWrQBw9/3TLYtLkiRJkqRBVrzh+ICIiGcDNwMvAA4ALwZuj4jLU0rf7XQ8d3zlKG+74+sAvPGZj+KqiyY7HQIRwRueeRVfPnSSfzp6hv/w3k/zG09/JD90yZ6OxyJJgls+9W1OzSwwNbPAybMLTM3Mc3JmgbPzSzx4zzjVyhAX7Bjlwp3buGBn7eODdo+xb3KMylDxdlXqrF7bi1ywYxs//LA9XLlvZ+lr7Nu1jW/dN81LP/xFPvj8H2bHtmoLI5QkSZIkafBESqnbMXRFRBwA/j6l9KLs6yHgIPCOlNKbGzh/J3Dy5MmT7NxZ/sWM3C9+4PPcdtchnnvtQ7jpGVc1da0/PvCdDR9/zmMfvOHj3/juGZ757k9yerbWquT7HzTJVRdN8uA942wbHmJbtcJodYiRSoUSLeu/R6dfQiz63V7kxyMVvHrRH73isbfvZ7t47G1em0J/T0WvXTD2gtcvckLvfY8VvX7jJ7Q79qJPUOToorEvp8SZ2UWmZhaYOrvAyZl5jp6a49DUDMem54tdLDM8FOzfNcbFe8bYMzHKrrEqu8arTI5V2VatUK0Ew0NDDFeCkcoQ/yabSdYKp06dYnJyEmAypXSqZRceQL22F2nURvuM+8/Mccunvs2x6Xn+5UN38/SrL2LXWLUlScNBTTv26k68V39FKPp/YSf17pr1rq36u2hZvbxcC0vLnDg7z7HpeY6fmefE2dobS07PLjI+UmHPxCjnTYxw3vYRdmyrMlYdYmykslKF+aTLL2BkuDWNPtyLSJKkfrMlkzQRMQKcBZ6VUrp1zf23ALtSSs84xzmjwNohMTuAew4ePNiSF0aWlhMf/nCVsxQAAAygSURBVNw93HD1RU1vTv/kMwc3fPynrrl402scPTnL7/71N/mfn7uXxeWt9z0iSb2iWgkmt1XZOV6tfRwbZmJkmMc8dDdzC0vcd2ae756a5b7Tcxw9Ncvhk7MsLDX+73ZlKLjrNU9uWbynTp3i4osvBl8Y2VAv7kUatdk+45H7dvKz7/s0Z+eXOxSRJKnfHXj5dUyMtqbRh3sRSZLUb7Zqu7PzgQpwdN39R4FH1DnnZcBr1t+Zbf5a5nktvVr3nkOS1D8mb27LZXcAvjBSX8/uRSRJ6rT9b2vLZd2LSJKkvrBVkzRlvIla3/i19gDHuxDLRnYA9wAPAk53OZZ+4HoV43oV43o1zrUqptfXawdwqNtBDKBe2Iv0+vdeL3PtmuP6Ncf1K8+1a0631s+9iCRJ6htbNUlzP7AEXLju/guBI+c6IaU0B8ytu7vn3pUTq0NiTlvavTnXqxjXqxjXq3GuVTF9sF69GFOv6cu9SB987/Us1645rl9zXL/yXLvmdHH9/LuSJEl9ozWT+fpMSmke+CxwXX5fNqz3OuDObsUlSZK2BvcikiRJkiQJtm4lDdTahdwSEZ8BPg28GJgA/qCrUUmSpK3CvYgkSZIkSVvclk3SpJQ+FBHfB9wE7AW+AFyfUlo/wLffzAGv5YHtUHRurlcxrlcxrlfjXKtiXK8B0Kd7Eb/3ynPtmuP6Ncf1K8+1a47rJ0mStIlIKXU7BkmSJEmSJEmSpC1nS86kkSRJkiRJkiRJ6jaTNJIkSZIkSZIkSV1gkkaSJEmSJEmSJKkLTNJIkiRJkiRJkiR1gUmaPhQRN0bEtyNiNiIORMQPbXL8T0bE17LjvxQRT+1UrL2gyHpFxPMi4m8i4kR2u2Oz9R00Rb+/1pz30xGRIuLWdsfYK0r8LO6KiHdFxOGImIuIf9pKP48l1uvFEfGPETETEQcj4rciYlun4u2miHhiRNwWEYeyn6sbGjjnSRHxuex76xsR8TMdCFUDyH1Gee45muMepDz3JM1xj1KO+xVJkqTWMEnTZyLi2cDNwGuBxwB3AbdHxAV1jn8c8AHg94FHA7cCt0bEVZ2JuLuKrhfwJGrr9SPAtcBB4P9GxEXtj7b7SqxXft5Dgf8K/E2bQ+wZJX4WR4CPAw8FngVcDjwPuLcT8XZbifV6DvDm7PgrgJ8Hng28sSMBd98EtTW6sZGDI+IS4KPAJ4CrgbcBvxcRT2lbhBpI7jPKc8/RHPcg5bknaY57lKa4X5EkSWqBSCl1OwYVEBEHgL9PKb0o+3qI2i/170gpvfkcx38ImEgp/fia+/4O+EJK6QUdCrtriq7XOc6vACeAF6WU3t/WYHtAmfXK1uivgfcCTwB2pZQ2fRddvyvxs/gC4L8Aj0gpLXQ02B5QYr3eCVyRUrpuzX3/DXhsSunxHQq7J0REAp6ZUqr7DvGIeAvwtJTSVWvu+yC1n8frOxCmBoT7jPLcczTHPUh57kma4x6lNdyvSJIklWclTR/J3vX2g8Ad+X0ppeXs62vrnHbt2uMzt29w/MAouV7rjQNV4HjLA+wxTazXq4HvppR+v70R9o6Sa/V04E7gXRFxNCL+ISJenr3ANNBKrtengB/M241ExMOApwL/u73R9q0t+2+9Wsd9RnnuOZrjHqQ89yTNcY/Scf6fIUmSdA7D3Q5AhZwPVICj6+4/Cjyizjl76xy/t7Wh9aQy67XeW4BDPPCXiUFUeL0i4vHUWjxc3d7Qek6Z762HAf8a+CNqv8hfCryb2gtyr21PmD2j8HqllP44Is4H/jYigtr/V7+TUtqKrUQaUe/f+p0RMZZSmulCTOo/7jPKc8/RHPcg5bknaY57lM5yvyJJknQOVtJIdUTErwM/Ta1sf7bb8fSaiNgB/CHwvJTS/d2Opw8MAd8Fnp9S+mxK6UPAG4At1Q6oURHxJODlwC9Q6w//E8DTIuJV3YxLktrBPUcx7kGa5p6kCe5RJEmS1GpW0vSX+4El4MJ1918IHKlzzpGCxw+SMusFQET8GvDrwI+mlL7YnvB6TtH1eji1gbO31d5ECGSJ34hYBC5PKX2zLZF2X5nvrcPAQkppac19XwX2RsRISmm+9WH2jDLr9TrgD1NKv5d9/aWImADeExFvyFqRaFW9f+tP+a5UFeA+ozz3HM1xD1Kee5LmuEfpLPcrkiRJ52AlTR/JfmH6LLB2SOVQ9vWddU67c+3xmR/b4PiBUXK9iIiXAK8Crk8pfabdcfaKEuv1NeBR1NqM5Lf/BXwi+/xgm0PumpLfW58ELs2Oy/0L4PCgvxhScr3GgfUvcuQvJgVab8v+W6/WcZ9RnnuO5rgHKc89SXPco3Sc/2dIkiSdg5U0/edm4JaI+AzwaeDFwATwBwAR8X7g3pTSy7Lj3w78VUT8Z+Cj1FppXAM8v9OBd0mh9YqIlwI3Ac8Bvh0ReU/9MymlM50OvgsaXq+sHcs/rD05IqYAUkrfc/+AKvqz+N+BFwFvj4h3AJdRa5Xx250OvEuKrtdtwK9GxOeBA9T65b8OuG3dO38HUkRsp/Znzl0SEVcDx1NK34mINwEXpZSemz3+O8CLIuKtwHupzRr4KeBpnYxbA8F9RnnuOZrjHqQ89yTNcY9SkvsVSZKk1jBJ02dSSh+KiO+j9kv9XuAL1N59mQ9gfDBr3tmVUvpURDwHeD3wRuDrwA1b5RfYousFvBAYAf7Huku9FviN9kbbfSXWa8sq8bN4MCKeAvwW8EXgXmovbr6lo4F3SYnvrdcDKft4EXAftRdFXtGxoLvrGmrvCM/dnH28BfgZYB+1NQMgpXR3RDyN2vfXLwP3AP8xpXR7R6LVwHCfUZ57jua4BynPPUlz3KM0xf2KJElSC0RKqdsxSJIkSZIkSZIkbTnOpJEkSZIkSZIkSeoCkzSSJEmSJEmSJEldYJJGkiRJkiRJkiSpC0zSSJIkSZIkSZIkdYFJGkmSJEmSJEmSpC4wSSNJkiRJkiRJktQFJmkkSZIkSZIkSZK6wCSNJEmSJEmSJElSF5ikkSRJkiRJkiRJ6gKTNJJ6VkRcHBHvjYhDETEfEf8cEW+PiPPWHHNJRPxxdsxsRNwTER+JiEesOSad4/a33flTSZKkXhcR71uzZ1iIiLsj4q0RsW3NMSnbezxk3bm3RsT76lxr7e3SDv6RJEmSJPUokzSSelJEPAz4DHAZ8O+AS4EXANcBd0bEnoioAh8HJoGfAC4Hng18Cdi17pI/C+xbc3t6B/4YkiSpf/0fanuGhwG/Avwn4LXrjknATQWutfZ2d8silSRJktS3hrsdgCTV8S5gHnhySmkmu+87EfF54JvAG4DfBR4OXJdS+ufsmH8GPnmO602llI60OWZJkjQ45tbsHQ5GxB3AjwEvXXPMO4FfjYjfTCn9Q4PXkiRJkqQVVtJI6jkRsQd4CvDuNQkaALIXOP6IWsXMfcAy8KyIqHQ8UEmStCVExFXA46i9gWStTwJ/Dry540FJkiRJGggmaST1osuAAL5a5/GvAruBBeCXqLUZORERfxERr8papa33gYg4s+Z2Q1silyRJg+LHsz3DLLVWqhcAv3mO414GXB8RT2jgWvntT9sRsCRJkqT+Y7szSb0sNjsgpfSuiHg/8CTgh4GfBF4eEU9PKX18zaG/Atyx5uvDrQxUkiQNnE8ALwQmqO0jFlNKH15/UErpK9le5M3Av9rkWrnpFscqSZIkqU9ZSSOpF32D2iDeK+o8fgVwglq7M1JKp1NKt6WUXgH8APA3wCvXnXMkpfSNNTdfHJEkSRuZzvYMdwE/Bzw2In6+zrGvAR6zQaXu9Lp9iG8WkSRJkgSYpJHUg1JKx4CPA78QEWNrH4uIvcC/Bz6UUkrnODcBX6P2rldJkqSmpZSWgTcCr1+/N8kePwi8MzvGOXmSJEmSGmaSRlKvehEwCtweEU+MiIsj4npqyZt7gVdExNUR8ZGIeFZEXBkRl2bvcP054CNdjF2SJA2ePwWWgBvrPP4mYD/wox2LSJIkSVLfM0kjqSellL4OXAN8C/gT4JvAe6j1dL82pXQcuAf4NrUWIweAzwG/nH39hs5HLUmSBlVKaZFatcxLIuIBFbvZ3uQtwLZOxyZJkiSpf8U5ugVJkiRJkiRJkiSpzaykkSRJkiRJkiRJ6gKTNJIkSZIkSZIkSV1gkkaSJEmSJEmSJKkLTNJIkiRJkiRJkiR1gUkaSZIkSZIkSZKkLjBJI0mSJEmSJEmS1AUmaSRJkiRJkiRJkrrAJI0kSZIkSZIkSVIXmKSRJEmSJEmSJEnqApM0kiRJkiRJkiRJXWCSRpIkSZIkSZIkqQv+P1QvRoaDmSgrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above distribution curves depit continuous values for the features. It can be seen that the data is are normally distributed and there is no noticeable skewness. It is also evident that the machine failure is imbalanced. To better visualize the imbalance, let's look at a count plot.\n"
      ],
      "metadata": {
        "id": "E_hxCQBIsP2q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10), dpi= 100)\n",
        "\n",
        "ax = sns.countplot(processed_data['Machine failure'],order=pd.value_counts(processed_data['Machine failure']).index)\n",
        "ax.bar_label(ax.containers[0], fontsize=17)\n",
        "plt.title('\\nCount Plot for Machine Failure \\n', fontsize=20)\n",
        "plt.xticks(fontsize=10,rotation=70)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dkPrnAzZuCn9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be seen that the output variable (y) is very unbalanced yielding 9661 entries indicating no failure and 339 entries indicating failure. This will be handled later using the oversampling technique."
      ],
      "metadata": {
        "id": "-k_Uk9Rbxxy9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, it is important to determine the relationships between the features in order to determine which will have a greater significance on the prediction."
      ],
      "metadata": {
        "id": "mzqVIjfdnRvJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10), dpi= 100)\n",
        "sns.heatmap(processed_data.corr(), xticklabels=processed_data.corr().columns, yticklabels=processed_data.corr().columns, cmap='RdYlGn', center=0, linewidths=0.2, annot=True)\n",
        "\n",
        "plt.title('\\nHeatmap for Machine Maintenance Features\\n', fontsize=20)\n",
        "plt.xticks(fontsize=10,rotation=70)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AT02RmIDg-6D",
        "outputId": "1be67ecd-9121-4754-ac14-aba3759af1c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graph, it is seeen that Air tempreature is highly correlated with Process temperature, which is sensible since the higher a process temperature goes, the higher the air temperature will be. Additionally, it is seen that torque and rotational speed are inversely proportional. There are no significant correlations between the input features and the output."
      ],
      "metadata": {
        "id": "5T-XA60LnmuF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "xJGu96fK-VIp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the purpose of the analysis and since normal machine learning models and classifiers work by determining the output through how closely it lies to the features without getting affected by the distance between the outputs. It is possible to group the machine failures into one column for multiclass classification. The success instance will be indicated by 0, 'TWF', 'HDF', 'PWF', 'OSF', 'RNF' will be indicated by 1, 2, 3, 4, and 5 respectively."
      ],
      "metadata": {
        "id": "-sGFEEWp5Gd2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Binary Classifcation:"
      ],
      "metadata": {
        "id": "i7GUCT29sHbB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data_grouped_binary = processed_data.copy()"
      ],
      "metadata": {
        "id": "pHPMpqocnmRb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10), dpi= 100)\n",
        "\n",
        "ax = sns.countplot(processed_data_grouped_binary['Machine failure'],order=pd.value_counts(processed_data_grouped_binary['Machine failure']).index)\n",
        "ax.bar_label(ax.containers[0], fontsize=17)\n",
        "plt.title('\\nCount Plot for Machine Failure after Grouping \\n', fontsize=20)\n",
        "plt.xticks(fontsize=10,rotation=70)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "rEgr6NkLd-fF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The columns can be dropped. Let's take a look at the correlation one more time to see if any interesting details have appeared."
      ],
      "metadata": {
        "id": "ntp5L0XUVVH3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "processed_data_grouped_binary.drop(['TWF','HDF','PWF','OSF','RNF'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "6ezkAX4F8XUG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10), dpi= 100)\n",
        "sns.heatmap(processed_data_grouped_binary.corr(), xticklabels=processed_data_grouped_binary.corr().columns, yticklabels=processed_data_grouped_binary.corr().columns, cmap='RdYlGn', center=0, linewidths=0.2, annot=True)\n",
        "\n",
        "plt.title('\\nHeatmap for Machine Maintenance Features after Grouping\\n', fontsize=20)\n",
        "plt.xticks(fontsize=10,rotation=70)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ujGxD9yb5z7N",
        "outputId": "de83162c-eaf7-44b6-ca07-dcb022e22f5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": 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gTS8V1UK9Kcl+j62nbUSaPVVUU+PuMjqnwuXZSXpI0dejh0NpekW8f2Ho/Qmx8hGRLi9+W+V/3MZq9g1P8Pl5Kqq9uUJSk1KU9fC2TlTy7+34PGZ8PsoPhJSwtpR8oGZFsPyNKc7XyPMtwbYOTpF2rhKfX4PjykfkORqkHaKi695BCdI0kQ9COUnvx723X+hzEtbgkw+cbnbtTrMMZHS+BGlGK/V3fLrHqYaKaue/ogT3OqF9u9l3cfB++Bi9kej8S3MfXZ3O8S7FeveJy2dkGQ/SZuMeKeVxiijj7ZOcH7FrdVTtz31VVPPv44j3k56PysJ9g/x3d+z9RZI6RaTZQdLi0GclLZ9MTEwlmyo8A0xMTJlPETcsJZ3ax62vvnwNBifp/BSffW7oZqBeKfJ+eCgfzSLeL7zhSGNdU0LrSnRj3yGUpkAJmoIFN0qxdH8p5XFJtW2jQ++PSrKeQaF0CZvRpchLyptM+WYoTj4Q2iBBmuqSvlFcgCXqeEl6NEvle278TaCKAiVjQvMslPbvofmx/IzOIA+xpiybHSdJ/xf6jMMz2D4naecEaUy+NpeTNCHi/fahdQyOeD98XBI2D1dR08kCSY3i3tsrtI7uKbZpfJDug1Lu79i5PDc078Fg3jtxad8J5j+QrMyUIg8vBOt4McVx26xcqfg1+VMlCKjI18RJeJ1Rmte/4PjHguBJmxXK165wkn5Nst+dEjRvDNJ9FKT5PIP9m+ycCpfn+UocqOscSndh3Ht5KnpYMk8Jmh0myd+jwbKfJDp+QbrG8rV6nUoR4Nbmwc1k0+jQcuV5Pt4ZLD8zwfuxcpP0fFPZBPzeTLIOk2/+6STdnuLz/hxa5/ah+SeG5pcqoJeNKdn5Erw/Wqm/49M9TicH6dYoxUNG+ZqJTtEPfWP7bZMSdB1TijIYea0MpautJEHziPT7hNb7naRqSdadjXuklMcpooxvtu9U/L7h6CTruS+Ubte495Kej8rOfcNxoXUMTbKO8MOZpOWTiYmpZBODdgCIMkC+hprkm8Ik827wt4ak3skSmu8ov72Z7WR+oIxuKj6iXI9S5XZzy+QDQptxzs2Rr60gSV86575JsI7wyG/bpfrALGzb40nee0F+myRf8yjrzHdY3jV4+YxzbkVUOufcRhXltYl88+VExiV5L1OxpptHmVnd4P+95GudFpT2s81rZWY7xI5jcCxjTSejjuOhwd8f5ZsCldZM59yXUW8455z8Dz4pjfKYQrJ981nw1+SD42F/Cf5+55ybmeIzYteF3dLpzDtNsWO+lwUD7wR/94p7v8TMD7ayfdwxjzUvyvS69FRw/KJ8Fvo/k+N6iHyzwNXyNXKSiR2bNma2bYI0y+T75koklu90ro2lOafCnnPOrYt6wzn3nXyNxai89JQUG9DjYVfypnWxZuLPJzl+cr55eex82KxZcRkqk/PR/AASHeO+y2LfPztagpGyK1Cy69mO8jUYpfTvZaTix/G30P/xzWCzLgvnSzbEytY7LnX3EbH9lqzsf+Ccm5thnhqE/l+VJF0X+fMx0ZTMeOfcpqg3yugeKRtiXcsk8ljo/0zuH0t73xD7zFT3ZU/KB/sAZBl9+AG553rn3PBUiYKRvU5N8HZ41MHfLPEgbPFaRXxOc/m+/o6W778s2coiRwIshdnJfqDJ/3hpIN/sN1mamAZRCbK4betVPMBYjHNug5l9IV/rMKofr2zoFvp/Woq04fe7ydf2iRIZvMqSCfJ9KdWX7ztpnHx/XpJ/OjyvJCszs0Pka6vurQTHO1DsOAY/fmP77v0U5S6Vb1O8vyT4myx/mX7OktD/8Z8Tuy50jo3al4Ya8k1+FqZKmIpz7n0z+1E+sHOSfNOpk+XPu/855z4oyfqCPqoulP9BstlIuSGZXpdKu79LInZs6kraWMJr9s8R82e7UN+YEVKWxdKeUxFSnRdL5a8D8Z/RK/T/eyqBIJDcIng5wsxGpLnoZt+BJTTGOTc4zbRZOx/NrLukS+RruSXbhjz5IEbG53MWJfueCd/LfFTKe5n35R/mbCfpLjP7q/xDuHclfeKcy0ofhVk8X7Ihtt8OLEHZSlZusnEvEA6w1cvC+qIky2dZ3CNlwxdBkDGR6fL3mDWV2f1jab/HYvvtRxfqezeec25J8P3eMVEaAKVDDT8AUVqWcrm64Rdm1lv+JmGYfB8dqe6265Tyc+OtTvF+7IdswnRxP3arxb+f5W1bkuipcsiC4G+ywEQmwutN9WPu9wTLxVta+uwk55xbLR/0k6STzay2pGOC12nX9ApqUzwi3xH3IUoddIk/jk1VdOx/U2bSLbeblceSCPZdqs+I+pysXBcy9GTw9+S4v09GpE3IzIbL/5AfpNTnVKbXpVJfZ0og28cm3bK42X1kFs6p0uYlfv+FAyMlPTcrQ1lPJVvf02fI9wF4mtILWGbrezpbkn3PZLyPnB/k5DD5ZpqSHwDl/+SvH8vMbLKZnRgM6FBiZXC+ZENp9luyfGXjXmBx6P8WiRI556Y75yw8yXf7kI5k+SyLe6RsSJqXIBgYC8iVOi8Z3Dc0Cf6mMyAHg3YAZYAafgCihL+wd1HxpqnJ/BL7x8xqyg980SxYfpR8s4PvJS2NNdEys+0k/S+2WGbZLh9lsG2VrRlDVvKTRhAzU0/I1+rbX34kykbywYHnS7CO0yWdEfw/XdJd8k/nf5W0OrYNZjZWRbXJtlSx68IM+Rp26YoaSbu0npAfJbazmZ0vH2yPzU+LmQ2UdF3w8kf50Svfl6/ptipWW8LMbpAfgbAqiB2bfPmawOmaUwZ5yYVzKvwdeIP8KOrpSNbUMNsyPh/NrIukB+R/DyyUH9TqLfn+wVYEwS6Z2enyfRpKle94JfueCR/Hw+S3Kx3FgijOua+DWpCHBdPe8qNd15Hvh/NASZea2cGu+CjG6aiM50tsv70i6W9ZWF827gXCrSB6JUyVmXTzWZnu2SpTXgBUQgT8AEQJP0ld5Jz7JWHKxPZTUZ9K5znnHkmQrqyffpaFbG9bMzOrliJAtlXwd0mSNJkIr3erhKm8cC2QsspPOt6S/1HUVtLNwbyJifrWSWBI8PcHSf2dc2sSpEt0LJfIP93Okx+tN5fFrgv1nXNfVUQGnHM/mNlH8v1F3RbM/tA5978ki8WLHfOlkvom6aOqKl2bYsemgaRvyiHYnkym51S25If+b63UTYPDwt+BGyqqvKeQjfNxsPxvgU2SBjjnEu2jbB2rcE2gVK2MstFsM3wcl2VyHINzamIwycxay4+APFS+/+Le8gMLHVnCVVeW8yVssaQ2kmpWorL/noq+aw80M8uwC42SytY9UsLa0XHSLf9J8xL02RkrOxVxvxarNZmwVmZIOmkAlBBNegFE+SL0/x6lXMdOof/HJ0m3a5L3pMr59DJb2xZTU0k64g5u2HoGL8vq5ju83t1TpO2TYLlyFTSHjHUCXTv4W9KBG2LH8r+JfmiZ7/gpsuPtoAZMbB/sZSXoJKoKKhw0xMwy7assE7FjnOkxfztFh/Tpnr9lKd3rX+zY1FLF5zujcyqLPg/9v3cJl/1R0h/B/6X9Dixr2TgfY8dqRpJgn5S97+nww5gmiRKZWVP5GvSZysa9TCTn3G/OucflHz7EytqhZlbSJrfleb6U9Hqya9CiocIF/b+9GLzsJB9sLU/ZukeKnQONU6xjhxTvx/RMMRBPD/l7zKi8lIdZwd/tzCzVOZ/pgGQAIhDwAxDlDRX1m3RhKYMY4RuQyCeVZpanoqfbiawNpa9VinyUhWxtW1iiAVQkX2MgdqP0RgnWmTbn3HwV9VE0yMzqR6UL+ikaHLxcquI/qivCE5LWBdM8Sa+XcPnYsUz2NP1wJa+9F/sR0iFIm6tiIxCbpIsqMB/j5X80rQv+PlPC5VMeczPrpdQ/6spDute/F1X0Y/7iMs1Ratk4p7Jhhvw1QZLOTHRNixLU5poUvPyTmXVNlr6CZON8TOdcaK2iUVsTiZXTpN/RzrmlKhoQK1kQ8Xhlp+nq5yrqauSsoK/XrAoe+sT6iKuu1IGceOV5vqR1nFRUthqpHEYmLoERKrrO3R8EicpFFu+RYt0oNDCzzgnWUVN+MLh0NFXRiOJRTg/9Xyb3jym8GfzNk3RiknQnqfJ1FwDkBAJ+ADYTPEm9J3jZX9KdQQArkpltZWZnxs2eHfp/cIJFRyj1U+twZ+uVZfSubG1b2Llmtmf8zKDmxu3By9WSxpRgnSV1b/C3haSRCdJcJ2nH4P+HY/0VVhTn3FfOudrBtG0pmjLGjuVhUT8ezKyjivZLIveoqO+uB82sW6KEZrZ1CfNXaTjnXpP0cfDyCjMblCy9mXU3s2Q/REqbjyXOuYbBMW/onCtpM6XYMd/TzDrFv2lmLVTyWoNlJa3rn3PuOxX1M3e8mV2abKVm1sHMTshC/qJk45zKWFADONbse2tJYxPVVjKzPDNrEzd7hHxT1zxJzyU7d82smpn9tTzP7yydj7Fjtb2Z9Y9Ypq6kp5R6oIhYOd0ujQeE7wZ/Dw/KQvxndpZ0Y4p1pCUoA/8Xy5t8GUgY7DKzhkHfoOF5e0VdJ0Lv15Q0IHi5UiUfeKA8z5d0j9MYFQXLbzezpDVkzWxPMxuQLE02OOemyZ+XktRO0vvJvm+DvFWXH8U7G7JxjxQeQOSyBOu4Q76rknTdYWabNe0NjslZwcvPnHOflGCd2fKCivrEHJ7gnN9eRf3qAsgy+vADkMi18jexu8vXHtjHzB6W71R6lXyNs53kB0z4s6SZksJ92b0q/yXfUtI/zay9/Bd/vnxzjCGSBkr6QMmb2nwY+v9OM7tJ/qY19pR3bqyT/XKUrW2LWSQfzHvdzO6Ur1myTr5ZyFXyfelI0jWl6BC8JB6Q9Ff5JkqnmVk7SffJP5FuLf+k+Kgg7f+UpR9lFWysfFCgjaSPzOwW+WYvteX7arxYvjbE50rcrPd3Mzs3WFdLSR8H58or8qP11ZfUTb6WTGdVnsB1aZwoH2R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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again, there are no significant correlations between the input features and the output."
      ],
      "metadata": {
        "id": "zbEkmdgF9Mza"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This project will consider comparisons between the applied models by using base data without any scaling, then using another version of the data with feature scaling applied, and finally an data version with feature scaling and oversampling applied. This will help draw iinsight as to how these modifications affect the accuracy of the results."
      ],
      "metadata": {
        "id": "XO2AUGk_Btu4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Base: Defining the Dependent and Independent Variables"
      ],
      "metadata": {
        "id": "ZFYTEa2a9uO9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=processed_data_grouped_binary.iloc[:,:-1].values\n",
        "y=processed_data_grouped_binary.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "dF3yz9OW9Kt0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLNIjFbH_oGf",
        "outputId": "d266e1a4-50ec-488f-f241-d20012215b70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 298.1  308.6 1551.    42.8    0. ]\n",
            " [ 298.2  308.7 1408.    46.3    3. ]\n",
            " [ 298.1  308.5 1498.    49.4    5. ]\n",
            " ...\n",
            " [ 299.   308.6 1645.    33.4   22. ]\n",
            " [ 299.   308.7 1408.    48.5   25. ]\n",
            " [ 299.   308.7 1500.    40.2   30. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1gpLz1U_pDn",
        "outputId": "f2bbefb7-e254-4933-aedd-8a8d1f6250b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 ... 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Base: Splitting the Data"
      ],
      "metadata": {
        "id": "m8rjAE7XDCTx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.20,random_state=0)"
      ],
      "metadata": {
        "id": "Z1XPio4oC7bv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Scaled: Defining the Dependent and Independent Variables\n",
        "\n"
      ],
      "metadata": {
        "id": "FAX960rhA0hM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df=processed_data_grouped_binary.copy() "
      ],
      "metadata": {
        "id": "xWbls02hDlw7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_scaled=processed_data_grouped_binary.iloc[:,:-1].values\n",
        "y_scaled=processed_data_grouped_binary.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "e8sa6CwxCoHB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scaled: Splitting the Data"
      ],
      "metadata": {
        "id": "Zn_zzaG_Educ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_scaled_train , X_scaled_test, y_scaled_train, y_scaled_test = train_test_split(X_scaled,y_scaled,test_size=0.20,random_state=0)"
      ],
      "metadata": {
        "id": "SEP0LSN-EKQo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scaling the Data"
      ],
      "metadata": {
        "id": "57eTN64lEzsv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_scaled_train = sc.fit_transform(X_scaled_train)\n",
        "X_scaled_test = sc.transform(X_scaled_test)"
      ],
      "metadata": {
        "id": "ILWdmYTvEDTD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Avgv-LJrEyLm",
        "outputId": "0285445a-b3cc-46f7-b032-fbeb90ebc83d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.09509118  0.40612254  0.93821897 -0.74940818  0.26299762]\n",
            " [-0.89093729 -0.8664902   0.36891261 -1.06077593 -0.94382689]\n",
            " [ 0.2530915  -0.1297144   0.06193369 -0.25724626  0.63915072]\n",
            " ...\n",
            " [ 0.65101456  0.07122445 -0.46830262  0.41570984  1.26607254]\n",
            " [-0.84119691 -0.59857173 -0.65807141  0.74716583 -1.16324953]\n",
            " [-0.14483156 -0.53159211 -1.07667902  1.75157792  0.48242026]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_scaled_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74dEP2aXFI8i",
        "outputId": "bc1b46d0-3ad2-407a-a39c-a2baab34107d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1.1396392  -1.2013883  -1.14365624  1.3096366   0.76453508]\n",
            " [-2.18418723 -2.54098066 -0.51853553  0.99826885  0.40405503]\n",
            " [-0.39353347 -1.00044944 -0.2729524   0.32531275  0.79588118]\n",
            " ...\n",
            " [-0.19457194  0.74102063  0.61449575 -0.61883461  0.40405503]\n",
            " [-0.19457194 -0.66555135 -0.07760218  0.28513627  0.82722727]\n",
            " [ 0.84997609  0.67404102 -1.09342333  0.92796001 -1.28863389]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Oversampled and Scaled: Defining the Dependent and Independent Variables\n"
      ],
      "metadata": {
        "id": "qy2UyyMqFdHb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "oversampled_scaled_df=processed_data_grouped_binary.copy() "
      ],
      "metadata": {
        "id": "Rfxp6nDpF0g2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_oversampled_scaled=processed_data_grouped_binary.iloc[:,:-1].values\n",
        "y_oversampled_scaled=processed_data_grouped_binary.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "AneLCtblF7tp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Oversampled and Scaled: Splitting the Data"
      ],
      "metadata": {
        "id": "obJN-uoZGVyU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_oversampled_scaled_train , X_oversampled_scaled_test, y_oversampled_scaled_train, y_oversampled_scaled_test = train_test_split(X_oversampled_scaled,y_oversampled_scaled,test_size=0.20,random_state=0)"
      ],
      "metadata": {
        "id": "s6u2TijeGGpZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data was oversampled with the failure (1 to 4) results yeilding to 9670 so that the data is balanced between failing or succeeding."
      ],
      "metadata": {
        "id": "0zdljwIAMLmo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "oversample = SMOTE()\n",
        "X_oversampled_scaled_train, y_oversampled_scaled_train = oversample.fit_resample(X_oversampled_scaled_train, y_oversampled_scaled_train)"
      ],
      "metadata": {
        "id": "0RnjvuZMHqXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_oversampled_scaled_train = sc.fit_transform(X_oversampled_scaled_train)\n",
        "X_oversampled_scaled_test = sc.transform(X_oversampled_scaled_test)"
      ],
      "metadata": {
        "id": "Garcrx4LLDsV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelling and Evaluation"
      ],
      "metadata": {
        "id": "CCF904qqLhJD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest"
      ],
      "metadata": {
        "id": "L44_UTn4LoDl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Base Model"
      ],
      "metadata": {
        "id": "hUjPvJDHL81t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier=RandomForestClassifier(n_estimators=10, criterion='entropy', max_depth=5, max_features=\"sqrt\", random_state=0) \n",
        "classifier.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKYQbDLTL5YH",
        "outputId": "54c12322-0639-4499-955d-353338180a2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', max_depth=5, max_features='sqrt',\n",
              "                       n_estimators=10, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uW6rnyQRMzPf",
        "outputId": "f13a0f33-5a7d-4c4b-dcb0-90fee95e9cf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.976"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score , recall_score, precision_score,f1_score, roc_curve,auc,confusion_matrix, ConfusionMatrixDisplay, roc_auc_score, classification_report\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "el0ghHt9Pri5",
        "outputId": "2641c016-38f1-4afb-ded8-b8f724362e56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      1925\n",
            "           1       0.97      0.37      0.54        75\n",
            "\n",
            "    accuracy                           0.98      2000\n",
            "   macro avg       0.97      0.69      0.76      2000\n",
            "weighted avg       0.98      0.98      0.97      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As evident, the base model yields very high accuracy. However, as expected, the recall is very low for the failures and the precision reaches 0 for some of the failures due to the imbalance in the data."
      ],
      "metadata": {
        "id": "FYq0fNDTPdta"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scaled Model"
      ],
      "metadata": {
        "id": "u7modxUvQuWR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.fit(X_scaled_train,y_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJenVcMKQtYz",
        "outputId": "732cbf54-783c-46ac-e6eb-39cf7424b007"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', max_depth=5, max_features='sqrt',\n",
              "                       n_estimators=10, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_scaled_pred = classifier.predict(X_scaled_test)"
      ],
      "metadata": {
        "id": "a1CvPS3qRaSZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_scaled_test,y_scaled_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8LrGxP22Rj1d",
        "outputId": "c9abfade-b1eb-4a17-f67b-a0178f44fb2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      1925\n",
            "           1       0.97      0.37      0.54        75\n",
            "\n",
            "    accuracy                           0.98      2000\n",
            "   macro avg       0.97      0.69      0.76      2000\n",
            "weighted avg       0.98      0.98      0.97      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Oversampled and Scaled Model"
      ],
      "metadata": {
        "id": "-GFor2uQSGJT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.fit(X_oversampled_scaled_train,y_oversampled_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zooQrmT5SFN9",
        "outputId": "b1d292a5-9a27-49a6-97e9-bbc07779d913"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', max_depth=5, max_features='sqrt',\n",
              "                       n_estimators=10, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_oversampled_scaled_pred = classifier.predict(X_oversampled_scaled_test)"
      ],
      "metadata": {
        "id": "AddMr7qFScir"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_oversampled_scaled_test,y_oversampled_scaled_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrJf0GljSlFN",
        "outputId": "c448ffa2-aa44-4e06-c90f-f04a437c74a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.92      0.96      1925\n",
            "           1       0.31      0.88      0.46        75\n",
            "\n",
            "    accuracy                           0.92      2000\n",
            "   macro avg       0.65      0.90      0.71      2000\n",
            "weighted avg       0.97      0.92      0.94      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "grid = {'n_estimators' : [30,50,60,70,80],\n",
        "    'max_features' : ['sqrt', 'log2'],\n",
        "    'max_depth' : [6,8,10,20],\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'random_state': [0]}\n",
        "\n",
        "gs=GridSearchCV(estimator= RandomForestClassifier(), param_grid=grid, scoring='accuracy', cv=5, n_jobs=-1, verbose=5)\n",
        "gs.fit(X_oversampled_scaled_train,y_oversampled_scaled_train)\n",
        "\n",
        "best_accuracy=gs.best_score_\n",
        "best_parameters=gs.best_params_\n",
        "\n",
        "print(\"Best Accuracy:\", best_accuracy)\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B8FAsakMTTHQ",
        "outputId": "c2cc181c-3610-4768-9ae2-0434f3eb4049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n",
            "Best Accuracy: 0.9759567164755799\n",
            "Best Parameters: {'criterion': 'entropy', 'max_depth': 20, 'max_features': 'sqrt', 'n_estimators': 60, 'random_state': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier=RandomForestClassifier(n_estimators=60, criterion='entropy', max_depth=20, max_features=\"sqrt\", random_state=0) \n",
        "classifier.fit(X_oversampled_scaled_train,y_oversampled_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTIJkDYAT-ov",
        "outputId": "465fda6d-5dda-4baa-d74e-961ca343d273"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', max_depth=20, max_features='sqrt',\n",
              "                       n_estimators=60, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "# Predict the X_test\n",
        "y_oversampled_scaled_pred=classifier.predict(X_oversampled_scaled_test)\n",
        "\n",
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_oversampled_scaled_test,y_oversampled_scaled_pred)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm ,display_labels=[\"No failure\",\"Machine Failure\"])\n",
        "disp.plot(cmap=plt.cm.OrRd)\n",
        "plt.xlabel(\"Predicted labels\")\n",
        "plt.ylabel(\"Actual labels\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpiklHHYVsKI",
        "outputId": "33849fc6-cc9e-4f1b-8d5f-32b2c73fc14c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_oversampled_scaled_test,y_oversampled_scaled_pred))\n",
        "# Plotting the ROC curve and calculating the AUC\n",
        "# get false and true positive rates\n",
        "y_pred_prob=classifier.predict_proba(X_oversampled_scaled_test)\n",
        "fpr, tpr, thresholds = roc_curve(y_oversampled_scaled_test, y_pred_prob[:,1])\n",
        "\n",
        "roc_auc = auc(fpr, tpr)\n",
        "print (\"AUC: \",roc_auc)\n",
        "\n",
        "plt.plot(fpr, tpr, color='green')\n",
        "plt.title('ROC Curve ')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate (Recall)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_hGt4pzYxKb",
        "outputId": "7fac0a47-9fde-449f-c37e-64d9a4d22b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.96      0.98      1925\n",
            "           1       0.44      0.79      0.56        75\n",
            "\n",
            "    accuracy                           0.95      2000\n",
            "   macro avg       0.72      0.87      0.77      2000\n",
            "weighted avg       0.97      0.95      0.96      2000\n",
            "\n",
            "AUC:  0.9505108225108225\n"
          ]
        },
        {
          "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": "markdown",
      "source": [
        "## Logistic Regression"
      ],
      "metadata": {
        "id": "rWvc1XFYXK1s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Base Model"
      ],
      "metadata": {
        "id": "lFnE7ob_X4o9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr_model= LogisticRegression(random_state=0)\n",
        "lr_model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvmLu2UXWMqH",
        "outputId": "92a0ed6f-f15c-4165-a13e-54e68433a698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lr_model.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avKP621Cai_G",
        "outputId": "ee7a5130-f91e-4fad-ab52-27fd5cd06cb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.964"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZaEqtw4atIm",
        "outputId": "660e36da-ade6-4f6c-831c-3dd0d0986754"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.98      1925\n",
            "           1       0.58      0.15      0.23        75\n",
            "\n",
            "    accuracy                           0.96      2000\n",
            "   macro avg       0.77      0.57      0.61      2000\n",
            "weighted avg       0.95      0.96      0.95      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scaled"
      ],
      "metadata": {
        "id": "jRw3i2Jwa1hM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model.fit(X_scaled_train,y_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qzd8v_YqbxH3",
        "outputId": "115f87f6-b1ba-428f-c811-fd3381107d64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_scaled_pred = lr_model.predict(X_scaled_test)"
      ],
      "metadata": {
        "id": "lCPolckLb8tD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_scaled_test,y_scaled_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usMx5vFXcEQF",
        "outputId": "80da7dd4-55a0-4815-ac30-06f8dfabab59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.98      1925\n",
            "           1       0.61      0.15      0.24        75\n",
            "\n",
            "    accuracy                           0.96      2000\n",
            "   macro avg       0.79      0.57      0.61      2000\n",
            "weighted avg       0.95      0.96      0.95      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Oversampled and Scaled"
      ],
      "metadata": {
        "id": "kY7EJt7Wcg0s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model.fit(X_oversampled_scaled_train,y_oversampled_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_Fi0U9KcgZO",
        "outputId": "9c5b82f2-c042-4391-94d4-be0d374825a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_oversampled_scaled_pred = lr_model.predict(X_oversampled_scaled_test)"
      ],
      "metadata": {
        "id": "sPjak-55dC1c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_oversampled_scaled_test,y_oversampled_scaled_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rHhx0iR_eAuL",
        "outputId": "15cad2f2-74fe-4c11-e11b-266eeb90e747"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.82      0.90      1925\n",
            "           1       0.16      0.84      0.26        75\n",
            "\n",
            "    accuracy                           0.82      2000\n",
            "   macro avg       0.57      0.83      0.58      2000\n",
            "weighted avg       0.96      0.82      0.88      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid={\"C\":np.logspace(-3,3,7), \"penalty\":[\"l1\",\"l2\"], \"max_iter\":[1000]}\n",
        "logreg=LogisticRegression()\n",
        "logreg_cv=GridSearchCV(logreg,grid,cv=10)\n",
        "logreg_cv.fit(X_oversampled_scaled_train,y_oversampled_scaled_train)\n",
        "\n",
        "print(\"tuned hyperparameters :(best parameters) \",logreg_cv.best_params_)\n",
        "print(\"accuracy :\",logreg_cv.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UYOnJyUeQHJ",
        "outputId": "fa16047a-1802-4f40-bd25-33e24e263c4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tuned hyperparameters :(best parameters)  {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2'}\n",
            "accuracy : 0.8313080748101267\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model= LogisticRegression(C = 0.1, penalty = 'l2', max_iter=1000, random_state=0)\n",
        "lr_model.fit(X_oversampled_scaled_train,y_oversampled_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-bfa8aMnejiy",
        "outputId": "ce74e9af-85a1-41a4-ee76-0c3200ca99a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.1, max_iter=1000, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_oversampled_scaled_pred=lr_model.predict(X_oversampled_scaled_test)\n",
        "\n",
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_oversampled_scaled_test, y_oversampled_scaled_pred)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm ,display_labels=[\"No failure\",\"Machine failure\"])\n",
        "disp.plot(cmap=plt.cm.OrRd)\n",
        "plt.xlabel(\"Predicted labels\")\n",
        "plt.ylabel(\"Actual labels\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "-zBvZaERepIz",
        "outputId": "4b4674b9-1c1f-4b2f-9a88-a0122e78cd2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_oversampled_scaled_test, y_oversampled_scaled_pred))\n",
        "# Plotting the ROC curve and calculating the AUC\n",
        "# get false and true positive rates\n",
        "y_pred_prob=lr_model.predict_proba(X_oversampled_scaled_test)\n",
        "fpr, tpr, thresholds = roc_curve(y_oversampled_scaled_test, y_pred_prob[:,1])\n",
        "\n",
        "roc_auc = auc(fpr, tpr)\n",
        "print (\"AUC: \",roc_auc)\n",
        "\n",
        "plt.plot(fpr, tpr, color='green')\n",
        "plt.title('ROC Curve ')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate (Recall)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "e783r73SjUIK",
        "outputId": "32afd7d6-8e19-49ad-b1dc-977aed3cd6dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.96      0.97      1925\n",
            "           1       0.40      0.73      0.51        75\n",
            "\n",
            "    accuracy                           0.95      2000\n",
            "   macro avg       0.69      0.84      0.74      2000\n",
            "weighted avg       0.97      0.95      0.96      2000\n",
            "\n",
            "AUC:  0.887965367965368\n"
          ]
        },
        {
          "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": "markdown",
      "source": [
        "## **KNN**"
      ],
      "metadata": {
        "id": "Ko5MnRMQ1wse"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Base Model"
      ],
      "metadata": {
        "id": "meelkjjjWOsb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "knn.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82A90zDD10UH",
        "outputId": "f0187b7b-2106-4ecb-cbdc-4965fb340c99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmWMucVL15Le",
        "outputId": "96aa47f9-c564-459c-ca54-05fd223e68b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9625"
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPyBIFGc2GxQ",
        "outputId": "b128dfd7-66e9-4c12-c929-c163b7813b6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98      1925\n",
            "           1       0.50      0.23      0.31        75\n",
            "\n",
            "    accuracy                           0.96      2000\n",
            "   macro avg       0.74      0.61      0.65      2000\n",
            "weighted avg       0.95      0.96      0.96      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scaled"
      ],
      "metadata": {
        "id": "3b_wgrd9WYSL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn.fit(X_scaled_train,y_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pX-iGd2EWY27",
        "outputId": "24718008-b108-4c89-c2f0-930c17c42568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_scaled_pred = knn.predict(X_scaled_test)"
      ],
      "metadata": {
        "id": "6V3ESxVRWZUt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_scaled_test,y_scaled_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fpyWm5iWayD",
        "outputId": "58b19544-5d48-4170-efdd-3e044c566172"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98      1925\n",
            "           1       0.64      0.37      0.47        75\n",
            "\n",
            "    accuracy                           0.97      2000\n",
            "   macro avg       0.81      0.68      0.73      2000\n",
            "weighted avg       0.96      0.97      0.96      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Oversampled and Scaled"
      ],
      "metadata": {
        "id": "b6_t2J-LWfNg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn.fit(X_oversampled_scaled_train,y_oversampled_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hAN_CZoWg1r",
        "outputId": "58a16367-0c89-4a43-8710-d4380d8ac45f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_oversampled_scaled_pred = knn.predict(X_oversampled_scaled_test)"
      ],
      "metadata": {
        "id": "d26VxokqWhIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_oversampled_scaled_test,y_oversampled_scaled_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBire0mZWjGD",
        "outputId": "a820720b-8a7d-4c49-e93a-b4468f1669cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.94      0.97      1925\n",
            "           1       0.34      0.80      0.48        75\n",
            "\n",
            "    accuracy                           0.93      2000\n",
            "   macro avg       0.67      0.87      0.72      2000\n",
            "weighted avg       0.97      0.93      0.95      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k_range = list(range(1, 31))\n",
        "param_grid = dict(n_neighbors=k_range)\n",
        "knn_classifier = KNeighborsClassifier()  \n",
        "\n",
        "grid = GridSearchCV(knn_classifier, param_grid, cv=10, scoring='accuracy', return_train_score=False,verbose=5)\n",
        "\n",
        "knn_grid = grid.fit(X_oversampled_scaled_train,y_oversampled_scaled_train)\n",
        "\n",
        "print(\"tuned hyperparameters :(best parameters) \",knn_grid.best_params_)\n",
        "print(\"accuracy :\",knn_grid.best_score_)"
      ],
      "metadata": {
        "id": "He2krlhxWkoj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_model=KNeighborsClassifier(n_neighbors=1)\n",
        "knn_model.fit(X_oversampled_scaled_train,y_oversampled_scaled_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PVsdRbfWtMN",
        "outputId": "61defc4e-f683-4fc2-805c-2dc3a2e39962"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=1)"
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_oversampled_scaled_pred=knn_model.predict(X_oversampled_scaled_test)\n",
        "\n",
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_oversampled_scaled_test, y_oversampled_scaled_pred)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm ,display_labels=[\"No failure\",\"Machine Failure\"])\n",
        "disp.plot(cmap=plt.cm.OrRd)\n",
        "plt.xlabel(\"Predicted labels\")\n",
        "plt.ylabel(\"Actual labels\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "8AXvvkWWW3yb",
        "outputId": "81bfa3d4-b46c-42c3-aeee-160505e4a83d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_oversampled_scaled_test, y_oversampled_scaled_pred))\n",
        "# Plotting the ROC curve and calculating the AUC\n",
        "# get false and true positive rates\n",
        "y_pred_prob=knn_model.predict_proba(X_oversampled_scaled_test)\n",
        "fpr, tpr, thresholds = roc_curve(y_oversampled_scaled_test, y_pred_prob[:,1])\n",
        "\n",
        "roc_auc = auc(fpr, tpr)\n",
        "print (\"AUC: \",roc_auc)\n",
        "\n",
        "plt.plot(fpr, tpr, color='green')\n",
        "plt.title('ROC Curve ')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate (Recall)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "vZiHIEzxW5Uj",
        "outputId": "1a7033f0-6f1b-4048-c0d5-7847b78826de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.96      0.97      1925\n",
            "           1       0.40      0.73      0.51        75\n",
            "\n",
            "    accuracy                           0.95      2000\n",
            "   macro avg       0.69      0.84      0.74      2000\n",
            "weighted avg       0.97      0.95      0.96      2000\n",
            "\n",
            "AUC:  0.8448484848484848\n"
          ]
        },
        {
          "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": "markdown",
      "source": [
        "## **ANN**"
      ],
      "metadata": {
        "id": "NEso3mIbjQaU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Base"
      ],
      "metadata": {
        "id": "YzDyL-fbj_DB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "ann_model=tf.keras.models.Sequential()"
      ],
      "metadata": {
        "id": "kDWvAtx5kCHz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann_model.add(tf.keras.layers.Dense(units=8,activation=\"relu\"))\n",
        "ann_model.add(tf.keras.layers.Dense(units=8,activation=\"relu\"))\n",
        "ann_model.add(tf.keras.layers.Dense(units=1,activation=\"sigmoid\"))"
      ],
      "metadata": {
        "id": "FE1C2Q-KkHZJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),loss=\"binary_crossentropy\",metrics=\"accuracy\" )"
      ],
      "metadata": {
        "id": "Cp_YZmYvkJvO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = ann_model.fit(X_train, y_train, epochs=40, batch_size=10, validation_split=0.2)"
      ],
      "metadata": {
        "id": "f3c4NwdqkV3L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['accurracy', 'loss'], loc='upper left')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TCcIp-fLk1cV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = ann_model.predict(X_test)\n",
        "y_pred = (y_pred > 0.5)"
      ],
      "metadata": {
        "id": "4rCS0LUnRVtD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scaled"
      ],
      "metadata": {
        "id": "yKazTRhmRgCe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = ann_model.fit(X_scaled_train, y_scaled_train, epochs=40, batch_size=10, validation_split=0.2)"
      ],
      "metadata": {
        "id": "atO_k2L2Rj6-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['accurracy', 'loss'], loc='upper left')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "KeBQ9rS4UDFC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = ann_model.predict(X_scaled_test)\n",
        "y_pred = (y_pred > 0.5)"
      ],
      "metadata": {
        "id": "SpokF1TfUEUr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Oversampled and Scaled"
      ],
      "metadata": {
        "id": "FPR1K4wHUb5y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = ann_model.fit(X_oversampled_scaled_train,y_oversampled_scaled_train, epochs=40, batch_size=10, validation_split=0.2)"
      ],
      "metadata": {
        "id": "0imw8dnlUg6E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['accurracy', 'loss'], loc='upper left')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Q-qPp1V6Umxe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = ann_model.predict(X_oversampled_scaled_test)\n",
        "y_pred = (y_pred > 0.5)\n",
        "cm = confusion_matrix(y_test_bi, y_pred)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm ,display_labels=[\"No failure\",\"Machine failure\"])\n",
        "disp.plot(cmap=plt.cm.OrRd)\n",
        "plt.xlabel(\"Predicted labels\")\n",
        "plt.ylabel(\"Actual labels\")\n",
        "plt.show()\n",
        "print(classification_report(y_oversampled_scaled_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "xWvg0kIVUoi8",
        "outputId": "431f61e3-65ef-4d05-d4f7-315b4a3e62ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 1ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.92      0.95      1925\n",
            "           1       0.28      0.80      0.42        75\n",
            "\n",
            "    accuracy                           0.92      2000\n",
            "   macro avg       0.64      0.86      0.69      2000\n",
            "weighted avg       0.96      0.92      0.93      2000\n",
            "\n"
          ]
        }
      ]
    }
  ]
}